Employees are internal customers whose processes are aimed at satisfying the external customers. Before an organization can satisfy its customers, it must first satisfy its own employees. Management can obtain feedback on the effectiveness of its training and continuous improvement efforts via servers' self- assessment service quality improvements. The justification for using self-assessment as a yardstick is because the selected organization have long been existing and are very familiar with customer needs based on feedbacks on services they received.
The study aimed to develop a proposed service quality training plan for frontline non-teaching employees based on the assessment of the level of competence of frontline non-teaching employees and the extent of delivery of service quality among SUCs in REgion VIII. This study was anchored on the conceptual model of the authors Frost and Kumar (2000), which have developed an internal service quality model based on the concept of the gap model. The model evaluated the service quality dimensions and their relationships to level of competence relative to job knowledge, quality and productivity, cost and materials management, customer satisfaction and work systems and on personal qualities relative to character and integrity, initiative, safety consciousness, team spirit and personality. Ther result suggest the perceptions of frontline non-teaching employees and their supervisors play a major role in recognizing extent of service quality delivery among SUCs in REgion VIII, thus acknowledging the usefulness of the service quality training program plan proposed in this study.
Final result of the study indicated that high percentage of frontline non-teaching employees and their supervisors have rated themselves highly competent in all areas on all level of competence and personal qualities. Significantly, the study proved that although frontline non-teaching employees of SUCs in Region VIII were highly competent, they remain to adopt the proposed service quality training plan and enhance their service quality delivery to offer services beyond the expectation of clients.
Submission Details: Dissertation Abstract submitted by Teresita Villa Lacaba from Philippines on 21-Feb-2016 14:20.
Abstract has been viewed 1577 times (since 7 Mar 2010).
Great care has been taken to ensure that this information is correct, however ThesisAbstracts.com cannot accept responsibility for the contents of this Dissertation abstract titled "A Proposed Service Quality Training Program Plan For Frontline Non-teaching Employees Of Sucs In Region Viii". This abstract has been submitted by Teresita Villa Lacaba on 21-Feb-2016 14:20. You may report a problem using the contact form.


text:
package institucion.Views.Loggin;

import institucion.Controllers.CtrlLoggin;
import institucion.Views.Principal.WelcomeP;
import institucion.Views.Secretary.WelcomeS;
import institucion.Views.Teacher.WelcomeT;
import java.util.HashMap;
import javax.swing.JOptionPane;

/*
 * To change this license header, choose License Headers in Project Properties.
 * To change this template file, choose Tools | Templates
 * and open the template in the editor.
 */

/**
 *
 * @author o5k4r1n
 */
public class Loggin extends javax.swing.JFrame {

	private CtrlLoggin ctrlL;
	public Loggin() {
            this.setUndecorated(true);
            initComponents();
            this.setLocationRelativeTo(null);

            ctrlL = new CtrlLoggin();
	}

	/**
	 * This method is called from within the constructor to initialize the form.
         * WARNING: Do NOT modify this code. The content of this method is always
         * regenerated by the Form Editor.
	 */
	@SuppressWarnings("unchecked")
    // <editor-fold defaultstate="collapsed" desc="Generated Code">//GEN-BEGIN:initComponents
    private void initComponents() {

        jPanel1 = new javax.swing.JPanel();
        lblUser = new javax.swing.JLabel();
        lblPassword = new javax.swing.JLabel();
        txtUser = new javax.swing.JTextField();
        txtPassword = new javax.swing.JPasswordField();
        btnEnter = new javax.swing.JButton();
        btnExit = new javax.swing.JButton();
        lblIcon = new javax.swing.JLabel();
        btnAttendance = new javax.swing.JButton();

        setDefaultCloseOperation(javax.swing.WindowConstants.EXIT_ON_CLOSE);

        jPanel1.setBackground(new java.awt.Color(0, 0, 51));

        lblUser.setFont(new java.awt.Font("Loma", 1, 18)); // NOI18N
        lblUser.setForeground(new java.awt.Color(51, 255, 255));
        lblUser.setText("Usuario:");

        lblPassword.setFont(new java.awt.Font("Loma", 1, 18)); // NOI18N
        lblPassword.setForeground(new java.awt.Color(51, 255, 255));
        lblPassword.setText("Contrasenia:");

        btnEnter.setFont(new java.awt.Font("Loma", 1, 14)); // NOI18N
        btnEnter.setForeground(new java.awt.Color(255, 255, 255));
        btnEnter.setIcon(new javax.swing.ImageIcon(getClass().getResource("/images/icons/enter.png"))); // NOI18N
        btnEnter.setText("INGRESAR");
        btnEnter.setBorderPainted(false);
        btnEnter.setContentAreaFilled(false);
        btnEnter.setCursor(new java.awt.Cursor(java.awt.Cursor.HAND_CURSOR));
        btnEnter.setHorizontalTextPosition(javax.swing.SwingConstants.CENTER);
        btnEnter.setVerticalTextPosition(javax.swing.SwingConstants.BOTTOM);
        btnEnter.addMouseListener(new java.awt.event.MouseAdapter() {
            public void mouseClicked(java.awt.event.MouseEvent evt) {
                btnEnterMouseClicked(evt);
            }
        });

        btnExit.setFont(new java.awt.Font("Loma", 1, 14)); // NOI18N
        btnExit.setForeground(new java.awt.Color(255, 255, 255));
        btnExit.setIcon(new javax.swing.ImageIcon(getClass().getResource("/images/icons/close.png"))); // NOI18N
        btnExit.setText("SALIR");
        btnExit.setBorderPainted(false);
        btnExit.setContentAreaFilled(false);
        btnExit.setCursor(new java.awt.Cursor(java.awt.Cursor.HAND_CURSOR));
        btnExit.setHorizontalTextPosition(javax.swing.SwingConstants.CENTER);
        btnExit.setVerticalTextPosition(javax.swing.SwingConstants.BOTTOM);
        btnExit.addMouseListener(new java.awt.event.MouseAdapter() {
            public void mouseClicked(java.awt.event.MouseEvent evt) {
                btnExitMouseClicked(evt);
            }
        });

        lblIcon.setIcon(new javax.swing.ImageIcon(getClass().getResource("/images/icons/manager.png"))); // NOI18N

        btnAttendance.setFont(new java.awt.Font("Lao UI", 1, 14)); // NOI18N
        btnAttendance.setForeground(new java.awt.Color(255, 255, 255));
        btnAttendance.setIcon(new javax.swing.ImageIcon(getClass().getResource("/images/icons/attendace_time.png"))); // NOI18N
        btnAttendance.setText("ASISTENCIA");
        btnAttendance.setBorderPainted(false);
        btnAttendance.setContentAreaFilled(false);
        btnAttendance.setCursor(new java.awt.Cursor(java.awt.Cursor.HAND_CURSOR));
        btnAttendance.setHorizontalTextPosition(javax.swing.SwingConstants.CENTER);
        btnAttendance.setVerticalTextPosition(javax.swing.SwingConstants.BOTTOM);
        btnAttendance.addMouseListener(new java.awt.event.MouseAdapter() {
            public void mouseClicked(java.awt.event.MouseEvent evt) {
                btnAttendanceMouseClicked(evt);
            }
        });

        javax.swing.GroupLayout jPanel1Layout = new javax.swing.GroupLayout(jPanel1);
        jPanel1.setLayout(jPanel1Layout);
        jPanel1Layout.setHorizontalGroup(
            jPanel1Layout.createParallelGroup(javax.swing.GroupLayout.Alignment.LEADING)
            .addGroup(jPanel1Layout.createSequentialGroup()
                .addGroup(jPanel1Layout.createParallelGroup(javax.swing.GroupLayout.Alignment.LEADING)
                    .addGroup(jPanel1Layout.createSequentialGroup()
                        .addGap(171, 171, 171)
                        .addComponent(lblIcon))
                    .addGroup(jPanel1Layout.createSequentialGroup()
                        .addGap(33, 33, 33)
                        .addGroup(jPanel1Layout.createParallelGroup(javax.swing.GroupLayout.Alignment.TRAILING)
                            .addComponent(lblPassword)
                            .addComponent(lblUser))
                        .addGap(32, 32, 32)
                        .addGroup(jPanel1Layout.createParallelGroup(javax.swing.GroupLayout.Alignment.LEADING)
                            .addComponent(txtUser, javax.swing.GroupLayout.PREFERRED_SIZE, 159, javax.swing.GroupLayout.PREFERRED_SIZE)
                            .addComponent(txtPassword, javax.swing.GroupLayout.PREFERRED_SIZE, 159, javax.swing.GroupLayout.PREFERRED_SIZE))))
                .addContainerGap(javax.swing.GroupLayout.DEFAULT_SIZE, Short.MAX_VALUE))
            .addGroup(javax.swing.GroupLayout.Alignment.TRAILING, jPanel1Layout.createSequentialGroup()
                .addGap(0, 42, Short.MAX_VALUE)
                .addComponent(btnEnter)
                .addPreferredGap(javax.swing.LayoutStyle.ComponentPlacement.UNRELATED)
                .addComponent(btnExit)
                .addPreferredGap(javax.swing.LayoutStyle.ComponentPlacement.UNRELATED)
                .addComponent(btnAttendance)
                .addGap(41, 41, 41))
        );
        jPanel1Layout.setVerticalGroup(
            jPanel1Layout.createParallelGroup(javax.swing.GroupLayout.Alignment.LEADING)
            .addGroup(jPanel1Layout.createSequentialGroup()
                .addGap(24, 24, 24)
                .addComponent(lblIcon)
                .addGap(32, 32, 32)
                .addGroup(jPanel1Layout.createParallelGroup(javax.swing.GroupLayout.Alignment.BASELINE)
                    .addComponent(lblUser)
                    .addComponent(txtUser, javax.swing.GroupLayout.PREFERRED_SIZE, javax.swing.GroupLayout.DEFAULT_SIZE, javax.swing.GroupLayout.PREFERRED_SIZE))
                .addGap(14, 14, 14)
                .addGroup(jPanel1Layout.createParallelGroup(javax.swing.GroupLayout.Alignment.BASELINE)
                    .addComponent(lblPassword)
                    .addComponent(txtPassword, javax.swing.GroupLayout.PREFERRED_SIZE, javax.swing.GroupLayout.DEFAULT_SIZE, javax.swing.GroupLayout.PREFERRED_SIZE))
                .addGap(31, 31, 31)
                .addGroup(jPanel1Layout.createParallelGroup(javax.swing.GroupLayout.Alignment.LEADING, false)
                    .addComponent(btnAttendance, javax.swing.GroupLayout.DEFAULT_SIZE, javax.swing.GroupLayout.DEFAULT_SIZE, Short.MAX_VALUE)
                    .addComponent(btnExit, javax.swing.GroupLayout.DEFAULT_SIZE, javax.swing.GroupLayout.DEFAULT_SIZE, Short.MAX_VALUE)
                    .addComponent(btnEnter, javax.swing.GroupLayout.PREFERRED_SIZE, 100, javax.swing.GroupLayout.PREFERRED_SIZE))
                .addContainerGap(41, Short.MAX_VALUE))
        );

        javax.swing.GroupLayout layout = new javax.swing.GroupLayout(getContentPane());
        getContentPane().setLayout(layout);
        layout.setHorizontalGroup(
            layout.createParallelGroup(javax.swing.GroupLayout.Alignment.LEADING)
            .addComponent(jPanel1, javax.swing.GroupLayout.PREFERRED_SIZE, javax.swing.GroupLayout.DEFAULT_SIZE, javax.swing.GroupLayout.PREFERRED_SIZE)
        );
        layout.setVerticalGroup(
            layout.createParallelGroup(javax.swing.GroupLayout.Alignment.LEADING)
            .addComponent(jPanel1, javax.swing.GroupLayout.PREFERRED_SIZE, javax.swing.GroupLayout.DEFAULT_SIZE, javax.swing.GroupLayout.PREFERRED_SIZE)
        );

        pack();
    }// </editor-fold>//GEN-END:initComponents

    private void btnEnterMouseClicked(java.awt.event.MouseEvent evt) {//GEN-FIRST:event_btnEnterMouseClicked
        String username = txtUser.getText();
        String pass     = txtPassword.getText();
        HashMap<Integer, String> data = ctrlL.validateUser(username, pass);
        if( data.isEmpty() ){
            JOptionPane.showMessageDialog(null,
                                        "Nombre de Usuario y/o contraseña no validos",
                                        "Usuario No Encontrado",
                                        JOptionPane.ERROR_MESSAGE);
            txtUser.setText("");
            txtPassword.setText("");
        }else{
            int id = 0;
            String rol = "";
            for( HashMap.Entry<Integer, String> d: data.entrySet()){
                id = d.getKey();
                rol = d.getValue();
            }
            switch(rol){
                case "principal":
                    WelcomeP p = new WelcomeP();
                    p.director_id = id;
                    this.setVisible(false);
                    p.setVisible(true);
                    break;
                case "teacher":
                    WelcomeT t = new WelcomeT();
                    t.teacher_id = id;
                    t.setVisible(true);
                    this.setVisible(false);
                    break;
                case "secretary":
                    WelcomeS s = new WelcomeS();
                    s.secretary_id = id;
                    s.setVisible(true);
                    this.setVisible(false);
                    break;
           }
        }
    }//GEN-LAST:event_btnEnterMouseClicked

    private void btnExitMouseClicked(java.awt.event.MouseEvent evt) {//GEN-FIRST:event_btnExitMouseClicked
        this.setVisible(false);
        System.exit(0);
    }//GEN-LAST:event_btnExitMouseClicked

    private void btnAttendanceMouseClicked(java.awt.event.MouseEvent evt) {//GEN-FIRST:event_btnAttendanceMouseClicked
        Access a = new Access();
        a.setVisible(true);
    }//GEN-LAST:event_btnAttendanceMouseClicked

    /**
    * @param args the command line arguments
    */
    public static void main(String args[]) {
        /* Set the Nimbus look and feel */
        //<editor-fold defaultstate="collapsed" desc=" Look and feel setting code (optional) ">
        /* If Nimbus (introduced in Java SE 6) is not available, stay with the default look and feel.
 * For details see http://download.oracle.com/javase/tutorial/uiswing/lookandfeel/plaf.html
         */
        try {
                for (javax.swing.UIManager.LookAndFeelInfo info : javax.swing.UIManager.getInstalledLookAndFeels()) {
                        if ("Nimbus".equals(info.getName())) {
                                javax.swing.UIManager.setLookAndFeel(info.getClassName());
                                break;
                        }
                }
        } catch (ClassNotFoundException ex) {
                java.util.logging.Logger.getLogger(Loggin.class.getName()).log(java.util.logging.Level.SEVERE, null, ex);
        } catch (InstantiationException ex) {
                java.util.logging.Logger.getLogger(Loggin.class.getName()).log(java.util.logging.Level.SEVERE, null, ex);
        } catch (IllegalAccessException ex) {
                java.util.logging.Logger.getLogger(Loggin.class.getName()).log(java.util.logging.Level.SEVERE, null, ex);
        } catch (javax.swing.UnsupportedLookAndFeelException ex) {
                java.util.logging.Logger.getLogger(Loggin.class.getName()).log(java.util.logging.Level.SEVERE, null, ex);
        }
        //</editor-fold>

        /* Create and display the form */
        java.awt.EventQueue.invokeLater(new Runnable() {
                public void run() {
                        new Loggin().setVisible(true);
                }
        });
    }

    // Variables declaration - do not modify//GEN-BEGIN:variables
    private javax.swing.JButton btnAttendance;
    private javax.swing.JButton btnEnter;
    private javax.swing.JButton btnExit;
    private javax.swing.JPanel jPanel1;
    private javax.swing.JLabel lblIcon;
    private javax.swing.JLabel lblPassword;
    private javax.swing.JLabel lblUser;
    private javax.swing.JPasswordField txtPassword;
    private javax.swing.JTextField txtUser;
    // End of variables declaration//GEN-END:variables
}

redpajama_set_name: RedPajamaGithub


text:
Daniel Alves case: medical report contradicts the victim's version
The premieres of the week |
Cutting out alcohol can be a weapon against anxiety and depression; know why
4 mistakes that are slowing down your Wi-Fi at home
Palmeiras shirt sold at R$ 179 does not deserve to be called popular – 01/27/2023
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Dad loses both legs saving daughters from snow plow accident
Galaxy S23 will have Gorilla Glass Victus 2 to protect the screen from falls – Tecnoblog
With remnants and rivalry on the rise, Palmeiras and Flamengo meet again in the Super Cup
Actress who was Globo's muse surprises at age 61; see how she is
Home/News/Most intense cyclone of the year on the planet advances through the Pacific
Most intense cyclone of the year on the planet advances through the Pacific
Admin August 31, 2022 News Leave a comment 10 Views
Super typhoon Hinnamnor impressed in satellite images of the last few hours | CIMSS
The most intense cyclone on the planet so far in 2022 is currently advancing in the Pacific Ocean towards the Asian continent. This is Super Typhoon Hinnamnor, the equivalent of a Category 5 hurricane in the Atlantic and East Pacific, currently moving from West to East towards islands that belong to Japan.
2.5-minute rapid scan # Himawari8 Infrared images showing Super Typhoon #Hinnamnor as it reached Category 5 intensity while approaching the island of Minamidaitōjima, Japan (station identifier ROMD): https://t.co/oPnRJDgHbY pic.twitter.com/zIkcWGDrEG
— UW-Madison CIMSS (@UWCIMSS) August 30, 2022
The storm's maximum sustained winds were estimated at 257 km/h by the Joint Pacific Typhoon Forecast Center (JTWC), which gives the tropical cyclone the classification of a rare super typhoon. Gusts of up to 300 km/h are likely being registered within the eyewall, the ring of destructive winds around the calm center of the storm. The storm is 500 kilometers south-southeast of the island of Kyushu, Japan.
Hinnamnor's central pressure dropped to 920 hPa, making it the strongest tropical system so far in 2022. Previously, the strongest tropical system of the year was intense tropical cyclone Batsirai, which had a central pressure of 934 hPa. Batsirai crossed the Southwest Indian Ocean in February before ravaging Madagascar with destructive winds and killing at least 21 people.
How is a typhoon different from a hurricane in the Atlantic or the East Pacific? Anything. It is the nomenclature that changes. In the Eastern Pacific and Atlantic, tropical cyclones with sustained winds above 120 km/h are called hurricanes, while in the Western Pacific, with the exception of Australia, they are called typhoons.
In the Atlantic and East Pacific, hurricanes are classified on the Saffir-Simpson scale, which typhoons do not. For a typhoon to be classified as a super typhoon in the West Pacific, the storm's sustained winds must be at least 241 km/h.
Where is the cyclone going?
As Hinnamnor moves west, most of Japan is under warning or alert, but the greatest risk is to the Daito Islands, southeast of Okinawa, where about 2,100 residents live. Two other small populated islands in the storm's path, Minamidaitojima and Kitadaitojima, stand about 60 meters above sea level at their highest point and are made of limestone that formed over ancient coral reefs.
Cyclone Hinnamnor has a very well defined eye with stadium effect | JMA
It's not yet clear how close the storm will come to Japan's most densely inhabited islands. In the last few hours, the Japanese satellite Himawari-8 captured impressive images of the super typhoon. The storm was a very compact "annular cyclone", characterized by an intense convection edge around the eye that had a stadium effect. Ring cyclones have a narrower radius of maximum winds and are more symmetrical, which helps them sustain their ferocity.
Hinnamnor advances to the West, will pass through islands in the south of Japan and may head for South Korea | JTWC
Hinnamnor will likely maintain its strength for another day or two before the possibility of some modest weakening. Regardless, it's already the strongest storm to hit Earth this year and could be very troublesome wherever it happens. It is projected to still be a Category 3 storm five days from now.
It's rare to get such persistent high-quality radar data on a West Pacific typhoon. #Hinnamnor has textbook traits reserved for the strongest storms:
– Almost perfectly concentric eyewalls, separated by a "moat" of weaker rain
– Self-induced erratic wobbling motion pic.twitter.com/SEzDUlEk90
— Dr. Jake Carstens (@JakeCarstens) August 31, 2022
Super Typhoon #Hinnamnor is 2 walls an eyewall replacement cycle with incentric rings of storm and outer eye walls evident on Kadena Air Base in Okinawa. pic.twitter.com/mPs3PrKhfj
— Ed Piotrowski (@EdPiotrowski) August 31, 2022
Numerical models indicate that Hinnamnor will pass very close to the coast of China, around Shanghai, and then advance north towards South Korea, which suffered disastrous flooding just three weeks ago. The European model projects a trajectory over southern Japan with hurricane-force winds and abundant rainfall.
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redpajama_set_name: RedPajamaCommonCrawl


Ms. Cathy Miller Tri-State Building Maintenance, Inc.
The decision came after a lawyer for Tastries Bakery in Bakersfield argued that owner Cathy Miller's right to free speech and free expression of religion trumps the argument that she violated a state anti-discrimination law.
In recognition of October as National Breast Cancer Awareness Month, we talked candidly to five midstate women about their experience. You'll read how Cathy Miller, Cynthia Rejoyce Lindsey, Liz Reid and Tina Navitsky faced their fears and found support and enduring hope.
A method of preparing a graft material for a bone surface at a surgical site includes providing a first syringe having continuous side wall having an inner surface and an outer surface wherein the syringe has a first end and a second end wherein the inner surface defines a chamber. An adapter is removably attached to the distal end of the syringe wherein the adapter has a throughbore in fluid communication with the chamber. A first selected amount of solid bone graft material is disposed within the chamber. A plunger is inserted into the chamber to define a selected volume between a distal end of the plunger and the second end of the first syringe. A second syringe containing a selected amount of a liquid biomaterial is attached to the adapter wherein the second syringe is in fluid communication with the adapter and the first syringe such that the liquid biomaterial is transferred from the second syringe to the first syringe. After a selected amount of time, the biomaterial forms a gel matrix containing the solid bone graft material. The adapter is removed to provide an unobstructed opening at the second end and the plunger is depressed to force the gel matrix from the chamber of the first syringe.
The present invention provides methods and compositions for detecting protein-protein and/or protein-nucleic acid interactions in cells.
A billiard cue stick extension comprises a shaft and an elongate rod. The shaft defines an elongate cavity which is open at one end to receive a cue stick. The other end of the shaft is combined with an elongate rod which effectively extends the length of the cue. The Shaft is made of a soft resilient material to friction-fit with the cue stick without marring it. The shaft is hollow and is made of a fiber reinforced plastic to emulate the performance of a cue stick without adding substantial weight to it.
A shoelace retainer includes a flexible, elongated strap member having a plurality of hook fastener elements on one surface thereof and a plurality of complementary loop fastener elements on an opposite surface thereof. A tab member having an elongated slot is disposed at a first end of the strap member for attaching the strap member to the footwear. The free ends of the shoelace are passed through the slot and the ends are tied in a conventional bowknot. The bow loops and free ends of the shoelace are placed on top of the strap member and the strap member is rolled up and onto itself, whereby the hook fastener elements become interlocked with the loop fastener elements, thereby confining the bow loops and free ends between convoluted layers.
Comp 105 Project- Prof. Cathy Miller --- By: Chris, Danny, Adam, and Matt.
It's down to Erika [White] Marcantonio and Nicole Rollolazo. Before the winner is announced, the top 11-20th competitors are shown with their routines. The only two I found hot were Carolyn Evans (maybe she shouldn't have worn hoses for her routine) and Cathy Miller.


text:
National Ploughing
Limerick's Rose of Tralee ploughs into busy schedule with appearance at agri extravaganza
Ryan O'Rourke
ryan.orourke@limerickleader.ie
Rose of Tralee SInead Flanagan will have her first official engagement at the Ploughing Championships
SINÉAD Flanagan, the International Rose of Tralee, says she's still getting used to her new title after winning the competition a week ago.
The junior doctor, from Adare, will have her first engagement as the Rose of Tralee at the agri-extravaganza that is the Ploughing Championships in Co. Carlow, which begin on September 17.
Before that however, she enjoyed a wonderful welcome home to Adare and told the Leader she is still coming to terms with all the attention.
"It's been fantastic. I am still getting used to being called the Rose of Tralee, but I am getting there. I'm just taking it all step by step at the moment. It's still very fresh for me. I am not used to all the media attention. It's still very early days," said Sinéad.
"The highlight so far was the homecoming reception in the Woodlands Hotel in my home town of Adare. My family and friends were there, as well as the Deputy Mayor. The Mayor made an appearance later in the evening. A few of the other roses and escorts also came so it was a great reception.
"The Woodlands have been neighbours of mine growing up, so it was really great to have it there," she added.
Sinéad was welcomed to the hotel by a corridor of fire along the driveway of the Woodlands House Hotel - where she worked for a time when she was younger.
Having turned 28 years old last Thursday her homecoming to Adare, where she has lived since she was a teenager was doubly special.
"My birthday happened to coincide with the homecoming reception, so I haven't really had a chance to have any official birthday celebrations as of yet," said Sinéad.
"There have been so many celebrations lately, I think I might postpone it for a while. Still, it has been a birthday I will never forget," she added.
The Ploughing Championships are the next official event in the Limerick Rose's diary, with the junior doctor taking it 'day by day'.
Under a newly revamped selection process for the Rose of Tralee brought about last year, there will only be one Limerick Rose every two years from 2019.
The changes in the competition, according to organisers, would see half of the regions showcasing a Rose each year, and have been made to "ensure that every Rose enjoys the ultimate positive experience". Roses representing Kerry, Cork, and Dublin will continue to be selected every year.
"At this moment there is no Limerick Rose for 2020. Whether that might change now that I have won, I don't know, but for now, there will be no Limerick Rose in the competition until 2021," said Sinéad.
"If any girl out there is thinking of going for the one in 2020, I cannot recommend it enough. Even before I was crowned, it was a fantastic experience. Even just taking part in the county level events is amazing," she added.


text:
Banéré, également orthographié Baniri, est une commune rurale située dans le département de Founzan de la province de Tuy dans la région des Hauts-Bassins au Burkina Faso.

Géographie

Banéré, qui est une commune rurale à l'habitat très dispersé sur son territoire, se trouve à  au sud-ouest de Kovio.

Histoire

Économie

Santé et éducation
Le centre de soins le plus proche de Banéré est le centre de santé et de promotion sociale (CSPS) de Nahi.

Culture

Notes et références

Ville dans la province du Tuy

redpajama_set_name: RedPajamaWikipedia


My name is Ronell K and I'm a freelance makeup artist working in the greater Nashville area. After working 6 years as a Regional manager for a pizza chain in Lincoln, Nebraska my husband and I agreed that it was time to pursue my dreams as makeup artist. So we packed up our bags and started over in a little town outside of Nashville. I then attended The Academy of Makeup Arts. There, I earned my professional certificate in the advanced makeup artist program and also received an excellence award for my punctuality, professional demeanor, and exceptional works during the course. I am very passionate about this art form, everything from beauty to special effects makeup to face/body painting excites me. I have joined MM so that I can become a part of an amazing artist filled community and to help grow my artistic profile.


Our high visibility sweatshirt design is available in these two-tone colour combinations. Constructed from superior brushed polycotton fabric, this stylish alternative is ultra-soft yet extremely durable, providing optimum comfort for the wearer.
They are made from a special blend of 65% polyester 35% cotton with a brushed back for extra warmth and comfort. The fabric provides breathability and flexibility combined with superior strength and durability and is manufactured to withstand the most rigorous working environments.


Perhaps the greatest platformer of all-time.
Super Sonic or Super Chronic?
What the macabre sixties sitcom were they thinking?
The Super Famicom version of Star Ocean might feature a lot of flashy technology, but it's the game's epic storyline and unique combat system that's made it such a beloved classic among 16-bit RPG fans over the years.
A Rare treat, or will it drive you bananas?
Does the final release in Enix's Gaia Trilogy hold up?
Fight, Mega Man, for everlasting re-release!
A Ride for all ages?
All the fun of the fare?


My brother has a youtube comedy channel called FleeceJackets, and after an 11 month hiatus he finally uploaded a new video last night. I love his sketches so much... they're just so totally random! If you like the video, pop over to youtube and leave him a comment, he'll really appreciate it! You should also check out his other videos while you're there, especially his corny tv commercials like Gnome Seller and Wonder Wood. Sheer genius!


text:
package com.github.twitch4j.helix.domain;

import com.fasterxml.jackson.annotation.JsonProperty;
import lombok.AccessLevel;
import lombok.Data;
import lombok.NoArgsConstructor;
import lombok.Setter;

import java.time.Instant;

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text:
Stephen McPartland MP
Stephen McPartland was elected to the House of Commons as Member of Parliament for Stevenage in 2010 and re-elected in 2015.
Stephen has secured over £300 million from the Government for infrastructure investment in Stevenage for our local schools, hospital, housing and jobs. He is leading a campaign for a satellite Radiotherapy Unit to be based at the Lister Hospital so cancer care can be brought closer to patients' homes. Stephen has also successfully secured the funds to widen the A1(M) to three lanes between junctions 6 and 8. Although it is not cheap, at £8 million a mile, it is a huge boost for local people and Hertfordshire's economy.
Stephen was born in Liverpool on 9th August 1976. He lives in Stevenage with his wife Emma who is a teacher at a local primary school. He is an avid reader and a football fan, often watching Stevenage play. He is involved in a number of locally-based charity organisations in Stevenage.
In Parliament, Stephen holds a number of roles including Select Committee memberships, Chairman of All Party Parliamentary Groups and a Board member of the Parliamentary Office of Science and Technology. He has a range of policy interests and Stephen is widely known as being outspoken on Welfare issues and use of technology, in particular. However, he does work closely with Government to deliver change and has succeeded in delivery a number of legislative changes, for example, asthma inhalers being made available in school first aid kits in 2014.
Prior to being elected an MP, Stephen was the Director of Membership for British American Business (the US Chamber of Commerce), based in London. Stephen commenced his political career in 1999 in Warrington where he managed a range of local council, Parliamentary and European election campaigns, before moving to Hertfordshire in 2001 to work as a Campaign Manager. Stephen studied for his first degree in History at the University of Liverpool, graduating in 1997. He studied for an MSc in Technology Management at Liverpool John Moores University the following year.


text:
\section{Background}
\subsection{Surveillance of Infectious Diseases}
Infectious diseases have long plagued developing countries, which often do not have the infrastructure and resources needed for effective eradication efforts \cite{morens2004challenge, lederberg2003microbial, binder1999emerging, amarasinghe2011dengue}. The need for a standardized infrastructure for data collection is highlighted by the fact that there is a large variance within individual nations in terms of ability to conduct surveillance efforts \cite{nsubuga2010strengthening, franco2006improving, sow2010trained}. In 1998 the World Health Organization Regional Office for Africa (WHO/AFRO), began to promote the Integrated Disease Surveillance and Response (IDSR) framework across all its members \cite{nsubuga2010implementing, perry2007planning, kabore2001technical, world1999regional}. The WHO IDSR framework paves the way towards realizing core capacities of International Health Regulations (IHR) to detect, assess, and respond to all events that may constitute public health emergencies of international concern (PHEICs) and report them to the WHO \cite{kasolo2013idsr}. Laboratory capacities, communication systems, logistics, and insufficient trained personnel are critical obstacles to successful implementation of IDSR \cite{phalkey2013challenges} \cite{sow2010trained} \cite{kebede2010trends}. Despite challenges in IDSR implementation, this led to an increase in surveillance data collection and created the opportunity to translate this surveillance data into actionable insights. Cameroon adopted the IDSR framework in 2003, recording district-level infectious disease counts on a weekly basis and forwarding them to the Ministry of Public Health for analysis. Figure 1 (taken from \cite{ngwa2015cholera}) provides a graphical overview of how surveillance data is collected in line with the WHO IDSR framework, beginning with data collection at the community/health district levels and final analysis at the central level. The IDSR framework in Cameroon has raised the consistency of district level data collection, but translation of this surveillance data into actionable insights has not been as successful. Additionally, although statistical techniques for time series analysis have existed for many years, they have not yet been widely applied to surveillance data collected through the WHO IDSR framework to drive actionable insights.

\begin{figure}[h]
\includegraphics[width=1\textwidth]{figures/Cameroon_health.png}
\caption{Graphic of Surveillance Disease Data Collection Infrastructure in Cameroon from \cite{ngwa2015cholera}}
\end{figure}

\subsection{Cross-Correlation Between Time Series}
In times series analysis, the cross-correlation of a pair of time-series is the correlation between the pair at different times, as a function of the different times. Let $(X_t, Y_t)$ be a pair of random processes and t be any point in time. The cross-correlation between X and Y at times $t_1$ and $t_2$ respectively is defined by the equation below:

\begin{equation}
R_{XY}(t_1, t_2) = \mathbb{E}[X_{t1}\overline{Y_{t2}}]
\end{equation}

\subsection{Related Work}

The statistical analysis of disease surveillance data has been a topic of academic study for many years. The methods proposed often use standard surveillance datasets which are often higher in sample size and quality than those available from surveillance data collected in low resource settings, and utilize additional information. Many efforts have focused on model-based efforts for multivariate time series surveillance data rather than analysis geared towards explicitly identifying shared patterns in infectious disease surveillance counts between health districts.

A nonlinear model for the analysis of multivariate time series data, with region-specific random effects to address varying transmission of a pathogens across regions has been shown \cite{paul2011predictive}. The model utilizes monthly counts of meningococcal disease cases in 94 departments of France (excluding Corsica) and weekly counts of influenza cases in 140 administrative districts of Southern Germany, and compare performance via one-step-ahead predictions.

 A framework for the statistical analysis from counts of infectious diseases which is based on Poisson branching process model with immigration has been proposed \cite{held2005statistical}. The authors additionally propose a multivariate formulation, which has the ability to capture patterns in the spatial spread of a disease over time. Another approach involves a multivariate time series model based approach that is enhanced to deal with possible dependence between different disease counts from different pathogens \cite{paul2008multivariate}. The results are demonstrated on weekly influenza and meningococcal disease counts from Germany and on influenza in the U.S.A. from 1996–2006, with air traffic information being integrated with the influenza data to provide information on the global dispersal of the pathogen into the model. The open source R package surveillance provides functionality for implementing statistical regression frameworks that can be applied to surveillance data \cite{meyer2014spatio}.

One proposed methodology is using ARIMA (autoregressive, integrated, moving average) forecasting of infectious disease counts using surveillance data \cite{allard1998use}. Another study investigated the relationship between joining the World Trade Organization (WTO) and the availability of several commodities with both harmful and protective effects related to the development of noncommunicable disease using a comparative interrupted time-series analysis \cite{cowling2019world}.

\section{Methods}
    \subsection{Central Idea}
    The core idea underlying the application of the multivariate cross-correlation based methodology presented here is straightforward: the correlations between time series of disease incidence in geographic regions can potentially provide insight on disease spreading patterns. Although such an analysis does not allow for inference of causality due to the number of confounding variables, it provides signals which can warrant deeper investigation from the public health perspective. More specifically, deeper analysis can be performed to understand how prior knowledge of variables involved in the spread of infectious disease patterns can explain the observed patterns and thus inform concrete mitigation strategies.

    \subsection{Methodology}
    Our computational approach for intelligent surveillance consists of three parts: a) obtaining and processing surveillance data of weekly disease counts, b) computing the multivariate cross-correlation between pairs of regions, and c) identifying highly correlated regions for a specific disease to gain insights on its spreading pattern.

    To obtain the WHO IDSR surveillance data on infectious disease incidence in Cameroon, we established a collaboration with officials from the WHO Country Office in Cameroon, the Ministry of Health of Cameroon, and Songhai Labs, a Cameroonian Knowledge Broker Platform.  The partnership was built through video calls, letters, and in-person meetings with health officials.  The data represents weekly counts of infectious diseases, in 189 different health districts in Cameroon for the years 2014 to 2017.

    From this data we extract the weekly counts of a disease D in $N$ different regions. Wherever a data point is missing in the time series, we use interpolation to fill in missing values with averages of the first available preceding and following entries. In cases in which the first or last entry of the time series is missing, we use the nearest point as an approximation. After data wrangling, we get $N$ number of time series, $\mathbf{t}_1, \mathbf{t}_2, \dots, \mathbf{t}_N$. For $i \in \{1,\dots,N\}$, $\mathbf{t}_i$ is the weekly counts of disease D in region $R_i$.

     We compute a correlation score for every pair of regions in Cameroon using the cross-correlation function ($ccf$) and a score function $s$. More complex methods for determining correlation between time series exist, however the $ccf$ serves as a simple standard starting point for such analyses. For quantifying general similarity in patterns between time series (including seasonality) the standard ccf is suitable, although the methodology can be repeated following pre-whitening of each time series to remove seasonality and auto-correlation in order to avoid 'spurious correlation' \cite{dean2016dangers, chatfield2003analysis}. For all $i, j \in \{1,\dots,N\}$ and $i \neq j$, $(R_i, R_j)$ is a region pair, and $\mathbf{t}_i$ and $\mathbf{t}_j$ are the time series for region $R_i$ and $R_j$ respectively. We adopt the $ccf$ function interface provided by the tseries \cite{ccfdocumentation} library in R. This implementation of $ccf$ takes as input two time series, $\mathbf{t}_1$ and $\mathbf{t}_2$, as well as a $lag$ parameter, and outputs a vector containing correlations of $\mathbf{t}_1$ and $\mathbf{t}_2$ from under $lag$-week lag to under $(-lag)$-week. Applying $ccf$ function to $\mathbf{t}_i$ and $\mathbf{t}_j$ with the parameter $lag=5$, we obtained the correlation vector $\mathbf{c}_{ij} \in \mathbb{R}^{11}$:
    \begin{equation} \label{corrVecDef}
        \mathbf{c}_{ij} = ccf(\mathbf{t}_i,\mathbf{t}_j,lag=5)
    \end{equation}
    For all $k \in \{-5,-4,\dots,0,\dots,4,5\}$, denote with ${c_{ij}}_k$ the correlation between $\mathbf{t}_i$ and $\mathbf{t}_j$ under $k$-week lag, where a positive $k$ means moving $\mathbf{t}_i$ ahead of $\mathbf{t}_j$ by $\abs{k}$ weeks, and a negative $k$ moving $\mathbf{t}_j$ ahead of $\mathbf{t}_i$ by $\abs{k}$ weeks. According to this definition, \begin{equation} \label{symmProp}
        {c_{ij}}_k = {c_{ji}}_{-k}
    \end{equation}
    Following Equation \ref{corrVecDef} and the definition of ${c_{ij}}_k$,
    \begin{equation}
        \mathbf{c}_{ij} = [{c_{ij}}_{-5},\dots,{c_{ij}}_0,\dots,{c_{ij}}_5]^T
    \end{equation}
    We define the score function $s:\mathbb{R}^{11}\rightarrow\mathbb{R}$ as
    \begin{equation}
        s(\mathbf{c}) = \sum_{k=-5}^{5} \frac{10}{\abs{k}+1} c_k
    \end{equation}
    The score function is the weighted sum of all the entries in the correlation vector $\mathbf{c}_{ij}$. The weight assigned to the correlation under $k$-week lag is stipulated as $\frac{10}{\abs{k}+1}$. The weight given to the correlation vector is inversely correlated with the absolute value of the lag, intuitively placing higher importance on closer times.
    Eventually, the correlation score $cs$ for $(R_i, R_j)$ is
    \begin{equation} \label{csDef}
        cs_{ij} = s(\mathbf{c}_{ij})
    \end{equation}
    Applying the computation outlined above for all regional pairs for a given disease, we obtain a set of corresponding correlation scores.
    \begin{equation}
        \mathbb{S} = \{ cs_{ij} \in \mathbb{R} \mid i,j = 1,2,\dots,N, i \neq j \}
    \end{equation}
    Importantly, the metric $cs$ is relative, with insight derived only in comparison with other regions, and is designed specifically with the goal of identification of positive trends between two time series. A negative correlation score between two regions indicates that when one region experiences an outbreak, the disease incidence in the other region tends to drop, which is not the trend of interest when using these scores to provide insight on the ramifications of an initial outbreak.

  To evaluate our methods we examined regions with high correlation scores to see how their alarm patterns aligned using an anomaly detection algorithm. We used the EARS (Early Aberration Detection System) C method for selected region pairs \cite{salmon2016monitoring}. This anomaly detection method computes a threshold (prediction interval) for the number of counts at each time step based on values from the recent past. If the number of counts for a step is higher than a threshold, an alarm is raised. Specifically for the threshold, we calculate an approximate (two-sided) $(1 - \alpha)\%$ prediction interval, based on the assumption that the difference between the expected value and the observed value divided by the standard derivation of counts over the sliding window, called $C$, follows a standard normal distribution in the absence of outbreaks.

  We utilized the computed correlation scores, $\mathbb{S}$, in tandem with the results of the EARS algorithm to identify how high weighted correlation scores aligned with alarms flagged using the EARS algorithm. Once we mark a region $R_i$ as experiencing an outbreak, we obtain the subset $\mathbb{S}_i$ of $\mathbb{S}$, the set of all the correlation scores of region pairs involving $R_i$.
    \begin{equation}
        \mathbb{S}_i = \{ cs_{ij} \in \mathbb{R} \mid j = 1,\dots,i-1,i+1,\dots,N \}
    \end{equation}
  In practice the regions with the highest correlation scores can be further investigated and flagged for warning as a result of the initial outbreak. While extrapolating causal relationships is difficult for such time series due to the multitude of factors that contribute, historical analysis of consistent infectious disease correlation patterns can create foundations for intelligent warning systems since these correlations inherently capture information about the nature of how infectious disease outbreaks occur.

 \section{Results}
    \subsection{Inter-regional Correlation Analysis}
    We show surveillance data of malaria as an example to demonstrate our methodology. Table 1 shows the format of the data: the $i$th row represents the time series $\mathbf{t}_i$ of region $R_i$. Weeks are marked consecutively W1, W2, ..., W207. The numbers under column W$i$ are the reported counts of Malaria cases in different regions during the corresponding week.

    Our analysis begins with a set of time series $\{\mathbf{t}_i \mid i = 1,2,\dots,N\}$ as represented in Table 1.

    \begin{figure}[h]
        \centering
        \includegraphics[width=1\textwidth]{figures/step2a_.png}
        \caption{A Snippet of Malaria Surveillance Data}
    \end{figure}

    \begin{figure}[h]
        \centering
        \begin{subfigure}[h]{0.45\textwidth}
            \includegraphics[width=1\textwidth]{figures/step2b_dist.png}
            \caption{Distribution: Mean=3.02, SD=7.44; Min=-24.22, Median=2.40, Max=29.96}
        \end{subfigure}
        \begin{subfigure}[h]{0.45\textwidth}
            \includegraphics[width=1\textwidth]{figures/step2b_heatmap.png}
            \caption{Heatmap of correlation scores. Strong green colors indicate regions that are associated, however proximity of indices does not reflect proximity in geographical distance}
        \end{subfigure}
        \caption{Distribution and Heatmap of Correlation Scores in $\mathbb{S}^{'}$}
    \end{figure}

    Figure 3a shows the histogram of correlation scores for malaria between different regions, which are normally distributed with a slight skew. We previously calculate $\mathbb{S}$, the set of correlation scores for all possible region pairs. According to Equation \ref{symmProp} and \ref{csDef}, $\forall i, j \in \{1,\dots,N\}, i \neq j,$
    \begin{equation}
        cs_{ij} =
        \sum_{k=-5}^{5} \frac{10}{\abs{k}+1} {c_{ij}}_k =
        \sum_{k=-5}^{5} \frac{10}{\abs{-k}+1} {c_{ji}}_{-k} =
        \sum_{{-k}=-5}^{5} \frac{10}{\abs{-k}+1} {c_{ji}}_{-k} =
        cs_{ji}
    \end{equation}
    Thus, we only compute a subset of $\mathbb{S}$:
    \begin{equation}
        \mathbb{S}^{'} = \{ cs_{ij} \in \mathbb{R} \mid i,j = 1,2,\dots,N, i < j \}
    \end{equation}
    Figure 3b presents a heatmap of the correlation scores in $\mathbb{S}^{'}$.  The heatmap shows which region pairs are strongly positively correlated (green), weakly correlated (white) and strongly negatively correlated (brown). The correlations represent the average correlation for the entire time period.  Each grid in the heatmap represents a region pair, indicated by the indices on horizontal and vertical axes. Indices that are next to each other are close geographically.

  When using historical correlation patterns to provide insight on the potential future spreading patterns of infectious disease, it is critical to have an understanding on the stability of such patterns over time. In Figure 4, we compare heatmaps between two years period (2014-15 and 2016-2017) with each other the heatmap for the entire four year period (2014-2017). The correlation heatmap does not noticeably change between 2014-2015 and 2016-2017, and the two heatmaps produce almost the same result as the one comparing 2014 and 2017 (Figure 3). Just as in the 2014-2017 heatmap, where regions with indices from 60 to 80 are strongly correlated, the same pattern exists in both 2014-2015 and 2016-2017, meaning that these regions have preserved their correlation pattern through these years.  We note that some regions (such as in the upper right) do change, however the major patterns remain intact.

    \begin{figure}[h!]
        \begin{subfigure}[h]{0.35\textwidth}
            \includegraphics[width=1\textwidth]{figures/14-15.png}
            \caption{14-15}
        \end{subfigure}
        \begin{subfigure}[h]{0.35\textwidth}
            \includegraphics[width=1\textwidth]{figures/16-17.png}
            \caption{16-17}
        \end{subfigure}
        \begin{subfigure}[h]{0.35\textwidth}
            \includegraphics[width=1\textwidth]{figures/14-17.png}
            \caption{14-17}
        \end{subfigure}
         \caption{Heatmap of Correlation Scores between Every Two Regions in Years 2014-2015, 2016-2017, 2014-2017}
    \end{figure}

    \begin{figure}[h!]
        \centering
        \begin{subfigure}[h]{0.3\textwidth}
            \includegraphics[width=1\textwidth]{figures/step2b_top1_ts.png}
        \end{subfigure}
        \begin{subfigure}[h]{0.3\textwidth}
            \includegraphics[width=1\textwidth]{figures/step2b_mid_ts.png}
        \end{subfigure}
        \begin{subfigure}[h]{0.3\textwidth}
            \includegraphics[width=1\textwidth]{figures/step2b_lower_ts.png}
        \end{subfigure}
        \begin{subfigure}[h]{0.3\textwidth}
            \includegraphics[width=1\textwidth]{figures/step2b_top1_ccf.png}
            \caption{$(R_{54},R_{67})$}
        \end{subfigure}
        \begin{subfigure}[h]{0.3\textwidth}
            \includegraphics[width=1\textwidth]{figures/step2b_mid_ccf.png}
            \caption{$(R_{77},R_{110})$}
        \end{subfigure}
        \begin{subfigure}[h]{0.3\textwidth}
            \includegraphics[width=1\textwidth]{figures/step2b_lower_ccf.png}
            \caption{$(R_{10},R_{111})$}
        \end{subfigure}
        \caption{Examples of Time Series and Cross-Correlation Plots}
    \end{figure}

\subsection{Evaluation}

    In order to further validate our method, we analyzed the correlation scores of several specific region pairs. The highest observed correlation score $29.96$, comes from the region pair $(R_{54},R_{67})$ representing the Bogo and Maga districts, which are next to each other in the Far North region. The time series $\mathbf{t}_{54}$ and $\mathbf{t}_{67}$ are plotted in Figure 5a along with their correlation vector $\mathbf{c}_{54,67}$. From the plot of the time series, it is clear that $\mathbf{t}_{54}$ and $\mathbf{t}_{64}$ are highly correlated. Moreover, in the correlation vector score plot where the blue dotted line is the threshold for the 0.05 significance level, under lags of various magnitude, the correlations of $\mathbf{t}_{54}$ and $\mathbf{t}_{64}$ computed by the $ccf$ function are always well above that threshold. Both plots support the high correlation score of $(R_{54},R_{67})$ and suggest strong association.

    Another region pair, $(R_{77}, R_{110})$ (Moulvoldaye and Garoua districts, which are not adjacent but relatively close together) has a relatively high correlation score $c_{54,67} = 22.78$. The corresponding plots are shown in Figure 5b. In the time series plot, $\mathbf{t}_{77}$ and $\mathbf{t}_{110}$ are somewhat correlated: there are time periods when they are in sync (from Week 25 to 40), and others where they deviate (from Week 160 to 170). The correlation of $(R_{77}, R_{110})$ is significant, but not as strong as that of $(R_{54}, R_{67})$.

    The region pair $(R_{10}, R_{111})$. $c_{10,111} = 1$, has a very low correlation score as shown in Figure 5c. There is very little observable correlation between $\mathbf{t}_{10}$ and $\mathbf{t}_{111}$, and the values within the cross-correlation plot are not measured to be significant.  This makes sense since these regions (Akonlinga and Garoua II), are far apart geographically and may not have a strong influence on each other in terms of respective malaria incidence patterns.

    The correlation plots can be used to dig deeper into how an outbreak in one region has historically been associated with changes in incidence levels in other regions. Looking at the correlations with respect to specific lags can further provide more granular information on the time scale in which relevant patterns are observed, giving insight on the temporal aspect of the observed correlation scores.

    A practical use case of the described methodology is to analyze the weighted cross-correlation scores for a given region once it is known that an outbreak has taken place. To illustrate, assume that an anomaly detection algorithm has detected an anomaly in $R_{54}$, which indicates a possible outbreak. We compute the $k$ largest correlation scores in $\mathbb{S}_{54} = \{c_{54,j} \mid j = 1,\dots,53,55,\dots,N \}$, and the regions corresponding to those scores. In this case, we stipulated $k = 5$. The results are shown in Table 2 and serve as a sample output for this method given an input region which has experienced an alarm for an outbreak.

    \begin{table}[h]
    \begin{tabular}{ |l|c| }
    \hline
    \multicolumn{1}{|c|}{Region Pairs} & Correlation Scores \\
    \hline
    54 BOGO,EXTREME NORD; 67 MAGA,EXTREME NORD & 29.96 \\
    54 BOGO,EXTREME NORD; 81 TOKOMBERE,EXTREME NORD & 28.87 \\
    54 BOGO,EXTREME NORD; 79 PETTE,EXTREME NORD & 28.31 \\
    54 BOGO,EXTREME NORD; 74 MOGODE,EXTREME NORD & 28.25 \\
    54 BOGO,EXTREME NORD; 77 MOULVOUDAYE,EXTREME NORD & 27.56 \\
    \hline
    \end{tabular}
    \vspace{1em}
    \caption{Top 5 Correlation Scores in $\mathbb{S}_{54}$}
    \label{table:1}
    \end{table}
    \begin{figure}[h!]
        \centering
        \begin{subfigure}[h]{0.4\textwidth}
            \includegraphics[width=1\textwidth]{figures/step2c_2ts.png}
        \end{subfigure}
        \begin{subfigure}[h]{0.4\textwidth}
            \includegraphics[width=1\textwidth]{figures/step2c_3ts.png}
        \end{subfigure}
        \begin{subfigure}[h]{0.4\textwidth}
            \includegraphics[width=1\textwidth]{figures/step2c_4ts.png}
        \end{subfigure}
        \begin{subfigure}[h]{0.4\textwidth}
            \includegraphics[width=1\textwidth]{figures/step2c_5ts.png}
        \end{subfigure}
    \iffalse
        \begin{subfigure}[h]{0.4\textwidth}
            \includegraphics[width=1\textwidth]{figures/step2c_2ccf.png}
            \caption{$(R_{54},R_{81})$}
        \end{subfigure}
        \begin{subfigure}[h]{0.4\textwidth}
            \includegraphics[width=1\textwidth]{figures/step2c_3ccf.png}
            \caption{$(R_{54},R_{79})$}
        \end{subfigure}
        \begin{subfigure}[h]{0.4\textwidth}
            \includegraphics[width=1\textwidth]{figures/step2c_4ccf.png}
            \caption{$(R_{54},R_{74})$}
        \end{subfigure}
        \begin{subfigure}[h]{0.4\textwidth}
            \includegraphics[width=1\textwidth]{figures/step2c_5ccf.png}
            \caption{$(R_{54},R_{77})$}
        \end{subfigure}
    \fi
        \caption{Time Series Corresponding to 4 Region Pairs Identified in Table 1}
    \end{figure}

    The two time series corresponding to $(R_{54},R_{67})$ are shown in Figure 5a, while the time series plots corresponding to the remaining entries of Table 1 can be found in Figure 6.

    \begin{figure}[h!]
        \centering
        \begin{subfigure}[h]{0.45\textwidth}
            \includegraphics[width=1\textwidth]{figures/dis_54-67.png}
        \end{subfigure}
        \begin{subfigure}[h]{0.45\textwidth}
            \includegraphics[width=1\textwidth]{figures/dis_54-77.png}
        \end{subfigure}
        \begin{subfigure}[h]{0.45\textwidth}
            \includegraphics[width=1\textwidth]{figures/dis_54-81.png}
        \end{subfigure}
         \caption{Outbreak Alarm Patterns Flagged using EARS C Anomaly Detection for 3 Region Pairs in Table 1}
    \end{figure}

    We then apply the EARS C method on the pairs of regions that have the highest correlation scores to analyze how patterns in flagging alarms are aligned. For example, we take the pairs $R_{54}$ and $R_{77}$, $R_{54}$ and $R_{67}$, and $R_{54}$ and $R_{77}$ and compare their outbreak patterns using the EARS C method with a look-back period of 7 weeks. This method will detect whether the disease counts from a given week are abnormal based on disease counts in the previous 7 weeks. As evident from Figure 7, the two regions in each pair have very similar alarm patterns.

    Furthermore, these alarms appear clustered around the same time. The yellow lines on Figure 7 indicate alarm clusters, which overlap in correlated regions. Instances where alarms in one region precede another could indicate disease spreading from one region to another. We observed similar alarm clustering in other regions with high correlation scores, confirming the reliability of this method.

\section{Discussion}

    The initial goal of the WHO IDSR strategy was to develop actionable insights to improve health outcomes of infectious disease outbreaks, but so far in Cameroon, analysis of surveillance data has been mainly descriptive. The lack of advanced analytics on surveillance data collected through the WHO IDSR strategy limits the ability of public health officials to improve their decision-making based on insights and trends from the collected data. The methodology introduced in this paper serves as a step towards bridging the gap between advanced analytics and surveillance data collected through the WHO IDSR strategy.

    We demonstrate the potential for a weighted cross-correlation analysis in quantifying similarity in surveillance time series for an infectious disease. Although causal relationships can't be ascertained from analysis of multivariate cross correlation, general trends can be assessed in evaluating the similarity of disease frequency patterns across many health districts. The evaluated similarity in disease frequency between health districts is a reflection of numerous factors, including general seasonality and outbreak spreading patterns. We further demonstrated that using a weighted cross correlation metric has the potential to cluster similar regions based on similar alarms flagged by an outbreak detection algorithm (EARS C1). This signals utility in providing insight on how infectious diseases may spread and flagging health districts that may be susceptible to the ramifications of an initial known outbreak. Using known weighted cross-correlation scores for a given disease can allow for more proactive monitoring of disease outbreaks even before the development of more sophisticated software systems, and serves as a gate to deploy more sophisticated techniques in artificial intelligence which can be better leveraged as size of the data increases. Proactive monitoring and utilization of historical surveillance to provide insight on correlation in disease frequency in different health districts can inform more efficient outbreak mitigation strategies as well.

    The trends revealed via multivariate cross correlation analysis be subsequently investigated to understand which factors may play a role in contributing to such trends. An analysis of such factors thereby may lead to a more mechanistic understanding of spreading between diseases, which can be leveraged by public health officials. For example, two highly correlated regions that are not necessarily close together geographically may share extensive modes of transportation. Investigating these modes of transportation and how they pertain to the spread of a certain infectious diseases can potentially lead to better preventative mechanisms for outbreak mitigation.

    One of the limitations of the current method is its use of the EARS C1 algorithm, which does not adequately capture the seasonality of disease outbreak patterns. As we can see from the outbreak patterns of different regions in Figure 7, there are clear seasonal trends in disease outbreaks. Specifically, the number of disease counts in the summer seems to be much higher than the average level every year, indicating a summertime peak period. Our current EARS C1 algorithm only considers a certain time period (7 weeks in this case), and thus misses seasonal trend. As a result, the algorithm tends to give unnecessary alerts when we are already on a high alert level. Investigating the use of anomaly detection algorithms that can take seasonal trends into account and understanding how the alarms cluster for highly correlated regions would be a natural extension to this work.

    Another limitation is that pairwise cross-correlation analysis does not directly capture the dynamics of multiple simultaneous outbreaks. Exploring methodologies that can analyze these trends in the context of multiple simultaneous outbreaks would be an ideal avenue for future work.

    It is important to note that the study only uses data over a four year period. As additional data collection infrastructure is put in place more surveillance data will become available for the continued development of methodologies for analysis.
    For the purpose of demonstrating our methodology, this paper uses Malaria as an example to demonstrate a proof of concept, largely due to improved data reporting quality and quantity for Malaria specifically. While Malaria is spread indirectly through parasites, a the same approach could be used for a disease that is passed between humans and compare how the identified patterns differed. Since different diseases naturally have various of modes of transmission and hence different inter-regional spread pattern, future research could use multivariate cross-correlation to delve deeper into the differences in observed trends between types of diseases.

\section{Conclusions}
WHO's Integrated Disease Surveillance and Response (IDSR) strategy has collected valuable disease surveillance data in Cameroon. Nevertheless, public health officials in Cameroon still face the challenge harnessing this information to obtain actionable insights. To issue this problem, we propose a computational methodology utilizing multivariate cross-correlation analysis to identify patterns of disease incidence between regions, which could in turn be used to drive more efficient mitigation efforts and drive investigation into mechanisms behind disease spread. In addition to presenting a mathematical formulation of this methodology, we present its application and analysis using real WHO surveillance data provided by officials from the WHO Country Office in Cameroon. Retroactive analysis, using malaria as an example, reveals the potential of the outlined methodology to provide strong signals for regional correlation of infectious diseases. As data collected through the WHO IDSR program increases in quantity and quality , computational approaches such as the one presented in this paper and future developed methods will become even more useful, ultimately improving disease mitigation and health outcomes.

 \section{Acknowledgments}
This work was partially supported through the generous funding from the UCLA Global Citizens Fellowship Award to VS.

redpajama_set_name: RedPajamaArXiv


text:
// import { ServerInteractService } from '../common/serverInteract.service';
// import { ScannerDownloadService } from './Scanner-web.service';
// import {async, ComponentFixture,inject, TestBed} from '@angular/core/testing';
// import {By} from '@angular/platform-browser';
// import {DebugElement} from '@angular/core';
// import {ScannerPageComponent} from './scanner-page.component';
// import {MatCardModule} from '@angular/material';
// import {MatMenuModule} from '@angular/material';
// import {MatTableModule} from '@angular/material';
// import {DataSource} from '@angular/cdk/collections';
//
// class ScannerDownloadServiceStub{
//
// }
//
// class ServerInteractServiceStub{
//
// }
//
//
// describe('TG:ScannerPageComponent', () => {
//     let component: ScannerPageComponent;
//     let fixture: ComponentFixture<ScannerPageComponent>;
//     // let comp: ScannerPageComponent;
//     // let de: DebugElement;
//     // let el: HTMLElement;
//     // let elb: HTMLButtonElement;
//     // let eli: HTMLInputElement;
//     // let de2: DebugElement;
//
//     beforeEach(async(() => {
//         TestBed.configureTestingModule({
//             imports: [
//                 MatCardModule,
//                 MatMenuModule,
//                 MatTableModule
//             ],
//             // providers:[ScannerDownloadService],
//             // providers:[{provide: ScannerDownloadService, useClass: ScannerDownloadServiceStub}],
//             providers:[{provide: ServerInteractService, useClass: ServerInteractServiceStub}],
//             declarations: [ScannerPageComponent]
//         })
//             .compileComponents();
//
//     }));
//
//     beforeEach(() => {
//         fixture = TestBed.createComponent(ScannerPageComponent);
//         component = fixture.componentInstance;
//         fixture.detectChanges();
//         // comp = fixture.componentInstance; // ScannerPageComponent test instance
//
//     });
//
//     // it('should create', () => {
//     //     const service=TestBed.get(ScannerDownloadService);
//     //     // expect(component).toBeTruthy();
//     // });
//
//     // testing search part
// //     xit('should display original head', () => {
// //         de = fixture.debugElement.query(By.css('h1'));
// //         // query for the head id = "start" by CSS element selector
// //         el = de.nativeElement;
// //         fixture.detectChanges();
// //         expect(el.textContent).toContain('Search Images');
// //     });
//
//
// //     xit('should be able search button at begin', () => {
// //         de = fixture.debugElement.query(By.css('#searchButton'));
// //         elb = de.nativeElement;
// //         expect(elb).toBeTruthy('search button is able');
// //     });
//
// //     xit('should be able reset button at begin', () => {
// //         de = fixture.debugElement.query(By.css('#resetButton1'));
// //         elb = de.nativeElement;
// //         expect(elb).toBeTruthy('reset button is able');
// //     });
//
//
// //     // testing image display part
// //     xit('should display CardTitle', () => {
// //         de = fixture.debugElement.query(By.css('#matCardTitle2'));
// //         el = de.nativeElement;
// //         fixture.detectChanges();
// //         expect(el.textContent).toContain('Voting Type: food');
// //     });
//
//
// //     xit('should disable previousPage button at begin', () => {
// //         de = fixture.debugElement.query(By.css('#previousButton'));
// //         elb = de.nativeElement;
// //         expect(elb.disabled).toBeTruthy('previousPage button is disabled');
//
// //     });
//
// //     xit('should be able nextPage button at begin', () => {
// //         de = fixture.debugElement.query(By.css('#nextButton'));
// //         elb = de.nativeElement;
// //         expect(elb).toBeTruthy('nextPage button is able');
// //     });
//
// //     xit('nextButton should be disabled when reach the last image', () =>{
// //         de = fixture.debugElement.query(By.css('#nextButton'));
// //         elb = de.nativeElement;
//
// //         de2 = fixture.debugElement.query(By.css('#count'));
// //         el = de2.nativeElement;
//
// //         // get the number of images
// //         let imageCount = parseInt(el.textContent, 10);
//
// //         // simulate click of next page button multiple times
// //         for(var i = 0; i < imageCount; i++) {
// //           de.triggerEventHandler('click', null);
// //         }
//
// //         // when it reaches the last image, it should disable the nextButton
// //         expect(elb.disabled ).toBeTruthy("nextPage button is disabled");
//
// //       })
//
// //     // testing leftside bar
//
//
// //     // testing count part
// //     xit('should display CardTitle', () => {
// //         de = fixture.debugElement.query(By.css('#matCardTitle1'));
// //         el = de.nativeElement;
// //         fixture.detectChanges();
// //         expect(el.textContent).toContain('Counting the total number');
// //     });
//
// //     xit('should be able count button at begin', () => {
// //         de = fixture.debugElement.query(By.css('#countButton'));
// //         elb = de.nativeElement;
// //         expect(elb).toBeTruthy('count button is able');
// //     });
//
// //    /* it('should be able cancel button at begin', () => {
// //         de = fixture.debugElement.query(By.css('#cancelButton'));
// //         elb = de.nativeElement;
// //         expect(elb).toBeTruthy('cancel button is able');
// //     }); */
//
// //     // testing navbar
// //     xit('should be able selectType button at begin', () => {
// //         de = fixture.debugElement.query(By.css('#votingType'));
// //         elb = de.nativeElement;
// //         expect(elb).toBeTruthy('votingType button is able');
// //     });
//
//
// });


text:
La basilique-sanctuaire de la Vierge des Larmes est un sanctuaire marial dans la commune de Syracuse, en Italie, dédié à la Vierge Marie.

Histoire et description de l'édifice
La construction d'un sanctuaire a été décidée pour conserver et vénérer une effigie de la Vierge Marie qui avait été l'objet de plusieurs lacrimations déclarées miraculeuses et qui a donné naissance au culte de la Vierge des Larmes.

Le projet a été lancé par un concours international auquel cent firmes d'architecture ont participé et que les architectes Michel Andrault et Pierre Parat ont remporté en 1957. L'ingénieur principal en a été Riccardo Morandi. Sa réalisation a commencé en 1966 et s'est terminée 28 ans plus tard. Le retard s'explique en partie par le fait que les excavations pour les fondations ont mis au jour une zone d'habitation du , ainsi que la principale voie de communication du quartier d'.

Description
L'édifice comprend deux niveaux (le sanctuaire et la crypte) surmontés d'un toit de forme conique, nervuré, en béton armé, de 103 mètres de hauteur totale. La hauteur intérieure est de 94,30 mètres et la pointe du cône est couronnée d'une statue de la Madone en bronze.

L'intérieur du sanctuaire a un diamètre de 71,40 mètres (sans les chapelles attenantes) et peut accueillir 11 000 personnes debout et environ 6 000 assises. L'autel a été réalisé en marbre blanc et en pierre de Modica; il est surmonté de l'effigie miraculeuse de la Madone et d'une croix du .

Le sanctuaire a été inauguré le 6 novembre 1994 par le pape Jean-Paul II, invité par , archevêque de Syracuse.

La crypte a été consacrée en 1968 par Giuseppe Bonfiglioli, archevêque de Syracuse et inaugurée la même année par Francesco Carpino, archevêque de Palerme.
Son diamètre externe est de 80 mètres et elle peut accommoder 3 000 personnes assises. L'autel est en granit rose. Dans la crypte est conservée la fiole contenant les larmes analysées pour confirmer le miracle. La crypte est entourée de chapelles ainsi que d'une salle où sont rassemblés les ex-voto des fidèles.
La crypte comprend également les vestiges d'un hypogée de l'Antiquité tardive sous lequel se trouvent deux salles gréco-romaines.

Cinéma
Dans son documentaire Sicilia di sabbia, Massimiliano Perrotta consacre un chapitre à la modernité de la basilique-sanctuaire.

Notes et références

Bibliographie
  Valentino Salvoldi, Basilica santuario Madonna delle lacrime, Turin, Elledici, 2011.

Lien externe
  du sanctuaire.
 Visite virtuelle du sanctuaire.

Sanctuaire en Italie
Église à Syracuse
Édifice religieux du XXe siècle en Italie
Lieu de pèlerinage marial
Syracuse


1. Remove power and ant.
3. Locate the digital unit.
Please notice that all functions only work if you switch off radio first.
RX/A : Turn ON/OFF SUB receiver marker on SM 230 Monitor in 100 kHz/250kHz and 25kHz sweep mode.
8.83 + RIT : Both 8.83 MHz and 455 kHz IF Filters are selectable on transmit. Each memory channel is also capable of storing this setting.
Memory protect: Cutting D 129 on control unit protects memory contents from beeing erased.
Write protect : Cutting D 18 on Control Unit write protects all memory channels.
Allows sub-rcvr to be on different band than Main receiver.
This is a Power On function mod.
Hold down buttons SUB, M/S, RX/SUB while turning on the power ON. Now you can listen to 15M on sub-rcvr with Main rcvr on 20M. or any other band or visa-versa. Both receivers will be in the same mode USB, LSB, CW etc.


text:
title: OER Glue goes Boom
author: Justin Ball
layout: post
permalink: "/2011/05/06/oer-glue-goes-boom/"
tags:
  - "business"
  - "OER"
date: '2011-05-06T06:00:00Z'
templateKey: blog-post
path: "/oer-glue-goes-boom"
description: ''
---

We've been building OER Glue since this last July and the result is a piece of technology that when people see it they have the same reaction they have during a magic show - HOLY SH@#$ how do you do that?

We think it's pretty cool.

Beyond pretty cool we feel like it's an idea that can have a significant impact on online education. OER Glue makes it simple to find and then remix Open Education Resources with popular services. [You can get a beta code here][1].

 [1]: http://www.oerglue.com/

Our big news for the day is that we have been selected to join the [BoomStartup][2] program for the summer. We are extremely excited for the opportunity and feel that the experience will help us bring the technology and subsequently a new kind of learning experience to learners world wide.

 [2]: http://www.boomstartup.com/

Here are a couple of links with the announcement:


text:
Back Pain in Outer Space
Radostin Penchev, Richard A. Scheuring, Adam T. Soto, Derek M. Miletich, Eric Kerstman, Steven P. Cohen
Space travel has grown during the past 2 decades, and is expected to surge in the future with the establishment of an American Space Force, businesses specializing in commercial space travel, and National Aeronautics and Space Administration's planned sustained presence on the moon. Accompanying this rise, treating physicians are bracing for a concomitant increase in space-related medical problems, including back pain. Back pain is highly prevalent in astronauts and space travelers, with most cases being transient and self-limiting (space adaptation back pain). Pathophysiologic changes that affect the spine occur during space travel and may be attributed to microgravity, rapid acceleration and deceleration, and increased radiation. These include a loss of spinal curvature, spinal muscle atrophy, a higher rate of disc herniation, decreased proteoglycan and collagen content in intervertebral discs, and a reduction in bone density that may predispose people to vertebral endplate fractures. In this article, the authors discuss epidemiology, pathophysiology, prevention, treatment, and future research.
https://doi.org/10.1097/ALN.0000000000003812
10.1097/ALN.0000000000003812
Dive into the research topics of 'Back Pain in Outer Space'. Together they form a unique fingerprint.
Space Flight Medicine & Life Sciences 100%
Back Pain Medicine & Life Sciences 79%
Spinal Curvatures Medicine & Life Sciences 39%
Astronauts Medicine & Life Sciences 35%
Weightlessness Medicine & Life Sciences 33%
Deceleration Medicine & Life Sciences 30%
Intervertebral Disc Medicine & Life Sciences 29%
Proteoglycans Medicine & Life Sciences 28%
Penchev, R., Scheuring, R. A., Soto, A. T., Miletich, D. M., Kerstman, E., & Cohen, S. P. (Accepted/In press). Back Pain in Outer Space. Anesthesiology, 384-395. https://doi.org/10.1097/ALN.0000000000003812
Back Pain in Outer Space. / Penchev, Radostin; Scheuring, Richard A.; Soto, Adam T.; Miletich, Derek M.; Kerstman, Eric; Cohen, Steven P.
In: Anesthesiology, 2021, p. 384-395.
Penchev, R, Scheuring, RA, Soto, AT, Miletich, DM, Kerstman, E & Cohen, SP 2021, 'Back Pain in Outer Space', Anesthesiology, pp. 384-395. https://doi.org/10.1097/ALN.0000000000003812
Penchev R, Scheuring RA, Soto AT, Miletich DM, Kerstman E, Cohen SP. Back Pain in Outer Space. Anesthesiology. 2021;384-395. https://doi.org/10.1097/ALN.0000000000003812
Penchev, Radostin ; Scheuring, Richard A. ; Soto, Adam T. ; Miletich, Derek M. ; Kerstman, Eric ; Cohen, Steven P. / Back Pain in Outer Space. In: Anesthesiology. 2021 ; pp. 384-395.
@article{a484cd4165f3431eaf774f286ea9c0e2,
title = "Back Pain in Outer Space",
abstract = "Space travel has grown during the past 2 decades, and is expected to surge in the future with the establishment of an American Space Force, businesses specializing in commercial space travel, and National Aeronautics and Space Administration's planned sustained presence on the moon. Accompanying this rise, treating physicians are bracing for a concomitant increase in space-related medical problems, including back pain. Back pain is highly prevalent in astronauts and space travelers, with most cases being transient and self-limiting (space adaptation back pain). Pathophysiologic changes that affect the spine occur during space travel and may be attributed to microgravity, rapid acceleration and deceleration, and increased radiation. These include a loss of spinal curvature, spinal muscle atrophy, a higher rate of disc herniation, decreased proteoglycan and collagen content in intervertebral discs, and a reduction in bone density that may predispose people to vertebral endplate fractures. In this article, the authors discuss epidemiology, pathophysiology, prevention, treatment, and future research.",
author = "Radostin Penchev and Scheuring, {Richard A.} and Soto, {Adam T.} and Miletich, {Derek M.} and Eric Kerstman and Cohen, {Steven P.}",
note = "Publisher Copyright: {\textcopyright} 2021, the American Society of Anesthesiologists. All Rights Reserved.",
doi = "10.1097/ALN.0000000000003812",
journal = "Anesthesiology",
T1 - Back Pain in Outer Space
AU - Penchev, Radostin
AU - Scheuring, Richard A.
AU - Soto, Adam T.
AU - Miletich, Derek M.
AU - Kerstman, Eric
AU - Cohen, Steven P.
N1 - Publisher Copyright: © 2021, the American Society of Anesthesiologists. All Rights Reserved.
N2 - Space travel has grown during the past 2 decades, and is expected to surge in the future with the establishment of an American Space Force, businesses specializing in commercial space travel, and National Aeronautics and Space Administration's planned sustained presence on the moon. Accompanying this rise, treating physicians are bracing for a concomitant increase in space-related medical problems, including back pain. Back pain is highly prevalent in astronauts and space travelers, with most cases being transient and self-limiting (space adaptation back pain). Pathophysiologic changes that affect the spine occur during space travel and may be attributed to microgravity, rapid acceleration and deceleration, and increased radiation. These include a loss of spinal curvature, spinal muscle atrophy, a higher rate of disc herniation, decreased proteoglycan and collagen content in intervertebral discs, and a reduction in bone density that may predispose people to vertebral endplate fractures. In this article, the authors discuss epidemiology, pathophysiology, prevention, treatment, and future research.
AB - Space travel has grown during the past 2 decades, and is expected to surge in the future with the establishment of an American Space Force, businesses specializing in commercial space travel, and National Aeronautics and Space Administration's planned sustained presence on the moon. Accompanying this rise, treating physicians are bracing for a concomitant increase in space-related medical problems, including back pain. Back pain is highly prevalent in astronauts and space travelers, with most cases being transient and self-limiting (space adaptation back pain). Pathophysiologic changes that affect the spine occur during space travel and may be attributed to microgravity, rapid acceleration and deceleration, and increased radiation. These include a loss of spinal curvature, spinal muscle atrophy, a higher rate of disc herniation, decreased proteoglycan and collagen content in intervertebral discs, and a reduction in bone density that may predispose people to vertebral endplate fractures. In this article, the authors discuss epidemiology, pathophysiology, prevention, treatment, and future research.
U2 - 10.1097/ALN.0000000000003812
DO - 10.1097/ALN.0000000000003812
JO - Anesthesiology
JF - Anesthesiology


text:
Plans Scrapped for Fourth Port Site
Thursday, September 17, 2020 03:14 PM EDT Updated: Friday, September 18, 2020 09:17 AM EDT
(photo courtesy of the Ports of Indiana)
Governor Eric Holcomb says the state will no longer pursue plans to acquire land near Lawrenceburg which would serve as the state's fourth port. The governor's office says the Ports of Indiana found the 725-acre site requires environmental remediation work that would take years to complete.
The property along the Ohio River previously served as a coal-fired power plant for American Electric Power Co. Inc. (NYSE: AEP), the parent Fort Wayne-based Indiana Michigan Power.
The site is owned by Tanners Creek Development LLC. The Ports of Indiana entered into an agreement in 2017 to acquire the property pending the results of an environmental and economic viability study.
Ultimately, the governor's office says, the amount of remediation work needed rendered the site economically unviable.
"I have been eager to pursue the opportunity for a fourth port in Indiana well before holding this office and remain so today," Holcomb said in a news release. "While I'm disappointed this particular site is not feasible for this purpose, I am committed to working with the Ports of Indiana and all the dedicated business and local elected leaders in southeastern Indiana to explore other possible locations in the region."
Ports of Indiana Chief Executive Officer Vanta Coda shared a similar sentiment.
"After a careful overall analysis, we feel it was not prudent to move forward with plans to develop the Lawrenceburg site. We echo the governor's disappointment," Coda said in a statement to Inside INdiana Business.
Indiana's ports at Burns Harbor in northwest Indiana, Mount Vernon and Jeffersonville generate an estimated $7.8 billion in annual economic impact. Last year, the three ports handled an estimated 12.3 million tons of cargo in 2019, down from a record 14.8 million tons the previous year.
In a recent interview on Inside INdiana Business with Gerry Dick, Coda said a proposal has been submitted the stretches of land where the ports are located as maritime districts, which would have a "great impact" on the visibility for the ports within the federal government.


you can contact MIC Higher Education Bureau. It should be offered this year as well. The Bureau has linked up with few IPTS other than HELP.
hi, pls contact MIC higher education bureau. you can find them on facebook. they will help facilitate for scholarship. but Help uni don't cover physiotherapy i think.


I am really really sorry for the radio silence last week guys, but I have a pretty good excuse: I am moving!
After four years of living in Dresden I am finally saying goodbye. Dresden has really treated me well. It's such a beautiful city. Every time I saw the historic town or visited the "Neustadt" with all its hipster cafés and colored walls I fell in love with it a little more. I felt like there has always been something to discover, some place to go that you haven't seen before. And of course I made really good friends here. It's really not easy for me to say goodbye.
However, after every goodbye comes a "hello" and that's gonna be in….drum roll please…. Amsterdam! I just love this city so much. I visited it 10 years ago or so with my family. It was our first time to see Amsterdam though we have driven past it so many times. I immediately knew: I wanna live here one day. All these tiny, slightly angular houses appeal to me so much. With all the town canals and bikes I feel like there is such a quality of life. And of course there is quite a lot to discover too. I'm a sucker for individual furniture and all that and I already found so many great stores, I can't wait to move into my own flat. Although that's not gonna happen that soon. First I'm only gonna be an interim tenant.
As for this outfit, I really couldn't believe that I was able to wear shorts one more time this year. I paired them with my new shoes from Asos and a simple white blouse. Pretty minimalistic, but that's what I enjoy most in outfits.
Beautiful outfit and scenery! Congratulations on getting an internship with a fashion company! Hoping your move to Amsterdam is stress-free!
Such a nice black and white look!
Oehh I love the white stripe. A simple detail but it does it all!
This is a lovely and very simple outfit! You look so stunning in that flower-y background!
Estas muy guapa corazón. me encantan los short.
Love this look! So effortlessly chic!


This thesis presents an experimental investigation into the feasibility of using a functionally thermal fluid to enhance the performance of a Proton Exchange Membrane (PEM) Fuel Cell. Specifically, a fluid was developed that utilised a liquid-solid phase change to enhance heat transport within the fuel cell. Increasing the convective heat transfer coefficient could permit the use of smaller volumetric flow rates and reduce pumping power.
The objective of the thermal fluid was to create isothermal conditions across a fuel cell and to reduce parasitic loadings from pumps and other components to enhance the overall system performance. Additionally, the fluid could reduce the system size and component cost, and stabilise temperature fluctuations within the system.
The thermal fluid that was developed constituted a mix of fine, Microencapsulated Phase Change Material (MicroPCM) particles suspended in a single-phase working fluid. For successful integration with the fuel cell, the microPCMs thermal and fluid properties, and their effectiveness in transferring heat, had to be fully characterised and understood.
Research consisted of experimental investigations of the fuel cell, followed by microPCM development. Experimentation on the fuel cell stack revealed a requirement for thermal stability and reduction in parasitic load from the pumps. Quantitative characterisation and development of the microPCM properties involved state of the art equipment to measure the latent heat of fusion, melting and freezing points, surface morphology and viscosity of the microPCM slurry. The effects of repeated use of solid to liquid phase change particles upon melting and solidification were studied. This lead to the further development of microPCM particles and experimentally examined in a fuel cell system.
The use of MicroPCM developed in this study balanced the improvement in thermal capacity of the fluid with the increase in pumping load, when compared to the use of water alone. The study suggested that with further development of the microPCM slurry, it has the potential to significantly increase the thermal capacity of the fluid and stabilise temperatures across the fuel cell, which in turn would results in improved stack performance and electrical conversion efficiency.


text:
Hu, Wenqing; Li, Chris Junchi
A convergence analysis of the perturbed compositional gradient flow: averaging principle and normal deviations. (English) Zbl 1397.34098
Discrete Contin. Dyn. Syst. 38, No. 10, 4951-4977 (2018).
Summary: We consider in this work a system of two stochastic differential equations named the perturbed compositional gradient flow. By introducing a separation of fast and slow scales of the two equations, we show that the limit of the slow motion is given by an averaged ordinary differential equation. We then demonstrate that the deviation of the slow motion from the averaged equation, after proper rescaling, converges to a stochastic process with Gaussian inputs. This indicates that the slow motion can be approximated in the weak sense by a standard perturbed gradient flow or the continuous-time stochastic gradient descent algorithm that solves the optimization problem for a composition of two functions. As an application, the perturbed compositional gradient flow corresponds to the diffusion limit of the Stochastic Composite Gradient Descent (SCGD) algorithm for minimizing a composition of two expected-value functions in the optimization literatures. For the strongly convex case, such an analysis implies that the SCGD algorithm has the same convergence time asymptotic as the classical stochastic gradient descent algorithm. Thus it validates, at the level of continuous approximation, the effectiveness of using the SCGD algorithm in the strongly convex case.
34F05 Ordinary differential equations and systems with randomness
34C29 Averaging method for ordinary differential equations
90C30 Nonlinear programming
perturbed compositional gradient flow; stochastic composite gradient descent; perturbed gradient flow; stochastic gradient descent; fast-slow dynamical systems; averaging principle; normal deviation
\textit{W. Hu} and \textit{C. J. Li}, Discrete Contin. Dyn. Syst. 38, No. 10, 4951--4977 (2018; Zbl 1397.34098)
[1] L. Arnold, Hasslemann's program revisited: The analysis of stochasticity in deterministic climate models, in Progress in Probability Book Series, Springer, 49, Stochastic Climate Models, 141-157. · Zbl 1015.34037
[2] V. I. Bakhtin, Asymptotics of superregular perturbations of fiber ergodic semigroups, Stochastics and Stochastic Reports, 75, 295, (2003) · Zbl 1039.60055
[3] V. Bakhtin; Y. Kifer, Diffusion approximation for slow motion in fully coupled averaging, Probability Theory and Related Fields, 129, 157, (2004) · Zbl 1069.34070
[4] A. Benveniste, M. Metivier and P. Priouret, Adaptive algorithms and stochastic approximations, Applications of Mathematics, Springer, 22 (1990), xii+365pp. · Zbl 0752.93073
[5] V. S. Borkar, Stochastic Approximation: A Dynamical Systems Viewpoint, Cambridge University Press, 2008. · Zbl 1181.62119
[6] S. Cerrai, A Khasminskii's averaging principle for stochastic reaction-diffusion equations, Annals of Applied Probability, 19, 899, (2009) · Zbl 1191.60076
[7] S. Cerrai, Normal deviations from the averaged motion for some reaction-diffusion equations with fast oscillating perturbation, Journal de Mathématiques Pures et Appliquées, 91, 614, (2009) · Zbl 1174.60034
[8] Y. Ermoliev, Methods of Stochastic Programming. Monographs in Optimization and OR, Nauka, Moscow, 1976.
[9] M. I. Freidlin, Functional Integration and Partial Differential Equations, Princeton University Press, Princeton, 1985. · Zbl 0568.60057
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[12] K. Hasselmann, Stochastic climate models, part ⅰ, theory, Tellus, 28, 473, (1976)
[13] W. Hu and C. J. Li, On the fast convergence of random perturbations of the gradient flow, preprint, arXiv: 1706.00837
[14] W. Hu, C. J. Li, L. Li and J. Liu, On the diffusion approximation of nonconvex stochastic gradient descent, Annals of Mathematical Science and Applications, to appear. arXiv: 1705.07562
[15] R. Khasminskii, On stochastic processes defined by differential equations with a small parameter, Theory of Probability and its Applications, 11, 211, (1966) · Zbl 0168.16002
[16] R. Khasminskii, On the principle of averaging the it ô's stochastic differential equations, Kybernetika (Prague), 4, 260, (1968)
[17] Y. Kifer, The exit problem for small random perturbations of dynamical systems with a hyperbolic fixed point, Israel Journal of Mathematics, 40, 74, (1981) · Zbl 0473.60067
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Dame Bradbury's continue their athletic prowess by becoming Cross Country County Athletics Champions.
The Year 6 girls cross country team travelled to Hadleigh Park to compete in the County Finals. Having won the Uttlesford Schools competition, they represented the district on a blisteringly hot day at the former Olympic mountain biking venue.
With 14 schools from around the county competing, the girls all ran superbly and scored a team total of 43 team points (lowest score being best). They were crowned county champions ahead of second place who scored 60 points. The girls have shown real commitment to training throughout the Easter holidays and their hard work was repaid with a fantastic result! We are all extremely proud of you, well done girls!


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Welcome to the site. There is a wealth of information on zebras here as well as on a sister site, https://www.planetcatfish.com/ which os owned by the same person.
If you are not aware of it, there is a great weekend fish event in Rocky Hill CT. in March. You can find out about it here https://northeastcouncil.org/ This will be my 3rd and final year in the vendor room. I usually bring some zebras as well as a few of the other B&W Hypans.