bluuebunny/crossref_metadata_2025 huggingface dataset:
FedLSTM: A Federated Learning Framework for Sensor Fault Detection in Wireless Sensor Networks
Authors: Khan Rehan , Saeed Umer , Koo Insoo
Published: December 2024

The rapid growth of Internet of Things (IoT) devices has significantly increased reliance on sensor-generated data, which are essential to a wide range of systems and services. Wireless sensor networks (WSNs), crucial to this ecosystem, are often deployed in diverse and challenging environments, making them susceptible to faults such as software bugs, communication breakdowns, and hardware malfunctions. These issues can compromise data accuracy, stability, and reliability, ultimately jeopardizing system security. While advanced sensor fault detection methods in WSNs leverage a machine learning approach to achieve high accuracy, they typically rely on centralized learning, and face scalability and privacy challenges, especially when transferring large volumes of data. In our experimental setup, we employ a decentralized approach using federated learning with long short-term memory (FedLSTM) for sensor fault detection in WSNs, thereby preserving client privacy. This study utilizes temperature data enhanced with synthetic sensor data to simulate various common sensor faults: bias, drift, spike, erratic, stuck, and data-loss. We evaluate the performance of FedLSTM against the centralized approach based on accuracy, precision, sensitivity, and F1-score. Additionally, we analyze the impacts of varying the client participation rates and the number of local training epochs. In federated learning environments, comparative analysis with established models like the one-dimensional convolutional neural network and multilayer perceptron demonstrate the promising results of FedLSTM in maintaining client privacy while reducing communication overheads and the server load.
Multimodal Large Language Model-Based Fault Detection and Diagnosis in Context of Industry 4.0
Authors: Alsaif Khalid M. , Albeshri Aiiad A. , Khemakhem Maher A. , Eassa Fathy E.
Published: December 2024

In this paper, a novel multimodal large language model-based fault detection and diagnosis framework that addresses the limitations of traditional fault detection and diagnosis approaches is proposed. The proposed framework leverages the Generative Pre-trained Transformer-4-Preview model to improve its scalability, generalizability, and efficiency in handling complex systems and various fault scenarios. Moreover, synthetic datasets generated via large language models augment the knowledge base and enhance the accuracy of fault detection and diagnosis of imbalanced scenarios. In the framework, a hybrid architecture that integrates online and offline processing, combining real-time data streams with fine-tuned large language models for dynamic, accurate, and context-aware fault detection suited to industrial settings, particularly focusing on security concerns, is introduced. This comprehensive approach aims to address traditional fault detection and diagnosis challenges and advance the field toward more adaptive and efficient fault diagnosis systems. This paper presents a detailed literature review, including a detailed taxonomy of fault detection and diagnosis methods and their applications across various industrial domains. This study discusses case study results and model comparisons, exploring the implications for future developments in industrial fault detection and diagnosis systems within Industry 4.0 technologies.
Research Status and Applications of Dual-Frequency Induction Heating Power Supply
Authors: Chen Xingru , Wang Jianchao , Ma Bendong , Duan Zhongxia , Wang Houshen
Published: December 2024

The advent of dual-frequency induction heating (DFIH) technology has revolutionized modern industrial applications by providing flexible regulation of the heating process, significantly boosting heating efficiency, and optimizing energy utilization. This comprehensive review delves into the state-of-the-art research on DFIH power supplies, with a particular emphasis on their topological structures, output power control mechanisms, and resonant network configurations. The paper critically examines the challenges inherent to this technology, such as enhancing power density and ensuring system stability. It also highlights recent innovations in multi-inverter bridge topologies, resonant circuit optimization, and the implementation of intelligent control algorithms. Case studies are employed to illustrate the practical applications of DFIH in metal processing and composite material treatment, showcasing its potential to revolutionize these industries. The review concludes by delineating future research directions, with a focus on the integration of multi-frequency technologies and the development of efficient control strategies, thereby setting the stage for the next generation of induction heating systems.
Improving Performance of ADRC Control Systems Affected by Measurement Noise Using Kalman Filter-Tuned Extended State Observer
Authors: Michalski Jacek , Mrotek Mikołaj , Pazderski Dariusz , Kozierski Piotr , Retinger Marek
Published: December 2024

This paper presents a novel tuning method for the extended state observer (ESO), which is applied in the active disturbance rejection control (ADRC) algorithm operating in a stochastic environment. Instead of the traditional pole placement (PP) method, the selection of ESO gains based on the noise variances of the Kalman filter (KF) is proposed. Also, a simple parametrization of ESO gains for the particular control process based on the observer bandwidth is introduced. A root locus and frequency analysis is conducted for the KF-based observer and presented with regard to the proposed tuning method. The presented results come from experiments carried out on the ball balancing table (BBT) real plant for various measurement noise levels. The possibilities of rejecting measurement noise by the estimation algorithm were investigated to ensure effective control and minimize the control signal energy. Based on the conducted experiments, one can conclude that the presented tuning method provides better results than the traditional PP algorithm in the stochastic environment in terms of control quality and reduction in measurement noise.
Monitoring of Transformer Hotspot Temperature Using Support Vector Regression Combined with Wireless Mesh Networks
Authors: Zhang Naming , Zhao Guozhi , Zou Liangshuai , Wang Shuhong , Ning Shuya
Published: December 2024

The accurate monitoring of the internal hotspot temperature in transformers is crucial for ensuring the stability of power grid operations. Traditional methods typically measure only the surface temperature of transformers, whereas this study proposes a non-invasive thermal inversion algorithm based on a wireless mesh network that effectively predicts the internal hotspot temperature. An electromagnetic-thermal-fluid coupling simulation model was developed to simulate the temperature distribution in transformers under various operating conditions. Subsequently, this study employed a Support Vector Regression (SVR) algorithm to train the sample dataset, optimizing the SVR model using a grid search and cross-validation to enhance the predictive accuracy. After training, the model estimates the hotspot temperature based on surface measurements obtained through a non-contact infrared sensor network. The wireless mesh network, based on the Wi-Fi protocol, provides robust and real-time monitoring even in harsh environments, with data transmitted to a central root node via multiple sensor nodes. The experimental results demonstrate that this method is highly accurate, with predicted temperatures closely matching the results from traditional measurement techniques. This method enhances transformer condition monitoring, helping to extend the transformer lifespan and improve power grid stability.
Numerical Simulation of Climate Change Impact on Energy, Environmental and Economic Performances of Small Single-Family Houses Equipped with Trombe Walls and Fixed Horizontal Overhangs
Authors: Kowalik Robert , Nešović Aleksandar , Cvetković Dragan , Janaszek Agata , Kozłowski Tomasz
Published: December 2024

Although the European residential sector has promoted various heating and cooling passive solar systems in many ways, ongoing climate changes affect these construction elements at an annual level. Using the weather files for three years in the recent past (2018, 2021 and 2023), this paper numerically investigates the energy, environmental and economic performance of two small single-family houses equipped with Trombe walls and fixed horizontal overhangs of different depths (0 m, 0.25 m, 0.5 m, 0.75 m and 1 m) for two characteristic European climate zones: continental (Kielce city, Poland) and moderate continental (Kragujevac city, Serbia). Both houses were created in Google SketchUp 8 software using current Statistical data and Rulebooks of energy efficiency, while adopted heating (gas boiler and radiators) and cooling (individual air-conditioning units) active thermo-technical systems were simulated in EnergyPlus 7.1 software using official specific energy, environmental and economic indicators. Compared to the appropriate reference houses—without mentioning passive solar systems—the main results of this study are as follows: (1) higher outdoor air temperatures can reduce final (thermal) energy consumption for heating by 37.74% (for the Kielce climate zone) and 52.49% (for the Kragujevac climate zone); (2) higher outdoor air temperatures can increase final (electricity) energy consumption for cooling between 5.71 and 11.75 times (for Kielce) and 4.36 and 9.81 times (for Kragujevac); (3) percentage savings of primary energy consumption and monetary savings are highest when houses are equipped with Trombe walls and 1 m deep overhangs; and (4) all considered cases of passive solar systems do not contribute to the reduction of greenhouse gas emissions. Since climate change is a consequence of greenhouse gas emissions, priority should be given to environmental indicators in future investigations.
Control Method for Improving Dynamic Characteristics of a DM-Coupled Inductor Boost Converter Using a 2D Look-Up Table
Authors: Jeong Seong-Wook , Lee Dong-In , Kwon Gyeong-Hyun , Youn Han-Shin
Published: December 2024

This paper proposes a control method to improve the dynamic performance of a two-phase DM (Differential Mode)-coupled boost converter designed for applications such as hybrid vehicles and railway systems. A conventional boost converter can be modified to a two-phase interleaved configuration to reduce current ripple and incorporate a differential mode (DM)-coupled inductor to reduce the volume of magnetic components, thereby achieving a decrease in cost and volume. However, when this converter is operated using a conventional PI controller, significant issues arise, particularly in the discontinuous conduction mode (DCM), where dynamic characteristics and response times are considerably slow. For a conventional boost converter, the steady-state duty cycle during DCM operation can be calculated analytically and used for feedforward compensation in a current-duty controller. In contrast, the duty cycle of a two-phase DM-coupled boost converter during DCM operation exhibits non-linear behavior depending on input/output voltages and load conditions, making analytical computation infeasible. To address this, steady-state duty cycle data is extracted through experiments and simulations, and a Look-Up Table is constructed to perform feedforward compensation. Given the multiple input and output specifications, multiple Look-Up Tables are required, leading to excessive MCU (Micro Controller Unit) computation load. The proposed correction algorithm enables feedforward compensation in the DCM region with a single Look-Up Table for all input and output specifications, achieving improvements in dynamic characteristics and reducing MCU computational load. This method achieves a reduction in settling time by up to 77 ms, with a minimum improvement of 10 ms, thereby significantly enhancing the responsiveness of the converter.
Combined Bioenergy and Food Potential of Opuntia ficus-indica Grown on Marginal Land in Rural Mexico
Authors: Varela Pérez Paola , Winkler Bastian , Röcker Philip , von Cossel Moritz
Published: December 2024

Opuntia ficus-indica (cactus pear) emerged as a promising crop for sustainable bioenergy production on marginal agricultural land, mitigating competition with food crops and lowering the risk of other indirect land use changes. In this study, the bioenergy potential is investigated of cactus pear residues within a smallholder farming context of Nopaltepec, a rural municipality in Central Mexico. Nopaltepec is a native environment of cactus pear and shows an annual production volume of 30 Gg of fresh matter. A bottom-up approach employing semi-structured interviews (n = 16) was utilized to assess the feasibility of transforming the pruning residues of cactus pear into a viable bioenergy source. The results indicate a substantial bioenergy potential, with 27 Mg of fresh matter biomass (equivalent to 9720 m3 biogas) per hectare obtainable annually without compromising fruit yields. Moreover, the digestate produced through anaerobic digestion can be recycled as biofertilizer, offering economic and ecological advantages to smallholders. Notably, farmers expressed keen interest in integrating this technology into their agricultural systems. This research underscores the potential of cactus pear residues for developing a decentralized bioenergy sector and provides valuable ideas for future bottom-up assessments in rural communities like Nopaltepec.
A Quantitative Assessment of the Economic Viability of Photovoltaic Battery Energy Storage Systems
Authors: Ahmad Aayesha S. , Chattopadhyay Sumit K. , Panigrahi B. K.
Published: December 2024

Rooftop PV-BESS installations often lose profitability despite policy support to accelerate capacity growth. This paper performs techno-economic analysis to assess the effect of heterogeneity in real-world conditions on the economic viability of residential rooftop PV-BESSs. The stochastic nature of generation and consumption is modeled as multiple deterministic scenarios that vary in the capacity rating of the PV system, climatic conditions (insolation and temperature), self-consumption ratio (SCR), generation–demand concurrence, and the presence/absence of capacity and storage subsidies. The results indicate that PV-BESSs are mostly profitable when operating at a capacity factor ≥ 18%. Furthermore, higher daytime electricity consumption enables greater savings with smaller storage capacities, thereby facilitating cost-effective installations at capacity factors ≥ 8%. However, low-yielding PV-BESSs and prosumers exhibiting low generation–demand concurrence require suitable subsidy allocations to become profitable.
Epigenome-Wide and Methylation Risk Score Analysis of Body Mass Index Among People with HIV
Authors: Abi Nanzha , Young Alexandra , Tiwari Pradeep , Chen Junyu , Liu Chang , Hui Qin , So-Armah Kaku , Freiberg Matthew S. , Justice Amy C. , Xu Ke , Gwinn Marta , Marconi Vincent C. , Sun Yan V.
Published: December 2024

Background/Objectives: People with HIV (PWH) on antiretroviral therapy (ART) often gain weight, which increases their risk of type 2 diabetes and cardiovascular disease. The role of DNA methylation (DNAm) markers in obesity among PWH is understudied. This research explores the relationship between body mass index (BMI) and epigenetic patterns to better understand and manage obesity-related risks in PWH. Methods: We conducted an epigenome-wide association study (EWAS) on 892 African American male PWH from the Veterans Aging Cohort Study, examining BMI associations with DNAm using linear mixed models, adjusting for covariates, including soluble CD14. We compared our results with BMI-associated DNAm markers from non-HIV individuals and developed a methylation risk score (MRS) for BMI using machine learning and a cross-validation approach. Results: We identified four epigenome-wide significant CpG sites, including one in the RAP1B gene, indicating shared and unique BMI-related epigenetic markers between PWH and non-HIV individuals. The constructed BMI MRS explained approximately 19% of the BMI variance in PWH. Conclusions: DNAm markers and MRS are significantly linked to BMI in PWH, suggesting shared and distinct molecular mechanisms with non-HIV populations. These insights could lead to targeted interventions to reduce cardiometabolic disease risks in PWH under ART.
Differential Responses of Tree Species to Elevated Ozone and Increasing Air Temperature: Implications for Foliar Functional Traits, Carbon Sequestration, and Their Relationship Under Mixed Planting
Authors: Wang Ruiting , Xu Sheng , Ping Qin , Li Kexin , Gao Kexin , He Xingyuan
Published: December 2024

Ozone pollution and global warming are affecting plant growth and ecosystem functions considerably. However, the information is limited on the effects of these factors on foliar traits and carbon sequestration (CS). This study evaluated the effects of elevated ozone (EO, ambient air +80 ppb) and increased air temperature (IT, ambient air +2 °C) alone and the combination of these on foliar traits and CS in Quercus mongolica and Pinus tabuliformis under single (SP) and mixed planting (MP) conditions. The results showed that CS increased by 24.3% in Q. mongolica and decreased by 5.3% in P. tabuliformis under MP. EO decreased CS, while IT increased it (p < 0.05). Under MP, IT mitigated ozone’s negative impact on CS of P. tabuliformis, but exacerbated it on Q. mongolica. Structural equation modeling revealed that ozone reduced CS by reducing the photosynthesis rate (Pn) under SP and by reducing leaf length under MP in Q. mongolica. IT enhanced CS by increasing Pn, leaf thickness (LT) under SP, and LT under MP only in P. tabuliformis. Pn had the highest total effect. Overall, MP can modulate environmental stress effects on CS, but this varies by species. Future research should focus on long-term, cross-species studies to provide practical strategies for ecosystem management.
Characteristics of Drought Events and Their Impact on Vegetation Dynamics in the Arid Region of Northwest China
Authors: Zhou Guixiang , Yao Junqiang , Chen Jing , Chen Yaning , Wang Chuan , Mo Yinxue , Ma Chenzhi , Yang Yuhui , Li Moyan , Zheng Peng
Published: December 2024

The arid region of Northwest China (ARNC) is responsive to global climate change, and drought events have occurred frequently in recent decades. However, studies about the effect of meteorological and drought stress on vegetation change in the ARNC are still insufficient. In this study, we analyzed the spatiotemporal trends of meteorological factors (temperature, TMP; precipitation, PRE; standardized precipitation evapotranspiration index, SPEI), drought stress factors (vapor pressure deficit, VPD; soil moisture, SM), and vegetation (normalized difference vegetation index, NDVI) during 1982–2021. We also investigated the characteristics of drought events by the run theory, including drought times, drought duration, drought severity, and drought intensity. The impacts of meteorological and drought stress factors on the vegetation were explored using Pearson correlation analysis and the structural equation model (SEM). We found that the annual and growing season TMP, PRE, VPD, SM, and NDVI showed an increasing trend in the ARNC during 1982–2021. In contrast, SPEI exhibited a decreasing trend in the annual and growing season. In addition, the characteristics of the drought events varied significantly in the ARNC. The drought events primarily occurred in the Tarim River Basin, Turpan-Hami Basin, and the Hexi Corridor. The Pearson correlation analysis and SEM results consistently demonstrated that TMP and SM exerted greater impacts on vegetation growth than PRE, VPD, and SPEI. The factors that determine vegetation change were TMP and PRE. Exploration of meteorological and drought stress factors that influence vegetation change is essential for comprehending the influence of dominant factors on vegetation change.
Effect of Photoperiod on Nutritional Quality of Muscle and Lipid Metabolism of Litopenaeus vannamei
Authors: Fang Yingying , Fei Fan , Guo Fulu , Zhu Chengliang , Gao Xiaoqiang , Li Wenyang , Yang Hongjun , Sun Yan , Zhang Chuanxin , Liu Baoliang
Published: December 2024

Photoperiod serves as a significant environmental signal for organisms and plays a critical role in regulating their metabolic processes. This research aimed to investigate the lipid metabolism and nutritional quality of adults Litopenaeus vannamei (wet weight: 11.27 ± 0.73 g, body length: 12.45 ± 0.42 cm) under five photoperiods (0L:24D, 8L:16D, 12L:12D, 16L:8D, and 24L:0D) for 40 days in recirculating water systems (RASs). The 24L:0D group increased lipid metabolism, as indicated by increased lipid metabolism enzyme levels and related gene expression linked to lipogenesis. Additionally, shrimp in the 24L:0D exhibited the highest value of crude fat. The 0L:24D showed a significantly reduced content of crude fat compared with the 8L:16D and 12L:12D. In 24L:0D, the content of total essential amino acids (TEAAs), total hydrolyzed essential amino acids (THEAAs), and total non-essential amino acids (TNEAAs) increased significantly. Similarly, the content of polyunsaturated fatty acids (PUFAs) in 24L:0D was also higher than in other groups. Conversely, 0L:24D resulted in lower metabolic activity and a reduction in PUFA content. In conclusion, prolonging light could benefit shrimp cultivation. This study thoroughly examined the effects of varying photoperiods on muscle quality and lipid metabolism in L. vannamei, providing essential insights for the improvement of indoor aquaculture environments. Provision of light for 24 h improves production but has some adverse effects on animal welfare, so a 16 h light cycle is recommended.
A Model for Selecting Kiwifruit (Actinidia eriantha) Germplasm Resources with Excellent Fruit Quality
Authors: Chen Lu , Liu Yansong , Gao Huan , Cao Jiale , Qian Jiquan , Zheng Kexin , Jia Dongfeng , Gao Zhu , Xu Xiaobiao
Published: December 2024

The evaluation of quality traits is an important procedure for kiwifruit breeding and comprehensive utilization. The present study aimed to establish a comprehensive system to assess Actinidia eriantha germplasms by analyzing 22 quality traits on kiwifruit samples collected from a wild population of 236 plants grown in the Jiangxi Province, China. Variability, correlation, principal components, and cluster analyses were carried out using the data collected from fruit quality evaluations. The coefficients of variation (CV) of fruit quality traits ranged from 11.66 to 66.16% (average coefficient = 35.09%), indicating a high level of variation among the 236 plants. In addition, different degrees of correlations were found between the traits, with similar traits demonstrating strong correlations. Principal component analysis (PCA) generated eight comprehensive and independent principal components, accounting for 77.93% of the original fruit quality information. Furthermore, an extensive evaluation from PCA ranked the plants based on cluster analysis and grouped them into seven categories. A stepwise regression analysis generated a prediction model, demonstrating a good fit (0.945) with the principal components of the comprehensive evaluation score. Overall, this study identifies nine quality traits, representing fruit appearance, sweetness, acidity, flavor, and nutritional attributes, as important traits for a comprehensive evaluation of A. eriantha fruits.
Chemometric Classification and Bioactivity Correlation of Black Instant Coffee and Coffee Bean Extract by Chlorogenic Acid Profiling
Authors: Chen Yumei , Yu Wei , Niu Yuge , Li Wenchen , Lu Weiying , Yu Liangli (Lucy)
Published: December 2024

Chlorogenic acids (CGAs) play a key role in defining the quality and functionality of coffee products. CGA fingerprints of black instant coffee (BIC) and coffee bean extract (CBE) were profiled using ultra-performance liquid chromatography–mass spectrometry and analyzed by chemometrics. A total of 25 CGAs were identified. The BICs yielded higher levels of major CGAs than the CBEs. Furthermore, chemometrics methods, including principal component analysis (PCA) and partial least squares–discriminant analysis (PLS–DA), successfully classified the CBEs and the BICs. In vitro cellular antioxidant activity and viability assays between coffee products further confirmed the relationship between phenolic compounds and bioactivities. Compared to the CBEs, the BICs provided higher cellular toxicity and oxidant activity for hepatocellular carcinoma G2 (HepG2) cells. These results demonstrated that CGAs and their derivatives could be markers for studying coffee-related products. This study revealed the unique phenolic profiles of various coffee products, highlighting the differences between whole beans and soluble coffee.
Structure Elucidation and Immunostimulatory Activity Evaluation of a Galactoglucan from Alpinia officinarum Hance
Authors: Xu Zhou , Xiong Yanxia , Hu Pei , Chen Long , Wan Jianhua , Huang Chenggang , Liu Wenjun
Published: December 2024

Alpinia officinarum Hance has a medicinal history of thousands of years in treating cough, diabetes, and gastrointestinal system diseases, and it is also a medicine food homology (MFH) plant in China. To evaluate the pharmacological activities of polysaccharides from the rhizomes of A. officinarum, polysaccharides were initially obtained by hot-water extraction and the ethanol precipitation method. A homogenous polysaccharide designated as AOP-w was isolated by a DE-52 column. The proposed structure was elucidated and the immunoregulatory effects on RAW 264.7 macrophage cells were evaluated. The results showed that AOP-w had a molecular weight of 5.26 kDa, and mainly consisted of galactose and glucose (molar ratio of 0.12:0.88). Its backbone comprised α-(1→4)-Glcp, α-(1→4,6)-Glcp and β-(1→3,4)-Galp residues, terminated by α-(1→6)-Glcp and T-Glcp residues. AOP-w was nontoxic to RAW 264.7 cells, but demonstrated promotion in cell proliferation within a 100 μg/mL concentration. The immunostimulatory effects of AOP-w were confirmed by the elevated NO production of AOP-w-treated cells. Moreover, the RNAseq was conducted and the results showed that AOP-w may activate the TNF and NF-κB signaling pathways by binding to Toll-like receptors, thereby affecting the immune modulatory activity of RAW264.7 cells. These results suggest a high potential of AOP-w from A. officinarum for immunotherapeutic applications.
Evaluation of Drying Characteristics and Quality Attributes for Microwave Vacuum Drying of Pork Skin Crisps
Authors: Li Yuangang , Zhang Jingming , Wang Junsheng , Ren Junpeng , Cao Chuanai , Liu Qian , Huang Xinning
Published: December 2024

As an important by-product of pork, pork skin can be processed into meat-based leisure food products to improve its utilization. In this study, microwave vacuum drying (MVD) technology was used to investigate the effects of microwave powers (600, 700, and 800 W) and processing duration on the drying characteristics and quality attributes of pork skin crisps (PSC). Five classical drying models were used to non-linearly fit the experimental data, and the Midilli et al. model was suitable for characterizing the MVD process of PSC. Before reaching a constant rate of drying, increasing microwave power and time can improve the brittleness and expansion ratio of PSC. In the constant rate drying stage, most of the free water in PSC was removed, showing the best brittleness and a stable expansion ratio. High power and long processing time can lead to serious lipid oxidation and change the flavor of PSC. Overall, the desired quality of PSC is recommended as 700 W for 6 min. This study can provide a reference for MVD application of meat-based by-product leisure foods.
Clinical Evaluation of Hovenia dulcis Extract Combinations for Effective Hangover Relief in Humans
Authors: Lee Ki Won , Xu Guangpeng , Paik Dong Hyun , Shim Youn Young , Reaney Martin J. T. , Park Ilbum , Lee Sang-Hun , Park Jong-Yul , Park Euddeum , Lee Sung-Bum , Kim In Ah , Hong Ji Youn , Kim Young Jun
Published: December 2024

Alcohol consumption is associated with both short- and long-term adverse effects, including hangover symptoms. The objective of this study was to examine the potential benefits of traditional beverages containing a combination of Hovenia dulcis extract (HD) with either Pueraria lobata extract (HDPB) or glutathione yeast extract (HDGB) in abbreviating alcohol intoxication and mitigating hangover symptoms. A total of 25 participants between the ages of 19 and 40 who had previously experienced a hangover were evaluated in a randomized, double-blind, crossover, placebo (PLA)-controlled clinical trial. Results showed that lower blood alcohol concentrations in the HDPB and HDGB groups were significantly lower than in the PLA group at 0.25 and 0.5 h, suggesting that HD aids in early alcohol metabolism (0 h, p < 0.05). Analysis of the hourly Acute Hangover Scale (AHS) showed that all treatment groups had significantly reduced gastrointestinal disorder symptoms compared to the PLA group (p < 0.05). It can be confirmed that hangover symptoms can be significantly improved by consuming HD combination drinks, apart from the effect of reducing blood alcohol and acetaldehyde concentrations. Therefore, it is predicted that the consumption of natural phytochemicals added to HD is safe for humans and may help accelerate recovery from hangover symptoms.
Is Football Unpredictable? Predicting Matches Using Neural Networks
Authors: Luiz Luiz E. , Fialho Gabriel , Teixeira João P.
Published: December 2024

The growing sports betting market works on the premise that sports are unpredictable, making it more likely to be wrong than right, as the user has to choose between win, draw, or lose. So could football, the world’s most popular sport, be predictable? This article studies this question using deep neural networks to predict the outcome of football matches using publicly available data. Data from 24,760 matches from 13 leagues over 2 to 10 years were used as input for the neural network and to generate a state-of-the-art validated feature, the pi-rating, and the parameters proposed in this work, such as relative attack, defence, and mid power. The data were pre-processed to improve the network’s interpretation and deal with missing or inconsistent data. With the validated pi-rating, data organisation methods were evaluated to find the most fitting option for this prediction system. The final network has four layers with 100, 80, 5, and 3 neurons, respectively, applying the dropout technique to reduce overfitting errors. The results showed that the most influential features are the proposed relative defending, playmaking, and midfield power, and the home team goal expectancy features, surpassing the pi-rating. Finally, the proposed model obtained an accuracy of 52.8% in 2589 matches, reaching 80.3% in specific situations. These results prove that football can be predictable and that some leagues are more predictable than others.
Fractional Partial Differential Equation Modeling for Solar Cell Charge Dynamics
Authors: Abdelfattah Waleed Mohammed , Ragb Ola , Salah Mohamed , Matbuly Mohamed S. , Mohamed Mokhtar
Published: December 2024

This paper presents a groundbreaking numerical approach, the fractional differential quadrature method (FDQM), to simulate the complex dynamics of organic polymer solar cells. The method, which leverages polynomial-based differential quadrature and Cardinal sine functions coupled with the Caputo-type fractional derivative, offers a significant improvement in accuracy and efficiency over traditional methods. By employing a block-marching technique, we effectively address the time-dependent nature of the governing equations. The efficacy of the proposed method is validated through rigorous numerical simulations and comparisons with existing analytical and numerical solutions. Each scheme’s computational characteristics are tailored to achieve high accuracy, ensuring an error margin on the order of 10−8  or less. Additionally, a comprehensive parametric study is conducted to investigate the impact of key parameters on device performance. These parameters include supporting conditions, time evolution, carrier mobilities, charge carrier densities, geminate pair distances, recombination rate constants, and generation efficiency. The findings of this research offer valuable insights for optimizing and enhancing the performance of organic polymer solar cell devices.
The Use of Gel Electrophoresis to Separate Multiplex Polymerase Chain Reaction Amplicons Allows for the Easy Identification and Assessment of the Spread of Toxigenic Clostridioides difficile Strains
Authors: Bogiel Tomasz , Kwiecińska Patrycja , Górniak Robert , Kanarek Piotr , Mikucka Agnieszka
Published: December 2024

Clostridioides difficile is a common etiological factor of hospital infections, which, in extreme cases, can lead to the death of patients. Most strains belonging to this bacterium species synthesize very dangerous toxins: toxin A (TcdA) and B (TcdB) and binary toxin (CDT). The aim of this study was to assess the suitability of agarose gel electrophoresis separation of multiplex PCR amplicons to investigate the toxinogenic potential of C. difficile strains. Additionally, the frequency of C. difficile toxin genes and the genotypes of toxin-producing strains were determined. Ninety-nine C. difficile strains were used in the detection of the presence of genes encoding all of these toxins using the multiplex PCR method. In 85 (85.9%) strains, the presence of tcdA genes encoding enterotoxin A was detected. In turn, in 66 (66.7%) isolates, the gene encoding toxin B (tcdB) was present. The lowest number of strains tested was positive for genes encoding a binary toxin. Only 31 (31.3%) strains possessed the cdtB gene and 22 (22.2%) contained both genes for the binary toxin subunits (the cdtB and cdtA genes). A relatively large number of the strains tested had genes encoding toxins, whose presence may result in a severe course of disease. Therefore, the accurate diagnosis of patients, including the detection of all known C. difficile toxin genes, is very important. The multiplex PCR method allows for the quick and accurate determination of whether the tested strains of this bacterium contain toxin genes. Agarose gel electrophoresis is a useful tool for visualizing amplification products, allowing one to confirm the presence of specific C. difficile toxin genes as well as investigate their dissemination for epidemiological purposes.
Various Hydrogel Types as a Potential In Vitro Angiogenesis Model
Authors: Radermacher Chloé , Rohde Annika , Kucikas Vytautas , Buhl Eva Miriam , Wein Svenja , Jonigk Danny , Jahnen-Dechent Willi , Neuss Sabine
Published: December 2024

Angiogenesis, the formation of new blood vessels, is a fundamental process in both physiological repair mechanisms and pathological conditions, including cancer and chronic inflammation. Hydrogels are commonly used as in vitro models to mimic the extracellular matrix (ECM) and support endothelial cell behavior during angiogenesis. Mesenchymal stem cells further augment cell and tissue growth and are therefore widely used in regenerative medicine. Here we examined the combination of distinct hydrogel types—fibrin, collagen, and human platelet lysate (HPL)—on the formation of capillaries in a co-culture system containing human umbilical vein endothelial cells (HUVECs) and bone marrow-derived mesenchymal stem cells (BM-MSCs). The mechanical properties and structural changes of the hydrogels were characterized through scanning electron microscopy (SEM) and nanoindentation over 10 days. Fibrin and HPL gels sustained complex network formations, with HPL gels promoting even vascular tube formation of up to 10-fold capillary caliber. Collagen gels supported negligible angiogenesis. Our results suggest that HPL gels in combination with MSC-EC co-culture may be employed to obtain robust vascularization in tissue engineering. This study provides a comparative analysis of fibrin, collagen, and HPL hydrogels, focusing on their ability to support angiogenesis under identical conditions. Our findings demonstrate the superior performance of HPL gels in promoting robust vascular structures, highlighting their potential as a versatile tool for in vitro angiogenesis modeling.
TExCNN: Leveraging Pre-Trained Models to Predict Gene Expression from Genomic Sequences
Authors: Dong Guohao , Wu Yuqian , Huang Lan , Li Fei , Zhou Fengfeng
Published: December 2024

Background/Objectives: Understanding the relationship between DNA sequences and gene expression levels is of significant biological importance. Recent advancements have demonstrated the ability of deep learning to predict gene expression levels directly from genomic data. However, traditional methods are limited by basic word encoding techniques, which fail to capture the inherent features and patterns of DNA sequences. Methods: We introduce TExCNN, a novel framework that integrates the pre-trained models DNABERT and DNABERT-2 to generate word embeddings for DNA sequences. We partitioned the DNA sequences into manageable segments and computed their respective embeddings using the pre-trained models. These embeddings were then utilized as inputs to our deep learning framework, which was based on convolutional neural network. Results: TExCNN outperformed current state-of-the-art models, achieving an average R2 score of 0.622, compared to the 0.596 score achieved by the DeepLncLoc model, which is based on the Word2Vec model and a text convolutional neural network. Furthermore, when the sequence length was extended from 10,500 bp to 50,000 bp, TExCNN achieved an even higher average R2 score of 0.639. The prediction accuracy improved further when additional biological features were incorporated. Conclusions: Our experimental results demonstrate that the use of pre-trained models for word embedding generation significantly improves the accuracy of predicting gene expression. The proposed TExCNN pipeline performes optimally with longer DNA sequences and is adaptable for both cell-type-independent and cell-type-dependent predictions.
Geology of the Mulkhura River Valley, Georgian Caucasus
Authors: Kumladze Roman M. , Tielidze Levan G. , Gamkrelidze Mamia , Cook Simon J. , Giorgadze Anzor
Published: December 2024

Geological mapping provides vital information about the structure, evolution, natural resource potential, and geohazards of a specific area. The role of geological mapping is especially valuable for mountainous countries like Georgia. In this context, we present a geological map of the Mulkhura River Valley in the Georgian Caucasus (43°3′ N, 42°52′ E) with accompanying cross-sections at a scale of 1:30,000, covering approximately 220 km2. The geological information in the map is based on a comprehensive review of previously published geological maps and literature, combined with original analysis of satellite imagery and hitherto unpublished field data. The extensive spatial coverage and accompanying cross-sections provide detailed insights into the structure of the region. This new map will serve as a foundation for future geological research, hazard management, and resource exploration in the area, as well as for geoconservation to develop the national geotourism industry in this region.
Multicentre Pilot Study to Evaluate the Efficacy of Targeted Exercise in Combination with Cytisinicline on Smoking Cessation at 12 Months: MEDSEC-CTA
Authors: Ruiz-Salcedo Sofia , Ranchal-Sanchez Antonio , Ruiz-Moruno Javier , Montserrat-Villatoro Jaime , Jurado-Castro Jose Manuel , Romero-Rodriguez Esperanza
Published: December 2024

Background/Objectives: Current scientific evidence shows both the relationship between good physical condition and a lower incidence of certain chronic diseases (including smoking), as well as the efficacy of cytisinicline. The aim of this protocol is to evaluate the efficacy of the synergistic effect of the combination of targeted physical exercise, together with brief advice and taking the drug cytisinicline, to achieve smoking cessation. Methods: We propose an experimental, multicentre, randomised, controlled study with two parallel arms to be carried out by a multidisciplinary team in the primary care setting of the Andalusian public health system (APHS) in Spain, with a follow-up of 12 months. The population sample will include people who are aged between 18 and 65 years and meet the criteria to be eligible for treatment with cytisinicline financed by the APHS: smokers of 10 or more cigarettes per day who are in the determination/action phase for smoking cessation of the transtheoretical model of Prochaska and Diclemente, willing to start anti-smoking treatment imminently and confirmed, with high dependence to nicotine (Fagerström’s test ≥ 7), with a motivation to quit smoking according to the Richmond test (≥6), and who have made a previous smoking cessation attempt in the last year. The study consists of two treatment arms. EXPERIMENTAL ARM: Smokers who are going to be prescribed targeted physical exercise and brief advice to stop smoking while receiving cytisinicline treatment (1.5 mg tablets) according to the care process. CONTROL ARM: Smokers will receive a standard leaflet explaining physical exercise to the general population and brief advice on smoking cessation when starting cytisinicline treatment, according to the care process. The calculated sample size is 75 participants per arm. Results: The primary results will allow us to estimate the efficacy of prescribing physical exercise as an adjuvant therapy to classic multicomponent treatment, incorporating it as an additional element to be considered when it is accepted by the smoker. Conclusions: This protocol (NCT06579846) evaluates the efficacy of combining physical exercise, brief advice, and cytisinicline to support smoking cessation, improve fitness, and reduce smoking-related comorbidities
A Systematic Review of Virtual Reality’s Impact on Anxiety During Palliative Care
Authors: Gaina Alexandra-Maria , Stefanescu Cristinel , Szalontay Andreea-Silvana , Gaina Marcel-Alexandru , Poroch Vladimir , Mosoiu Daniela Viorica , Stefanescu Bogdan-Victor , Axinte Magdalena , Tofan Cristina-Maria , Magurianu Liviu Adrian
Published: December 2024

Background: Virtual Reality (VR) is an emerging non-pharmacological treatment for anxiety in palliative care patients. Anxiety, a common symptom in this population, has a significant effect on living standards. The aim of this systematic review is to evaluate the effectiveness of VR interventions in reducing anxiety and improving quality of life in palliative care. Methods: The systematic review protocol was registered in PROSPERO (CRD42024517914). The comprehensive search was performed in nine databases, including PubMed, PsycINFO, and EMBASE, and included studies published up to 15 November 2024. RCTs, pilot studies, and feasibility trials involving adult palliative care patients in which VR interventions were used, were compared with standard care. Two reviewers independently extracted and assessed the quality of the data using the RoB 2 tool. Results: A total of 13 studies were considered eligible, with 333 participants aged 15 to 85 years old. In these experiments, anxiety decreased steadily, and in some cases mood and quality of life were improved significantly. The VR interventions ranged from guided nature walks to memory-training sessions. Sessions lasted between 5 and 30 min and ranged in duration from single sessions to daily use. Personalized and participatory VR-based content was particularly effective in alleviating anxiety. The heterogeneity of the study designs and VR protocols hampered meta-analysis, despite promising findings. Conclusions: VR has considerable potential as an adjunctive anxiety therapy for palliative care. The personalization and immersion that VR provides are psychologically unique and have the potential to lower anxiety and improve well-being. But standardizing intervention protocols and more studies are required to define the best VR strategies and evaluate outcomes over the long term. The article highlights the promise of VR as part of palliative care as a viable bio-psycho-socio-spiritual approach.
The Impact of Moral Hazard on Healthcare Utilization in Public Hospitals from Romania: Evidence from Patient Behaviors and Insurance Systems
Authors: Huțu Daniela , Cumpăt Carmen Marinela , Grădinaru Andreea , Rusu Bogdan
Published: December 2024

Background/Objectives: Moral hazard represents a significant challenge in healthcare systems globally, reflecting the tendency of insured individuals to over-utilize medical services when shielded from the full costs of care. Methods: This paper investigates the dynamics and implications of moral hazard within the Romanian public hospital sector, offering practical recommendations for healthcare policymakers to mitigate the financial risks associated with excessive healthcare utilization and ensure long-term sustainability. To achieve the objectives of this study, a quantitative research approach utilizing vignettes was employed. Vignettes allow for the simulation of real-world decision-making under conditions of insurance coverage, capturing nuanced behaviors that traditional surveys may overlook. Results: The study examined patient behaviors in the context of moral hazard in public hospitals in Romania, employing a quantitative approach based on vignettes. A total of 303 valid responses were collected. The findings indicate a significant tendency among insured patients, both publicly and privately insured, to opt for more expensive treatments compared to uninsured patients, who preferred more affordable options such as medication or physiotherapy. In the case of treatments for severe conditions, insured patients frequently chose combinations of higher-cost therapies, while uninsured individuals either delayed treatment or opted for less expensive alternatives. These results highlight the impact of moral hazard, driven by a reduced sensitivity to costs in the presence of insurance, and underscore the need for cost-sharing policies to mitigate the overutilization of medical resources. Conclusions: This paper uniquely contributes to the understanding of moral hazard by integrating insights from both Romanian public hospitals and international case studies, offering practical policy recommendations for mitigating the financial risks associated with excessive healthcare utilization.