bluuebunny/crossref_metadata_2025 huggingface dataset:
Blockchain-Based Federated Learning: A Survey and New Perspectives
Authors: Ning Weiguang , Zhu Yingjuan , Song Caixia , Li Hongxia , Zhu Lihui , Xie Jinbao , Chen Tianyu , Xu Tong , Xu Xi , Gao Jiwei
Published: October 2024

Federated learning, as a novel distributed machine learning mode, enables the training of machine learning models on multiple devices while ensuring data privacy. However, the existence of single-point-of-failure bottlenecks, malicious threats, scalability of federated learning implementation, and lack of incentive mechanisms have seriously hindered the development of federated learning technology. In recent years, as a distributed ledger, blockchain has the characteristics of decentralization, tamper-proof, transparency, security, etc., which can solve the issues encountered in the above-mentioned federated learning. Particularly, the integration of federated learning and blockchain leads to a new paradigm, called blockchain-based federated learning (BFL), which has been successfully applied in many application scenarios. This paper aims to provide a comprehensive review of recent efforts on blockchain-based federated learning. More concretely, we propose and design a taxonomy of blockchain-based federated learning models, along with providing a comprehensive summary of the state of the art. Various applications of federated learning based on blockchain are introduced. Finally, we expand on current trends and provide new perspectives pertaining to this new and exciting development in the field.
Spotting Leaders in Organizations with Graph Convolutional Networks, Explainable Artificial Intelligence, and Automated Machine Learning
Authors: Xie Yunbo , Meisel Jose D. , Meisel Carlos A. , Betancourt Juan Jose , Yan Jianqi , Bugiolacchi Roberto
Published: October 2024

Over the past few decades, the study of leadership theory has expanded across various disciplines, delving into the intricacies of human behavior and defining the roles of individuals within organizations. Its primary objective is to identify leaders who play significant roles in the communication flow. In addition, behavioral theory posits that leaders can be distinguished based on their daily conduct, while social network analysis provides valuable insights into behavioral patterns. Our study investigates five and six types of social networks frequently observed in different organizations. This study is conducted using datasets we collected from an IT company and public datasets collected from a manufacturing company for the thorough evaluation of prediction performance. We leverage PageRank and effective word embedding techniques to obtain novel features. State-of-the-art performance is obtained using various statistical machine learning methods, graph convolutional networks (GCN), automated machine learning (AutoML), and explainable artificial intelligence (XAI). More specifically, our approach can achieve state-of-the-art performance with an accuracy close to 90% for leaders identification with data from projects of different types. This investigation contributes to the establishment of sustainable leadership practices by aiding organizations in retaining their leadership talent.
Evaluating Airport Service Quality Based on the Statistical and Predictive Analysis of Skytrax Passenger Reviews
Authors: Alanazi Mohammed Saad M. , Li Jun , Jenkins Karl W.
Published: October 2024

This study leverages approximately 7500 reviews from Skytrax to explore the determinants of airport service quality and their influence on passenger recommendations. The dataset includes various features such as terminal cleanliness, terminal seating, terminal signs, food and beverages, airport shopping, WiFi connectivity, and airport staff. The research employs a comprehensive methodology encompassing statistical data analysis, predictive modelling, and interaction effects analysis. The descriptive analysis of time-series data highlighted trends and fluctuations in service quality and recommendations, providing insights into temporal dynamics. Multiple machine learning models, including logistic regression, Random Forest, SVM, KNN, Gradient Boosting, and Neural Networks, were developed in this study and cross-validated for airport recommendation based on Skytrax’s online reviews. Among others, Gradient Boosting emerged as the most accurate model with an 88.15% mean accuracy. Interaction effects revealed significant combined influences, such as terminal cleanliness and terminal seating, on passenger recommendations. This multifaceted approach offers robust insights into factors influencing airport recommendations and guides improvements in airport management to enhance passenger satisfaction. Future work will focus on a general-purpose machine learning framework and its toolbox development for airport service quality analysis based on online reviews from various sources.
A Bi-Objective Optimization Strategy of a Distribution Network Including a Distributed Energy System Using Stepper Search
Authors: Ma Suliang , Meng Zeqing , Cui Yilin , Sha Guanglin
Published: October 2024

The optimal scheduling of DES is to solve a multi-objective optimization problem (MOP) with complex constraints. However, the potential contradiction between multiple optimization objectives leads to the diversity of feasible solutions, which has a serious impact on the selection of optimal scheduling strategies. Therefore, a stepper search optimization (SSO) method has been proposed for a bi-objective optimization problem (BiOP). Firstly, a constrained single-objective optimization problem (CSiOP) has been established to transform a BiOP and describe an accurate pareto front curve. Then, based on the characteristics of pareto front, the rate of the pareto front is analyzed by the SSO, and the best recommended solution of the BiOP is obtained. Finally, in the IEEE 33 with a DES simulation, by comparing other methods, the SSO method can better control the bi-objective optimization results to be 1–2.5 times as much as the optimal result under each single optimization objective and avoid the imbalance between the two optimization objectives. Additionally, the optimization speed of the SSO method is more than 10 times faster than that of the non-dominated sorting genetic algorithm (NSGA). Further, the SSO method will provide a novel idea for solving MOP.
A Method for Developing Seismic Hazard-Consistent Fragility Curves for Soil Liquefaction Using Monte Carlo Simulation
Authors: Huang Fu-Kuo , Wang Grace S.
Published: October 2024

The objective of this study is to present a method for developing fragility curves for soil liquefaction that align with seismic hazards using Monte Carlo simulation. This approach can incorporate all uncertainties and variabilities in the input parameters. The seismic parameters, including earthquake magnitude (M) and associated peak ground acceleration (PGA), are jointly considered for the liquefaction assessment. The liquefaction potential and the resulting damages obtained by this method are more realistic. A case study is conducted using data from a sand-boil site in Yuanlin, Changhua County, where liquefaction occurred during the 1999 Chi-Chi earthquake in Taiwan. The findings indicate that the liquefaction potential index, IL, the post-liquefaction settlement, St, and the liquefaction probability index, PW, are all appropriate parameters for assessing liquefaction damages. The fragility curves for soil liquefaction developed through this method can support the performance-based earthquake engineering (PBEE) approach, provide guidance for liquefaction evaluation to the Taiwan Earthquake Loss Estimation System (TELES), and serve as a foundation for scenario simulation and an earthquake early warning system for liquefaction damages.
Adaptive Point Learning with Uncertainty Quantification to Generate Margin Lines on Prepared Teeth
Authors: Alsheghri Ammar , Ladini Yoan , Hosseinimanesh Golriz , Chafi Imane , Keren Julia , Cheriet Farida , Guibault François
Published: October 2024

During a crown generation procedure, dental technicians depend on commercial software to generate a margin line to define the design boundary for the crown. The margin line generation remains a non-reproducible, inconsistent, and challenging procedure. In this work, we propose to generate margin line points on prepared teeth meshes using adaptive point learning inspired by the AdaPointTr model. We extracted ground truth margin lines as point clouds from the prepared teeth and crown bottom meshes. The chamfer distance (CD) and infoCD loss functions were used for training a supervised deep learning model that outputs a margin line as a point cloud. To enhance the generation results, the deep learning model was trained based on three different resolutions of the target margin lines, which were used to back-propagate the losses. Five folds were trained and an ensemble model was constructed. The training and test sets contained 913 and 134 samples, respectively, covering all teeth positions. Intraoral scanning was used to collect all samples. Our post-processing involves removing outlier points based on local point density and principal component analysis (PCA) followed by a spline prediction. Comparing our final spline predictions with the ground truth margin line using CD, we achieved a median distance of 0.137 mm. The median Hausdorff distance was 0.242 mm. We also propose a novel confidence metric for uncertainty quantification of generated margin lines during deployment. The metric was defined based on the percentage of removed outliers during the post-processing stage. The proposed end-to-end framework helps dental professionals in generating and evaluating margin lines consistently. The findings underscore the potential of deep learning to revolutionize the detection and extraction of 3D landmarks, offering personalized and robust methods to meet the increasing demands for precision and efficiency in the medical field.
Investigation of the Automatic Monitoring System of a Solar Power Plant with Flexible PV Modules
Authors: Kavaliauskas Žydrūnas , Šajev Igor , Blažiūnas Giedrius , Gecevičius Giedrius
Published: October 2024

During this research, an automatic monitoring system was developed to monitor the working parameters in a solar power plant consisting of two flexible silicon modules. The first stage of the monitoring system relies on a microcontroller, which collects data from wattmeter modules made using a microcontroller. This tier also includes DC/DC converter and RS232-TCP converter modules for data transfer. The second stage, the industrial PLC, receives data from the first stage and transmits them to the PC, where the information is stored and the processes are visualized on the HMI screen. During this study, the charging process was analyzed using PWM- and MPPT-type charging controllers, as well as the power supply of Fito LED strips for lighting plants. Using the created monitoring system, the parameters of the solar power plant with flexible PV modules were monitored. This study compared PWM and MPPT battery charging methods, finding that MPPT is more efficient, especially under unstable solar conditions. MPPT technology optimizes energy usage more efficiently, resulting in faster battery charging compared to PWM technology.
Strengthening Transformer Tank Structural Integrity through Economic Stiffener Design Configurations Using Computational Analysis
Authors: Hasan Md Milon , Rahman Arafater , Islam Asif , Khondoker Mohammad Abu Hasan
Published: October 2024

Power transformers play a vital role in adjusting voltage levels during transmission. This study focuses on optimizing the structural design of power transformer tanks, particularly high-voltage (HV) tank walls, to enhance their mechanical robustness, performance, and operational reliability. This research investigates various stiffener designs and their impact on stress distribution and deformation through finite element analysis (FEA). Ten different configurations of stiffeners, including thickness, width, type, and position variations, were evaluated to identify the optimal design that minimizes stress and deflection while considering weight constraints. The results indicate that specific configurations, particularly those incorporating 16 mm thick H beams, significantly enhance structural integrity. Experimental validation through pressure testing corroborated the simulation findings, ensuring the practical applicability of the optimized designs. This study’s findings have implications for enhancing the longevity and reliability of power transformers, ultimately contributing to more efficient and resilient power transmission systems.
Integration of Whole-Genome Sequencing with ddPCR Kit for Detection of Omicron Subvariants in Wastewater in the Upper Peninsula of Michigan
Authors: Jarvie Michelle M. , Nguyen Thu N. T. , Southwell Benjamin , Wright Derek
Published: October 2024

This study explores the integration of genome sequencing and digital droplet polymerase chain reaction (ddPCR)-based methods for tracking the diversity of COVID-19 variants in wastewater. The research focuses on monitoring various Omicron subvariants during a period of significant viral evolution. Genome sequencing, particularly using Oxford Nanopore Technology (ONT), provides a detailed view of emerging variants, surpassing the limitations of PCR-based detection kits that rely on known sequences. Of the 43 samples analyzed, 39.5% showed matching results between the GT Molecular ddPCR kits and sequencing, though only 4% were exact matches. Some mismatches occurred due to newer subvariants like XBB and BQ.1, which the ddPCR kits could not detect. This emphasized the limitations of ddPCR kits, which rely on known variant sequences, while sequencing provides real-time data on emerging variants, offering a more comprehensive view of circulating strains. This study highlights the effectiveness of combining these methodologies to enhance early detection and inform public health strategies, especially in regions with limited clinical sequencing capabilities.
Phenolic and Acidic Compounds in Radiation Fog at Strasbourg Metropolitan
Authors: Khoury Dani , Millet Maurice , Jabali Yasmine , Delhomme Olivier
Published: October 2024

Sixty-four phenols grouped as nitrated, bromo, amino, methyl, chloro-phenols, and cresols, and thirty-eight organic acids grouped as mono-carboxylic and dicarboxylic are analyzed in forty-two fog samples collected in the Alsace region between 2015 and 2021 to check their atmospheric behavior. Fogwater samples are collected using the Caltech Active Strand Cloudwater Collector (CASCC2), extracted using liquid–liquid extraction (LLE) on a solid cartridge (XTR Chromabond), and then analyzed using gas chromatography coupled with mass spectrometry (GC-MS). The results show the high capability of phenols and acids to be scavenged by fogwater due to their high solubility. Nitro-phenols and mono-carboxylic acids have the highest contributions to the total phenolic and acidic concentrations, respectively. 2,5-dinitrophenol, 3-methyl-4-nitrophenol, 4-nitrophenol, and 3,4-dinitrophenol have the highest concentration, originating mainly from vehicular emissions and some photochemical reactions. The top three mono-carboxylic acids are hexadecenoic acid (C16), eicosanoic acid (C18), and dodecanoic acid (C12), whereas succinic acid, suberic acid, sebacic acid, and oxalic acid are the most concentrated dicarboxylic acids, originated either from atmospheric oxidation (mainly secondary organic aerosols (SOAs)) or vehicular transport. Pearson’s correlations show positive correlations between organic acids and previously analyzed metals (p < 0.05), between mono- and dicarboxylic acids (p < 0.001), and between the analyzed acidic compounds (p < 0.001), whereas no correlations are observed with previously analyzed inorganic ions. Total phenolic and acidic fractions are found to be much higher than those observed for pesticides, polycyclic aromatic hydrocarbons (PAHs), and polychlorinated biphenyls (PCBs) measured at the same region due to their higher scavenging by fogwater.
Correction: Patias et al. Effect of Liposomal Protium heptaphyllum (Alb.) March Extract in the Treatment of Obesity Induced by High-Calorie Diet. Biology 2024, 13, 535
Authors: Patias Naiéle Sartori , de Queiroz Eveline Aparecida Isquierdo Fonseca , Ferrarini Stela Regina , Bomfim Gisele Facholi , Aguiar Danilo Henrique , Sinhorin Adilson Paulo , Bello Alexandre Aymberé , da Silva Geovana Vicentini Fazolo , Cavalheiro Larissa , Sinhorin Valéria Dornelles Gindri
Published: October 2024

In the original publication [...]
Effects of Acute Hypoxic Exposure in Simulated Altitude in Healthy Adults on Cognitive Performance: A Systematic Review and Meta-Analysis
Authors: Ramírez-delaCruz María , Bravo-Sánchez Alfredo , Sánchez-Infante Jorge , Abián Pablo , Abián-Vicén Javier
Published: October 2024

The neurocognitive response following hypoxia has received special interest. However, it is necessary to understand the impact of acute hypoxic exposure induced by simulated altitude on cognitive performance. This study aimed to determine the effects of acute hypoxic exposure in simulated altitude in healthy adults on reaction time, response accuracy, memory, and attention. Five electronic databases were searched. The inclusion criteria were: (1) Experimental studies involving a hypoxia intervention induced by a hypoxic air generator to determine the effects on cognitive performance; and (2) Conducted in adults (males and/or females; aged 18–50 years) without pathologies or health/mental problems. Four meta-analyses were performed: (1) reaction time, (2) response accuracy, (3) memory, and (4) attention. Finally, 37 studies were included in the meta-analysis. Hypoxia exposure induced detrimental effects on reaction time (standard mean difference (SMD) −0.23; 95% confidence interval (CI) −0.38–−0.07; p = 0.004), response accuracy (SMD −0.20; 95% CI −0.38–−0.03; p = 0.02), and memory (SMD −0.93; 95% CI: −1.68–−0.17; p = 0.02). Nevertheless, attention was not affected during hypoxia exposure (SMD −0.06; 95% CI: −0.23–0.11; p = 0.47). Acute exposure to hypoxia in controlled lab conditions appears to be detrimental to cognitive performance, specifically in reaction time, response accuracy, and memory.
N6-Methyladenosine Methyltransferase Component KIAA1429 Is a Potential Target of Cancer Therapy
Authors: Huang Junjun , Guo Jihua , Jia Rong
Published: October 2024

N6-methyladenosine (m6A), the most abundant RNA modification in eukaryotes, has a crucial impact on tumorigenesis. KIAA1429 is the key component of the m6A methyltransferase complex, in which KIAA1429 functions as a scaffold to bridge the catalytic core proteins. KIAA1429 is often overexpressed in malignances, associated with patient prognosis, and required for tumorigenesis. KIAA1429 regulates the expression of a number of tumor-associated genes in an m6A -dependent manner, and thus, contributes to cell proliferation, migration, drug resistance, tumor formation and metastasis. This review focuses on recent progress in the understanding of roles and mechanisms of KIAA1429 in cancers, and offers ideas for potential anti-cancer therapeutic methods by targeting KIAA1429.
The Ambivalence of Post COVID-19 Vaccination Responses in Humans
Authors: Gopalaswamy Radha , Aravindhan Vivekanandhan , Subbian Selvakumar
Published: October 2024

The Coronavirus disease 2019 (COVID-19) pandemic, caused by severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2), has prompted a massive global vaccination campaign, leading to the rapid development and deployment of several vaccines. Various COVID-19 vaccines are under different phases of clinical trials and include the whole virus or its parts like DNA, mRNA, or protein subunits administered directly or through vectors. Beginning in 2020, a few mRNA (Pfizer-BioNTech BNT162b2 and Moderna mRNA-1273) and adenovirus-based (AstraZeneca ChAdOx1-S and the Janssen Ad26.COV2.S) vaccines were recommended by WHO for emergency use before the completion of the phase 3 and 4 trials. These vaccines were mostly administered in two or three doses at a defined frequency between the two doses. While these vaccines, mainly based on viral nucleic acids or protein conferred protection against the progression of SARS-CoV-2 infection into severe COVID-19, and prevented death due to the disease, their use has also been accompanied by a plethora of side effects. Common side effects include localized reactions such as pain at the injection site, as well as systemic reactions like fever, fatigue, and headache. These symptoms are generally mild to moderate and resolve within a few days. However, rare but more serious side effects have been reported, including allergic reactions such as anaphylaxis and, in some cases, myocarditis or pericarditis, particularly in younger males. Ongoing surveillance and research efforts continue to refine the understanding of these adverse effects, providing critical insights into the risk-benefit profile of COVID-19 vaccines. Nonetheless, the overall safety profile supports the continued use of these vaccines in combating the pandemic, with regulatory agencies and health organizations emphasizing the importance of vaccination in preventing COVID-19’s severe outcomes. In this review, we describe different types of COVID-19 vaccines and summarize various adverse effects due to autoimmune and inflammatory response(s) manifesting predominantly as cardiac, hematological, neurological, and psychological dysfunctions. The incidence, clinical presentation, risk factors, diagnosis, and management of different adverse effects and possible mechanisms contributing to these effects are discussed. The review highlights the potential ambivalence of human response post-COVID-19 vaccination and necessitates the need to mitigate the adverse side effects.
Modulatory Impact of Tefluthrin, Telmisartan, and KB-R7943 on Voltage-Gated Na+ Currents
Authors: Huang Hsun-Yu , Huang Yi-Bo , Wu Chao-Liang , Wu Sheng-Nan
Published: October 2024

Tefluthrin (Tef) is categorized as a type-I pyrethroid insecticide, telmisartan (Tel) functions as an angiotensin II receptor blocker, and KB-R7943 has been identified as an inhibitor of the Na+-Ca2+ exchange process. However, the influence of these compounds on the amplitude and gating properties of voltage-gated Na+ current (INa) in neurons associated with pain signaling remains unclear. In cultured dorsal root ganglion (DRG) neurons, whole-cell current recordings revealed that Tef or Tel increased the peak amplitude of INa, concomitant with an elevation in the time constant of INa inactivation, particularly in the slow component. Conversely, exposure to KB-R7943 resulted in a depression in INa, coupled with a decrease in the slow component of the inactivation time constant of INa. Theoretical simulations and bifurcation analyses were performed on a modeled interneuron in the spinal dorsal horn. The occurrence of INa inactivation accentuated the subthreshold oscillations (SO) in the membrane potential. With an increase in applied current, SO became more pronounced, accompanied by the emergence of high-frequency spiking (HS) with a frequency of approximately 150 Hz. Moreover, an elevation in INa conductance further intensified both SO and HF. Consequently, through experimental and in silico studies, this work reflects that Tef, Tel, or KB-R7943 significantly impacts the magnitude and gating properties of INa in neurons associated with pain signaling. The alterations in INa magnitude and gating in these neurons suggest a close relationship with pain transmission.
The Past, Present and Future of the Common Crane (Grus grus) in Poland
Authors: Polak Marcin , Mikusek Romuald
Published: October 2024

In Poland, it is highly probable that in past centuries, Common Cranes (Grus grus) were widespread all over the country. In the 19th century, a particularly dramatic decline in this population took place when Common Cranes abandoned many regions. At present, this species inhabits the whole of lowland Poland, only avoiding the uplands and mountainous regions in the south of the country. The rate of population growth between 2001 and 2018 was c. 5% per annum. The breeding population increased tenfold in the 30 years from 1990 to 2020. The results of the Polish Bird Monitoring Programme in 2018–2020 show that Common Crane numbers had stabilized. The latest count (2013–2018) of breeding pairs in Poland is 23,000–30,000, while that for the years 2010–2012 was 20,000–22,000, i.e., 15% of the European population or 19% of the breeding population in the EU. The greatest threats to the Polish Common Crane population come from habitat and climate changes, two factors that are inextricably interlinked. Experts are alerting that climate warming, in combination with increased pressure from human activities, can, in the long term, reverse this trend and lead to declines in Common Crane populations in this part of Europe.
Association Between Glaucoma and Brain Structural Connectivity Based on Diffusion Tensor Tractography: A Bidirectional Mendelian Randomization Study
Authors: Shu Lian , Chen Xiaoxiao , Sun Xinghuai
Published: October 2024

Background: Glaucoma is a neurodegenerative ocular disease that is accompanied by cerebral damage extending beyond the visual system. Recent studies based on diffusion tensor tractography have suggested an association between glaucoma and brain structural connectivity but have not clarified causality. Methods: To explore the causal associations between glaucoma and brain structural connectivity, a bidirectional Mendelian randomization (MR) study was conducted involving glaucoma and 206 diffusion tensor tractography traits. Highly associated genetic variations were applied as instrumental variables and statistical data were sourced from the database of FinnGen and UK Biobank. The inverse-variance weighted method was applied to assess causal relationships. Additional sensitivity analyses were also performed. Results: Glaucoma was potentially causally associated with alterations in three brain structural connectivities (from the SN to the thalamus, from the DAN to the putamen, and within the LN network) in the forward MR analysis, whereas the inverse MR results identified thirteen brain structural connectivity traits with a potential causal relationship to the risk of glaucoma. Both forward and reverse MR analyses satisfied the sensitivity test with no significant horizontal pleiotropy or heterogeneity. Conclusions: This study offered suggestive evidence for the potential causality between the risk of glaucoma and brain structural connectivity. Our findings also provided novel insights into the neurodegenerative mechanism of glaucoma.
Predicting the Risk of Loneliness in Children and Adolescents: A Machine Learning Study
Authors: Zhang Jie , Feng Xinyi , Wang Wenhe , Liu Shudan , Zhang Qin , Wu Di , Liu Qin
Published: October 2024

Background: Loneliness is increasingly emerging as a significant public health problem in children and adolescents. Predicting loneliness and finding its risk factors in children and adolescents is lacking and necessary, and would greatly help determine intervention actions. Objective: This study aimed to find appropriate machine learning techniques to predict loneliness and its associated risk factors among schoolchildren. Methods: The data were collected from an ongoing prospective puberty cohort that was established in Chongqing, Southwest China. This study used 822 subjects (46.84% boys, age range: 11–16) followed in 2019. Five models, (a) random forest, (b) extreme gradient boosting (XGBoost), (c) logistic regression, (d) neural network, and (e) support vector machine were applied to predict loneliness. A total of 39 indicators were collected and 28 predictors were finally included for prediction after data pre-processing, including demographic, parental relationship, mental health, pubertal development, behaviors, and environmental factors. Model performance was determined by accuracy and AUC. Additionally, random forest and XGBoost were applied to identify the important factors. The XGBoost algorithm with SHAP was also used to interpret the results of our ML model. Results: All machine learning performed with favorable accuracy. Compared to random forest (AUC: 0.87 (95%CI: 0.80, 0.93)), logistic regression (AUC: 0.80 (95%CI: 0.70, 0.89)), neural network (AUC: 0.80 (95%CI: 0.71, 0.89)), and support vector machine (AUC: 0.79 (95%CI: 0.79, 0.89)), XGBoost algorithm had the highest AUC values 0.87 (95%CI: 0.80, 0.93) in the test set, although the difference was not significant between models. Peer communication, index of general affect, peer alienation, and internet addiction were the top four significant factors of loneliness in children and adolescents. Conclusions: The results of this study suggest that machine learning has considerable potential to predict loneliness in children. This may be valuable for the early identification and intervention of loneliness.
The Effect of Item Strength on Retrieval-Induced Forgetting in Social Interaction
Authors: Chu Yue , Xu Hui , Tang Weihai , Liu Xiping
Published: October 2024

Retrieval-induced forgetting (RIF) refers to the phenomenon in which people retrieve information, but forget related information. RIF also occurs when people interact with each other. In social interactions, information recalled by the speaker can lead the listener to forget related information, a phenomenon known as socially shared retrieval-induced forgetting (SS-RIF). How does SS-RIF occur? Does it have similar mechanisms to RIF? By observing SS-RIF and RIF with different item strengths, this study investigated the mechanisms of SS-RIF. Item strength was manipulated based on exemplar taxonomic frequency, with high-frequency exemplars designated as strong items and low-frequency exemplars as weak. Experiment 1 found that only strong items exhibited SS-RIF and RIF, while weak items did not exhibit either SS-RIF or RIF. In Experiment 2, participants were asked to restudy the materials, and only the strong items still exhibited SS-RIF and RIF. Additionally, the magnitude of SS-RIF observed in Experiment 2 was similar to that observed in Experiment 1, as well as the performance on RIF. The findings of this study provide evidence for the inhibition mechanism of both SS-RIF and RIF, indicating a shared underlying mechanism.
Physical Activity Behaviour and Motivation During and Following Pulmonary and Cardiac Rehabilitation: A Repeated Measures Study
Authors: Alfrey Kristie Lee , Gardner Benjamin , Judd Jenni , Askew Christopher D. , Vandelanotte Corneel , Rebar Amanda L.
Published: October 2024

Background: Exercise rehabilitation programmes are important for long-term health and wellbeing among people with cardiac and pulmonary diseases. Despite this, many people struggle to maintain their physical activity once rehabilitation ends. This repeated measures study tracked changes in physical activity behaviour and motivation during and after completing a community-based exercise rehabilitation programme. Methods: Cardiac and pulmonary exercise rehabilitation patients (N = 31) completed six once-monthly measures of physical activity (MET·min), self-determined motivation, intention, and habit strength for rehabilitation exercise (within rehabilitation sessions) and lifestyle physical activity (outside of rehabilitation sessions). Linear regression and random effects models with estimated marginal means were used to test for associations between physical activity motivation and behaviour and change during and post-rehabilitation. Results: Overall physical activity decreased after rehabilitation (823 MET·min) despite patients becoming more self-determined for lifestyle physical activity during rehabilitation. More self-determined motivation, stronger intentions, and stronger habits were associated with more lifestyle physical activity behaviour. However, none of these motivation variables were significantly associated with rehabilitation exercise behaviour. Conclusions: Among community-based cardiac and pulmonary rehabilitation patients, physical activity levels decreased following exercise rehabilitation programmes. The findings revealed clear distinctions in the motivation of rehabilitation exercise compared to lifestyle physical activity. Exercise rehabilitation programmes might improve the longevity of outcomes by integrating approaches to enhance lifestyle physical activity beyond the clinic.
Rib Alignment Control of Long-Span Arch Bridge in Cable-Stayed Buckle by Multi-Objective Optimization
Authors: Yu Mengsheng , Yao Xinyu , Wang Longlin , Hao Tianzhi , Deng Nianchun
Published: October 2024

The construction duration of long-span arch bridges is excessively prolonged due to insufficient closing precision and the non-convergence of traditional cable adjustment calculation methods. This study investigates cable force management in long-span concrete-filled steel tubular (CFST) arch bridges during cable-stayed buckle construction, aiming to improve construction safety and precision in arch rib alignment. Using the Pingnan Third Bridge and Tian’e Longtan Bridge as practical examples, the research develops a multi-objective optimization method for cable forces that integrates influence matrices, constrained minimization, and a forward iterative approach. This method offers a robust strategy for tensioning and cable-stayed buckling, enabling real-time monitoring, calculation, and adjustment during the construction of large-span CFST arch bridges. The results reveal that the iterative approach notably enhances calculation efficiency compared to conventional methods. For instance, field measurements at the Pingnan Third Bridge show a minimal arch closure error of only 3 mm. Additionally, the study addresses concerns about excessive stress in exposed steel tubes during concrete casting. By optimizing the sequence of main arch closure and concrete casting, stress in the exposed steel tube is reduced from 373 MPa to 316 MPa, thus meeting specification requirements. In summary, the multi-objective cable force optimization method demonstrates superior efficiency in determining cable tension and controlling rib alignment during cable-stayed buckle construction of long-span CFST arch bridges.
Exploring the Link Between Social Media Engagement, Social Anxiety, and Environmental Preferences in University Students
Authors: Wang Lan , Zhang Xia , Xie Aolin
Published: October 2024

As mental health issues become increasingly prominent, social anxiety is prevalent among university students and significantly affects their social behaviors and environmental preferences. With the increasing reliance on social media, understanding how these behaviors shape preferences for physical social environments is crucial for designing supportive campus spaces. This study investigates the relationship between social anxiety, social media usage, and environmental preferences in campus settings. We aim to identify the key environmental factors that can promote healthier social interactions for students with varying levels of social anxiety and social media use. Data were collected from university students through a structured questionnaire assessing demographic characteristics, online and offline behavior, social media usage intensity, social anxiety level, and environmental preferences. Using K-means clustering, participants were grouped into four clusters based on their social media usage and social anxiety levels. ANOVA and Multivariate Multiple Regression (MMR) were conducted to examine differences in environmental preferences across these clusters. Significant differences in environmental preferences were observed across the four clusters. Gender and grade influenced social anxiety levels, with females and undergraduate students reporting greater anxiety. Individuals with high social media use, regardless of their anxiety levels, generally exhibited stronger preferences for various environmental dimensions, including openness, convenience, facility provision, safety/comfort, and cultural landscape. However, individuals with higher social anxiety tended to prefer enclosed, facility-rich, and more controlled environments. The relationship between social anxiety and preferences for convenience, safety/comfort, and cultural landscape was minimal or non-significant. This study highlights the importance of considering both behavioral and psychological factors when designing university social environments. By identifying specific environmental preferences of students with high social anxiety, the findings offer evidence-based recommendations for creating more inclusive and supportive campus spaces that promote positive social interactions and well-being.
Technological Innovation in Architectural Design Education: Empirical Analysis and Future Directions of Midjourney Intelligent Drawing Software
Authors: Xu Chong , Huang Yi
Published: October 2024

This study investigates the role of Midjourney intelligent drawing software in architectural design education, particularly its impact on design efficiency and creativity among sophomore architecture students. While Midjourney serves as a powerful tool for rapid visual concept generation, it does not replace traditional design skills but complements them by enhancing the visualization process. The study also identifies some key limitations of Midjourney, especially in areas of functionality and sustainable design, which require students to integrate additional tools for a more comprehensive design approach. The recommendation to expand the use of intelligent drawing technologies like Midjourney in future research is supported by the observed improvements in student performance, including enhanced creative expression and efficiency in design processes. These conclusions are based on qualitative data derived from student feedback, as well as quantitative measurements of their design outcomes before and after using the software. Furthermore, it highlights the need to broaden the scope of studies to include a wider variety of educational contexts and source materials to improve understanding of the software’s potential in design education.
Enhancing Lymphoma Diagnosis, Treatment, and Follow-Up Using 18F-FDG PET/CT Imaging: Contribution of Artificial Intelligence and Radiomics Analysis
Authors: Hasanabadi Setareh , Aghamiri Seyed Mahmud Reza , Abin Ahmad Ali , Abdollahi Hamid , Arabi Hossein , Zaidi Habib
Published: October 2024

Lymphoma, encompassing a wide spectrum of immune system malignancies, presents significant complexities in its early detection, management, and prognosis assessment since it can mimic post-infectious/inflammatory diseases. The heterogeneous nature of lymphoma makes it challenging to definitively pinpoint valuable biomarkers for predicting tumor biology and selecting the most effective treatment strategies. Although molecular imaging modalities, such as positron emission tomography/computed tomography (PET/CT), specifically 18F-FDG PET/CT, hold significant importance in the diagnosis of lymphoma, prognostication, and assessment of treatment response, they still face significant challenges. Over the past few years, radiomics and artificial intelligence (AI) have surfaced as valuable tools for detecting subtle features within medical images that may not be easily discerned by visual assessment. The rapid expansion of AI and its application in medicine/radiomics is opening up new opportunities in the nuclear medicine field. Radiomics and AI capabilities seem to hold promise across various clinical scenarios related to lymphoma. Nevertheless, the need for more extensive prospective trials is evident to substantiate their reliability and standardize their applications. This review aims to provide a comprehensive perspective on the current literature regarding the application of AI and radiomics applied/extracted on/from 18F-FDG PET/CT in the management of lymphoma patients.
Synthesis, Characterization and Structural Study of the Two Ionic Hydrogen-Bonded Organic Frameworks Based on Sterically Crowded Bifunctional Moieties
Authors: Vostrikova Kira E. , Kirin Vladimir P. , Samsonenko Denis G.
Published: October 2024

Small bifunctional molecules are attractive for use as models in different areas of knowledge. How can their functional groups interact in solids? This is important to know for the prediction of the physical and chemical properties of the materials based on them. In this study, two new hydrogen-bonded organic frameworks (HOFs) based on sterically demanding molecular compounds, bis(1-hydroxy-2-methylpropane-2-aminium) sulfate (1) and 2-methyl-4-oxopentan-2-aminium hydrogen ethanedioate hydrate (2), were synthesized and fully characterized by means of FTIR and NMR spectroscopies, as well as by X-ray powder diffraction and thermogravimetric analyses. Their molecular and crystal structures were established through single-crystal X-ray diffraction analysis. It was shown that both compounds have a layered structure due to the formation of a 2D hydrogen-bonding network, the layers being linked by systematically arranged Van der Waals contacts between the methyl groups of organic cations. To unveil some dependencies between the chemical nature of bifunctional molecules and their solid structure, Hirschfeld surface (HS) analysis was carried out for HOFs 1, 2, and their known congeners 1-hydroxy-2-methylpropan-2-aminium hemicarbonate (3) and 1-hydroxy-2-methylpropan-2-aminium (1-hydroxy-2-methylpropan-2-yl) carbamate (4). HS was performed to quantify and visualize the close intermolecular atomic contacts in the crystal structures. It is clearly seen that H–H contacts make the highest contributions to the amino alcohol based compounds 1, 3 and 4, with a maximal value of 65.2% for compound 3 having CO32− as a counterion. A slightly lower contribution of H–H contacts (64.4%) was found for compound 4, in which the anionic part is represented by 1-hydroxy-2-methylpropan-2-yl carbamate. The significant contribution of the H–H contacts in the bifunctional moieties is due to the presence of a quaternary carbon atom with a short three-carbon chain.
Ginsenoside Re Regulates Oxidative Stress through the PI3K/Akt/Nrf2 Signaling Pathway in Mice with Scopolamine-Induced Memory Impairments
Authors: Li Xin , Zheng Kai , Chen Hao , Li Wei
Published: October 2024

While Ginsenoside Re has been shown to protect the central nervous system, reports of its effects on memory in the model of scopolamine-induced memory impairment are rare. The aim of this study was to investigate the effects of Ginsenoside Re on scopolamine (SCOP)-induced memory damage and the mechanism of action. Male ICR mice were treated with SCOP (3 mg/kg) for 7 days and with or without Ginsenoside Re for 14 days. As evidenced by behavioral studies (escape latency and cross platform position), brain tissue morphology, and oxidative stress indicators after Ginsenoside Re treatment, the memory damage caused by SCOP was significantly ameliorated. Further mechanism research indicated that Ginsenoside Re inhibited cell apoptosis by regulating the PI3K/Akt/Nrf2 pathway, thereby exerting a cognitive impairment improvement effect. This research suggests that Ginsenoside Re could protect against SCOP-induced memory defects possibly through inhibiting oxidative stress and cell apoptosis.
Structural Performance of a Hollow-Core Square Concrete Column Longitudinally Reinforced with GFRP Bars under Concentric Load
Authors: Said AbdulMuttalib I. , Hilfi Hussein A. , Allawi Abbas A. , Wardeh George
Published: October 2024

Concrete columns with hollow-core sections find widespread application owing to their excellent structural efficiency and efficient material utilization. However, corrosion poses a challenge in concrete buildings with steel reinforcement. This paper explores the possibility of using glass fiber-reinforced polymer (GFRP) reinforcement as a non-corrosive and economically viable substitute for steel reinforcement in short square hollow concrete columns. Twelve hollow short columns were meticulously prepared in the laboratory experiments and subjected to pure axial compressive loads until failure. All columns featured a hollow square section with exterior dimensions of (180 × 180) mm and 900 mm height. The columns were categorized into four separate groups with different variables: steel and GFRP longitudinal reinforcement ratio, hollow ratio, spacing between ties, and reinforcement type. The experimental findings point to the compressive participation of longitudinal GFRP bars, estimated to be approximately 35% of the tensile strength of GFRP bars. Notably, increasing GFRP longitudinal reinforcement significantly improved the ultimate load capability of hollow square GFRP column specimens. Specifically, elevating the ratio of GFRP reinforcement from 1.46% to 2.9%, 3.29%, 4.9%, and 5.85% resulted in axial load capacity improvements of 32.3%, 43.9%, 60.5%, and 71.7%, respectively. Specifically, the GFRP specimens showed a decrease in capacity of 13.1%, 9.2%, and 9.4%, respectively. Notably, the load contribution of steel reinforcement to GFRP reinforcement (with similar sectional areas) was from approximately three to four times the axial peak load, highlighting the greater load participation of steel reinforcement due to its higher elastic modulus. In addition, the numerical modeling and analysis conducted using ABAQUS/CAE 2019 software exhibited strong concordance with experimental findings concerning failure modes and capacity to carry axial loads.
Effect of Treatment of Neuropathic and Ischemic Diabetic Foot Ulcers with the Use of Local Ozone Therapy Procedures—An Observational Single Center Study
Authors: Pasek Jarosław , Szajkowski Sebastian , Cieślar Grzegorz
Published: October 2024

Background: Diabetes ranks high among worldwide global health problems, and diabetic foot ulcer syndrome (DFU) is considered as one of its most serious complications. The purpose of this study was to evaluate the impact of local ozone therapy procedures on the wound healing process in patients with two DFU types: neuropathic and ischemic. Material and Methods: In the retrospective study reported here, the treatment outcomes of 90 patients were analyzed: 44 males (48.8%) and 46 females (51.2%), in the age range between 38 and 87 years of age, with neuropathic (group 1) and ischemic (group 2) diabetic foot ulcers treated by means of local ozone therapy. The assessment of therapeutic effects in both groups of patients included an analysis of the rate of ulcer healing using planimetry and an analysis of the intensity of pain associated with ulcers performed using the VAS scale. Results: After the application of ozone therapy procedures, a statistically significant decrease in the surface area of the ulcers was obtained in both groups of patients, respectively: in group 1 from 7 (6–7.5) cm2 to 3 (2–3.5) cm2 and in group 2 from 7.5 (6.5–8) cm2 to 5 (4.5–5.5) cm2 (p < 0.001), with a complete healing of ulcers not observed in any patients from groups 1 and 2. After treatment, the surface area of the assessed ulcers was smaller in the neuropathic group. The intensity of pain experienced after treatment also decreased with statistical significance in both groups (p < 0.001). Conclusions: Short-term local ozone therapy was effective in promoting wound healing and alleviating pain in patients with DFUs of both neuropathic and ischemic etiology. The effectiveness of therapy in the neuropathic type of DFUs was significantly higher than in the ischemic type, in which patients had a higher incidence of risk factors and more advanced lesions, characterized by a larger initial ulcer area and greater intensity of pain.