Miracle-dz/UniFAR-FTUs huggingface dataset:
Age patterns of under-5 mortality in sub-Saharan Africa during 1990–2018: A comparison of estimates from demographic surveillance with full birth histories and the historic record

BACKGROUND Estimates of under-5 mortality (U5M) for sub-Saharan African populations often rely heavily on full birth histories (FBHs) collected in surveys and model age patterns of mortality calibrated against vital statistics from other populations. Health and Demographic Surveillance Systems (HDSSs) are alternate sources of population-based data in much of sub-Saharan Africa, which are less formally utilized in estimation. OBJECTIVE In this study we compare the age pattern of U5M in different African data sources (HDSSs, Demographic and Health Surveys (DHS), and Multiple Indicator Cluster Surveys (MICS)), and contrast these with the historical record as summarized in the Human Mortality Database and model age patterns. METHODS We examined the relative levels of neonatal, postneonatal, infant, and child mortality across data sources. We directly compared estimates for DHS and MICS subnational regions with HDSS, and used linear regression to identify data and contextual attributes that correlated with the disparity between estimates. RESULTS HDSS and FBH data suggests that African populations have higher levels of child mortality and lower infant mortality than the historic record. This age pattern is most explicit for Western African populations, but also characterizes data for other subregions. The comparison between HDSS and FBH data suggests that FBH estimates of child mortality are biased downward. The comparison is less conclusive for neonatal and infant mortality. CONTRIBUTION This study questions the practice of using model age patterns derived from largely high-income settings for inferring or correcting U5M estimates for African populations. It also highlights the considerable uncertainty around the consistency of HDSS and FBH estimates of U5M.
Visions of Personal Future among Adolescents with Different Orientations Toward Educational Mobility

Research of contemporary societies emphasises the importance of global economic circumstances, political uncertainties and social inequalities for young people’s visions of their personal future. This research is focused on general and career-specific aspects of adolescents’ visions of personal future and how these relate to adolescents’ orientation toward educational mobility. Educational mobility is determined by the equivalence/nonequivalence of parents’ levels of education and their offspring’s educational aspirations as expressed at the end of secondary schooling. According to this principle, three groups of participants were defined and their differences were analysed with respect to (a) general aspects of their visions of personal future, (b) career-specific aspects of their envisioned future, and (c) the perception of factors on which the achievement of career visions will depend. Significant differences among the three groups have been found in general and career-specific visions of the future. The findings of the study indicate that students who plan to attend university are more preoccupied with career and perceive personal characteristics as more important factors for achieving career goals than students without such plans. Finally, this paper suggests that, in order to fully understand young people’s visions of personal future from a micro and a macro perspective, it is fruitful to integrate psychological and sociological approaches.
Grammar-Based Sampling

Recent software systems tend to be highly configurable. Configurations of these systems are combinations of configuration options, e.g., on-off switches for runtime or compile-time options that might enable or disable certain functionality of the program. These configurations have not only an impact to the functionality of the software but also to its performance. Performance in this context has many different meanings, e.g., execution time or power consumption. Finding the best performing configuration in a brute-force manner by measuring all configurations is almost always infeasible. State-of-the-art solutions create a sample set (i.e., a subset of all valid configurations), measure the sampled configurations, and apply machinelearning algorithms to predict the performance of configurations in the configuration space. Creating a valuable sample set for machine-learning algorithms present many challenges. In this thesis we present a new sampling strategy that is lightweight in terms of memory footprint and execution time but selects the configurations uniformly over the valid configuration space. To evaluate the quality of our sampling strategy, we compare the prediction results to other state-of-the-art sampling strategies, e.g., t-wise sampling, on 10 variability models of real-world software systems. For the prediction results we use the most promising three different ML algorithms, i.e., random forest, multiple linear regression, and support vector machines, to learn on our sample sets. In a second step we investigate the performance of our approach regarding execution time. In our results we show also the connection between the valid configuration space and the grammar configuration space.
Data-Driven Approaches for Diagnosis of Incipient Faults in Cutting Arms of the Roadheader

Incipient fault detection and identification (IFDI) of cutting arms is a crucial guarantee for the smooth operation of a roadheader. However, the shortage of fault samples restricts the application of the fault diagnosis technique, and the data analysis tools should be optimized efficiently. In this study, four machine learning tools (the back-propagation neural network based on genetic algorithm optimization, the naive Bayes based on genetic algorithm optimization, the support vector machines based on particle swarm optimization, and the support vector machines based on dynamic cuckoo) are applied to address the challenge in the IFDI of cutting arms. The commonly measured current and vibration data cutting arms are used in the IFDI. The experimental results show that the support vector machines based on dynamic cuckoo outperform the other methods. Besides, the performance of the four methods under different operating conditions is compared. The fault cause of cutting arms of the roadheader is analyzed and the design improvement scheme for cutting arms is provided. This study provides a reference for improving the fault diagnosis of the roadheader.
Detection and Discrimination of Barley- and Wheat-specific Forms of Wheat Dwarf Virus in Poland

Wheat dwarf virus (WDV) is one of the most common viruses on cereal crops in Poland. Studies were undertaken aiming at molecular characterization of Polish isolates of the virus. The presence of two main groups, WDV-barleyand WDV-wheat-specific forms, in field samples has been confirmed. Detection and differentiation of WDV isolates was conducted using immuno-capture polymerase chain reactions. The studies were carried out on a set of 68 samples collected from different parts of the country. Obtained results demonstrated that WDV-wheat-specific form can infect all tested cereals: wheat, triticale, rye and barley while WDV-barley-specific form was identified mainly in barley and in rare cases in wheat plants. Comparative analysis of coat protein gene was performed using 16 WDV isolates originated from different hosts revealed high (>98%) nucleotide sequence identity. Moreover, WDVwheat-specific (Pol-WDV-W) and WDV-barley-specific (WDV-B) isolates were fully sequenced. Based on nucleotide sequence similarity, Pol-WDV-W should be classified as WDV-E and WDV-B as WDV-F strains. This is the first report of the complete sequence of WDV isolates from Poland.
Prediction of Anti and gauche vicinal proton‐proton coupling constants for hexapyranose rings using a generalized karplus equation

A new coupling constant - torsion angle relation for the three-bond 1H-1H spin-spin coupling constants is utilized to explore the effect of endocyclic and exocyclic oxygen atoms on Jae, Jee and Jaa in hexapyranoid systems. Attention is paid to common stereochemical surroundings rather than to individual compounds. It is shown that a simplified model for the flattened six-membered ring, incorporating a constant geometry irrespective of the location of the coupled protons, correctly reproduces all the main trends observed.
The effect of nonsinusoidal voltage on intrinsic aging of cable and capacitor insulating materials

This paper investigates the effect of nonsinusoidal voltage, i.e. voltage affected by the presence of harmonics, on intrinsic aging of cable and capacitor insulating materials, i.e. cross-linked polyethylene (XLPE) and polypropylene (PP). The results of life tests under sinusoidal and distorted voltage, the latter obtained superimposing one or more harmonics on the 50 Hz component, are processed by statistical techniques derived from design-of-experiment procedures. It is shown that the factor predominant on aging acceleration due to the voltage distortion is voltage-peak increase, but also waveform slope and voltage rms value have statistical significance. Life models are obtained for XLPE and PP insulating materials, which show the endurance of the two materials to aging under nonsinusoidal conditions.
Learning High-Dimensional DAGs: Provable Statistical Guarantees and Scalable Approximation

We introduce a recently developed score-based framework for structure learning of directed acyclic graphs (DAGs) on high-dimensional data. Compared to undirected graphs—which are well understood and for which there are algorithms that scale to millions of nodes—the situation for directed graphs is far less advanced, with meth-ods still struggling to handle datasets with thousands of variables on commodity hardware. To address this, we developed a novel framework for DAG learning that simultaneously provides high-dimensional statistical guarantees, scalable computation to tens of thousands of nodes, and user-friendly software, in addition to being able to learn causal networks in the presence of experimental data. Furthermore, this framework avoids commonly used but uncheckable assumptions found in the literature such as faithfulness and irrepresentability, giving a sense of what happens when score-based methods are naïvely applied to high-dimensional datasets. In particular, our results yield—for the ﬁrst time—ﬁnite-sample guarantees for structure learning of Gaussian DAGs in high-dimensions via score-based estimation.
Effects of active meditation on physical and psycho-emotional stress indicators in Chilean health sciences students. A pilot study

Introduction: The multiple academic demands in the university environment generate high levels of stress among students. Although said stress may be useful to meet such demands, in many cases, it can have a negative impact on their health. Objective: To assess whether the implementation of active meditation in the daily routine of health sciences university students impacts their physical and psycho-emotional stress indicators. Materials and methods: Exploratory and quantitative pilot study carried out in a sample of 22 university students who agreed to participate in a 3-month active meditation elective course (18 sessions). Participants were asked to fill out the Global State Perception Questionnaire (CPGE) ex-ante and ex-post taking the course. Results: 18 participants completed the GSPC ex-ante and ex-post, but only 16 were valid for data processing. Most students reported an improvement in their perception of coping with different stressful situations after completing the course. For example, 87.5% reported remaining in a relaxed state during stressful situations, 81% stated feeling decreased mental exhaustion, and 81% said they were better at handling stressful situations. Conclusions: The implementation of active meditation in the daily routine of this group of university students decreased their physical and psycho-emotional indicators of stress. Therefore, it is necessary to carry out new studies with larger samples to confirm the positive effect of this type of course on the health of university students, which will undoubtedly lead to better academic performance as a result of a better mental state.
Depression and its relationship with quality of life in frontline psychiatric clinicians during the COVID-19 pandemic in China: a national survey

This was a national survey that determined the prevalence of depressive symptoms (depression thereafter) and its relationship with quality of life (QOL) in frontline clinicians working in psychiatric hospitals in China during the COVID-19 pandemic. Depression and QOL were assessed using the Patient Health Questionnaire nine items (PHQ-9) and the World Health Organization Quality of Life Questionnaire-brief version (WHOQOL-BREF), respectively. Multivariable logistic regression analyses and analysis of covariance were used. A total of 10,516 frontline clinicians participated in this study, of which, 28.52% (n=2,999) met screening criteria for depression. Compared to those without depression, clinicians with depression had a lower quality of life (F (1, 10515) =2874.66, P<0.001). Higher educational level (OR=1.225, P=0.014), if the number of COVID-19 patients in the hospital catchment area surpassed 500 (OR=1.146, P=0.032), having family/friends/colleagues who were infected (OR=1.695, P<0.001), being a current smoker (OR=1.533, P<0.001), and longer working hours (OR=1.020, P=0.022) were independently associated with higher risk of depression. Living with family members (OR=0.786, P<0.001), and being junior clinicians (OR=0.851, P=0.011) were independently associated with lower odds of depression. The results showed that depression was common in frontline psychiatric clinicians during the COVID-19 pandemic. Timely assessment and effective interventions of depression for frontline clinicians in psychiatric hospitals were warranted.
Dynamic mechanical relaxation behavior of block copolymers at high temperatures

We carried out dynamic mechanical measurements to investigate three different examples of block copolymers: styrene–isoprene diblock copolymers and styrene–butadiene–styrene and styrene–(styrene butadiene)–styrene triblock copolymers. Isochronal and isothermal measurements of the real and imaginary parts of the complex shear modulus were performed over wide ranges of temperature and frequency. The measurements showed the presence of an additional relaxation process appearing at temperatures higher than those of the glass relaxation of the polystyrene phase, which has been misinterpreted by some authors as an order–disorder transition. The frequency dependence revealed that this process was a relaxation process and did not belong to a first-order transition. Moreover, the influence of crosslinking via dicumylperoxide was measured, and we constructed complete master curves to confirm the presence of two relaxation processes. The high-temperature relaxation process was strongly suppressed by crosslinking. Therefore, it was possible to detect the glass relaxation process of the polystyrene phase in a precise manner. The results were compared with those of homopolymers. © 2001 John Wiley & Sons, Inc. J Polym Sci Part B: Polym Phys 39: 2198–2206, 2001
Normality Testing of High-Dimensional Data Based on Principle Component and Jarque–Bera Statistics

The testing of high-dimensional normality is an important issue and has been intensively studied in the literature, it depends on the variance–covariance matrix of the sample and numerous methods have been proposed to reduce its complexity. Principle component analysis (PCA) has been widely used in high dimensions, since it can project high-dimensional data into a lower-dimensional orthogonal space. The normality of the reduced data can then be evaluated by Jarque–Bera (JB) statistics in each principle direction. We propose a combined test statistic—the summation of one-way JB statistics upon the independence of the principle directions—to test the multivariate normality of data in high dimensions. The performance of the proposed method is illustrated by the empirical power of the simulated normal and non-normal data. Two real data examples show the validity of our proposed method.
Towards an organic thermally regenerative fuel cell for truck engines

A thermally regenerative fuel cell (TRFC) on board a long-haul truck would convert waste heat from the engine or exhaust into electrical energy. We propose a TRFC in which waste-heat drives the endothermic dehydrogenation of a secondary benzylic alcohol to the corresponding ketone in a catalytic reaction chamber adjacent to the engine. 1-Phenyl-1-propanol has been identified as a suitable alcohol. In a fully functioning TRFC, the H2 produced from the dehydrogenation will be sent to the fuel cell's anode, while the ketone, propiophenone, will be sent to the cathode to serve as an oxidant, together producing electricity to charge a battery that could power auxiliary vehicle components. The electrochemical reduction of the ketone back to the original benzylic alcohol would take place at the cathode. For such a system to be viable, both reactions must be very selective and rapid. Herein, the initial development of this system and the implications of catalyst and benzylic alcohol choice on the performance of both of these reactions are discussed. For the dehydrogenation of 1-phenyl-1-propanol, Pd/SiO2 offers the highest selectivity (99.65%) at 200 °C. For the re-hydrogenation of the analogous ketone, propiophenone, palladium catalysts offer the highest selectivity, although the highest rates are observed for platinum immobilized on Vulcan XC-72 carbon support; surface modification of commercial catalysts with n-butyl tin also affects the selectivity and rate in a manner that depends on the choice of catalyst support. It has also been shown that the electronic and steric properties of the phenyl ring substituents and the identity of the alkyl chain affect the rate and selectivity.
Evaluation of factors fuelling global antimicrobial resistance and its economic and clinical burden

The aim of the research is to assess the factors fuelling Antimicrobial Resistance (AMR) and its economic and clinical burden. For this purpose, a systematic review was conducted, which included an analysis of factors and economic and clinical burden of AMR. It was found that poorly treated sewage; discharges from antibiotic manufacturing plants, empirical prescribing, lack of antimicrobial stewardship, poor AMR monitoring; hospital-acquired infections and human-to-human contacts with environmental pathogens through food; and increase in global trade and travel are the factors behind the spread of AMR. Further, based on previous research, the study found a significant economic and clinical burden caused by AMR infections. It is recommended that well-organised antimicrobial stewardship be in place, AMR monitoring, limit access to antibiotics via over the counter dispensing without prescriptions, mandatory sensitivity tests for antimicrobial prescription be considered, proper hygiene in hospitals and medicine manufacturing sites must be ensured to reduce global AMR. Key words: Antimicrobial Resistance, antimicrobial resistance (AMR), antibiotic resistance, economic burden, clinical burden.
Running head: RETROSPECTIVE CHILDHOOD FANTASY PLAY SCALE Development and Validation of the Retrospective Childhood Fantasy Play Scale (RCFPS)

The current article describes the development and initial psychometric properties of the Retrospective Childhood Fantasy Play Scale (RCFPS), a brief 11 item retrospective self-report measure of preference for, and engagement with, fantasy play during childhood. Five studies were conducted to: (1) develop the initial items for the scale (n=77), (2) determine the underlying factor structure (n =200), (3) test the fit of the model (n =530), and (4) & (5) ascertain construct validity (n =200) and (n =263). Overall, the results suggest that the RCFPS is a uni-dimensional measure with acceptable fit and preliminary validity. The RCFPS may prove useful in educational and developmental research as an alternative to longitudinal studies to further investigate how childhood fantasy play relates to individual differences in adulthood (e.g., in the areas of creativity, theory of mind and narrative skills).
Some Remarks on the Classification of Water Vapor Sorption Isotherms and Blahovec and Yanniotis Isotherm Equation

Mathematical analysis was performed on equations describing water vapor sorption isotherms. It was shown that an identity exists between the Blahovec and Yanniotis (BY), D'Arcy and Watt (DW), and generalized D'Arcy and Watt (GDW) models. The original BY model has been modified to account for temperature dependence of the model parameters. This modification enables the application of this model to describe sorption data obtained at different temperatures. A new method for classification of sorption isotherms has been proposed that is much simpler than the classification established by Blahovec and Yanniotis.
SURFB OARD : R EPRODUCIBLE P ERFORMANCE A NALYSIS FOR D ISTRIBUTED M ACHINE L EARNING W ORKFLOWS

Large-scale HPC infrastructures are enablers for scientiﬁc research in many domains. The recent advances in machine learning (ML) have led to an ever increasing demand for computation power, as well as the design of complex operational workﬂows. Understanding the performance and efﬁciency of these workﬂows is key to productivity, knowledge and model sharing, and energy efﬁciency. Even though there have been efforts in studying and designing portability protocols, performance analysis of large-scale ML is still an expert-driven task, tightly locked-in to speciﬁc physical and software infrastructure. Much like in other domains, this hinders reproducibility of both results and overall workﬂow performance. To overcome this challenge, we propose the design of a container-based framework for reproducible performance analysis of ML workﬂows at scale. We validate our framework using a case-study on two different large-scale production systems running ML workﬂows. We show empirically that our containerized approach is portable and allows arbitrarily low-level performance evaluation when run on two different, production-based HPC clusters with hundreds of GPUs. We report our ﬁndings on widely-used open-source software stacks and datasets and offer practitioners insights into what types of analyses our framework enables. To beneﬁt the community, we open-source our software and results.
Mesoporous Silica Nanoparticles as a System for Ciprofloxacin Drug Delivery; Kinetic of Adsorption and Releasing

حضرت السليكا متوسطة المسام ذات الحبيبات النانوية كحامل في نقل الدواء بواسطة طريقة sol-gel باستخدام سلكيات الصوديوم كمصدر رخيص للسليكا والمادة الفعالة سطحيا cocamidopropyl betaine شخصت حبيبات السليكا باستخدام التقنيات XRD-AFM-TEM-SEM و ايزوثيرمات امتزاز – امتزاز غاز النيتروجين , واثبتت النتائج ان الحبيبات هي من النوع النانوي ضمن المدى nm (80-40) كمعدل 62.15 نانوميتر وعلى شكل قضبان ويملك مساحة سطحية تساوي 1096.122 متر2/ غم وحجم مساحة مقداره 0.9 سم3/غم مع معدل قطر مسام يساوي 2.902 نانومتر, مما يؤهلها لتكون حاملة للدواء بكفاءة. درست حركيات امتزاز الدواء سيبروفلوكساسين (ciprofloxacin) ثم حللت النتائج ووجد انها تنطبق جيدا مع معادلة المرتبة الاولى الكاذبة وكانت سعة تحميل الدواء على حبيبات السليكا النانوية بمقدار 16. 3ملغم دواء لكل ملغم سليكا وكذلك نسبة ازالة مقدارها 26% و98. 6% من الدواء المحمل بعد مرور 90 دقيقة في الوسط المائي ومحلول الفوسفات بفر سلاين (PBS) ذو الاس الهيدروجيني pH=7.4 على التوالي أجريت عمليات حركيات الازالة في كلا المحلولين ( الماء ومحلول البفر) تحت التحريك وباستخدام معادلاتKorsmeyer-Peppass وحركيات المرتبة الاولى و Kopcha ودلت النتائج على ان معادلتي Korsmeyer-Peppas و Kopcha هي الاكثر انطباقا.