Statistical Approach Research Articles

Enhancing service excellence: analyzing natural language question answering with advanced cosine similarity

<span lang="EN-US">Information related to student services in higher education must be produced and disseminated in various forms. Covid-19 pandemic, student services with a remote model related to this question and answer become very important. To carry out this automation process, the advanced cosine similarity method is used to check the similarity of the questions to the database and statistics to calculate the similarity value of each word. The proposed paper proceeds with three phases. The first stage to solve this problem is the data processed in question; the professional next step is word insertion. It converts alphanumeric words to vector format. Each word is a vector that represents a point in space with a certain dimension. The recommended advanced cosine similarity data still must be analyzed into a statistical approach. We will measure accuracy to get results so that optimal results and answers are obtained, research procedures are carried out based on literature study, initial data collection and observation, system development, system testing, system analysis, and system evaluation. This research implemented in universities with student chat automation applications providing an accuracy 83.90% given by natural language question answering system (NLQAS) so that it can improve excellent service in universities.</span>

Automatic translation from English to Amazigh using transformer learning

Due to the lack of parallel data, to our knowledge, no study has been conducted on the Amazigh-English language pair, despite the numerous machine translation studies completed between major European language pairs. We decided to utilize the neural machine translation (NMT) method on a parallel corpus of 137,322 sentences. The attention-based encoder-decoder architecture is used to construct statistical machine translation (SMT) models based on Moses, as well as NMT models using long short-term memory (LSTM), gated recurrent units (GRU), and transformers. Various outcomes were obtained for each strategy after several simulations: 80.7% accuracy was achieved using the statistical approach, 85.2% with the GRU model, 87.9% with the LSTM model, and 91.37% with the transformer.

Cholinesterase Inhibitory and In Silico Toxicity Assessment of Thirty-Four Isoquinoline Alkaloids - Berberine as the Lead Compound

Cholinesterase (ChE) inhibitors used currently in clinics for the treatment of Alzheimer's disease (AD) are the most prescribed drug class with nitrogen-containing chemical formula. Galanthamine, the latest generation anti-ChE drug, contains an isoquinoline structure. The aim of the current study was to investigate the inhibitory potential of thirty-four isoquinoline alkaloids, e.g. (-)-adlumidine, β-allocryptopine, berberine, (+)-bicuculline, (-)-bicuculline, (+)-bulbocapnine, (-)-canadine, (±)-chelidimerine, corydaldine, (±)-corydalidzine, (-)-corydalmine, (+)-cularicine, dehydrocavidine, (+)-fumariline, (-)-fumarophycine, (+)-α-hydrastine, (+)-isoboldine, 13-methylcolumbamine, (-)-norjuziphine, norsanguinarine, (-)-ophiocarpine, (-)-ophiocarpine-N-oxide, oxocularine, oxosarcocapnine, palmatine, (+)-parfumine, protopine, (+)-reticuline, sanguinarine, (+)-scoulerine, (±)-sibiricine, (±)-sibiricine acetate, (-)-sinactine, and (-)-stylopine isolated from several Fumaria (fumitory) and Corydalis species towards acetyl- (AChE) and butyrylcholinesterase (BChE) by microtiter plate assays. The alkaloids with strong ChE inhibition were proceeded to molecular docking simulations as well as in silico toxicity screening for their mutagenic capacity through VEGA QSAR (AMES test) consensus model and VEGA platform as statistical approaches. The inputs were evaluated in a simplified molecular input-line entry system (SMILES). ChE inhibition assays indicated that the highest AChE inhibition was caused by berberine (IC50: 0.72 ± 0.04 µg/mL), palmatine (IC50: 6.29 ± 0.61 µg/mL), β-allocryptopine (IC50: 10.62 ± 0.45 µg/mL), (-)-sinactine (IC50: 11.94 ± 0.44 µg/mL), and dehydrocavidine (IC50: 15.01 ± 1.87 µg/mL) as compared to that of galanthamine (IC50: 0.74 ± 0.01 µg/mL), the reference drug with isoquinoline skeleton. Less number of the tested alkaloids exhibited notable BChE inhibition. Among them, berberine (IC50: 7.67 ± 0.36 µg/mL) and (-)-corydalmine (IC50: 7.78 ± 0.38 µg/mL) displayed a stronger inhibition than that of galanthamine (IC50: 12.02 ± 0.25 µg/mL). The mutagenic activity was shown for β-allocryptopine, (+)- and (-)-bicuculline, (±)-corydalidzine, (-)-corydalmine, (+)-cularicine, (-)-fumarophycine, (-)-norjuziphine, (-)-ophiocarpine-N-oxide, (+)-scoulerine, (-)-sinactine, and (-)-stylopine by means of in silico experiments. The results obtained by molecular docking simulations of berberine, palmatine, and (-)-corydalmine suggested that the estimated free ligand-binding energies of these compounds inside the binding domains of their targets are reasonable to make them capable of establishing strong polar and nonpolar bonds with the atoms of the active site amino acids. Our findings revealed that berberine, palmatin, and (-)-corydalmine stand out as the most promising isoquinoline alkaloids in terms of ChE inhibition. Among them, berberine has displayed a robust dual inhibition against both ChEs and could be evaluated further as a lead compound for AD.

A statistical approach to evaluate the effect of obstacles on Green Building Development in Northeast India

The practice of "green building" (GB) is gaining popularity in the building sector as a practical means of meeting the rising demand for eco-friendly or sustainable structures. However, the adoption of GB has been slower than it ought to have been. This study examines the significance of several obstacles to the widespread acceptance of GB. The findings gathered through statistical analysis of the responses received from a questionnaire survey provided to experts in the field of GB throughout the Northeastern states, are provided. A thorough review was done to identify 18 possible barriers to GBD in Northeast India. Descriptive and inferential statistical studies (ANOVA and Exploratory Factor Analysis) were done to evaluate the effects of different obstacles on GBD and categorize them into manageable factors. To examine the impact of various obstacles on GBD, a partial least squares structural equation model (PLS-SEM) was established. The results revealed that poor geographic location, rating system limitations, enormous financial consequences, inadequate market conditions and policies, project viability, and poor public perception of GB contribute to Northeast India's GBD challenges. The conclusions of this paper might assist policymakers in identifying issues in GB adoption and developing appropriate measures for their wider acceptance.

Influence of regional and yearly weather patterns on multi-mycotoxin occurrence in Austrian wheat: a liquid chromatographic-tandem mass spectrometric and multivariate statistics approach.

Mycotoxin surveys play an essential role in our food safety system. The obtained occurrence data form the basis for the assessment of the exposure of humans and animals to these toxic fungal secondary metabolites. Liquid chromatography coupled with tandem mass spectrometry (LC-MS/MS) has become the gold standard for mycotoxin determination because it enables selective and sensitive multi-toxin analysis. Simultaneous determination of several hundreds of secondary fungal metabolites is feasible using this technique. In this study, we combined a targeted dilute-and-shoot LC-MS/MS-based multi-analyte approach with multivariate statistics for the analysis of Austrian wheat from two different years and different geographical origins. We quantified 47 secondary fungal metabolites, including regulated emerging and masked mycotoxins. The resulting multi-mycotoxin occurrence data were further analyzed using both multivariate and univariate statistics. Principal component analysis (PCA) and analysis of variance (ANOVA) simultaneous component analysis (ASCA) were employed to identify regional and yearly trends within the dataset and to quantify the variance in metabolite occurrence attributed to the different effects. In addition, secondary fungal metabolites significantly impacted by these factors were selected via ANOVA. Of the 47 secondary metabolites identified, 39 were affected by the year, region or a combined effect. Moreover, our findings show that 43 of the secondary fungal metabolites were significantly influenced by the weather conditions. The results presented in this study underline the added value of combining targeted LC-MS/MS with multivariate statistics for monitoring a broad spectrum of secondary fungal metabolites in food crops. Through multivariate statistics, trends associated with the year or region can be readily studied. The approach presented could pave the way for a better understanding of the impact of climate change on plant pathogenic fungi and its implications for food safety. © 2024 The Author(s). Journal of The Science of Food and Agriculture published by John Wiley & Sons Ltd on behalf of Society of Chemical Industry.

Causal effect of blood osteocalcin on the risk of Alzheimer’s disease and the mediating role of energy metabolism

Growing evidence suggests an association between osteocalcin (OCN), a peptide derived from bone and involved in regulating glucose and lipid metabolism, and the risk of Alzheimer’s disease (AD). However, the causality of these associations and the underlying mechanisms remain uncertain. We utilized a Mendelian randomization (MR) approach to investigate the causal effects of blood OCN levels on AD and to assess the potential involvement of glucose and lipid metabolism. Independent instrumental variables strongly associated (P < 5E-08) with blood OCN levels were obtained from three independent genome-wide association studies (GWAS) on the human blood proteome (N = 3301 to 35,892). Two distinct summary statistics datasets on AD from the International Genomics of Alzheimer’s Project (IGAP, N = 63,926) and a recent study including familial-proxy AD patients (FPAD, N = 472,868) were used. Summary-level data for fasting glucose (FG), 2h-glucose post-challenge, fasting insulin, HbA1c, low-density lipoprotein cholesterol, high-density lipoprotein cholesterol, total cholesterol (TC), and triglycerides were incorporated to evaluate the potential role of glucose and lipid metabolism in mediating the impact of OCN on AD risk. Our findings consistently demonstrate a significantly negative correlation between genetically determined blood OCN levels and the risk of AD (IGAP: odds ratio [OR, 95%CI] = 0.83[0.72–0.96], P = 0.013; FPAD: OR = 0.81 [0.70–0.93], P = 0.002). Similar estimates with the same trend direction were obtained using other statistical approaches. Furthermore, employing multivariable MR analysis, we found that the causal relationship between OCN levels and AD was disappeared after adjustment of FG and TC (IGAP: OR = 0.97[0.80–1.17], P = 0.753; FPAD: OR = 0.98 [0.84–1.15], P = 0.831). There were no apparent instances of horizontal pleiotropy, and leave-one-out analysis showed good stability of the estimates. Our study provides evidence supporting a protective effect of blood OCN levels on AD, which is primarily mediated through regulating FG and TC levels. Further studies are warranted to elucidate the underlying physio-pathological mechanisms.

Statistical Mechanics Approaches for Studying Temperature and Rate Effects in Multistable Systems

Systems with a multistable energy landscape are widespread in physics, biophysics, technology, and materials science. They are strongly influenced by thermal fluctuations and external mechanical actions that can be applied at different rates, moving the system from equilibrium to non-equilibrium regimes. In this paper, we focus on a simple system involving a single breaking phenomenon to describe the various theoretical approaches used to study these problems. To begin with, we propose the exact solution at thermodynamic equilibrium based on the calculation of the partition function without approximations. We then introduce the technique of spin variables, which is able to simplify the treatment even for systems with a large number of coordinates. We then analyze the energy balance of the system to better understand its underlying physics. Finally, we introduce a technique based on transition state theory useful for studying the non-equilibrium dynamical regimes of these systems. This method is appropriate for the evaluation of rate effects and hysteresis loops. These approaches are developed for both the Helmholtz ensemble (prescribed extension) and the Gibbs ensemble (applied force) of statistical mechanics. The symmetry and duality of these two ensembles is discussed in depth. While these techniques are used here for a simple system with theoretical purposes, they can be applied to complex systems of interest for several physical, biophysical, and technological applications.

Hydrogeochemical evolution and processes based on multivariable statistical and inverse simulation modeling: A coal mine in the Northern Coalfield, China

ABSTRACT Considering groundwater from the aquifers overlying the bedrock is an important water source for drinking purposes. As such, the investigation of its property is essential. Based on the spatial structure of aquifers in the study area, the aquifers in the Cenozoic strata are divided into three groups. The multivariate statistical approaches are employed to identify the hydrogeochemical processes and hydro-chemical types, and hydrogeochemical inverse modeling is applied to further validate and elucidate the hydrogeochemical process and water–rock interaction. The results are as follows: (1) The hydro-chemical type of the upper aquifer is dominated by the K + Na-HCO3 type, while others have similar water quality types, which are dominated by the K + Na-Cl type and the K + Na-SO4 type. (2) The saturation index of anhydrite, gypsum, halite, and CO2(g) is below zero in three aquifers, indicating that they are unsaturated. While aragonite, calcite, and dolomite in the middle aquifer remain in the unsaturated–saturated state. (3) The cation exchange process accelerating the reduction of Ca2+ concentration and the increase of SO42− concentration occurs in three aquifers, and the dissolution of calcite and dolomite minerals occurs in most cases. This study supports the fundamental evidence for the hydrogeochemical processes and water resource utilization and has a certain practical significance.

Comparison of fatigue crack growth design curves on GMAW and EBW joints of high strength steels

AbstractThere is a growing demand in the industrial sector for the use of high-strength structural steels (HSSSs), which can achieve a significant weight reduction in structures. These structural steels are usually produced by quenching and tempering (Q + T) or thermomechanical treatment (TM), and their applications in welded structures pose several challenges for the users. In industrial practice, gas metal arc welding (GMAW) is basically the most commonly used fusion welding process, which has a relatively high heat input. However, at HSSSs, there is a need for low heat input but, at the same time, productive welding processes. High-energy density welding processes, e.g., electron beam welding (EBW), offer a unique opportunity to weld these steels. The widespread use of HSSSs is also hampered by the fact that the benefits of high strength can be exploited primarily under static loading. At the same time, different welded structures made of HSSSs are often subjected to cyclic loading, and possible weld defects and material discontinuities are major risks in this case. During our experiments, GMAW and autogenous EBW processes were applied to make welded joints from S960 Q + T and TM structural steels. The fatigue resistance of the welded joints was characterized by fatigue crack growth (FCG) tests, considering the increased crack sensitivity of HSSSs. A statistical approach was followed both in the design of the experiments and in the evaluation of their results. Based on the test results fatigue crack propagation design curves were determined for the investigated GMAW and EBW welded joints. The design curves were compared with each other, with design curves of lower strength material (S690QL) and with the recommended fatigue crack growth laws of BS 7910.

Inverse design of a pyrochlore lattice of DNA origami through model-driven experiments.

Sophisticated statistical mechanics approaches and human intuition have demonstrated the possibility of self-assembling complex lattices or finite-size constructs. However, attempts so far have mostly only been successful in silico and often fail in experiment because of unpredicted traps associated with kinetic slowing down (gelation, glass transition) and competing ordered structures. Theoretical predictions also face the difficulty of encoding the desired interparticle interaction potential with the experimentally available nano- and micrometer-sized particles. To overcome these issues, we combine SAT assembly (a patchy-particle interaction design algorithm based on constrained optimization) with coarse-grained simulations of DNA nanotechnology to experimentally realize trap-free self-assembly pathways. We use this approach to assemble a pyrochlore three-dimensional lattice, coveted for its promise in the construction of optical metamaterials, and characterize it with small-angle x-ray scattering and scanning electron microscopy visualization.

Exploring Innovative Approaches to Synthetic Tabular Data Generation

The rapid advancement of data generation techniques has spurred innovation across multiple domains. This comprehensive review delves into the realm of data generation methodologies, with a keen focus on statistical and machine learning-based approaches. Notably, novel strategies like the divide-and-conquer (DC) approach and cutting-edge models such as GANBLR have emerged to tackle a spectrum of challenges, spanning from preserving intricate data relationships to enhancing interpretability. Furthermore, the integration of generative adversarial networks (GANs) has sparked a revolution in data generation across sectors like healthcare, cybersecurity, and retail. This review meticulously examines how these techniques mitigate issues such as class imbalance, data scarcity, and privacy concerns. Through a meticulous analysis of evaluation metrics and diverse applications, it underscores the efficacy and potential of synthetic data in refining predictive models and decision-making software. Concluding with insights into prospective research trajectories and the evolving role of synthetic data in propelling machine learning and data-driven solutions across disciplines, this work provides a holistic understanding of the transformative power of contemporary data generation methodologies.

Comparison between Statistical Approaches and Data Mining Algorithms for Outlier Detection

Outliers are observation values that are very different from most observations. The presence of outliers in data can have a negative impact on research but can contain important information for other research. So, identifying outliers before conducting data analysis is a crucial thing to do. Outlier detection methods/techniques were first pioneered by researchers in statistics. However, due to rapid technological advances which have an impact on the ease of collecting extensive data, the development of outlier detection techniques is now handled mainly by researchers in the field of computer science (data mining) using computing facilities. This research aims to examine the results of simulation studies by comparing methods for identifying several outliers using statistical approaches and data mining algorithm approaches in various predetermined data scenarios. Based on the scenario carried out, the outlier detection method using a statistical approach is generally better than the outlier detection method using a data mining-based approach. Suggestions for further research are to improve the data mining method by focusing more on statistical analysis apart from focusing on data processing computing time so that the expected results of outlier detection are faster and more precise.

Laparotomy for Traumatic Splenic Injury: Should we Increase our Efforts to Preserve the Spleen?

Abstract Background In trauma laparotomy the spleen is removed very liberally, and often little effort is made for splenic preservation. Aims The aim of this study was to assess the effect of surgical management (splenic repair vs. splenectomy) on outcomes in patients undergoing trauma laparotomy. Methods This is a trauma registry (TQIP) study (2013–2019), including adult patients with severe splenic injuries (grades III-V) undergoing trauma laparotomy. Characteristics and outcomes of patients who underwent splenic repair vs. splenectomy within 6 hours of admission were compared using different statistical approaches, i.e. 1:1 exact matching with consecutive conditional logistic regression analysis as primary analysis, multivariable logistic regression, propensity score matching and inverse-probability weighting as sensitivity analyses. The primary outcome was in-hospital mortality. Results A total of 11,247 patients with a severe splenic injury undergoing trauma laparotomy within 6 hours of admission were identified. Of these, 10,820 (96.2%) patients underwent splenectomy and 427 (3.8%) patients splenic repair within 6 hours of admission. Among patients who underwent an initial splenic salvage procedure, 23 (5.3%) patients underwent a splenectomy during the subsequent hospital stay. 400 patients with splenic preservation, were matched with 400 patients who underwent splenectomy (matched for age, sex, hypotension, trauma mechanism as well as AIS spleen grades 3,4,5 and AIS groups (AIS 0-2, AIS 3, AIS 4-5) for head, face, neck, thorax, spine, and lower &amp; upper extremity). The mortality was significantly lower in the group of patients with splenic repair (6.5% vs 12.8%, p=0.002). The mortality benefit was subsequently verified by conditional regression analysis (aOR 0.4, CI 95% 0.2; 0.9). Multivariable logistic regression, propensity score matching, and inverse-probability weighting confirmed the lower mortality in the splenic repair group. Conclusion In trauma laparotomy splenic repair was independently associated with lower mortality compared with splenectomy. Splenectomy was required in approximately 5% of the patients after initial splenic repair. The findings provide evidence that efforts for spleen preservation in trauma laparotomy should be considered in selected cases.

Estimation of planned and unplanned missing individual scores in longitudinal designs using continuous-time state-space models.

Latent change score (LCS) models within a continuous-time state-space modeling framework provide a convenient statistical approach for analyzing developmental data. In this study, we evaluate the robustness of such an approach in the context of accelerated longitudinal designs (ALDs). ALDs are especially interesting because they imply a very high rate of planned data missingness. Additionally, most longitudinal studies present unexpected participant attrition leading to unplanned missing data. Therefore, in ALDs, both sources of data missingness are combined. Previous research has shown that ALDs for developmental research allow recovering the population generating process. However, it is unknown how participant attrition impacts the model estimates. We have three goals: (a) to evaluate the robustness of the group-level parameter estimates in scenarios with empirically plausible unplanned data missingness; (b) to evaluate the performance of Kalman scores (KS) imputations for individual data points that were expected but unobserved; and (c) to evaluate the performance of KS imputations for individual data points that were outside the age ranged observed for each case (i.e., to estimate the individual trajectories for the complete age range under study). In general, results showed lack of bias in the simulated conditions. The variability of the estimates increased with lower sample sizes and higher missingness severity. Similarly, we found very accurate estimates of individual scores for both planned and unplanned missing data points. These results are very important for applied practitioners in terms of forecasting and making individual-level decisions. R code is provided to facilitate its implementation by applied researchers. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Accommodating detection limits of multiple exposures in environmental mixture analyses: an overview of statistical approaches

BackgroundIdentifying the impact of environmental mixtures on human health is an important topic. However, such studies face challenges when exposure measurements lie below limit of detection (LOD). While various approaches for accommodating a single exposure subject to LOD have been used, their impact on mixture analysis has not been thoroughly investigated. Our study aims to understand the impact of five popular LOD accommodation approaches on mixture analysis results with multiple exposures subject to LOD, including omitting subjects with any exposures below LOD (complete case analysis); single imputations by LOD/2\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\sqrt{2}$$\\end{document}, and by estimates from a censored accelerated failure time (AFT) model; and multiple imputation (MI) with or without truncation based on LOD.MethodsIn extensive simulation studies with high-dimensional and highly correlated exposures and a continuous health outcome, we examined the performance of each LOD approach on three mixture analysis methods: elastic net regression, weighted quantile sum regression (WQS) and Bayesian kernel machine regression (BKMR). We further analyzed data from the National Health and Nutrition Examination Survey (NHANES) on how persistent organic pollutants (POPs) influenced leukocyte telomere length (LTL).ResultsComplete case analysis was inefficient and could result in severe bias for some mixture methods. Imputation by LOD/2\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\sqrt{2}$$\\end{document} showed unstable performance across mixture methods. Conventional MI was associated with consistent mild biases, which can be reduced by using a truncated distribution for imputation. Estimating censored values by AFT models had a minimal impact on the results. In the NHANES analysis, imputation by LOD/2\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\sqrt{2}$$\\end{document}, truncated MI and censored AFT models performed similarly, with a positive overall effect of POPs on LTL while PCB126, PCB169 and furan 2,3,4,7,8-pncdf being the most important exposures.ConclusionsOur study favored using truncated MI and censored AFT models to accommodate values below LOD for the stability of downstream mixture analysis.

Mitigating Malware Threats on Emerging Technology: A Machine Learning Approach

Malicious programs and malware threats lead to a substantial vulnerability and pose a fundamental problem. Nowadays, smart devices are becoming more common, and consequently, the risk of malware intrusion is highly observed. This paper presents a comprehensive exploration from the initial to the final phase of an effective strategy and the deployment of a model to detect malware efficiently. The proposed Mitigating Malware Threats on Emerging Technology framework “MMTET” will help mitigate the risk of intrusion. This study explores the complexity of handling datasets. Random Forests and Decision Trees serve as machine learning algorithms for training and testing. Starting with a data collection method to obtain relevant parameters, this paper highlights the importance of well-curated datasets in training using effective machine learning models. Data analysis follows a statistical approach, and the visualization tools are used for identifying inherent biases, imbalances, and trends in datasets. For boosting the dataset’s quality, feature engineering and selection take a central stage to balance the data with new methodologies and detect relevant features with correlation analysis. Experimental result shows that Random Forest has the best performance compared to other methods obtained from different algorithms, with accuracy 98.30%.

Assessment of Anharmonicities in Clusters: Developing and Validating a Minimum-Information Partition Function.

Precise thermodynamic calculations are essential for understanding the dynamics of cluster systems and new particle formation. However, the widely employed harmonic statistical mechanical approach often falls short in terms of accuracy. In this study, we present an improved statistical model that incorporates vibrational anharmonicity via a novel partition function that requires only one additional system-specific input parameter. In addition to considering vibrational aspects, we also account for anharmonicity related to the configurational space. The role of anharmonicities is thoroughly examined in the case of general clusters, where the complete sets of conformers, mechanically stable spatial arrangements, are known up to clusters composed of 14 monomers. By performing consistent Monte Carlo simulations on these systems, we benchmark the statistical model's efficacy in reproducing key thermodynamic properties (formation free energy and potential energy) in the classical limit. The model exhibits exceptional alignment with simulations, accurately reproducing free energies within a precision of 2kBT and reliably capturing cluster melting temperatures. Furthermore, we demonstrate the significance and applicability of the model by reproducing thermodynamic barriers in homogeneous gas-phase nucleation of larger clusters. The transferability of our developed approach extends to more complex molecular systems and bears relevance for atmospheric multicomponent clusters, in particular.

Using portable X-ray fluorescence elemental analysis to explore porous skeletal lesions: Interplay of sex, age at death, and cause of death.

Search for possible associations between bone elemental concentration and the presence of porous skeletal lesions (PSLs), considering the sex, age, and cause of death (COD) of the individuals. The sample comprised 107 non-adult individuals (56 females, 51 males) aged 0-20 (x̄ = 13.2, SD = 5.8) from the Coimbra and Lisbon Identified Skeletal Collections. Cribra cranii, orbitalia, humeralis, and femoralis were recorded as present/absent, and elemental concentrations were assessed by portable x-ray fluorescence (pXRF). A multivariate statistical approach was applied. Well-preserved skeletons with minimal diagenesis showed no sex-related elemental variations or PSL associations. In contrast, age-at-death correlated with elevated Ca, P, Sr, and Pb levels. Cribra cranii increased with age while other cribra declined post-adolescence. Higher concentrations of Fe and lower of S were linked to cribra cranii. Respiratory infections as COD increased the odds of expressing cribra femoralis (OR = 5.25, CI = 1.25-15.14), cribra cranii (OR = 2.91, CI = 0.97-8.69), and cribra orbitalia (OR = 2.76, CI = 1.06-7.24). Feasible pXRF results and low cribra intraobserver error assure replicability. Elevated Ca, P, and Sr in older individuals may relate to skeletal growth, while increased Pb suggests bioaccumulation. Cribra's increase with age reflects different rates of marrow conversion and bone remodeling. Higher Fe and lower S in individuals with cribra cranii possibly reflects poor nutrition, early alcohol use, and sideroblastic anemia, aligning with 19th-20th-century Portugal's living conditions. Respiratory infections increased cribra expression, revealing intricate interplays among inflammation, anemia(s), marrow expansion, and diet. This research highlights a complex scenario and blazes a new path for cribra interpretation.

Assessment of the Dynamics of Structural Changes in the Socio-Economic Environment and Their Typology in the Context of a Statistical Approach

The purpose of the study is to substantiate the documentation for assessing the dynamics of structural changes in the socio-economic environment and their typology. The relevance of the study is related to the problems of reflecting the socio-economic environment, which affect the efficiency of economic activity. To address these issues, many regions of the country have adopted regional development strategies for the period up to 2030, which correspond to the national development goals of the Russian Federation. Various approaches of scientists to determining the essence and significance of the socio-economic environment, which is defined as a set of conditions and factors that influence all sectors of the economy, are studied.Methods: structural, index, comparative estimates and methodology of the system of national accounts.Results: the article uses a digital meter as a reproduction of the population and gross regional product (GRP), which objectively reflect the structural changes and typologies of the regions. GRP is calculated by types of economic activity, at the stages of: production, income generation and final consumption of households 2012-2021. Typologies of the Ural regions have been developed according to the following criteria: specialization of the economy, the position of the region in production, by types of primary income and final consumption of households per capita.Conclusion: the advantage of the statistical approach of studying the assessment of the dynamics of structural changes in the socio-economic environment and their typology allows us to get a more objective idea of the prevailing trends and the state of the regional economy. The results obtained made it possible to formulate a methodological basis for future scientific research in the field of structural changes in the meso-environment and socio-economic behavior of the population. They can also be used as a methodological basis for the development of public policy measures and strategies for sustainable development of territories in the future.

Optimizing wheelchair basketball lineups: A statistical approach to coaching strategies.

This study was designed to support the tactical decisions of wheelchair basketball (WB) coaches in identifying the best players to form winning lineups. Data related to a complete regular season of a top-level WB Championship were examined. By analyzing game-related statistics from the first round, two clusters were identified that accounted for approximately 35% of the total variance. Cluster 1 was composed of low-performing athletes, while Cluster 2 was composed of high-performing athletes. Based on data related to the second round of the Championship, we conducted a two-fold evaluation of the clusters identified in the first round with the team's net performance as the outcome variable. The results showed that teams where players belonging to Cluster 2 had played more time during the second round of the championship were also those with the better team performance (R-squared = 0.48, p = 0.035), while increasing the playing time for players from Classes III and IV does not necessarily improve team performance (r2 = -0.14, p = 0.59). These results of the present study suggest that a collaborative approach between coaches and data scientists would significantly advance this Paralympic sport.