Miracle-dz/UniFAR-FTUs huggingface dataset:
Medical Oxygen as a Life-Saving Medicine: A rapid review of the oxygen landscape and innovative efforts in the World Health Organization Eastern Mediterranean Region in Response to COVID-19 and Beyond

Background: Medical oxygen is an essential treatment for life-threatening hypoxemic conditions and is commonly indicated for the clinical management of many leading causes of mortality. Many countries of the World Health Organization (WHO) Eastern Mediterranean Region (EMR) lacked robust medical oxygen systems prior to the COVID-19 (corona virus disease) pandemic and this situation was exacerbated by increased needs, particularly in remote and rural health facilities, resulting in many unfortunate deaths. The aim of this article is to describe the oxygen landscape in the region and the regional initiatives undertaken by countries and WHO. Methodology: We conducted a rapid review to synthesize the available literature on the needs and availability of oxygen and its related resources and the regional initiatives undertaken. We conducted search in PubMed, relevant WHO and World Bank websites, and in general using google to understand the health of conditions that could benefit from the availability of medical oxygen, oxygen related resources including health workforce available for support and usage of medical oxygen, and the initiatives by WHO, countries and partners to improve the situation. We used a snowballing technique and reviewed all available databases for reports, surveys, assessments, and studies related to medical oxygen, besides WHO internal records, assessments, and consultation reports. Results: The data on oxygen availability, supply demand gap, infrastructure facilities, and human resources were sparse. The regional initiatives have led to increase in resources, including human resources and oxygen production infrastructure. The Live Oxygen Platform (LOP), contributed to improved availability of quality data needed for supply demand assessments. Conclusion: A regional enterprise strategy to promote sustainable, decentralized, and contextualized production, supply, and monitoring of oxygen together with human resource support including training and placement by WHO, partners, and governments contributed to improved availability of oxygen in the region. Additionally, with the LOP, governments, WHO, and partners have access to better data availability for policy decision making and timely resource allocation.
Price Perturbations for Privacy Preserving Demand Response With Distribution Network Awareness

Demand response (DR), where electricity consumption is shifted in response to incentive signals, can ease the transition to renewable generation. However, when many devices simultaneously respond to these signals there is the potential for violating local network constraints. Many of the proposed solutions require consumers to hand over control of their devices to third-parties. Here, we propose a method of using price distortions to coordinate distributed resources, which is both robust to local constraints and privacy preserving. We formulate the price distortion setting problem as a mixed-integer linear programming problem. Conditions are derived under which the method can guarantee constraint violations are eliminated within one time step. The method was tested using case studies involving both electric vehicles and smart heating/cooling systems. We show that the proposed method, under a scenario with maximum DR participation, can achieve 98% of the theoretical lower bound on the number of constraint violations. Furthermore, our method out-performs the benchmark where some devices opt-out of DR.
An Artificial Neural Network Model to Analyze Maize Price Behavior in Mexico

Mexican producers were supported by the Government from 1993-2013 through the PROCAMPO (support program, direct subsidy from the federal government), in order to keep the prices stable. The tool used to analyze the basic products price behavior was Artificial Neural Network (ANN) models. Data from the period 1980 to 2014 was available, and two ANN models were implemented, one including only internal prices of related products and the second takes into account also the international maize price. The purpose of this paper was to analyze the impact of internal related products and international maize price on the behavior of internal maize price. Both models demonstrated the powerful of the ANN for prediction of the maize price 3 and 5 years ahead. Also, a sensitivity analysis was performed using Garson’s algorithm, through which was demonstrated that rice, safflower and sorghum are corn’s complementary products. The rice price was the variable with a major positive impact on the national corn price. On the other hand, wheat, soybean and barley behave as corn’s substitute products, with wheat price being the variable with major impact on the corn price. Including the international corn price improves the model performance, besides there was a negative sensitivity between the international and national corn price; this behavior is explained by the fact that during PROCAMPO existence, the national prices were forced to be stable while the international maize price was increasing. Finally, this study showed the application of the ANN models in a particular product price behavior, with the possibility of being used in the decision making process for the application of subsidies to the agricultural producers in public policies.
Predicting speech quality under random packet loss: Individual impairment and additivity with other network impairments

Packet loss is the most important type of degradation particular of packet-based transmission as applied in Voice over Internet Protocol (VoIP). In this paper, a model for predicting speech quality under random packet loss is presented and compared to auditory test data from the literature. The approach is intended for network planning and is an extension of the so-called E-model. Packet loss may be present in a network in addition to other types of degradations, like those known from traditional, circuit-switched networks. Hence, in a second step, the paper discusses how speech quality is perceived in case of packet loss and additional impairments like line noise, transmission delay or talker echo. A series of short conversation tests is described for this aim. In particular, it is studied whether the perceived impairments behave additively on the applied scales. The results show a limited impairment additivity: For example, the impairment due to packet loss is partially masked by additional line noise.
Boundedness of solutions to a quasilinear higher-dimensional chemotaxis-haptotaxis model with nonlinear diffusion

This paper deals with the Neumann problem for the coupled quasilinear chemotaxis-haptotaxis model of cancer invasion given by \begin{document}$\left\{ \begin{gathered} ut = \nabla \cdot \left( {{{\left( {u + 1} \right)}^{m - 1}}\nabla u} \right) - \nabla \cdot \left( {u\nabla v} \right) - \nabla \cdot \left( {u\nabla w} \right) + u\left( {1 - u - w} \right), \hfill \\ ut = \Delta v - v + u, \hfill \\ wt = - vw, \hfill \\ \end{gathered} \right. $ \end{document} where the parameter $m≥q1$ and $\mathbb{R}^N(N≥q2)$ is a bounded domain with smooth boundary. If $m>\frac{2N}{N+2}$, then for any sufficiently smooth initial data there exists a classical solution which is global in time and bounded. The results of this paper partly extend previous results of several authors.
A Review of YOLO Object Detection Algorithms based on Deep Learning

Object detection is a research hotspot in the field of computer vision, and YOLO series shows good performance in object detection, and has been widely used in robot vision, unmanned driving and other fields in recent years. This paper first introduces the YOLO series algorithm, including the principle, innovation points, advantages and disadvantages of various algorithms, then introduces the application field of YOLO series, and finally analyzes its future development trend to provide reference for the topic research.
Estimation of covariance, correlation and precision matrices for high-dimensional data

The thesis concerns estimating large correlation and covariance matrices and their inverses. Two new methods are proposed. First, tilting-based methods are proposed to estimate the precision matrix of a p-dimensional random variable, X, when p is possibly much larger than the sample size n. Each 2 by 2 block indexed by (i, j) of the precision matrix can be estimated by the inversion of the pairwise sample conditional covariance matrix of Xi and Xj controlling for all the other variables. However, in the high dimensional setting, including too many or irrelevant controlling variables may distort the results. To determine the controlling subsets, the tilting technique is applied to measure the contribution of each remaining variable to the covariance matrix of Xi and Xj , and only puts the (hopefully) highly relevant remaining variables into the controlling subsets. Four types of tilting-based methods are introduced and the properties are demonstrated. The simulation results are presented under different scenarios for the underlying precision matrix. The second method NOVEL Integration of the Sample and Thresholded covariance estimators (NOVELIST) performs shrinkage of the sample covariance (correlation) towards its thresholded version. The sample covariance (correlation) component is non-sparse and can be low-rank in high dimensions. The thresholded sample covariance (correlation) component is sparse, and its addition ensures the stable invertibility of NOVELIST. The benefits of the NOVELIST estimator include simplicity, ease of implementation, computational efficiency and the fact that its application avoids eigenanalysis. We obtain an explicit convergence rate in the operator norm over a large class of covariance (correlation) matrices when p and n satisfy log p/n → 0. In empirical comparisons with several popular estimators, the NOVELIST estimator performs well in estimating covariance and precision matrices over a wide range of models. An automatic algorithm for NOVELIST is developed. Comprehensive applications and real data examples of NOVELIST are presented. Moreover, intensive real data applications of NOVELIST are presented.
Predictors of Success in the National Physical Therapy Examination for Hispanic Doctor of Physical Therapy Students

Introduction. Hispanic/Latino students often underachieve in higher education programs in the health professions and have shown lower first-time pass rates on the National Physical Therapy Examination than their White peers. A plausible explanation for this difference is the lack of English proficiency. The purpose of this study was to determine predictive academic variables for passing licensure examination on the first attempt for Hispanic/Latino Doctor of Physical Therapy (DPT) students. Review of the Literature. Numerous studies have been conducted to determine the factors that predict success on the National Physical Therapy Examination; however, there is a scarcity of literature on the predictors of success for Hispanic/Latino DPT students, particularly on the influence of English proficiency. Subjects. A nonprobability convenience sample of 67 Hispanic students from a single DPT program was used. Methods. A retrospective, predictive, correlational study was conducted. Data collection was performed by secondary analysis of student academic profiles. Predictor variables included proficiency in the English language; undergraduate grade point average; verbal, quantitative, and written skills; and first-year and third-year grade point average while enrolled in the physical therapist education program. Results. The first-time pass rate was 59.7% (n = 40). Results showed that students who passed and those who did not pass on their first attempt differed in first-year and third-year program grade point average with large effect sizes of d = 1.13 and d = 1.48, respectively; however, third-year grade point average was the only significant independent predictor of success. English proficiency and preadmission variables did not predict first-time success. Discussion and Conclusion. The results suggest first-year and third-year grade point average may be used to identify at-risk students. Use of these variables is encouraged to monitor the progression of students. The study highlighted the need to identify additional predictors of performance.
Assessment of suboptimal control for turbulent skin friction reduction via resolvent analysis

This paper extends the resolvent analysis of McKeon & Sharma (J. Fluid Mech., vol. 658, 2010, pp. 336–382) to elucidate the drag reduction mechanisms for the suboptimal control laws proposed by Lee, Kim & Choi (J. Fluid Mech., vol. 358, 1998, pp. 245–258). Under the resolvent formulation, the turbulent velocity field is expressed as a linear superposition of propagating modes identified via a gain-based decomposition of the Navier–Stokes equations. This decomposition enables targeted analyses of the effects of suboptimal control on high-gain modes that serve as useful low-order models for dynamically important coherent structures such as the near-wall (NW) cycle or very-large-scale motions. The control laws generate blowing and suction at the wall that is proportional to the fluctuating streamwise (Case ST) or spanwise (Case SP) wall shear stress, with the magnitude of blowing and suction being a design parameter. It is shown that both Case ST and SP can suppress resolvent modes resembling the NW cycle. However, for Case ST, the analysis reveals that control leads to substantial amplification of flow structures that are long in the spanwise direction. Quantitative comparisons show that these predictions are broadly consistent with results obtained in previous direct numerical simulations. Further, the predicted changes in mode structure suggest that suboptimal control can be considered a modified version of opposition control. In addition to the study of modes resembling the NW cycle, this paper also considers modes of varying speed and wavelength to provide insight into the effects of suboptimal control across spectral space.
Themed Issue Conservation Physiology of Animal Migrations Stress physiology of migrant birds during stopover in natural and anthropogenic woodland habitats of the Northern Prairie region

Anthropogenic alterations of woodland habitat may influence stopover biology, which in turn could alter the stress physiology of migratory landbirds. Woodland stopover habitats are scarce in the Northern Prairie region of North America and consist of native riparian corridor woodlands (corridors) and smaller, more isolated woodlots of anthropogenic origin around farmsteads (woodlots). Corridor habitats have been greatly reduced since the time of European settlement, but woodlot habitats have appeared over this same time period. In this study, we compared stopover biology and stress physiology of migratory landbirds using natural and anthropogenic woodland habitats. We first tested for differences between birds in the two habitats for baseline corticosterone (CORT B ) and the magnitude of the stress response for individual species, taxonomic families and foraging guilds. Plasma corticosterone increased significantly for all bird groups in both habitats following 30 min of restraint stress (CORT 30 ), and neither CORT B nor the magnitude of the stress response (CORT 30 − CORT B ) differed significantly between birds in the two habitats. Secondly, because CORT B levels are often elevated and CORT secretion following a stressor is often suppressed for birds in poor body condition, we hypothesized that woodland migrants with higher fattening rates would show reduced CORT B and a robust stress response. We tested this hypothesis by assessing the relationships between plasma corticosterone and plasma metabolites associated with refuelling. We found that CORT B was negatively associated and the magnitude of the stress response positively associated with plasma triglycerides (an indicator of fat deposition), with opposite patterns for corticosterone and plasma β -hydroxybutyrate (an indicator of fat catabolism). These data suggest that both corridor and woodlot habitats serve as effective stopover habitat and that the reduction of corridor habitat and increased reliance on anthropogenic woodlots is not detrimental to the stress physiology of migrants in a region with limited woodland habitats.
Scalable Data Servers for Large Multivariate Volume Visualization

Volumetric datasets with multiple variables on each voxel over multiple time steps are often complex, especially when considering the exponentially large attribute space formed by the variables in combination with the spatial and temporal dimensions. It is intuitive, practical, and thus often desirable, to interactively select a subset of the data from within that high-dimensional value space for efficient visualization. This approach is straightforward to implement if the dataset is small enough to be stored entirely in-core. However, to handle datasets sized at hundreds of gigabytes and beyond, this simplistic approach becomes infeasible and thus, more sophisticated solutions are needed. In this work, we developed a system that supports efficient visualization of an arbitrary subset, selected by range-queries, of a large multivariate time-varying dataset. By employing specialized data structures and schemes of data distribution, our system can leverage a large number of networked computers as parallel data servers, and guarantees a near optimal load-balance. We demonstrate our system of scalable data servers using two large time-varying simulation datasets
Effects of variable temperature drying on total flavonoids, amino acids, and antioxidative characteristics along with textural properties of germinated brown rice

Germinated brown rice (GBR) is a kind of nutritional whole cereal food but faces an issue of long-term storage due to its high moisture content. In this study, the variable temperature (VT) drying condition for GBR was established by drying at 50°C for 60 min, followed by further drying at 70°C for 130 min. The results showed a lower percentage of fissured grains of GBR in the VT drying group (27%) than that dried at 70°C (35%) and 90°C (78%). The contents of total flavonoid, γ-aminobutyric acid (GABA), and antioxidant capacities in vitro of GBR in the VT drying group were higher than those of GBR in 90 and 110°C drying groups. The texture, color, and flavor of GBR were effectively maintained by the VT drying method compared with those of GBR dried under a higher constant temperature. This study provided reference data for the GBR drying process.
Fiscal Competition and the Composition of Public Expenditure : An Empirical Study

H2; H4; H7; C33 KEy woRdS: JEL Classification: fiscal competition, public expenditure, panel data econometrics 1 University of Salzburg and Austrian Institute of Economic Research (WIFO), Austria Corespondence concerning to this article should be addressed to: hannes.winner@sbg.ac. at Hannes Winner1
RANDOM NEURAL NETWORK LEARNING HEURISTICS

The random neural network (RNN) is a probabilitsic queueing theory-based model for artificial neural networks, and it requires the use of optimization algorithms for training. Commonly used gradient descent learning algorithms may reside in local minima, evolutionary algorithms can be also used to avoid local minima. Other techniques such as artificial bee colony (ABC), particle swarm optimization (PSO), and differential evolution algorithms also perform well in finding the global minimum but they converge slowly. The sequential quadratic programming (SQP) optimization algorithm can find the optimum neural network weights, but can also get stuck in local minima. We propose to overcome the shortcomings of these various approaches by using hybridized ABC/PSO and SQP. The resulting algorithm is shown to compare favorably with other known techniques for training the RNN. The results show that hybrid ABC learning with SQP outperforms other training algorithms in terms of mean-squared error and normalized root-mean-squared error.
Prediabetes in rural adolescent girls from DERVAN cohort: data from the KONKAN region of the state of Maharashtra, India (DERVAN-4)

Background India is witnessing an epidemic of type 2 diabetes. Overweight/obesity, overnutrition, physical inactivity, and family history are well-known risk factors for diabetes. We investigated the role of undernutrition in the development of diabetes among rural adolescent girls. Methods DERVAN cohort study was set up in the KONKAN region of the western Indian state of Maharashtra. It enrolled 1,520 adolescent girls (16–18 years old at the time of enrollment). We measured glycemic parameters (glucose, insulin, and HbA1C) and body size using anthropometry and body composition using bioimpedance. Prediabetes was diagnosed using the American Diabetic Association (ADA) criteria. We also calculated various HOMA indices for insulin resistance (HOMA-IR), β-cell function (HOMA-β), insulin sensitivity (HOMA-S), and compensatory β-cell response using a homeostasis model. BMI, body fat%, and waist circumferences were treated as exposures and all the glycemic parameters and indices as outcomes. Results The median age of the subjects was 16.6 years. The median weight, height, and BMI were 40.7 kg, 151.7 cm, and 17.5 kg/m2, respectively. Prevalence of underweight was 28.8%, and stunting was observed in 30.4%. Thinness and obesity using BMI were observed in 58.4% and 4.2%, respectively. The median body fat% was 22.5, and excess body fat (>35%) was observed in 5.7%. The prevalence of prediabetes was 39.4%. Fasting insulin concentrations, HOMA-IR, and HOMA-β showed a positive trend across body composition quartiles (p < 0.001). HOMA-S and compensatory β-cell response showed an inverse trend (p < 0.001). Compared with prediabetic girls in the overweight/obese group, girls most undernourished group had lower median insulin concentrations (8.1 μIU/ml vs. 17.1 μIU/ml), lower HOMA-IR (1.1 vs. 2.3), and lower HOMA-β (75.6 vs. 129.2) but higher sensitivity (87.4 vs. 43.7) (p < 0.001) for all. Conclusion We have reported a high prevalence of prediabetes among rural adolescent girls with a very low prevalence of obesity. Prediabetes in obesity is driven by hyperinsulinemia and overworking of the pancreas while poor β-cell function and poor insulin secretion are major drivers in the undernourished group. The high-risk diabetes screening programs are much needed for the undernourished populations. Caution should be exercised for planning the interventions as overfeeding (or overnutrition) is likely to put the populations at risk of development of obesity and insulin resistance.