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They have fled war-torn countries, given up livelihoods, and left behind possessions and family for the safety of a foreign world of cowboy hats and Walmarts. But the refugees who land in Amarillo's Astoria Park have an ally who understands their confusion and loss: a 64-year-old former teacher named Miss Evelyn.
Katy Vine
https://www.texasmonthly.com/articles/refugees-amarillo-texas/
Dim Niang (second from left) and Zam Kap (seated) with, from left, their children, Cing Kim, Go Hau Mang "Abner," Zen Zuun, and Awi Lin.
Photograph by Wyatt McSpadden
From the December 2016 Issue Subscribe
The wind is what struck Dim Niang in those first days—a constant dry wind that knew no boundaries, wind that could lift a stretch of road dust and seem to cast it up to Oklahoma, New Mexico, and beyond. Back home in Myanmar, the tropical humidity would have pounded dirt like this into submission. How different Amarillo was. Paved roads that did not buckle under monsoons. Grocery stores built like giant boxes. Big trucks with cowboys for drivers.
Dim was more curious about Amarillo than she was intimidated by it. A slim beauty, the second oldest of six in a family that had farmed rice and cotton in Kalaymyo, a remote village on the southern edge of Chin State, she'd always been resilient. This was a quality of her people, the Zomi, a tight-knit ethnic group in the lush, green mountains that border India and Bangladesh. After moving from western China as late as the eighth century, the Zomi had staked out an existence in the isolated mountains, preserving their dialects and ceremonial dress even after adopting Christianity when American missionaries arrived, in the late 1800s. Dim had studied hard in school and eventually graduated from a local Bible college, an achievement that shaped her conditions for a suitor: he had to be Zomi, he had to be educated, and he had to be kind.
Zam Kap was aware of these criteria. A bold young man from the same village, he had met Dim's family as a teenager, and as he and Dim reached their mid-twenties, he began to court her openly, announcing to everyone in the neighborhood that he loved her. He was a handsome college graduate with a degree in psychology and a fondness for story who showed promise as a community leader, and his enthusiasm seemed boundless. Standing before Dim, he'd stretch his arms wide, flash a contagious smile, and say, "I love you. Do you love me?"—a candor that made a shy but flattered Dim laugh. She dropped enough hints with her family about her admiration for him that when Zam's father approached hers about a wedding, the ceremony took place within a week.
In 1999 the couple moved into their own house, a three-room wood structure on stilts, where, eventually, they had four children—one boy and three girls—who filled the yard with shouts and laughter and games of hide-and-seek. From nine to four o'clock each day, Dim and Zam taught at the local school, a concrete building that housed all grades, and when they returned home, they navigated their way around a duck, a chicken, a cat, and a pig, as well as a number of rats—vexing pests that had long ravaged the countryside's corn and rice crops and often drove Zam into a hunting frenzy. (Dim and the kids cheered him on one night as he caught five rodents, a personal record.)
On a combined monthly salary of $25, the family ate the pumpkin, beans, spinach, and cabbage they grew in their garden and saved enough to pay the rates for the children's school tuition: $2 a month for girls and $2.50 for boys. They didn't own a car—only an old motorcycle and a bicycle—and had no electricity. Once a year, Dim dressed everyone up and splurged on a family photo that cost 50 cents. Three of the kids had been born with disabilities—the boy, Go Hau Mang, had a cleft palate; their oldest daughter, Zen Zuun, had a club foot; and their middle daughter, Cing Kim, had developmental delays—but except for an initial cleft-palate operation, there was no money for surgery or therapy. At night, Dim sang her children a popular lullaby that revealed her and Zam's exhaustion: "Baby, grow! Hurry, hurry, and grow. Help your parents. Take on their strength and help do their work."
It was a mostly happy life, despite its limitations, but around them, a dark political reality was encroaching. A military dictatorship, in place since 1962, had required all ethnic minorities, including the Zomi, to adopt the customs, language, and religion of Myanmar's Buddhist majority; a series of pro-democracy protests around the country in 1988 had led to a bloody crackdown and the death of an estimated three thousand demonstrators. After that, the authorities had tightened their grip on Myanmar's villages, so that by the time Dim and Zam were raising their family, it was not uncommon for the police to show up and arrest community leaders, torture villagers to get political intel, or shoot suspected dissidents in the street. They were also known to seize property, extort money, and coerce villagers into building Buddhist temples and army housing. Though media coverage of the government was prohibited—the sentence for just listening to the BBC was three years in prison—the Zomi didn't need any news outlets to understand what was happening. Zam watched from his home's doorway as neighbor after neighbor got escorted into army trucks in broad daylight. "Another one arrested," he'd tell Dim.
A visit from a newly released community leader in 2010 confirmed what the couple had begun to fear: Zam, the leader told them, was most likely next. The following morning, Dim and Zam hired a driver, said goodbye to their parents, and set out with their children on a two-day drive to the closest border, with India. They did not have much of a plan, but when they reached New Delhi, a few other Zomi exiles there told them to register with the United Nations High Commissioner for Refugees. The family found a single-room flat, where they settled in to wait for word of their future.
It took two years and ten months for Zam to receive the call from a UN official saying that his family had been approved for relocation. He gripped the phone as he heard the name of their new homeland: Texas. Within days, he and Dim and their children found themselves staring out of airplane windows at the cities that came and went beneath them—Dubai, Chicago, Dallas—until their final descent into Amarillo, on September 11, 2013. Stepping off the plane, Zam had taken a deep breath. He was 45 and Dim was 40. They had four mouths to feed. How to start over? Who would help them?
It was only a few days later, as he and Dim sat in a seemingly enormous three-bedroom apartment, dazed and overwhelmed, that a thin, perky woman with glasses knocked on their door.
"Hello!" she said. "I'm Miss Evelyn!"
Evelyn in the apartment complex's "community center," translating and explaining medical paperwork to a refugee who is on a call with a medical hotline.
Evelyn Lyles is a tough and practical 64-year-old, with gray hair she wears in a pixie cut. She has the patience and mannerisms of a teacher, which she was until 1994. She is devoted to her church, First Baptist of Amarillo, where she attends services every Sunday. More often, you will catch her around town leaning in as she listens to an immigrant mother ask a question about health insurance or to an excited, if confused, father with limited English who is gripping an envelope announcing "You Are the Winner of a Luxury Cruise!" She isn't particularly interested in news headlines or political hand-wringing about refugees. Her calling is local and personal, a commitment to the biblical mandate to welcome the stranger, to look out from her front porch and see a neighborhood that goes deep and wide.
Which is why, for the past four years, she's shown up almost every morning at an apartment complex in central Amarillo called Astoria Park, whose 164 units house refugees from Myanmar, Somalia, and a few other distant and war-torn countries. Texas has long served as a destination for refugees, accepting more arrivals than any other state; in 2015, for example, it received 11 percent of the 69,933 refugees admitted to the U.S. Though most end up in Houston—which takes in roughly 38 percent of the state's total—what is significant for Amarillo is the ratio: it currently accepts more per capita than anywhere in Texas. For decades now, the city has served as a harbor for new immigrants because of its meatpacking plants, which require minimal language skills and offer health insurance as well as quick and steady wages of $12 to $14 an hour. Since 2007, the city of nearly 200,000 has absorbed more than 4,000 refugees.
Until this year, when Governor Greg Abbott made headlines by announcing that Texas was withdrawing from the U.S. Refugee Resettlement Program, the arrival and assimilation of refugees was funded by the federal government and managed by the state, which distributed the dollars to local agencies, such as Catholic Charities and Refugee Services of Texas, to help arrivals adapt as quickly as possible. This withdrawal, though it removes Texas as a middleman, does not effectively change the number of refugees sent to the state. Nor does it change the reality that has long greeted those who arrive: from the day a refugee steps onto American soil, the State Department gives him or her ninety days to find a job, enroll his or her children in school, and begin to pay rent, bills, and taxes.
This notion, of course—rebuilding a life in ninety days—is a farce for anyone who, like Dim and Zam, does not speak English or, say, has never used a gas stove. City councils and overloaded agencies do not have the time or resources to teach arrivals how to mail a letter or change a lightbulb, and so into this void often step energetic local volunteers. One of these is Evelyn, who devotes around forty hours a week to helping the newly displaced figure out the vagaries of modern American life. In navigating the mysterious protocols of Amarillo, she is their Sherpa, their fairy godmother, their tour guide, and their interpreter.
This is how she came to be sitting one cold, sunny morning last December in her 2006 Toyota Corolla, talking in Mister Rogers–paced sentences as she drove Dim to a doctor's appointment.
"Dim, does Zam like to fish?" she asked, pointing to a pond. "You can come here fishing."
"Fishing," Dim repeated, with a nod.
"I heard there was a van wreck last week with ten people," Evelyn said. Dim groaned. Evelyn continued, "Did Zam have trouble driving on the ice?"
Dim put effort into her words. "Friday no work."
She was bundled in purple stretch pants, sky-blue fuzzy socks, a maroon velvet jacket, and a blue turban-style yarn cap she'd woven herself and pulled down over her long jet-black hair. Though the doctor visit was just a checkup, she had reached the age of requiring a mammogram, and she furrowed her brow, trying to ignore her anxiety.
"Now, Dim," Evelyn said. "When you go to the clinic, you will give address. They will give you paper. Then they will ask you to take everything on top off." Evelyn stabbed a look in Dim's direction to make sure she understood. Dim chuckled.
"Then they will say, 'No breathe!' " Evelyn sucked in air and puffed out her cheeks as she held her breath. " 'Hold your breath' means 'no breathe.' Okay?"
"Okay," Dim said. Evelyn often resorted to this sort of improvised slapstick routine, acting out toothaches or ear infections like one of the Marx brothers.
"And you are going to the dentist Saturday?" Evelyn asked. Dim had received her driver's permit only a few months earlier, and she still drove tentatively, practicing in parking lots. "Six lights," Evelyn instructed. "See, it says 'Port'? You turn. And it's the same way to the West Clinic. Now you remember where we go?"
Dim looked confused.
"Under," said Evelyn, as the car went under a bridge, and Dim nodded, the overpass jogging her memory.
"Under," Dim repeated.
"Here, by the horse, you turn," Evelyn said, pointing to a statue. "You pay no money to go inside. They will not give you an appointment for next year. You have to remember to call."
Would Dim remember? Did she know what was going on? Evelyn couldn't be sure, but they'd reached the clinic and she was out of time to explain any more. She parked and located a folder containing her day's to-do list, holding it close as she walked Dim to the entrance.
Elia San with her husband, Sang, and children, Elizabeth and William, outside the family's ground-floor apartment.
Evelyn does a lot of walking, mostly down the sidewalks of Astoria, waving and calling residents by name, asking about births, deaths, marriages, surgeries, jobs. She remembers everyone's name—and can usually spell it—as well as his or her apartment number. ("It's not like West Texas names," said Jeff Parsons, of Mission Amarillo, one of the nonprofits that help refugees. "Ha No. Pa Ka. She knows them all.") She can recall the names of their relatives around town and knows which churches or mosques or temples they attend. Once, when a toddler from the complex was injured in a car accident, a refugee informed the police that the best person to identify the girl was Evelyn.
"I do feel like their mother," she said one day in her makeshift office, a "community center" apartment in Astoria that she's filled with folding chairs and tables. But her brand of parenting is less helicopter and more birds-out-of-the-nest: her charges know not to count on sentimentalities so much as drills in independence and resolve. These are qualities that Evelyn herself has had to rely on. She grew up on a farm in nearby Hereford, where she rose early to milk cows and gather eggs, and after marrying in college, she worked as an elementary school teacher, then as an educational diagnostician. She and her husband tried their hand at milo, wheat, and corn farming, but a severe drought devastated the land, and eventually the couple lost their farm. "Best thing that happened to us, though at the time it seemed like the end of the world," she said.
In 2002 her husband, who had made a career as a school principal, retired, and the couple moved from Hereford to Amarillo, where the youngest of their two sons, a nineteen-year-old who was bipolar, could find more support. Soon afterward Evelyn was hired as a diagnostician at Sam Houston Middle School, where, about a year in, she noticed an influx of young South Asians and Africans. "I'd see them walking home and in the cafeteria," she recalled. She had also volunteered with Mission Amarillo to teach a Bible class for kids at a nearby apartment complex, and she was surprised by the great number of refugee children she saw living in the area. "One time, eight children who knew me from the apartments approached me at school—to fortify each other, right? They'd see me in the hall and pass along questions from their parents: 'My mom asked me to ask you where to get eyeglasses.' Then it was a dentist," she said.
She enjoyed helping them so much that she considered volunteering full-time, but before she'd given it much thought, her own family was struck with a devastating loss. "My youngest son died in 2004," she said. "We were in a fog for at least a year." She paused, considering whether to go on. "Suicide," she added.
She says she stopped eating and slept in fits. Though she continued to spend a few hours a week with the refugees, it was not until she retired, in 2012, that she threw herself fully into Astoria, an independent, one-woman operation taking on the wildly diverse needs of people who had also experienced deep loss. "I decided one day that you can sit around and be sad the rest of your life or you can go and do something," she said, forcing a smile. "Now the void left by my one lost son is filled with two hundred families."
On one particular winter morning, she was preoccupied with a few of the newest arrivals, and she'd enlisted Dim to help. "They speak Burmese and you speak Burmese. Can you translate?" Dim nodded, and Evelyn led the way. The family in question—a mother and her two grown sons—had found jobs at a meatpacking plant, but as recent hires they were most likely on the night shift and most likely still asleep. Outside their apartment, Evelyn knocked. The door opened to reveal a young man named Ha sporting a wraparound skirt and a hairstyle that indicated he had indeed just woken up.
"Hello!" Evelyn said brightly, stepping inside. An agency had outfitted the apartment with the basics—a thrift-store dining room set, a sofa bed, a clock, a desk—but the space had yet to feel lived-in. After landing at the airport a few weeks earlier, the family had been given 24 hours to rest, then shuttled to an office for paperwork. There, they'd been handed forms on the travel loan they'd have to pay back—roughly $3,000 for the three of them—and shown how to apply for Social Security, Medicaid, and food stamps. Then they'd been enrolled in an employment program so that they could wean themselves off Social Security, Medicaid, and food stamps within three months.
"I need more information to make the appointments you requested," Evelyn informed Ha. His thermostat hovered below 60 degrees—a money-saving practice—and she rubbed her shoulders to stay warm. She tried to explain that Medicaid was waiting on additional records from him. "Yesterday I called," Evelyn said, pointing to a sheet. "They said, 'No!' "
She knew that when a resident didn't know where exactly to find a document, there was a good chance it was on top of the refrigerator, under a couch cushion, or in a Walmart bag hanging on a wall. This time, Evelyn pointed to the desk. Ha rummaged around, returning with a plastic envelope. Inside was the paperwork. "We need to make a copy," she said, gesturing as if she were taking a photo.
Ha grunted his permission, and after making a few notes, Evelyn looked up and smiled, relieved to have solved the issue so easily. Too often forms went missing or were ignored, which led to other problems. ("Yesterday a refugee came to me with evidence that Medicaid had chosen a clinic for him that had been closed for two years," she told me, a system glitch he could have avoided if he'd read the government letter.) Evelyn did her best to fix these messes, wading through piles of forms, dialing 1-800 numbers, and waiting on hold to Muzak, the sound track of bureaucracy.
Dim and Zam, in traditional Zomi dress, outside their apartment in Astoria Park.
Photographs by Wyatt McSpadden
Evelyn remembers when Dim's family was new three years ago. "I was impressed because they already knew some English, especially Abner," she said, referring to Go Hau Mang, who chose the eleventh entry in The Name Book for himself soon after arriving. "He could tell the family what I was there for." School had just started, so Evelyn brought backpacks, notebooks, and pencils for the kids: Abner, who was then fourteen; Zen Zuun, who was twelve; Cing Kim, who was ten; and Awi Lun, who was eight.
It took a while for the items to get any use: at first the family had such jet lag that they mixed up their nights and days, and then the children were hesitant to go to class. When Dim finally ventured out to Walmart, she wandered the aisles, struck by the wall of televisions blasting in English, the rows of fresh vegetables, the aisle of cereals. How could one possibly choose from so many types of eggs?
At Evelyn's urging, Dim began attending two free weekly English classes at First Baptist. Soon she could understand basic phrases, but replying was another matter; the words, difficult to mimic, seemed to collide in her mouth. Zam, meanwhile, landed a job at Tyson Fresh Meats within six weeks. His routine was vastly different from the one he had when he was a teacher: rise at 5:40, carpool for the thirty-minute drive to the plant, cut cow knees with giant scissors for hours in a warehouse, finish at 2:30, utterly exhausted. Still, he marveled at his cultural discoveries, like the city's ranchers and their funny names, the importance of being punctual, and the baffling monument that was Cadillac Ranch. That winter, when his family saw snow for the first time, Dim made the children stay inside, because she wasn't sure it was safe. They watched from the window, mesmerized by the powdery flakes that landed on the yellow grass.
It wasn't until a teacher at Margaret Wills Elementary School called a parent-teacher conference that Evelyn really got to know the family. As the campus nearest to Astoria, the school was used to receiving refugee students, but administrators had taken note of the particular challenges faced by Dim and Zam's children—in a conversation about Cing Kim's learning disability, they'd heard about Abner's cleft palate and Zen Zuun's club foot—and reached out to Evelyn for help. The volunteer matriarch sprang into action, enlisting a charity to pay for additional surgery and orthodontics for Abner and raising the funds for the girls to visit doctors in Fort Worth and Dallas, where Zen Zuun was fitted for a foot brace and Cing Kim received a full medical assessment. Specialists found a small hole in her heart that would eventually require surgery.
Grateful, Zam worked hard to cover what costs he could. His work, difficult and repetitive, made his arms tired, and he took ibuprofen and wore wrist braces to keep pain and carpal tunnel at bay. At least there were friends to be made: eighty other Zomi, he'd learned, lived in Amarillo, and his family was soon attending picnics, prayer gatherings, and council meetings. In 2014 Zam was voted head of the Zomi community, an honor he took seriously; within six months, he'd used group funds to buy Zomi-English dictionaries and had delivered them to local middle and high schools, public libraries, and Amarillo College. It was important for his people to be engaged in their city, he felt, even as he and Dim tried to instill in their children a sense of their culture. ("Always remember the traditional days of celebration," he'd instruct at home.) By December 2015, two years after their arrival, Zam had proudly created a wall display in the living room with Christmas lights outlining the shapes of the U.S., Myanmar, and Texas. On a whiteboard near the kitchen, he wrote down the family's priorities using the English words he knew: "1. Jesus. 2. Law. 3. Education. 4. Native (race). 5. Culture. 6. Human behavior life. 7. Personality. 8. Family. 9. Relationship."
The kids, at this point, had adopted the mannerisms and outlook of their American peers; Abner had made the honor roll at school, as had Zen Zuun, and the two spoke of going to college. He hoped to play professional soccer and become an animé director; she wanted to be a scientist, "one who knows if it will be raining or snowing." The two younger sisters had their own hopes—maybe they'd be teachers, they said, or doctors, or singers, like Selena Gomez, or a combination of all three. But despite their parents' fears of them losing their cultural ties, their responsibility to their community was never far from their minds, thanks to the roles continually thrust upon them—translator, babysitter, errand-runner. For as often as Abner could expound on his love for the Avengers, he could also relay the finer points of his family's affairs, from hospital bills to plumbing issues to Medicaid. (He provided translation for this article.) "Food stamps, or SNAP, is very low," he said one day last winter. "So that is a concern. It is thirty-nine dollars a month. We're praying to get a new car. Small car. We don't have the money, but we trust in God."
Four times a year, Dim would use $10 from their savings to call her parents in Kalaymyo. She'd tell them that everything was going well, that she was looking for work. The kids, she'd say, were strong and smart. Eight thousand miles away, her parents listened on a landline phone given to them by a cousin, their understanding obscured by their awe. "They think that we will be rich and already have lots of money," said Abner. "That's all our people believe."
School principal Chris Altman, at Margaret Wills Elementary, where about 40 percent of students are refugee children.
Those displaced by war or persecution have stories that can seem almost fantastical, warped by whatever horrors impinged on their life's path. Evelyn is used to hearing about kidnappings, shootings, and limbs being blown off. She's met an Iraqi who was a cook for Saddam Hussein before the war led her to work as a laundress at Abu Ghraib and then as an interpreter for the U.S. troops; her ears rang for a week after landing in the mortar-free silence of the Panhandle. There are Somali who dodged warlords and lions as they fled to Kenya. One boy told Evelyn that when he and his brother and sister were looking for driftwood one night, to start a fire, the "night witches" came and killed his siblings; he escaped by scooting up a tree.
Among the first to arrive with these sorts of stories in Amarillo were the Vietnamese, in the seventies. Fleeing the Vietnam War, they eventually set up a few grocery stores and restaurants around town. But refugees remained relatively few until three decades later, when raids on the meatpacking plants by Immigration and Customs Enforcement in 2006 created a labor shortage. After agents arrested 297 undocumented workers from Mexico and Central America, prompting hundreds more to scatter, desperate company owners reached out to some of the city's legal residents—recent arrivals from Myanmar—and offered bonuses of $650 to $1,500 for each new worker they recruited. Word quickly spread through Texas and beyond, and refugees began to flood Amarillo in such numbers that in 2008 Refugee Services of Texas formally expanded its branch in the city. Together with Catholic Charities, the agency began placing about four hundred to six hundred refugees each year—a number separate from the two hundred or more moving from other American cities on their own.
The schools felt the impact first. "We have six hundred kids in our school, and sixty-two percent are limited in their English proficiency. It used to be about forty-eight," said Terri Huseman, the principal at Will Rogers Elementary School. More critically, the number of languages spoken in the district's 55 schools grew exponentially, to more than forty, including Zomi, Kayah Li, Somali, Arabic, and Swahili. Because schools were not given advance notice of a refugee's arrival, teachers and staff found they had to improvise, never sure whether a new student could read or had even ever held a pencil.
Around Amarillo, the influx created a sort of culture shock in reverse. "What happened is, our schools, which had always been rated acceptable, came back with poor ratings, and people began asking why," Huseman continued. (Refugees' test scores count toward a school's accountability results, even though the district receives no additional money or resources to help with refugee assimilation.) "Then our nurses and social work staff began hearing from others in the medical profession: 'Oh, yes, we know, because we have them at the hospital, we have them at the clinic.' " Around town, 911 dispatchers and police officers found they suddenly required more than just Spanish translators.
Before long, stories began to spread alongside exaggerated rumors: about clashes in an apartment complex between Somali of rival ethnicities; about refugees setting up shadow governments; about foreign fathers bothered by the authority that female teachers exercised over their sons in the classroom; about young arrivals bullying one another in school hallways; about teachers having to train their new charges in the basics of toilet-flushing. ("Yes, it's true the four-year-olds were scared by the automatic commodes," said Evelyn, annoyed. "But we weren't spending hours on that.") Flustered residents called the mayor, Paul Harpole, to complain. Principals and teachers decried the challenges before them, pleading for understanding and help.
Tensions escalated so much that in 2011 the executive director of Catholic Charities of the Texas Panhandle, Nancy Koons, arranged for some twenty school nurses, principals, and social workers to meet with her agency's national partners to discuss its placement policies ("You have to hear what I'm hearing," she explained to her counterparts). In a conference room in Amarillo, the locals tried to explain just how taxed they were. How could their schools possibly catch up with the influx of new students? Where were the resources for helping so many diverse children with so many diverse needs? How was this fair to the kids, both refugee and not? "It was intense," recalled Principal Huseman. "Everybody had a stake in the game."
In response, Catholic Charities, which alone had been placing four hundred refugees in Amarillo annually, agreed to cut its number in half. But Refugee Services increased its number, essentially negating the change, and so in 2013 Mayor Harpole, losing patience, decided to make his city's case to the federal government himself. In a meeting with U.S. representative Mac Thornberry, Texas senator Kel Seliger, and several members of the State Department, he tried to convey Amarillo's plight with words he echoed to me last spring: "Let me explain that I have no problem with refugees coming here," he said. "I think we could double the number that come into the United States, so long as we spread them out across the whole country. We just can't handle such a high concentration."
Conceding, the State Department agreed to a reduction in placements to Amarillo, and the numbers tapered: this year Catholic Charities placed 160 refugees, and Refugee Services placed 264. (Both agencies have also limited resettlement to refugees who have family already in Amarillo.) Even so, not everyone has felt the change. "I haven't seen a dramatic difference in the numbers," said Chris Altman, the principal at Margaret Wills. "We went from six non-English-speaking kids to thirty in one year, and then to eighty or ninety. Now about forty percent of our six hundred students are ESL." To help, the school has added a pre-K program for three-year-olds, after-school activities, Saturday tutoring, and summer school in July. Principal Altman bristles when she recalls articles and op-eds that have emphasized the strain caused by refugees. "It upsets me and other teachers, when we're listening to the news, when we hear about the burden and don't hear any of the good—like the drive our students have to succeed," she said. "It's a challenge more than a burden. When I get home, I often ask my husband, 'What if I had never moved to Wills?' My life is so rich."
The parking lots in Astoria serve as a timecard of sorts, marking a shift change at the meatpacking plants as early-morning workers pull into empty spots left by those headed out for the afternoon. Making her rounds through the complex, Evelyn watches. She knows there are other ways she could spend her time—women her age play Bunco, or quilt, or travel—but it's here, among her brood, that she is happiest. "When I consider the past fifteen years, I see how God was directing this all along," she told me. "I feel like he has placed me here. The families need me."
She was careful not to slip on the icy pavement as she knocked on doors one day last December, checking first on a young mother bathing her child in a plastic bathtub in her living room, then on a group of women who had gathered in another apartment, their cheeks decorated with thanaka, a yellowish cosmetic paste often used by Burmese. Also on Evelyn's list was Elia San, a 23-year-old mother with two small children who had recently been homebound.
Elia believed the future held big things for her family; she imagined her daughter, who was two, would become a doctor. Her four-year-old son, she hoped, would be a pastor. She had noticed he was already starting to read out of the same picture dictionary she used, and he often sang the songs he'd learned in preschool at Margaret Wills.
She was glad her children had each other. She had grown up an only child in a farming town in Myanmar called Matupi; despite having friends with whom she played hopscotch under the lemon trees, she'd struggled with a sense of loneliness until she met her husband, Sang, at eighteen. When the military began interfering with their village, the couple had fled by negotiating with a driver to take them to Thailand, then crossing into Malaysia on foot, walking at night to escape the notice of the authorities. In Kuala Lumpur, they'd ended up in an apartment with two other families, where they'd lived for three and a half years—and welcomed their two babies—as they waited for the U.N. to process their refugee applications. By the time they arrived in Amarillo, in November 2014, their children were two years and six months old.
The family had not required Evelyn's assistance much at first. After a few months of helping her children adjust, Elia had landed a job at Tyson cutting pork ribs and trimming meat for $14 an hour. Her shift went from 4 a.m. until 2:30 p.m.; Sang worked from 2:30 p.m. until 12:30 a.m. Sometimes Elia's knife dulled under the endless supply of meat, making her hands hurt. But she had paid off her family's $3,500 travel loan, and Sang was able to buy a used car—a Toyota Camry—within eleven months. Elia carpooled to work so that Sang could use the car for his commute, but she enjoyed their weekend drives to church. They'd bought the Camry on loan so they could build their credit; Elia had heard that within five years of arrival, many refugees had saved enough to buy a house on nearby Julian Boulevard.
But then, in the early morning of July 24, 2015, Elia fell asleep on the way to work, leaning against the passenger door in a friend's car. As she slept, her friend pulled around a slow truck on the highway and—Elia has no memory of this—slammed head-on into an oncoming car. Elia was thrown from the vehicle and did not wake up until a week later, at Northwest Texas Hospital. She had lost hearing in one ear, torn a bicep, broken a leg and a hand, and required surgery on her knee. While she was in the intensive care unit, her uncle helped watch the children and Sang took a month off work. Her medical bills amounted to more than $200,000. "How will we pay for this?" she'd asked Sang.
Though she was no longer in a wheelchair, stairs were difficult, and the family lived in an upstairs apartment, with no elevator. This had left Elia stranded, confined for months with only TV as a distraction. In the mornings, Thomas the Train. In the afternoons, Dr. Phil. Late at night, The Walking Dead. She'd been feeling a little like a zombie herself.
"Elia?" Evelyn called. She knocked on the door. "Elia? It's Miss Evelyn!"
A boy peeked out from behind a curtain, and Evelyn brightened. "William!" she said. "William! Get your mama."
Elia appeared, and as Evelyn stepped inside, she made note of her agility. "You don't even see the limp anymore!" she exclaimed. Elia smiled, covering her mouth to hide a few missing teeth. William made "strong man" arms to impress Evelyn as the toddler, Elizabeth, wandered around the room with a baby bottle.
"You're getting ready to move?" Evelyn asked. A downstairs apartment had opened up, and the family had made arrangements to transfer that weekend. Elia nodded. All that was left to do was take the final meter reading, which Evelyn explained. Elia, who had received an education in Matupi, understood; she liked reading the Burmese newspaper sold in Amarillo, and details like this did not escape her.
"You take English classes?" Evelyn continued. This time Elia looked sheepish. She'd let her attendance lapse, for fear that too much walking would worsen the pain in her steel-plated shins.
"Tell me, Elia, what has been hard?" asked Evelyn.
"English speaking, a year without a car, then the accident," Elia replied, her hand brushing her daughter's head.
"At the hospital, what made it hard?" Evelyn asked.
"English. Sometimes Sui [Elia's cousin] would translate," said Elia.
"So English soon?" Evelyn suggested.
Elia nodded. The new ESL semester was starting before long. "Upstairs very boring," she said with a laugh. She had received some pleasure, she said, from watching her husband cook; she'd taught him from the couch, and his skills were improving. But she wanted to reclaim her dreams. She wanted a tidy, landscaped home like the ones she saw on television.
Evelyn Lyles with children from the Astoria Park apartment complex.
Just before spring, as Donald Trump was surging in the primaries and boasting that he could look refugees in the face and refuse them entry, Dim settled into a folding chair in Evelyn's office. She had received good news: Cing Kim's doctors had decided that the hole in her heart was small enough that they could postpone the girl's operation a little while longer. Around Amarillo, residents spoke less about politics than the economy. At 2.9 percent, the city's unemployment rate was lower than the national average.
All this meant that Dim had finally started to consider her own future. She'd thought, at first, about working for herself. She could import cloth from India to make bedspreads and slipcovers, like the floral number she'd made for her own sofa. But the shipping proved too costly, so instead she'd found work with a local tailor, calling in each day to see what help was needed. Some days she got a few hours' work, but other days there was nothing for her to do, and the money wasn't adding up. She had sought out Evelyn for guidance.
"I want you to apply at United Market Street," Evelyn told her. The grocery store, a mile up the road, was holding open interviews that afternoon and offered part-time positions. "I'm going to ask you some questions they might ask you," she said. "We're going to practice, okay?"
Dim nodded and straightened up in her chair.
"Tell me about your family," said Evelyn.
"My family, six people," said Dim.
"What are your hobbies?"
"Hobbies? What are?"
" 'Hobby' means something you like to do for fun. You like to sew. You like to cook. Tell me more things you like. Say in English."
Dim asked, "How spell?"
"H-o-b-b-y," Evelyn said. Dim wrote the word down.
"H-o-b-b-y," Dim repeated. "Cooking, c-o-o-k-i-n-g. Sewing, s-e-w-i-n-g."
"Can you work evenings?"
"Evening?" Dim said with intensity, unsure again of a word's meaning. Then she laughed. "I don't know, Miss Evelyn!"
Evelyn remained serious. "You say, 'Mmm. Evening is hard.' Did you have a job before?"
"I don't know, Miss Evelyn!"
Evelyn reassessed her hopes for Dim. Perhaps she might do better in the grocery's kitchen, where language skills weren't as important. She said as much to Dim, and they began again. "So you come in and say, 'I want to talk to Tina about a job," said Evelyn.
"I want to talk to Tina," Dim said.
"What kind of job?"
"Cooking, cleaning, Miss Evelyn!" said Dim proudly.
"Do you want to work in the kitchen?"
"Kitchen. Cooking, cleaning, washing dishes," said Dim.
Evelyn nodded encouragingly, adding, "Make food, cut the salad."
Dim twisted her face in confusion. "Cut the salad?" she asked.
By the time she made it to the grocery store, a few hours later, the interviews had ended for the day. Dim returned to Astoria visibly relieved, glad for the chance to review her notes again. At home, when her children gave her a version of her usual pep talk—chin up, keep trying—she shot Abner a look, as if to say she didn't want to hear it.
Across the complex, settled in her ground-floor apartment, Elia was assessing her own progress. To Evelyn's delight, she had successfully contacted companies with her family's change of address so that they wouldn't miss any bills or notices—especially those from a lawyer who had offered to help settle her hospital charges. Best of all, she'd returned to ESL classes, negotiating the lingering pain from the injuries in her right arm by writing left-handed. Classes were going well, she announced when Evelyn stopped by for a visit. She was improving.
Elia had been in the U.S. for just over a year. This meant she had four more before she could take a citizenship test, a $680 exam that required her to stand before a federal adjudicator in Dallas and display her command of English and the nation's history and government. For a fee, a leasing agent at Astoria named Grace Pum helped people with the necessary paperwork. (Evelyn has tried it, but it requires a familiarity with citizenship rules that she lacks. "It's time-consuming, and if you make a mistake, oh my goodness," she said.) Beginning at the five-year mark, Elia would have two years to pass the test; if she didn't pass, or if time ran out, there would be little further opportunity to acquire citizenship, and she would be cut off from all benefits, including Social Security.
Evelyn knew people at Astoria in this situation—people who had been unable to take the test. Some were blind, or couldn't read, or had never grasped the language. When they got too old to work, they became dependent on relatives or fellow immigrants. No one wanted that fate. Learning English, as Elia knew, was not just a key to a better job, it was also a race against the clock.
"Did you think about telling your story this week?" asked Evelyn. Elia had informed Evelyn that she wanted to share a personal story, in English, with her ESL class. She'd been practicing it repeatedly. "God has been good to me," she planned to say. "God saved my life. In July I had a bad car accident. Everyone thought I was dead. I had a broken leg, broken arm, and head. I could not walk for two months. Then friends prayed for me. I also pray. God loves me and healed me. God gave me Jesus as my savior. God loves you. Thank you."
This was an impressively lengthy speech for a refugee who had been in the U.S. for only fifteen months, and Elia beamed with pride at Evelyn's question. She nodded, ready to practice her story once more when, suddenly, Evelyn's phone rang.
"Hello? Zaw?" said Evelyn, recognizing the number. Zaw, a refugee who had been in Amarillo seven years, had agreed to drive a family to a pediatric dentist clinic a few blocks away, but he had not been confident in the directions. (His name has been changed.) There was no answer. "He's supposed to get them there at two-thirty, and it's two-fourteen," said Evelyn, nervously. "Could be that a friend is playing with the phone. That happens quite a bit."
She turned back to Elia, but the phone rang again. "Hello, Zaw?" she answered. "Are you at the apartment? I'm going to come and show you, okay? I'm going to come right now." She paused. "Oh, you're already gone? Okay."
Zaw had overshot the clinic. Evelyn tried to give him directions, but finally, doubting he would know where to turn, she excused herself from Elia's apartment and drove to the clinic herself. Soon she was on Georgia Street, standing with her hands on her hips, ready to flag Zaw down. He called her several more times, lost again. "His whole life revolves around Georgia Street," she said, annoyed. "He's told me ten times, 'Yes, I know where Georgia and I-40 is.' He's in Soncy—five miles from here. He says, 'Where is it? Is it by Academy?' Nooooo. I don't know if he's going to get here. And if he does, he's going to be late, and they're going to send him away. This is a specialty clinic."
Evelyn doesn't dream much these days; she's too light a sleeper. But sometimes, in the middle of the night, she wakes with a start, panicked about some aspect of life at Astoria. She worries that she neglected to tell a resident some vital fact, that one day something terrible will happen because of a misstep she could have prevented—some paperwork that wasn't right, some phone call that went unreturned, some word in English that didn't get understood, directions she gave wrong. And then what would she say?
On April 21, Mayor Harpole found himself in Austin, in the cavernous chambers of the Texas Senate. He was dressed in a dark-blue suit, and he leaned into a small microphone as he prepared to speak. Events in the previous few months—the ever-worsening conflict in Syria, the Paris attacks, the shooting in San Bernardino—had renewed public fear about terrorists who might infiltrate the country's immigration system. The number of refugees worldwide had risen to 63.5 million, the highest ever. Shortly after President Obama announced that the U.S. would be increasing the number of refugees it admitted to 85,000 for 2016, Lieutenant Governor Dan Patrick had charged the Senate Committee on Health and Human Services with studying "the impact to the state of the increasing number of refugees relocating to Texas."
This is how Harpole had ended up at the Capitol to testify, once again, about his city's experience. He had no issue with refugees, he reiterated, or their assimilation. But it was unfair to resettle so many of them, with so many different needs and languages, in one place—especially with locals having so little say in the matter. "Who makes the decision about how many can come to our city? We cannot get a definitive answer on that," he said. "We have not, as a community, been involved in the consultation meetings at all."
It was a complaint that would grow only more widespread in the weeks that followed. In June a federal court threw out a lawsuit filed by attorney general Ken Paxton that had sought to prevent the resettlement of Syrian refugees in Texas; in September, citing security concerns and the need for local control, Governor Abbott announced the state's withdrawal from the federal resettlement program altogether. In Amarillo, meanwhile, unfounded rumors began to spread once again, this time about refugees causing an uptick in crime (as one internet headline blared, "Non-Assimilating Refugees Flooding Amarillo Texas: The chaos taking place in Europe may be coming to your town").
Amarillo didn't seem very chaotic to Dim, unless you considered the rush hour at her new job—cooking chicken at McDonald's—chaotic. She'd applied in September, after Cing Kim had undergone her surgery successfully and Dim could commit to a permanent job. She'd planned on walking the two miles to work, but Zam had saved enough to buy her a used Honda. Now she was saving too, for Abner's driver's education class. Her son would be graduating in the spring, and she hoped that with enough financial aid, he'd be able to attend Amarillo College.
Elia, meanwhile, was still not well enough to apply for a job. But her body was stronger, as was her English, and she had begun pouring her energies into helping out the family of a new neighbor, a man named Haffizullah, whom she'd taken to calling Muhammad because, she explained with a laugh, "his real name is too long." Haffizullah and his wife are Rohingya, a Muslim minority in Myanmar that suffers violent persecution despite the country's recent democratic reforms, and speak almost no English, but Elia had discovered they could communicate in Malay, a language she had picked up during her time in Kuala Lumpur. Evelyn had noticed that she'd been gathering vegetables for them from the community garden, giving them Pampers, and babysitting their boys. "There's a circle," Evelyn observed. "They come in, we help them, and later they help others."
Naturally, Evelyn too was helping Haffizullah, and one day in the fall she stopped by his bare apartment to assist him in finding a ride to Walmart, where he could catch a bus to work. He'd just lost his bike to a thief, and his ride had stood him up, he explained; if he started walking now, he'd be late, and without a way back home from the bus stop at 1 a.m., he'd have to stay at a relative's place near the meatpacking plant the rest of the week.
Evelyn, glancing out the window, spotted two Congolese men who were getting into a car. "I know them! They work the B shift!" she exclaimed. She power walked out and got the driver's attention. "Could this man go with you to the bus?" she asked. After some discussion in broken English, Haffizullah got into the backseat.
"Every day?" he asked Evelyn.
"I think so," she replied. "Right now you need to go."
The car took off, and Evelyn headed to her office. This would not be Haffizullah's last hurdle, not by a long shot—just as there were always more hurdles for the hundreds of other families at Astoria. The moment she got to her desk, she knew, there'd be a line of residents forming, coming to her with questions, so many of them that she'd have trouble locking up that evening.
But Haffizullah was taken care of for now, and that was a victory. "How fortunate," she said, marveling, "that the solution was right across the street."
Tags: Family, Longreads, The Culture, Politics, Refugees
sandrasugarbaker
Mrs. Lyles embodies the meaning of generosity, kindness and helpfulness. She is an example to us all and the city of Amarillo is as lucky to have her as are the refugees she devotes her time and efforts to. As a Christian she is a role model of accemptance and grace. There's plenty of money in Texas and I hope some of it finds its way to her and the teachers at the elementary school so that they can put it to good use with translators, tutors, and other life skill courses that would support the transition to life in America for the refugees. God Bless you Miss Evelyn!
sandrasugarbaker,
Your kind comments and observation were certainly a refreshing and heart-warming change from the misinformed and, to put a finer point on the subject, diatribes rife with mistrust virtually bordering on xenophobia. On the contrary, over the course of your life, you have managed to find the beauty in the differences of the various cultures to which you have had access. If only your refreshing (and, more importantly, educated) way of looking at the world were as common as they were in the past, imagine how much richer everyone's lives could actually be. Thank you again for enlightening those who, for untold reasons, have been unable to do the same.
Why thank you, John! Your words warmed my heart and I couldn't agree more that diversity and appreciation for other cultures enriches cities and the citizens who reside there. I pray for others to find empathy when considering the plight of many refugees. So many would love nothing more than to return to their countries and to families left behind but they cannot; for most it isn't safe and there are not opportunities for a fair and free life. I struggle to understand why others can't comprehend that fact and as a next step, welcome them to our country with open arms and a lending hand. I subscribe to the build a longer table not a higher fence view and wish more would do the same. Fear will eat you alive! Thanks again, John. Blessings to you.
DefendAmerica
Texas Monthly…a liberal rag attempting to steal our national sovereignty while hiding behind the skirts of old ladies.
Give it up. It just doesn't work.
WestTexan70
Go away.
Welcoming the Stranger: Faces of the Refugee Crisis
By Kimberly Meyer
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Right Aid
By Dave Mann
Boomtown, Episode 6: A Thin Line
By Christian Wallace
Texas Monthly Recommends: A UNT Alum's National Book Award-Winning Memoir 'The Yellow House'


UPDATE: Our Peanut Butter and Pumpkin Dog Treat Recipe has been one of our all-time most popular treat recipes, appearing not only in our The Healthy Hound Cookbook but also in several magazines. We wanted to share it once again and hope you enjoy it!
Now that it's October, we wanted to feature a whole month of special Halloween dog treat recipes, starting with our Peanut Butter and Pumpkin Dog Treat. Whether they're just for you to enjoy at home or whether you're headed to a doggie party this month, these treats feature seasonal ingredients and, as always, just take a few minutes to make!
The pumpkin you'll need is canned pumpkin…but not pumpkin pie filling (which has too many spices and too much sugar). You're looking for just plain, ol' canned pumpkin. Mine was in a 15-ounce can so there's plenty more in the refrigerator now for the next batch of treats.
Mix all your ingredients except the water then add it sparingly. Depending on the amount of oil in your peanut butter, you may need the entire 1/2 cup…or not. The dough should turn out sticky but not dry; if it feels dry, you need a little more water. If it's too sticky (if it's sticking to your fingers), you need a little more flour.
Roll the dough out on a lightly floured surface; I used pumpkin-shaped cookie cutters but you can use any shape then placed them on a greased cookie sheet.
After baking for about 20 minutes, they were ready to take out and cool. Be sure to cool the peanut butter and pumpkin dog treats completely before serving or refrigerating.
And be warned: the smell of these treats baking is frighteningly good! You just might have to test one before giving it to your dog!
We've used a similar recipe for our Husky. The biscuits are baked twice to give them some extra crunch. (And yes, I've been known to sample them — but only in between the first bake and the second; the finished product is a little too robust for human teeth.) They keep for a long time, too, and Katya loves them. We're lucky enough to have pumpkins growing in the garden, so when it's jack o' lantern time we cook up a couple and make these with fresh pumpkin. So good!
Pumpkin soup…mmm, that sounds so good!
Our dogs have been eating Proportions dog food lately; it comes with a dry food and then a chicken mixture you pour over the kibble…the chicken mixture is in a pumpkin soup that they LOVE! I've got to make some pumpkin soup for our girls!
Is it possible to substitute mashed cooked sweet potatoes for the pumpkin in this recipe?
Just to follow-up, I made the treats with mashed baked sweet potatoes instead of the pumpkin and they came out great! The dogs loved them. The dough was perfect to use with a dog bone shaped cookie cutter.
I'm so glad they turned out well…I've got to make those for our dogs! This weekend I cooked them (in the slow cooker) some sweet potatoes with chicken breast and they LOVED it!


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Orbite Technologies Inc. (TSX:ORT)(OTCQX:EORBF) ("Orbite", or the "Company") today announced that, further to its press release of October 13, 2016, production activities continue as planned at its high purity alumina ("HPA") plant.
Feedstock digestion, which commenced on September 30, is now operating automatically, smoothly and as anticipated, and production times have come down, as planned.14 digestion batches have been completed, and batch production times have decreased to four hours per batch, well within design parameters.
The digestion liquors continue to be transferred to the crystallisation circuit for the production of Aluminum Chloride Hexahydrate (ACH) crystals, the precursor to High Purity Alumina (HPA). To date, 35 tonnes of ACH crystals have been produced, corresponding to close to 10 tonnes of HPA, once calcined.
Purity levels of the ACH crystals, produced in the first batches, were in excess of 5N, ahead of expectations. This corresponds to HPA purity levels in excess of 4N5. Recently produced ACH purity levels have increased further and are very close to reaching the levels corresponding to 5N HPA.
Based on this positive progress, the company will commence decomposer and calciner heat up in the coming days to commence calcination of ACH crystals into HPA.
"Purity of the initial batches of ACH crystals is excellent, and continues to surpass our expectations so soon after start of production," stated Glenn Kelly, CEO of Orbite. "Operations to date clearly validate our technology for the production of ACH and we look forward to commencing the conversion of the ACH crystals into HPA of 5N+ levels. As we intend to be a producer of ultra-high purity alumina, our production sequence is to only transfer ACH crystals to the calcination circuit once the required purity levels have been achieved. While we could have started HPA production with earlier batches, we aim to deliver higher purity and therefore higher value HPA to the market in response to demand from our potential customers."


Currently, with the rapid development of technology for the Internet of Things [1, 2], condition monitoring systems (CMSs) [3, 4] are widely used to monitor the working condition of equipment. KOMTRAX (KOMTRAX: http://www.komatsuamerica.com/komtrax) from Komatsu and IEM (IEM: http://www.sanygroup.com/group/en-us/) from SANY are well-known CMSs that generate a large amount of telemetry data while monitoring the working condition of equipment at runtime. Event sequence data especially are one main type of telemetry data, which record a sequence of the operations on the equipment. If we can analyze the working mode of equipment according to these event sequence data, it will help us better understand the working condition of equipment at runtime.
Diagnosis of working condition of equipment mainly depends on analyzing the telemetry data collected at the runtime of the equipment. In most CMSs, data analysis is the only way for engineers to diagnose the working condition of equipment. Telemetry data mainly contain operation events, performance counters, alert events, and others in the real CMSs. Most telemetry data can be classified into two categories: continuous time series data and temporal event data. Time series data is a sequence of real-valued data points, captured and sampled typically at successive time points equally spaced with a uniform time interval. For example, the engine temperature of equipment is a typical example of time series in the CMSs. An event sequence in CMSs is used to record the occurrences of a specific message indicating that something such as an operation has happened in the equipment. For example, an event sequence of "pumping concrete" in a concrete pump truck contains an event pumping concrete, which represents the idea that the concrete pump truck starts pumping the concrete.
As illustrated in Figure 1, an event sequence records all the specific operations on the equipment at runtime, which usually provides enough information to engineers for working condition diagnosis of equipment. The previous studies on analysis of event sequence data for working condition diagnosis are mainly grouped into two categories: the correlation analysis between distinct event sequences [6–8] and events based process mining algorithms [9, 10]. The correlation analysis between distinct event sequences usually provides useful hints for causality analysis. Although correlated metrics may not exactly be the root causes of events, they could also provide intermediate useful information that pinpoints the root causes of events. The process mining algorithms focus on the occurrence order of distinct events with the help of process models [11, 12], which mainly indicate the working process of equipment.
Figure 1: Actual example of telemetry data of a concrete pump truck at one runtime. Oil temperature is a typical continuous time series, which records the variation of oil temperature at one runtime of a concrete pump truck. Operation event sequence is a typical event sequence, which records the occurrences of six operation events (warm-up, landing leg unfolding, cantilever unfolding, concrete pumping, landing leg folding, and cantilever folding) on the concrete pump truck at one runtime.
However, the occurrence frequency of events also provides us with important information for working condition diagnosis, which is ignored by the previous studies. We take a concrete pump truck as an example. As illustrated in Figure 1, at a normal runtime of concrete pump truck, a concrete pump truck needs five concrete pumping events to finish pumping a hopper of the concrete. With the wear and tear of concrete pump truck, the concrete pump truck needs seven or more concrete pumping events to finish pumping a hopper of the concrete. Although the occurrence order of the six events (warm-up landing leg unfolding cantilever unfolding concrete pumping landing leg folding cantilever folding) is the same, yet the working condition of the concrete pump truck has changed a lot. If the occurrence frequencies of events are taken into consideration for working condition diagnosis, it will enhance the ability of working condition diagnosis for engineers, which will help us better understand the working condition of equipment.
In this paper, we proposed a probabilistic generative model called working condition model (WCM), which is capable of depicting the working condition of equipment at runtime. According to event sequence data in different working condition of equipment, we simulated the process of event sequence data generated in order to get the WCM of equipment. Furthermore, with the help of WCM, we extended the application of event sequence data to more domains such as anomaly detection and the variation trend analysis of working condition. Motivated by the real requirement of working condition diagnosis, our working condition model tries to answer the following three questions:(a)How many types of working modes (details in Section 3) does the equipment have at runtime?(b)In each type of working mode, how does an event sequence behave?(c)For a new event sequence at runtime, which type of working mode does it belong to?
Our evaluation consists of multiple phases. First, we model the WCM of the real event sequence data sets collected from 279 concrete pump trucks over a period of 6 months. We analyze the performance of the WCM of the concrete pump truck. Then, we apply the WCM of the concrete pump truck for more applications including anomaly detection and the variation trend analysis of working condition.
Our work presents a probabilistic generative model named WCM to simulate the process of event sequence data generated and to depict the working condition of equipment at runtime. The contributions of this paper are as follows:(i)Motivated by real applications, we propose the WCM to depict the working condition of equipment. To the best of our knowledge, this is the first attempt to simulate the process of event sequence data generated for working condition diagnosis.(ii)We illustrate two useful applications based on WCM: automated detection for a new work cycle and automated detection for anomalous work cycles.(iii)The experiments on real data from a well-known Chinese construction machinery manufacturer show the effectiveness of our model.
The rest of the paper is organized as follows: In Section 2, we introduce some related works. The problem statement and formulation are introduced in Section 3. We introduce our approach in Sections 4 and 5. The empirical evaluation is shown in Section 6. Finally, we conclude our work in Section 7.
An event is a happening of interest [13, 14]. In the surveillance of equipment, the interest in events comes mostly from the state of equipment changes that are produced by equipment manipulation operations . Example events in the actual surveillance of the concrete pump truck include warm-up, landing leg unfolding, cantilever unfolding, concrete pumping, landing leg folding, and cantilever folding as shown in Figure 1. When a sequence of events takes place, we refer to these occurrences to get the event sequence data. The main idea of analysis of event sequence data is to process events to gather meaningful or valuable information and then to derive actions from them.
Events in an event sequence are often interrelated and form complex relationships. The correlation analysis of event sequence data [7, 8, 16, 17] focuses on detecting these relationships and is extended to other related applications such as anomaly detection [18–20]. A temporal, spatial, or causal relationship of events can determine the partial order between events . Hence, event sequence data based process mining algorithms focus on the causal relationship of events by analyzing the occurrence order of distinct events [21, 22]. There have been many existing process mining algorithms [23, 24] and tools [25, 26] to mine the causal relationship of events, which is capable of instructing engineers to better understand the operation procedure of equipment .
Working condition of equipment  is the condition in which the equipment works, including but not limited to such things as amenities, physical environment, stress and noise levels, degree of safety or danger, and the like. The working condition diagnosis usually uses specific models or variables for different applications. In the correlation analysis of event sequence data for working condition diagnosis, the correlation coefficients of event sequence data, for example, the Pearson correlation [6, 7, 29, 30] and the Rank correlation , are used to depict the working conditions of equipment. In the process mining algorithms of event sequence data, varieties of process models, for example, Petri net  and business process modeling notation [33, 34], are specific to depicting the working condition of equipment for different process mining algorithms. In this paper, we take the occurrences frequencies of events into consideration and simulate the process of event sequences generated in different working modes of equipment. Hence, we use the occurrence probability of events in the event sequence to depict the working condition of equipment at runtime.
In probability and statistics, a generative model  is a model for randomly generating observable data, typically given some hidden parameters. It specifies a joint probability distribution over observable data. Generative models are used in machine learning  either for modeling data directly (i.e., modeling observations drawn from a probability density function), or as an intermediate model to forming a conditional probability density function. A conditional distribution can be formed from a generative model through the Bayesian rule .
For example, latent Dirichlet allocation (LDA) [5, 37, 38] is a typical generative model, which is widely used in many domains. In natural language processing, LDA is capable of simulating the process of documents generated well, where observations are words collected into documents, and it posits that each document is a mixture of a small number of topics and that each word's creation is attributable to one of the document's topics. Figure 2 illustrates the graphic model  of LDA . With plate notation, the dependencies among variables can be captured concisely.
Figure 2: Graphic model of a typical generative model LDA. The boxes are plates representing replicates. The outer plate represents documents, while the inner plate represents the repeated choice of topics and words within a document. denotes the number of documents and the number of words in a document. are hidden parameters and is observations. Details about LDA refer to .
As the working condition of equipment is always corresponding to a period, for example, one day or one week, we first determine the basic unit of observation for working condition diagnosis, intended to ease the working condition diagnosis according to the event sequence data.
Definition 1 (work cycle). A work cycle of a piece of equipment, denoted by , is a complete work period, that is, a complete usage period of the equipment from the time the equipment starts working until it shuts down.
We define the idea that consists of elements that are integers from , where is the number of work cycles. There is one important advantage in adopting the work cycle as the basic unit of observation in terms of event sequence data analysis for working condition diagnosis. In our opinion, no matter in what kind of circumstances (e.g., different places and different climates) the equipment works, the working condition of equipment in one work cycle mainly behaves similarly. For example, in a work cycle of the concrete pump truck illustrated in Figure 1, different concrete pump trucks usually have a similar working process: warm-up landing leg unfolding cantilever unfolding concrete pumping landing leg folding cantilever folding, even though the concrete pump truck works in different working circumstances.
Definition 2 (event). An event of the equipment, denoted by , is to record an occurrence of a specific message indicating that something such as an operation has happened in the equipment.
For example, in Figure 1, there are six events (warm-up, landing leg unfolding, cantilever unfolding, concrete pumping, landing leg folding, and cantilever folding), which reflect the working condition of some component in the concrete pump truck, respectively. We will use integers to denote the entries in the event set, with each event taking a value from , where is the number of unique events in the event set denoted by .
Definition 3 (event sequence). An event sequence, denoted by , of the equipment consists of a sequence of events that occur in work cycle .
An event sequence is represented as a vector of events, , with entries. For example, in Figure 1, the event set of the concrete pump truck contains six () events, denoted by , where the integers represent the entry of the events warm-up, landing leg unfolding, cantilever unfolding, concrete pumping, landing leg folding, and cantilever folding, respectively. Hence, the event sequence is equal to a vector with the length , denoted by .
Suppose that the data set has work cycles of the equipment, corresponding to event sequences. The data set with event sequences is represented as a concatenation of the event sequence vectors, which we will denote by , having .
In a work cycle, an event sequence provides us a main working process of the equipment. However, an occurrence of the event is also related with the working place and working date of the equipment. For example, the concrete pump truck will add an operation event concrete mixing in order to prevent the concrete setting if the working temperature is low. The working temperature is directly related with the working place (e.g., north or south of China) and working date (e.g., winter or summer).
In addition to these events, we have the information about the characteristics of each event sequence (work cycle): working place, working date, and equipment pieces number of the work cycle. We define to be the set of working places of work cycle . consists of elements that are integers from , where is the number of working places which generated the event sequences in the data set. will be used to denote the number of working places of work cycle . We define to be the set of working dates of work cycle . consists of elements that are integers from , where is the number of working dates. (In order to ease the notation, in the working date, we just record the working month of the work cycle, which means .) will be used to denote the number of working dates of work cycle . We define to be the set of equipment number of work cycle . consists of elements that are integers from , where is the number of the equipment pieces.
Definition 4 (work cycle characteristic). A work cycle characteristic (WCC) is five-tuple set, denoted by , which record all the information about the work cycle .
A WCC is corresponding to a work cycle, so the original data set is redefined as a group of WCCs, denoted by . The WCCs of two work cycles are likely to be different, though they have the same working place and working date. The main differences between the work cycles center on the occurrence of the events. However, the occurrence disciplines of the events are akin to each other for the work cycles in the same working mode. For example, the concrete pump truck has two main working modes: pumping mode and traveling model. For the work cycle in the pumping mode of the concrete pump truck, the occurrence of the event concrete pumping is frequent, as shown in Figure 1. However, for the work cycle in the traveling mode, the occurrence of the event concrete pumping is none, since the concrete pump truck can not pump concrete in the traveling mode.
Definition 5 (working mode). A working mode, denoted by , is on behalf of a kind of work cycles that is about a specific subject, has an identifiable purpose, and can stand alone.
For event set , we define working mode vector (WMV) to be the set of events associated with its occurrence frequency , where . The WMV is able to depict the occurrence disciplines of events according to the occurrence frequency of events. Therefore, if we can get a group of WMVs for a group of work cycles, it will help us better understand the occurrence disciplines of events.
Definition 6 (working mode space (WMS)). A working mode space (WMS), denoted by , is a set of WMVs for a group of given work cycles of equipment.
Actually, the WMS is akin to a group of cluster centers, each of which depicts the working condition of equipment in different working modes.
In this section, we develop effective algorithms for the inference of the WMS for a group of given work cycles of equipment. Before proceeding, we formulate our problem as follows.
WMS Inference Problem. Given a group of work cycles associated with the corresponding WCCs , the inference problem is to infer the WMS model , where represents the number of working modes.
With the help of WMS, we can find that, in different working places and different working dates, the work cycles of equipment have different working modes. Meanwhile, there are several working modes in the same working place and the same working date. The WMV of working mode reflects the working condition of its corresponding work cycle, especially the occurrence disciplines of events.
In the remainder of this section, we first introduce the WCM for learning the WMS for a group of given work cycles, and then introduce the inference framework of the WCM.
The WCM is a hierarchical generative model in which each event in a work cycle is associated with three latent variables: a working place, , a working date, , and a working mode, . These latent variables augment the -dimensional vector (indicating the values of all events in the event set ) with three additional -dimensional vectors, , , and , indicating working place, working date, and working mode assignments for the events.
As we observed, the sets of working places and the sets of working dates for each work cycle are observed. This leaves the unresolved issue of having unobserved working places and working dates and avoids the need to define a prior on working places and working dates, which is outside of the scope of our model. Each working place is associated with a multinomial distribution over working mode, and each working date is also associated with a multinomial distribution over working mode. Conditioned on the set of working places and the set of working dates, associated with their distributions over working modes, the process by which the corresponding event sequence for a work cycle is simulated can be summarized as follows: first, a working place and a working date are, respectively, chosen uniformly at random for each event that will appear in the work cycle; next, a working mode is sampled for each event both from the distribution over working mode associated with the working place of that event and from the distribution over working mode associated with the working date of that event; finally, the events themselves are sampled from the distribution over events associated with each working mode.
This simulating process can be expressed more formally by defining some of the other variables in the WCM. Assume we have working modes. We can parameterize the multinomial distribution over working modes for each working place using matrix of size , with elements that stand for the probability of assigning working mode to an event occurring in working place . Thus , and for simplicity of notation we will drop the index when convenient and use to stand for the th column of the matrix . Similarly, we use matrix of size to parameterize the multinomial distribution over working modes for each working date, where elements stand for the probability of assigning working mode to an event occurring in the working date . Thus, , and we will also drop the index when convenient and use to stand for the th column of the matrix , intended to simplify the notation. The multinomial distributions over events associated with each working mode are parameterized by matrix of size , with elements that stand for the probability of simulating to make event occur in the working mode . Again, , and stands for the th column of the matrix . These three multinomial distributions are assumed to be generated from symmetric Dirichlet priors with hyperparameters , , and , respectively. In the results of this paper, we assume that these hyperparameters are fixed. This notation is summarized in Notations.
The sequential simulating procedure of first picking a working place and a working date, respectively, followed by picking a working mode, and then simulating an event to occur in this working mode according to the probability distributions, leads to the following generative process:(1)For each working place choose ; for each working date choose ; for each working mode choose Dirichlet.(2)For each work cycle , given the vector of working places , given the vector of working dates , for each event , conditioned on choose working place , conditioned on choose working date , conditioned on and choose working mode , conditioned on choose event .
Figure 3: The graphic representation of WCM.
In (3), the factorization makes use of the conditional independence assumptions of model. Meanwhile, the variables and are mutually stochastically independent. Equation (3) represents the probability of the events in terms of the entries of the parameter matrices , , and as introduced above. The probability distribution over working place assignments, , is assumed to be uniform over the elements of and deterministic if . Similarly, the probability distribution over working date assignments, , is assumed to be uniform over the elements of and deterministic if . The probability distribution over working mode assignments, both and , is the multinomial distributions and in and , respectively, that corresponds to working place and working date , respectively. The probability of an event given a working mode assignment, , is the multinomial distribution in that corresponds to working mode .
In (4) and (5), we treat , , and as random variables and compute the marginal probability of a corpus by integrating them out. are the Dirichlet priors on , , and , respectively, as we defined before.
The WCM contains three continuous random variables: , , and . Various approximate inference approaches have recently been proposed for estimating the posterior distribution for continuous random variables in hierarchical Bayesian models. In this paper our inference method is Gibbs sampling , which is a special form of Markov chain Monte Carlo.
Our target of estimation is to compute the posterior distribution . In order to sample the values of the distribution, we have to use the latent variables , , and to estimate the posterior distribution:The estimation process mainly involves two steps: first, we use Gibbs sampling to get approximate posterior ; second, can be computed directly for each sample, by exploiting the fact that the Dirichlet distribution is conjugate to the multinomial.
According to (1)~(5), we can derive a basic equation needed for the Gibbs sampler as shown in (7). In (7), means working mode assigned to working place count matrix, where means the number of events assigned to working mode in the working place excluding the working mode assignment to event . Similarly means working mode assigned to working date count matrix, where means the number of events assigned to working mode in the working date excluding the working mode assignment to event . Similarly, represents event assigned to working mode count matrix, where represents the number of events from the th entry in the event set assigned to working mode excluding the topic assignment to event . Meanwhile, represents the vector of working place assignment, vector of working date assignment, vector of working mode assignments, and vector of event observations in the data set except for the th event in the th work cycle, respectively.
The main sampling steps are as follows: we first initialize the working place, working date, and working mode assignments, , , and , randomly. In each Gibbs sampling iteration, we sequentially draw the working mode, working place, and working date assignment of the th event from the joint conditional distribution in (7). With the increasing of iterations, the Gibbs sampler will approach its stationary distribution—the posterior distribution .
Given , , and , computing posterior distributions on , and is straightforward. Based on the fact that the Dirichlet distribution is conjugate to the multinomial distribution, then we can getwhere represents the vector of counts of the number of times each event has been assigned to working mode . and are similar to . Then we can evaluate the posterior probability of each element of , and as follows:where is the matrix of working mode assigned to event counts exhibited in and refers to sample from the Gibbs sampler. These posterior probabilities also provide point estimates for , , and and correspond to the posterior predictive distribution for the next event from a working mode, the next event from a working date, and the next working mode in a work cycle, respectively.
We trained the WCM on a real world data set collected from a well-known Chinese construction machinery manufacturer. The data set is a set of event sequence data from the concrete pump truck in months (from June 2012 to November 2012). This data set contains work cycles, different working places, different working dates, a total of event tokens, and an event set size of unique events. The working date of each work cycle is according to its real working month, which means the working date set . Because the event sequence data are all collected in the Chinese Mainland, we divide the working places into 5 regions according to administrative region of China: Northern China, Northeastern China, Eastern China, Mid-Southern China, and Western China.
The concrete pump truck is a type of construction machinery, which is a truck associated with a concrete pump. It alternates between two working statuses: traveling and pumping. In the pumping status, it will push the concrete to the specified location. In the traveling status, it is just a truck. In the experiment, we mainly focus on events in the pumping status. Figure 4 shows the stream of the concrete in the concrete pump truck at runtime and operation sequence of different systems in the concrete pump truck. The concrete pump truck first switches to pumping status and then unfolds and fixes the landing leg. Next, it unfolds cantilever to the specified location. Afterwards, the concrete is poured to the hopper, and meanwhile the stirring system initiates stirring the concrete. Finally, the pumping system initiates pumping the concrete in the hopper to the specified location. When the pumping ends, the concrete pump truck stops the pumping system and then folds the cantilever and landing leg successively. Table 1 shows the relations between systems and events in the concrete pump truck.
Figure 4: The stream of the concrete in the concrete pump truck and the operation sequence of the concrete pump truck at runtime.
Table 1 shows all the events in the event set. There are two types of events: alert event and operation event. The occurrence of an alarm event is to remind the operator that some emergency happens. For example, the occurrence of event RCI means to remind the operator to import concrete into the hopper. The alarm event is not a regular operation. The operation event is the real record of regular operations in the concrete pump truck.
As mentioned earlier, in the experiment described in this paper we do not estimate the hyperparameters , , and . Instead they are fixed at , , and , respectively. In this paper we use the perplexity of the model on test work cycles to evaluate when the performance of the model begins to stabilize.
The perplexity of new unobserved work cycle that contains events and is conditioned on the working places and working dates of the work cycle is defined aswhere is the probability assigned by the WCM. To simplify notation here, we do not consider the explicit dependency on the hyperparameters. For multiple work cycles, we report the average perplexity over work cycles defined as follows:The lower the perplexity the better the performance of the model. We can obtain an approximate estimate of perplexity by averaging over multiple samples according to (9), calculated as follows:Experimental results, using different values for , indicated that samples is a reasonable choice to get a good approximation of the perplexity. Because of the exchangeability of the working modes, it is possible that quite different solutions of working modes are detected across different samples. In practice, however, we have also found that the solutions of working modes are relatively stable across samples, with only a small subset of unique working modes appearing in any sample. Hence, we use the average perplexity values across samples in the experiment.
Figure 5 illustrates the perplexity as a function of iterations of the Gibbs sampler, for a model to fit the data set, respectively. It appears from Figure 5 that performance of models (for different settings of parameter ) trained using the Gibbs sampler appears to stabilize rather quickly (after about 100 iterations), at least in terms of perplexity on the data set. This indicates that the perplexity values flatten out after a or so iterations of the Gibbs sampler.
Figure 5: Perplexity as a function of iterations of the Gibbs sampler for a model, respectively. Each curve shows the perplexity from averaging for different settings of , but now over a larger range of sampling iterations.
Although the perplexity computation is able to be averaged over different Gibbs sampler runs, other applications of the model rely on the analysis of each working mode and are based on the analysis of each sample. Meanwhile, the setting of the parameter is also determined according to the perplexity. The parameter represents the number of working modes.
Figure 6 illustrates the perplexity as a function of the parameter in Gibbs samples. Empirical settings of the parameter show that the average perplexity over the data set decreases with the increase of the parameter . Experimental results confirm that the average perplexity indeed decreases as we made analysis. In particular, the perplexity values flatten out after the parameter is set to . This indicates that the parameter fits the data set in the model.
Figure 6: Perplexity as a function of the parameter of the Gibbs sampler for samples.
About the analysis of the WCM results, we can use the point estimate of the WCM parameters to look at specific , , and distributions and related quantities that can be derived from these parameters (such as the probability of a working place and a working date given a randomly selected event from a working mode). In the following results, we take a specific sample, , , and , after iterations from a single arbitrarily selected Gibbs run and then generate point estimates of , , and using (9).
There are totally working modes (parameter ). Each working mode, using a WMV, helps us to better understand the occurrences of events. For the sake of analysis, we list the highest probability working modes for each working place and each working date from the WCM in Table 2. In each working mode, we list the top events most likely to be generated in the most likely working mode conditioned on both the working place and working date. For example, in the working place of Northern China, for the most likely working mode (numbered in the working modes), the top events (OPI, SPM, EAI, HOS, BVI, MCI, AOM, APTS, CPD, and TCI) are most likely to occur in the working date of June.
Table 2: The highest probability working mode for each working place and each working date from the WCM.
Experimental results show that different working places have different working modes in spite of the same working date, and the same working place also has different working modes for different working dates. It indicates that the working mode is indeed related with the working place and working date. Events related with the pumping system, such as OPI, MCI, and CPI, are most likely to occur in most working modes, which indicates that the working modes of the concrete pump truck are consistent with the actual situations. Meanwhile events related with the cantilever system and landing leg system, such as LLF and CFI, have less occurrences as compared with events of the pumping system. Moreover, in the working date of summer (working date = June, July, and August), the alert event SPM is more likely to occur, which indicates that the concrete pump truck more likely fails in the hot climate. The operation event AOM is more likely to occur, which indicates that the operators prefer to operate the concrete pump truck in the remote manner.
Because the probability of working mode reflects the probability of its occurrence, we can analyze the work loads of different working places in different working dates. According to the probability of the working mode in Table 2, we can find that the working modes in the working place of Eastern China are more likely to occur than the working modes in the working place of Western China. It indicates that the concrete pump trucks in the working place of Eastern China have more work loads than that in the working place of Western China. Meanwhile, the concrete pump trucks in the working date of June have more work loads than that in the working date of November. Generally, we can analyze different working modes according to the probability.
In this section we provide some illustrative examples of how the WCM can be used to answer different types of questions and prediction problems concerning working modes of the equipment.
In real cases, we would like to quickly assess working mode assignments for new work cycles not contained in the training data set, especially for the real-time event sequence flow.
Our automated detection strategy is to apply the Gibbs sampling algorithm that runs only on the event tokens in the new work cycle, instead of rerunning the algorithm for every new work cycle again. Afterwards, the event tokens in the new work cycles are quickly assigned to the most likely working places, working dates, and working modes. The main procedure is as follows: first, we start by assigning events randomly to working places, working dates, and working modes; second, we then sample new assignments of events by applying the Gibbs sampler only to the event tokens in the new work cycle, each time temporarily updating the count matrices , , and shown in (7).
Table 3 shows the occurrences of events for a new work cycle. After the sampling, the WCM has assigned each event to its most likely working mode. Table 3 illustrates the top 3 most likely working modes assigned to each event for the new work cycle. Note that each event is assigned to different working modes according to its occurrence count. According to (7), although events of this new work cycle are assigned to different working modes, they are assigned to the number 107 working mode with the probability . The top most likely events in the number 107 working mode are shown as follows: RTM, CM, OPI, BVI, SPM, CPI, MCI, SCI, ISC, and SoEThe automated detection result for the new work cycle is indeed consistent with the actual situations, in comparison with the real occurrences of events.
Table 3: Actual example of automated detection for a new work cycle. Each event is assigned to its most likely working mode according to its corresponding occurrence count. In the table, we list the top 3 most likely working modes for each event for the new work cycle.
We illustrate in this section how our model could be useful for detecting anomalous work cycles. A work cycle assigned to a working mode with low probability is considered as an anomalous work cycle.
We also take the work cycle as an example for the automated detection of an anomalous work cycle, shown in Table 3. The work cycle is assigned to the number working mode with the probability . As compared with most of other working modes, number working mode has lower probability, so this work cycle is detected as an anomalous work cycle. The alert events SPM and SoE have frequent occurrences both in the work cycle and in number working mode, which indicates that this work cycle is an anomalous work cycle. Meanwhile, we analyzed the real failure records and confirmed that the engine indeed failed frequently during this work cycle. Generally, these anomalous work cycles can be automatically detected efficiently, with the help of the WCM.
The working condition model proposed in this paper provides a relatively simple probabilistic model for exploring the relationships between working place, working place, working mode, and events in a work cycle. This model provides significantly improved predictive power in terms of the analysis of working condition according to the event sequence data.
Our future works mainly include the optimization of the model, the model training, and the conduction experiments on different data sets. Furthermore, the further analysis of the anomalous work cycles detected by our model is also an interesting question.
: Probabilities of working modes given working dates (-dimensional vector).
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Sources tell Code/red the company will hold its next special event on Thursday, Oct. 16 — not the 21st. Headlining the gathering: The latest updates to its iPad line, along with those new iMacs that 9to5Mac told us about earlier this week. Also: OS X Yosemite.
Tim Cook walks on stage, talks iPhone sales, Apple Watch amazingness, shows video of iPhone retail launch. Craig up. Talks Yosemite.
New iPad Air comes in gold, is 10% thinner, slightly faster, has the Touch ID button from 2013's iPhone. Ships end of October.
iOS 8 is likely to supercharge the functionality of Apple's iPad with a new split-screen multitasking feature, according to sources with knowledge of the enhancement in development. These people say that the feature will allow iPad users to run and interact with two iPad applications at once. Up until now, each iPad application either developed by Apple or available on the App Store is only usable individually in a full-screen view.
In addition to allowing for two iPad apps to be used at the same time, the feature is designed to allow for apps to more easily interact, according to the sources. For example, a user may be able to drag content, such as text, video, or images, from one app to another. Apple is said to be developing capabilities for developers to be able to design their apps to interact with each other. This functionality may mean that Apple is finally ready to enable "XPC" support in iOS (or improved inter-app communication), which means that developers could design App Store apps that could share content.
IF iOS 8 split screen is true, then the whole much time spent in apps vs web stupidity may finally die. Not zero sum.


Argentinian artist Catalina Chervin's drawings are not planned, and she does not make preparatory sketches. Instead she builds up an initial 'framework' by first covering the paper with short lines. She lets her hand move freely, allowing the drawing to emerge. The result here is a dense labyrinth of visceral forms, perhaps derived from the artist's early medical training.
The background of the drawing is shaded in pencil. In the lower half of the drawing is an abstract pen and ink design.