Miracle-dz/UniFAR-FTUs huggingface dataset:
Use of stool culture as a non invasive method for the diagnosis of Helicobacter pylori from stool of diarrhoeic children in western Nigeria

Background: Helicobacter pylori has been associated with chronic diarrhoea, iron deficiency anaemia, growth retardation, gastric malignancies, peptic ulcer disease, and gastritis among children. Diagnosis of this infection has been invasive using biopsies while stool culture is not common or routinely practiced. This study was designed to detect and isolate H.pylori from stool of diarrhoeic children and highlight possible use of such for routine laboratory diagnosis of H.pylori infections. Material and methods: Two hundred and sixty faecal samples obtained from diarrhoeic children were screened for H. pylori antigen, using H. pylori stool antigen test kit (HpSA) and cultured on modified DENTS medium. Cholestyramine and nitrobluetetrazolium salt were added to the stool and the medium respectively to aid isolation of H. pylori . Correlation of BMI and H. pylori infection of the children was also evaluated. Results: Twenty-six (10%) samples showed growth on culture while 91 (35%) tested positive for H. pylori antigen. Of the 26 children with positive culture, 16 had a low BMI. HpSA has sensitivity and specificity of 11.5% and 62.4%. There was a significant association (χ 2 = 12.86, df=2, P-value = 0.004) between age group of participants and use of HpSA kit. Conclusion: Stool culture for recovery of H. pylori is feasible in our environment and diarrhoeic children should be screened for H. pylori using both HpSA and culture. H. pylori is suggested to be screened routinely especially among children having diarrhoea and are underweight. Albeit, other causes should be eliminated before concluding on the reason for the underweight. Keywords: Helicobacter pylori , stool culture, HpSA, BMI, Cholestyramine, Nitrobluetetrazolium salt
Study of Miniaturization of a Silicon Vapor Chamber for Compact 3D Microelectronics, via a Hybrid Analytical and Finite Element Method

The interest in silicon vapor chambers (SVCs) has increased in the recent years as they have been identified as efficient cooling systems for microelectronics. They present the advantage of higher thermal conductivity and smaller form factor compared to conventional heat spreaders. This work aims to investigate the potential miniaturization of these devices, preliminary to integration on the backside of mobile device chips, located as close as possible to hotspots. While detailed numerical models of vapor chamber operation are developed, an easy modeling with low computational cost is needed for an effective parametric study. Based on the study of the operating limits, this paper shows the thinning potential of a water filled micropillar for a device operating below 10 W and identify the corresponding vapour core height, and wick thickness.
Reliable localization of pedestrians in a smart home using multi-sensor data fusion

The integration of sensors in future intelligent habitats as well as on people with the objective of helping them in their daily tasks is a concept that has become common. This concept is based on the deployment of sensor networks in the environment ( floor tiles, cameras, RFID.) that became active and communicating, in addition to wearable sensors carried by people (inertial unit, Gyroscope .). The work carries out the global task of assistance to the person which breaks down into several functions, amongst them are: the sure and reliable localization of pedestrians and the tracking of movements of static and dynamic objects. For the methodological part, it primarily consists of developing robust fusion algorithms for the localization of one or more persons. However, recognition generally fails when several people stand or walk together, preventing successful tracking. This work provides a tracking and association technique which uses the previous state information to associate between tracks. Secondly, a fault tolerance part, consists of integrating a layer of diagnosis for detection and identification of sensor faults to ensure a safe and reliable system using informational based approaches (using KL divergence for example for information measures). From the standpoint application, a part of this project is dedicated to data acquisition campaigns on the smart apartment platform of INRIA Nancy Grand Est. Data fusion algorithms will be tested and validated with real data. This Project is carried out within the framework of a collaboration between the Lebanese University, the laboratory CRIStAL and INRIA Nancy Grand-Est.
mHealth Compendium: Volume Four

Volume 5 of the mHealth Compendium series compiles forty-one case studies submitted by various implementing partners primarily in Africa but also in other regions of the world. These applications have been used for tasks including the monitoring and implementation of health interventions; making electronic payments for services; and informing local communities about potential outbreaks of disease as was done during the recent Ebola epidemic in West Africa.
Enabling the Usability Heuristics of Agile Base Systems to Improve Quality of Local Software Industry

Many agile software development practices are promoted to improve the quality of software products. In recent years agile software development overlooked the usability features that effected system productivity.Usability is a main feature of interaction. Interaction is a way of a farming relationship between people and designed objects. An interactive model provides the way to band application together to achieve target user’s need. Usability gained attention of researchers and engineers because of its own importance. Agile software methods and usability engineering played a major role for producing better and reliable products, because both of them are concepts of methods as well as practices. The purpose of this research was to highlight the need of usability practices. The proposed model demonstrates that usability heuristics were much compatible with agile methodologies and would help to improve its productivity by reducing time and cost. Action research was applied for the development of framework proposed. The framework was evaluated using case study and further results were compared with existing related work.
Improving nursing research reporting: a guide to reporting guidelines

Enhancing the quality of the nursing research reporting through structured approach is the key in solving the problems related to reporting. Complete and transparent reporting of nursing research with the standard guidelines such as, The Strengthening the Reporting of Observational Studies in Epidemiology (STROBE), Consolidated Standards of Reporting Trials (CONSORT), Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA), Standards for reporting qualitative research (SRQR), Consensus-based Clinical Case Reporting Guideline (CARE) and Consolidated criteria for reporting qualitative research (COREQ)will ensure the transparency through the minimum set of items required to reflect what was one done? What are the outcomes? What are the benefits? What are the issues that bring bias and what are the harms? The ‘Enhancing the Quality and Transparency of Health Research’ (EQUATOR) network and ‘Penelope Research’ also helps the authors to solve problem of finding out of relevant reporting guidelines. Whatever may be the reason writing and publishing high impact nursing and health research is the need of the day.
Loyalty or Incentives? How Party Alignment Affects Bureaucratic Performance

Existing studies show that party alignment between national and subnational politicians has a positive impact on government spending. However, public programs often need the approval or input from career bureaucrats. Since party politics do not directly affect these agents’ incentives, it is unclear whether party alignment will affect their performance and the programs they supervise. To examine this question, I rely on a uniquely large and granular data set of projects implemented under the Member of Parliament Local Area Development Scheme in India. The evidence shows that party alignment leads to lower project approval time and a higher utilization of program resources without compromising the overall quality of projects. Career concerns emerge, over political selection, as an important mechanism explaining bureaucratic behavior. The overall findings suggest that bureaucrats’ incentives combined with the structure of promotions in the civil service are important factors explaining the impact of party alignment on the distribution of resources.
Real Circuit Delay Measurement Method by Variable Frequency Operation with On-Chip Fine Resolution Oscillator

: With the progress of semiconductor process miniaturization, delay degradation by aging increases and threatens the reliability of fabricated chips. The amount of delay degradation is known to be circuit and workload dependent, but previous evaluations are based on simulations, and delay degradation measurement of real circuit under realistic workload has not been reported yet. This paper proposes real circuit delay measurement method, which achieves enough accuracy to measure circuit and workload dependent delay degradation. In the proposed method, on-chip oscillator supplies ﬁne resolution variable frequency clock to internal circuit. Internal circuit execute test pattern to activate critical paths at various frequency and determine the maximum frequency at which correct results can be obtained. The maximum frequency corresponds to the delay of the critical paths activated by the test pattern. Clock multiplication improves delay resolution, and repetitive measurement reduces measurement error caused by time dependent random delay variation. The proposed method has been implemented on a 65nm low power process test chip. Variable frequency oscillator utilizes only standard cells and is designed with automatic layout ﬂow without any timing tuning. The area overhead of the proposed method is 0.09% of the total random logic. The evaluation result show that 0.18% average measurement accuracy has been achieved.
Knowledge of stroke among hypertensive patients in selected hospitals in the Tanga region, Tanzania

Meeting the need for knowledge relating to the nature of stroke and risk factors for stroke is still a substantial challenge in the world. Lack of knowledge about stroke in general and specific knowledge of the risk factors, signs, and symptoms of stroke results in the late presentation of patients at hospital. The result is delay in prompt initiation of stroke management and non-compliance with follow-up rehabilitation. There is therefore a need to empower people to take control of their own health. In Tanzania it is likely that baseline knowledge about stroke is poor due to general lack of access to education and knowledge sources, such as newspaper, television and other literature. If the baseline knowledge of the causes and symptoms is established, appropriate education programmes targeted at lifestyle changes and prevention of stroke can be developed. In addition, those who do develop warning signs will be more likely to seek appropriate care as soon as possible. The aim of this study was to determine the knowledge of stroke among hypertensive patients in the Tanga region of Tanzania. To investigate this, a cross-sectional survey using a quantitative research design was used. The study was carried out in selected district hospitals in the Tanga region of Tanzania. A sample of convenient of 256 hypertensive patients was recruited and a self-administered questionnaire, with both closed and open-ended questions were used for data collection. Descriptive and inferential statistics analysis were carried out to ascertain frequencies and percentages, and to assess the influence of demographic characteristics on participants’ knowledge of stroke. The Kruskal-Wallis test was used to identify the relationship between the overall knowledge of stroke and the socio-demographic
Correlation of Selected Trunk and Hip Muscle Cross-Sectional Areas with Incidence and Severity of Low Back Pain in Adult Males and Females

ii Dedication . iv Acknowledgements.v Vita.vi List of Tables.ix List of Figures.x Chapter 1: Introduction.1 Chapter 2: Review of Literature.5 Scope of the Problem of Low Back Pain.6 Defining Low Back Pain.6 Definition of Pain and Overview of Basic Nociception.6 Definition of Low Back Pain.8 Causes of and Risk Factors for Low Back Pain.8 Age/Sex.9 Muscle Strength/Endurance/Motor Control.9 Lifting . 12 Sitting/Posture/Sedentary Lifestyle.14 Psychological and Emotional Factors.15 Work Exposure and Biomechanical/Ergonomic Factors . 15 Underlying Medical Conditions.16 Hyperalgesia and Peripheral/Central Sensitization.17 Measuring Low Back Pain Severity.19 Pain Scales . 19 Chronicity . 19 Functional/Impairment Scales.20 Peripheralization of Symptoms.20 Functional Motion.21 Treatment Interventions for Low Back Pain.22 viii Exercise.22 Pharmacological, Thermal, Electrotherapeutic and Manual Interventions . 27 Surgery . 28 Evidence of the Role of Selected Trunk and Hip Muscles in Low Back Pain . 29 Multifidus . 29 Gluteus Maximus . 31 Psoas Major . 33 Erector Spinae.34 Measuring Muscle Strength.36 Electromyography . 36 Manual Muscle Testing.36 Muscle Morphometry.36 Theories of Muscle Atrophy with Low Back Pain.40 Conclusion . 43 Chapter 3: Correlation of Gluteus Maximus and Psoas Major Cross-Sectional Areas with Multifidus Cross-Sectional Area in a Cadaver Sample.44 Chapter 4: Correlation of Gluteus Maximus Cross-Sectional Area with Incidence and Severity of Low Back Pain in Adult Females.57 Chapter 5: Correlation of Pre-Surgical Muscle Morphometry and Various Measures of Low Back Pain Severity with Treatment Recommendations and Surgical Outcomes in Adult Females and Males.67 Chapter 6: Conclusions and Future Research.94 References.98 Appendix A Oswestry Disability Index Version 2.1a. 115
Comparative analysis of prediction algorithms for building energy usage prediction at an urban scale

Strategic planning for efficient and sustainable urban environments necessitates identification of scalable energy saving opportunities for the buildings sector. A possible resolution is the analysis of building energy use data at urban scale, although the available data is often sparse, inconsistent, diverse and heterogeneous in nature. Over the past decades, predictive modeling using sparse data has aided with the forecasting of building energy use. However, most studies of energy use prediction focus on individual buildings. This paper proposes the integration of building archetypes simulation, parametric analysis, and machine learning techniques as a solution to accurately predict individual building energy use at an urban level. The aim of the research described in this paper is to achieve accurate prediction of building energy performance, which will allow stakeholders, such as energy policymakers and urban planners, to make informed decisions when planning retrofit measures at large scale. The methodology generates synthetic building data for training the predictive model and predicts building energy use at urban scale with limited resources. The experimentation focuses on Dublin city through the development of synthetic building dataset using parametric analysis on previously identified key variables of two distinct building archetypes. Having compared different prediction algorithms, we show that the Gradient Boosted Trees algorithm gives a better prediction when compared to other algorithms.
Relation Inference among Sensor Time Series in Smart Buildings with Metric Learning

Smart Building Technologies hold promise for better livability for residents and lower energy footprints. Yet, the rollout of these technologies, from demand response controls to fault detection and diagnosis, significantly lags behind and is impeded by the current practice of manual identification of sensing point relationships, e.g., how equipment is connected or which sensors are co-located in the same space. This manual process is still error-prone, albeit costly and laborious.We study relation inference among sensor time series. Our key insight is that, as equipment is connected or sensors co-locate in the same physical environment, they are affected by the same real-world events, e.g., a fan turning on or a person entering the room, thus exhibiting correlated changes in their time series data. To this end, we develop a deep metric learning solution that first converts the primitive sensor time series to the frequency domain, and then optimizes a representation of sensors that encodes their relations. Built upon the learned representation, our solution pinpoints the relationships among sensors via solving a combinatorial optimization problem. Extensive experiments on real-world buildings demonstrate the effectiveness of our solution.
Testing Suicide Risk Prediction Algorithms Using Phone Measurements With Patients in Acute Mental Health Settings: Feasibility Study

Background Digital phenotyping and machine learning are currently being used to augment or even replace traditional analytic procedures in many domains, including health care. Given the heavy reliance on smartphones and mobile devices around the world, this readily available source of data is an important and highly underutilized source that has the potential to improve mental health risk prediction and prevention and advance mental health globally. Objective This study aimed to apply machine learning in an acute mental health setting for suicide risk prediction. This study uses a nascent approach, adding to existing knowledge by using data collected through a smartphone in place of clinical data, which have typically been collected from health care records. Methods We created a smartphone app called Strength Within Me, which was linked to Fitbit, Apple Health kit, and Facebook, to collect salient clinical information such as sleep behavior and mood, step frequency and count, and engagement patterns with the phone from a cohort of inpatients with acute mental health (n=66). In addition, clinical research interviews were used to assess mood, sleep, and suicide risk. Multiple machine learning algorithms were tested to determine the best fit. Results K-nearest neighbors (KNN; k=2) with uniform weighting and the Euclidean distance metric emerged as the most promising algorithm, with 68% mean accuracy (averaged over 10,000 simulations of splitting the training and testing data via 10-fold cross-validation) and an average area under the curve of 0.65. We applied a combined 5×2 F test to test the model performance of KNN against the baseline classifier that guesses training majority, random forest, support vector machine and logistic regression, and achieved F statistics of 10.7 (P=.009) and 17.6 (P=.003) for training majority and random forest, respectively, rejecting the null of performance being the same. Therefore, we have taken the first steps in prototyping a system that could continuously and accurately assess the risk of suicide via mobile devices. Conclusions Predicting for suicidality is an underaddressed area of research to which this paper makes a useful contribution. This is part of the first generation of studies to suggest that it is feasible to utilize smartphone-generated user input and passive sensor data to generate a risk algorithm among inpatients at suicide risk. The model reveals fair concordance between phone-derived and research-generated clinical data, and with iterative development, it has the potential for accurate discriminant risk prediction. However, although full automation and independence of clinical judgment or input would be a worthy development for those individuals who are less likely to access specialist mental health services, and for providing a timely response in a crisis situation, the ethical and legal implications of such advances in the field of psychiatry need to be acknowledged.
Revisiting the neuroticism – performance link: A dynamic approach to individual differences

In the present paper, the relationship between neuroticism and supervisory ratings of performanceisexaminedusingadynamicapproachtopersonality.Thisapproachintegrates both within- and between-person differencesby looking at individual differences inbaseline neuroticism, neuroticism variability and neuroticism attractor strength. Our ﬁndings showed that baseline neuroticismrelated tolower supervisoryratings ofperformance, and that a high level of baseline neuroticism is particularly detrimental for people who fail to returntotheirbaselineswiftly.Altogether,theseﬁndingsdemonstratethatadoptingamoreintegrative,dynamicapproachtopersonalityhasthepotentialtocontributetoabetterunderstandingofthepersonality – performance relationship. Hypothesis 3 : Baseline neuroticism moderates the link between neuroticism variability and performance, with the relation between variability and performance being more positive for people low than for people high on baseline neuroticism
The effects of cold water immersion on medial gastrocnemius muscle architecture and performance post-exhaustive stretch-shortening cycle exercise

Kositsky, Adam. The effects of cold water immersion on medial gastrocnemius muscle architecture and performance post-exhaustive stretch-shortening cycle exercise. Biology of Physical Activity, University of Jyväskylä. Master’s Thesis in Biomechanics. 128 pp. Cold water immersion (CWI) is a method highly used in the recovery from fatiguing exercise yet its efficacy is debated. It has been proposed that the resultant suppressed inflammation decreases pain and muscle soreness and thus improves performance. However, it has been shown that this long-term inhibition of inflammatory markers reduces adaptations to resistance training. Changes in muscle architecture have been shown after fatiguing exercise, potentially due to the increased inflammation, yet it is not currently known how these architectural parameters respond to different recovery methods, such as CWI. The aim of this study was to investigate how muscle architecture of the medial gastrocnemius (MG) and neuromuscular performance change after exhaustive stretchshortening cycle (SSC) exercise and what effects a single CWI has on those parameters over 48 hours (48H). Male athletes (n = 11) were randomized into either a passive recovery control group (CON) or CWI. Maximal voluntary contraction (MVC) and maximal drop jump (DJ) tests were performed preand post-exhaustive SSC exercise on a sledge apparatus and again at 24 hours (24H) and 48H. Muscle architecture of MG was measured using ultrasonography at rest and during MVC. Kinetics and kinematics during maximal DJs were determined via two-dimensional motion analysis. MVC was reduced for CON post-exercise but was not significantly different from baseline for either group at 24H or 48H. However, MG fascicle length (FL) during MVC increased for both groups (>10%) at 24H and 48H. For both groups, rebound height from maximal DJ was lower post (~15%) and remained non-significantly reduced at 24H and 48H. Recovery of DJ rebound height performance (e.g. change from post measurements) was significant only for the CWI group. Ankle joint stiffness was lower for both groups post-exercise, with CON gradually recovering and CWI remaining decreased. In summary, exhaustive SSC exercise alters MG architecture, possibly in response to a diminished stretch reflex function. CWI may slightly enhance DJ rebound height at 48H, however it may come at the expense of a less efficient SSC function due to decreased ankle joint stiffness. Thus, after purely SSC exerciseinduced fatigue, at 24H and particularly at 48H CWI may aid global performance during a single SSC task but the effects to the mechanisms of performance may not be beneficial for repeated SSC tasks. However, data from other muscles and joints are needed to confirm this.