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There is a plethora of research attempting to contrast high- and low-velocity pitchers to identify traits to target for increasing velocity. However, pitch velocity exists on a continuum. Therefore, our purpose is to display the analytical discrepancies between creating velocity subgroups and leaving velocity as a continuous variable by examining the influence of ball velocity on elbow valgus torque. Motion capture data for 1315 actively competing pitchers were retrospectively extracted from a private database. We compared three analytic methods: (1) linear regression of valgus torque on ball velocity, (2) t-test between low- and high-velocity groups formed by a median split, and (3) t-test between very low- and very high-velocity groups formed by upper and lower velocity quartiles. Linear regression indicates ball velocity influenced valgus torque (p < 0.001, R2 = 0.280). Median splitting reduced the predictability of ball velocity on valgus torque (p < 0.001, R2 = 0.180). Conversely, extreme group splitting artificially inflated the effect size (p < 0.001, R2 = 0.347). We recommend sports biomechanics researchers not discretise a continuous variable to form subgroups for analysis because (1) it distorts the relationship between the variables of interest and (2) a regression equation can be used to estimate the dependent variable at any value of the independent variable, not just the group means. [ABSTRACT FROM AUTHOR]

AbstractIn this critical quantitative study, we used regression with moderation analysis to investigate the factors that exacerbated or mitigated plurisexual (bisexual and queer) student affairs educators’ workplace trauma responses during COVID-19. Using the concept of monosexism and critical trauma theory, we assessed distinctions amongst 914 monosexual and plurisexual student affairs educators’ experiences with workplace trauma, finding a significant moderation effect of plurisexuality when it came to institutional leadership support’s prediction of workplace trauma response. Implications for student affairs research and practice are also discussed. [ABSTRACT FROM AUTHOR]

The ability to accurately predict when a job on a supercomputer will leave the queue and start to run is not only beneficial for providing insights to users, but can also help enable non‐traditional HPC workloads that are not necessarily suited to the batch queue style‐approach that is ubiquitous on production HPC machines. However there are numerous challenges in achieving such a prediction with high accuracy, not least because the queue's state can change rapidly and depend upon many factors. In this work, we explore a novel machine learning approach for predicting queue wait times, hypothesising that such a model can capture the complex behavior resulting from the queue policy and other interactions to generate accurate job start times. For ARCHER2 (HPE Cray EX), Cirrus (HPE 8600), and 4‐cabinet (HPE Cray EX) we explore how different machine learning approaches and techniques improve the accuracy of our predictions, comparing against the estimation generated by Slurm. By combining categorization and regression models, we demonstrate that our approach delivers the most accurate predictions across our machines of interest, with the result of this work being the ability to predict job start times within 1 min of the actual start time for around 65% of jobs on ARCHER2 and 4‐cabinet, and 76% of jobs on Cirrus. When compared against what Slurm can deliver, via the backfill plugin, this represents around 3.8 times better accuracy on ARCHER2 and 18 times better for Cirrus. Furthermore our approach can accurately predicting the start time for three quarters of all job within 10 min of the actual start time on ARCHER2 and 4‐cabinet, and for 90% of jobs on Cirrus. Whilst the initial driver of this work was to better facilitate non‐traditional, interactive and urgent, workloads on HPC machines, the insights gained can also be used to provide wider benefits to users, enrich existing batch queue systems, and inform supercomputing center policy also. [ABSTRACT FROM AUTHOR]

The increasing prevalence of Diabetes Mellitus (DM) as a global health concern highlights the paramount importance of accurately predicting its progression. This necessity has propelled the use of deep learning's advanced analytical and predictive capabilities to the forefront of current research. However, this approach is confronted with significant challenges, notably the prevalence of incomplete data and the need for more robust predictive models. Our research aims to address these critical issues, leveraging deep learning to enhance the precision and reliability of diabetes progression predictions. We address the issue of missing data by first locating individuals with data gaps within specific patient clusters, and then applying targeted imputation strategies for effective data imputation. To enhance the robustness of our model, we implement strategies such as data augmentation and the development of advanced group-level feature analysis. A cornerstone of our approach is the implementation of a deep attentive transformer that is sensitive to group characteristics. This framework excels in processing a wide array of data, including clinical and physical examination information, to accurately predict the progression of DM. Beyond its predictive capabilities, our model is engineered to perform advanced feature selection and reasoning. This is crucial for understanding the impact of both individual and group-level factors on deep models' predictions, providing invaluable insights into the dynamics of DM progression. Our approach not only marks a significant advancement in the prediction of diabetes progression but also contributes to a deeper understanding of the multifaceted factors influencing this chronic disease, thereby aiding in more effective diabetes management and research. [ABSTRACT FROM AUTHOR]

Student socialisation into the norms and environment of their institution is a crucial process during their college experience. During the COVID-19 pandemic, these processes were disrupted, and little published research exists exploring comparisons of these student socialisation processes before and during the pandemic. Accordingly, we report on a quantitative study conducted at a large mid-western university in the United States, which compared survey data from 2018 to data collected in 2021. We operationalised student socialisation processes using Ashford and Black’s proactive behaviours scale to explore how these behaviours and relevant socialisation outcomes changed from before to during the pandemic. The primary socialisation outcomes of interest were engineering identity and social capital. During the COVID pandemic, students reported lower levels of social capital. However, we simultaneously observed higher ratings of students’ engineering identity. Next, using multivariate linear regression, we examined which proactive behaviours best predicted socialisation outcomes in both datasets. We found all proactive behaviours were significant predictors of social capital. Proactive behaviours did not influence student’s sense of engineering identity. This study contributes to the growing literature on the pandemic's impact on engineering undergraduate students and how proactive behaviours are related to their socialisation outcomes. [ABSTRACT FROM AUTHOR]

Imbalances in electrolyte concentrations can have severe consequences, but accurate and accessible measurements could improve patient outcomes. The current measurement method based on blood tests is accurate but invasive and time-consuming and is often unavailable for example in remote locations or an ambulance setting. In this paper, we explore the use of deep neural networks (DNNs) for regression tasks to accurately predict continuous electrolyte concentrations from electrocardiograms (ECGs), a quick and widely adopted tool. We analyze our DNN models on a novel dataset of over 290,000 ECGs across four major electrolytes and compare their performance with traditional machine learning models. For improved understanding, we also study the full spectrum from continuous predictions to a binary classification of extreme concentration levels. Finally, we investigate probabilistic regression approaches and explore uncertainty estimates for enhanced clinical usefulness. Our results show that DNNs outperform traditional models but model performance varies significantly across different electrolytes. While discretization leads to good classification performance, it does not address the original problem of continuous concentration level prediction. Probabilistic regression has practical potential, but our uncertainty estimates are not perfectly calibrated. Our study is therefore a first step towards developing an accurate and reliable ECG-based method for electrolyte concentration level prediction—a method with high potential impact within multiple clinical scenarios. [ABSTRACT FROM AUTHOR]

This research investigates the impact of dry machining AA4015/B4C MMCs (metal matrix composites) utilizing an uncoated B4C insert on Flank wear (VBc) and surface roughness (Ra). This work attempts to close the knowledge gap on the effects of cutting parameters (feed, depth of cut, and speed) on tool wear and surface quality in machining technology. The primary rationale lies in optimizing machining processes for MMCs, known for their challenging machinability due to their tough metallic matrix and hard ceramic reinforcement. The study’s significance is underscored by the exploration of optimal processing parameters to minimize Flank wear and improve surface roughness, crucial factors influencing component quality and lifespan. Specifically, the research identifies v1-f1-d3 (VBc) and v3-f1-d3 (Ra) as the best process parameter combinations, significantly reducing both VBc and Ra. The obtained mathematical models for VBc and Ra provide statistically significant insights into the relationships between cutting variables and performance characteristics. The employment of Taguchi’s L9 orthogonal array proves invaluable in achieving these optimized process parameters efficiently. The Taguchi method’s advantage lies in its ability to systematically explore numerous variables and their interactions with minimal experiments. By reducing the number of trials required, this methodology streamlines the optimization process, saving time, resources, and costs while delivering enhanced machining performance for MMCs. This research, through its systematic approach and emphasis on optimized parameters, contributes to the advancement of machining techniques for MMCs, holding implications for various industrial applications demanding high-performance materials. [ABSTRACT FROM AUTHOR]

Generalized linear models (GLMs) are an integral tool in ecology. Like general linear models, GLMs assume linearity, which entails a linear relationship between independent and dependent variables. However, because this assumption acts on the link rather than the natural scale in GLMs, it is more easily overlooked. We reviewed recent ecological literature to quantify the use of linearity. We then used two case studies to confront the linearity assumption via two GLMs fit to empirical data. In the first case study we compared GLMs to generalized additive models (GAMs) fit to mammal relative abundance data. In the second case study we tested for linearity in occupancy models using passerine point‐count data. We reviewed 162 studies published in the last 5 years in five leading ecology journals and found less than 15% reported testing for linearity. These studies used transformations and GAMs more often than they reported a linearity test. In the first case study, GAMs strongly out‐performed GLMs as measured by AIC in modeling relative abundance, and GAMs helped uncover nonlinear responses of carnivore species to landscape development. In the second case study, 14% of species‐specific models failed a formal statistical test for linearity. We also found that differences between linear and nonlinear (i.e., those with a transformed independent variable) model predictions were similar for some species but not for others, with implications for inference and conservation decision‐making. Our review suggests that reporting tests for linearity are rare in recent studies employing GLMs. Our case studies show how formally comparing models that allow for nonlinear relationships between the dependent and independent variables has the potential to impact inference, generate new hypotheses, and alter conservation implications. We conclude by suggesting that ecological studies report tests for linearity and use formal methods to address linearity assumption violations in GLMs. [ABSTRACT FROM AUTHOR]

The cognitive state of a person can be categorized using the circumplex model of emotional states, a continuous model of two dimensions: arousal and valence. The purpose of this research is to select a machine learning model(s) to be integrated into a virtual reality (VR) system that runs cognitive remediation exercises for people with mental health disorders. As such, the prediction of emotional states is essential to customize treatments for those individuals. We exploit the Remote Collaborative and Affective Interactions (RECOLA) database to predict arousal and valence values using machine learning techniques. RECOLA includes audio, video, and physiological recordings of interactions between human participants. To allow learners to focus on the most relevant data, features are extracted from raw data. Such features can be predesigned, learned, or extracted implicitly using deep learners. Our previous work on video recordings focused on predesigned and learned visual features. In this paper, we extend our work onto deep visual features. Our deep visual features are extracted using the MobileNet-v2 convolutional neural network (CNN) that we previously trained on RECOLA's video frames of full/half faces. As the final purpose of our work is to integrate our solution into a practical VR application using head-mounted displays, we experimented with half faces as a proof of concept. The extracted deep features were then used to predict arousal and valence values via optimizable ensemble regression. We also fused the extracted visual features with the predesigned visual features and predicted arousal and valence values using the combined feature set. In an attempt to enhance our prediction performance, we further fused the predictions of the optimizable ensemble model with the predictions of the MobileNet-v2 model. After decision fusion, we achieved a root mean squared error (RMSE) of 0.1140, a Pearson's correlation coefficient (PCC) of 0.8000, and a concordance correlation coefficient (CCC) of 0.7868 on arousal predictions. We achieved an RMSE of 0.0790, a PCC of 0.7904, and a CCC of 0.7645 on valence predictions. [ABSTRACT FROM AUTHOR]

Drawing on Arnold Schönberg's seminal opera "Moses and Aron", the comment focuses on the role of holistic politics in Andrew Arato's and Jean L. Cohen's "Populism and Civil Society". It argues that their anti-populist stance is too quick in dismissing a politics which is driven by representing and re-constituting the whole of the social order. Against this backdrop, a rejuvenation of the political left may not consist in a rejection of holism as such but in a popular politics which relies on functional equivalences. [ABSTRACT FROM AUTHOR]

This research addresses the gap in comprehensive predictive models for construction project success rating by exploring the potential of regression models to evaluate project success rating. By analyzing 130 datasets from the National Capital Region, the study utilizes Support Vector Regression (SVR), Multiple Linear Regression (MLR), and Artificial Neural Network (ANN) with a 22-30-1 configuration (22 input neurons, 30 neurons in a single hidden layer, and 1 output neuron). The input variables represent critical success factors rated on a scale of 1-5, while the output variable represents the predicted project success percentage rating. Various statistical tools, including ANOVA, Lasso Regression, R², MAE, and MSE, are utilized for evaluation. The findings reveal that SVR achieved the highest accuracy (R² = 0.881, MAE = 2.172, MSE = 7.054), followed closely by MLR (R² = 0.874, MAE = 2.180, MSE = 7.470), while ANN (R² = 0.743, MAE = 3.076, MSE = 15.239) may require refinement. Lasso Regression identified 22 critical success factors, with Financial Condition, Effectiveness in Decision-Making, and Compliance to Quality Standards ranking as the top three. This research contributes to the advancement of construction predictive analytics, which can lead to improved decision-making and more efficient, effective, and ethical construction practices. [ABSTRACT FROM AUTHOR]

Permutation techniques have been used extensively in machine learning algorithms for evaluating variable importance. In ordinary regression, however, variables are often removed to gauge their importance. In this paper, we compared the results for permuting variables to removing variables in regression to assess relations between these two methods. We compared permute-and-predict (PaP) methods with leave-one-covariate-out (LOCO) techniques. We also compared these results with conventional metrics such as regression coefficient estimates, t-statistics, and random forest out-of-bag (OOB) PaP importance. Our results indicate that permutation importance metrics are practically equivalent to those obtained from removing variables in a regression setting. We demonstrate a strong association between the PaP metrics, true coefficients, and regression-estimated coefficients. We also show a strong relation between the LOCO metrics and the regression t-statistics. Finally, we illustrate that manual PaP methods are not equivalent to the OOB PaP technique and suggest prioritizing the use of manual PaP methods on validation data. [ABSTRACT FROM AUTHOR]

A dose‐response experiment was conducted to evaluate the effects of graded levels of dietary digestible threonine (dThr) during the first laying cycle on productive and reproductive performance, egg quality and immune responses of Japanese quail breeders (Coturnix coturnix japonica). Also, dThr requirements were determined based on nutrient dose‐response data. A total of 450 (360 females and 90 males) 11‐week‐old breeders were allocated to five increment (+0.03%) levels of dThr (0.49%, 0.52%, 0.55%, 0.58%, 0.61% and 0.64%) with five replicates per treatment and 15 (12 females and three males) birds each. The experiment lasted for 12 weeks. In response to increasing dietary dThr levels, egg production, egg mass, feed efficiency, egg specific gravity, eggshell relative weight, eggshell thickness, egg fertility (EF) and immune response against sheep red blood cell (SRBC) inoculation were improved with quadratic trends and egg hatchability as set eggs was improved with linear trends. Japanese quail breeders fed a diet with 0.58% dThr concentration (threonine/lysine ratio of 59%) showed the productive performance traits, EF, eggshell quality and immune response against SRBC inoculation in the highest values. However, feed intake, egg weight, egg albumen and yolk relative weight, egg shape index, haugh unit and egg composition were not affected by increasing dietary dThr level. Based on the broken‐line regression model, the dThr requirements to optimize productive performance, eggshell quality, EF and immune response against SRBC inoculation were estimated at 159−188, 169−183, 175 and 178 mg/bird per day, respectively. It is concluded, in the Japanese quail breeders during the first laying phase a daily dThr intake of 188 mg/bird, dietary dThr concentration at 0.58% (threonine/lysine ratio of 59%) is adequate for optimized productive and reproductive performance, eggshell quality and immune responses. The estimated requirements depend on what production parameter is taken into considered for optimization. [ABSTRACT FROM AUTHOR]

The synergy of edge computing and Machine Learning (ML) holds immense potential for revolutionizing Internet of Things (IoT) applications, particularly in scenarios characterized by high-speed, continuous data generation. Offline ML algorithms struggle with streaming data as they rely on static datasets for model construction. In contrast, Online Machine Learning (OML) adapts to changing environments by training the model with each new observation in real-time. However, developing OML algorithms introduces complexities such as bias and variance considerations, making the selection of suitable estimators challenging. In this challenging landscape, ensemble learning emerges as a promising approach, offering a strategic framework to navigate the bias-variance tradeoff and enhance prediction accuracy by amalgamating outputs from diverse ML models. This paper introduces a novel ensemble method tailored for edge computing environments, designed to efficiently operate on resource-constrained devices while accommodating various online learning scenarios. The primary objective is to enhance predictive accuracy at the edge, thereby empowering IoT applications with robust decision-making capabilities. Our study addresses the critical challenges of ML in resource-constrained edge computing environments, offering practical insights for enhancing predictive accuracy and scalability in IoT applications. To validate our ensemble's efficacy, we conducted comprehensive experimental evaluations leveraging both synthetic and real-world datasets. The results indicate that our ensemble surpassed state-of-the-art data stream algorithms and ensemble regressors across a range of regression metrics, underlining its superior predictive prowess. Furthermore, we scrutinized the ensemble's performance within the realm of auto-scaling for Virtual Network Function (VNF)-based applications situated at the network's edge, thereby elucidating its applicability and scalability in real-world scenarios. [ABSTRACT FROM AUTHOR]

Geographically weighted regression (GWR) offers a local approach to modeling spatial data, considering geographical location and spatial relationships between observations. A salient feature of GWR is the emphasis on geographical proximity, in accordance with Tobler's First Law of Geography, which assumes that closer entities have a greater influence on the target location. Traditional GWR models have been augmented to consider various forms of physical distances aimed at enhancing model performance, and they often disregarded the potential influence of other data attributes, a shortcoming that extends to most GWR extensions. In this study, we introduce a novel weight matrix construction, which integrates data attribute similarity alongside the conventional geographically weighted matrix. The two weights are integrated in a manner that results in improved model performance. The proposed model, called Similarity and Geographically Weighted Regression or SGWR, was applied to five distinct datasets: housing prices, crime rates, and three health outcomes including mental health, depression, and HIV. Results show that SGWR significantly improved model performance based on several statistical measures, outperforming the global regression model and the traditional GWR. [ABSTRACT FROM AUTHOR]

We design four Artificial Neural Network (ANN) models, namely a Multilayer Perceptron (MLP), a Convolutional Neural Network (CNN), an Extreme Learning Machine network (ELM) and an ensemble model based on stacking (STACKED) to forecast the ionospheric vertical Total Electron Content (vTEC) from 1 to 24 hours in advance on a single station at mid latitude. The time series used, spanning from January 2006 to December 2018, includes vTEC, provided by the Global Navigation Satellite System (GNSS) receiver at Tsukuba (TSKB), Japan (36.06 o N, 140.05 o E) and suitable external drivers selected among several helio-geophysical parameters. The selection of appropriate external drivers is made by rating their relevance on the vTEC at forecasting timescales (from 1 to 24 hours). The process, based on eleven machine learning models, highlights that the most important external drivers are: the 10.7 cm Solar Flux (F10.7), the magnitude B T of the Interplanetary Magnetic Field (IMF), and the Auroral Electrojet (AE) index. The forecasting performance of the four models (MLP, CNN, ELM, STACKED) is then analysed. The analysis relies on three statistical metrics to compare actual and forecasted vTEC: the coefficient of determination ( R 2 ), the Mean Absolute Error (MAE), and the Root Mean Squared Error (RMSE). Additionally, descriptive statistics are presented using box and whisker plots. The four ANN models show a quite satisfactory capability to forecast vTEC when applied to the test dataset which represents 10% of the available data from 2006 to 2018. Furthermore, by conducting a Wilcoxon signed rank test, it is shown that statistically significant improvements are achieved by the STACKED model with regard to MLP, CNN and ELM. On average, by analysing the forecasted (from 1 to 24 hours in advance) vs the actual vTEC, the STACKED model achieves R 2 = 0.816 , RMSE = 0.426 TECu, and MAE = 0.296 TECu (1 TECunit = 10 16 electrons/ m 2 ) whilst MLP, CNN and ELM show respectively R 2 = 0.808 , 0.812 , 0.803 , RMSE = 0.436 TECu, 0.431 TECu, 0.441 TECu and MAE = 0.304 TECu, 0.299 TECu, 0.312 TECu. [ABSTRACT FROM AUTHOR]

Bacground: Morphologic measurements such as body lenght, wither height, heart girth, chest width, body leght, cannon‐bone circumference is used to predict carcass weight. For this purpose, estimating carcass weight with measurements of key bones such as ankle bones, which play a significant role in the balance distribution of body weight, seems possible. Objectives: The aim of this study is to create new regression models for effective carcass weight estimation by using the morphometric data of the talus and calcaneus bones of hair goats. Methods: Study materials consisted of talus and calcaneus bones obtained from abattoir products of hair goat kids (12–18 months old, 20 female and 20 male) and adult hair goats (36–48 months old, 20 female and 20 male). Morphometric measurements of the talus and calcaneus of each animal were taken by a digital caliper. Using the morphometric measurements, an index and a factor were calculated for each bone. Regression analysis and correlations were examined in IBM SPSS 21 programme. Results: As a result, statistical analysis of GLc, GLt, Bd, Calfactor and Talfactor were statistically significant on predicting carcass weight. Conclusion: Specific anatomical structures, such as certain bone measurements, such as talus and calnaneus could serve as indicators of growth performance and also carcass weight performance. In addition new anatomical factors and indices may be produced and new regression methods may be applied with these new parameters to predict carcass weight. [ABSTRACT FROM AUTHOR]

• Serum RC levels were positively and independently associated with the severity of hepatic steatosis and liver fibrosis. • During follow-up, baseline serum RC levels were independently associated with increased risk of development and decreased likelihood of regression of MASLD. • Baseline serum RC levels were positively correlated with the incident of liver fibrosis. The aim of this study was to explore the associations of serum remnant cholesterol (RC) levels with the progression and regression of metabolic dysfunction-associated steatotic liver disease (MASLD) in Chinese adults. We conducted a cross-sectional study in 13,903 individuals who underwent transient elastography tests (cohort 1) and a longitudinal study in 17,752 individuals who underwent at least two health check-up exams with abdominal ultrasound (cohort 2). Anthropometric and biochemical parameters were collected. Serum RC levels were calculated. Noninvasive fibrosis indices such as FIB-4 were evaluated in cohort 2. In cohort 1, serum RC levels were positively and independently associated with the severity of hepatic steatosis and liver fibrosis according to logistic regression analysis. In cohort 2, baseline serum RC levels were increased in participants with the incidence of MASLD and decreased in participants with the regression of MASLD during the follow-up period. Baseline serum RC levels were independently associated with an increased risk of development and a decreased likelihood of regression of MASLD: the fully adjusted hazard ratios (HR) were 2.785 (95 % CI 2.332–3.236, P < 0.001) and 2.925 (95 % CI 2.361–3.623, P < 0.001), respectively. In addition, when we used FIB-4 to evaluate liver fibrosis, baseline serum RC levels were positively correlated with the incidence of high-intermediate probability of advanced fibrosis. However, we did not find an association between serum RC levels and the regression of liver fibrosis. Serum RC levels are independently correlated with the progression and regression of MASLD in Chinese adults, suggesting that RC may participate in the pathophysiological process of MASLD. [ABSTRACT FROM AUTHOR]

Educational system is significantly changing in today's world. Recently, the New Education Policy (NEP)-2020 isstarted implementing in India. Students can have various options for getting education according to their choices and requirements. NEP-2020 is more student-centric rather than making them compulsory to get the degree with prescribed syllabus. AI has a major role in NEP-2020. Data mining technology plays a vital role in this new higher education system. As the Higher Education Institutions are growing rapidly, it is necessary for them to impart quality education for enrollment of students. Institutions can maintain educational quality by improving their results. This can be achieved by predicting student academic performance with the help of data mining algorithms. Classification, clustering, regression and association rule mining are the data mining techniques which can be implemented on student dataset to predict the final grade. This study focuses on prediction of student performance using classification and regression data mining techniques. The aim of this literature review is to study various data mining tools, algorithms and the important attributes that affect the student academic performance. [ABSTRACT FROM AUTHOR]

The success of supervised models for emotion recognition on images heavily depends on the availability of images properly annotated. Although millions of images are presently available, only a few are annotated with reliable emotional information. Current emotion recognition solutions either use large amounts of weakly-labeled web images, which often contain noise that is unrelated to the emotions of the image, or transfer learning, which usually results in performance losses. Thus, it would be desirable to know which images would be useful to be annotated to avoid an extensive annotation effort. In this paper, we propose a novel approach based on active learning to choose which images are more relevant to be annotated. Our approach dynamically combines multiple active learning strategies and learns the best ones (without prior knowledge of the best ones). Experiments using nine benchmark datasets revealed that: (i) active learning allows to reduce the annotation effort, while reaching or surpassing the performance of a supervised baseline with as little as 3% to 18% of the baseline's training set, in classification tasks; (ii) our online combination of multiple strategies converges to the performance of the best individual strategies, while avoiding the experimentation overhead needed to identify them. [ABSTRACT FROM AUTHOR]

Data analysis methods for the study of treatments or exposures in relation to a clinical outcome in the presence of competing risks have a long history, often with inference targets that are hypothetical, thereby requiring strong assumptions for identifiability with available data. Here data analysis methods are considered that are based on single and higher dimensional marginal hazard rates, quantities that are identifiable under standard independent censoring assumptions. These lead naturally to joint survival function estimators for outcomes of interest, including competing risk outcomes, and provide the basis for addressing a variety of data analysis questions. These methods will be illustrated using simulations and Women's Health Initiative cohort and clinical trial data sets, and additional research needs will be described. [ABSTRACT FROM AUTHOR]

: The association between live body weight and morphometric traits plays a major role in the daily management and improvement of poultry. The objective of the current study was to determine the association between morphometric features and live body weight, as well as to investigate the direct and indirect effects of morphometric traits on White Leghorn laying hens' live body weight. Live body weight (BW) and morphometric traits including shank circumference (SC), body length (BL), wing length (WL), shank length (SL), toe to back length (TBL), beak length (BKL), beak to comb length (BCL), height (CH) and chest girth (CG) were collected from one hundred (n = 100) White Leghorn laying hens aged 40 weeks. The correlation findings showed that BW was positively correlated to SL, WL, SC, BL, and CH (p < 0.05). The Path analysis results reported that BL (0.45) had the highest direct effect on BW while WL (0.14) had the highest indirect effect on BW via BL. Correlation results propose that improvement of BL, SL, SC, CG and CH might increase the BW of White Leghorn hens. Path analysis results on the other hand, imply that BL and SC may be used as selection basis during breeding to improve BW in chickens. This study suggests that BW of White Leghorn is correlated with some morphometric traits that might be used during breeding. The findings also suggest that body length directly influence the live body weight of White Leghorn chicken breed. [ABSTRACT FROM AUTHOR]

This study investigated the use of separate and combined stratified random sampling to estimate population mean scores for two important courses in Statistics using their pre-requisites, by comparing two estimators (ratio and regression). For Probability Distribution, the separate ratio estimator emerged as the optimal choice, providing a mean score estimate of 31.66 with a variance of 6612.18. This indicates that using the pre-requisites course score as auxiliary information improved the estimation accuracy compared to the combined ratio estimator. In contrast, Statistical Inference, the combined ratio estimator proved to be more effective, yielding a mean score estimate of 33.99 with a variance of 84.54. The separate regression estimator was also evaluated, demonstrating its suitability for Probability Distribution with a mean score estimate of 32.18 and a variance of 7.22. However, for Statistical Inference, the combined regression estimator offered a more precise estimate (32.99) with a lower variance (17.59). The findings highlight the effectiveness of auxiliary information when selecting estimation methods in stratified sampling. [ABSTRACT FROM AUTHOR]

In the current study, tool life and production time during harsh machining of SS304 using coated carbide inserts are examined. The experiment was conducted in a dry condition. Using a Grey-based Taguchi technique, the process parameters were optimized. Regression analysis has also been used to build prediction models for tool life and production time, and their suitability has been assessed. Hard turning operations result in good quality tool life of around 136 and production time of approximately 78. The ideal parametric combination for quality characteristics has been revealed to be speed of 580 m/min, Feed Rate of 0.13 mm/rev, and Depth of Cut of 0.25 mm, respectively, using the grey-based Taguchi technique. Cutting Speed is regarded as most significant aspect in determining tool life and production time. The models for prediction have a good correlation coefficient. This clearly demonstrates improved model fit and was determined to be highly significant. [ABSTRACT FROM AUTHOR]

In recent years, the direction has turned to non-stationary time series. Here the situation is more complicated: it is often unclear how to set down a meaningful asymptotic for non-stationary processes. For this reason, the theory of locally stationary processes arose, and it is based on infill asymptotics created from non-parametric statistics. The present paper aims to develop a framework for inference of locally stationary functional time series based on the so-called conditional U-statistics introduced by Stute (Ann Probab 19:812–825, 1991), and may be viewed as a generalization of the Nadaraya-Watson regression function estimates. In this paper, we introduce an estimator of the conditional U-statistics operator that takes into account the nonstationary behavior of the data-generating process. We are mainly interested in establishing weak convergence of conditional U-processes in the locally stationary functional mixing data framework. More precisely, we investigate the weak convergence of conditional U-processes when the explicative variable is functional. We treat the weak convergence when the class of functions is bounded or unbounded, satisfying some moment conditions. These results are established under fairly general structural conditions on the classes of functions and the underlying models. The theoretical results established in this paper are (or will be) critical tools for further functional data analysis developments. [ABSTRACT FROM AUTHOR]

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A generalization of Passing–Bablok regression is proposed for comparing multiple measurement methods simultaneously. Possible applications include assay migration studies or interlaboratory trials. When comparing only two methods, the method boils down to the usual Passing–Bablok estimator. It is close in spirit to reduced major axis regression, which is, however, not robust. To obtain a robust estimator, the major axis is replaced by the (hyper‐)spherical median axis. This technique has been applied to compare SARS‐CoV‐2 serological tests, bilirubin in neonates, and an in vitro diagnostic test using different instruments, sample preparations, and reagent lots. In addition, plots similar to the well‐known Bland–Altman plots have been developed to represent the variance structure. [ABSTRACT FROM AUTHOR]

Air pollution is now one of the world's most serious environmental problems. It represents a significant hazard to both health and the climate. Urban air quality is steadily declining, affecting not only the air itself but also impacting the quality of water and land. This paper explores the utilization of machine learning-based algorithms for analysis and prediction of air quality in smart cities. In this paper, smart cities for which air quality index (AQI) is calculated are Ahmedabad, Delhi, Lucknow, Gurugram, and Mumbai. The comparative analysis of different Machine Learning algorithms such as Random Forest Regression (RF), Decision Tree Regression, Linear regression, XgBoost and proposed hybrid model which is combination of Random forest and Xgboost model, have been discussed in the paper. The analysis has been carried out using a machine learning-based algorithm to determine which pollutant is the primary source of pollution in a smart city so that preventative steps can be implemented to reduce air pollution. [ABSTRACT FROM AUTHOR]

Selecting suitable biodiesel for the intended application is challenging due to the significant variations in the feedstock for producing biodiesel. The available models to predict biodiesel properties have limited applicability and reliability. The present work addresses these two challenges by developing reliable models based on machine learning algorithms for predicting engine fuel properties of biodiesel and optimizing biodiesel composition for better fuel properties. The models are developed using multilinear regression (MLR), artificial neural networks (ANN), support vector machine regression with grid search (SVMGS), Bayesian optimization (SVMBO) and grey-wolf optimization (SVMGWO) for hyperparameter tuning, Gaussian process regression (GPR), random forest (RF), and adaptive neuro-fuzzy inference system (ANFIS) algorithms. The models are trained to predict viscosity, cetane number, and calorific value from 13 methyl ester constituents of 70 biodiesels. SVMGS models predicted the viscosity, cetane number, and calorific value of 33 validation samples with a mean absolute percentage error of 1.54%, 1%, and 0.43%. Biodiesel composition was optimized to minimize viscosity and maximize cetane number and calorific value. The optimized composition exhibits 3.72 cSt viscosity, 57 cetane number, and 43 MJ/kg calorific value, which can be prepared by blending 68% ± 1% camelina and 32% ± 1% coconut oil. Applying machine learning algorithms to predict biodiesel properties yielded more accurate predictions than available models. It helped find the optimal composition for improved engine characteristics. [ABSTRACT FROM AUTHOR]

The growing interest in chemoinformatic model uncertainty calls for a summary of the most widely used regression techniques and how to estimate their reliability. Regression models learn a mapping from the space of explanatory variables to the space of continuous output values. Among other limitations, the predictive performance of the model is restricted by the training data used for model fitting. Identification of unusual objects by outlier detection methods can improve model performance. Additionally, proper model evaluation necessitates defining the limitations of the model, often called the applicability domain. Comparable to certain classifiers, some regression techniques come with built‐in methods or augmentations to quantify their (un)certainty, while others rely on generic procedures. The theoretical background of their working principles and how to deduce specific and general definitions for their domain of applicability shall be explained. [ABSTRACT FROM AUTHOR]

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Several sectors, such as agriculture and renewable energy systems, rely heavily on weather variables that are characterized by intermittent patterns. Many studies use regression and deep learning methods for weather forecasting to deal with this variability. This research employs regression models to estimate missing historical data and three different time horizons, incorporating long short-term memory (LSTM) to forecast short- to medium-term weather conditions at Quinta de Santa Bárbara in the Douro region. Additionally, a genetic algorithm (GA) is used to optimize the LSTM hyperparameters. The results obtained show that the proposed optimized LSTM effectively reduced the evaluation metrics across different time horizons. The obtained results underscore the importance of accurate weather forecasting in making important decisions in various sectors. [ABSTRACT FROM AUTHOR]

Response functions that link regression predictors to properties of the response distribution are fundamental components in many statistical models. However, the choice of these functions is typically based on the domain of the modeled quantities and is usually not further scrutinized. For example, the exponential response function is often assumed for parameters restricted to be positive, although it implies a multiplicative model, which is not necessarily desirable or adequate. Consequently, applied researchers might face misleading results when relying on such defaults. For parameters restricted to be positive, we propose to construct alternative response functions based on the softplus function. These response functions are differentiable and correspond closely to the identity function for positive values of the regression predictor implying a quasi-additive model. Consequently, the proposed response functions allow for an additive interpretation of the estimated effects by practitioners and can be a better fit in certain data situations. We study the properties of the newly constructed response functions and demonstrate the applicability in the context of count data regression and Bayesian distributional regression. We contrast our approach to the commonly used exponential response function. [ABSTRACT FROM AUTHOR]

In this study, the prediction of n-value (index-value) surfaces—a key indicator of the field and temperature dependence of critical current density in superconductors—across various high-temperature superconducting materials is addressed using a deep learning modeling approach. As superconductors play a crucial role in advanced technological applications in aerospace and fusion energy sectors, improving their performance model is essential for both practical and academic research purposes. The feed-forward deep learning network technique is employed for the predictive modeling of n-value surfaces, utilizing a comprehensive dataset that includes experimental data on material properties and operational conditions affecting superconductors' behavior. The model demonstrates enhanced accuracy in predicting n-value surfaces when compared to traditional regression methods by a 99.62% goodness of fit to the experimental data for unseen data points. In this paper, we have demonstrated both the interpolation and extrapolation capabilities of our proposed DFFNN technique. This research advances intelligent modeling in the field of superconductivity and provides a foundation for further exploration into deep learning predictive models for different superconducting devices. [ABSTRACT FROM AUTHOR]

The number of positive words in scientific papers has exhibited a notable upwards trend over the past few decades. However, there remains a gap in our comprehensive understanding of the relationship between positive words and research impact. In this study, we conduct a multifaceted exploration of the citation advantage associated with positive words based on social cognitive theory, examining its predictability, temporal evolution, and universality across journals of varying quality grades. Drawing from a corpus encompassing 124,144 papers published in the management field between 2001 and 2020, our regression results provide compelling evidence suggesting that positive words can serve as a significant predictor of the citation counts of academic papers, supporting the citation advantage of positive words. However, it is essential to recognize that over time, the citation advantage attributed to positive words is experiencing a conspicuous decline. The universality of the above phenomenon has been further verified in the analysis of journals of different quality. Our findings prompt a discussion regarding the need to pay more attention to the overuse and misuse of positive words, as well as practical considerations for enhancing scientific communication within the academic community. [ABSTRACT FROM AUTHOR]

Community mobilisation is essential to prevent further biodiversity loss and protect the natural environment. This includes engaging in public- and private-sphere behaviours, and community support of relevant government policies. This purpose of this paper is to explore and compare the factors associated with public- and private-sphere nature conservation behaviour and policy support. We utilised data from a survey of 4,048 Australians, representative for age, gender, and geographic location, which measured engagement in nature conservation behaviours and support for diverse conservation policies. Hierarchical Multiple Regressions were conducted to assess relationships between behaviour and policy support with the key independent variables of awareness (of biodiversity issues), connection (to nature), and concern (about biodiversity issues), and socio-demographic covariates. Overall, community support for nature conservation policies was high, not just for incentive measures but for regulations and penalties. Regression models explained 22.9% of the variance in public-sphere behaviour, 30.2% of the variance in private-sphere behaviour, and 43.5% of the variance in policy support. Concern made one of the strongest contributions for all outcomes, and was substantially stronger for policy support compared to behaviour. Awareness and connection also made independent contributions to all outcomes. Socio-demographic variables explained a smaller proportion of unique variance for policy support (1.0%) compared to behaviour (public-sphere: 9.6%, private-sphere: 6.3%). Notably, for public-sphere behaviour, younger age explained almost as much variance as concern. Implications for practitioners and policymakers seeking to engage the general population in conservation support are discussed. [ABSTRACT FROM AUTHOR]

This work focuses on Generalized Linear Mixed Models that incorporate spatiotemporal random effects structured via Factor Model (FM) with nonlinear interaction between latent factors. A central aspect is to model continuous responses from Normal, Gamma, and Beta distributions. Discrete cases (Bernoulli and Poisson) have been previously explored in the literature. Spatial dependence is established through Conditional Autoregressive (CAR) modeling for the columns of the loadings matrix. Temporal dependence is defined through an Autoregressive AR(1) process for the rows of the factor scores matrix. By incorporating the nonlinear interaction, we can capture more detailed associations between regions and factors. Regions are grouped based on the impact of the main factors or their interaction. It is important to address identification issues arising in the FM, and this study discusses strategies to handle this obstacle. To evaluate the performance of the models, a comprehensive simulation study, including a Monte Carlo scheme, is conducted. Lastly, a real application is presented using the Beta model to analyze a nationwide high school exam called ENEM, administered between 2015 and 2021 to students in Brazil. ENEM scores are accepted by many Brazilian universities for admission purposes. The real analysis aims to estimate and interpret the behavior of the factors and identify groups of municipalities that share similar associations with them. [ABSTRACT FROM AUTHOR]

Background and Aim: We aimed to explore the associations of baseline and cumulative cardiovascular health with nonalcoholic fatty liver disease (NAFLD) development and regression using the new Life's Essential 8 score. Methods: From a health screening database, participants who underwent at least 4 health examinations between 2012 and 2022 were recruited and categorized into two cohorts: (a) the NAFLD development cohort with no history of NAFLD prior to Exam 4 and (b) the NAFLD regression cohort with diagnosed NAFLD prior to Exam 4. The LE8 score was calculated from each component. The outcomes were defined as newly incident NAFLD or regression of existing NAFLD from Exam 4 to the end of follow-up. Results: In the NAFLD development cohort, of 21,844 participants, 3,510 experienced incident NAFLD over a median follow-up of 2.3 years. Compared with the lowest quartile of cumulative LE8, individuals in the highest quartile conferred statistically significant 76% lower odds (hazard ratio [HR] 0.24, 95% confidence interval [CI], 0.21–0.28) of NAFLD incidence, and corresponding values for baseline LE8 were 42% (HR 0.58, 95% CI 0.53–0.65). In the NAFLD regression cohort, of 6,566 participants, 469 experienced NAFLD regression over a median follow-up of 2.4 years. Subjects with the highest quartile of cumulative LE8 had 2.03-fold (95% CI, 1.51–2.74) higher odds of NAFLD regression, and corresponding values for baseline LE8 were 1.61-fold (95% CI, 1.24–2.10). Conclusion: Cumulative ideal cardiovascular health exposure is associated with reduced NAFLD development and increased NAFLD regression. Improving and preserving health behaviors and factors should be emphasized as an important part of NAFLD prevention and intervention strategies. [ABSTRACT FROM AUTHOR]

Often the question arises whether Y$$ Y $$ can be predicted based on X$$ X $$ using a certain model. Especially for highly flexible models such as neural networks one may ask whether a seemingly good prediction is actually better than fitting pure noise or whether it has to be attributed to the flexibility of the model. This paper proposes a rigorous permutation test to assess whether the prediction is better than the prediction of pure noise. The test avoids any sample splitting and is based instead on generating new pairings of (Xi,Yj)$$ \left({X}_i,{Y}_j\right) $$. It introduces a new formulation of the null hypothesis and rigorous justification for the test, which distinguishes it from the previous literature. The theoretical findings are applied both to simulated data and to sensor data of tennis serves in an experimental context. The simulation study underscores how the available information affects the test. It shows that the less informative the predictors, the lower the probability of rejecting the null hypothesis of fitting pure noise and emphasizes that detecting weaker dependence between variables requires a sufficient sample size. [ABSTRACT FROM AUTHOR]

In mechanical weed control research, there is a tendency to prioritise practical application over the establishment of a solid theoretical foundation. This emphasis can lead to a fragmented knowledge base and relatively modest research progress. The widespread use of ANOVA exacerbates this issue by primarily focusing on the differences between treatments, neglecting an exploration of the mechanisms behind the observed results. In response to these limitations and in line with rapid advances in sensor and robotics technology, this paper presents a proposal to establish a theoretical framework for mechanical weed control. The framework aims to emphasise understanding of the underlying mechanisms of mechanical weed control, rather than solely focusing on their observable outcomes. As part of this proposal, a simulation model known as HarrowSim based on regression parameters is presented. Simulation runs show the importance of factors such as treatment intensity, selectivity, crop tolerance and weed pressure in relation to crop yield response to post‐emergence weed harrowing. While currently parameterised for post‐emergence weed harrowing, it is argued that HarrowSim is relevant for all post‐emergence mechanical weed control methods, and is useful for teaching purposes and for inspiring future research. While the theoretical framework and HarrowSim have their weaknesses, these can also be perceived as opportunities since they can help focus attention on key issues for future research. [ABSTRACT FROM AUTHOR]

Most patients with lumbar disc herniation (LDH) derive benefit from conservative treatment, prompting growing global interest in non-surgical approaches. Despite being recognized as one of the effective conservative therapies, there is currently limited evidence to support the sole efficacy of acupuncture in treating patients with acute extruded LDH. Here we report the case of a 52-year-old male who presented with low back pain and persistent radiating lower limb pain and numbness. Physical examination and magnetic resonance imaging (MRI) revealed an extruded LDH at the L5/S1 level, compressing the nerve root and causing severe radiculopathy symptoms. After 23 days of continuous inpatient acupuncture treatment, followed by 5 intermittent outpatient acupuncture treatment over 2 months, patient’s pain and numbness was significantly alleviated and a followed-up MRI showed a remarkable regression of the extruded disc fragment. In this case, acupuncture as a monotherapy is safe and effective, but more conclusive evidence is needed. [ABSTRACT FROM AUTHOR]

In this paper, the optimal approximation algorithm is proposed to simplify non-linear functions and/or discrete data as piecewise polynomials by using the constrained least squares. In time-sensitive applications or in embedded systems with limited resources, the runtime of the approximate function is as crucial as its accuracy. The proposed algorithm searches for the optimal piecewise polynomial (OPP) with the minimum computational cost while ensuring that the error is below a specified threshold. This was accomplished by using smooth piecewise polynomials with optimal order and numbers of intervals. The computational cost only depended on polynomial complexity, i.e., the order and the number of intervals at runtime function call. In previous studies, the user had to decide one or all of the orders and the number of intervals. In contrast, the OPP approximation algorithm determines both of them. For the optimal approximation, computational costs for all the possible combinations of piecewise polynomials were calculated and tabulated in ascending order for the specific target CPU off-line. Each combination was optimized through constrained least squares and the random selection method for the given sample points. Afterward, whether the approximation error was below the predetermined value was examined. When the error was permissible, the combination was selected as the optimal approximation, or the next combination was examined. To verify the performance, several representative functions were examined and analyzed. [ABSTRACT FROM AUTHOR]

Various approaches have been proposed for bridge structural health monitoring. One of the earliest approaches proposed was tracking a bridge's natural frequency over time to look for abnormal shifts in frequency that might indicate a change in stiffness. However, bridge frequencies change naturally as the structure's temperature changes. Data models can be used to overcome this problem by predicting normal changes to a structure's natural frequency and comparing it to the historical normal behaviour of the bridge and, therefore, identifying abnormal behaviour. Most of the proposed data modelling work has been from long-span bridges where you generally have large datasets to work with. A more limited body of research has been conducted where there is a sparse amount of data, but even this has only been demonstrated on single bridges. Therefore, the novelty of this work is that it expands on previous work using sparse instrumentation across a network of bridges. The data collected from four in-operation bridges were used to validate data models and test the capabilities of the data models across a range of bridge types/sizes. The MID approach was found to be able to detect an average frequency shift of 0.021 Hz across all of the data models. The significance of this demonstration across different bridge types is the practical utility of these data models to be used across entire bridge networks, enabling accurate and informed decision making in bridge maintenance and management. [ABSTRACT FROM AUTHOR]

In war the world falls into good and evil. This calls for clear positioning. A Ukrainian colleague demanded that the patient know which side the therapist is on. Dealing with this attack on the evenly suspended attention catapulted the author into a relationship determined by war. How do early childhood affects and drive impulses shape the relationship between people at war and those not at war? What psychological prerequisites contribute to finding a bearable way of facing the trauma of war? In lieu of an answer, Bion’s war experiences and the soldiers’ calling for their mother point to the central function of the object relationship. Touching on Winnicott’s concept of ruthless love, the author finally arrives at unconditional love as a prerequisite. [ABSTRACT FROM AUTHOR]

The expansive utility of polymeric 3D-printing technologies and demand for high- performance lightweight structures has prompted the emergence of various carbon-reinforced polymer composite filaments. However, detailed characterization of the processing–microstructure–property relationships of these materials is still required to realize their full potential. In this study, acrylonitrile butadiene styrene (ABS) and two carbon-reinforced ABS variants, with either carbon nanotubes (CNT) or 5 wt.% chopped carbon fiber (CF), were designed in a bio-inspired honeycomb geometry. These structures were manufactured by fused filament fabrication (FFF) and investigated across a range of layer thicknesses and hexagonal (hex) sizes. Microscopy of material cross-sections was conducted to evaluate the relationship between print parameters and porosity. Analyses determined a trend of reduced porosity with lower print-layer heights and hex sizes compared to larger print-layer heights and hex sizes. Mechanical properties were evaluated through compression testing, with ABS specimens achieving higher compressive yield strength, while CNT-ABS achieved higher ultimate compressive strength due to the reduction in porosity and subsequent strengthening. A trend of decreasing strength with increasing hex size across all materials was supported by the negative correlation between porosity and increasing print-layer height and hex size. We elucidated the potential of honeycomb ABS, CNT-ABS, and ABS-5wt.% CF polymer composites for novel 3D-printed structures. These studies were supported by the development of a predictive classification and regression supervised machine learning model with 0.92 accuracy and a 0.96 coefficient of determination to help inform and guide design for targeted performance. [ABSTRACT FROM AUTHOR]

Background: Prediabetes is a condition preceding the development of diabetes and is associated with an increased risk of a number of complications. The primary mode of management is thought to be lifestyle modification. Pharmacological therapy, such as glucagon-like peptide-1 receptor agonists (GLP-1RAs), were not well addressed in the literature and were only evaluated in trials as secondary and exploratory outcomes with a limited sample size. Here, GLP-1RAs are evaluated as a comprehensive therapy approach for patients with prediabetes. Methods: A comprehensive search of Web of Science, SCOPUS, PubMed, and Cochrane was performed on May 5, 2023, to retrieve randomized controlled trials (RCTs) comparing the effect of GLP-1RAs to placebo and/or lifestyle modification on prediabetes reversion to normoglycemia, prevention of overt diabetes, glycemic control, anthropometric parameters, and lipid profiles. Review Manager (RevMan) version 5.4 was used. The quality of RCTs was assessed using the revised version of the Cochrane Risk of Bias Tool. GRADE was performed to evaluate the certainty of evidence. Results: Twelve trials involving 2903 patients in the GLP-1RAs group and 1413 in the control group were included in the meta-analysis. Low quality of evidence revealed that GLP-1RAs significantly increased the incidence of prediabetes reversion to the normoglycemic state [RR = 1.76, 95% CI (1.45, 2.13), P < 0.00001] and moderate quality of evidence showed that GLP-1RAs significantly prevented new-onset diabetes [RR = 0.28, 95% CI (0.19, 0.43), P < 0.00001]. Significant reductions in HbA1c, fasting plasma glucose, body weight, waist circumference, triglycerides, and LDL were observed in the GLP-1RAs arm (P < 0.05). However, higher incidences of gastrointestinal disorders were reported in the GLP-1RAs group (P < 0.05). Conclusions: GLP-1RAs combined with lifestyle modification proved to be a more effective therapy for managing prediabetic patients than lifestyle modification alone, with a tolerable safety profile. Future guidelines should consider GLP-1RAs as an adjunct to lifestyle modification in the management of prediabetic patients to provide better management and improve treatment adherence. [ABSTRACT FROM AUTHOR]

We analyzed the relationship between recovery from coronary artery aneurysms (CAAs) and concurrent infections in patients with Kawasaki disease (KD). The estimated median time of aneurysm persistence between patients with and without infections was compared using Kaplan-Meier survival analyses. Risk factors associated with persistent CAAs at 2 years were identified using multivariable analyses. Co-infection was confirmed in 20.5% (106/518) of patients diagnosed with KD. No significant differences regarding treatment or coronary artery outcome were identified between patients with and without infections. The estimated median time of aneurysm persistence was higher in the co-infected group (9 vs. 6 months). A maximum Z -score ≥ 4.00 at 1 month had 78% sensitivity and 83% specificity in predicting CAAs without recovery within 1 year of onset, whereas the predictability was higher within 2 years of onset, with a Z -score ≥ 4.88 (sensitivity, 92%; specificity, 91%). Concomitant infections did not affect the response to treatment or coronary artery outcomes in patients with KD. [ABSTRACT FROM AUTHOR]

This study deals with the statistical analysis of climate variables (temperature, relative humidity, and rainfall) and the analysis of data and values of climate characteristics for the period of 2006- 2016 in Aden Governorate. The normal distribution of climate variable data was verified, and the values of basic descriptive statistics were determined. The differences between their means were identified, the relationship between climate variables and rainfall was studied, and the effect of both temperature and relative humidity on the rainfall was considered. The data for the climate variables under study was found to be normally distributed, and there were no statistically significant differences between the values of the maximum and minimum temperature variables. It was found that there were statistically significant differences between the values of the relative humidity variable and the rainfall variable, as well as a strong statistically significant direct relationship between the maximum and minimum temperatures, and a strong statistically significant inverse relationship between maximum temperatures and relative humidity, as well as a moderate statistically significant inverse relationship between minimum temperatures and relative humidity, while there was a weak direct relationship between temperatures (Maximum and minimum) and the rainfall. Furthermore, there was a weak inverse relationship between relative humidity and rainfall, and this is a reflection of the location of Aden Governorate on the southern coastal plains of Yemen, which is located within the hot tropical region. There was no statistically significant effect of climate variables (maximum and minimum temperatures, and relative humidity) on the rainfall in Aden Governorate, due to its location and the influence of other factors such as the direction of the winds blowing onto it, especially in the summer. [ABSTRACT FROM AUTHOR]

Background: Cesarean section-induced postpartum hemorrhage (PPH) potentially causes anemia and hypovolemic shock in pregnant women. Hence, it is helpful for obstetricians and anesthesiologists to prepare pre-emptive prevention when predicting PPH occurrence in advance. However, current works on PPH prediction focus on whether PPH occurs rather than assessing PPH amount. To this end, this work studies quantitative PPH prediction with machine learning (ML). Methods: The study cohort in this paper was selected from individuals with PPH who were hospitalized at Shijiazhuang Obstetrics and Gynecology Hospital from 2020 to 2022. In this study cohort, we built a dataset with 6,144 subjects covering clinical parameters, anesthesia operation records, laboratory examination results, and other information in the electronic medical record system. Based on our built dataset, we exploit six different ML models, including logistic regression, linear regression, gradient boosting, XGBoost, multilayer perceptron, and random forest, to automatically predict the amount of bleeding during cesarean section. Eighty percent of the dataset was used as model training, and 20 % was used for verification. Those ML models are constantly verified and improved by root mean squared error(RMSE) and mean absolute error(MAE). Moreover, we also leverage the importance of permutation and partial dependence plot (PDP) to discuss their feasibility. Result: The experiment results show that random forest obtains the highest accuracy for PPH amount prediction compared to other ML methods. Random forest reaches the mean absolute error of 21.7, less than 5.4 % prediction error. It also gains the root mean squared error of 33.75, less than 9.3 % prediction error. On the other hand, the experimental results also disclose indicators that contributed most to PPH prediction, including Ca, hemoglobin, white blood cells, platelets, Na, and K. Conclusion: It effectively predicts the amount of PPH during a cesarean section by ML methods, especially random forest. With the above insight, ML predicting PPH amounts provides early warning for clinicians, thus reducing complications and improving cesarean sections' safety. Furthermore, the importance of ML and permutation, complemented by incorporating PDP, promises to provide clinicians with a transparent indication of individual risk prediction. [ABSTRACT FROM AUTHOR]

Background: Ghana was one of the few countries commended to have halved extreme poverty after the MDG era yet the under-five mortality rate (U5MR) was dire. Ghana's U5MR is still above the national and global SDG target. As a result, the government has over the years implemented other complementary social and policy interventions to improve the socioeconomic status of households to ensure better child health outcomes. Yet key gaps exist and threaten child health outcome sustainability, and child health-related SDG targets. In this regard, we hypothesize that household wealth should therefore not influence under-five child deaths significantly. Methods: The paper first reports the under-five mortality trend over the past 30 years in Ghana. It uniquely analyses the levels and trends in wealth as measured by the Gross National Income per capita at purchasing power parity (ln GNI/p, ppp) as a proxy of the mean Comparative Wealth Index (CWI) against under-five child deaths in Ghana. Using data from the 2014 Ghana Demographic and Health Survey and employing the logistic regression estimation technique, the paper further estimates the effect of household wealth and other key covariates on under-five child deaths. Implemented government poverty-alleviating policies and intervention programs to reduce childhood mortality in Ghana were reviewed to identify risk gaps to child deaths. Results: The 30 year trend analysis from 1990 to 2020 shows that the under-five mortality rate in Ghana is still high, falling short of the rate of decline that is expected toward achieving the 2030 SDG target for U5MR of 25 deaths per 1000 live births. Our empirical estimations show that household wealth still has a negative and significant relationship with under-five child deaths despite the government's existing poverty-alleviating and pro-poor health policies. In addition, the results show that regional differences, maternal age, the number of children alive, and attendance to postnatal healthcare services are crucial for child survival. Conclusion: The results imply that the government should consolidate universal programmes with intensified targeted interventions toward improving household wealth, especially in the Northern Savannah regions. A new and dynamic child health policy is imminent to reflect strategies towards achieving the SDG target on U5MR. Additionally, intensive education on active maternal participation in postnatal care should be given critical consideration to improve child survival. [ABSTRACT FROM AUTHOR]