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King Richard III remains one of the most infamous and recognisable monarchs in English or British history, despite only sitting on the throne for two years and fifty-eight days.
Fact and Fictions
King Richard III remains one of the most infamous and recognizable monarchs in English or British history, despite only sitting on the throne for two years and fifty-eight days. His hold on the popular imagination is largely due to the fictional portrayal of him by William Shakespeare which, combined with the workings of five centuries of rumor and gossip, has created two opposing versions of Richard. In fiction he is the evil, scheming murderer who revels in his plots, but many of the facts point towards a very different man.
Dissecting a real Richard III from the fictional versions that have taken hold is made difficult by the inability to discern motives in many instances, leaving a wide gap for interpretation that can be favorable or damning in varying degrees. It is the facts that will act as the scalpel to begin the operation of finding a truth obscured by fiction.
Richard III may have been a monster, a saint, or just a man trying to survive, but any view of him should be based in the realities of his life, not the myths built on rumor and theater. How much of what we think we know about England's most controversial monarch will remain when the facts are sifted from the fictions?
Matthew Lewis trained in law and is now a full time author of historical fiction and non-fiction. He also blogs on his website, Matt's History Blog, and can be found on Twitter as @mattlewisauthor. His main interest is medieval history and he has a number of books on that topic, including The Wars of the Roses: The Key Players in the Struggle for Supremacy and Richard, Duke of York: King by Right.
The informed and informative result of remarkably painstaking and meticulous research that is deftly organized and presented so as to be of especially impressive value to scholars and non-specialist general readers alike, this edition of "Richard III" by Matthew Lewis will prove to be an enduringly popular addition to community and academic library late Medieval English Biography collections in general, and King Richard III supplemental studies reading lists in particular
- Midwest Book Review
Richard III Reviews


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39 Canciones (3 horas y 10 minutos) Publicado el 4 de marzo de 2007
Concerto No. 1 in E Major, RV 269, 'La primavera': I. Allegro
Nigel Kennedy & Antonio Vivaldi
Concerto No. 1 in E Major, RV 269, ''La primavera'': II. Largo
Nigel Kennedy/English Chamber Orchestra & Antonio Vivaldi
Concerto No. 1 in E Major, RV 269, ''La primavera'': III. Allegro
Concerto No. 2 in G Minor, RV 315, 'L'estate': I. Allegro non molto
Nigel Kennedy/Charles Tunnell/Olga Hegedus/Ivor Bolton/John Constable/Robin Jeffrey/David Miller/English Chamber Orchestra & Antonio Vivaldi
Concerto No. 2 in G Minor, RV 315, 'L'estate': II. Adagio
Concerto No. 2 in G Minor, RV 315, 'L'estate': III. Presto
Concerto No. 3 in F Major, RV 293, 'L'autumno': I. Allegro
Concerto No. 3 in F Major, RV 293, 'L'autumno': II. Adagio molto
Concerto No. 3 in F Major, RV 293, 'L'autumno': III. Allegro
Concerto No. 4 in F Minor, RV 297, 'L'inverno': I. Allegro non molto
Concerto No. 4 in F Minor, RV 297, 'L'inverno': II. Largo
Concerto No. 4 in F Minor, RV 297, 'L'inverno': III. Allegro
The Four Seasons - Summer - III. Presto
Nigel Kennedy/English Chamber Orchestra
Meditation/Méditation from Thaïs
Hungarian Dance No. 5
Nocturne in C Sharp Minor Op. posth.
Gymnopédie No. 2
Prelude No. 1
Air on the G string (from Orchestral Suite No. 3 in D BWV1068)
Czardas
Sinfonia (The Arrival of the Queen of Sheba)
The Girl with the Flaxen Hair/La fille aux cheveux de lin (No. 8 of Preludes, Book 1)
The Flight of the Bumble-Bee from The Tale of Tsar Saltan
Banjo and Fiddle
Zigeunerweisen Op. 20
Urge for Going
Melody in the Wind
Violin Concerto No. 1 in G Minor, Op.26: I. Vorspiel (Allegro moderato) -
Nigel Kennedy/English Chamber Orchestra/Jeffrey Tate
Concerto for Violin and Orchestra No. 1 in G minor Op. 26: II. Adagio
Violin Concerto No. 1 in G Minor, Op.26: III. Finale (Allegro energico)
Rondo in A, D.438
Violin Concerto in E Minor, Op.64: I. Allegro molto appassionato -
Violin Concerto in E Minor, Op.64: II. Andante
Violin Concerto in E Minor, Op.64: III. Allegretto non troppo - Allegro molto vivace
℗ This compilation 2007 EMI Records Ltd © 2007 EMI Records Ltd

redpajama_set_name: RedPajamaCommonCrawl


Human resource planning in healthcare can employ machine learning to effectively predict length of stay of recruited health workers who are stationed in rural areas. While prior studies have identified a number of demographic factors related to general health practitioners' decision to stay in public health practice, recruitment agencies have no validated methods to predict how long these health workers will commit to their placement. We aim to use machine learning methods to predict health professional's length of practice in the rural public healthcare sector based on their demographic information.
Recruitment and retention data from Africa Health Placements was used to develop machine-learning models to predict health workers' length of practice. A cross-validation technique was used to validate the models, and to evaluate which model performs better, based on their respective aggregated error rates of prediction. Length of stay was categorized into four groups for classification (less than 1 year, less than 2 years, less than 3 years, and more than 3 years). R, a statistical computing language, was used to train three machine learning models and apply 10-fold cross validation techniques in order to attain evaluative statistics.
The three models attain almost identical results, with negligible difference in accuracy. The "best"-performing model (Multinomial logistic classifier) achieved a 47.34% [SD 1.63] classification accuracy while the decision tree model achieved an almost comparable 45.82% [SD 1.69]. The three models achieved an average AUC of approximately 0.66 suggesting sufficient predictive signal at the four categorical variables selected.
Machine-learning models give us a demonstrably effective tool to predict the recruited health workers' length of practice. These models can be adapted in future studies to incorporate other information beside demographic details such as information about placement location and income. Beyond the scope of predicting length of practice, this modelling technique will also allow strategic planning and optimization of public healthcare recruitment.
The lack of health workforce is a global crisis which numerous countries have proposed and implemented intervention plans [1, 2]. However, there is limited data regarding the impact of these interventions and their sustainability over a long period of time. Research shows that the loss of healthcare workers in African countries (such as South Africa and Ghana) cripples the pre-existing delicate health system [3, 4]. Hence, the retention of health workers is essential for the healthcare system performance. These studies also point out that the recruitment of health workers should not only focus on nurses and physicians, but also on community health workers (CHWs) to help the primary healthcare systems boost the coverage and address the basic health needs of societies .
Specifically, healthcare systems in sub-Saharan Africa (SSA) face a serious human resource crisis, with recent estimates pointing to a shortfall of more than half a million nurses and midwives needed to meet the Millennium Development Goals of improving the health and wellbeing of the SSA population by 2015 . One of the reasons for this phenomenon is due to human capital flight ("brain drain") in the health profession, especially in the public sector [1, 6]. Migration of health workers from low- and middle-income countries (LMICs) to high-income countries is a controversial aspect of globalization, having attracted considerable attention in health policy discourse at both the technical and political levels [1, 7–9]. The migration of skilled healthcare workforce translates into a direct loss of considerable resources to the public sector of LMICs, as direct benefits only accrue to countries, which have not invested in educating young professionals. To make matters worse, in many sub-Saharan countries such as Sierra Leone and South Africa, there are limited alternatives for the population to seek healthcare services from the private sector or next health facility due to inaccessible distance or cost factor .
To maintain a functional health system, most countries have altered their retirement age in order to extend the working life of their staffs. Furthermore, Botswana and South Africa have recruited from other countries within and outside the continent . Despite various local and international frameworks, the effectiveness of these interventions is yet to be seen [7, 8]. Another challenge lies in the monitoring and evaluation of these frameworks. Recent cross-sectional reviews of currently available healthcare workforce database show that in most cases, the systems are fragmented, unreliable, and cannot be integrated at both national and international levels, and that in order for policy-makers to make data-driven decisions, better database management systems still need to be developed [1, 2, 8].
A high turnover rate in the health workforce is another concern as it is costly and detrimental to organizational performance and quality of care. Healthcare organizations with high attrition rate not only face issues with the quality, consistency and stability of services provided to people in need, but also issues regarding the working conditions of the remaining staffs such as increased workloads, disrupted team cohesion and decreased morale [11, 12].
Some studies have focused on the influence of individual and organizational factors on an employee's intention to leave . A World Health Organization (WHO) study of four African countries shows that the major reasons behind health worker migration are better salary, safer environment, living conditions, lack of facilities, lack of promotion, and heavy workloads . Other studies conclude that better compensation package with good work-life balance is the primary reason to migrate [6, 14, 15]. On the other hand, one of the obstacles to migration is language barrier, which lies at the basis of patient care [16, 17]. Patients express their distress by describing their symptoms and pain and report changes in health status to professionals. Nurses or doctors need the current and technical language fluency to communicate under stress and duress with one another, members of the teams, and patient families .
Another healthcare policy concern is the misdistribution of healthcare workforce between urban and rural areas. It prevents equitable access to health services, contributes to increased health-care costs and underutilization of health professional skills in urban areas, and remains a barrier to universal health coverage .
Overall, the human capital flight of local health professionals, the high turnover rate, and the shortage of workers in the public sector of South Africa thus demands further investment in attracting and retaining foreign healthcare staffs that stay for an extended period of time. The WHO has also issued global recommendations to improve the rural recruitment and retention of the health workforce . This is pivotal to the delivery of healthcare in rural and remote areas of South Africa. A study has shown that 84% of South African population uses public healthcare, served by only 30% of the trained and certified doctors . Generally, sub-Saharan Africa faces severe lack of healthcare workers, with only 3% of the world's total medical staff while facing 24% of the global burden of disease . The arrival of foreign medical workforce and their placement in the public health sector reduces the two-front misdistribution of physicians, alleviates the lack of human resources in public rural facilities, and improves access to healthcare to people in rural areas .
To date, greater efforts have focused on recruitment, with significantly less attention to workforce retention. As aforementioned, a challenge to improve health access in rural areas is to maintain high retention rate of the medical workforce. Currently, there are few empirical studies regarding the factors that influence the length of practice [14, 17]. Previous attempts to identify these factors mainly focus on worker satisfaction at medical facilities and retention strategy of staffing agencies . There are some recent research into the correlation between employee demographic information and the success of retention effort in public health facilities .
This paper aims to develop a predicting tool for the length of practice of foreign healthcare workers, given their demographic information. Machine learning methods are well-suited for this challenge. Rather than traditionally considering the effect of demographic variables on the length of practice one after another, machine learning method examines all potential predictors simultaneously in an unbiased manner, and identifies pattern of information that are useful to make prediction.
A quantitative retrospective cohort study was conducted using secondary data, collected from the Africa Health Placements (AHP).
South Africa Health, healthcare worker population in underserved communities and distribution and retention levels. AHP recruits foreign and locally qualified health professionals to be placed in underserved communities in South Africa. Underserved areas like rural areas often face challenges in recruiting and retaining health workers, government has responded with programmes like compulsory community service and rural allowance to address this challenge.
Longitudinal individual health worker records are maintained at AHP. These health workers included professionals from South Africa and the rest of the world seeking employment in underserved facilities in South Africa. Data was collected using two methods (i) customized online portal completed by healthcare workers (HCW) and (ii) interviews by recruitment officers through email, Skype, and telephonic conversations. Data were captured onto a database and customer management system called Docwize. The online portal is available at the AHP website as a contact form. Once registered, the HCW receives login details to complete their application on Docwize. This system allows them to input personal and professional information, upload certificates, which would then be verified with the respective regulatory authorities, and be informed about the next steps until they secured a job offer. The HCW have an option of completing the application online or supplying the details to the recruitment officers who then update the system. It takes an average of 18 months to complete the recruitment process, 75% of the HCW were discouraged by the regulatory delays resulting in incomplete data. The length of stay was continuously monitored during their employment contract. Emails and telephonic contact are used to establish their last date of employment at a particular facility.
We took a complete cases approach, using only data from successfully recruited health workers without missing observations. The Africa Health Placements dataset contains 62 variables and 13 698 entries, in which there were 2079 successfully recruited practitioners. Among these 2079 professionals, some chose not to provide personal information such as marital status or gender. After data cleaning, there were 1838 entries with completed fields to meet the requirements of this study.
Nationality: categorical data of 145 different countries. Instead of recording nationality as it is, the nationality variable is transcribed based on World Bank's classification of countries into 4 categories: low income, lower middle income, upper middle income, and high income.
Relationship status: categorical data of 3 levels: married, single, or other.
With a large recruitment and retention dataset from AHP, we built three machine learning predictive models using relevant demographic data. We evaluated the models' performance by doing 10-fold cross-validation. The aim was to choose a model that performs significantly better in predicting length of practice.
As shown on Table 1, three different machine learning classification models (multinomial logistic regression, decision tree, and Naive Bayes Classification) were used to train the dataset. The issue was approached as a classification, rather than a regression problem, as we aimed to classify a successful recruit into one of the four mutually exclusive groups (less than 1 year, less than 2 years, less than 3 years, and more than 3 years). The use of a regression method is not optimal in this case, due to (i) the lack of quantitative numerical variables in our demographic information, (ii) the wide range of value of the dependent variables (length of practice measured in days), and (iii) the non-continuous nature of the dependent variables. A regression method would require a much larger dataset to arrive at a model of relatively acceptable fit. With our current available dataset, the experimental fit is approximately 18% with high internal sum of squares. Moreover, in strategic workforce planning, a precise prediction of the length of practice in days (or months) is generally not expected. A prediction of whether a specific healthcare worker will stay for 1 year, 2 years, or longer is usually acceptable for most intents and purposes.
To decide which of the three models perform best, we have to see their ability to generalize and predict new, unseen data. A challenge to our research was the lack of test data which we could have used for model evaluation. Conventionally splitting our existing data into a 80/20 ratio—80% of the data for training and 20% for testing—was an option, but not optimal as we wanted to use all data available for training.
We examined our three models with a technique called 10-fold cross-validation. Ten-fold cross-validation works as follows: we randomly partition the original dataset into 10 disjoint subsets, use nine of those subsets in the training process, make predictions about the remaining subset, and record the misclassification error. To avoid opportune data splits, we average misclassification error across the 10 folds. A comparison between the average misclassification errors of the three machine learning models allowed us to decide which model performs best on unseen data.
Three machine learning models were trained, and a 10-fold cross validation technique was used to attain evaluative statistics. The three models attain almost identical results, with negligible difference in accuracy. The "best"-performing model (multinomial logistic classifier) achieves a 47.34% [SD 1.63] while the decision tree model achieves an almost comparable 45.82% [SD 1.69] (Table 1).
Multiclass area under the curve (AUC) was computed by building multiple receiver operating characteristic (ROC) curves (one class versus another) and taking the average, as defined by Hand and Till . The three models achieve an average AUC of 0.66 (multinomial logistic at 0.6652, decision tree 0.6635, Naive Bayes 0.6602), suggesting sufficient predictive signal at the four selected categorical variables.
Overall, the three models had significant accuracy in classifying the length of stay of healthcare workers (p value < 2.2e−16) (Table 1). Additionally, Kappa statistics was also computed, in order to measure how much better each of the classifiers is performing over the performance of a classifier that simply guesses at random according to the frequency of each class . The Cohen's Kappa statistics of the multinomial logistics, decision tree, and Naive Bayes are 0.2658, 0.2649, and 0.2521 respectively, suggesting a fair (but not substantial) agreement between prediction and response adjusted by the amount of agreement expected by chance.
All three models perform reasonably well at identifying those who are likely to stay for less than 1 year (Table 2). The sensitivity of this class was greater than 75% for all three models, showing that they correctly identify more than ¾ of those who are likely to stay less than 1 year. Specificity of this class is not particularly high (all lower than 65%), so all three models do not do as well in identifying those who are staying for more than 1 year. However, with a negative positive rate as high as 84% across the three techniques, it means that when the model negatively classifies a person out of those who stay for less than 1 year, such classification is likely to be correct.
In contrast, all three models perform poorly at identifying those who are staying between 2 and 3 years (Table 2). With sensitivity at as low as 0% (decision tree) and specificity up to 100%, the three models must have learned to negatively assign a majority (all in decision tree case) out of this class. This is likely the result of imbalanced data sample with too little sample data of this class (Fig. 1).
In general, more males (997, 54%) than females (861, 46%) were recruited (Table 3). Males stay on average 187.78 days more than females do. South Africa has supplied the greatest number of health workers (381, 41%), followed by the United Kingdom (361, 39%), Nigeria (106, 11%), and Netherlands (86, 9%) (Table 3). Doctors (1538, 83%) were the most recruited health workers and then nurses (107, 6%) and other professionals (193, 10%). With regard to relationship status, single healthcare workers constituted 61% of the recruited, 31% were married, and 8% were cohabiting (Table 3, Figs. 1, 2, and 3).
Figure 4 shows two world heat maps that represent (a) the number of successful recruits from each country and (b) the average length of practice among those in these countries. The two maps point to an observation: AHP as a health placement organization is not very successful in recruiting from some countries, e.g. Russia, but once we do, the recruits tend to stay for an extended period of time. However, the sample size casts some doubts on this observation. Some countries have very high average length of stay, simply because we have a very small sample size of them.
This research shows that a majority of foreign qualified healthcare workers (1497 out of 1838, 81%) stay at their placement facilities for less than 3 years. While a constant rate of foreign recruitment per year can "fill the gap" in paper, the low average length of practice signifies a hidden cost of recruiting, relocating, and training of new healthcare professionals. Effective workforce planning from government or non-profit organizations, thus, requires a tool to predict the length of practice of incoming health professionals.
The three models attain significantly above chance results, with the average AUC of approximately 0.66 (multinomial logistic at 0.6652, decision tree at 0.6635, Naive Bayes at 0.6602), suggesting sufficient predictive signal at the four categorical variables selected. This is an indication that applying and retraining machine learning models with available datasets, Human Resource for Health decision makers can effectively source healthcare workers who are most likely to stay the longest in underserved communities.
Machine learning must be applied together with other qualitative methods like exit interviews so as to give an in-depth understanding of the healthcare worker perceptions and experiences that relate to their length of stay. A mixed method would have generated a better understanding of why certain gender, countries, age, and experience tend to stay longer than others.
Incomplete fields in the data were another issue as many candidates were excluded from the study due to missing information. We could not obtain age as one of the predictors, although we recognized that it could potentially influence health worker long-term plan to stay. Our issue with incomplete data relates directly to the ineffective database system issue that is common among the public sector in South Africa [1, 2, 8]. Although in the short run, installing and enabling a more effective database system imposes a cost challenge to healthcare non-profits and public sector, such system is likely to make tremendous impacts as the machine learning models can be further improved by learning from a larger, high-quality dataset. In the meantime, there is a potential for the public sectors and NGOs to collaborate and involve in data sharing that could empower the training process of machine learning algorithms.
Machine learning models give us an effective tool to predict the recruited health workers' length of practice. These models can be adapted beyond the scope of demographic information (i.e. information about placement location, income), allowing strategic planning and optimization of public healthcare recruitment.
The authors would like to thank the African Health Placement for providing the dataset used in the study.
The dataset supporting the conclusions of this manuscript is available with the corresponding author and will be made available in an anonymized version on reasonable request.
All authors contributed toward conceptualization, data analysis, drafting, and critically revising the paper and agree to be accountable for all aspects of the work. All authors also read and approved the final manuscript.
Permission to conduct the study was obtained from Africa Health Placements. The researchers followed the highest standards to protect confidentiality and anonymity of subject data. All identifying information of individual subjects such as name, address and date of birth were removed from the dataset prior to the study.
Tshuma N, Mosikare O, Alaba OA, Muloongo K, Nyasulu PS. Acceptability of community-based adherence clubs among health facility staff in South Africa: a qualitative study. Patient Prefer Adherence. 2017;11:1523–31. https://doi.org/10.2147/ppa.s116826.
Agyepong IA, Anafi P, Asiamah E, et al. Health worker (internal customer) satisfaction and motivation in the public sector in Ghana. Hum Resour Heal. 2012;11(247). https://doi.org/10.1186/1472-698X-12-25.
Delobelle P, Rawlinson JL, Ntuli S, Malatsi I, Decock R, Depoorter AM. Job satisfaction and turnover intent of primary healthcare nurses in rural South Africa: a questionnaire survey. 2010:371–83. https://doi.org/10.1111/j.1365-2648.2010.05496.x.


Nineteenth time is the charm! You can listen to the audio below, subscribe to the podcast feed , subscribe and rate in iTunes.
On the nineteenth episode of Mad at Dad, Michael Butler and Dave Slusher discuss their weeks; we have fun with our first real Blab troll and spend most of the show doing a track by track of the albums THE DOCTOR and LAP OF LUXURY to decide which is the worst Cheap Trick album.
Eighteenth time is the charm! You can listen to the audio below, subscribe to the podcast feed , subscribe and rate in iTunes.
On the eighteenth episode of Mad at Dad, Michael Butler and Dave Slusher discuss their weeks; we discuss Butler's gig and Japanese dinner, plus some cooking; Slusher talks about his trip to Goldsboro; Slusher praises the Frank Dimino episode of the Rock and Roll Geek Show; talk a little more about Celebrity Deathpool; we close with some Cheap Trick and Robin Zander songs and talk about the worst Cheap Trick album.
Seventeenth time is the charm! You can listen to the audio below, subscribe to the podcast feed , subscribe and rate in iTunes.
On the seventeenth episode of Mad at Dad, Michael Butler and Dave Slusher discuss their weeks; we discuss Butler's week and Superbowl weekend; Slusher talks about his business trip to San Diego; we review some of the picks in the Celebrity Deathpool; we close with a Cheap Trick song.


Everything you need to know about bathroom fan installation costs and prices in Beaumont, TX.
Q: What is the cost of bathroom exhaust fan installation in Beaumont, TX?
Cost of bathroom exhaust fan installation is included. Price estimate includes mid-grade fan and brackets. Does not include ductwork, electrical rewiring or roof vents.


The Hough Ear Institute has a passion to educate others in audiology and surgical techniques so that as many patients as possible can receive the benefits of the technology that is already available for curing deafness. This endeavor is met through three initiatives: otology workshops, international education, and fellowship programs.
Temporal Bone Surgical Dissection Courses are held at the Hough Ear Institute.
Otolaryngologists and residents from across the country attend courses on temporal bone dissection, providing them with advanced training in otologic treatment options and surgery.
The course provides instruction pertaining to the latest techniques for treating chronic ear disease and are exposed to a variety of surgical and treatment procedures, including the performance of tympanoplasty, ossicular chain reconstruction, mastoidectomy, stapedectomy, treatment of vertigo and management of the facial nerve in chronic ear surgery. In addition participants experience an actual surgery using video-conferencing and students experience "hands on" surgery techniques as they work in the HEI dissection laboratory. A unique aspect of this workshop is telemedicine training accomplished by observing a live otologic surgery done in the operating room at INTEGRIS Baptist Medical Center.
For many years surgeons, audiologists and scientists at HEI have freely given their time and energy to teach others their skills and technology. From the beginning, HEI personnel recognized that sharing knowledge within the profession is of utmost importance. Thousands of physicians and allied clinicians have been trained by HEI personnel in short courses or seminars, either at HEI or in their own states or countries.
Teams from HEI have made overseas trips to such countries as Vietnam, Zimbabwe, Kenya, Zaire, Moldova, Siberia, Ukraine and Jordan, performing ear surgery and/or teaching doctors.
In 1995, Dr. Jack Hough went to Vietnam to determine if the ENT sector in Vietnam was interested in a cooperative effort with REI-Vietnam to educate, instruct and train Vietnamese ENT physicians in state-of-the-art techniques and improve the quality of medical services for the Vietnamese people. The National ENT Institute in Hanoi and the University of Medicine and Pharmacy in Ho Chi Minh Ctiy accepted Dr. Hough's offer to initiate and develop an ENT program for their institutions. The first ENT exchange was in 1996 with Dr. Hough as team leader. Dr. Hough, along with seven other otorhinolaryngologists, conducted seminars at the National ENT Institute in Hanoi and the University of Medicine and Pharmacy in Ho Chi Minh City. During this same year, the first of many Vietnamese ENT physicians visited this country to study and observe. Along with conducting seminars and training sessions, the ENT teams from HEI have hand carried state-of-the-art equipment that was donated to the ENT instutions in Hanoi, Ho Chi Minh City, and Hue.
A major program at HEI is a formal research and education fellowship in otology and neurotology that is ongoing for graduate-certified otolaryngologists who wish to restrict their specialty practice to ear surgery. As a result, those trained at HEI are now practicing otology and teaching others all over the world.
A large part of what Hough Ear Institute does is the training and mentoring of international doctors. Doctors come to HEI from developing countries for training fellowships.
This Fellowship has been an exceptional experience for me in my career and in my life. I am so excited and wanted to stop and tell you how much I appreciate your help over the 9 weeks. You were patient, inspirational, and actually made things fun. I have learned so much, seen lots of new things, made lifelong friends, and had a ton of fun. It not only enhances my knowledge and technical skills in ear surgery, especially in cochlear implants, but also enables me to know about your beautiful country, your culture with hospitable people. Also please send my deep gratitude to your families, you truly made me feel at home and part of your families. Thank you, Dr. Kopke and the rest of the HEI team, for such a fantastic Fellowship. The impact of your help is so significant. It will help me for the rest o f my life. Thank you so much for everything. I will not forget it.
To finish this project, we need your help. Please consider a donation today which can be made by check or through our secure online form.
To ensure your donation is directed properly, please add "International Education" in the comment box below.
With your help, we're closer to breakthroughs that could help restore hearing loss. Read more.


When you take our Exercise Health Assessment you'll answer questions about how much exercise you do, how intense it is, what your physical limitations might be, if you have an illness that can be helped by exercise, and coached (if you want) about nutritional supplementation.
While is is very important to consume the raw materials in our diet to produce sufficient energy (ATP) to be able to exercise, there are also particular nutrients that are important in enhancing athletic performance that can be obtained in special diets or with supplemetation. For optimal performance in sport it is important to prepare physically, biochemically, mentally and emotionally.


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Q: Generalization of the chain rule using upper derivatives I'm having trouble with the following exercise, taken from Chapter 6 of Royden and Fitzpatrick's Real Analysis:
Let $f$ be defined on $[a,b]$ and $g$ a continuous function on $[\alpha, \beta]$ that is differentiable at $\gamma \in (\alpha, \beta)$ with $g(\gamma) = c \in (a,b)$. Show that if $g'(\gamma) > 0$, then $\bar D(f \circ g)(\gamma) = \bar D f(c) \cdot g'(\gamma)$ where $$\bar Df(x) = \lim_{h \to 0} \sup_{0 < |t| \leq h} \frac{f(x + t) - f(x)}{t}$$
If $g$ were linear, the problem is straight-forward:
\begin{multline}
\bar D(f \circ g)(\gamma) = \lim_{h \to 0}\sup_{0 < |t| \leq h}\frac{f(c + g'(\gamma)\cdot t) - f(c)}{t} = \\\lim_{h \to 0}\sup_{0 < |s| \leq g'(\gamma)\cdot h}\frac{f(c + s) - f(c)}{s}\cdot g'(\gamma) = \bar D f(c) \cdot g'(\gamma)
\end{multline}
I'm having trouble in the general case, in part because the problem places so little structure on $f$. The error from linearly approximating $g(\cdot)$ at $\gamma$ can be made arbitrarily small, but without a continuity condition on $f$ I'm unsure how to make the approximation error "go away". Thank you!

A: Because $g'(\gamma)>0,$ there exists an open interval $I$ containing $\gamma$ such that $(g(x)-g(\gamma))/(x-\gamma)>0 $ for $x\in I\setminus \gamma.$ Let $x_n$ approach $\gamma$ within $I\setminus \gamma.$ We have
$$\frac{f(g(x_n))- f(g(\gamma))}{x_n-\gamma}= \frac{f(g(x_n))- f(g(\gamma))}{g(x_n)-g(\gamma)}\frac{g(x_n)-g(\gamma)}{x_n-\gamma}.$$
The continuity of $g$ implies $g(x_n)\to g(\gamma).$ Let $\epsilon > 0.$ For large $n,$ the first fraction on the right is less than $\bar D f(g(\gamma))+\epsilon.$ For such $n$ we have
$$ \frac{f(g(x_n)- f(g(\gamma))}{x_n-\gamma} \le (\bar D f(g(\gamma))+\epsilon)\cdot \frac{g(x_n)-g(\gamma)}{x_n-\gamma}.$$
Apply $\limsup$ to both sides to get
$$  \limsup_{n\to \infty} \frac{f(g(x_n)- f(g(\gamma))}{x_n-\gamma} \le (\bar D f(g(\gamma))+\epsilon)g'(\gamma).$$
Since $\epsilon$ is arbitrary, the $\limsup $ on the left is $\le \bar D f(g(\gamma))g'(\gamma).$ Now $x_n$ was an arbitrary sequence approaching $\gamma,$ so we have shown
$$\bar D(f\circ g)(\gamma) \le \bar D f(g(\gamma))g'(\gamma).$$
The argument above would be reversible if we knew $g$ was injective on $I.$ But it need not be. However, shrinking $I$ if necessary, we can say this: $g(I)$ is an interval whose interior contains $g(\gamma)$ and furthermore, if $y_n \to g(\gamma)$ within $g(I),$ then there is a sequence $x_n$ in $I$ converging to $\gamma$ such that $g(x_n)=y_n.$ I'll omit the proof of this for now; perhaps you would like to have a go at it. And maybe the first thing to check is to see if the reverse inequality follows from this result.

A: To just throw out an idea, have you tried the usual chain rule proof method of re-writing
$$\frac{f(g(x + t)) - f(g(x))}{t}$$
as
$$\frac{f(g(x + t)) - f(g(x))}{g(x+t) - g(x)} \cdot \frac{g(x + t) - g(x)}{t}.$$  (Below I'll refer these two fractions as "the first term" and "the second term".)
Because $g'$ exists at $\gamma$ you can pick a small interval around $\gamma$ and bound the second term to be arbitrarily close to $g'(\gamma)$.
Because $g'(\gamma) > 0$ there is an interval around $\gamma$ where the denominator of the first term is non-zero, which means you don't have to fuss about possible division by zero. (You might have to think about that point for a bit).
Because $\bar D f(c)$ exists, you can pick an $h_1$ small enough that $\sup_{0 < |t| \leq h_1} \frac{f(x + t) - f(x)}{t}$ is arbitrarily close to $\bar D f(c)$. Because $g$ is continuous at $\gamma$ you can find an $h_2$ so that $|\tau| < h_2$ implies $|g(x+\tau) - g(x)| < h_1$, and so the first term actually ranges over a subset of the terms we took the $\sup$ of when finding $\bar D f(c)$, so it will go to the same limit. (There are some ugly details hiding in there too.)
My guess is that writing it up this way would be an inglorious slog through many epsilons and deltas, and picking intervals that depend on other intervals, etc., etc.. but it could get the job done. But even if you don't use most of the ideas, I'll point out that I think that it's the continuity of $g$ at $\gamma$ that will let you translate "how $f(g(t))$ reacts to small changes in $t$" to "how $f(t)$ reacts to small changes in $t$". The $\sup$ is there in the definition of $\bar D$, in distinction to a regular derivative, to be able to handle arbitrarily badly-behaved functions -- we shouldn't expect much help from $f$ behaving nicely.

redpajama_set_name: RedPajamaStackExchange


Friends in the Garden #1: Do Art Your Way by Denise Bossarte is a creative venture by the artist who has come up with a unique idea to create books of copyright free images that SoulCollage®, mixed media, and collage artists could use without infringing copyright. The images in this series are of the insects and creatures you see in the garden regularly on flowers; bees, butterflies, dragonflies, beetles, bumblebees, and other insects which the author calls 'friends in the garden.' The pictures are aesthetically shot and highlight the author's creative eye and love for photography.
The 30 individual photographs in the book are spaced in a way that they can be cut out without damaging any other picture in the book. The thought of having an individual page for each photograph is very clever. The pictures are clear and colorful and the author does an excellent job in capturing life in the garden and its radiance. Her pictures will also encourage you to become observant while walking in the garden and to notice the insects and creatures that you usually will not look at. The author's idea of bringing out a book for this purpose is thoughtful. The pictures have an amazing life to them and the clarity and angles make them tangible. This is definitely a good book for all those who work in digital art and who are looking out for images that are free from copyright and can be downloaded without any complications.


Let your dreams take flight at the White Rock Theatre, starring comedy legend Bobby Davro as Smee!
Don't miss another spectacular family pantomime with an all star cast, impressive live music, energetic dance routines, lots and lots of laughter and flying that will thrill the child in all of us, come and see Peter Pan soar to new heights and join Wendy, Michael and John as they take off to Neverland with the boy who never grows up.
Book early to avoid disappointment…The 'Croc' is ticking!
A £1.00 per ticket booking fee applies, capped at six per order. Fee-free booking for White Rock Friends; Groups of 10+ please call 01424 462288 to buy fee-free.
*A £1.00 per ticket booking fee applies, capped at six per order. Fee-free booking for White Rock Friends; Groups of 10+ please call 01424 462288 to buy fee-free.


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Public Safety & Homeland Security
Northern California Hospitals Preparing for COVID-19 Vaccine
The UC Davis Medical Center is administering an experimental vaccine or a placebo to about 230 local volunteers as part of the national Pfizer trial, one of a handful of tests underway internationally.
by Tony Bizjak and Hannah Wiley, The Sacramento Bee / November 11, 2020
Nov. 11—News this week from Pfizer pharmaceuticals of a coronavirus vaccine breakthrough is prompting Sacramento and Northern California healthcare providers to step up preparations for mass distribution.
That includes the UC Davis Medical Center in Sacramento, which is in talks with state officials about serving as a vaccine storage and distribution site for Northern California. The vaccine requires specialized low-temperature freezers for storage, and UC Davis has some and is buying more.
Dr. David Lubarsky, chief executive for UC Davis Health, said a limited number of vaccine doses could be available as early as December in California for first responders and healthcare workers. The general public, he said, probably won't have access until after the middle of next year.
The UC Davis Medical Center is administering an experimental vaccine or a placebo to about 230 local volunteers as part of the national Pfizer trial, one of a handful of tests underway internationally. Pfizer reported on Monday the tests nationally have shown an initial 90% success rate.
"The news is tremendously good," Lubarsky said. "If it holds up, the possibility to achieve herd immunity is within reach for the U.S. within a year."
It's a race against time as the U.S. struggles to contain an autumn surge of potentially lethal COVID-19 cases. The virus has been connected with 240,000 deaths in the United States since the pandemic hit in February. Worldwide, it has killed 1,270,000, according to the World Health Organization.
Amid a regional surge in infections, Sacramento, Placer and El Dorado counties were demoted to more restrictive tiers of business activity on Tuesday. The six county Sacramento region has combined for more than 660 deaths and 42,000 confirmed infections since the pandemic began. Nearly 1 million people in California have been sickened by the coronavirus and more than 18,000 have died.
California Health and Human Services Secretary Dr. Mark Ghaly declined this week to offer a timeline for vaccine distribution in California, but said, "we anticipate that we are getting near approvals of some vaccines. We aren't done yet. Nothing is complete. We eagerly wait for more information."
Pfizer still must monitor its clinical test subjects to observe the vaccine effects, then win emergency authorization from the U.S. Food and Drug Administration.
Several of Sacramento's major healthcare providers — UC Davis, Kaiser Permanente, Sutter Health and Dignity Health — told The Sacramento Bee this week they are in active preparation to get vaccines to their members once it becomes available.
Sacramento County public health officials also said this week they are in talks with the state about obtaining doses that they plan to administer to people who are not otherwise able to get the vaccine from a health provider, including small community health clinics and congregate care facilities.
But health officials say they are still in the dark about timing and details of vaccine distribution. "We don't know how much (doses) we are getting," Sacramento County Health Officer Dr. Olivia Kasirye said.
Newsom and health equity
Gov. Gavin Newsom on Monday reiterated that the first general public vaccine injections will go to essential healthcare workers and first responders. "A vaccine is on the way. That's good news," he said. "But the availability to you and me and others outside our first responders, outside our health care professionals is many, many months off."
The California Department of Public Health's draft COVID-19 vaccination planning report includes the general public in the last stages of its outline.
In the second part of California's first vaccine phase, as outlined in the state health department's report, Californians who face the worst of COVID-19 would be prioritized. These individuals might have underlying medical conditions, like cancer, heart conditions, immunocompromised systems, type 2 diabetes or Sickle cell disease, according to the Centers for Disease Control and Prevention's list of qualifying conditions.
When a larger supply reaches California, Newsom said certain populations would be next in line, including those in nursing homes, with disabilities or in under-served Latino, Black and rural communities.
"Improving health equity will also occur by examining the demographic and social factors that contribute to the overall health disparities in California," the department's report notes, "such as racism, income, ageism, English proficiency, lack of access to healthcare, crowded housing, precarious employment, and discrimination related to disabilities, sexual orientation, gender identity, ethnicity, and immigration status."
Newsom's administration is also preparing a list of storage, safety and transportation requirements for when the vaccine does become available, including dry ice and personal protective equipment for health workers administering the shots.
UC Davis executive on herd immunity
Lubarsky said he is concerned that some members of the public might not be willing get vaccinated, which would make it harder to achieve herd immunity.
Some are opposed to vaccinations in general, and others may worry about the safety of the vaccine, given the political push from the Trump Administration to get a vaccine to market.
"There has been way too much talk of politics involved with science," Lubarsky said. Technology and pharmaceutical companies have been working quickly, but with a public health focus, not a political one, he said.
Sutter Health prepares
At Sutter Health, which operates 200-plus clinics in Northern California, officials say they have acquired about 15 freezers, "both large and smaller, portable options — that can each store about 12,000 to 30,000 doses depending on their size. The smaller portable freezers can be used to transport doses from one location to another or as mobile 'pop-up' vaccination units."
The healthcare provider said it plans to be judicious in the name of safety. Sutter's medical director of infectious disease is serving on the state's COVID-19 Vaccine Drafting Guidelines Workgroup.
"We are committed to following established standards despite the significant urgency and rapid operationalization of vaccine development and deployment," Sutter said in an email to The Bee. "Like other health systems, we are taking an informed and carefully considered approach to vaccine prioritization."
Kaiser Permanente in COVID vaccine trial
Kaiser Permanente, a national healthcare provider based in Oakland, is participating in clinical trials for both the NIH/ Moderna and Pfizer/ BioNTech vaccines.
Officials with Kaiser Permanente said limited initial supplies of the vaccine will require government officials and others "to balance equity considerations and the need to maximize the benefits of an initially limited supply."
"We will work with our partners at the Centers for Disease Control and U.S. Department of Health and Human Services, as well as health officials in each state where we operate, to comply with local and regional allocation and distribution procedures when those plans are approved by the CDC," Kaiser said in a statement to The Bee.
"While the CDC has yet to announce its criteria for prioritizing which populations should receive the vaccine first, when it is announced we will leverage the extensive data and advantages of our integrated system to identify, reach out and vaccinate members and employees who meet that criteria. We expect to use many different venues for administering the vaccine, with an emphasis on patient safety, convenience, and the most efficient use of resources."
Kaiser officials said they also plan to work to educate their members on the value of the vaccine and of continued safety steps.
"We also are committed to educating our members, workforce, and the communities we serve about the safety and effectiveness of the vaccine and to promote understanding of how a vaccine — coupled with continued public health measures like the wearing of masks, appropriate physical distancing, and frequent hand washing — can help control and ultimately bring about the end of the COVID-19 pandemic."
Officials with San Francisco-based Dignity Health, which operates in three states, said they too are encouraged about this week's Pfizer announcement.
" Dignity Health is closely monitoring the development of a vaccine for COVID-19 and are encouraged by initial results from Pfizer's vaccine trial," the healthcare company said in an email to The Bee. "While we anticipate federal, state and local health agencies will take the lead on determining distribution plans and recommending who should receive the vaccine first, we expect priority may initially be given to health care workers involved directly in patient care and those who are most vulnerable in our community and at an increased risk for the virus.
" Dignity Health has well-established protocols in place to receive, store, and administer vaccinations and stand ready to partner with our health agencies to help safely distribute any COVID-19 vaccines that are approved for distribution. The care and safety of our employees and the patients we serve is always our top priority, and we look forward to sharing more with our community when we learn more."
(c)2020 The Sacramento Bee (Sacramento, Calif.)
Visit The Sacramento Bee (Sacramento, Calif.) at www.sacbee.com
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Charlotte Cooper was one of the first women tennis players in history, and she was a champion: over the course of her career, she won Wimbledon five times, and was the first woman to win an Olympic gold in tennis.
Looking at photos from the peak of her career, it is almost impossible to imagine today how she achieved those results. She arrived on the tennis court in true Belle Époque athletic propriety: a starched cotton shirt with a high-buttoned collar and tie, and a billowing linen skirt brushing her ankles—complete with requisite petticoats—all while carrying the heavy wooden racket of the time.
Comparing Charlotte Cooper's turn-of-the-20th-century look to the state-of-the-art garments worn by today's elite female athletes, one can fully appreciate the materials revolution that has propelled active sportswear into the modern day.
Speed, strength, endurance, and athletic ability in women's sports continue to improve, reinforcing the unwritten rule that records are made to be broken. This cannot happen, however, without technological innovation in activewear, fabrics, and fibers.
In 1989, French vascular physician Michaël Prüfer, a world champion downhill skier and gold medal winner at the 1982 Albertville Olympics, put to good use his medical knowledge to co-develop a compression sock that significantly improves muscle recovery and performance. The result? The patented Booster Veines Sport system, from which the French company BV Sport was born.
Since then, top European sports teams and world-class athletes have relied on BV Sport's technology. In 1998, the French national football team wore the first BV Sport recovery socks, created specially for the FIFA World Cup being held that year in France, and the home team triumphed.
After the sock came the Booster sleeve design, whose stiff yet elastic construction delivers targeted, perfect compression. French sprinter Odiah Sidibé, gold medalist in the 4×100 relay at the 2001 European Championships in Munich, convinced her teammates to try out BV Sport technology for better performance, reduced risk of injury, and faster recovery. For runners who were hesitant to go sockless or who were willing to try a new sock, the company created the Booster sock.
The superior performance attributes of BV Sport products have been achieved through years of medical studies and development alongside professional sports doctors and federations, taking into account the unique physical specifications of individual athletes. The products are designed to optimize muscle biomechanics in movement, improving blood flow back to the heart, and reducing vibrations, harmful oscillations, and overall fatigue.
Over the past several years, BV Sport has been focusing on women's apparel and legwear with the goal of supplying the rapidly growing female athlete population with the superior products that men have enjoyed. The technology found in BV Sport legwear has been extended to other apparel categories, such as shirts, leggings, and pants.
BV Sport's most recent innovations are made of Sensil® Innergy. This Nylon 6.6 made by NILIT enhances athletic performance and increases physical fitness by using far infrared technology. This technology reflects the wearer's naturally emitted thermal energy back to the body, causing a gentle, virtually imperceptible warming that improves muscle elasticity and speeds up recovery. Sensil® Innergy has also been known to reduce the appearance of cellulite and enhance overall well-being, making it the perfect fabric not only for performance sports apparel but also for yoga pants, shapers, and base layers.
How would a trailblazer like Charlotte Cooper have done with state-of-the-art tennis equipment and apparel made of Sensil® Innergy?
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Exxon: When all else fails, whip-out the psuedoscientific technobabble?
...Anyone else heard the Exxon ads lately? The ones about Earth's electromagnetic field wave resonance, and how they can apparently 'tap into it' to 'sense' where oil is?
I'm not a geologist or physicist, but I have been getting a bit of a feel for recognizing pseudoscientific terms whenever I hear them (they tend to include the words 'resonance' and 'waves' ). Are these guys just making stuff up now? Or is there a legitimate science that they're pursuing?
The ones about Earth's electromagnetic field wave resonance, and how they can apparently 'tap into it' to 'sense' where oil is?
Do you remember that time when you pointed out that there is no "layman's guide to quantum mechanics", and as a result, anyone can bamboozle the gullible with QM and call it a day?
It's sort of the same with EM. Electromagnetism is one of the most complex topics in physics. To understand classical EM, you must be intimately familiar with vector calculus and field equations.
The term "electromagnetic field wave resonance" does not mean anything. Let's start decomposing this and understand what internal terms do make sense. I can use this topic to articulate terms that people often use carelessly. Terms like "field", "wave", "electromagnetism" and "resonance" all have the potential, for some reason, to induce pseudoscientific claims. Definition is important, so lets start with that.
Electromagnetic fields: Numerous pseudoscientific institutions and snake oil sellers love the term "field", and use it to justify a wide variety of nonsense. Virtually entirely, these people are unfamiliar with field equations and mechanics. Field theory arose to replace action-at-a-distance theories. Coulomb's Law, the Biot-Savart Law and Newton's Law of Gravitation are such examples. These are field equations which tell us the magnitude and direction of the field at the unprimed coordinates (called the field coordinates) as a function of the distance from the primed coordinates (source coordinates). According to these laws, any object which can feel a force in a region of space based on an action at a distance based on a source of that field will also exert an equal and opposite force on the source. The field generated by a particular source is therefore independent of other objects which can be affected by the field but the force that is exerted between two objects depends on both objects. Action at distance theories are problematic because they predict that the response of an object to a change in the field is instantaneous. But if a source were suddenly eliminated, or vanished, then an instantaneous response would imply that the rate of propagation of information is greater than c, violating Relativity. We could deny that the object responds immediately, but this means that Newton's Third Law is not true, because in the time between the object being removed and the information being transmitted, the object is still responding, so there is a reaction but no action. Field theory is employed to replace action at a distance theories. According to a field theory, the field is what transmits information between the source and the other object. The rate at which information is transmitted is finite, and the field can store energy. Even if the source were removed, the other object would still respond, and this response would not violate Relativity or Newton's Third Law.
Field: A field has a definition in mathematical physics that must be clarified. A function in n dimensions has an input in the form of an (n-1)-tupule. There is usually only one output value, but sometimes (like for holomorphic functions) there are more. Most functions have one output and that one output is (in n dimensions) a function of n-1 variables. But a field in n-dimensions will always have components, each of which is a function of n variables. If it is a scalar field, then there is one component, if it is a vector field, then there are n components, and if it is a kth-rank tensor, then there are nk components. So, for example a 2D scalar field would assign a scalar value to every point in the 2D region, whereas a function in 2D would take one input dimension and produce an output in the second dimension. In field equations, the dimensions are all input values. The output is a function of n variables.
Wave: "Electromagnetic field wave" doesn't really mean anything. What we say is that there are wave-solutions to the Maxwell equations. The differential forms of the Maxwell equations are all source functions. According to them, there are two sources to an electric field (charges and changing magnetic fields), and there are two sources to a magnetic field (moving charges and changing electric fields). As a result, electric and magnetic fields are the result of each other, and are both fundamentally the result of a unified property called electromagnetism. A magnetic field is usually denoted B and an electric field E. These fields (remember, if one is present and changing, the other must be present) can propagate through space as plane waves.
The wave solutions state that the d'lAmbertian operation over B and E fields is zero. Electromagnetic waves are plane propagations. The E and B fields are mutually perpendicular and perpendicular to the direction of propagation. The phase velocity of an electromagnetic propagation is a constant depending on the electric and magnetic constants for the medium. That's why Newton was wrong, by the way, since he predicts that velocity follows Galilean transforms. But magnetic and electric constants are invariant under coordinate transforms. The "Maxwell equations" are actually a bit of a misnomer. Two of them were formulated by Gauss, one by Ampere, and the other by Faraday, and the modern form with four equations in modern vector notation was formulated by Heaviside.
The primary contribution Maxwell made to the Maxwell equations was correcting Ampere's Law to make it consistent with the continuity equation. But Maxwell's primary contribution to our understanding of reality was to realize that there are wave solutions to these equations.
Resonance: Totally different thing. To understand resonance, it is necessary to understand that a wave propagates energy without the propagation of matter. For something like a water wave, we model this by saying that individual particles (which oscillate about fixed equilibrium points in phase with the other particles) are exhibiting simple harmonic motion. The nice thing about SHM is that the time period T for a system left to oscillate by itself (like a pendulum) depends only on one variable. That variable is the degree to which the medium allows the propagation of the wave. For a spring exhibiting SHM, one could change the mass and amplitude of the body oscillating and nothing would happen to T. The only way to change T would be to change k, a constant for a particular system that refers to the degree to which it is deformed by force being exerted on it. A wave whose particles oscillate with a particular speed in water would oscillate with a different speed in, say, syrup. Within any system, there is a natural frequency at which it oscillates which depends only on the constant for the medium. If you let a pendulum swing back and forth, it will tend to oscillate at that natural frequency determined only by its length (not by mass, angle of incline or amplitude).
Where c is a constant which will differ for the material in question and A0 is the initial amplitude. This is an example of first order functions. The rate at which the amplitude dies away in an uncontrolled system depends purely on the number of oscillations that have occurred.Radioactive decay and First order chemical reactions work on the same principle.
Basically, what you should understand by familiarizing yourself with this is that when someone says they can "tap into electromagnetic field wave resonance" they are seeing who they can impress when they stick random large words together.
The world's largest oil company is seeking a distinct competitive advantage through, and is betting billions on, the use of electromagnetics to locate offshore oil fields.
The Wall Street Journal reported that since the 1970s when he'd studied the moon for NASA, Dr. Len Srnka liked the prospect of using the electromagnetic properties of earth, water, and rock to decipher underground terrain.
Exxon hired him in the 1980s to apply the method to finding oil, but for years technological and funding hurdles intervened. Finally Dr. Srnka got a chance to test his theory in a proven offshore oil field. Exxon Mobil had drilled off the coast of Angola since 1997 and knew exactly where the oil was. So, could Dr. Srnka's electromagnetic contraption show what Exxon already knew?
It could, mapping the oil with unerring precision.
Two years later, Exxon is making a multimillion-dollar bet that Dr. Srnka's technology, which he calls R3M, will work in offshore oil and natural-gas fields around the world. If Exxon is right, it could give the world's largest publicly traded oil company a competitive advantage over its rivals.
The stakes are higher than one company's profits. The global economy needs more technological home runs to slake its growing thirst for oil. Current world consumption of 80 million barrels a day may hit 120 million daily by 2030, according to the International Energy Agency.
Easy-to-find, easy-to-get oil is a thing of the past, maintains one school of industry experts. When companies find big new fields, they're often in hostile environments—icy Arctic frontiers or far underwater—where getting the oil out is an exercise in human ingenuity. The problem has cast a shadow over the major oil companies.
Better technology has helped them improve their exploration odds in the past. But many companies, under pressure to deliver short-term profit growth, are spending less on research and development today than they were 10 years ago. As a result, it's been decades since the industry has hit upon the kind of breakthrough technologies—such as three-dimensional seismic surveys or horizontal drilling—that can unlock major new supplies.
Oil is a "resistive" material—a poor conductor of electricity. So when electromagnetic energy waves transmit into the earth, oil-soaked rocks show up as a resistive layer that contrasts with more conductive substances like water-soaked rocks. Schlumberger Ltd. pioneered electromagnetics as the standard for "well logging," the process of mapping the layers of oil in a newly drilled well.
There was one big problem: Electromagnetic "noise" in the atmosphere wreaked havoc on measurements, muddying the picture scientists tried to get from deep in the earth.
Dr. Srnka realized that salt water would be the perfect insulator from atmospheric noise. After two years of work, he developed a process that used sensors placed on the sea floor in very deep water. A high-power transmitter would blast electromagnetic energy waves into the sea floor, and the sensors would record the response. The data would then micro-process into an image. Oil would light up as a resistive layer.
Exxon Mobil began drilling its first wells with R3M this summer. It hopes to use the technology to reduce costly cases where it drills underwater and finds nothing. It also hopes R3M will enable it to spot oil where others might miss it, allowing it to lock up permits and perhaps negotiate better contracts with countries where the oil is located.
The method ExxonMobile uses is based on the quasistatic approximation to Maxwell's equations; the permittivity is not a significant factor. The technique looks at contrasts in the electrical resistivity between highly-resistive oil-saturated formations, and highly-conductive saltwater-saturated formations or otherwise less resistive rocks and sediments. There are some secondary effects that are also of interest, but this is the primary indicator of the presence or absence of significant levels of petroleum and gas.