bluuebunny/crossref_metadata_2025 huggingface dataset:
Study on the Impact of Design Parameters of Photovoltaic Com-Bined Vacuum Glazing (PVCVG) on the Energy Consumption of Residential Buildings in Lhasa
Authors: Zhong Luyang , Wu Dan , Zhang Bo , Chen Lixing , Xie Yibing , Zhang Yingzi , Liang Xinchun
Published: January 2025

Synergistic active-passive utilization of solar energy in buildings plays an important role in achieving nearly zero energy buildings. Building-integrated photovoltaic (BIPV) is a crucial initiative to reduce heating energy consumption, especially in cold climate zones with abundant solar radiation. However, few studies have examined the impact of design parameter of photovoltaic combined vacuum glazing (PVCVG) on building energy efficiency. This study assessed the energy performance of Several typical Win-dows and PVCVG with various Window-to-Wall Ratio (WWR) design conditions in residential buildings in Lhasa, and investigated how the WWR and orientation of PVCVG influence energy consumption by DesignBuilder software. The findings indi-cate that PVCVG exhibits great energy-saving potential in Lhasa, with both orientation and WWR of PVCVG substantially affecting energy consumption. Specifically, When the south-facing WWR > 40%, the energy generated by the three kinds of PV Windows can meet the building's requirements. the optimal orientation for PVCVG is southward, followed by east and west orientations. PVCVG reaches optimal energy satisfaction when the South WWR is 85%. This study is expected to provide useful information for improving the energy use efficiency in cold climate zones with abundant solar radiation and promoting sustainable building development.
Considerations About Melanocytic Nevi
Authors: Chesca Antonella A
Published: January 2025

The purpose of the study is to analyse and to identify specific structural characteristics of melanocytic nevi, to youth pacients. Using the optical and electronic microscope, could be possible a proper describtion related melanocytic nevi. In this study play a significant role, different factors, such as genetic, epigenetic, microbiomic and proteomic factors. Disease diagnostic management and future trends, directions are also important key points As future directions good to mention preventive and prophylactic methods.
Compositionality in Visual Representations and the Hardwiring of Our Visual System
Authors: Raftopoulos Athanassios
Published: January 2025

It is widely accepted that visual representations are iconic and differ from the symbolic representations of propositional attitudes. Iconic representations compose differently from symbolic representations. Symbolic compositionality is canonical (it conforms to a set of rules determined by symbolic logic or by some grammar). Iconic representations display a whole/parts compositionality, in the way parts of objects combine to form whole objects. In this paper, I take recourse to Mereotopology as it applies to the compositionality of parts to form wholes to explain, first, the compositionality of icons, and to argue, second, that the hardwiring of our visual system is such as to reflect the basic compositional rules of Mereotopology.
Synergistic Effects of Green Nanoparticles on Antitumor Drug Efficacy in Hepatocellular Cancer
Authors: Rîmbu Mirela Claudia , Popescu Liliana , Mihăilă Mirela , Sandulovici Roxana Colette , Cord Daniel , Mihăilescu Carmen Marinela , Gălățanu Mona Luciana , Panțuroiu Mariana , Manea Carmen Elisabeta , Boldeiu Adina , Brîncoveanu Oana , Savin Mihaela , Grigoroiu Alexandru , Ungureanu Florin Dan , Amzoiu Emilia , Popescu Mariana , Truță Elena
Published: January 2025

Cancer is one of the leading causes of mortality worldwide, and despite significant advances in treatment plans and drug development, survival prospects are still low, and treatment side effects are life altering. This article examines plant-derived extracts and their therapeutic potential against various types of hepatocellular carcinoma as a means of inducing fewer side effects compared to existing treatments. The focus is on the in vitro synergistic effect of silver bio-nanoparticles obtained from Clematis vitalba, Melissa officinalis, and Taraxacum officinale extracts (Clematis vitalbae extractum—CVE, Melissae extractum—ME, Taraxaci extractum—TE), alongside important drugs employed in liver cancer treatments (sunitinib—SNTB and imatinib—IMTB), and their effect on HepG2 cell lines (human hepatocellular carcinoma) and normal HUVEC cell lines (human umbilical vein endothelial cells). The nanoparticles (AgNPs) were characterized in terms of their dimensions, maximum absorption, and stability in solutions using techniques such as UV-Vis, DLS, zeta potential, and SEM. Meanwhile, antitumor effect was determined based on the viability of the HepG2 cells after 24 and 48 hours of treatment with each extract, mixtures of extracts with nanoparticles, and combinations of the extracts with antitumor drugs. Tests were repeated on the HUVEC cell line to determine normal cell toxicity. M. officinalis-derived silver nanoparticles (ME AgNPs) and the Clematis vitalba extract with silver nanoparticles (CVE AgNPs) significantly reduced HepG2 cell viability, enhancing efficacy when combined with conventional therapies (SNTB + ME AgNPs 1:1 vs SNTB: 20.01% vs 25.73% (p = 0.002), IMTB + ME AgNPs 1:1 vs IMTB: 17.80% vs 18.08% (p = 0.036); SNTB + CVE AgNPs 1:1 vs SNTB: 18.73% vs 25.73% (p = 0.000), SNTB + CVE AgNPs 1:2 vs SNTB: 26.62% vs 41.00% (p = 0.018), IMTB + CVE AgNPs 1:1 vs IMTB: 12.99% vs 18.08% (p = 0.001)). In contrast, the Taraxacum extract showed comparable cytotoxicity to its silver nanoparticles but did not exceed the efficacy of the extract alone after 24 hours. Of the three extracts, CVE demonstrated the most potent antitumor effect, resulting in an increase in the anticancer efficacy of the synthetic drug with remarkable therapeutic benefits in the fight against cancer. The findings of this study highlight the promising potential of plant-derived silver bio-nanoparticles to enhance the efficacy of conventional liver cancer treatments, paving the way for future research into more effective and less toxic therapeutic options.
Response to In Vitro Micropropagation of Plants with Different Degrees of Variegation of the Commercial Gymnocalycium cv. Fancy (Cactaceae)
Authors: Cortés-Olmos Carles , Guerra-Sandoval Vladimir Marín , Guijarro-Real Carla , Pineda Benito , Fita Ana , Rodríguez-Burruezo Adrián
Published: January 2025

Gymnocalycium cv. Fancy is an ornamental cactus with a heterogeneous genetic background, which includes variegated variants whose in vitro culture response has not been studied. So plants exhibiting different degrees of variegation (from 0% to 100%) were also classified by their initial size and used to obtain different types of explants (apices, central discs, epicotyls and hypocotyls). The effects of three plant growth regulators (BAP at 8µM, KIN at 4µM and TDZ at 1µM) was evaluated. The response of explants was measured in the number of shoots, calluses and rhizogenesis events per explant; as well as the appearance of variegated shoots with specific color percentages. Central discs treated with 1 µM TDZ provided the best results in terms of shoot production. Additionally, a correlation was observed between the type of activated areole (green, mixed or fully colored) and the percentage of color of the obtained shoots, enabling precise explant selection based on desired morphological characteristics. Additionally, the appearance of shoots with different colors confirms the possibility of selecting new lines from this cultivar. These findings could be of great value not only for breeding and multiplication of ornamental cactaceae but also other edible species and relatives.
Employing the Binary Logistic Regression in Modeling the Effectiveness of Agricultural Extension in Clove Farming: Facts and Findings from Sidrap Regency, Indonesia
Authors: Hasim Hasim , Salam Muslim , Sulaeman Andi Amran , Jamil Muhammad Hatta , Iswoyo Hari , Diansari Pipi , Arsal Ariady , Tenriawaru A. Nixia , Akhsan Akhsan , Muslim Ahmad Imam
Published: January 2025

The research objective was to examine the factors influencing the effectiveness of agriculture in clove farming. Primary data were using structured interviews and a simple random sampling method to select 140 respondents, clove farmers. The binary logistic regression model was employed to test the influence of fourteen independent variables on the effectiveness of agricultural extension in clove farming. The research results indicated that the variables of family size, mass communication approach, electronic media, and clove cultivation materials significantly and positively impacted the effectiveness of agricultural extension in clove farming. Meanwhile, the variables of educational attainment, farming experiences, farmer cosmopolitan, print media, and land area had a negative significant effect, and the other variables had no significant effect. These findings are important indicators and valuable insights, promoting and encouraging the effectiveness of extension in clove farming. The findings emphasize the significant role of family characteristics, agricultural extension approaches, and communication delivery methods in the diffusion of agricultural technology and enhancing the effectiveness of agricultural extension. Based on the research results, policy recommendations are proposed to enhance extension effectiveness in clove farming, specifically augmented farmer education on clove cultivation and the intensification and extensification of mass extension methods alongside using electronic media in extension activities.
The Impact of AI and IoT in Reducing Urban Carbon Footprints
Authors: Luz Ayuns , Iseal Sheed
Published: January 2025

As urbanization accelerates, cities are becoming major contributors to global carbon emissions, necessitating innovative solutions to mitigate their environmental impact. This paper explores the transformative role of Artificial Intelligence (AI) and the Internet of Things (IoT) in reducing urban carbon footprints. AI enables smarter energy management, efficient transportation systems, and data-driven decision-making, while IoT facilitates real-time monitoring, resource optimization, and enhanced connectivity. Together, these technologies create synergistic systems that drive urban sustainability, from smart grids and buildings to integrated mobility solutions. Despite challenges such as high implementation costs and data privacy concerns, case studies from pioneering smart cities demonstrate the feasibility and effectiveness of AI and IoT in addressing urban environmental challenges. By leveraging these technologies and fostering collaborative efforts, cities can significantly advance their journey toward achieving carbon neutrality and sustainable urban development.
The Role of CXCR2, MMP-2, and MMP-9 in Pathogenesis of Placenta Accreta: A Molecular Expression Study
Authors: Mirani Putri , Murti Krisna , Lestari Peby Maulina , Liberty Iche Andriyani , Kesty Cindy , Andrina Hana , Stevanny Bella
Published: January 2025

Background and Objectives: The pathogenesis of placenta accreta spectrum disorder (PASD) is influenced by the inflammatory process. Therefore, the examination of biomarkers related to the inflammatory process, namely Matrix Metalloproteinase (MMP) and CXC Motif Chemokine Receptor 2 (CXCR2) is expected to bring researchers to a bright spot regarding the pathogenesis of PASD. This study analyzes the role of CXCR2, MMP-2, and MMP-9 in the pathogenesis of PASD as well as their potential correlation with 25-OHD, zinc, and calcium ion levels. Methods: An observational study with a case control design was conducted to assess differences in the mean density of CXCR2, MMP-2, and MMP-9 immunostaining in placental and uterine tissue, as well as their correlation with serum 25-OHD, zinc, and calcium ion levels in 17 patients with PASD and 34 patients without PASD at the Department of Obstetrics and Gynecology, Dr. Mohammad Hoesin Hospital Palembang. CXCR2, MMP-2, and MMP-9 expression was measured by immunohistochemistry analysis. The data were analyzed using STATA version 15: There were no significant differences in the mean levels of MMP-2 expression in patients with and without PASD. There were significant differences in the expression of placental CXCR2 (p=0.003), uterine CXCR2 (p<0.001), and uterine MMP-9 (p=0.018) in patients with and without PASD. No significant correlation was found between serum 25-OHD levels, serum zinc levels, serum calcium ion levels and CXCR2, MMP-2, and MMP-9 expression. Conclusions: CXCR2 and MMP-9 may play a role in the pathogenesis of PASD.
A Neural Network Constitutive Model, and Automatic Stiffness Evaluation for Multiscale Finite Elements
Authors: Mouratidou Aliki D. , Stavroulakis Georgios E.
Published: January 2025

A neural network model for a constitutive law in nonlinear structures is proposed. The neural model is constructed based on a data set of responses of representative volume elements, calculated by finite elements. An open scientific software machine learning platform Tensorflow and an application programming interface, intended for a deep learning Keras library, provided by Python are used for the development of the artificial neural network. The tangential stiffness matrix within a multi-scale model is calculated via the method of automatic differentiation of Tensorflow. The results are compared with given data set. The loss function, including the Sobolev metrics is computed. The results can be integrated into a multiscale finite element analysis and provide results with less effort. The technique is also tested on hyperelastic materials.
Antiparasitic Pharmacology Goes to the Movies: Leveraging Generative AI to Create Educational Short Films
Authors: Worthley Benjamin , Guo Meize , Sheneman Lucas , Bland Tyler
Published: January 2025

Medical education faces the dual challenge of addressing cognitive overload and sustaining student engagement, particularly in complex subjects such as pharmacology. This study introduces Cinematic Clinical Narratives (CCNs) as an innovative approach to teaching antiparasitic pharmacology, combining generative artificial intelligence (genAI), edutainment, and mnemonic-based learning. The intervention involved two short films, Alien: Parasites Within and Wormquest, designed to teach antiparasitic pharmacology to first-year medical students. A control group of students only received traditional text-based clinical cases, while the experimental group engaged with the CCNs in an active learning environment.
Students who received the CCN material scored an average of 8% higher on exam questions related to the material covered by the CCN compared to students in the control group. Results also showed that the CCNs improved engagement and interest among students, as evidenced by significantly higher scores on the Situational Interest Survey for Multimedia (SIS-M) compared to traditional methods. Notably, students preferred CCNs for their storytelling, visuals, and interactive elements.
This study underscores the potential of CCNs as a supplementary educational tool, and suggests the potential for broader applications across other medical disciplines outside of antiparasitic pharmacology. By leveraging genAI and edutainment, CCNs represent a scalable and innovative approach to enhancing the medical learning experience.
Document Relevance Filtering by Natural Language Processing and Machine Learning: A Multidisciplinary Case Study of Patents
Authors: Bridgelall Raj
Published: January 2025

The exponential growth of patent datasets poses a significant challenge in filtering relevant documents for research and innovation. Traditional semantic search methods based on keywords often fail to capture the complexity and variability of multidisciplinary terminology, leading to inefficiencies. This study addresses the problem by systematically evaluating supervised and unsupervised machine learning (ML) techniques for document relevance filtering across five technology domains: solid-state batteries, electric vehicle chargers, connected vehicles, electric vertical takeoff and landing aircraft, and light detecting and ranging (LiDAR) sensors. The contributions include benchmarking the performance of 10 classical models. These models include extreme gradient boosting, random forest, and support vector machines; a deep artificial neural network; and three natural language processing methods: latent Dirichlet allocation, non-negative matrix factorization, and k-means clustering of a manifold-learned reduced feature dimension. Applying these methods to more than 4,200 patents filtered from a database of 9.6 million patents revealed that most supervised ML models outperform the unsupervised methods. An average of seven supervised ML models achieved significantly higher precision, recall, and F1-scores across all technology domains, while unsupervised methods show variability depending on domain characteristics. These results offer a practical framework for optimizing document relevance filtering, enabling researchers and practitioners to efficiently manage large datasets and enhance innovation.
Investigation of the Discrepancy between Optically and Gravimetrically Calculated Fiber Volume Fraction in Flax Fiber-Reinforced Polymer
Authors: Maier Christoph , Beckmann Alexander , Wittmann Armin , Koch Klaus Peter , Fischer Georg
Published: January 2025

The fiber volume fraction significantly influences the mechanical properties of fiber-reinforced composites. However, accurate measurements can be particularly challenging in natural fiber-reinforced polymers. This study compares indirect methods using gravimetric and volumetric measurements with a U-Net-based direct method using micro-CT images for flax fiber-reinforced polymers made via compression molding at 2.33–13.5 bar. A notable discrepancy was observed between the direct and indirect methods, with the latter yielding a fiber volume fraction approximately 25% lower than what can be determined optically. This difference may arise from the matrix being absorbed by the fibers, resulting in a mixed region between dry fiber and pure matrix, further explained using a four-phase model. Our findings indicate that the volume fraction is dependent on the applied pressure. Specifically, we established a linear relationship between the fiber volume fraction and the pressure up to 9.4 bar, beyond which the fiber volume fraction plateaued. Furthermore, we examined the impact of void distribution in relation to pressure. At lower pressures, voids are distributed irregularly throughout the composite, whereas at higher pressures, the overall number of voids decreases, and they tend to concentrate primarily in the center.
Incremental Hemodialysis: Review of Clinical Trials Comprising Patients Undergoing Once-Weekly-Hemodialysis
Authors: Bolasco Piergiorgio
Published: January 2025

see paper
Variation of Fatty Acids, Lignans, and Tocols of Sesame (Sesamum indicum) Genotypes
Authors: Kurt Cemal
Published: January 2025

Sesame is one of the neglected crops. As a result, there are not many studies on the fatty acid, tocol and lignan composition of sesame genotypes. This study aimed to evaluate the diversity in fatty acid, lignan and tocol composition among different sesame accessions from Türkiye and 12 other countries. Fifty sesame genotypes were investigated under field conditions in Adana, Türkiye. The sesame genotypes varied widely in their fatty acid, lignan and tocol compositions. The content of palmitic acid, stearic acid, oleic acid, linoleic acid, linolenic acid, α-tocopherol, γ-tocopherol, α-tocotrienol, total tocopherol, sesamin, sesamolin, sesamol varied between 8.39-10.11%, 4.19-5.36%, 35.83-43.49%, 40.64-48.8%, 0.28-0.49%, 1.08-2.79 µg/g, 41.5-75.74 µg/g, 0.96-1.8 µg/g, 42.93-76.92 µg/g, 2.34-14.31 mg/g, 0.23-1.81 mg/g, 0-0.59 mg/g respectively. While the sesamin content of the sesame genotypes used in the study was higher than previously reported values, it was determined that the sesamol content was at very low levels. The sesame genotypes are rich in sesamin content and can be used as parents in breeding programs. Sesamol was not detected in almost all of the cultivars from Türkiye. In addition, it was determined that the γ-tocopherol contents of these cultivars were below the average value for Turkish cultivars. The same applies to sesamin and sesamolin. The results obtained from the study showed that the lignan and tocol composition of the cultivars produced in Türkiye were not at the desired levels. Therefore, improving the lignan and tocol composition of the cultivars in the breeding programs designed to obtain new cultivars will benefit consumers in Türkiye, which consumes sesame extensively.
Global Trends and Action Items for the Prevention and Control of Emerging and Reemerging Infectious
Authors: De Gaetano Silvia , Ponzo Elena , Midiri Angelina , Mancuso Giuseppe , Filippone Daniele , Infortuna Giovanni , Zummo Sebastiana , Biondo Carmelo
Published: January 2025

In recent decades, the world has observed the emergence and reemergence of a multitude of previously non-existent or re-emerging infectious diseases, for which there is a paucity of timely and effective preventative measures. The WHO has published a catalogue of priority pathogens that are likely to trigger future epidemics, with the objective of designing effective prophylactic and therapeutic interventions. The rationale behind these interventions is rooted in a comprehensive understanding of the aetiology, epidemiology and pathogenesis of the target diseases. While it is imperative to acknowledge the pivotal role that evolutionary changes in pathogens play, it is equally important to recognise the influence of a multifaceted interplay of factors on the emergence and re-emergence of infectious diseases. These include changes in human populations, vectors and reservoirs of exposure, and environmental changes. This review summarises the aetiology, epidemiology and pathogenesis of the ten WHO priority diseases, as well as those with a high epidemic potential that are already the focus of specific control programme initiatives, such as HIV/AIDS, tuberculosis, and malaria diseases. Furthermore, the review concentrated on the means of addressing these infections through public health surveillance and response systems. Such systems must be designed to rapidly detect unusual and unexpected disease patterns, track and share information in real time, and rapidly mobilise global responses, which are the most important way to effectively contain transmission.
Structural Monophyly Analysis Allows Estimation of Self-Sustainability at Supraspecific Level Across 88 Million Years In Mosses
Authors: Zander Richard H.
Published: January 2025

A new method of macroevolutionary analysis, high-resolution phylogenetics, using both mor-phological and molecular traits has revealed well-supported evidence of complexity-based pro-cesses generating and controlling biodiversity. A novel technique of using evolutionary rates following a strict morphological clock, at least approximately, may allow detailed information on speciation and extinction events across geologic time. Branching series of minimally mono-phyletic genera are used to characterize in detail the branching lineage of the widely distributed moss family Streptotrichaceae. A strict morphological clock is calibrated by timing of genera new to recently exposed islands, molecular scaling against fossil taxa, and fossil evidence of the origin of the modern bryoflora. The numbers of genera generated in each 22 my interval are similar, while only one genus is inferred as extinct. The general outline of the phylogeny is tadpole-shaped because cumulative extinction is less than cumulative speciation, thus sustaining the family over vast time spans. Extant species per genus increase significantly over time through extinction, not as increased number over the optimal four descendants per ancestor, that is, not as secondarily descendant species. Ancient traits are preserved throughout the lineage. It is hypothesized that descendant species are protected from coeval competition through bursts of speciation. This study supports a complexity-based explanation of the interaction of major evo-lutionary processes resulting in sustainability.
Climate Dynamics and Rain-Fed Tuber Crop Yield Cultivated by Small-Scale Farming System Under Global Warming in Wolaita Zone, Southern Ethiopia
Authors: Badacho Tadele Badebo
Published: January 2025

Agriculture is a backbone of Ethiopian economy, contributing a vital role to food security and employment in rural communities. Climate change and variability have been adversely influenced, challenging the country’s efforts to ensure food security. As a result, this study investigated the association between climate variability and tuber crop yields in southern Ethiopia. Modified Mann-Kendal trend test and coefficient of variation were implemented to examine trend and variability while Seaborne bivariate kernel density was used to assess how climate variability has been related with tuber crop yields. The study has also evaluated the predictive potential of multivariate regression by means of coefficient of determination and root mean square error metrics. The rainfall characteristics showed increasing trend during spring, autumn and annually at a rate of 0.32 mm, 1.67 mm and 0.25 mm, whereas significantly decreasing in summer rainy season at a rate of 0.455 mm/year. Spring and autumn rainfall revealed moderate to high variability, posing risks to rain-fed farming. Days and night time showed increasing trend at a rate of 0.053 oC and 0.16 oC 1981-2021 period. A reasonable tuber crop yield was harvested with cumulative rainfall ranging from 450.0 to 650.0 mm during the growing season, day and night time temperature was between 23.0-26.0 oC and 11.5-14.0 oC. When day time temperature above 26.0 oC and night time temperature below 11.5 oC, sweet potato and taro yields decrease, and harvesting is utterly unexpected. The RF regression model proved to be the best model performing algorithm allowing for optimal yield prediction, assisting farmers and decision makers in better planning crop production and management. The high variability of spring rainfall and the decreasing trend of summer rainfall, combined with an increasing of temperatures, could reduce agricultural productivity, leading to food insecurity. Therefore, the yield of tuber crops can be improved by supplementing the rain-fed farming system with irrigation and applying modern farming techniques and operations by farmers. Moreover, the finding suggests that the need to carefully select plant varieties tolerant to high ambient temperature conditions, which will be more prevalent in the context of climate change. There is a need to intensify adaptation measures to minimize the negative consequences of climate variability to improve the adaptive capacity of sweet potato and taro farmers.
Conservation of Fragmented Ecosystems in Urban Environments: Sustainability Indicators and Strategies to Mitigate Biodiversity Loss
Authors: Ixtepan Turrent María del Carmen , Colmenero Fonseca Fabiola , Gómez Ponce Carlos Andrés , Cárcel-Carrasco Javier , Rodríguez Pérez Ramiro
Published: January 2025

This article analyzes the impact of urbanization and industrialization on biodiversity and ecosystems, with special attention to habitat fragmentation and loss of ecological connectivity. The methodology includes a comprehensive literature review and a case study in a specific city to evaluate the effects of these urban transformations. Qualitative and quantitative methods were used to develop and validate innovative sustainability indicators designed to measure the health of urban ecosystems and their ability to support biodiversity. These indicators, a key contribution of the study, offer practical and adaptable tools for urban planners and policy makers, facilitating the assessment of ecological connectivity and sustainability. The findings reveal that conserving urban ecosystem fragments improves ecological resilience, mitigates heat island effects, reduces pollution, and fosters long-term sustainability. Furthermore, the article proposes new conservation strategies, integrating these fragments into urban planning, which contributes to the creation of more livable and sustainable cities, with direct benefits for both ecosystems and human well-being.
Overview of Deep Learning Research
Authors: Tan Lianghao
Published: January 2025

Deep learning, as a strong trend in the fields of machine learning and artificial intelligence, is not only a hot topic in current academic research, but also a key force driving technological progress. As one of the latest trends in artificial intelligence research, deep learning has become one of the most eye-catching scientific research trends today due to its powerful data processing capabilities and pattern recognition accuracy. This paper summarizes the deep learning model and summarizes the application of deep learning in many fields.
Attention Dynamics in Spatial-Temporal Contexts
Authors: Wang Yuying , Zhang Xuemin , Van den Bussche Eva
Published: January 2025

This study systematically explored the impact of the spatial metaphor of time on attentional bias using visual order stimuli, ranging from fully order to 50% disorder, highlighting the regulatory roles of short-term memory and Gestalt perceptual grouping. Three experiments were conducted. Exper-iment 1, comprising Experiments 1a and 1b, investigated how the spatial metaphor of time shaped attentional bias across five disorder levels. Experiment 1a examined horizontal orientations, while Experiment 1b focused on vertical orientations. Experiment 2 compared attentional biases toward stimuli with the same disorder level in both orientations. The key distinction between the stimuli was that one represented short-term memory, while the other did not involve memory. Experiment 3 extended Experiment 2. It compared attentional biases between fully ordered structures (intact Gestalt structures) as non-memory representations and partially disordered structures in short-term memory. The results revealed a significant preference for future-related information, particularly on the right in horizontal orientations. Short-term memory representations enhanced attentional at-traction and triggered Inhibition of Return (IOR), while fully ordered structures attracted attention as effectively as partially disordered structures, thereby neutralizing attentional biases. Overall, this study contributes to a deeper understanding of the cognitive mechanisms underlying visual atten-tion and the influence of temporal metaphors.
Optimizing Charging Control for Fast and Efficient Electric Vehicle Charging
Authors: Han Li , Liu Haijuan , Zhang Yanxue , Pan Linyan
Published: January 2025

Background: Electric vehicles (EVs) are at the heart of sustainable transportation, and the need for effective charging systems is steadily increasing. The difficulty is to optimize charging tactics that reduce charging time while retaining grid stability. To maximize performance and energy utilization, charging must be managed effectively using variables like battery levels, grid load, and environmental conditions.

Objectives: The objective of this research is to create a sophisticated machine-learning technique for predicting the optimal charging control for electric vehicles. Particularly, it aims to use a Dual-Level Voting Boost (DLVB) algorithm to determine whether an EV charging session will be optimized or not, using input features such as battery level, charging rate, ambient temperature, and charging mode.

Methodology: This paper proposes the Dual-Level Voting Boost (DLVB) algorithm, which is a two-stage ensemble learning method. The first level employs fundamental classifiers like Decision Trees, Logistic Regression, and K-Nearest Neighbors, whereas the second level employs more sophisticated models like Random Forest, Support Vector Machines, and Naive Bayes. This dual-layer structure enables a more precise classification of optimized and non-optimized charging sessions. The model's efficacy is assessed utilizing metrics like Accuracy, Precision, Recall, F1-Score, and the Matthews Correlation Coefficient (MCC).

Results: On the test dataset, the DLVB algorithm obtained 95% accuracy, 94% precision, 93% recall, 94% F1 score, and 92% MCC. These findings show the efficiency of the dual-level voting system in correctly predicting optimized charging sessions, substantially surpassing conventional single-model methods.

Conclusion: The DLVB algorithm has significant potential for improving EV charging control by correctly predicting optimization results. This approach provides a potential strategy for enhancing charging effectiveness, decreasing energy waste, and maintaining grid stability.
Relative Influence Of Salinity In The Flow And Accumulation Of Organic Carbon In Open-water Karstic Mangroves
Authors: Quintero Alvarado Angelica Paola , Medina Calderón Jairo Humberto , Mancera-Pineda José Ernesto
Published: January 2025

Carbonate - open-water mangroves have high OC content, apparently due to sediments’ biophysical characteristics. However, the role of key regulators such as salinity and hydroperiod, which modulate the forest structure and therefore carbon dynamics, has been little explored. This study evaluates the influence of salinity on the accumulation of aerial and underground OC (production of litter and roots), in open-water karstic forests. For this purpose, an experimental design was implemented on San Andrés Island, where there is an edaphic salinity gradient because of the water regime. Three physiographic types of mangroves characterized by different saline regimes were selected. Two inland forests of mesohaline regime (9.63 ± 6.26 and 11.54 ± 7.46 PSU), a euhaline fringe forest (37.47 ± 5.76 PSU), and a hyperhaline regime basin forest (62.36 ± 10.54 PSU). The working hypothesis was the existence of an inverse relationship between salinity and litter production, and a direct relationship between salinity and root production. Root production was evaluated using the growth core implantation technique (108 soil cores), selecting live roots according to diameter (<2, 2-5 and 5-20 mm). The mean (±SD) OC content in dry litter (Mg C ha-1 y-1) was 8.96 ± 0.28; 5.57 ± 0.15; 6.31 ± 0.27; and 4.54 ± 0.8; while the production of dry roots was 0.41 ± 0.08; 1.19 ± 0.46; 1.30 ± 0.5 and 0.24 ± 0.20, for the mesohaline forests, the euhaline forest and the hyperhaline forest, respectively. The proposed hypothesis was sustained among forests with marked salinity ranges. Leaf litter production was not very high in forests with intermediate salinity, and root production was very low in high salinity, suggesting that salinity acts in synergy with other variables influencing the species composition and their functioning. These results affirm the high productivity of carbonate environments and the contribution of autochthonous production.
ICT Teachers’ Vision and Experience in Developing Digital Skills of Primary School Students in Computer Science Lessons
Authors: Aliya Katyetova , Symbat Issabayeva
Published: January 2025

The rapid development of technology sets its own rules not only for adults but also for children. At the same time, the former need to develop digital skills to improve their competitiveness in the labour market or to continue working in the changing conditions of global digitalization; for elementary schoolchildren, acquiring such skills from elementary school will give them the confidence to apply correctly in middle and high school, and at university. The development of digital literacy in elementary school students is considered relevant and timely in the article. Schools should be interested in providing the necessary conditions to develop children's digital skills. Teachers can equip them with the basic skills needed to live successfully in the digital age by teaching them digital literacy skills. They can help children move consciously in the digital environment. This article examines the vision and experiences of elementary school computer science teachers in developing students' digital skills in informatics classes. The article discusses research methods such as questionnaires, interviewing information and communication technologies (ICT) teachers, observation and participation in computer science lessons, and qualitative analysis of teachers' responses. The study's results will be helpful for schools and are suggested for improving computer science curricula.
Reference Phone Number: A Secure and QoS-Improved SIP-Based Phone System
Authors: Hsieh WenBin
Published: January 2025

With the improvement of the internet and the widespread adoption of digital communication devices such as smartphones, VoIP has largely replaced traditional telephone systems. Many companies are deploying VoIP systems due to their scalability and low cost. In this paper, address the issue of remote clients or traveling employees being unable to contact business partners due to specific phone numbers. We propose a reference phone number mechanism that combines a set of related business partners' phone numbers to enhance call availability. To ensure the confidentiality of calls, we also designed an algorithm to integrate key exchange protocols into the proposed mechanism. The mechanism can flexibly customize the required security protocols. A performance analysis is conducted by deploying the proposed mechanism in a medium-sized company. The results prove that the mechanism is feasible and the effect is satisfactory.
Evaluation of the Ongoing Use of Approximate Construction Cost Estimation
Authors: Ameya Frikot
Published: January 2025

Cost estimation in any project is the prime concern. Early stage cost estimate plays a significant role in the success of any construction project. Cost is probably the first to be considered when it comes to construction projects. Accurate estimation of quantities and costs incurred in a construction project is a crucial factor in its achievement. Because of the complexity of the construction industry and the individuality of every project undertaking, several factors may affect the overall project cost. Several objects, such as the structural, architectural, sanitary, electrical, and air-conditioning system workings conclude the total cost of buildings. Meanwhile, the structural casing covers 25% of the total construction cost in a multistory reinforced concrete residential building. All parties involved in the construction of a project; owners, contractors, and donors need reliable information about the cost in the early stages of the project, where very limited drawings and details are available during this stage. This research aims at developing a model to estimate the cost of building construction projects with a high degree of accuracy and without the need for detailed information or drawings by using an Artificial Neural Network (ANN). ANN is a new approach that is used in cost estimation, which can learn from experience and examples and deal with non-linear problems.
Assessment of Heavy Metal Contamination and Ecological Risk in Urban River Sediments: A Case Study from Leyte, Philippines
Authors: Siddique Abu Bakar , Al Helal Abu Sayed , Patindol Teofanes A. , Lumanao Deejay M. , Longatang Kleer Jeann G. , Rahman Md. Alinur , Catalvas Lorene Paula A. , Tulin Anabella B. , Shaibur Molla Rahman
Published: January 2025

This study evaluated heavy metal (HM) contamination in sediments from the Malbasag River in Ormoc City Port, Leyte, Philippines. A total of thirty sediment samples were collected randomly from ten locations along the river using an Ekman grab sampler. Atomic absorption spectrophotometry revealed HM concentrations in the order of Mn > Zn > Cu > Ni > Pb > Cd. All HMs exceeded sediment quality guideline (SQG) thresholds except for Mn. Contamination was assessed using indices such as the contamination factor (CF), pollution load index (PLI), geo-accumulation index (Igeo), and enrichment factor (EF). The CF values indicated “moderate to considerable” contamination for Zn, Ni, and Cd, while Cu and Pb showed “very high” contamination levels. The PLI results indicated severe sediment degradation in 20% of samples. The Igeo analysis classified 60% of the samples as “heavily to extremely polluted” for Cd, Cu, and Pb. EF analysis suggested that anthropogenic sources contributed to elevated HM levels, including port activities and agricultural runoff. Ecological risk index (RI) analysis revealed moderate risk in 40% and considerable risk in 20% of sampling locations. Multivariate analyses suggested significant anthropogenic contributions to HM contamination, highlighting the need for further studies to assess the ecological impacts.
Detection of Myocardial Ischemia from ECG Signal Using Max30001
Authors: Imran Iffa , Asif Rameez , Rehman Ateeq Ur , Mustafa Adil
Published: January 2025

Among the commonly occurring medical emergencies such as heart attack, arrhythmia, valve diseases and high blood pressure, reduction in blood flow to the heart prevents the reception of sufficient oxygen to the heath muscles known as myocardial ischemia caused the partial of complete blockage of coronary arteries. Myocardial ischemia prevents the heart muscles to pump the blood efficiently that can ultimately lead to the heart attack or may cause abnormal heart rhythms. Electrocardiogram (ECG) an electrical activity of the heart is mostly used by the cardiologist for diagnosing MI patients. Identifying the MI manually is time consuming and a possibility of misinterpretation exists to the changes in the patient ECG. An automated method for the detection of MI pattern in the ECG is proposed using wavelet transform. It is observed that the difference in the height of PR segment and J-point allows to distinguish between a normal ECG and abnormal MI ECG. Furthermore, this study also finds that there is a significant difference in the J-point, R-peak amplitude and ST- wave of the MI patient than the normal healthy person when the patients ECG is recorded using MAX30001.
Deep Reinforcement Learning Based Robotic Arm’s Target Reaching Performance Enhancement
Authors: Honelign Ldet , Abebe Yoseph , Jung Young Seok , Tullu Abera , Jung Sunghun
Published: January 2025

This work presents the implementation of Deep Deterministic Policy Gradient (DDPG) algorithm to enhance target reaching capability of the seven Degree-of-Freedom (7-DoF) Franka Panda robot arm. A simulated environment is established by employing OpenAI Gym, PyBullet, and Panda Gym. Upon completion of 100,000 training time steps, the DDPG algorithm attains a success rate of 100% and an average reward of -1.8. The actor loss and critic loss values are 0.0846 and 0.00486, respectively, indicating improved decision-making and accurate value function estimations. The simulation results demonstrate the efficiency of DDPG in improving robotic arm performance, highlighting its potential for application to improve robot arm manipulation.
Evolving Botnet Defenses: A Survey of Machine Learning Approaches for Identifying Polymorphic and Evasive Malware
Authors: Ahmadi Sina
Published: January 2025

The advancement of polymorphic and evasive malware helps botnets overcome traditional security mechanisms rendering the latter obsolete. This phenomenon, coupled with the skillful development of botnets, threateningly endangers the computer systems and networks of the modern day. This paper highlights the various ML techniques used for botnet detection, outlining their advantages, limitations, and possible uses. In particular, this study focused on supervised, unsupervised, and deep learning methods and their contributions in aiding the detection of botnets. This paper builds upon existing studies and experiments to provide a new perspective on assessment of the ML detector systems, their effectiveness, and the problems they encounter. This survey also sets out the primary pillars that new research needs to rest upon, especially for improving botnet defense systems.
Validation Insights on Heat Transfer and Entropy Generation in Enclosures: A Beginners Guide
Authors: Saha Goutam
Published: January 2025

In Computational Fluid Dynamics (CFD), validation is the process of assessing the accuracy and reliability of a numerical model by comparing its results with experimental data, analytical solutions, or high-fidelity numerical results. This critical step ensures the model accurately represents real-world physical phenomena and can be trusted for predictive simulations. Validation typically involves comparing simulation outputs, such as velocity profiles, temperature distributions, or pressure fields, with experimental measurements, which are considered the gold standard. For simpler cases, analytical solutions like Poiseuille or Couette flow serve as benchmarks, while high-fidelity numerical results, such as those from Direct Numerical Simulations, are used for more complex scenarios. The validation process includes both quantitative measures, such as error norms and deviation percentages, and qualitative assessments like visual comparisons of flow patterns or temperature fields. Discrepancies revealed during validation help identify limitations in the model, guiding refinements such as improved mesh quality or adjustments to turbulence models. Validation is distinct from verification, which focuses on ensuring the accuracy and consistency of the numerical implementation. Together, validation and verification establish the foundation for credible and reliable CFD simulations in engineering and scientific applications.
EEG Data Augmentation Method Based on the Gaussian Mixture Model
Authors: Liao Chuncheng , Zhao Shiyu , Wang Xiangcun , Zhang Jiacai , Liao Yongzhong , Wu Xia
Published: January 2025

Traditional methods of electroencephalograms(EEG) data augmentation, such as segmentation-reassembly and noise mixing, suffer from data distortion that can alter the original temporal and spatial feature distributions of the brain signals. Deep learning-based methods for generating augmentation EEG data, such as Generative Adversarial Networks (GANs) and Variational Autoencoders (VAEs), have shown promising performance but require a large number of comparative learning samples for model training. To address these issues, this paper introduces an EEG data augmentation method based on Gaussian Mixture Model microstates, which retains the spatiotemporal dynamic features of the EEG signals in the generated data. The method first performs Gaussian mixture clustering on data samples of the same class, using the product of the probability coefficients and weight matrices of each Gaussian model as corresponding microstate features. Next, it randomly selects two EEG data samples of the same type, analyzes the similarity of the main components of the microstate features, and swaps the similar main components to form new Gaussian mixture model features. Finally, new data is generated according to the Gaussian mixture model using the respective model probabilities, weights, means, and variances. Experimental results on publicly available datasets demonstrate that the proposed method effectively characterizes the original data's spatiotemporal and microstate features, improving the accuracy of subject task classification.