bluuebunny/crossref_metadata_2025 huggingface dataset:
AI-Driven Enhancement of Skin Cancer Diagnosis: A Two-Stage Voting Ensemble Approach Using Dermoscopic Data
Authors: Chiu Tsu-Man , Li Yun-Chang , Chi I-Chun , Tseng Ming-Hseng
Published: January 2025

Background: Skin cancer is the most common cancer worldwide, with melanoma being the deadliest type, though it accounts for less than 5% of cases. Traditional skin cancer detection methods are effective but are often costly and time-consuming. Recent advances in artificial intelligence have improved skin cancer diagnosis by helping dermatologists identify suspicious lesions. Methods: The study used datasets from two ethnic groups, sourced from the ISIC platform and CSMU Hospital, to develop an AI diagnostic model. Eight pre-trained models, including convolutional neural networks and vision transformers, were fine-tuned. The three best-performing models were combined into an ensemble model, which underwent multiple random experiments to ensure stability. To improve diagnostic accuracy and reduce false negatives, a two-stage classification strategy was employed: a three-class model for initial classification, followed by a binary model for secondary prediction of benign cases. Results: In the ISIC dataset, the false negative rate for malignant lesions was significantly reduced, and the number of malignant cases misclassified as benign dropped from 124 to 45. In the CSMUH dataset, false negatives for malignant cases were completely eliminated, reducing the number of misclassified malignant cases to zero, resulting in a notable improvement in diagnostic precision and a reduction in the false negative rate. Conclusions: Through the proposed method, the study demonstrated clear success in both datasets. First, a three-class AI model can assist doctors in distinguishing between melanoma patients who require urgent treatment, non-melanoma skin cancer patients who can be treated later, and benign cases that do not require intervention. Subsequently, a two-stage classification strategy effectively reduces false negatives in malignant lesions. These findings highlight the potential of AI technology in skin cancer diagnosis, particularly in resource-limited medical settings, where it could become a valuable clinical tool to improve diagnostic accuracy, reduce skin cancer mortality, and reduce healthcare costs.
Encapsulation and Melanization Are Not Correlated to Successful Immune Defense Against Parasitoid Wasps in Drosophila melanogaster
Authors: Magyar Lilla B. , Andó István , Cinege Gyöngyi
Published: January 2025

Parasitoid elimination in Drosophila melanogaster involves special hemocytes, called lamellocytes, which encapsulate the eggs or larvae of the parasitoid wasps. The capsules are melanized, and metabolites of the melanization reaction may play a potential role in parasitoid killing. We have observed a variation in the melanization capacity of different, commonly used D. melanogaster strains, such as Canton-S, Oregon-R, and BL5905, BL6326. In this work, we aimed to clarify a possible connection between the effectiveness of capsule melanization and the success of parasitoid elimination following infection with Leptopilina parasitoid wasps. Circulating hemocytes and lamellocyte attachment were visualized by confocal and epifluorescence microscopy using indirect immunofluorescence. Expression profiles of the PPO2 and PPO3 prophenoloxidase genes, which encode key enzymes in the melanization reaction, were detected by qRT-PCR. Parasitization assays were used to analyze fly and wasp eclosion success. Active encapsulation and melanization reactions against Leptopilina boulardi were observed in the BL5905 and the BL6326 strains, though restricted to the dead supernumerary parasitoids, while fly and wasp eclosion rates were essentially the same in the four examined D. melanogaster strains. We conclude that encapsulation and melanization carried out by D. melanogaster following L. boulardi infection have no impact on survival.
The Effects of a Web-Based Need-Supportive Intervention for Physical Education Teachers on Students’ Physical Activity and Related Outcomes: A Randomized Controlled Trial
Authors: Paap Hasso , Koka Andre , Meerits Pille-Riin , Tilga Henri
Published: January 2025

Background/Objectives: Globally, adolescents are insufficiently active, highlighting the need for effective strategies to enhance their activity levels. This study evaluated a web-based intervention program designed for physical education (PE) teachers, aimed at fostering students’ basic psychological needs—autonomy, competence, and relatedness—in PE settings. Methods: Secondary school PE teachers and their students were randomly assigned into either an experimental (teachers n = 36; students n = 463, Mage = 13.94, SD = 0.88) or a control group (teachers n = 49; students n = 820, Mage = 13.93, SD = 0.87). Teachers in the experimental group underwent a 4-week web-based intervention program focused on autonomy-, competence-, and relatedness-supportive motivational techniques. Students completed questionnaires assessing psychological measures and self-reported physical activity (PA) before and after the teachers’ training. Results: Results indicated that students in the experimental group perceived significantly higher autonomy, competence, and relatedness support compared to their control group counterparts at post-test. Conclusions: Consequently, the web-based need-supportive intervention was effective in promoting need-supportive teacher behavior. As the developed training for PE teachers increased students’ perceptions of need support from their PE teachers, it holds promise for future PE teacher education. Furthermore, the web-based format offers scalability for broader implementation.
Suicidal Behaviors Among United States Adolescents: Increasing Clinical and Public Health Challenges
Authors: Ostanin Jhon , Miranda Helena , Shugar Simon , Abdo Dina , Mejia Maria Carmenza , Hennekens Charles H. , Kitsantas Panagiota
Published: January 2025

Background/Objectives: Suicide in the United States (US) adolescents is a major clinical and public health problem. In this original investigation, we explored trends in suicidal behaviors (ideation, planning, and attempts) among US adolescents from 2011 to 2021. Methods: The study sample included 90,306 adolescents from the 2011–2021 Youth Risk Behavior Surveillance System. Descriptive statistics and the chi-squared test were used to assess differences in suicidal behaviors across gender, race/ethnicity, and grade between 2011 and 2021. Results: The overall percentage of female adolescents reporting suicidal behaviors increased significantly between 2011 and 2021, and it was higher than males. In 2021, females exhibited significantly higher rates of considering suicide (30.0% vs. 14.3%), planning (23.6% vs. 11.6%), and attempts (13.3% vs. 6.6%) compared to their male counterparts. Whites were more likely to report suicidal ideation (22.7%) while Black non-Hispanic youth had a higher likelihood of making a suicide plan (17.7%), attempting suicide (14.5%), or making a suicide attempt requiring medical treatment (4.4%) relative to other racial/ethnic groups. Overall, ninth graders were more likely to report suicide attempts (11.6%) compared to 12th graders (8.6%). Conclusions: The results demonstrate significant increases in suicidal behavior among US adolescents, particularly in females. They suggest the need for gender-sensitive approaches in mental health support and prevention strategies. Overall, given the significant increase in suicidal behaviors, healthcare providers as well as public health professionals should prioritize mental health initiatives, promote awareness, and ensure access to mental health resources for adolescents.
Nexus of Foreign Direct Investment (FDI) and Environmental Emissions in South Africa: A Markov-Switching Regression
Authors: Mosikari Teboho , Xaba Diteboho
Published: January 2025

The study on the link concerning FDI and environmental emissions has been the interest in recent environmental economics subject. The interest of this research work is to dynamically understand the effect of FDI on environmental emissions in South Africa. The research applied the renowned Markov-switching regression to explore the association among the variables. Prior to the formal estimation, the data were subjected to a linearity test, non-linear unit root test and cusum test so to ascertain whether the variables conform to non-linearity modeling. The results demonstrated that in both regimes (lower or higher emissions), the influence of FDI is positive and statistically significant. This finding implies that foreign investment is detrimental to our environment, irrespective of regime changes. This finding supports the Pollution Haven Hypothesis (PHH). Furthermore, the results show that emissions in South Africa stay in a low or high regime for a short period between one and two years. Policy implications to the results are that economic and climate change policy makers in South Africa should start to regulate FDI to be environmentally friendly.
Proportional Condylectomy Using a Titanium 3D-Printed Cutting Guide in Patients with Condylar Hyperplasia
Authors: Smolka Wenko , Cornelius Carl-Peter , Obermeier Katharina Theresa , Otto Sven , Liokatis Paris
Published: January 2025

Background: The purpose of the study was to describe proportional condylectomy in patients with condylar hyperplasia using a titanium 3D-printed ultrathin wire mesh cutting guide placed below the planned bone resection. Methods: Eight patients with condylar hyperplasia underwent proportional condylectomy using an ultrathin titanium 3D-printed cutting guide placed below the planned bone resection. The placement of the guide was facilitated by the incorporation of anatomical landmarks. The accuracy of bone resections guided by such devices was evaluated on postoperative radiographs. The mean postoperative follow-up was 30 months. Results: Surgery could be performed in all patients in the same manner as virtually planned. The fitting accuracy of the cutting guides was judged as good. Postoperative radiographs revealed that the virtually planned shape of the newly formed condylar head after condylectomy could be achieved. Conclusions: In conclusion, the use of virtual computer-assisted planning and CAD/CAM-based cutting guides for proportional condylectomy in unilateral condylar hyperplasia of the mandible offers high accuracy and guarantees very predictable results.
Understanding Technology Use During the COVID-19 Pandemic Through the Lens of Age-Friendly Cities and Communities: An International, Multi-Centre Study
Authors: Marston Hannah R. , Girishan Prabhu Vishnunarayan , Ivan Loredana
Published: January 2025

Research on age-friendly cities and communities (AFCC) has primarily taken a qualitative approach. This article extends insights from a quantitative perspective to understand the international perspectives of community living and well-being during the COVID-19 pandemic. Employing an intersectional approach, this online survey aimed to understand human behaviour within AFCC. This article contextualises the digital practices and the impact of technology experienced through the age-friendly city lens of adults aged 18–50+ years living in different types of communities. Using an original dataset collected from 2020 to 2021 across 11 sites and in 13 languages, the study gathered responses from a sample size of 3422 participants. Findings indicate that adults aged 50+ years reported significantly lower loneliness scores, and higher well-being scores compared to adults aged below 40. Factors including gender, education level, and marital and employment status were found to impact loneliness and well-being significantly. From a community perspective, individuals living in rural areas and small towns reported significantly lower loneliness scores and higher well-being scores than those living in metros and cities. These findings contribute to the ongoing discourse in AFCC and have the potential to aid policy responses intended to reduce loneliness and improve well-being through public health and pandemic preparedness planning.
An Erdős-Révész Type Law for the Length of the Longest Match of Two Coin-Tossing Sequences
Authors: Grill Karl
Published: January 2025

Consider a coin-tossing sequence, i.e., a sequence of independent variables, taking values 0 and 1 with probability 1/2. The famous Erdős-Rényi law of large numbers implies that the longest run of ones in the first n observations has a length Rn that behaves like log(n), as n tends to infinity (throughout this article, log denotes logarithm with base 2). Erdős and Révész refined this result by providing a description of the Lévy upper and lower classes of the process Rn. In another direction, Arratia and Waterman extended the Erdős-Rényi result to the longest matching subsequence (with shifts) of two coin-tossing sequences, finding that it behaves asymptotically like 2log(n). The present paper provides some Erdős-Révész type results in this situation, obtaining a complete description of the upper classes and a partial result on the lower ones.
AFHRE: An Accurate and Fast Hardware Resources Estimation Method for Convolutional Accelerator with Systolic Array Structure on FPGA
Authors: Wang Yongchang , Zhao Hongzhi , Zhao Jinyao
Published: January 2025

FPGA-based convolutional accelerators have been widely used in image recognition scenarios. Many convolutional accelerators utilize the systolic array structure to enhance parallelism. Developing a method to efficiently estimate the utilized hardware resources of an FPGA for such a structure would be helpful in improving the speed of achieving an optimal systolic array structure with the best performance on a given FPGA device. Currently, most estimations of work have either focused on the evaluation of hardware resources for general structures or have not adequately assessed hardware resources specifically for systolic arrays. To reduce estimation latency, this paper proposes an Accurate and Fast Hardware Resources Estimation method (AFHRE) that addresses these shortcomings by analyzing the structure of systolic arrays and utilizing mathematical formulas to describe their characteristics. Experiments show that the DSP resource occupancy estimated by AFHRE is fully consistent with that by Vivado HLS. The error rates of other three types of hardware resources (BRAM, LUT, and FF) are within 11%. In addition, the speed of resource estimation using this method is 40X to 610X faster than that of Vivado HLS. AFHRE can serve as a preprocessing step for Vivado HLS, achieving some optimal or sub-optimal solutions systolic array parameters much faster than original simulation manners of Vivado HLS.
Improving Localization Accuracy Through Optimal Selection Strategy
Authors: Wu Na , Yan Xiaozhen , Luo Qinghua , Xing Yuexiu
Published: January 2025

A localization system is essential for providing crucial position information in various applications, such as three-dimensional (3D) warehousing, smart cities, uncrewed aerial vehicle (UAV) control, and other services that heavily rely on accurate localization. However, the transmission of wireless signals can be impacted by diverse environmental factors, leading to decreased accuracy in determining localization in scenarios involving multiple signal paths, None Line of Sight (NLOS) situations, and different types of interference. In some cases, this may render the localization system unsuitable for subsequent applications. To enhance the localization accuracy, we propose a 3D localization method using an optimization selection strategy. With this method, we make the following innovations: (1) We utilize an evaluation of feature points to minimize the negative impact of NLOS. (2) Through the backward assessment and the optimal selection of distance estimations, we obtain a more accurate localization result. In more detail, our approach implements a specific strategy for distance estimation, followed by defining the feature points within the localization field and selecting the most optimized one. Subsequently, using the chosen feature points, we evaluate the quality of the distances in reverse. We then select suitable distance estimation outcomes for further localization calculations. Ultimately, by employing the proposed 3D localization technique, we achieve a highly precise localization result. We perform simulations and experiments to assess the presented localization system. More specifically, compared with certain strategies, we improve the localization accuracy by 58.33% and 43.83% using the selection strategy. Compared with the other methods, we enhance the localization accuracy from 17.94% to 32.54%. The results from these evaluations demonstrate that our method significantly enhances 3D localization accuracy.
Masking and Homomorphic Encryption-Combined Secure Aggregation for Privacy-Preserving Federated Learning
Authors: Park Soyoung , Lee Junyoung , Harada Kaho , Chi Jeonghee
Published: January 2025

Secure aggregation of local learning model parameters is crucial for achieving privacy-preserving federated learning. This paper presents a novel and practical aggregation method that effectively combines the advantages of masking-based aggregation with those of homomorphic encryption-based techniques. Each node conceals its local parameters using a randomly selected mask, independently chosen, thereby eliminating the need for additional computations to generate or exchange mask values with other nodes. Instead, each node homomorphically encrypts its random mask using its own encryption key. During each federated learning round, nodes send their masked parameters and the homomorphically encrypted mask to the federated learning server. The server then aggregates these updates in an encrypted state, directly calculating the average of actual local parameters across all nodes without the necessity to decrypt the aggregated result separately. To facilitate this, we introduce a new multi-key homomorphic encryption technique tailored for secure aggregation in federated learning environments. Each node uses a different encryption key to encrypt its mask value. Importantly, the ciphertext of each mask includes a partial decryption component from the node, allowing the collective sum of encrypted masks to be automatically decrypted once all are aggregated. Consequently, the server computes the average of the actual local parameters by simply subtracting the decrypted total sum of mask values from the cumulative sum of the masked local parameters. Our approach effectively eliminates the need for interactions between nodes and the server for mask generation and sharing, while addressing the limitation of a single key homomorphic encryption. Moreover, the proposed aggregation process completes the global model update in just two interactions (in the absence of dropouts), significantly simplifying the aggregation procedure. Utilizing the CKKS (Cheon-Kim-Kim-Song) homomorphic encryption scheme, our method ensures efficient aggregation without compromising security or accuracy. We demonstrate the accuracy and efficiency of the proposed method through varied experiments on MNIST data.
Research Progress on Nano-Confinement Effects in Unconventional Oil and Gas Energy—With a Major Focus on Shale Reservoirs
Authors: Wang Guo , Shen Rui , Xiong Shengchun , Mei Yuhao , Dong Qinghao , Chu Shasha , Su Heying , Liu Xuewei
Published: January 2025

Compared to conventional reservoirs, the abundant nanopores developed in unconventional oil and gas reservoirs influence fluid properties, with nano-confinement effects. The phase behavior, flow characteristics, and solid–liquid interactions of fluids are different from those in conventional reservoirs. This review investigates the physical experiments, numerical simulations, and theoretical calculation methods used in the study of nano-confinement effects in unconventional oil and gas energy. The impact of different methods used in the analysis of fluid phase behavior and movement in nanopores is analyzed. Nanofluidic, Monte Carlo method, and modified equation of state are commonly used to study changes in fluid phase behavior. Nano-confinement effects become significant when pore sizes are below 10 nm, generally leading to a reduction in the fluid’s critical parameters. The molecular dynamic simulation, Monte Carlo, and lattice Boltzmann methods are commonly used to study fluid movement. The diffusion rate of fluids decreases as nanopore confinement increases, and the permeability of nanoscale pores is not only an inherent property of the rock but is also influenced by pressure and fluid–solid interactions. In the future, it will be essential to combine various research methods, achieve progress in small-scale experimental analysis and multiscale simulation.
Method of Helicopter Turboshaft Engines’ Protection During Surge in Starting Mode
Authors: Baranovskyi Denys , Vladov Serhii , Bulakh Maryna , Vysotska Victoria , Vasylenko Viktor , Czyżewski Jan
Published: January 2025

This article proposes a mathematical model for protecting helicopter turboshaft engines from surges, starting with fuel metering supply and maintaining stable compressor operation. The model includes several stages: first, fuel is supplied according to a specified program; second, an unstable compressor operation signal is determined based on the gas temperature in front of the compressor turbine and the gas generator rotor speed derivatives ratio; at the third stage, when the ratios’ threshold value is exceeded, fuel supply is stopped, and the ignition system is turned on. Then, the fuel supply is restored with reduced consumption, and the rotor speed is corrected, followed by a return to regular operation. The neural network model implementing this method consists of several layers, including derivatives calculation, comparison with the threshold, and correction of fuel consumption and rotor speed. The input data for the neural network are the gas temperature in front of the compressor turbine and the rotor speed. A compressor instability signal is generated if the temperature and rotor speed derivatives ratio exceed the threshold value, which leads to fuel consumption adjustment and rotor speed regulation by 28…32%. The backpropagation algorithm with hyperparameter optimization via Bayesian optimization was used to train the network. The computational experiments result with the TV3-117 turboshaft engine on a semi-naturalistic simulation stand showed that the proposed model effectively prevents compressor surge by stabilizing pressure, vibration, and gas temperature and reduces rotor speed by 29.7% under start-up conditions. Neural network quality metrics such as accuracy (0.995), precision (0.989), recall (1.0), and F1-score (0.995) indicate high efficiency of the proposed method.
Identification of Tree-Related High-Impedance Earth Faults Based on Long-Term Fluctuations in Zero-Sequence Current
Authors: Wang Pengwei , Xu Bingyin , Yip Tony , Liang Dong , Zou Guofeng
Published: January 2025

To address the challenge of accurately identifying tree-related high-impedance earth faults (THIEFs), a method based on long-term fluctuations in zero-sequence current is proposed. The analysis of the recorded data from staged tests in a real test network reveals the development patterns of THIEFs and their zero-sequence characteristics during the fault process. It was found that, over an extended duration, the fluctuation characteristics of the zero-sequence current RMS value curves for THIEFs differ significantly from those of the other types of high-impedance earth faults (HIEFs). By applying approximate arc length parameterization, the RMS value curve is standardized. The curvature standard deviation, the average curvature variation rate, and its standard deviation are used as feature parameters. An identification method based on an improved grey wolf optimization probabilistic neural network is constructed. Validation with staged test results demonstrates that the proposed method achieves a success rate of 97.5%, accurately distinguishing THIEFs from other types of HIEF.
Design and Optimization of the Heatsink of a Level 1 Electric Vehicle Charger
Authors: Ebere Iheanyi Emmanuel , Khan Ashraf Ali , Ogundahunsi Samuel , Ugwuemeaju Emeka , Khan Usman Ali , Ahmed Shehab
Published: January 2025

The onboard circuits of EV chargers comprise heat-producing electronic devices such as MOSFETs and diodes for switching and power conversion operations. A heatsink must dissipate this generated heat to extend the devices’ life and prevent component thermal stress or failure. This study primarily investigates the optimal heatsink geometry and pin configuration, which offers the most efficient temperature versus cost performance. MATLAB/Simulink (R2024a) was used to model a Level 1 charger using eight MOSFETs and four diodes. Various heatsink geometries were modeled using the ANSYS (2024 R1) Workbench and Fluent software to optimize the sink’s thermal performance. The analyses were performed under transient conditions using natural and forced cooling scenarios. The 2 mm wide plate fin heatsink with 44 fins yielded the best result. Further enhancement of the best-performing naturally cooled model improved the switches and diodes temperatures by 14% and 4%, respectively. The performance of the heatsink was further improved by applying a cooling fan to achieve an up to 25% diode and 40% MOSFET thermal dissipation efficiency. The results of this study show that the most efficient cooling performance and cost are realized when the optimum combination of fin spacing, proximity from the cooling fan, and fin geometry is selected.
Overhead Power Line Tension Estimation Method Using Accelerometers
Authors: Kim Sang-Hyun , Chun Kwan-Ho
Published: January 2025

Overhead power lines are important components of power grids, and the status of transmission line equipment directly affects the safe and reliable operation of power grids. In order to guarantee the reliable operation of lines and efficient usage of the power grid, the tension of overhead power is an important parameter to be measured. The tension of power lines can be calculated from the modal frequency, but the measured acceleration data obtained from the accelerometer is severely contaminated with noises. In this paper, a multiscale-based peak detection (M-AMPD) algorithm is used to find possible modal frequencies in the power spectral density of acceleration data. To obtain a reliable noise-free signal, median absolute deviations with baseline correction (MAD-BS) algorithm are applied. An accurate estimation of modal frequencies used for tension estimation is obtained by iteration of the MAD-BS algorithm and reduction in frequency range technique. The iterative range reduction technique improves the accuracy of the estimated tension of overhead power lines. An accurate estimation of overhead power line tension can contribute to improving the reliability and efficiency of the power grid. The proposed algorithm is implemented in MATLAB R2020a and verified by comparison with measured data by a tensiometer.
Theory and Practice of Burning Solid Biofuels in Low-Power Heating Devices
Authors: Dula Małgorzata , Kraszkiewicz Artur
Published: January 2025

Combustion is the most advanced and proven method on the market for using agricultural by-product residues and waste from the agri-food industry. Currently, a wide range of combustion technologies is used to produce heat and electricity in low-power heating devices (>50 kW) using various types of biofuels from biomass (woody biomass, herbaceous biomass, waste and residues from the agri-food industry). Combustion of biomass fuels, especially those of wood origin, causes lower carbon dioxide (CO2) and sulfur oxides (SOx) emissions into the atmosphere compared to coal combustion. The growing interest in solid biofuels has contributed to intensive activities on improving the combustion process and energy devices enabling effective and economic conversion of chemical energy contained in biomass into other usable forms such as heat, electricity. Having good quality fuel, it is necessary to ensure an appropriate, clean combustion technique, which allows to achieve the highest thermal efficiency of the heating device and at the same time the lowest emission of pollutants. The article presents issues related to the theory, characteristics of the combustion process and problems related to the formation of harmful chemical compounds nitrogen oxides (NOx), SOx, carbon monoxide (CO), particulate matter (PM) emitted to the atmosphere during the combustion process in low-power heating devices. The analysis indicates the possibility of minimizing undesirable phenomena during the combustion of these biofuels related to ash sintering, the formation of deposits, corrosion and improving the amount of condensable solid particles formed and therefore reducing the emission of gaseous products to the environment.
Multi-Level Matching Optimization Design of Thin-Walled Beam Cross-Section for Tri-Axle Unmanned Forestry Vehicle Frame
Authors: Chen Qiang , Zhao Yilu , Wang Dequan , Chen Zhongjia , Wang Qingchun , Yuan Xiangyue
Published: January 2025

With the advancement of forestry modernization, the research and development of forestry vehicles provide solid technical support for the efficiency and sustainability of forest operations. This study aims to reduce the mass of the forest-use tri-axle unmanned vehicle frame through structural optimization design, improve its static and dynamic characteristics, and enhance vehicle mobility and environmental adaptability while maintaining or enhancing its structural strength and stability. Initially, the finite element model of the vehicle frame was established using the finite element software Hypermesh (2022), and its static and dynamic characteristics were analyzed using OptiStruct (2022) software. The accuracy of the finite element calculations was verified through experiments. Subsequently, a sensitivity analysis method was employed to screen the design variables of the thin-walled beam structure of the forest-use tri-axle unmanned vehicle. Response surface models were created using least squares regression (LSR) and radial basis function network (RBF). Considering indicators such as frame mass, modal frequency, and maximum bending and torsional stresses, the multi-objective genetic algorithm (MOGA) was applied to achieve a multi-objective lightweight design of the vehicle frame. This comprehensive optimization method is rarely reported in forestry vehicle design. By employing the proposed optimization approach, a weight reduction of 10.1 kg (a 7.44% reduction) was achieved for the vehicle frame without compromising its original static and dynamic performance. This significant lightweighting result demonstrates considerable practical application potential in the field of forestry vehicle lightweight design. It responds to the demand for efficient and environmentally friendly forestry machinery under forestry modernization and holds important implications for reducing energy consumption and operational costs.
Cassava Waste Starch as a Source of Bioplastics: Development of a Polymeric Film with Antimicrobial Properties
Authors: Silveira Yuri D. O. , Franca Adriana S. , Oliveira Leandro S.
Published: January 2025

Polysaccharides represent the most abundant biopolymers in agri-food wastes and thus are the most studied polymers to produce biodegradable films for use in packaging. Starch is among the major polysaccharides extracted from food and agricultural waste that have been used as precursor material for film production. Therefore, the present study aimed at producing an active film with antimicrobial properties using starch extracted from cassava waste and oil extracted from cloves. The antimicrobial activity of the produced films was tested against Staphylococcus aureus, Salmonella Typhimurium and Listeria monocytogenes. Cassava periderm and cortex were bleached with either NaClO or H2O2 before starch aqueous extraction. The active films’ antimicrobial effectiveness was assessed by the formation of inhibitory halos around film disc samples in an agar diffusion method. The inhibition zone diameters were statistically similar for all microorganisms, with an average diameter of 11.87 ± 1.62 mm. The films presented an average water vapor permeability of 0.14 g mm/m2 h kPa, an average tensile strength of 0.17 MPa and an elongation at break of 32.90%. Based on the determined properties, the produced films were deemed adequate for use in food packaging, in which antimicrobial activity is paramount.
The Beneficial Effects of Lactobacillus Strains on Gut Microbiome in Alzheimer’s Disease: A Systematic Review
Authors: Quansah Michael , David Monique Antoinette , Martins Ralph , El-Omar Emad , Aliberti Silvana Mirella , Capunzo Mario , Jensen Slade O. , Tayebi Mourad
Published: January 2025

Background/Objectives: Growing evidence suggests that the gut–brain axis influences brain function, particularly the role of intestinal microbiota in modulating cognitive processes. Probiotics may alter brain function and behavior by modulating gut microbiota, with implications for neurodegenerative diseases like Alzheimer’s disease (AD). The purpose of this review is to systematically review the current literature exploring the effects of probiotic supplementation on gut microbiota and cognitive function in AD and mild cognitive impairment (MCI). Methods: A comprehensive literature search was conducted across PubMed/Medline, Embase, and Scopus to identify relevant randomized controlled trials (RCTs) from inception to 20 August 2024. The search focused on comparing outcomes between intervention and control/placebo groups. Data searches, article selection, data extraction, and risk of bias assessment were performed in accordance with Cochrane guidelines. Systematic Review Registration: PROSPERO registration no: CRD42023446796. Results: Data from four RCTs involving 293 Individuals (AD and MCI patients) receiving mainly Lactobacillus and Bifidobacterium strains showed some beneficial effects on cognitive function, altered gut microbiota composition, and positively affected metabolic biomarkers. However, variability in microbiota assessment across studies limits the interpretation of results. The limited number and quality of the existing studies make it difficult to draw definitive conclusions from the data. Additional high-quality research is clearly needed. Conclusions: Probiotics show promise as an adjunctive intervention for cognitive decline, but larger, long-term trials are needed to confirm their efficacy and clinical applicability in neurodegenerative diseases like AD.
4-Alkyl-4H-thieno[2′,3′:4,5]pyrrolo[2,3-b]quinoxaline Derivatives as New Heterocyclic Analogues of Indolo[2,3-b]quinoxalines: Synthesis and Antitubercular Activity
Authors: Sadykhov Gusein A. , Belyaev Danila V. , Khramtsova Ekaterina E. , Vakhrusheva Diana V. , Krasnoborova Svetlana Yu. , Dianov Dmitry V. , Pervova Marina G. , Rusinov Gennady L. , Verbitskiy Egor V. , Charushin Valery N.
Published: January 2025

The synthetic approach based on a sequence of Buchwald–Hartwig cross-coupling and annulation through intramolecular oxidative cyclodehydrogenation has been used for the construction of novel 4-alkyl-4H-thieno[2′,3′:4,5]pyrrolo[2,3-b]quinoxaline derivatives. For the first time, these polycyclic compounds were evaluated for antimycobacterial activity, including extensively drug-resistant strains. A reasonable bacteriostatic effect against Mycobacterium tuberculosis H37Rv was demonstrated. A plausible mechanism for antimycobacterial activity of heterocyclic analogues of indolo[2,3-b]quinoxalines has been advanced on the basis of their molecular docking data.
Investigating Antecedents to Older Adults’ Uptake of Health Information Systems: A Quantitative Case Study of Electronic Personal Health Records
Authors: Torkman Reza , Ghapanchi Amir Hossein , Ghanbarzadeh Reza
Published: January 2025

Introduction and Aim: older adults are among the primary beneficiaries of health information systems such as electronic Personal Health Records (PHRs), yet they are notably underrepresented in existing research. This study addresses this gap by focusing on this vulnerable group, aiming to enhance their engagement with PHR systems to improve health management. This research seeks to identify the key determinants that influence the intention of older adults to utilize PHR systems, thereby supporting the development of more user-friendly health information technologies for this demographic. Methods: to better understand the functionality of PHR systems and improve user competence, hands-on workshops were conducted for 135 older adults. The workshops aimed to enhance their capability to manage chronic conditions using these systems. Post-training, participants’ intentions to use PHRs were assessed via a survey, applying the Health Belief Model (HBM) as the analytical framework. Data analysis was performed using Partial Least Squares (PLS) path modeling to underscore the robustness of the methodological approach. Findings: the data analysis of the participants’ responses uncovered that seniors’ usage intention toward PHRs is a function of the PHR’s perceived usefulness (p < 0.01, OR = 2.5, 95% CI 1.5–4.1), perceived barriers (p < 0.05, OR = 1.3, 95% CI 0.8–2.1), perceived confidence (p < 0.05, OR = 1.6, 95% CI 1.0–2.5), and cues to action (p < 0.05, OR = 1.8, 95% CI 1.1–2.9). Of these, perceived usefulness was found to be the strongest predictor. The results also indicate that perceived susceptibility and perceived severity did not significantly impact seniors’ intention to use PHRs. Conclusions: according to the findings and based on the HBM literature, if individuals perceive more benefits and fewer barriers to the use of PHRs, greater self-efficacy, and better cues to action, they are more likely to adopt the system.
Effect of Deposition Temperature and Thermal Annealing on the Properties of Sputtered NiOx/Si Heterojunction Photodiodes
Authors: Nedev Roumen , Mateos-Anzaldo David , Osuna-Escalante Eddue , Perez-Landeros Oscar , Curiel-Alvarez Mario , Osorio-Urquizo Esteban , Castillo-Saenz Jhonathan , Lopez-Medina Javier , Valdez-Salas Benjamin , Nedev Nicola
Published: January 2025

NiOx is a p-type semiconductor with excellent stability, which makes it interesting for a wide range of applications. Broadband photodetectors with high responsivity (R) were fabricated by depositing r.f.-sputtered NiOx layers on n-Si at room temperature (RT), 50 °C and 100 °C. In self-powered mode the RT diodes have R between 0.95 and 0.39 A/W for wavelengths between 365 and 635 nm, while at a reverse bias of −4 V, the responsivity increases to values between 22 A/W and 10.7 A/W for wavelengths in the same range. The increase of the deposition temperature leads to a decrease of R but also to a smaller reverse dark current. Thus, the 100 °C photodiodes might be more appropriate for applications where high responsivity is required, because of their smaller power consumption compared to the RT diodes. In addition, it was found that the increase of the deposition temperature leads to an increase of the diodes’ series resistance and the resistivity of NiOx. The effect of Rapid Thermal Annealing (RTA) on the properties of the photodiodes was studied. Annealing at 550 °C for 6 min leads to much higher responsivity compared to R of diodes with as-deposited NiOx. However, a disadvantage of the annealed diode is that the reverse current depends on the amplitude and polarity of previously applied bias voltage. The higher responsivity of the RTA photodiodes makes them useful as light sensors.
Technical and Tactical Performance in Women’s Singles Pickleball: A Notational Analysis of Key Match Indicators
Authors: Prieto-Lage Iván , Reguera-López-de-la-Osa Xoana , Vázquez-Estévez Christopher , Gutiérrez-Santiago Alfonso
Published: January 2025

Background: Pickleball has experienced remarkable growth in recent years, yet studies exploring its specific characteristics are scarce. This investigation provides a detailed notational analysis of women’s singles pickleball, evaluating the technical and tactical performance indicators in the game. Method: An observational methodology was used to analyze all points from five PPA Tour tournaments. The matches were recorded and coded using LINCE PLUS software, version 2.1.0, with a category system designed for this sport. A descriptive analysis was conducted with IBM SPSS version 25.0, and Theme 6.0 Edu software was used to detect gameplay patterns. The statistical significance was set at p < 0.05. Results: The findings indicate that serving players have a slight advantage, winning 55.1% of points. Most of the points were resolved through unforced errors, accounting for 63.7% of the total, primarily from forehand strokes in short rallies and backhand strokes in medium rallies. The most frequent hitting zones for point termination were near the non-volley zone (35.8%) and behind the baseline (38.6%). Conclusions: This study provides a deeper understanding of performance in women’s pickleball, highlighting technical and tactical patterns that offer guidelines for optimizing strategies and techniques in the sport.
Economic News, Social Media Sentiments, and Stock Returns: Which Is a Bigger Driver?
Authors: Verma Rahul , Verma Priti
Published: January 2025

This study provides empirical evidence on the relative impact of innovations in information content and noise embedded in economic news and social media sentiments on DJIA, S&P 500, NASDAQ, and Russell 2000 index returns. We find that economic news sentiments are relatively more rational and have a greater impact than irrational social media sentiments. There exist significant negative effects of three distinct categories of social media sentiments and a significant positive impact of economic news sentiments on stock returns. The magnitude of the impact of the economic news sentiments is larger. In addition, the economic news sentiments seem to have greater information content and are driven by risk factors to a greater extent than the sentiments of social media, which probably contain more noise. There are significant negative responses of stock returns to irrational components of social media sentiments while significant positive responses to rational components of economic news sentiments. Lastly, the magnitude of the impact of rational economic news sentiments is higher than that of irrational social media sentiments. Our results are consistent with the view that business news is a manifestation of a rational outlook to a larger extent than social media and can drive stock valuations.
The Effect of the MgO/Al2O3 Ratio on the Thermal and Refractory Behaviors of Cordierite Ceramics
Authors: Lee Jae-Seung , Kim Jin-Woo , Park Joo-Seok , Lee Min-Ho , Lee Heesoo
Published: January 2025

In this study, cordierite-based ceramics (2MgO·2Al2O3·5SiO2) were synthesized using high-purity MgO, Al2O3, and SiO2 as starting materials. The influence of the MgO/Al2O3 ratio on various properties, including the thermal behavior, pyrometric cone refractory behavior, phase formation, physical properties, and microstructure of the synthesized ceramics, was systematically analyzed. Increasing the MgO/Al2O3 ratio progressively weakened the cordierite network, leading to lower temperatures for liquid formation and melting. This resulted in reduced viscosity and increased fluidity. Subsequently, the thermal and refractory behaviors were observed at lower temperatures with higher deformation rates under higher MgO/Al2O3 ratios. The lower viscosity of the liquid formed at reduced temperatures contributed to an increase in the density of sintered bodies, reduced porosity, and enhanced shrinkage. X-ray diffraction analysis confirmed that cordierite was the predominant phase in samples sintered at 1300, 1350, and 1400 °C, with higher cordierite formation at higher temperatures. Conversely, the formation of secondary phases, such as spinel, cristobalite, and enstatite, decreased with increasing sintering temperature. Pyrometric cones were then constructed for a range of temperature settings, and their deformation characteristics at specific temperatures were used to evaluate the refractoriness under diverse conditions.
Multilevel Constrained Bandits: A Hierarchical Upper Confidence Bound Approach with Safety Guarantees
Authors: Baheri Ali
Published: January 2025

The multi-armed bandit (MAB) problem is a foundational model for sequential decision-making under uncertainty. While MAB has proven valuable in applications such as clinical trials and online advertising, traditional formulations have limitations; specifically, they struggle to handle three key real-world scenarios: (1) when decisions must follow a hierarchical structure (as in autonomous systems where high-level strategy guides low-level actions); (2) when there are constraints at multiple levels of decision-making (such as both system-wide and component-level resource limits); and (3) when available actions depend on previous choices or context. To address these challenges, we introduce the hierarchical constrained bandits (HCB) framework, which extends contextual bandits to incorporate both hierarchical decisions and multilevel constraints. We propose the HC-UCB (hierarchical constrained upper confidence bound) algorithm to solve the HCB problem. The algorithm uses confidence bounds within a hierarchical setting to balance exploration and exploitation while respecting constraints at all levels. Our theoretical analysis establishes that HC-UCB achieves sublinear regret, guarantees constraint satisfaction at all hierarchical levels, and is near-optimal in terms of achievable performance. Simple experimental results demonstrate the algorithm’s effectiveness in balancing reward maximization with constraint satisfaction.
Deep Reinforcement Learning for Intraday Multireservoir Hydropower Management
Authors: Castro-Freibott Rodrigo , García-Sánchez Álvaro , Espiga-Fernández Francisco , González-Santander de la Cruz Guillermo
Published: January 2025

This study investigates the application of Reinforcement Learning (RL) to optimize intraday operations of hydropower reservoirs. Unlike previous approaches that focus on long-term planning with coarse temporal resolutions and discretized state-action spaces, we propose an RL framework tailored to the Hydropower Reservoirs Intraday Economic Optimization problem. This framework manages continuous state-action spaces while accounting for fine-grained temporal dynamics, including dam-to-turbine delays, gate movement constraints, and power group operations. Our methodology evaluates three distinct action space formulations (continuous, discrete, and adjustments) implemented using modern RL algorithms (A2C, PPO, and SAC). We compare them against both a greedy baseline and Mixed-Integer Linear Programming (MILP) solutions. Experiments on real-world data from a two-reservoir system and a simulated six-reservoir system demonstrate that while MILP achieves superior performance in the smaller system, its performance degrades significantly when scaled to six reservoirs. In contrast, RL agents, particularly those using discrete action spaces and trained with PPO, maintain consistent performance across both configurations, achieving considerable improvements with less than one second of execution time. These results suggest that RL offers a scalable alternative to traditional optimization methods for hydropower operations, particularly in scenarios requiring real-time decision making or involving larger systems.