Perform Data Analysis Research Articles

Long-Term Performance Evaluation and Fouling Characterization of a Full-Scale Brackish Water Reverse Osmosis Desalination Plant

Water scarcity in Tunisia’s semi-arid regions necessitates advanced brackish water desalination solutions. This study evaluates the long-term performance and fouling characteristics of the largest brackish water reverse osmosis desalination plant in southern Tunisia over a period of 5026 days. The plant employs two-stage spiral-wound membrane elements to treat groundwater with a salinity of 3.2 g L−1. The pre-treatment process includes oxidation, sand filtration, and cartridge filtration, along with polyphosphonate antiscalant dosing. Membrane performance was assessed through the analysis of operational data, standardization of permeate flow (Qps) and salt passage (SPs), and the calculation of water (A), solute (B), and ionic (Bj) permeability coefficients. Over the operational period, there was an increase in operating pressure, pressure drop, and permeate conductivity, accompanied by a gradual increase in SPs as well as in the solute B and ionic Bj permeability coefficients. The average B increased by 82%, reflecting a decrease in solute rejection over time. Additionally, the ionic permeability coefficients for both SO42− and Cl− ions increased, with Cl− showing an 88% increase and SO42− showing an 87% increase. The produced water’s salinity increased by 67%, indicating a significant loss of membrane performance. To identify the cause of these problems, membrane characterization was analyzed using visual inspection, X-ray fluorescence (XRF), and Fourier transform infrared spectroscopy (FTIR). The characterization revealed the complex nature of the foulants, with a predominant presence of calcium sulfate, along with minor quantities of calcite, dolomite, and silica. The extent of CaSO4 deposition suggests poor antiscaling efficiency, highlighting the critical importance of selecting an effective antiscalant to mitigate membrane fouling.

Performance analysis of ground source heat pump systems for constant temperature and humidity air conditioning in industrial buildings: a case study

Abstract The ground source heat pump (GSHP) system has gained widespread popularity for its provision of efficient and environmentally sustainable cooling and heating solutions for buildings. While the performance of GSHP systems has raised significant concerns, research predominantly focuses on residential and office buildings, leaving a gap in understanding their applicability in industrial settings with constant temperature and humidity air conditioning needs. This study aims to address this gap by evaluating the performance of a hybrid ground source heat pump (HGSHP) system implemented within a cigarette factory located in the hot summer and cold winter (HSCW) of China. Through a comprehensive analysis of operational data and long-term monitoring under three cooling conditions, essential parameters including water temperature characteristics, indoor temperature and humidity, system efficiency, power consumption, and soil temperature distribution were examined. The findings revealed that the weighted average coefficient of performance (COP) of the heat pumps (COPhp) and the whole system (COPsys) was decreased from 5.05 to 4.32 and 3.32 to 2.89, respectively. A positive correlation was observed between low cooling load high energy consumption, and low COP. The inlet temperature of the condenser exhibited a declining trend, which was attributed to the intermittent operation of condensation heat recovery. Furthermore, a comprehensive analysis of indoor temperature and humidity distribution within the controlled room was presented and found that almost all the rooms serviced by this system meet the specified design requirements. This research contributes to an enhanced understanding of the viability of HGSHP systems in industrial settings and provides valuable references for improving their energy efficiency and overall performance.

The Significance of Facilities Management Aspect in Developing Maintenance Management Plans during Pre- Development Stages of Educational Private Finance Initiative (PFI) Projects

Facilities Management (FM) is one of the essential aspects that needs to be considered in the building maintenance management plan. Generally, the scope of FM involves thorough and effective administration, finance, operation and maintenance of the building. In the context of the Private Finance Initiative (PFI) projects, the operation and maintenance stage is longer compared to the design and construction stages. Therefore, the focus during this stage should be emphasised proactively. This research aims to determine the significance of FM aspect towards maintenance management plan of PFI projects during pre-development stages. This research used previous literature, mainly from journals articles to construct the models and formulate the literature reviews. Additionally, a quantitative approach was employed as the research methodology. A questionnaire survey was distributed to the maintenance personnel involved in the PFI projects in Malaysia. The survey was conducted towards the educational PFI projects only. This research also used Partial Least Square-Structural Equation Modelling (PLS-SEM) software to perform data analysis. There are two constructs in this research which are the FM aspects and maintenance management plan. The measurement and structural models were developed to determine the significance of the FM aspect towards the maintenance management plans. There are five indicators that represents FM aspects and six indicators of maintenance management plan highlighted in this research. All these indicators are proven to be reliable and valid through the internal consistency, convergent validity, cross loading value. The Fornell Lacker Criterion was conducted to prove discriminant validity between FM aspect and maintenance management plan. The findings of this research found that the FM aspects are very significant towards the maintenance management plan of the PFI project during the predevelopment stages. It was proven through structural model analysis using p and t-value. This research can be beneficial for better implementation of maintenance management.

Breaking the Subjective Bias: Rethinking the Talent Management Model in the Ministry of Home Affairs

Objectives: The objective of this research is to address subjective elements in talent management assessment in civil service organizations, focusing particularly on the Ministry of Home Affairs. By investigating the effectiveness of the Sikerja application and proposing solutions to enhance objectivity, the study seeks to contribute practical insights for advancing talent management practices.Methodology: This research employed qualitative research, utilizing a case study approach. Through in-depth interviews with key personnel and a thorough literature review, data is collected and analyzed comprehensively.Finding: The study reveals significant shortcomings in Sikerja's ability to address subjectivity in performance appraisal at the Ministry of Home Affairs. It lacks rigorous testing and is influenced by personal biases, undermining its reliability. To develop a more effective talent management framework, the study proposes adopting a Data-Driven Decision-Making (DDDM) approach. This involves establishing an independent team to analyze performance data objectively, ensuring fairness and transparency. Finally, the study recommends developing a talent management model based on Berger's concept and data for continuous improvement.Conclusion: Developing a talent management model for the Ministry of Home Affairs requires an understanding of the current situation. The review of the Sikerja application highlights its limitations; subjectivity in assessments and the lack of scientific validation in its methods, which hinder its efficacy. Therefore, the author suggests adopting a Data-Driven Decision-Making (DDDM) approach in talent management. DDDM offers a robust solution by establishing an independent data analysis team, ensuring objectivity, and identifying areas for improvement. Integrating DDDM principles into all talent management stages will establish a framework promoting objectivity, transparency, and fairness, ultimately optimizing the workforce's potential.

The impact of family social support on academic resilience in Indonesian and Turkish students: the mediating role of self-regulated learning

ABSTRACT This study examines the influence of family social support on student resilience and self-regulated learning (SRL), as well as the role of SRL as a mediator for Indonesian and Turkish students. This quantitative method used three research scales to answer the research questions: resilience, family social support, and self-regulated learning. The study included 606 Indonesian respondents and 504 Turkish respondents. In this study, the researchers used a structural equation model based on partial least squares to perform data analysis. The results showed that Indonesian students had a higher average academic resilience score than Turkish students. Turkish students’ family social support scores were higher than Indonesian students. The bootstrapping method demonstrated a positive indirect effect of SRL on the relationship between family social support and academic resilience. Hence, SRL is a good variable that connects family social support with academic resilience.

Evaluating Integrated Social Studies Learning Programs in Indonesian Middle Schools: An Application of the Stufflebeam Model

Many studies have been conducted to evaluate the nonintegrated approach. Each model claimed to have certain advantages, but the current research used a combined social science learning model called Stufflebeam's CIPP (context, input, process, and product) model, which included input, process, and product components due its comprehensiveness in assessment and students’ interest based. The assessment model appropriate for Integrated Social Studies learning programs in public middle schools because it could fulfil pedagogical aspects for students in schools. The research group included respondents from eight public middle schools, as well as school administrators, 16 instructors, and 350 pupils. Documentation, questionnaires, and tests are examples of data gathering methods. Content validity is tested using expert opinion, construct validity is tested using a limited test and evaluated using Product Moment correlation, and reliability is tested using Cronbach Alpha. The SPSS software is used to perform data analysis methods such as descriptive statistics. 20. The development of a combined social study learning program evaluation model for public middle schools yielded outstanding assessment criteria. The quality of integrated social studies learning inputs includes: teachers’ competence, students’ motivation, and infrastructure, which are all very good categories; the quality of integrated social studies learning processes, which include initial activities, core activities, and closing activities, which are all excellent categories; and product quality (results) integrated social studies learning includes: attitude competencies, knowledge, and skills.

Performance Analysis of Fifth-Semester Physics Education: Assessing an Online Pascal Compiler in the Department of Mathematics and Natural Sciences Education at University of Mataram

This study evaluates the integration of online Pascal compilers in undergraduate physics education at the University of Mataram, focusing on its impact on students' programming skills. Over one semester, three classes (A, B, and C) of physics education students from the Department of Mathematics and Natural Sciences Education (PMIPA), Faculty of Teacher Training and Education (FKIP), University of Mataram, participated in lectures, discussions, and practical exercises using the GDB Online Pascal Compiler. Course content included Pascal fundamentals, arrays, matrices, and applications in physics, evaluated through assignments, mid-semester, and final exams. Quantitative analysis of student performance data revealed significant differences in mean scores and variability among the classes, with Class A achieving the highest average score and Class C exhibiting the greatest variability. Statistical tests confirmed that performance data for all classes deviated significantly from normal distribution, necessitating non-parametric tests. The Mann-Whitney U test indicated no statistically significant differences in median scores between class A vs. class B, class A vs. class C, and class B vs. class C, suggesting consistent performance outcomes across classes post-course. These findings underscore the potential of the online Pascal compiler in enhancing students' programming skills within the context of physics education.

Formation of the ability to perform data analysis using information technology in the training of masters

Formation of the ability to perform data analysis using information technology in the training of masters

Why are Indonesian consumers buying on live streaming platforms? Research on consumer perceived value theory

Along with the economic and technological development in Indonesia, one of the innovations is in the field of social media commerce and it has managed to attract the attention of Indonesian consumers. Different from other social media, TikTok offers a new feature, namely TikTok Live, participants can use this social media for their entertainment as well as a buying and selling platform. At the same time, live streaming also faces several factors where consumers are not yet sure about buying it. However, the marketing strategy has recently become a hot topic of discussion. Therefore, this empirical research aimed to investigate why Indonesian consumers buy on live streaming using perceived trust as a mediating variable, such as trust in the products and trust in the sellers. The factors used to analyse the influence on consumers' purchase intention include utilitarian value, hedonic value and social value. This study collected data in a questionnaire created in Google Forms and performed data analysis using SPSS 26 and SPSS Amos software, using structural equation models to analyse validation and theoretical hypotheses. The results of this analysis are expected to provide knowledge to social commerce providers, especially in the area of live streaming.This study shows that perceived values such as utilitarian, hedonic and social values have a significant positive impact on purchase intention. Utilitarian value and social value both have a significant and positive influence on both consumer trust in the products and consumer trust in the platform, but not on hedonic value, where hedonic value only has a significant positive influence on trust in the platform, but an insignificant effect on trust in the product. Even though hedonic value has no influence on the product, sellers still need to provide a more pleasant atmosphere to attract the attention of users or consumers. And for further research, this study hopes to uncover additional elements that influence consumer behaviour on the TikTok live streaming platform.

Understanding the heterogeneity in liver hepatocellular carcinoma with a special focus on malignant cell through single-cell analysis

IntroductionHepatocellular carcinoma (HCC) is the most common form of liver cancer globally and remains a major cause of cancer-related deaths. HCC exhibits significant intra-tumoral and interpatient heterogeneity, impacting treatment efficacy and patient prognosis.MethodsWe acquired transcriptome data from the TCGA and ICGC databases, as well as liver cancer chip data from the GEO database, and processed the data for subsequent analysis. We also obtained single cell data from the GEO database and performed data analysis using the Seurat package. To further investigate epithelial cell subgroups and their copy number variations, we used the Seurat workflow for subgroup classification and the InferCNV software for CNV analysis, utilizing endothelial cells as a reference. Pseudo-time analysis and transcription factor analysis of epithelial cells were performed using the monocle2 and SCENIC software, respectively. To assess intercellular communication, we employed the CellChat package to identify potential ligand-receptor interactions. We also analyzed gene expression differences and conducted enrichment analysis using the limma and clusterProfiler packages. Additionally, we established tumor-related risk characteristics using Cox analysis and Lasso regression, and predicted immunotherapy response using various datasets.ResultsThe samples were classified into 23 clusters, with malignant epithelial cells being the majority. Trajectory analysis revealed the differentiation states of the malignant epithelial cells, with cluster 1 being in the terminal state. Functional analysis revealed higher aggressiveness and epithelial-mesenchymal transition (EMT) scores in cluster 1, indicating a higher propensity for metastasis. RBP4+ tumor cells were highly enriched with hypoxia process and intensive cell-to-cell communication. A prognostic model was established, and immune infiltration analysis showed increased infiltration in the high-risk group. TP53 demonstrated significant differences in mutation rate between the two risk groups. Validation analysis confirmed the up-regulation of model genes, including AKR1B10, ARL6IP4, ATP6V0B, and BSG in tumor tissues.ConclusionA prognostic model was established based on HCC malignant cell associated gene signature, displaying decent prognosis guiding effectiveness in the multiple cohorts. The study provided comprehensive insights into the heterogeneity and potential therapeutic targets of LIHC.

Business Secret behind the Tagline Business Resilience behind the Tagline: Open 24 Hours / 7 Days, Apocalypse Opened for Half a Day

This research investigates the marketing strategies used by shops in Madura with a focus on the tagline "Open 24 Hours / 7 Days; Stop Opening Half A Day". Through a SWOT analysis approach and the Porter's Diamond framework, this research aims to understand how the tagline supports the competitiveness and operational success of stores in a unique local context. Research methods include in-depth interviews with shop owners, customer surveys, and operational data analysis. This research model uses a simple design with Triangulation Theory. The data collection techniques used are observation, interviews and documentation. The data analysis technique used is the Manual Data Analysis Procedure (MDAP). The research results show that the tagline not only acts as a creative marketing tool, but also reflects a commitment to high customer service and operational continuity in the face of changing market conditions. A SWOT analysis identifies strengths such as responsive customer service and weaknesses such as reliance on local resources, while a Porter's Diamond analysis highlights related factors and industry conditions that support the stores' operational strategies. Suggestions for future research include data collection, comparative studies with other industrial contexts, and collaboration with local governments to support demand and development of local shops in Madura.

Assessment of the safety of Roxadustat for cardiovascular events in chronic kidney disease-related anemia using meta-analysis and bioinformatics.

This study compares the cardiovascular risk in anemic chronic kidney disease patients treated with Roxadustat versus erythropoietin stimulating agents (ESAs). It also explores the cardiovascular impact of Roxadustat. We searched PubMed, EMBASE, Cochrane, Scopus, and Web of Science databases up to 13 August 2023, using terms such as "ESA," "Roxadustat," "MACE," "stroke," "death," "myocardial infarction," and "heart failure." Two researchers independently selected and extracted data based on predefined criteria. We assessed the risk of bias with the Cochrane tool and analyzed statistical heterogeneity using the Q and I2 tests. We conducted subgroup analyses by geographical region and performed data analysis with Stata 14.0 and RevMan 5.4 software. Data were sourced from the NCBI database by filtering for "Roxadustat" and "human," and differentially expressed genes were identified using R software, setting the significance at p < 0.01 and a 2-fold logFC, followed by GO enrichment analysis, KEGG pathway analysis, and protein interaction network analysis. A total of 15 articles encompassing 1,43,065 patients were analyzed, including 1,38,739 patients treated with ESA and 4,326 patients treated with Roxadustat. In the overall population meta-analysis, the incidences of Major Adverse Cardiovascular Events (MACE), death, and heart failure (HF) were 13%, 8%, and 4% in the Roxadustat group, compared to 17%, 12%, and 6% in the ESA group, respectively, with P-values greater than 0.05. In the subgroup analysis, the incidences were 13%, 11%, and 4% for the Roxadustat group versus 17%, 15%, and 5% for the ESA group, also with p-values greater than 0.05. Bioinformatics analysis identified 59 differentially expressed genes, mainly involved in the inflammatory response. GO enrichment analysis revealed that these genes are primarily related to integrin binding. The main pathways identified were the TNF signaling pathway, NF-κB signaling pathway, and lipid metabolism related to atherosclerosis. The protein interaction network highlighted IL1B, CXCL8, ICAM1, CCL2, and CCL5 as the top five significantly different genes, all involved in the inflammatory response and downregulated by Roxadustat, suggesting a potential role in reducing inflammation. The meta-analysis suggests that the use of Roxadustat and ESA in treating anemia associated with chronic kidney disease does not significantly alter the likelihood of cardiovascular events in the overall and American populations. However, Roxadustat exhibited a safer profile with respect to MACE, death, and heart failure. The bioinformatics findings suggest that Roxadustat may influence integrin adhesion and affect the TNF and NF-κB signaling pathways, along with lipid and atherosclerosis pathways, potentially reducing inflammation.

Effect of Otago exercise on fear of falling in older adults: a systematic review and meta-analysis

BackgroundApproximately 40–70% of older adults who have experienced falls develop fear of falling (FOF), with the incidence rate in nursing home residents reaching as high as 79.4%. An increasing number of studies have focused on the effect of the Otago Exercise Programme (OEP) on reducing FOF among older adults, yet comprehensive analysis is lacking due to regional and demographic variations. Therefore, this study integrates the relevant literature to provide evidence supporting interventions aimed at alleviating FOF among older adults.ObjectiveTo evaluate the impact of OEP on FOF in older adults through meta-analysis.MethodsWe searched ten databases using computer systems, covering all records up to May 1, 2024. Two researchers independently conducted the literature screening, bias risk assessment, and data extraction. We performed data analysis using RevMan 5.3 and Stata 15.0 software, assessed result stability through sensitivity analysis, and examined publication bias with funnel plots and Egger’s test.ResultsSixteen RCTs were included. Meta-analysis revealed that the OEP significantly reduced FOF among older adults [SMD = 0.96, 95%CI (0.68, 1.23), P < 0.00001]. Subgroup analysis revealed that interventions lasting more than 16 weeks [SMD = 1.12, 95%CI (0.75, 1.49), P < 0.00001], with a frequency of more than twice a week [SMD = 0.99, 95%CI (0.64, 1.35), P < 0.00001], and for older adults in community and nursing institutions [SMD = 1.03, 95%CI (0.50, 1.57), P = 0.0002] were more effective. A comparison of the 16-week and 24-week interventions revealed that the latter had better outcomes [SMD = 0.87, 95%CI (0.66, 1.08), P = 0.0004].ConclusionCurrent evidence indicates that OEP effectively reduces FOF among older adults. It is recommended that interventions last for more than 24 weeks, occur more than twice a week, and suitable for application among older adults in community settings or elder care institutions.

Hypermethylation and low expression of FOXM1 predisposes women to unexplained recurrent miscarriage by impairing trophoblast stem cell proliferation

Recurrent miscarriage (RM) is a distressing pregnancy complication with an unknown etiology. Increasing evidence indicates the relevance of dysregulation of human trophoblast stem cells (hTSCs), which may play a role in the development of RM. However, the potential molecular regulatory mechanism underlying the initiation and maintenance of hTSCs is yet to be fully elucidated. In this study, we performed data analysis and identified Forkhead box M1 (FOXM1) as a potential factor associated with RM. FOXM1 is a typical transcription factor known for its involvement in various pathophysiological processes, while the precise function of FOXM1 functions in hTSCs and RM remains incompletely understood. Utilizing RNA-seq, CUT&Tag, ChIP-qPCR, and sodium bisulfite conversion methods for methylation analysis, we elucidate the underlying regulatory mechanisms of FOXM1 in hTSCs and its implications in RM. Our findings demonstrate the relative high expression of FOXM1 in proliferating cytotrophoblasts (CTBs) compared to differentiated extravillous cytotrophoblasts (EVTs) and syncytiotrophoblasts (STBs). Besides, we provide evidence supporting a significant correlation between FOXM1 downregulation and the incidence of RM. Furthermore, we demonstrate the significant role of FOXM1 in regulating hTSCs proliferation and cell cycle through the transcriptional regulation of CDKN3, CCNB2, CCNA2, MAD2L1 and CDC25C. Notably, we observed a correlation between the downregulation of FOXM1 in RM and hypermethylation in its promoter region. Collectively, these results provide insights into the impact of FOXM1 on trophoblast regulation and offer a novel perspective on RM.

Exploring the relationship between talent management, work-life balance, and job satisfaction in state-owned banking: a mediation analysis

The objective of this study is to examine how job satisfaction mediates the relationship between talent management and work-life balance, and employee performance in state-owned banks. The analysis employed quantitative analysis and data collection approaches, namely a questionnaire issued to 127 employees using Google Forms. Performing data analysis utilising the structural partial least squares (SEM-PLS) equation model. The findings indicated that both talent management and work-life balance have a direct and substantial favourable impact on employee performance. This study also discovered that job satisfaction had a role in partially mediating the impact of talent management on employee performance. Job satisfaction somewhat mediates the impact of talent management and work-life balance on employee performance. These findings indicate the importance of consistently focusing on and enhancing talent management methods and work-life balance in order to enhance employee performance.

Trends of fear and anger on YouTube during the initial stage of the COVID-19 outbreak in South Korea

BackgroundThe COVID-19 pandemic has been the most widespread and threatening health crisis experienced by the Korean society. Faced with an unprecedented threat to survival, society has been gripped by social fear and anger, questioning the culpability of this pandemic. This study explored the correlation between social cognitions and negative emotions and their changes in response to the severe events stemming from the COVID-19 pandemic in South Korea.MethodsThe analysis was based on a cognitive-emotional model that links fear and anger to the social causes that trigger them and used discursive content from comments posted on YouTube’s COVID-19-related videos. A total of 182,915 comments from 1,200 videos were collected between January and December 2020. We performed data analyses and visualizations using R, Netminer 4.0, and Gephi software and calculated Pearson’s correlation coefficients between emotions.ResultsYouTube videos were analyzed for keywords indicating cognitive assessments of major events related to COVID-19 and keywords indicating negative emotions. Eight topics were identified through topic modeling: causes and risks, perceptions of China, media and information, infection prevention rules, economic activity, school and infection, political leaders, and religion, politics, and infection. The correlation coefficient between fear and anger was 0.462 (p < .001), indicating a moderate linear relationship between the two emotions. Fear was the highest from January to March in the first year of the COVID-19 outbreak, while anger occurred before and after the outbreak, with fluctuations in both emotions during this period.ConclusionsThis study confirmed that social cognitions and negative emotions are intertwined in response to major events related to the COVID-19 pandemic, with each emotion varying individually rather than being ambiguously mixed. These findings could aid in developing social cognition-emotion-based public health strategies through education and communication during future pandemic outbreaks.

Modeling indoor thermal comfort in buildings using digital twin and machine learning

Digital Twin (DT) concept is used in different domains and industries, including the building industry, as it has physical and digital assets with the help of Building Information Modeling (BIM). Technologies and methodologies constantly enrich the building industry because the amount of data generated during different building stages is considerable and has a tremendous effect on the lifecycle of a building. Previous research underscores the importance of seamlessly exchanging information between physical and digital assets within a comprehensive framework, particularly emphasizing the integration of BIM data with various systems to enhance efficiency and prevent information loss. Despite advancements in technologies, challenges persist in optimizing methods for integrating BIM data into DT frameworks, including ensuring interoperability, scalability, and real-time monitor and control. This study addresses this research gap by proposing a comprehensive platform that integrates the DT concept with IoT and BIM technologies. The platform is developed in five main stages: 1) acquiring electronic data of the building from the laser scanner, 2) developing a Wi-Fi IoT module and BIM data for physical assets and digital replica, 3) constructing the DT elements of the platform, 4) performing data analysis 5) implementing thermal comfort prediction models. Two machine learning models (Facebook prophet, NeuralProphet) are implemented to predict thermal comfort. The best predictive model is identified by evaluating its error function using historical training data collected during facility operation. A case study demonstrates the practical application of the proposed framework. The case study involves a real building where the platform is implemented to monitor and control indoor environments. By utilizing predefined data in BIM models, the platform ensures data accuracy, consistency, and usability. The case outputs reveal that Neuralprophet provides good prediction results.

Integrating deep learning techniques for personalized learning pathways in higher education

The rapid improvement of artificial intelligence (AI) in the educational domain has opened new possibilities for enhancing the learning experiences for students. This research discusses the critical need for personalized education in higher education by integrating deep learning (DL) techniques to create customized learning pathways for students. This research intends to bridge the gap between constant educational content and dynamic student needs. This research presents an AI-driven adaptive learning platform implemented across four different courses and 300 students at a university in Faisalabad-Pakistan. A controlled experiment compares student outcomes between those using the AI platform and those undergoing traditional instruction. Quantitative results demonstrate a 25 % improvement in grades, test scores, and engagement for the AI group, with a statistical significance of a p-value of 0.00045. Qualitative feedback highlights enhanced experiences attributed to personalized pathways. The DL analysis of student performance data highlights key parameters, including enhanced learning outcomes and engagement metrices over time. Surveys reveal increased satisfaction compared to one-size-fits-all content. Unlike prior AI research lacking rigorous validation, our methodology and significant results deliver a concrete framework for institutions to implement personalized, AI-driven education at scale. This data-driven approach builds on previous attempts by tying adaptations to actual student needs, yielding measurable improvements in key outcomes. Overall, this work empirically validates that AI platforms leveraging robust analytics to provide customized and adaptive learning can significantly enhance student academic performance, engagement, and satisfaction compared to traditional approaches. These findings have insightful consequences for the future of higher education. The research contributes to the growing demand for AI in education research and provides a practical framework for institutions seeking to implement more adaptive and student-centric teaching methodologies.

Wake Effect Quantification using SCADA Data and LES Modelling of an Operational Offshore Wind Farm

Wake effects in the Anholt offshore wind farm have been investigated using both operational data and a Large Eddy Simulation (LES) model of a group of five turbines within the wind farm. Analysis of operational data showed that the variations of main shaft speeds of the downstream turbines were almost six times those of the upstream turbine at near-rated operation. The aim of the LES was to study the impact of atmospheric stability on the wind turbine array performance and compare this with the field data. An LES precursor method was used to model the near-neutral and unstably stratified atmospheric boundary layers that represent typical conditions in winter and summer, respectively, and the turbines in wind farm model were simulated using an actuator line method. It was found that LES with the actuator line method and generic turbine design data can generate a reasonable mean power generation trend for the Anholt wind farm under near-neutral and unstable conditions. The maximum difference in the mean power output between the LES and averaged operational data was approximately 20%.

Maturation-specific enhancements in lower extremity explosive strength following plyometric training in adolescent soccer players: A systematic review and meta-analysis

BackgroundFor adolescent soccer players, good sprinting and jumping abilities are crucial for their athletic performance. The application of plyometric training on boosting explosive strength in adolescent soccer players is contingent upon the maturation phase, which can mediate the training-induced adaptations. PurposeThis systematic review and meta-analysis aim to explore the maturation effect of plyometric training on the lower limb explosive power of adolescent soccer players, with vertical countermovement jump (CMJ) and 20-m sprint as the main outcome indicators. MethodsAn extensive search of the literature was carried out on various databases including PubMed, Web of Science, Scopus, ProQuest, and the China National Knowledge Infrastructure (CNKI), covering the time period from the establishment of each database to February 6, 2023. The search was conducted using English keywords such as ‘Plyometric,’ ‘Adolescent,’ ‘football,’ and ‘Explosive strength.’ This study utilized the Cochrane risk of bias assessment tool to conduct a standardized quality evaluation of all the included literature. Additionally, the Review Manager 5.4 software was employed to perform data analysis on all the extracted data. ResultsA total of 17 studies involving 681 adolescent soccer players aged 10 to 19 were included. Plyometric training significantly improved CMJ performance across different maturation stages, especially in the post-peak height velocity stage (POST-PHV) [MD = 4.35, 95 % CI (2.11, 6.59), P < 0.01, I2 = 60 %]. The pre-peak height velocity stage (PRE-PHV) showed the next best improvement [MD = 3.00, 95 % CI (1.63, 4.37)], while the middle-peak height velocity stage (MID-PHV) showed the least improvement [MD = 2.79, 95 % CI (1.16, 4.41), P < 0.01, I2 = 49 %]. However, improvements in 20 m sprint ability were only observed in the PRE-PHV [MD = −0.06, 95 % CI (−0.12, 0), P < 0.01, I2 = 0 %] and MID-PHV [MD = −0.18, 95 % CI (−0.27, −0.08), P < 0.01, I2 = 0 %] stages. ConclusionPlyometric training serves as a potent strategy for boosting the lower limb explosive strength of adolescent soccer players, and the training effect is closely related to the players' biological maturity. Considering biological maturity is a key aspect that this study deems essential for the formulation of effective training programs for these adolescent players.