Robustness Of Method Research Articles

Robust kinetics estimation from kinematics via direct collocation

IntroductionAccurate joint moment analysis is essential in biomechanics, and the integration of direct collocation with markerless motion capture offers a promising approach for its estimation. However, markerless motion capture can introduce varying degrees of error in tracking trajectories. This study aims to evaluate the effectiveness of the direct collocation method in estimating kinetics when joint trajectory data are impacted by noise.MethodsWe focused on walking and squatting movements as our target activities. To assess the method's robustness, we created five groups with differing noise levels—noise-free, mild noise, noisy group1, noisy group2, and a Gaussian noise group—in the joint center trajectories. Our approach involved combining joint center tracking with biological terms within the direct collocation scheme to address noise-related challenges. We calculated kinematics, joint moments, and ground reaction forces for comparison across the different noise groups.ResultsFor the walking task, the mean absolute errors (MAEs) for the knee flexion moments were 0.103, 0.113, 0.127, 0.129, and 0.116 Nm/kg across the respective noise levels. The corresponding MAEs of the ankle flexion moment were 0.130, 0.133, 0.145, 0.131, and 0.138 Nm/kg. The hip flexion moment had MAEs of 0.182, 0.204, 0.242, 0.246, and 0.249 Nm/kg in the respective groups. In squatting, the MAEs of ankle flexion moments were 0.207, 0.219, 0.217, 0.253, and 0.227 Nm/kg in the noise-free, mild noise, noisy group1, noisy group2, and the Gaussian noise group, respectively. The MAEs of the knee flexion moments were 0.177, 0.196, 0.198, 0.197, and 0.221 Nm/kg, whereas the mean MAEs of the hip flexion moments were 0.125, 0.135, 0.141, 0.161, and 0.178 Nm/kg in the respective groups.ConclusionThe results highlight that the direct collocation method incorporating both tracking and biological terms in the cost function could robustly estimate joint moments during walking and squatting across various noise levels. Currently, this method is better suited to reflect general activity dynamics than subject-specific dynamics in clinical practice. Future research should focus on refining cost functions to achieve an optimal balance between robustness and accuracy.

Does Structural Empowerment of Nurse Leaders Moderate the Relationship Between Destructive Leadership and Nursing Outcomes?-A Cross-Sectional Study.

To describe how nursing staff assess their superiors' destructive leadership and nurse leaders' structural empowerment. A further aim is to examine the associations between destructive leadership and staff outcomes and how nurse leaders' structural empowerment moderates these associations. A cross-sectional study. The data were collected during November 2022-April 2023. Nursing staff assessed their work well-being, commitment, decision-making and career advancement. Destructive leadership was evaluated using the Employment Precariousness Scale and some additional questions. Nurse leaders' structural empowerment was assessed using the Conditions For Work Effectiveness Questionnaire-II. The data were analysed using statistical methods including linear regression. Nursing staff (n = 381) assessed destructive leadership as rare, but it negatively associated with their commitment, decision-making, career advancement and work well-being. Nurse leaders' (n = 97) structural empowerment had a positive association with nursing staff outcomes. The interaction between destructive leadership and nurse leaders' structural empowerment positively affected the work well-being and commitment of nursing staff. The models explained 32% of nurses' work well-being and 19% of their commitment. Destructive leadership undermines nursing staff outcomes. Structural empowerment of nurse leaders should be enhanced to protect against destructive leadership and other protective factors should be explored in the future to identify and prevent destructive leadership. Organisations should provide nurse leaders with appropriate resources and support to enhance their empowerment and opportunities to improve their leadership skills and expertise and should develop ways of identifying destructive leadership patterns and policies to resolve situations in which destructive leadership occurs. A work environment that empowers nurse leaders may reduce the effect of destructive leadership on staff well-being, commitment, decision-making and career advancement. The significance of protective factors against destructive leadership should be explored using more robust methods in future research. A STROBE checklist. None.

Causal relationship between gut microbiota and lung squamous cell carcinoma: a bidirectional two-sample Mendelian randomization study.

This study aims to explore the potential causal relationship between gut microbiota and lung squamous cell carcinoma (LUSC). A bidirectional two-sample Mendelian randomization analysis was conducted using genome-wide association study (GWAS) data from gut microbiota and LUSC. Gut microbiota served as the exposure factor, with instrumental variables selected from a GWAS involving 18 340 participants. LUSC data were drawn from a European cohort including 29 266 LUSC cases and 56 450 controls. Inverse-variance weighted (IVW) method was used as the primary method, with the Benjamini-Hochberg method applied to adjust for multiple comparisons. An independent dataset (ieu-a-967, containing 3275 LUSC cases and 15 038 controls) was used for replication analysis to ensure robustness. IVW analysis found that Butyricicoccus (OR = 0.79, 95% CI: 0.63-0.99, P = .042) and Coprobacter (OR = 0.85, 95% CI: 0.74-0.97, P = .018) were significantly protective against LUSC. In contrast, Victivallis (OR = 1.11, 95% CI: 1.00-1.23, P = .045) and Ruminococcus (OR = 1.28, 95% CI: 1.03-1.60, P = .028) increased LUSC risk. Replication analysis in the independent dataset confirmed significant associations for Ruminococcus and Coprobacter. No reverse causality or pleiotropy was detected. This study provides evidence of a causal relationship between specific gut microbiota and LUSC risk, highlighting new microbial targets for potential prevention and treatment strategies in lung cancer. Key messages What is already known on this topic? Previous studies have suggested potential links between gut microbiota composition and the development of various cancers, including lung cancer. However, the exact causal relationship between specific gut microbiota and lung squamous cell carcinoma (LUSC) has remained unclear. Traditional observational studies have struggled to determine the direction of causality due to confounding factors, making further investigation necessary through more robust methods such as Mendelian randomization (MR). What this study adds? This bidirectional MR study provides novel genetic evidence indicating that certain gut microbiotas are causally associated with LUSC risk. Specifically, Butyricicoccus appears to reduce the risk of LUSC, while Victivallis increases the risk. These findings highlight the role of the gut-lung axis in LUSC and open up new avenues for exploring gut microbiota as potential modulators of lung cancer risk. How this study might affect research, practice, or policy? The implications of this study may significantly influence future research into cancer prevention strategies by targeting gut microbiota. Additionally, it could inform clinical practices aimed at modulating gut microbiota to lower the risk of LUSC, potentially influencing dietary or probiotic interventions to reduce cancer susceptibility. Furthermore, these results might shape public health policies that focus on the gut-lung axis as a novel avenue for cancer prevention and management.

CGRclust: Chaos Game Representation for twin contrastive clustering of unlabelled DNA sequences

BackgroundTraditional supervised learning methods applied to DNA sequence taxonomic classification rely on the labor-intensive and time-consuming step of labelling the primary DNA sequences. Additionally, standard DNA classification/clustering methods involve time-intensive multiple sequence alignments, which impacts their applicability to large genomic datasets or distantly related organisms. These limitations indicate a need for robust, efficient, and scalable unsupervised DNA sequence clustering methods that do not depend on sequence labels or alignment.ResultsThis study proposes CGRclust, a novel combination of unsupervised twin contrastive clustering of Chaos Game Representations (CGR) of DNA sequences, with convolutional neural networks (CNNs). To the best of our knowledge, CGRclust is the first method to use unsupervised learning for image classification (herein applied to two-dimensional CGR images) for clustering datasets of DNA sequences. CGRclust overcomes the limitations of traditional sequence classification methods by leveraging unsupervised twin contrastive learning to detect distinctive sequence patterns, without requiring DNA sequence alignment or biological/taxonomic labels. CGRclust accurately clustered twenty-five diverse datasets, with sequence lengths ranging from 664 bp to 100 kbp, including mitochondrial genomes of fish, fungi, and protists, as well as viral whole genome assemblies and synthetic DNA sequences. Compared with three recent clustering methods for DNA sequences (DeLUCS, iDeLUCS, and MeShClust v3.0.), CGRclust is the only method that surpasses 81.70% accuracy across all four taxonomic levels tested for mitochondrial DNA genomes of fish. Moreover, CGRclust also consistently demonstrates superior performance across all the viral genomic datasets. The high clustering accuracy of CGRclust on these twenty-five datasets, which vary significantly in terms of sequence length, number of genomes, number of clusters, and level of taxonomy, demonstrates its robustness, scalability, and versatility.ConclusionCGRclust is a novel, scalable, alignment-free DNA sequence clustering method that uses CGR images of DNA sequences and CNNs for twin contrastive clustering of unlabelled primary DNA sequences, achieving superior or comparable accuracy and performance over current approaches. CGRclust demonstrated enhanced reliability, by consistently achieving over 80% accuracy in more than 90% of the datasets analyzed. In particular, CGRclust performed especially well in clustering viral DNA datasets, where it consistently outperformed all competing methods.

Developing institution-specific admission competency criteria for prospective health sciences students

BackgroundGiven the critical role of health professionals in societal health, the development of robust and effective selection methods is of fundamental concern for educational institutions within the field of health sciences education. Conventionally, admission competencies have been determined by institutional authorities. Developing institution-specific competency criteria enables an admission process that is mission- and value-aware, evidence-based, and strategically adaptable. However, few schools have established their admission competency criteria, although the majority possess their own models of graduation competencies. This study reports the process of developing and validating an institution-specific admission competency model that addresses the need for evidence-based and mission-aligned selection processes that are distinct from standardized models.MethodsThis study was conducted in two phases, using both qualitative and quantitative analyses. Phase I involved constructing an admission competency model through a qualitative approach facilitated by workshops with 17 faculty members and 92 first-year pre-doctoral students of a dental school. Through constant comparative analysis, this phase focused on the extraction and refinement of competencies for entering dental students. In Phase II, a questionnaire developed from the workshops asked respondents to rate the importance of 47 attributes across 10 constructs on a 5-point Likert scale. A total of 301 individuals participated in the survey. Exploratory Factor Analysis (EFA) identified the factor structure, and Confirmatory Factor Analysis (CFA) examined construct validity and assessed the model fit with the data.ResultsThe EFA of the 47 attributes identified 10 factors, and the CFA results indicated a good-to-acceptable level of fit for the ten-factor model. Aligned with the American Association of Medical Colleges Premed competencies, this study identified unique attributes specific to the institution, such as confidence, leadership, and entrepreneurship. These findings highlight the importance of developing tailored competencies reflecting the unique needs of institutions and their fields.ConclusionsThis study demonstrates the feasibility and value of creating institution-specific admission competency models, offering a methodology that aligns with evidence-based mission-driven selection processes. The distinct competencies identified emphasize the need for educational institutions to consider unique institutional and field-specific requirements and move beyond standardized models to enhance the selection of medical students.

Low-Voltage Biological Electric Shock Fault Diagnosis Based on the Attention Mechanism Fusion Parallel Convolutional Neural Network/Bidirectional Long Short-Term Memory Model

Electric shock protection is critical for ensuring power safety in low-voltage grids, and robust fault diagnosis methods provide an essential foundation for the accurate operation of such protection devices. However, current low-voltage electric shock protection devices often suffer from limitations in operational precision and in their ability to effectively recognize electric shock types. To address these challenges, this paper proposes a fault diagnosis method for low-voltage electric shocks based on an attention-enhanced parallel CNN-BiLSTM model. The method first utilizes CNN to extract local spatial features of the electric shock signal and BiLSTM to capture temporal features. An attention mechanism is then introduced to fuse the local spatial and temporal features with weighted emphasis. Finally, a fully connected layer maps the fused features to the output layer, generating diagnostic results. Visualization through T-SNE analysis validates the improvement in model performance due to the attention mechanism. Comparative experiments show that the proposed model outperforms single models and other combined models in terms of accuracy, precision, recall, F1 score, and convergence speed. The results demonstrate that the proposed model achieves a fault diagnosis accuracy of 99.55%.

Greater baseline cortical atrophy in the dorsal attention network predicts faster clinical decline in Posterior Cortical Atrophy

BackgroundPosterior Cortical Atrophy (PCA) is a clinical syndrome characterized by progressive visuospatial and visuoperceptual impairment. As the neurodegenerative disease progresses, patients lose independent functioning due to the worsening of initial symptoms and development of symptoms in other cognitive domains. The timeline of clinical progression is variable across patients, and the field currently lacks robust methods for prognostication. Here, evaluated the utility of MRI-based cortical atrophy as a predictor of longitudinal clinical decline in a sample of PCA patients.MethodsPCA patients were recruited through the Massachusetts General Hospital Frontotemporal Disorders Unit PCA Program. All patients had cortical thickness estimates from baseline MRI scans, which were used to predict longitudinal change in clinical impairment assessed by the CDR Sum-of-Boxes (CDR-SB) score. Multivariable linear regression was used to estimate the magnitude of cortical atrophy in PCA patients relative to a group of amyloid-negative cognitively unimpaired participants. Linear mixed-effects models were used to test hypotheses about the utility of baseline cortical atrophy for predicting longitudinal clinical decline.ResultsData acquired from 34 PCA patients (mean age = 65.41 ± 7.90, 71% females) and 24 controls (mean age = 67.34 ± 4.93, 50% females) were analyzed. 62% of the PCA patients were classified as having mild cognitive impairment (CDR 0.5) at baseline, with the rest having mild dementia (CDR 1). Each patient had at least one clinical follow-up, with the mean duration of 2.78 ± 1.62 years. Relative to controls, PCA patients showed prominent baseline atrophy in the posterior cortical regions, with the largest effect size observed in the visual network of the cerebral cortex. Cortical atrophy localized to the dorsal attention network, which supports higher-order visuospatial function, selectively predicted the rate of subsequent clinical decline.ConclusionsThese results demonstrate the utility of a snapshot measure of cortical atrophy of the dorsal attention network for predicting the rate of subsequent clinical decline in PCA. If replicated, this topographically-specific MRI-based biomarker could be useful as a clinical prognostication tool that facilitates personalized care planning.

Estimation of minimal data sets sizes for machine learning predictions in digital mental health interventions

Artificial intelligence promises to revolutionize mental health care, but small dataset sizes and lack of robust methods raise concerns about result generalizability. To provide insights on minimal necessary data set sizes, we explore domain-specific learning curves for digital intervention dropout predictions based on 3654 users from a single study (ISRCTN13716228, 26/02/2016). Prediction performance is analyzed based on dataset size (N = 100–3654), feature groups (F = 2–129), and algorithm choice (from Naive Bayes to Neural Networks). The results substantiate the concern that small datasets (N ≤ 300) overestimate predictive power. For uninformative feature groups, in-sample prediction performance was negatively correlated with dataset size. Sophisticated models overfitted in small datasets but maximized holdout test results in larger datasets. While N = 500 mitigated overfitting, performance did not converge until N = 750–1500. Consequently, we propose minimum dataset sizes of N = 500–1000. As such, this study offers an empirical reference for researchers designing or interpreting AI studies on Digital Mental Health Intervention data.

Semi‐empirically modelling barrier sediment transport in response to hydrodynamic forcing using UAV‐derived topographical data (Holgate, New Jersey)

AbstractWe conducted monthly surveys, from October 2020 to May 2021, of coastal topography in Holgate, New Jersey. Using unmanned aerial vehicle (UAV)‐photogrammetry and RTK‐GNSS equipment, we generated digital elevations models and cross‐section profiles, capturing spatiotemporal variability in volumetric change. We measured a total loss of 27 500 ± 10 500 m3 of subaerial sediment through the study. From October 2020 to February 2021, over 59 600 ± 10 500 m3 of sediment was eroded, followed by 32 100 ± 10 500 m3 of deposition from February to May 2021. We developed a semi‐empirical model correlating the measured geomorphological change to wave energy and water‐level variations. The calibrated model identified storm conditions that caused erosion and that waves from the south to southeast caused more erosion than those from the east to northeast. These results emphasise that alongshore transport represents a critical component of sediment transport dynamics relevant to beach erosion. Using the calibrated model, we quantified the impact of water‐level variations and wave energy on net sediment transport for a stretch of barrier coastline. Specifically, a water‐level increase of 0.14 m (equivalent to a 1σ variation) generated slightly less erosion (1.18 m3/m per 48 h) than the same variance‐based increase in wave energy, which generates 1.44 m3/m of erosion per 48 h. These variables strongly covary. Alongshore transport modulates the relationship, increasing erosion 0.9 m3/m per 48 h with a 1σ shift in wave energy directed alongshore. Forcing from strong storms, hindcast from 8 years of data, can produce 15–20 m3/m of erosion per 48 h. This modelling approach represents a methodology to produce estimates of potential erosion under predicted storm conditions, which is inherently valuable to coastal management and resilience planning. Our study demonstrates cost‐effective data collection and robust analytical methods that can be applied globally to benefit both the understanding of coastal geomorphology and local communities through data‐driven natural resource management.

A Two-Parameter Ridge Estimator for Handling Extreme Multicollinearity Problems in Logistic Regression

This paper introduces a robust two-parameter ridge estimator that is customized for logistic regression models, which tend to be sensitive to extreme multicollinearity problems. Inflated standard errors and unreliability in the results stem from the problem of multicollinearity characterized by high correlations among predictor variables in logistic regression model. Traditional approaches like the Maximum Likelihood Estimator (MLE) and one-parameter ridge-type estimators often perform poorly under these settings, thus calling for the development of more robust approaches. This new proposal is called New Biased Two Parameter (NBTP), which extends the ridge regression framework by introducing additional biasing parameters customized for an extreme multicollinearity problem. The paper combines a theoretical analysis with extensive Monte Carlo simulations and real application to Pena data. It demonstrates that the new estimator, New Biased Two Parameter (NBTP), provides much more stable and accurate parameter estimates than previous methods. The results underscore the importance of using robust estimation methods within logistic regression, especially when multicollinearity may be widespread in fields such as medical research, finance, and the social sciences.

Unsupervised surface water mapping with airborne LiDAR data by leveraging physical properties of water

ABSTRACT Comprehensive mapping of surface water, especially smaller bodies of water (<1 ha), remains challenging due to the lack of robust and scalable extraction methods. Traditional methods require the use of either training procedures or the repetitive tuning of site-specific parameters, which present hurdles to automated mapping and introduce biases tied to training data and parameters. The dependence on water’s reflectance properties, including LiDAR intensity, further complicates the issue, as higher-resolution images inherently introduce increased noise. In response to these challenges, we propose a unique, unsupervised method that focuses on the geometric characteristics of water instead of its variable reflectance properties. Unlike existing approaches, our method relies exclusively on 3D coordinate observations from airborne LiDAR data, taking advantage of the presumption that connected surface water remains flat due to surface tension. Leveraging this physical constraint and spatial connectivity, our method precisely extracts water bodies of diverse sizes and reflectance without the need for training procedures or intensive parameter tuning. Notably, by relying solely on 3D coordinate observations, our approach significantly facilitates the fully automated generation of comprehensive 3D topographical maps of both water and terrain, eliminating the need for human intervention or supplementary optical imagery. We validated the robustness and scalability of this method across diverse terrains, including urban, coastal, and mountainous areas. Overall, the proposed method achieved a 11% higher accuracy, measured by intersection over union, compared to the highly competitive NDWI-based method. Moreover, it proved its effectiveness in both accuracy and scalability compared to supervised machine learning and deep learning methods.

High order moment conserving method of classes in CFD code

Abstract Dispersed multiphase flows are widely present in the majority of chemical process industry equipment. For the purpose of design, optimization, and scale up of these industrial systems, robust, accurate and fast simulation methods are needed, especially when coupling computational fluid dynamics (CFD) with population balance models (PBM). In this work, the high-order moment conserving method of classes (HMMC) is implemented and tested within the commercial CFD software of ANSYS Fluent with two simple well-defined test cases and a realistic 3D simulation of rotating disc (RDC) extractor. Firstly, a zero-dimensional PBM with well-mixed assumption and no convection was solved for a single computational cell in Fluent to verify the implementation in comparison with a simple reactor model in MATLAB. Secondly, the convection was considered by solving a one-dimensional PBM for three computational cells side by side in Fluent and compared with an equivalent three staged reactor model in MATLAB. Eventually, realistic 3D RDC simulations were conducted to fully couple the HMMC and CFD in Fluent. The implementation of HMMC was compared to predictions with other state-of-the-art PBM solution methods, e.g., the quadrature method of moments (QMOM) and the fixed pivot technique (FPT) in Fluent and MATLAB platforms. The implementation of the HMMC in Fluent platform was straightforward with no additional challenges. The verification shows that the HMMC approach is robust and efficient for polydispersed multiphase CFD simulations.

Climate change and its impacts in extreme events in Morocco (observation, monitoring, and forecasting)

ABSTRACT Rising extreme weather events in Morocco pose a growing threat to various socioeconomic sectors. These events, including heatwaves and cold spells, are exhibiting an alarming increase in intensity, frequency, and duration. To understand this trend and its future implications, a comprehensive study is essential. So, this research investigates the link between climate change and extreme weather events. It specifically focuses on how climate change influences the occurrence and intensity of these extremes. The study employs two main phases: Phase 1 is about the historical analysis (1984–2018). This phase uses robust methods like Sen's slope test for trend estimation and Mann–Kendall test for significance to evaluate observed changes in climatic extremes. Phase 2 is about future projections (2041–2060). This phase utilizes four climate models to project future changes in thermal and rainfall extremes under the RCP 4.5 scenario. Five climate indices (Tmm, Tx90p, WSDI, PRCPTOT, and SPI) are employed for analysis. Historical analysis (1984–2018) reveals a significant increase in hot extremes across Morocco. Rainfall extremes, however, show no significant changes in trends. Future projections (2041–2060) for all four climate models agree on significant warming in Morocco. They also project a decrease in annual precipitation across most of the country.

An MST-based assay reveals new binding preferences of IFIT1 for canonically and non-canonically capped RNAs.

IFIT proteins (interferon-induced proteins with tetratricopeptide repeats) are key components of the innate immune response that bind to viral and cellular RNA targets to inhibit viral translation and replication. The RNA target recognition is guided by molecular patterns, particularly at the RNA 5' ends. IFIT1 preferably binds RNAs modified with the 7-methylguanosine (m7G) cap-0 structure, while RNAs with cap-1 structure are recognized with lower affinity. Less is known about the propensity of IFIT1 to recognize non-canonical RNA 5' ends, including hypermethylated and non-canonical RNA caps. Deciphering the structure-function relationship for IFIT1-RNA interaction may improve the understanding of cellular selection of IFIT targets and guide the design of exogenously delivered therapeutic RNAs, but requires high-throughput and robust analytical methods. Here, we report a biophysical assay for quick, direct, in-solution affinity assessment of differently capped RNAs with IFIT1. The procedure, which relies on measuring microscale thermophoresis (MST) of fluorescently labelled protein as a function of increasing ligand concentration, is applicable to RNAs of various lengths and sequences without the need for their labelling or affinity tagging. Using the assay, we examined thirteen canonically and non-canonically 5'-capped RNAs, revealing new binding preferences of IFIT1. The 5' terminal m6A mark in the m7G cap had a protective function against IFIT1, which was additive with the effect observed for the 2'-O position (m6Am cap-1). In contrast, an increased affinity for IFIT1 was observed for several non-canonical caps, including trimethylguanosine (TMG), unmethylated (G), and flavin-adenine dinucleotide (FAD) caps. The results suggest new potential cellular targets of IFIT1 and may contribute to broadening the knowledge on the mechanisms of the innate immune response as well as the more effective design of chemically modified mRNAs.

Energy development and industrialization: evidence from Africa

Purpose The purpose of this study is to analyze the impact of energy development on industrialization in African economies from 1990 to 2017. It aims to assess how access to electricity, energy use and overall energy development contribute to industrial growth. By using panel data analysis and advanced econometric techniques such as fixed effects, two-stage least squares (2SLS) and system-GMM estimation, this study seeks to provide empirical evidence and insights into the complex dynamics between energy policies and industrialization outcomes. The findings are intended to inform policymakers and stakeholders on effective strategies to promote sustainable industrial development in the region. Design/methodology/approach This study uses a quantitative approach using panel data analysis spanning 1990–2017 from various sources. It uses fixed effects, 2SLS and system-GMM estimation methods to investigate the nuanced relationship between energy development and industrialization in African economies. This comprehensive methodology facilitates a robust examination of how different dimensions of energy development influence industrialization outcomes, aiming to fill gaps in existing literature and provide insights for policy formulation and implementation. Findings The findings reveal that energy development significantly enhances industrialization in African economies. Access to electricity and energy use both positively impact industrial growth, with access to electricity showing a stronger influence. This study underscores the critical role of energy infrastructure in fostering industrial expansion and economic development. Moreover, robust econometric methods confirm these relationships across various specifications, highlighting the importance of tailored energy policies to sustain industrialization efforts in Africa. These findings contribute empirical insights to the literature and provide actionable recommendations for policymakers aiming to enhance energy access and promote sustainable industrial development in the region. Originality/value This study contributes to the literature by offering new insights into the relationship between energy development and industrialization in African economies. By using advanced econometric techniques and focusing on nuanced dimensions of energy access and use, it addresses gaps in previous research. The findings underscore the significance of energy infrastructure for industrial growth, highlighting policy implications for sustainable development in Africa. This research adds value by providing empirical evidence that informs strategies to enhance energy efficiency, expand access to electricity and promote industrialization, thus contributing to broader discussions on economic development and energy policy in the region.

Achievement emotions in online language learning: domain-specific components and interactions with self-regulation strategies and language performance

Abstract Although previous research has underscored the crucial impact of discrete emotions on language learning, scant attention has been given to exploring domain-specific multiple achievement emotions experienced by second/foreign language (L2) learners in digital learning environments. This study aims to bridge the gap by validating a questionnaire to measure multiple achievement emotions experienced by Chinese English as a foreign language (EFL) learners during synchronous online English learning. One primary goal is to investigate the factorial structure and cultural sensitivity of achievement emotions in online English learning. This study further examines the interactions of achievement emotions with self-regulated learning (SRL) strategies and English performance to validate the criterion validity. Data were collected from 371 Chinese EFL students with intermediate to upper-intermediate L2 proficiency. Robust statistical methods, including confirmatory factor analysis and exploratory structural equation modeling (ESEM), were employed to elucidate the internal structure of achievement emotions. Model comparisons confirmed a seven-factor, correlated ESEM model comprising enjoyment, hope, pride, anger, shame-related anxiety, hopelessness, and boredom. Moderate correlations between achievement emotions and SRL strategies highlighted the interconnectedness of cognitive and emotional variables in L2 learning. Notably, this study revealed significant predictive effects of positive activating emotion (i.e. hope) and negative demotivating emotions (i.e. hopelessness and boredom) on English performance. This study concludes that the internal structure of multiple achievement emotions in online learning is embedded with situation-specific and culture-sensitivity characteristics. Moreover, the findings lend empirical support to the control-value theory, which holds that different achievement emotions have a joint and synergistic function in affecting learners’ SRL and academic performance.

Evaluating the Performances of Robust Logistic Regression Models in the Presence of Outliers

Logistic regression models are widely used in the field of medical and behavioral sciences. These models are used to describe the effect of explanatory variables on a binary response variable. The maximum likelihood estimator (MLE) is commonly used to estimate the parameters of logistic regression models due to its efficiency under a parametric model. However, evidence has shown that the MLE is highly sensitive to outlying observations which might affect the parameter estimates. Robust methods are put forward to rectify this problem. This paper investigated the robustness of GM-Mallows and GM-Schweppes as an alternative to the commonly used ordinary logistic regression model in the presence of outliers. The study used a Monte Carlos Simulation, by generating a logistic regression model with Five independent normally distributed covariates. 5% of outliers was contaminated to the data on sample sizes 50, 200 and 400 respectively. The results showed that the GM-Mallows estimator perform best across all metrics having the lowest AIC, BIC, MSE and MAE except for n=50. This suggests that the robust methods, especially GM-Mallows, provide more reliable estimates in the presence of outliers. The finding suggests that if there is presence of outliers’ GM-Mallows appears to be the top choice, where the GM-Schweppes offers a middle ground, providing some robustness with perhaps less extreme adjustments. The ordinary logistic regression might be preferred if simplicity and interpretability are prioritized, and there's confidence that outliers are not a significant issue in the data.

Assessing the Causal Role of Walking Pace and Hand Grip Strength with Chronic Obstructive Pulmonary Disease Hospital: A Mendelian Randomization Study

Background Chronic obstructive pulmonary disease(COPD) hospitalization heightens risks for patients, including mortality, reduced quality of life, and financial strain. Walking pace (WP) and hand grip strength (HGS) are key indicators, their direct connection to COPD hospitalization is uncertain. Objective To investigate the relationship between genetic determinants of walking pace, hand grip strength, and the risk of COPD hospitalization as well as lung function. Methods The data pertaining to WP (n = 459,915), HGS (n = 922,115), COPD hospitalizations (n = 309,154), and lung function (n = 79,055) were procured from comprehensive large-scale genome-wide association studies. In carrying out the causal inference analysis, robust statistical methods were utilized, encompassing inverse variance weighted (IVW), MR-Egger, weighted median, simple median and Weighted mode. To address issues of heterogeneity, pleiotropy, and outliers, we incorporated sensitivity analyses and Mendelian randomization (MR) techniques. Results The IVW analysis suggests that a faster WP reduces the risk of COPD hospitalization (OR = 0.3559, 95% CI: 0.22-0.52; p = 5.197 × 10−5). It also reveals a potential association between gait speed and the likelihood of developing early-onset COPD (OR = 0.189, 95% CI 0.09 to 0.39; p = 8.89 × 10-6) as well as late-onset COPD (OR = 0.44, 95% CI 0.25 to 0.76; p = 0.0036). The IVW analysis further indicates a potential correlation between an increased WP and enhanced peak expiratory flow (PEF) (OR = 1.699, 95% CI: 1.23 to 2.35; p = 0.0014), forced expiratory volume in 1 s(FEV1, OR = 1.557, 95% CI 1.24 to 1.95; p = 0.0001), and forced vital capacity(FVC, OR = 1.584, 95% CI 1.26 to 1.99; p = 8.89 × 10-5). The IVW analysis suggests a possible causal link between stronger left-hand grip strength and elevated levels of FVC (OR = 1.29, 95% CI: 1.15 to 1.46; p = 1.68 × 10-5), FEV1 (OR = 1.24, 95% CI: 1.11 to 1.39; p = 1.63 × 10-4), and PEF (OR = 1.2, 95% CI: 1.07 to 1.36; p = 2.67 × 10-3). Similarly, right-hand grip strength exhibits a comparable causal relationship with FVC and PEF as left-hand grip strength. Conclusions Our research shows a link between slower walking pace and higher COPD hospitalization risk, as well as decreased lung function (PEF, FEV1, FVC). We also found a significant correlation between weaker hand grip and reduced lung function, especially FVC. These findings have the potential to improve risk assessment approaches, intervention strategies, and management methods for COPD patients, while simultaneously enhancing their overall quality of life and health status.

A Bayesian Approach Toward Robust Multidimensional Ellipsoid-Specific Fitting.

This work presents a novel and effective method for fitting multidimensional ellipsoids (i.e., ellipsoids embedded in [Formula: see text]) to scattered data in the contamination of noise and outliers. Unlike conventional algebraic or geometric fitting paradigms that assume each measurement point is a noisy version of its nearest point on the ellipsoid, we approach the problem as a Bayesian parameter estimate process and maximize the posterior probability of a certain ellipsoidal solution given the data. We establish a more robust correlation between these points based on the predictive distribution within the Bayesian framework, i.e., considering each model point as a potential source for generating each measurement. Concretely, we incorporate a uniform prior distribution to constrain the search for primitive parameters within an ellipsoidal domain, ensuring ellipsoid-specific results regardless of inputs. We then establish the connection between measurement point and model data via Bayes' rule to enhance the method's robustness against noise. Due to independent of spatial dimensions, the proposed method not only delivers high-quality fittings to challenging elongated ellipsoids but also generalizes well to multidimensional spaces. To address outlier disturbances, often overlooked by previous approaches, we further introduce a uniform distribution on top of the predictive distribution to significantly enhance the algorithm's robustness against outliers. Thanks to the uniform prior, our maximum a posterior probability coincides with a more tractable maximum likelihood estimation problem, which is subsequently solved by a numerically stable Expectation Maximization (EM) framework. Moreover, we introduce an ε-accelerated technique to expedite the convergence of EM considerably. We also investigate the relationship between our algorithm and conventional least-squares-based ones, during which we theoretically prove our method's superior robustness. To the best of our knowledge, this is the first comprehensive method capable of performing multidimensional ellipsoid-specific fitting within the Bayesian optimization paradigm under diverse disturbances. We evaluate it across lower and higher dimensional spaces in the presence of heavy noise, outliers, and substantial variations in axis ratios. Also, we apply it to a wide range of practical applications such as microscopy cell counting, 3D reconstruction, geometric shape approximation, and magnetometer calibration tasks. In all these test contexts, our method consistently delivers flexible, robust, ellipsoid-specific performance, and achieves the state-of-the-art results.

Cropformer: An Interpretable Deep Learning Framework for Crop Genome Prediction.

Machine learning and deep learning have been employed in genomic selection (GS) to expedite the identification of superior genotypes and accelerate breeding cycles. However, a significant challenge for current data-driven deep learning models in GS is their low robustness and interpretability. To address this challenge, we developed Cropformer, a deep learning framework for predicting crop phenotypes and exploring downstream tasks. The framework consists of a combination of convolutional neural networks and multiple self-attention mechanisms to improve accuracy. Here, Cropformers ability to predict complex phenotypic traits was extensively evaluated on more than 20 traits across five major crops: maize, rice, wheat, foxtail millet, and tomato. Evaluation results show that Cropformer outperforms other GS methods in precision and robustness. Compared to the runner-up model, Cropformer's prediction accuracy improved by up to 7.5%. Additionally, Cropformer enhances the ability to analyze and assist the mining of genes associated with traits. With Cropformer, we identify dozens of single nucleotide polymorphisms (SNPs) with potential effects on maize phenotypic traits and reveal key genetic variations t underlying these differences. Cropformer makes considerable advances in predictive performance and assisted gene identification, representing a powerful general approach to facilitating the genomic design of crop breeding. Cropformer is freely accessible at https://cgris.net/cropformer.