Advanced Statistical Techniques Research Articles

Enhancing prediction of elemental composition through machine learning decision tree models for biomass gasification optimization

Abstract The worldwide transition to cleaner, sustainable energy sources, prompted by population growth and industrialization, responds to uncertain fossil fuel prices and environmental concerns, highlighting the substantial benefits of renewable energy in reducing greenhouse gas emissions and addressing climate change. Derived from non-fossilized organic materials, biomass emerges as a significant and sustainable contributor to renewable energy. Its diverse nature is complemented by a range of conversion technologies, encompassing combustion, pyrolysis, gasification, and liquefaction, providing versatile avenues for biomass energy transformation. Gasification, the transformative process of converting organic matter into combustible gases under controlled oxygen levels, is accomplished through direct oxygen supply or pyrolysis. This method yields a dependable gaseous fuel versatile for heating, industrial processes, power generation, and liquid fuel production. Machine learning employs advanced statistical techniques for modeling across diverse industries, showcasing particular efficacy in optimizing thermochemical processes by precisely identifying the optimal operating conditions required for achieving desired product properties. These models utilize proximate biomass data to predict the elemental compositions of N2 and H2. Assessment of both single and two hybrid models indicated that the introduced optimizers significantly enhanced the estimation of N2 and H2 when combined with Decision Tree (DT), with Decision Tree Coupled with Artificial Hummingbird Algorithm (DTAH) proving particularly effective. Notably, DTAH demonstrated outstanding performance with remarkable R 2 values of 0.990 for N2 and 0.992 for H2. Additionally, the minimal Root Mean Square Error (RMSE) values of 1.291 and 1.550 for N2 and H2 predictions respectively underscore the precision of DTAH, establishing it as a suitable choice for practical real-world applications.

Assessing changes in intra- and inter-annual precipitation variability using advanced statistical inference techniques

ABSTRACT Aiming to find statistically significant changes on the usual monthly weather conditions in Alentejo, Portugal, in the past 40 years, time series of precipitation from a grid of locations in Alentejo were studied using an unconventional approach. The time series were divided into 4 decades and disposed in contingency tables with two factors, Year and Month. Then, log-linear models were fitted to those tables. The model deviances for Year and Month represent the weight of each factor in explaning precipitation varibility and Year:Month deviance is an estimate of the lack of independence between both factors, representing changes in intra-annual precipitation variability. The set of all the models deviances, per decade and location, were analysed using ANOVA techniques, first to compare the four decades, then to perform a crossed comparison between decades and between four pre-defined zones. Results indicate significant differences between the oldest and recent decades in terms of intra-annual precipitation variability, which could be interpreted as a trend towards softening the differences in precipitation between the wet months of the year. Furthermore, two homogeneous sub-regions could be defined in Alentejo, a large interior region and a smaller one close to the Atlantic Ocean.

ANALIZA STATYSTYK SIECI KOMPUTEROWYCH W CELU IDENTYFIKACJI PRZEPŁYWÓW INFORMACJI ZAKŁÓCAJĄCYCH STABILNOŚĆ W WOJSKOWYCH SIECIACH LOKALNYCH

The increasing complexity and scope of military computer networks necessitate robust methods to ensure network stability and security. This study presents a comprehensive analysis of computer network statistics in military local networks to develop a method for detecting information flows that disrupt stability. By leveraging advanced statistical techniques and machine learning algorithms, this research aims to enhance the cybersecurity posture of military local networks globally. Military networks are vital for communication, data exchange, and operational coordination. However, the dynamic nature of network traffic and the persistent threat of cyberattacks pose significant challenges to maintaining network stability. Traditional monitoring techniques often fail to meet the unique requirements of military networks, which demand high levels of security and rapid response capabilities. This study employs a multi-faceted approach to detect anomalies in network traffic, utilizing statistical methods such as Z-score analysis, Principal Component Analysis (PCA), and Autoregressive Integrated Moving Average (ARIMA) models. Machine learning techniques, including Support Vector Machines (SVM), Random Forests, Neural Networks, K-means clustering, and Reinforcement Learning, are also applied to identify patterns indicative of stability-disrupting information flows. The integration of statistical and machine learning methods forms a hybrid model that enhances anomaly detection, providing a robust framework for network security. The research problem is formulated as follows: does data collection include comprehensive network traffic data from various segments of military local area networks, including packet flows, transmission rates, and error rates over a specified period? Statistical analysis identifies patterns in the network traffic, which are then used to train machine learning models to classify normal and abnormal traffic. The research hypothesis states that machine learning models achieve high accuracy in detecting stability-disrupting information flows, with a precision rate exceeding 90%. The models identified several instances of stability-disrupting events, correlating these with known security incidents to validate the effectiveness of the detection method. This study underscores the importance of continuous monitoring and analysis of network statistics to ensure stability and security. The proposed method can be integrated with existing network monitoring and intrusion detection systems, providing a comprehensive approach to network security. Future research can build on these findings to develop more sophisticated models and explore additional factors influencing network stability, including the incorporation of advanced machine learning techniques, such as deep learning, and the exploration of other network metrics, like latency and packet loss. This comprehensive approach aims to enhance the security and operational reliability of military local networks.

Utilizing machine learning to forecast mechanical characteristics of NaOH-Treated jute fiber reinforced composite materials

The prediction of mechanical properties in composite materials is crucial for optimizing their performance in various engineering applications. This study focuses on NaOH treated jute fiber reinforced glass epoxy composites, aiming to predict key mechanical characteristics like flexural strength, tensile strength, and the hardness. A comprehensive dataset of 974 samples was analyzed, encompassing a range of input parameters including fiber content, fiber orientation, matrix type, and processing conditions. Advanced statistical and machine learning techniques were employed to develop predictive models, with XGBoost showing itself to be the best model in terms of predicting each of the three mechanical properties. Feature importance analysis revealed that fiber content is the most significant factor influencing the predictions.

Investigating the Connection Among Quality Management Effectiveness and Organizational Culture

The success of an organization depends on its quality management (QM) and organizational culture (OC). While QM seeks to improve the caliber of products and services, OC consider to share beliefs and behaviors that impact working relationships. Together, they deliver excellent performance and a competitive advantage. The goal of the study is to evaluate how various OC factors affect QM methods and their effectiveness. The comprehensive evaluation provided by the study is based on data from 320 experts, validated questionnaires and advanced statistical techniques like correlation analysis, ANOVA and multiple regression. The study shows a significant relationship between certain OC variables and QM outcomes, highlighting the advantages of process adherence, communication process and encouraging leadership style on the effectiveness of QM and customer satisfaction. OC must be in line with QM objectives to improve customer experiences and organizational success. The research highlights the significance of integrating OC elements into QM strategies to foster an environment that drives higher output and improved customer satisfaction.

Application of Advanced Statistical Techniques to Improve the Prediction of Student Performance in Mathematics

The present study explores the application of advanced statistical techniques to predict the academic performance of students in the area of mathematics. Through the use of logistic regression models, decision trees, and neural networks, data from 500 high school students in public institutions were analyzed. The results show that advanced statistical techniques allow a more accurate prediction of academic performance, with a success rate of 87% in neural network models. These findings suggest that the integration of these tools can facilitate the early identification of students at risk of low achievement and improve educational interventions.

Mapping Transmission Dynamics and Drug Resistance Surveillance in the Cyprus HIV-1 Epidemic (2017-2021).

The human immunodeficiency virus type 1 (HIV-1) epidemic has been a major public health threat on a global scale since the early 1980s. Despite the introduction of combination antiretroviral therapy (cART), the incidence of new HIV-1 infections continues to rise in some regions around the world. Thus, with the continuous transmission of HIV-1 and the lack of a cure, it is imperative for molecular epidemiological studies to be performed, to monitor the infection and ultimately be able to control the spread of this virus. This work provides a comprehensive molecular epidemiological analysis of the HIV-1 infection in Cyprus, through examining 305 HIV-1 sequences collected between 9 March 2017 and 14 October 2021. Employing advanced statistical and bioinformatic techniques, the research delved deeply into understanding the transmission dynamics of the HIV-1 epidemic in Cyprus, as well as the monitoring of HIV-1's genetic diversity and the surveillance of transmitted drug resistance. The characterization of Cyprus's HIV-1 epidemic revealed a diverse landscape, comprising 21 HIV-1 group M pure subtypes and circulating recombinant forms (CRFs), alongside numerous uncharacterized recombinant strains. Subtypes A1 and B emerged as the most prevalent strains, followed by CRF02_AG. The findings of this study also revealed high levels of transmitted drug resistance (TDR) patterns, raising concerns for the efficacy of cART. The demographic profiles of individuals involved in HIV-1 transmission underscored the disproportionate burden borne by young to middle-aged Cypriot males, particularly those in the MSM community, who reported contracting the virus in Cyprus. An assessment of the spatiotemporal evolutionary dynamics illustrated the global interconnectedness of HIV-1 transmission networks, implicating five continents in the dissemination of strains within Cyprus: Europe, Africa, Asia, North America, and Oceania. Overall, this study advances the comprehension of the HIV-1 epidemic in Cyprus and highlights the importance of understanding HIV-1's transmission dynamics through continuous surveillance efforts. Furthermore, this work emphasizes the critical role of state-of-the-art bioinformatics analyses in addressing the challenges posed by HIV-1 transmission globally, laying the groundwork for public health interventions aimed at curbing its spread and improving patient outcomes.

Sleep Quality and Urinary Incontinence in Prostate Cancer Patients: A Data Analytics Approach with the ASCAPE Dataset.

The ASCAPE project aims to improve the health-related quality of life of cancer patients using artificial intelligence (AI)-driven solutions. The current study employs a comprehensive dataset to evaluate sleep and urinary incontinence, thus enabling the development of personalized interventions. This study focuses on prostate cancer patients eligible for curative treatment with surgery. Forty-two participants were enrolled following their diagnosis and were followed up at baseline and 3, 6, 9, and 12 months after surgical treatment. The data collection process involved a combination of standardized questionnaires and wearable devices, providing a holistic view of patients' QoL and health outcomes. The dataset is systematically organized and stored in a centralized database, with advanced statistical and AI techniques being employed to reveal correlations, patterns, and predictive markers that can ultimately lead to implementing personalized intervention strategies, ultimately enhancing patient QoL outcomes. The correlation analysis between sleep quality and urinary symptoms post-surgery revealed a moderate positive correlation between baseline insomnia and baseline urinary symptoms (r = 0.407, p = 0.011), a positive correlation between baseline insomnia and urinary symptoms at 3 months (r = 0.321, p = 0.049), and significant correlations between insomnia at 12 months and urinary symptoms at 3 months (r = 0.396, p = 0.014) and at 6 months (r = 0.384, p = 0.017). Furthermore, modeling the relationship between baseline insomnia and baseline urinary symptoms showed that baseline insomnia is significantly associated with baseline urinary symptoms (coef = 0.222, p = 0.036). The investigation of sleep quality and urinary incontinence via data analysis through the ASCAPE project suggests that better sleep quality could improve urinary disorders.

Deep autoencoder-based behavioral pattern recognition outperforms standard statistical methods in high-dimensional zebrafish studies.

Zebrafish have become an essential model organism in screening for developmental neurotoxic chemicals and their molecular targets. The success of zebrafish as a screening model is partially due to their physical characteristics including their relatively simple nervous system, rapid development, experimental tractability, and genetic diversity combined with technical advantages that allow for the generation of large amounts of high-dimensional behavioral data. These data are complex and require advanced machine learning and statistical techniques to comprehensively analyze and capture spatiotemporal responses. To accomplish this goal, we have trained semi-supervised deep autoencoders using behavior data from unexposed larval zebrafish to extract quintessential "normal" behavior. Following training, our network was evaluated using data from larvae shown to have significant changes in behavior (using a traditional statistical framework) following exposure to toxicants that include nanomaterials, aromatics, per- and polyfluoroalkyl substances (PFAS), and other environmental contaminants. Further, our model identified new chemicals (Perfluoro-n-octadecanoic acid, 8-Chloroperfluorooctylphosphonic acid, and Nonafluoropentanamide) as capable of inducing abnormal behavior at multiple chemical-concentrations pairs not captured using distance moved alone. Leveraging this deep learning model will allow for better characterization of the different exposure-induced behavioral phenotypes, facilitate improved genetic and neurobehavioral analysis in mechanistic determination studies and provide a robust framework for analyzing complex behaviors found in higher-order model systems.

Assessing Financial Stability through ESG: The Impact of Sustainable Finance on Commercial Banks listed in Pakistan Stock Exchange (PSX)

The notion of sustainable finance, which encompasses environmental, social, and governance (ESG) considerations, has become increasingly prominent in assessing the financial resilience of companies, particularly within the banking industry, in recent times. The primary objective of this research is to examine the influence of ESG practices on the financial stability of commercial banks that are publicly traded on the Pakistan Stock Exchange (PSX). The study utilizes an extensive dataset of 10 years from 2014 to 2023 that includes significant ESG indicators as well as financial performance metrics, spanning multiple years. The study seeks to analyze the impact of sustainable finance practices on the overall financial stability of the chosen banks using advanced statistical techniques and regression analysis. The results of this study are anticipated to provide insight into the possible advantages and drawbacks of incorporating ESG factors into the operational plans of commercial banks. Moreover, this study will make a valuable contribution to the current body of knowledge on sustainable finance, particularly in the context of emerging economies like Pakistan. In such markets, the importance of ESG factors in shaping financial decision-making is undergoing rapid transformation.

Unraveling stock exchange connections: an empirical study of India, US, Hong Kong, Germany, France and Amsterdam

PurposeThe current study immerses itself in the realm of diversification prospects within a select group of preeminent global stock exchanges. Specifically, the study casts its discerning gaze upon the financial hubs of the United States, Hong Kong, Germany, France, Amsterdam and India. In this expansive vista of international financial markets, the present analytical study aims to unravel the multifaceted opportunities that lie therein for astute portfolio management and strategic investment decisions.Design/methodology/approachThe study encompasses daily time series data spanning from 2019 to 2022. To assess the interconnectedness among these stock indices, advanced statistical techniques, including Johansen cointegration methods and vector autoregressive (VAR) models, have been applied.FindingsThe research outcomes reveal both unidirectional and bidirectional relationships between the Indian, Hong Kong and US stock exchanges, encompassing both short-term and long-term time frames. Interestingly, the empirical findings indicate the presence of diversification opportunities between the Indian stock exchange and the stock exchanges of Germany, France and Amsterdam.Research limitations/implicationsThese insights hold significant value for both Indian and international investors, including foreign institutional investors (FIIs), domestic institutional investors (DIIs) and retail investors, as they can utilize this knowledge to construct more effective and diversified investment portfolios by understanding the intricate interconnections between these prominent global stock exchanges.Originality/valueThis research undertaking aspires to bring coherence to a landscape rife with divergent interpretations and methodological divergences. We are poised to offer a comprehensive analysis, a beacon of clarity amidst the murkiness, to shed light on the intricate web of interconnections that underpin the world's stock exchanges. In so doing, we seek to contribute a seminal piece of scholarship that transcends the existing ambiguities and thus empowers the field with a deeper understanding of the multifaceted dynamics governing international stock markets.

Interwoven Lives: The Impact of Social Support and Behavioral Changes on Health Behaviors among the Chinese Residents

Objectives: It is found that there is a growing level of mortality among people with diabetic diseases. However, factors like social support and positive behavior change tend to play a substantial role in dealing with such mental worries. Methods: Based upon the given thoughts, this research examines the impact of social support and behavioral changes on health behavior in the form of diabetic health worries. For this purpose, the study will collect the sample using the survey questionnaire technique. A sample of 357 respondents has empirically been analyzed by applying advanced statistical techniques, such as testing for the reliability and validity of the latent constructs and relative items, discriminant validity, and structural equation modeling techniques. Results: The study findings confirm that the latent constructs are significantly represented by the latent items of social support, behavioral changes, and health behaviors, respectively. Moreover, the findings through structural equation modeling reveal a significant and productive impact of social support and behavioral changes in improving the health behaviors like low mental worries of and diabetes. Conclusion: The study provides some meaningful policy implications in determining how the role of social support and behavioral changes would be reflected in the form of low diabetic health worries. Besides, a few limitations have also been expressed.

Combined Influence of Multiple Factors on Lung Cancer and Anxiety: A Probit Model Analysis

Lung cancer remains a leading cause of cancer-related mortality worldwide, significantly impacting public health. Concurrently, anxiety is a prevalent psychological disorder known to influence the development and progression of various cancers. This research paper aims to examine the combined influence of various factors, including demographic characteristics, environmental exposures, and physical conditioning data, on the incidence of lung cancer and anxiety. Given the binary nature of both lung cancer and anxiety outcomes, the analyses will first employ separate binary probit regression models to identify significant predictors for each condition independently. Then, joint modeling techniques will be implemented with dual purposes. First, by comparing the results of the individual and joint models, the reliability and robustness of the findings will be enhanced through cross-validation. Second, joint models will enable an investigation into potential endogeneity between lung cancer and anxiety. Addressing this endogeneity is crucial as it can potentially improve model robustness and provide deeper insights into the interrelationship between these two health outcomes. For the study purpose, the demographic factors considered include age and gender. Environmental factors encompass smoking history, alcohol consumption, and peer pressure. Physical conditioning data includes pre-existing health conditions such as yellow fingers, anxiety, chronic disease, fatigue, allergy, wheezing, coughing, shortness of breath, swallowing difficulty, and chest pain. By leveraging advanced statistical modeling techniques, this research seeks to uncover nuanced relationships and potential causal pathways that may exist between lung cancer and anxiety. The findings from this study will contribute to the existing body of knowledge by providing additional case study showing the relationship among a multitude of factors on lung cancer and anxiety, respectively. Also, the endogeneity checking among lung cancer and anxiety may enhance the efficiency of models done by others in the future. The research paper's analysis on lung cancer incidences reveals that age and wheezing are significant predictors. In examining anxiety levels, smoking emerged as a significant predictor, indicating a higher likelihood of anxiety among smokers. In addition, the joint probit models confirmed these findings, with age and wheezing significantly predicting lung cancer incidence, and smoking significantly predicting anxiety levels. No significant endogeneity was observed between lung cancer and anxiety, suggesting that these health outcomes are influenced by different sets of factors. These findings underscore the importance of considering demographic, environmental, and physical conditioning data in understanding and addressing lung cancer and anxiety, and checking their potential endogeneity

UNDERSTANDING THE IMPACT OF TEACHER DEMOGRAPHICS ON ASSESSMENT FOR LEARNING STRATEGIES IN GHANA'S EDUCATIONAL SYSTEM

This study explores the implementation and effectiveness of Assessment for Learning (AfL) strategies among basic schoolteachers in Ghana, focusing on the influences of gender, teaching experience, and teaching division. Utilising a survey approach, data were collected through a 16-item Assessment for Learning Questionnaire (AfLQ) from a randomly selected sample of 100 teachers. The instrument's reliability and validity were confirmed through a pilot study and expert reviews, achieving a Cronbach's alpha of over 0.7 for all variables. Multinomial regression analysis provided key insights into the effectiveness, variability, and reliability of AfL practices, guiding professional development and future research. Multiple linear regression analyses identified significant correlations among AfL practices, with strong positive relationships between self-assessment, peer assessment, and immediate feedback. Variance plots showed higher variability in the implementation of certain practices among female and subject teachers, indicating a need for more tailored professional development. Additionally, the Test Information Function (TIF) plot revealed distinct proficiency levels, highlighting areas where teachers excel and potential gaps requiring intervention. These findings emphasise the critical role of demographic factors in the application and success of AfL strategies and offer actionable insights for enhancing teacher training programmes. By integrating advanced statistical techniques, the study provides a comprehensive understanding of AfL practices in diverse educational settings, contributing valuable knowledge to the field of educational assessment.<p> </p><p><strong> Article visualizations:</strong></p><p><img src="/-counters-/soc/0871/a.php" alt="Hit counter" /></p>

Older adults’ compliance with mobile ecological momentary assessments in behavioral nutrition and physical activity research: pooled results of four intensive longitudinal studies and recommendations for future research

BackgroundMobile Ecological Momentary Assessment (EMA) is increasingly used to gather intensive, longitudinal data on behavioral nutrition, physical activity and sedentary behavior and their underlying determinants. However, a relevant concern is the risk of non-random non-compliance with mobile EMA protocols, especially in older adults. This study aimed to examine older adults’ compliance with mobile EMA in health behavior studies according to participant characteristics, and prompt timing, and to provide recommendations for future EMA research.MethodsData of four intensive longitudinal observational studies employing mobile EMA to understand health behavior, involving 271 community-dwelling older adults (M = 71.8 years, SD = 6.8; 52% female) in Flanders, were pooled. EMA questionnaires were prompted by a smartphone application during specific time slots or events. Data on compliance (i.e. information whether a participant answered at least one item following the prompt), time slot (morning, afternoon or evening) and day (week or weekend day) of each prompt were extracted from the EMA applications. Participant characteristics, including demographics, body mass index, and smartphone ownership, were collected via self-report. Descriptive statistics of compliance were computed, and logistic mixed models were run to examine inter- and intrapersonal variability in compliance.ResultsEMA compliance averaged 77.5%, varying from 70.0 to 86.1% across studies. Compliance differed among subgroups and throughout the day. Age was associated with lower compliance (OR = 0.96, 95%CI = 0.93–0.99), while marital/cohabiting status and smartphone ownership were associated with higher compliance (OR = 1.83, 95%CI = 1.21–2.77, and OR = 4.43, 95%CI = 2.22–8.83, respectively). Compliance was lower in the evening than in the morning (OR = 0.82, 95%CI = 0.69–0.97), indicating non-random patterns that could impact study validity.ConclusionsThe findings of this study shed light on the complexities surrounding compliance with mobile EMA protocols among older adults in health behavior studies. Our analysis revealed that non-compliance within our pooled dataset was not completely random. This non-randomness could introduce bias into study findings, potentially compromising the validity of research findings. To address these challenges, we recommend adopting tailored approaches that take into account individual characteristics and temporal dynamics. Additionally, the utilization of Directed Acyclic Graphs, and advanced statistical techniques can help mitigate the impact of non-compliance on study validity.

Identifying the multiple nexus between geopolitical risk, energy resilience, and carbon emissions: Evidence from global data

Under the frequent occurrence of extreme weather events and the increasingly complex geopolitical situation in recent years, how to balance the nexus between energy security, economic stability, and the achievement of sustainable development goals has gradually become the forefront of international attention. In light of this context, this paper examines the multiple nexus between geopolitical risks (GR), energy resilience (ER), and carbon emissions (PCO2) using annual panel data from 31 countries between 2000 and 2019. The second-generation unit root, cointegration tests, wavelet quantile correlation analysis, and other advanced statistical techniques are adopted to address methodological challenges effectively and avoid spurious regression. The findings consistently indicate the inhibitory effects of geopolitical risks and energy resilience on PCO2 across all quantiles and periods. And the restraining effect exhibits significant heterogeneity and asymmetry, particularly in non-emerging economies. Besides, the bidirectional causality among the three variables has been confirmed. Further, we confirm that the combined effects of energy resilience and geopolitical risks are particularly noteworthy for future carbon reduction efforts, especially in scenarios with low-ER and high-GR. In a short, the findings provide novel insights for global emissions reduction and offer specific policy recommendation to attain sustainable development goals.

The The Influence Of Service Quality, Brand Trust And Customer Perceived Value On Costumer Satisfaction Study Of Amaris Hotel Customer In Indonesia

The hotel business can grow rapidly quantitatively by capturing the market in the tourism sector which continues to grow very rapidly. A hotel company will be able to compete and win market share if the hotel is able to provide good quality products and services. This study investigates the correlation between service quality, brand trust, customer perceived value, and their collective influence on customer satisfaction, focusing study of Amaris Hotel Indonesia. Drawing upon a comprehensive review of literature and employing a quantitative research approach, data were collected from a sample of hotel customers through structured surveys. Utilizing advanced statistical techniques such as regression analysis, the study examines the influence of service quality, brand trust, customer perceived value on customer satisfaction. Target included Amaris Hotel Indonesia customers. A total of 272 valid questionnaires were collected and verified with PASW Statistics 18. The conditions were: Service quality positive and significant impact on customer satisfaction. Brand trust has a positive and significant impact on customer satisfaction. Customer perceived value positive and significant impact on customer satisfaction. This indicates that customers who perceive higher service quality, exhibit greater trust in the hotel brand, and perceive greater value are more likely to be satisfied with their hotel experience. Additionally, this study reveals distinct interactions and moderation effects among these variables, highlighting the complex dynamics underlying customer satisfaction in the hospitality industry. These insights contribute to both theoretical understanding and practical implications for hotel managers seeking to enhance customer satisfaction in a highly competitive market environment.

Parsimonious statistical techniques for the detection of drifts toward dangerous operational conditions in tokamaks

The approaching initial operation of major new tokomaks is rendering more pressing the need for effective disruption prediction techniques. The required tools should be not only accurate but also capable of operating with a minimum number of signals, because in the first campaigns of new devices typically only a very limited number of diagnostics is available. The present work reports the results obtained with two advanced statistical techniques, chaos onset and concept drift (CD). These methods are capable of detecting the plasma drifting towards dangerous regions of the operational space using only global signals such as the plasma current or the locked mode amplitude. The obtained performances meet the requirements of mitigation in devices such as JT60-SA and ITER at the beginning of their operation. The computational times of the chaos onset approach are already of the order of 1 ms on laptop computers, whereas the CD technique requires implementation on FPGAs or GPUs to be compatible with real time requirements.

Novel Genes Associated With Atrial Fibrillation and the Predictive Models for AF Incorporating Polygenic Risk Score and PheWAS-Derived Risk Factors

BackgroundAtrial fibrillation (AF), the most common atrial arrhythmia, presents with varied clinical manifestations. Despite the identification of genetic loci associated with AF, particularly in specific populations, research within Asian ethnicities remains limited. In this study we aimed to develop predictive models for AF using AF-associated single-nucleotide polymorphisms (SNPs) from a genome-wide association study (GWAS) on a substantial cohort of Taiwanese individuals, to evaluate the predictive efficacy of the model. MethodsThere were 75,121 subjects, that included 5694 AF patients and 69,427 normal control subjects with GWAS data, and we merged polygenic risk scores from AF-associated SNPs with phenome-wide association study-derived risk factors. Advanced statistical and machine learning techniques were used to develop and evaluate AF predictive models for discrimination and calibration. ResultsThe study identified the top 30 significant SNPs associated with AF, predominantly on chromosomes 10 and 16, implicating genes like NEURL1, SH3PXD2A, INA, NT5C2, STN1, and ZFHX3. Notably, INA, NT5C2, and STN1 were newly linked to AF. The GWAS predictive power using polygenic risk score-continuous shrinkage analysis for AF exhibited an area under the curve of 0.600 (P < 0.001), which improved to 0.855 (P < 0.001) after adjusting for age and sex. Phenome-wide association study analysis showed the top 10 diseases associated with these genes were circulatory system diseases. ConclusionsIntegrating genetic and phenotypic data enhanced the accuracy and clinical relevance of AF predictive models. The findings suggest promise for refining AF risk assessment, enabling personalized interventions, and reducing AF-related morbidity and mortality burdens.

Unlocking Adolescent Mental Health: Exploring Social Determinants and Depression With the Social Ecological Model

Employing the robust framework of the Social Ecological Model (SEM) and drawing insights from the Social Determinants of Health (SDOH), this research sought to uncover the interplay between SDOH and mental health among adolescents. With a focus on depression, the study unraveled how various social determinants, both individually and collectively, shape the mental well-being of adolescents, leveraging the rich dataset provided by the National Survey of Children’s Health 2018. Utilizing advanced statistical techniques, the study revealed the predictive power of SDOH in understanding adolescent mental health. Results illuminate the significant influence of social determinants on depression, underscoring the imperative for a comprehensive approach to mental health interventions. Notably, the findings highlight how the absence of adverse determinants mitigates the risk of negative mental health outcomes among adolescents, while also shedding light on the correlation between adverse determinants and depression. These findings underscore the critical role of interdisciplinary collaboration among healthcare professionals in minimizing the detrimental impact of adverse determinants on adolescent mental health. By recognizing and addressing the multifaceted influences of social determinants, stakeholders can work together to foster environments conducive to positive mental well-being among adolescents.