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Large-scale manufacturers aim to reduce the number of faulty products by finding tools or process factors that cause product faults through tool monitoring. The causes of faulty electronic components such as semiconductor chips and printed circuit boards (PCBs) include abnormalities in single tools and abnormalities caused by interactions between the tools of a specific process and a related process. Here, the tools exhibiting an interaction effect are called the fault-introducing tool group. This study presents a numerical association rule mining method for discovering the fault-introducing tool groups based on a genetic algorithm. A novel fitness function and rule pruning process are developed to identify the fault-introducing tool groups. The effectiveness of the method is verified using simulations and a case study of actual PCB production lines. The proposed method can discover fault-introducing tool groups better than machine learning algorithms. Additionally, the method can accurately identify fault-introducing tool groups in various manufacturing environments, such as those with highly skewed yield distributions or variations in yield distributions over time. In an actual PCB production line, the groups identified by the proposed method produced up to 36.5% more faulty chips than those identified by the comparison models. [ABSTRACT FROM AUTHOR]

In the era of big data, it is necessary to provide novel and efficient platforms for training machine learning models over large volumes of data. The MapReduce approach and its Apache Spark implementation are among the most popular methods that provide high-performance computing for classification algorithms. However, they require dedicated implementations that will take advantage of such architectures. Additionally, many real-world big data problems are plagued by class imbalance, posing challenges to the classifier training step. Existing solutions for alleviating skewed distributions do not work well in the MapReduce environment. In this paper, we propose a novel KD-tree based classifier, together with a variation of the SMOTE algorithm dedicated to the Spark platform. Our algorithms offer excellent predictive power and can work simultaneously with binary and multi-class imbalanced data. Exhaustive experiments conducted using the Amazon Web Service platform showcase the high efficiency and flexibility of our proposed algorithms. [ABSTRACT FROM AUTHOR]

Large old trees (LOTs) are important ecological assets that contribute significantly to biodiversity, ecosystem functioning and local culture. This study analyzed the abundance, species composition, spatial and altitudinal distribution patterns, and conservation needs of LOTs in Baisha County, tropical southern China. We conducted a comprehensive field survey of 301 LOTs and recorded their biological characteristics, geographical locations, and environmental conditions. Species importance values were calculated, and the spatial distribution was analyzed using GIS techniques. Redundancy analysis (RDA) examined the relationships between LOT diversity and environmental factors. The results indicated a complex and diverse stock dominated by species from the Moraceae family, particularly of the genus Ficus. The structural analysis displayed a skewed age distribution, with a higher frequency of younger trees and a decline in older classes. Spatial analysis showed that LOTs are concentrated in the northwestern and central areas and are favored by microclimatic conditions, soil types, and historical land-use practices. The abundance and species richness of LOTs were greater at intermediate elevations. Redundancy analysis highlighted the intricate relationships between LOT diversity, abundance, and socioeconomic factors. This study provided crucial insights into the ecology and conservation requirements for LOTs in Baisha. The findings underscored the importance of targeted conservation efforts, particularly for older trees and mid-elevation habitats. We recommended integrating ecological research, long-term monitoring, traditional ecological knowledge, and community involvement in formulating conservation strategies to preserve these ecologically and culturally significant trees for future generations. [ABSTRACT FROM AUTHOR]

Skewed steel I-girder bridges experience complex load distribution under live load that is not thoroughly understood, while standard design practice for such bridges consists of simplifications that should be further evaluated and verified. Commonly used line girder analysis (LGA) can estimate strong-axis bending stress through the application of a live load distribution factor (LLDF) that considers the skew effect from 30° to 60°, and it accounts for skew-related lateral response by simply adding a flange lateral bending stress for skew exceeding 20°. Since LGA calculations related to skew do not account for bridge width, and because girder lateral bending response is considered in a simplified fashion, further refinement may be possible. In addition, the widely used practices of designing exterior and interior girders with the same demand and analyzing stub and integral abutment bridges in a similar way need to be further assessed. This paper evaluates the effect of bridge geometric parameters—including skew of 0°–70°, bridge width ranging from 8 to 26 m (27–84 ft), and abutment type (stub versus integral)—on skewed steel I-girder bridge response through a numerical parametric study (using field-validated models), considering live load positioning across the width of a bridge. The distribution of girder strong-axis and lateral bending stress was analyzed, with peak stress compared to LGA calculations. Exterior girders were generally observed with larger strong-axis bending stress but smaller lateral bending stress (versus interior girders) when directly loaded; estimating girder strong-axis bending stress using LGA with a controlling LLDF for all girders can be overly conservative for interior girders. The distribution of strong-axis and lateral bending stress on a skewed bridge with either stub or integral abutments was also found to be dependent on live load positioning, with peak stress closer to bridge obtuse corners (away from bridge midspan) as skew increases. The standard practice of providing a minimum distance between the bridge end and the first intermediate cross-frame was confirmed to be important to avoid lateral bending stress concentration near bridge obtuse corners. Girder response near the bridge pier was generally less significant than that along bridge spans under live loading, except for exterior girder flange lateral bending stress. Near the pier, bottom flange lateral bending stress increases with increasing skew, while interior and exterior girders behave differently under the skew effect for strong-axis bending stress. [ABSTRACT FROM AUTHOR]

In this paper, we studied a multiobjective linear fractional programming (MOLFP) problem with pentagonal and hexagonal fuzzy numbers, while the decision variables are binary integer numbers. Initially, a multiobjective fuzzy binary integer linear fractional programming (MOFBILFP) problem was transformed into a multiobjective binary linear fractional programming problem by using the geometric average method; second, a multiobjective binary integer linear fractional programming (MOBILFP) problem was converted into a binary integer linear fractional programming (BILFP) problem using the Pearson 2 skewness coefficient technique; and third, a BILFP problem was solved by using LINGO (version 20.0) mathematical software. Finally, some numerical examples and case studies have been illustrated to show the efficiency of the proposed technique and the algorithm. The performance of this technique was evaluated by comparing their results with those of other existing methods. The numerical results have shown that the proposed technique is better than other techniques. [ABSTRACT FROM AUTHOR]

Mental health (MH) disorders are one of the most prevalent global public health issues for a significant portion of the population. The aim of this study was to investigate the relationship between the mealtime distribution of macronutrients and MH in Iranian adults. We used a convenience sampling technique to perform this cross-sectional study. The Depression, Anxiety, and Stress Scale (DASS-21) was used to assess the MH outcome. Dietary consumption was assessed using multiple 24-hour dietary recalls. The coefficient of variation (CV) was then used to compute the macronutrient distribution at mealtime. In all, 54.6%, 53.9%, and 56.6% were classified as having anxiety, depression, and stress symptoms, respectively. The CV scores for carbohydrate were relatively evenly distributed across all meal times, with a mean of 0.38, 0.38, and 0.39 for breakfast, lunch, and dinner, respectively. We observed no significant association between the mealtime distribution of carbohydrates and mental health indices in any of the analyzed models. However, a more skewed distribution of protein intake towards dinner (i.e. a higher protein CV value) was positively associated with anxiety (OR: 1.69; CI: 1.02-2.78) and stress symptoms (OR: 1.93; CI: 1.17-3.16). We also observed a significant association between fat distribution and depression symptoms, with subjects in tertile 2 having higher odds of depression compared to T1 (OR: 1.63; CI: 1.00-2.63). In conclusion, a skewed distribution of protein and fat intake may be associated with mental health problems. Future studies are warranted to validate these findings and gain a better understanding of the underlying mechanisms. [ABSTRACT FROM AUTHOR]

Understanding patterns of diversification necessarily requires accounting for both the generation and the persistence of species. Formal models of speciation genetics, however, focus on the generation of new species without explicitly considering the maintenance of biodiversity (e.g., coexistence, the focus of ecological studies of diversity). Consequently, it remains unclear whether and how new species will coexist following a speciation event, a gap limiting our ability to understand the rate-limiting controls of diversification over macroevolutionary timescales. To connect coexistence and speciation theory and assess the relative importance of ecological versus genetic constraints in diversification events, we develop a deterministic, three-locus, population-genetic model that includes a skewed distribution of available resources (to generate variation in fitness differences), frequency-dependent competition, and assortative mating. Both ecology and genetics play vital and interacting roles in shaping initial speciation events and long-term eco-evolutionary outcomes. Ecological constraints are especially important when fitness differences are large and competition remains strong among dissimilar phenotypes. Ephemeral species can occur in our model and are typically lost because of competitive exclusion, a result demonstrating that species persistence may serve as the rate-limiting control of long-term diversification rates. More broadly, our model adds evidence that the unification of ecological and evolutionary (including genetic) perspectives on biodiversity is needed to predict large-scale patterns. [ABSTRACT FROM AUTHOR]

Madagascar is acclaimed for its floral and faunal endemism and biodiversity. Among the island nation's most emblematic fauna are its native mammalian carnivores; they are members of the threatened and endemic Eupleridae family. The Corridor of Marojejy, Anjanaharibe‐Sud Special Reserve, Tsaratanana (COMATSA) is a newly protected corridor system that faces deforestation and lacks detailed assessments of its native carnivore community. We deployed 44 motion‐activated trail cameras to identify which terrestrial carnivores are found within the rainforest corridor landscape of COMATSA‐Sud and Marojejy National Park, and to examine carnivore occupancy, relative activity and the impacts of habitat edge. Our sampling array operated from October 20, 2021, to February 10, 2022 (113 days) and confirmed the presence of four species in Eupleridae: Cryptoprocta ferox, Fossa fossana, Galidia elegans, and Galidictis fasciata, as well as a non‐native carnivore species: Canis familiaris. We show that carnivore occurrences significantly increase with distance to forest edges. We discuss the negative impacts of forest loss on native carnivores and highlight the threats posed by the non‐native carnivore C. familiaris. Our findings spotlight faunal detections across the corridor and indicate that fewer native carnivores are found in the corridor compared to nearby protected areas, suggesting that deforestation correlates with a skewed distribution of carnivore detections. Based on our data, we provide management recommendations for the protection of the forest corridor and its endemic faunal community. [ABSTRACT FROM AUTHOR]

This article investigates the relationship between international trade and asymmetrical labor income risk. Using the case study of Brazil, we inspect how an increase in import penetration following the China shock impacted the distribution of idiosyncratic earnings changes across the country's local labor markets. We find that an increase in import penetration leads to a more dispersed and negatively skewed distribution. These effects can be explained by an increase in the volatility of hours worked following job and industry transitions, particularly from involuntary job separations. [ABSTRACT FROM AUTHOR]

Aggregated long and short trading risk positions of speculative assets over time are likely to be unequal. This may be because of irrational decisions of traders and investors as well as catastrophic events that lead to pronounce or salient market crashes. Returns of such assets are therefore more likely to have one polynomial tail and one exponential tail. The generalized hyperbolic (GH) skewed Student‐t distribution is known to handle such situations quite well. In this paper, we use generalized autoregressive conditional heteroscedasticity (GARCH) models to empirically show the superiority of the GH skewed Student‐t distribution in forecasting the extreme tail risks of cryptocurrency returns in the presence of substantial skewness in comparison with some competing distributions. Furthermore, we show the practical significance of the GH skewed Student‐t distribution‐based risk forecasts in computing daily capital requirements. Evidence from the study suggests that the GH skewed Student‐t distribution model tends to be superior in forecasting volatility and expected shortfall (ES) but not value‐at‐risk. In addition, the distribution yields higher value‐at‐risk (VaR) exceptions but surprisingly avoids the red zone of the Basel II accord penalty zones and produces lower but optimal daily capital requirements. Therefore, in the presence of substantially skewed returns having exponential‐polynomial tails, we recommend the use of the GH skewed Student‐t distribution for parametric GARCH models in forecasting extreme tail risk. [ABSTRACT FROM AUTHOR]

Finite mixture model is a useful probabilistic model for representing the probability distributions of observations. Gaussian mixture model (GMM) is a widely used one whose parameters are always estimated by the famous EM algorithm. But when probability distributions contain asymmetric modes, GMM requires much more constituent components to achieve a satisfied accuracy. Therefore, a generalized mixture finite model is proposed. First, it adopts the derivative λ -PDF which can well represent the asymmetric probability distributions as the probability density. However, the differential operation and solving the likelihood equations are scarcely possible in the M step of EM algorithm for the complex expression of the derivative λ -PDF. Second, a pseudo EM method is proposed to avoid the abovementioned difficulty which finds the pseudo maximum likelihood estimates of the parameters by utilizing the moment matching principle. It more easily estimates the parameters by solving a series of moment matching equations. Finally, four examples are presented to verify that the proposed generalized finite mixture model has advantage on representing the asymmetric probability distributions observations. [ABSTRACT FROM AUTHOR]

ABSTRACT In pre‐clinical and medical quality control, it is of interest to assess the stability of the process under monitoring or to validate a current observation using historical control data. Classically, this is done by the application of historical control limits (HCL) graphically displayed in control charts. In many applications, HCL are applied to count data, for example, the number of revertant colonies (Ames assay) or the number of relapses per multiple sclerosis patient. Count data may be overdispersed, can be heavily right‐skewed and clusters may differ in cluster size or other baseline quantities (e.g., number of petri dishes per control group or different length of monitoring times per patient). Based on the quasi‐Poisson assumption or the negative‐binomial distribution, we propose prediction intervals for overdispersed count data to be used as HCL. Variable baseline quantities are accounted for by offsets. Furthermore, we provide a bootstrap calibration algorithm that accounts for the skewed distribution and achieves equal tail probabilities. Comprehensive Monte‐Carlo simulations assessing the coverage probabilities of eight different methods for HCL calculation reveal, that the bootstrap calibrated prediction intervals control the type‐1‐error best. Heuristics traditionally used in control charts (e.g., the limits in Shewhart c‐ or u‐charts or the mean ± 2 SD) fail to control a pre‐specified coverage probability. The application of HCL is demonstrated based on data from the Ames assay and for numbers of relapses of multiple sclerosis patients. The proposed prediction intervals and the algorithm for bootstrap calibration are publicly available via the R package predint. [ABSTRACT FROM AUTHOR]

Studies on automatically predicting student learning outcomes often focus on developing and optimizing machine learning algorithms that fit the data captured from different education systems. This approach has a fatal weakness when it is used for disadvantaged groups, such as those with academic warnings or who have dropped out, because these groups are often much smaller than other common groups in number. The imbalanced data that have class distribution skew create a big challenge to training good classification models. The significant approach to tackle this challenge is applying oversampling methods to increase the number of minor classes; however, generating good new samples from the existing instances of a minor class is still a hard issue and requires new investigation. This study presents two new methods of handling data imbalance based on the original algorithms SMOTE and adaptive synthetic sampling (ADASYN), called Improved SMOTE (I_SMOTE) and Improved ADASYN (I_ADASYN). These modifications involve a new selecting fit candidate method based on a new similarity measurement and a roulette wheel selection to generate synthetic data samples. The aim is to rebalance data and therefore improve the prediction accuracy of minor groups. The proposal methods were designed and applied to education datasets, and they were tested on public datasets and a dataset collected from a Vietnamese university for evaluation. The experimental results on learning datasets showed the high potential of novel algorithms, I_SMOTE and I_ADASYN, for student academic performance problems in general and at‐risk student groups especially. Empirical results proved that the recall, precision, and F1‐score of the minority class of I_SMOTE and I_ADASYN are strongly better than the original balancing algorithms. Besides, the I_SMOTE and I_ADASYN also improve relatively by 6.6% and 8.0% of the ROC area compared to the original SMOTE and ADASYN, respectively. [ABSTRACT FROM AUTHOR]

There is robust evidence implicating inhibitory deficits as a fundamental behavioural phenotype in children with attention‐deficit/hyperactivity disorder (ADHD). However, prior studies have not directly investigated the role in which white matter properties within the fronto‐basal‐ganglia circuit may play in the development of inhibitory control deficits in this group. Combining recent advancements in brain‐behavioural modelling, we mapped the development of stop‐signal task (SST) performance and fronto‐basal‐ganglia maturation in a longitudinal sample of children aged 9–14 with and without ADHD. In a large sample of 135 ADHD and 138 non‐ADHD children, we found that the ADHD group had poorer inhibitory control (i.e., longer stop‐signal reaction times) across age compared to non‐ADHD controls. When applying the novel parametric race model, this group effect was driven by higher within‐subject variability (sigma) and higher number of extreme responses (tau) on stop trials. The ADHD group also displayed higher within‐subject variability on correct responses to go stimuli. Moreover, we observed the ADHD group committing more task‐based failures such as responding on stop trials (trigger failures) and omissions on go trials (go failures) compared to non‐ADHD controls, suggesting the contribution of attentional lapses to poorer response inhibition performance. In contrast, longitudinal modelling of fixel‐based analysis measures revealed no significant group differences in the maturation of fronto‐basal‐ganglia fibre cross‐section in a subsample (74 ADHD and 73 non‐ADHD children). Finally, brain‐behavioural models revealed that age‐related changes in fronto‐basal‐ganglia morphology (fibre cross‐section) were significantly associated with reductions in the variability of the correct go‐trial responses (sigma.true) and skew of the stop‐trial distribution (tauS). However, this effect did not differ between ADHD and typically developing children. Overall, our findings support the growing consensus suggesting that attentional deficits subserve ADHD‐related inhibitory dysfunction. Furthermore, we show novel evidence suggesting that while children with ADHD are consistently performing worse on the SST than their non‐affected peers, they appear to have comparable rates of neurocognitive maturation across this period. [ABSTRACT FROM AUTHOR]

Analysis of matrix variate data is becoming increasingly common in the literature, particularly in the field of clustering and classification. It is well known that real data, including real matrix variate data, often exhibit high levels of asymmetry. To address this issue, one common approach is to introduce a tail or skewness parameter to a symmetric distribution. In this regard, we introduce here a new distribution called the matrix variate skew-t distribution (MVST), which provides flexibility, in terms of heavy tail and skewness. We then conduct a thorough investigation of various characterizations and probabilistic properties of the MVST distribution. We also explore extensions of this distribution to a finite mixture model. To estimate the parameters of the MVST distribution, we develop an EM-type algorithm that computes maximum likelihood (ML) estimates of the model parameters. To validate the effectiveness and usefulness of the developed models and associated methods, we performed empirical experiments, using simulated data as well as three real data examples, including an application in skin cancer detection. Our results demonstrate the efficacy of the developed approach in handling asymmetric matrix variate data. [ABSTRACT FROM AUTHOR]

This study investigates the role of the probability distribution in forecasting the volatility and value-at-risk (VaR) of cryptocurrency returns using generalized auto-regressive conditional heteroskedasticity (GARCH)-type models. We consider GARCH, EGARCH, GJR-GARCH, TGARCH and Realized GARCH models and show that the role of the probability distribution varies across different situations. A skewed and heavy-tailed distribution contributes to better performance in forecasting the VaR; however, it does not improve the accuracy of volatility forecasting. The results help us to better understand the role of the probability distribution in GARCH-type models. [ABSTRACT FROM AUTHOR]

Utilizing imaging spectroscopy technology to identify methane super‐emitters plays a vital role in mitigating methane emissions in the Oil & Gas (O&G) sector. While earlier research has uncovered significant point‐source methane emissions from O&G production in the US and Canada, which are key regions with large methane emissions, a comprehensive post‐COVID‐19 survey has been notably absent. Here, we perform a detailed survey of methane super‐emitters across multiple basins of North America (Marcellus Shale, Haynesville/Bossier Shale, Permian Basin and Montney Shale) using the new Chinese Gaofen5‐01A/02 (GF5‐01A/02) satellite measurements during 2022–2023. We detect 139 individual methane plumes emanating from 122 point sources, with flux rates ranging from 519 to 16,071 kg hr−1. These emissions exhibit a highly skewed and heavy‐tailed distribution, constituting approximately 23% of the flux inversion with TROPOMI in the sample region, with a range of 13%–40%. Moreover, we observe a 66.7% reduction in methane emissions in Permian Basin during COVID‐19, followed by fluctuations until spring 2023. By summer 2023, methane emissions rebound to twice their previous magnitude (1.68 ± 0.58 Tg a−1). Using these point‐source surveys, we further quantify a regional methane emission of 2.69 ± 0.86 Tg a−1 in Permian Basin. This estimation closely aligns with top‐down inversions (2.22 ± 0.40 Tg a−1) from TROPOMI. The upscale estimation underscores the effectiveness of high‐resolution remote sensing measurements in improving bottom‐up emissions inventories and refining regional methane emission assessments. Our results highlight the potential climate benefits derived from regular monitoring and specific remediation efforts focused on relatively few strong point‐source emissions. Plain Language Summary: Reducing methane (CH4) leaks from Oil & Gas (O&G) production is crucial for abating climate change. However, detecting these abnormal CH4 emissions globally is challenging as they often occur unexpectedly. Satellite remote sensing with hyperspectral imaging spectrometer provides an effective approach for top‐down monitoring. These instruments produce CH4 plume maps, enabling the quantification of emissions. In this research, we conduct a comprehensive survey in major O&G basins of North America during 2022–2023 using the new Chinese Gaofen5‐01A/02 satellite. Through repeated observations by high‐resolution satellites, we capture CH4 emission dynamics in sample basins and quantify their contribution to regional methane budget. Our results demonstrate the value of high‐resolution satellite observations in reducing uncertainties in quantifying anthropogenic CH4 emission and supporting strategies for emission mitigation. Key Points: The new Chinese Gaofen5‐01A/02 hyperspectral satellite missions have great capability in methane mappingWe find substantial methane point‐source emissions in Permian Basin after COVID‐19 during 2022–2023Satellite‐based survey can effectively improve bottom‐up regional methane emissions inventories [ABSTRACT FROM AUTHOR]

Background: Peer support workers provide support for people experiencing mental health conditions based on their own lived experience of mental health problems. Assessing fidelity to core ingredients of peer support is vital for successful implementation and intervention delivery. Modifications to its implementation are needed when scaling up to different socio-economic settings, raising further uncertainty about fidelity. As part of a large multi-centre study on peer support called Using Peer Support In Developing Empowering Mental Health Services (UPSIDES), we developed and evaluated the psychometric properties of the UPSIDES Fidelity Scale. Methods: We constructed the fidelity scale based on an initial item pool developed through international expert consultation and iterative feedback. Scale refinement involved site-level expert consultation and translation, resulting in a service user-rated 28-item version and a peer support worker-rated 21-item version assessing receipt, engagement, enactment, competence, communication and peer support-specific components. Both versions are available in six languages: English, German, Luganda, Kiswahili, Hebrew and Gujarati. The scale was then evaluated at six study sites across five countries, with peer support workers and their clients completing their respective ratings four and eight months after initial peer support worker contact. Psychometric evaluation included analysis of internal consistency, construct validity and criterion validity. Results: For the 315 participants, item statistics showed a skewed distribution of fidelity values but no restriction of range. Internal consistency was adequate (range α = 0.675 to 0.969) for total scores and all subscales in both versions. Confirmatory factor analysis indicated acceptable fit of the proposed factor structure for the service user version (χ2/df = 2.746; RMSEA = 0.084) and moderate fit for the peer support worker version (χ2/df = 3.087; RMSEA = 0.093). Both versions showed significant correlations with external criteria: number of peer support sessions; perceived recovery orientation of the intervention; and severity of illness. Conclusions: The scale demonstrates good reliability, construct and criterion validity, making it a pragmatic and psychometrically acceptable measure for assessing fidelity to a manualised peer support worker intervention. Recommendations for use, along with research and practical implications, are addressed. As validated, multi-lingual tool that adapts to diverse settings this scale is uniquely positioned for global application. Trial registration: ISRCTN, ISRCTN26008944. Registered on 30 October 2019. [ABSTRACT FROM AUTHOR]

This study addresses the horizontal and vertical dispersion of passive tracers in idealized wind-driven subtropical gyres. Synthetic particles within a closed basin are numerically advected to analyze their dispersion under different theoretical velocity fields. Horizontal dispersion simulations incorporate the classic wind-driven Stommel circulation along with (i) surface Ekman drift associated with the Stommel wind field and (ii) inertial effects due to particle size and buoyancy. Results reveal that the Ekman drift inhibits particle dispersion across the entire domain leading to tracer concentration in a quasi-stable distribution skewed toward the western side of the basin. Similar behavior is observed with inertial particles. The equilibrium state is quantified for different diffusivity values, particle sizes, and buoyancies. For vertical dispersion, simulations incorporate the three-dimensional Ekman velocity, which includes a negative vertical component, while ignoring inertial effects. Initially, surface particles accumulate around the gyre center while slowly sinking, but they disperse across the basin once they surpass the Ekman layer and are free from surface effects. Tracers sink more on the western side of the basin, regardless of horizontal diffusivity. On average, ignoring inertial effects, particles sink less with higher diffusivity and more with lower diffusivity, suggesting a potential for high horizontal distribution of sunken tracers in the ocean. [ABSTRACT FROM AUTHOR]

Trade-offs are a fundamental concept in evolutionary biology because they are thought to explain much of nature's biological diversity, from variation in life-histories to differences in metabolism. Despite the predicted importance of trade-offs, they are notoriously difficult to detect. Here we contribute to the existing rich theoretical literature on trade-offs by examining how the shape of the distribution of resources or metabolites acquired in an allocation pathway influences the strength of trade-offs between traits. We further explore how variation in resource distribution interacts with two aspects of pathway complexity (i.e., the number of branches and hierarchical structure) affects tradeoffs. We simulate variation in the shape of the distribution of a resource by sampling 106 individuals from a beta distribution with varying parameters to alter the resource shape. In a simple "Y-model" allocation of resources to two traits, any variation in a resource leads to slopes less than −1, with left skewed and symmetrical distributions leading to negative relationships between traits, and highly right skewed distributions associated with positive relationships between traits. Adding more branches further weakens negative and positive relationships between traits, and the hierarchical structure of pathways typically weakens relationships between traits, although in some contexts hierarchical complexity can strengthen positive relationships between traits. Our results further illuminate how variation in the acquisition and allocation of resources, and particularly the shape of a resource distribution and how it interacts with pathway complexity, makes its challenging to detect trade-offs. We offer several practical suggestions on how to detect trade-offs given these challenges. [ABSTRACT FROM AUTHOR]

Seaweeds are widely assumed to be phenotypically plastic across hydrodynamic gradients, yet while many marine macroalgae exhibit intraspecific phenotypic variation that correlates with flow, researchers often fail to test whether such variation is due to plasticity or another mechanism, such as local adaptation. In this minireview, we considered mechanisms for sensing flow in seaweeds that could facilitate adaptive phenotypic plasticity across hydrodynamic gradients. We then reviewed the literature from 1900 to 2024 to see how often phenotypic variation and plasticity across hydrodynamic gradients had been observed and demonstrated in different groups of seaweeds. In the last 124 years, phenotypic variation and plasticity in response to flow have been well documented in brown algae but scarcely documented in red and green algae. This could suggest that brown algae are better able to sense and respond to flow than red and green algae, perhaps due to the intercalary meristem of many brown algae, including most kelps. However, this skewed distribution could also be the result of publication bias, as most studies involving flow have been conducted on brown algae. Only 30% of 141 papers specifically investigated if observations of phenotypic variation along hydrodynamic gradients were due to plasticity. To date, phenotypic plasticity in response to flow has been demonstrated in 20 brown algal species, five red algal species, and two green algal species. Thus, the assumption that phenotypic plasticity to flow is common across seaweeds is not particularly well supported by the literature. Mechanisms underlying plasticity to flow are poorly understood and remain a critical avenue for future research. [ABSTRACT FROM AUTHOR]

Objective: The Attention-Deficit/Hyperactivity Disorder Rating Scale-IV (ADHD-RS-IV) assesses ADHD symptoms in children and adolescents. The original United States norms comprise percentiles. Yet, no Nordic percentile norms exist, and only T-scores, which (often falsely) assume normally distributed data, are currently available. Here, we for the first time provide Danish percentile norms for children aged 6–9 based on parent/caregiver-reports, and illustrate the potential consequences of T-scores when derived based on the expected skewed distribution of an ADHD scale in the population. Materials and methods: The sample comprised 1895 Danish schoolchildren (879 girls and 1016 boys) in 1st, 2nd, or 3rd grade from the general population. Their parents/caregivers completed the ADHD-RS-IV. Sex and age differences were investigated, percentiles were derived based on the observed score distributions, and for comparison, T-scores > 70 were estimated, which are expected to identify the top 2.3% under the assumption of normality. Results: Boys were rated to have higher ADHD-RS-IV scores than girls except on the impulsivity score. No age effects were found on the majority of scores. Sex-stratified and unisex percentiles (80, 90, 93, 98) were reported. The distribution of ADHD-RS-IV scores were highly skewed. T-score cutoffs identified a significantly higher proportion of and about twice as many children as having elevated ADHD symptoms than expected (4.3–5.2% vs. 2.3%). Conclusions: ADHD-RS-IV (parent/caregiver-report) percentile norms for young Danish schoolchildren are now available for future reference. The use of percentiles is considered appropriate given the skewed score distribution and since T-scores appear to over-identify children as having clinically elevated ADHD symptoms. [ABSTRACT FROM AUTHOR]

Multi-label text classification refers to assigning multiple relevant category labels to each text, which has been widely applied in the real world. To enhance the performance of multi-label text classification, most existing methods only focus on optimizing document and label representations, assuming accurate label-document similarity is crucial. However, whether the potential relevance between labels and if the problem of the long-tail distribution of labels could be solved are also key factors affecting the performance of multi-label classification. To this end, we propose a multi-label text classification model called DV-MLTC, which is based on a dual-view graph convolutional network to predict multiple labels for text. Specifically, we utilize graph convolutional neural networks to explore the potential correlation between labels in both the global and local views. First, we capture the global consistency of labels on the global label graph based on existing statistical information and generate label paths through a random walk algorithm to reconstruct the label graph. Then, to capture relationships between low-frequency co-occurring labels on the reconstructed graph, we guide the generation of reasonable co-occurring label pairs within the local neighborhood by utilizing the local consistency of labels, which also helps alleviate the long-tail distribution of labels. Finally, we integrate the global and local consistency of labels to address the problem of highly skewed distribution caused by incomplete label co-occurrence patterns in the label co-occurrence graph. The Evaluation shows that our proposed model achieves competitive results compared to existing state-of-the-art methods. Moreover, our model achieves a better balance between efficiency and performance. [ABSTRACT FROM AUTHOR]

This paper aims to propose Local Neighborhood Encodings (LNE)-a hybrid data preprocessing method dedicated to skewed class distribution balancing. The proposed LNE algorithm uses both over- and undersampling methods. The intensity of the methods is chosen separately for each fraction of minority and majority class objects. It is selected depending on the type of neighborhoods of objects of a given class, understood as the number of neighbors from the same class closest to a given object. The process of selecting the over- and undersampling intensities is treated as an optimization problem for which an evolutionary algorithm is used. The quality of the proposed method was evaluated through computer experiments. Compared with SOTA resampling strategies, LNE shows very good results. In addition, an experimental analysis of the algorithms behavior was performed, i.e., the determination of data preprocessing parameters depending on the selected characteristics of the decision problem, as well as the type of classifier used. An ablation study was also performed to evaluate the influence of components on the quality of the obtained classifiers. The evaluation of how the quality of classification is influenced by the evaluation of the objective function in an evolutionary algorithm is presented. In the considered task, the objective function is not de facto deterministic and its value is subject to estimation. Hence, it was important from the point of view of computational efficiency to investigate the possibility of using for quality assessment the so-called proxy classifier, i.e., a classifier of low computational complexity, although the final model was learned using a different model. The proposed data preprocessing method has high quality compared to SOTA, however, it should be noted that it requires significantly more computational effort. Nevertheless, it can be successfully applied to the case as no very restrictive model building time constraints are imposed. [ABSTRACT FROM AUTHOR]

In this paper, we enhanced change point detection in skew normal distribution models by integrating the EM algorithm's Q-function with the modified information criterion (MIC). The new QMIC framework improves sensitivity and accuracy in detecting changes, outperforming the modified information criterion (MIC) and the traditional Bayesian information criterion (BIC). Due to the complexity of deriving analytic asymptotic distributions, bootstrap simulations were used to determine critical values at various significance levels. Extensive simulations demonstrate that QMIC offers superior detection capabilities. We applied the QMIC method to two stock market datasets, successfully identifying multiple change points, and highlighting its effectiveness for real-world financial data analysis. [ABSTRACT FROM AUTHOR]

Previous studies have found a possible association between nickel and metabolic syndrome (MetS), but with conflicting results. No studies have determined whether nickel exposure increases the prevalence of MetS in the general U.S. population. Therefore, we used data from the National Health and Nutrition Examination Survey (NHANES) to assess the association between urinary nickel and MetS. Since urinary nickel levels were presented as a skewed distribution, they were normalized using a logarithmic transformation. Weighted multivariate logistic models, restricted cubic spline, threshold effect analysis, and subgroup analyses were used to examine the association between urinary nickel concentration and the risk of MetS and its components. Based on data from 1577 participants, individuals in the second, third, and fourth quartiles of urinary nickel had an adjusted OR for MetS of 1.42 (95% CI: 0.88, 2.28), 2.00 (95% CI: 1.22, 3.28), and 1.68 (95% CI: 1.05, 2.70), respectively, representing an inverted "L"-shaped nonlinear dose–response relationship with an inflection point at 0.2141 ng/L. Patients over the age of 40, males, less educated, and smokers are more susceptible to nickel exposure. In addition, there were significant associations between nickel and most components of the MetS, with the strongest to weakest correlations being high fasting glucose, reduced high-density lipoprotein, abdominal obesity, and elevated blood pressure; however, there was no significant correlation between nickel and hyperlipidemia. In conclusion, environmental nickel exposure increases the prevalence of MetS in U.S. adults, particularly in males over 40 years of age, those with less education, and smokers. [ABSTRACT FROM AUTHOR]

We study the interaction between noisy demand and skewed asset payoffs. In our model, price as a function of quantities is convex in a neighborhood around zero if and only if skewness is positive. The combination of convexity and noise produces the idiosyncratic skewness effect, a documented negative relationship between an asset's idiosyncratic skewness and its expected return. We further offer an explanation for the idiosyncratic volatility puzzle. Finally, our theory predicts that higher idiosyncratic skewness strengthens the idiosyncratic volatility effect (and vice versa). We find support for this prediction in the cross-section of stock returns. [ABSTRACT FROM AUTHOR]

Background: Many approaches have been developed to overcome technical noise in single cell RNA-sequencing (scRNAseq). As researchers dig deeper into data—looking for rare cell types, subtleties of cell states, and details of gene regulatory networks—there is a growing need for algorithms with controllable accuracy and fewer ad hoc parameters and thresholds. Impeding this goal is the fact that an appropriate null distribution for scRNAseq cannot simply be extracted from data in which ground truth about biological variation is unknown (i.e., usually). Results: We approach this problem analytically, assuming that scRNAseq data reflect only cell heterogeneity (what we seek to characterize), transcriptional noise (temporal fluctuations randomly distributed across cells), and sampling error (i.e., Poisson noise). We analyze scRNAseq data without normalization—a step that skews distributions, particularly for sparse data—and calculate p values associated with key statistics. We develop an improved method for selecting features for cell clustering and identifying gene–gene correlations, both positive and negative. Using simulated data, we show that this method, which we call BigSur (Basic Informatics and Gene Statistics from Unnormalized Reads), captures even weak yet significant correlation structures in scRNAseq data. Applying BigSur to data from a clonal human melanoma cell line, we identify thousands of correlations that, when clustered without supervision into gene communities, align with known cellular components and biological processes, and highlight potentially novel cell biological relationships. Conclusions: New insights into functionally relevant gene regulatory networks can be obtained using a statistically grounded approach to the identification of gene–gene correlations. [ABSTRACT FROM AUTHOR]

The world is facing an unprecedented loss of biodiversity, with nearly one million species on the brink of extinction, and the extinction rate accelerating. Conservation efforts are often hindered by insufficient information on crucial ecosystems. To address this issue, our paper leverages advances in machine-based pattern recognition to estimate species occurrence maps using georeferenced data from the Global Biodiversity Information Facility (GBIF). Our algorithms have generated maps for more than 600,000 species, including vertebrates, arthropods, mollusks, other animals, vascular plants, fungi, and other organisms. Validation involved comparing these maps with expert maps for mammals, ants, and vascular plants. We found a close similarity in global distribution patterns, with regional differences attributed to technical variations or necessary revisions in existing expert maps based on GBIF data. As a practical application, we identified the global distributions of approximately 68,000 species with small ranges (25 km x 25 km or less) confined to a single country. Our maps reveal a skewed international distribution of these species, identifying 30 countries where 78.2 percent are concentrated. These results highlight the need to integrate the newly mapped GBIF data into global conservation planning. Our algorithms support rapid updates and the creation of new maps as GBIF occurrence reports increase. The data are available on the World Bank Development Data Hub at https://doi.org/10.57966/h21e-vq42 (Dasgupta et al. 2024). [ABSTRACT FROM AUTHOR]

Detecting student behavior in smart classrooms is a critical area of research in educational technology that significantly enhances teaching quality and student engagement. This paper introduces an innovative approach using advanced computer vision and artificial intelligence technologies to monitor and analyze student behavior in real time. Such monitoring assists educators in adjusting their teaching strategies effectively, thereby optimizing classroom instruction. However, the application of this technology faces substantial challenges, including the variability in student sizes, the diversity of behaviors, and occlusions among students in complex classroom settings. Additionally, the uneven distribution of student behaviors presents a significant hurdle. To overcome these challenges, we propose Student Behavior Detection Network (SBD-Net), a lightweight target detection model enhanced by the Focal Modulation module for robust multi-level feature fusion, which augments feature extraction capabilities. Furthermore, the model incorporates the ESLoss function to address the imbalance in behavior sample detection effectively. The innovation continues with the Dyhead detection head, which integrates three-dimensional attention mechanisms, enhancing behavioral representation without escalating computational demands. This balance achieves both a high detection accuracy and manageable computational complexity. Empirical results from our bespoke student behavior dataset, Student Classroom Behavior (SCBehavior), demonstrate that SBD-Net achieves a mean Average Precision (mAP) of 0.824 with a low computational complexity of just 9.8 G. These figures represent a 4.3% improvement in accuracy and a 3.8% increase in recall compared to the baseline model. These advancements underscore the capability of SBD-Net to handle the skewed distribution of student behaviors and to perform high-precision detection in dynamically challenging classroom environments. [ABSTRACT FROM AUTHOR]

The novel object recognition (NOR) paradigm is a cognitive test that has been used with many species to detect differences in ability. Various iterations of the paradigm have been implemented, making it difficult to compare results both within and across species. Interpretations of the results are equally diverse, threatening the integrity of the paradigm. These inconsistencies have prompted a deeper dive into the variability of the resultant data. For the purposes of this meta-analysis, data originated from 12 studies involving 367 pigs that were subjected to the same NOR paradigm beginning between postnatal days 21 and 24. The main cognitive measure from the NOR paradigm is recognition index (RI), which was the focus of most of the analyses in this meta-analysis. RI was chosen as the main outcome as it determines a pig's preference for novelty, an innate behavior of cognitively intact pigs. A histogram of RI values (range 0 to 1) showed a bimodal distribution skewed to the right, suggesting that the interpretation of positive performance on the task may need to be stricter. Correlational analyses proved that the number of investigations and investigation time with both the novel and familiar objects were the strongest predictors of resultant RI values. Objective data inclusion criteria were then considered to eliminate non-compliant pigs. Results indicated that requiring at least 5 s of investigation over a minimum of 3 investigations with the novel object reduced overall variability for RI with a concomitant increase in the mean. Further analyses showed that pigs preferred to spend more time with and interact more with the novel object across the entire testing trial, especially in the first minute. Together, these findings suggest that future interpretations of NOR should consider applying stricter statistical analyses as well as additional data processing, such as binning, with emphasis on novel object and familiar object investigation. Overall, modifications to the existing iterations of the NOR paradigm are necessary to improve paradigm reliability. [ABSTRACT FROM AUTHOR]

In this study, a modified 2-parameter skew t distribution called the transmuted skew student t distribution (TSStD) was presented. Some statistical and reliability properties of TSStD such as the quantile function, the raw moments, and the moment generating function (among others), were derived. Through the method of maximum likelihood, the two parameters of the model were estimated. The stability of the model was studied via Montecarlo simulations utilizing bias, mean square error, and root mean square error as metrics. The results from the stability study revealed that the TSStD was well-behaved. Four datasets were modeled with the transmuted skewed student t distribution and four other probability density models. On the basis of information criteria, the results revealed that the transmuted skew student t distribution provides a better fit for all the datasets compared to the other competing models. [ABSTRACT FROM AUTHOR]

Estimation of unknown process parameters is critical in statistical process control/monitoring (SPC/M). Retrospective data is usually used to identify the process in-control state behavior in terms of estimating the process parameters. Practitioners analyze the retrospective data in order to handle out-of-control observations before estimating the parameters. However, chances to overlook some out-of-control observations do exist; in which case, the Phase I data is said to be contaminated. In this study, we aim to assess the impact of having contaminated retrospective data on the performance of the EWMA chart; assuming non-normal processes. Two distributions are considered; the t-distribution to represent a symmetric heavy-tailed distribution with two contamination scenarios, and the Gamma distribution to represent a skewed one. A weighted variance EWMA chart is used with the Gamma distribution to account for the skewness. Our results revealed that under the t-distribution a contamination with respect to the mean is more serious than a contamination with respect to the degrees of freedom. Under the gamma distribution, the estimation effect generally and contamination effect specifically are more serious on smaller shape parameter values (heavily skewed). [ABSTRACT FROM AUTHOR]

The increasing sophistication of cyberattacks necessitates the development of advanced detection systems capable of accurately identifying and mitigating potential threats. This research addresses the critical challenge of cyberattack detection by employing a comprehensive approach that includes generating a realistic yet imbalanced dataset simulating various types of cyberattacks. Recognizing the inherent limitations posed by imbalanced data, we explored multiple data augmentation techniques to enhance the model's learning effectiveness and ensure robust performance across different attack scenarios. Firstly, we constructed a detailed dataset reflecting real-world conditions of network intrusions by simulating a range of cyberattack types, ensuring it embodies the typical imbalances observed in genuine cybersecurity threats. Subsequently, we applied several data augmentation techniques, including SMOTE and ADASYN, to address the skew in class distribution, thereby providing a more balanced dataset for training supervised machine learning models. Our evaluation of these techniques across various models, such as Random Forests and Neural Networks, demonstrates significant improvements in detection capabilities. Moreover, the analysis also extends to the investigation of feature importance, providing critical insights into which attributes most significantly influence the predictive outcomes of the models. This not only enhances the interpretability of the models but also aids in refining feature engineering and selection processes to optimize performance. [ABSTRACT FROM AUTHOR]

The skew distribution has the characteristic of appropriately modeling asymmetric unimodal data. However, in practice, there are several cases in which the data present more than one mode. In the literature, it is possible to find a large number of authors who have studied extensions based on the skew distribution to model this type of data. In this article, a new family is introduced, consisting of a multimodal modification to the family of skew distributions. Using the methodology of the weighted version of a function, we perform the product of the density function of a family of skew distributions with a polynomial of degree 4, thus obtaining a more flexible model that allows modeling data sets, whose distribution contains at most three modes. The density function, some properties, moments, skewness coefficients, and kurtosis of this new family are presented. This study focuses on the particular cases of skew-normal and Laplace distributions, although it can be applied to any other distribution. A simulation study was carried out, to study the behavior of the model parameter estimates. Illustrations with real data, referring to medicine and environmental data, show the practical performance of the proposed model in the two particular cases presented. [ABSTRACT FROM AUTHOR]

Background: Intracellular methotrexate polyglutamates (MTX‐PGs) concentrations are measurable in red blood cells (RBCs) during MTX treatment. MTX‐PG3 concentrations correlate with efficacy in patients with Crohn's disease (CD). Since RBCs are not involved in pathogenesis of CD and lack extended MTX metabolism, we determined MTX‐PGs accumulation in peripheral blood mononuclear cells (PBMCs: effector cells) and intestinal mucosa (target cells) and compared those with RBCs as a potential more precise biomarker. Methods: In a multicentre prospective cohort study, blood samples of patients with CD were collected during the first year of MTX therapy. Mucosal biopsies were obtained from non‐inflamed rectum and/or inflamed intestine. MTX‐PGs concentrations in mucosa, PBMCs and RBCs were measured by liquid chromatography–tandem mass spectrometry. Results: From 80 patients with CD, a total of 27 mucosal biopsies, 9 PBMC and 212 RBC samples were collected. From 12 weeks of MTX therapy onwards, MTX‐PG3 was the most predominant species (33%) in RBCs. In PBMCs, the distribution was skewed towards MTX‐PG1 (48%), which accounted for an 18 times higher concentration than in RBCs. Long‐chain MTX‐PGs were highly present in mucosa: 21% of MTX‐PGtotal was MTX‐PG5. MTX‐PG6 was measurable in all biopsies. Conclusions: MTX‐PG patterns differ between mucosa, PBMCs and RBCs of patients with CD. [ABSTRACT FROM AUTHOR]

Item nonresponse is a common issue in surveys. Because unadjusted estimators may be biased in the presence of nonresponse, it is common practice to impute the missing values with the objective of reducing the nonresponse bias as much as possible. However, commonly used imputation procedures may lead to unstable estimators of population totals/means when influential units are present in the set of respondents. In this article, we consider the class of multiply robust imputation procedures that provide some protection against the failure of underlying model assumptions. We develop an efficient version of multiply robust estimators based on the concept of conditional bias, a measure of influence. We present the results of a simulation study to show the benefits of our proposed method in terms of bias and efficiency. [ABSTRACT FROM AUTHOR]

The distribution of financial data usually shows strong skewness and kurtosis. In this article, we propose a probability distribution with parameters explicitly representing skewness and kurtosis. This distribution extends logistic distribution to a wide range of distributions bounded by Gaussian and Laplace. The properties of the new distribution are studied, and parameter estimation methods are proposed. As an application, the new distribution can be used for pricing volatility skew and smile in option quotes and constructing local volatility models for the evaluation of path-dependent financial derivatives. [ABSTRACT FROM AUTHOR]

In this paper, we deal with the problem of monitoring network latency. Indeed, latency is a key network metric related to both network performance and quality of service, since it directly impacts on the overall user's experience. High latency leads to unacceptably slow response times of network services, and may increase network congestion and reduce the throughput, in turn disrupting communications and the user's experience. A common approach to monitoring network latency takes into account the frequently skewed distribution of latency values, and therefore specific quantiles are monitored, such as the 95th, 98th, and 99th percentiles. We present a comparative analysis of the speed of convergence of the sequential FRUGAL-1U, FRUGAL-2U, and EASYQUANTILE algorithms and the design and analysis of parallel, message-passing-based versions of these algorithms that can be used for monitoring network latency quickly and accurately. Distributed versions are also discussed. Extensive experimental results are provided and discussed as well. [ABSTRACT FROM AUTHOR]

Summary: Imbalanced samples are widespread, which impairs the generalization and fairness of models. Semi‐supervised learning can overcome the deficiency of rare labeled samples, but it is challenging to select high‐quality pseudo‐label data. Unlike discrete labels that can be matched one‐to‐one with points on a numerical axis, labels in regression tasks are consecutive and cannot be directly chosen. Besides, the distribution of unlabeled data is imbalanced, which easily leads to an imbalanced distribution of pseudo‐label data, exacerbating the imbalance in the semi‐supervised dataset. To solve this problem, this article proposes a semi‐supervised imbalanced regression network (SIRN), which consists of two components: A, designed to learn the relationship between features and labels (targets), and B, dedicated to learning the relationship between features and target deviations. To measure target deviations under imbalanced distribution, the target deviation function is introduced. To select continuous pseudo‐labels, the deviation matching strategy is designed. Furthermore, an adaptive selection function is developed to mitigate the risk of skewed distributions due to imbalanced pseudo‐label data. Finally, the effectiveness of the proposed method is validated through evaluations of two regression tasks. The results show a great reduction in predicted value error, particularly in few‐shot regions. This empirical evidence confirms the efficacy of our method in addressing the issue of imbalanced samples in regression tasks. [ABSTRACT FROM AUTHOR]

With the advent of big data, the fields of biomedicine and economics generate massive data with skew characteristics. Numerous methods have been proposed for modeling either skewed or massive data, whereas most existing methods cannot allow a direct handling of massive and skewed data. We first investigate the subsampling algorithms for skew-normal mode regression model, which include uniform subsampling, leverage subsampling, optimal subsampling, and vector mode subsampling. Since the aforementioned algorithms mainly leverage the value of the information module to calculate the sampling probability without accounting for the residuals in the modeling process. This observation motivates us to propose a novel residual subsampling method with applications to massive data. We then employ the signal-to-noise ratio (SNR) to carry out simulation studies to compare the performance of various sampling methods under various information quantities. Finally, a real-data example is provided for illustrative methods. [ABSTRACT FROM AUTHOR]

Recently, fault diagnosing supervised classifiers have been widely proposed to diagnose both electric and mechanical faults in induction motors (IM). However, many of them require a large amount of data, which implies a great effort required for processing fault-related features and building the training set. Furthermore, in real-world datasets, it is required to deal with highly skewed data distributions, also known as class imbalance, which is a limiting issue and can misguide the tuning of machine learning algorithms. Resampling techniques based on a synthetic generation of minority class observations aim to address this problem. Last but not least is the fact that inverter-fed IM introduces undesired harmonics in the monitoring signal altering the diagnosis patterns. This diagnosis scheme is evaluated on experimental imbalanced data oriented to deal with the diagnosis of a rotor in situations where it is fed with an inverter. The results show how this imbalanced approach determines the actual diagnosis performance on a small amount of data. The experimental results demonstrate that balanced training sets built with class balancing techniques improve the classifier and therefore its performance for diagnosing incipient rotor faults in inverter-fed IM with studied and interpretable features recently proposed in this field of study. [ABSTRACT FROM AUTHOR]

Summary The mixture models are widely used to analyze data with cluster structures and the mixture of Gaussians is most common in practical applications. The use of mixtures involving other multivariate distributions, like the multivariate skew normal and multivariate generalised hyperbolic, is also found in the literature. However, in all such cases, only the mixtures of identical distributions are used to form a mixture model. We present an innovative and versatile approach for constructing mixture models involving identical and non‐identical distributions combined in all conceivable permutations (e.g. a mixture of multivariate skew normal and multivariate generalised hyperbolic). We also establish any conventional mixture model as a distinctive particular case of our proposed framework. The practical efficacy of our model is shown through its application to both simulated and real‐world data sets. Our comprehensive and flexible model excels at recognising inherent patterns and accurately estimating parameters. [ABSTRACT FROM AUTHOR]

Strong selection on complex traits can lead to skewed trait means and reduced trait variability in populations. An example of this phenomenon can be evidenced in allele frequency changes and skewed trait distributions driven by persistent human-directed selective pressures in domesticated species. Dog domestication is linked to several genomic variants; however, the functional impacts of these variants may not always be straightforward when found in non-coding regions of the genome. Four polymorphic transposable elements (TE) found within non-coding sites along a 5 Mb region on canine CFA6 have evolved due to directional selection associated with heightened human-directed hyper-sociability in domesticated dogs. We found that the polymorphic TE in intron 17 of the canine GTF2I gene, which was previously reported to be negatively correlated with canid human-directed hyper-sociability, is associated with altered chromatin looping and hence distinct cis-regulatory landscapes. We reported supporting evidence of an E2F1-DNA binding peak concordant with the altered loop and higher expression of GTF2I exon 18, indicative of alternative splicing. Globally, we discovered differences in pathways regulating the extra-cellular matrix with respect to TE copy number. Overall, we reported evidence suggesting an intriguing molecular convergence between the emergence of hypersocial behaviors in dogs and the same genes that, when hemizygous, produce human Williams Beuren Syndrome characterized by cranio-facial defects and heightened social behaviors. Our results additionally emphasize the often-overlooked potential role of chromatin architecture in social evolution. [ABSTRACT FROM AUTHOR]

Objective: In order to facilitate the tracing of infectious diseases in a small area and to effectively carry out disease control and epidemiological investigations, this research proposes a novel spatiotemporal model to estimate effective reproduction number(Re)for infectious diseases, based on the fundamental concept of contact tracing. Methods: This study utilizes the incidence of hand, foot, and mouth disease (HFMD) among children in Bishan District, Chongqing, China from 2015 to 2019. The study incorporates the epidemiological characteristics of HFMD and aims to construct a Spatiotemporal Correlation Discrimination of HFMD. Utilizing ARC ENGINE and C# programming for the creation of a spatio-temporal database dedicated to HFMD to facilitate data collection and analysis. The scientific validity of the proposed method was verified by comparing the effective reproduction number obtained by the traditional SEIR model. Results: We have ascertained the optimal search radius for the spatiotemporal search model to be 1.5 km. Upon analyzing the resulting Re values, which range from 1.14 to 4.75, we observe a skewed distribution pattern from 2015 to 2019. The median and quartile Re value recorded is 2.42 (1.98, 2.72). Except for 2018, the similarity coefficient r of the years 2015, 2016, 2017, and 2019 were all close to 1, and p <0.05 in the comparison of the two models, indicating that the Re values obtained by using the search model and the traditional SEIR model are correlated and closely related. The results exhibited similarity between the Re curves of both models and the epidemiological characteristics of HFMD. Finally, we illustrated the regional distribution of Re values obtained by the search model at various time intervals on Geographic Information System (GIS) maps which highlighted variations in the incidence of diseases across different communities, neighborhoods, and even smaller areas. Conclusion: The model comprehensively considers both temporal variation and spatial heterogeneity in disease transmission and accounts for each individual's distinct time of onset and spatial location. This proposed method differs significantly from existing mathematical models used for estimating Re in that it is founded on reasonable scientific assumptions and computer algorithms programming that take into account real-world spatiotemporal factors. It is particularly well-suited for estimating the Re of infectious diseases in relatively stable mobile populations within small geographical areas. [ABSTRACT FROM AUTHOR]

Diabetic nephropathy (DN) is a common secondary kidney disease. Immune and inflammatory responses play an influential role in the development of DN. This study aims to explore the role and mechanisms of immune- and inflammatory-related factors in DN. Participants from the NHANES 2013–2018 were included to evaluate the association between the SII and DN. Considering the skewed distribution of SII, log SII was used for subsequent analysis. Then, the DEGs were extracted from the GSE96804 dataset by the "limma" package of R, which were further screened out genes in the key module based on WGCNA. The intersection genes between DEGs and key module genes were the key genes for the following mechanism exploration. The CyTargetlinker plug-in of Cytoscape software was used to construct the drug-genes network. Molecular docking was used to calculate the binding affinity between potential drugs and the hub genes. Among the 8236 participants from NHANES 2013–2018, Log SII was significantly associated with DN (p < 0.05). DEG and WGCNA revealed 30 DN-related genes, which mainly regulated immune- and inflammation pathways, and the NOD-like receptor signaling pathway was the core pathway highly involved in the DN occurrence. Moreover, NAIP, ZFP36, and DUSP1 were identified as hub genes in DN progression and there was a strong binding interaction between resveratrol and DUSP1.In conclusion, immune inflammation plays an influential role in the occurrence and development of DN. SII is an effective diagnostic marker for DN and resveratrol might have potential value in treating DN. [ABSTRACT FROM AUTHOR]

Multilevel network meta-regression (ML-NMR) leverages individual patient data (IPD) and aggregate data from a network of randomized controlled trials (RCTs) to assess the comparative efficacy of multiple treatments, while adjusting for between-study differences. We provide an overview of ML-NMR for time-to-event outcomes and apply it to an illustrative case study, including example R code. The case study evaluated the comparative efficacy of idecabtagene vicleucel (ide-cel), selinexor+dexamethasone (Sd), belantamab mafodotin (BM), and conventional care (CC) for patients with triple-class exposed relapsed/refractory multiple myeloma in terms of overall survival. Single-arm clinical trials and real-world data were naively combined to create an aggregate data artificial RCT (aRCT) (MAMMOTH-CC versus DREAMM-2-BM versus STORM-2-Sd) and an IPD aRCT (KarMMa-ide-cel versus KarMMa-RW-CC). With some assumptions, we incorporated continuous covariates with skewed distributions, reported as median and range. The ML-NMR models adjusted for number of prior lines, triple-class refractory status, and age and were compared using the leave-one-out information criterion. We summarized predicted hazard ratios and survival (95% credible intervals) in the IPD aRCT population. The Weibull ML-NMR model had the lowest leave-one-out information criterion. Ide-cel was more efficacious than Sd, BM, and CC in terms of overall survival. Effect modifiers had minimal impact on the model, and only triple-class refractory was a prognostic factor. We demonstrate an application of ML-NMR for time-to-event outcomes and introduce code that can be used to aid implementation. Given its benefits, we encourage practitioners to utilize ML-NMR when population adjustment is necessary for comparisons of multiple treatments. • Multilevel network meta-regression (ML-NMR) can leverage individual patient data and aggregate data when comparing multiple treatments in a network of any size, while adjusting for differences in populations between trials. • We demonstrate an application of ML-NMR for time-to-event outcomes and introduce code for implementation. • ML-NMR reflects an extension of standard network meta-analysis framework to help address challenges of existing alternative indirect treatment methods, which can be applied to time-to-event outcomes to compare multiple direct treatment comparisons simultaneously in health technology assessments. [ABSTRACT FROM AUTHOR]

Bandwidth selection is key for kernel quantile estimators (KQEs), which estimate quantiles by averaging all of the order statistics with appropriate kernel-weighting functions. This paper provides a new data-driven bandwidth selection method for KQEs, named the exact bootstrap-based bandwidth selection (EBS) rule. By relying on the exact analytic expressions for the bootstrap mean and variance of KQEs, the error due to bootstrap resampling is eliminated, and thus the optimal bandwidth can be obtained by minimizing the mean squared error (MSE) estimate. The effectiveness of this EBS rule is confirmed by numerical experiments. First, the bandwidth selection performance of the EBS method is compared to that of a benchmark approach. Simulation studies show that the EBS method performs well, especially in selecting bandwidths for extreme quantiles and when applied to small sample sizes with skewed distributions and relatively large variances. Second, KQEs with a bandwidth determined by our EBS rule is compared with five other state-of-the-art quantile estimators over six typical distributions. The results further validate the efficiency of the EBS method. Third, the results of simulations of controlling actual type-I error rates that occur when two independent groups are compared through quantiles further demonstrate the precision of our EBS-based KQEs. [ABSTRACT FROM AUTHOR]

Graph Neural Networks (GNNs) have been successfully adopted in recommender systems by virtue of the message-passing that implicitly captures collaborative effect. However, in practical recommendation scenarios, user-item interaction data is often sparse and exhibits a skewed distribution. To address these issues, some contrastive learning methods based on data augmentation are applied to recommender systems to enhance the representation of users and items. Nevertheless, many data enhancements solely rely on graph topology, missing crucial structural information and potentially biasing the model. In this paper, we propose a contrastive learning recommendation framework called GDA-GCL based on a generative model data augmentation strategy. Specifically, we use the Conditional Variational Autoencoder(CVAE) generative model to learn the distribution of neighbor node features conditioned on the features of the central node. Due to the randomness of resampling, we design a mirror graph comparison strategy to generate different comparison views, which introduces additional high-quality training signals into the GNN paradigm. Experimental results on three real-world public datasets demonstrate that GDA-GCL achieves significant improvement in performance over various baseline methods. Extensive analysis, including ablation studies, has demonstrated the effectiveness and robustness of our proposed generative data-augmented contrastive recommendation framework in addressing the data sparsity issue in recommendation systems. [ABSTRACT FROM AUTHOR]

We study noisy aggregation of dispersed information in financial markets without imposing parametric restrictions on preferences, information, and return distributions. We provide a general characterization of asset returns by means of a risk‐neutral probability measure that features excess weight on tail risks. Moreover, we link excess weight on tail risks to observable moments such as forecast dispersion and accuracy, and argue that it provides a unified explanation for several prominent cross‐sectional return anomalies. Simple calibrations suggest the model can account for a significant fraction of empirical returns to skewness, returns to disagreement, and interaction effects between the two. [ABSTRACT FROM AUTHOR]