bluuebunny/crossref_metadata_2025 huggingface dataset:
Using Thin Ultra-High-Molecular-Weight Polyethylene Coatings to Reduce Friction in Frost-Resistant Rubbers
Authors: Torskaya Elena , Shkalei Ivan , Stepanov Fedor , Makhovskaya Yulia , Dyakonov Afanasy , Petrova Natalia
Published: October 2024

Frost-resistant rubbers retain their highly elastic properties over a wide temperature range. They are used in various friction units (e.g., seals), but their high friction coefficient and low wear resistance lead to the need for frequent replacement. In this paper, we propose applying thin (several hundred microns) UHMWPE coatings to formed rubber rings. The application technology depends on the required coating thickness. Friction tests of the coatings and pure UHMWPE were performed using the ball-on-disk (unidirectional sliding) scheme for various loads and velocities. In the experiments, the friction coefficients and temperatures near the contact area were determined. Friction tracks were studied using microscopy methods. The sliding contact of the ball and the two-layer material was modeled to obtain the dependences of the deformation component of friction on the sliding velocity for coatings of different thicknesses. UHMWPE is sensitive to frictional heating, so the thermal problem of determining the temperature in the contact area was also solved. It is shown that the minimum friction coefficient occurs for coatings with a thickness of 600 μm. At the same time, in the case of the 300 μm coating, the surface of the friction track is practically no different from the initial one. Thus, the studied combination of polymers provides antifrictional properties and wear resistance to the surface layer while maintaining the damping properties of rubber.
Effect of Blade Gap Ratio on Turbine Performance in Drag-Based Vertical-Axis Wind Turbines
Authors: Altan Gurkan , Altan Burcin Deda
Published: October 2024

The aim of this study was to improve the performances of three-bladed vertical-axis wind turbines with gap distance. For this purpose, turbine design conditions such as a gap ratio between the blades and the addition of blades were changed to provide performance increase, and an aerodynamic performance-enhancing performance setup placed in front of the vertical-axis wind turbine was also used. Therefore, in this study, the effects of gap distance and blade addition on wind turbine performance were investigated, especially by keeping the aspect ratio of the vertical-axis wind turbine constant. The investigation of the effects of turbine design parameters, such as the gap distance and additional blade number, on turbine performance was carried out using the ANSYS Fluent program with a numerical analysis method validated with experimental data. The turbine gap ratio, at which the maximum torque coefficient of a three-bladed vertical-axis wind turbine is obtained, was determined to be 40% without performance setup and 30% with performance setup. It was determined that the average torque coefficient value obtained from the turbine with the addition of turbine blades and with the performance setup increased by approximately 66% compared to the torque coefficient value obtained from the three-bladed turbine without the performance setup. It was also determined that the maximum power coefficient obtained from the vertical-axis wind turbine with optimum turbine design parameters with the performance setup increased by approximately 46% compared to the maximum power coefficient obtained from the version of the turbine without the performance setup.
Extraction, Exploitation, and Religious Surplus in the Capitalocene
Authors: Rieger Joerg
Published: October 2024

Efforts to address the logic of extraction, which arguably is at the core of our current environmental catastrophe, are examples for a non-reductive material turn in the study of religion and theology. These efforts are linked with the logics of property, possession, human/nature, and human/land relations. This emphasis on materiality and relationship creates welcome openings for another set of relationships that is still under-reflected in the material turn in religion and theology, namely the various connections between extraction and exploitation, specifically of labor, both productive and reproductive, human and other-than-human. In this article, the logic of extraction will be interpreted and reevaluated in its relation to exploitative relationships of labor, which in turn will be deepened in conversation with extraction. Relationships of extraction, production, and reproduction will further be investigated in terms of the notion of a religious surplus, which examines the multiple contributions of religion and theology as generated in broader surplus-producing relationships.
Contributing to Public Deliberation by Religious Behavior: Beyond the Inclusivism–Exclusivism Debate
Authors: Wong Baldwin
Published: October 2024

Recently, political philosophers have debated the role of religious reasons in public deliberations, such as appealing to religious convictions and religious classics. Exclusivists, such as Rawls, Quong, Hartley, and Watson, argue that democratic governments and citizens should restrict or exclude the use of religious reasons in making laws and policies, while inclusivists, such as Gaus, Vallier, and Billingham, oppose such categorical exclusion. Nevertheless, the debate mainly focuses on the role of religious reasons in public deliberation. In this paper, I will argue that religious behaviors—defined as highly altruistic actions motivated by religious beliefs, such as dedicating substantial time and effort to serving the poor and advancing the common good—can exert positive influences on public deliberation. Through this kind of altruistic action, religious believers can subtly influence non-religious citizens. While religious believers may not rationally persuade non-religious citizens through religious reasoning, the altruistic actions exhibited by religious believers could emotionally inspire admiration and motivate non-religious citizens to learn more about those religions. This enhances mutual understanding among different religious and secular sects and thus improves public deliberation. Furthermore, I argue that the improved understanding fostered by religious behaviors can facilitate exclusivism and inclusivism to overcome certain philosophical challenges, such as the problems of incompleteness and anarchy, which are among the most frequent criticisms directed at exclusivism and inclusivism. Hence, this paper highlights an aspect overlooked in the exclusivism–inclusivism debate: no matter whether the democratic government and citizens should permit or restrict religious reasons, religious behavior is still beneficial in public deliberation.
Underwater Optical Imaging: Methods, Applications and Perspectives
Authors: Hao Yansheng , Yuan Yaoyao , Zhang Hongman , Zhang Ze
Published: October 2024

Underwater optical imaging is essential for exploring the underwater environment to provide information for planning and regulating underwater activities in various underwater applications, such as aquaculture farm observation, underwater topographical survey, and underwater infrastructure monitoring. Thus, there is a need to investigate the underwater imaging process and propose clear and long-range underwater optical imaging methods to fulfill the demands of academia and industry. In this manuscript, we classify the eighteen most commonly used underwater optical imaging methods into two groups regarding the imaging principle, (1) hardware and (2) software-based methods, each with an explanation of the theory, features, and applications. Furthermore, we also discuss the current challenges and future directions for improving the performance of current methods, such as improving the accuracy of underwater image formation model estimation, enlarging the underwater image dataset, proposing comprehensive underwater imaging evaluation metrics, estimating underwater depth and integrating different methods (e.g., hardware- and software-based methods for computational imaging) to promote the imaging performance not only in the laboratory but also in practical underwater scenarios.
Unsupervised Multi-Scale Hybrid Feature Extraction Network for Semantic Segmentation of High-Resolution Remote Sensing Images
Authors: Song Wanying , Nie Fangxin , Wang Chi , Jiang Yinyin , Wu Yan
Published: October 2024

Generating pixel-level annotations for semantic segmentation tasks of high-resolution remote sensing images is both time-consuming and labor-intensive, which has led to increased interest in unsupervised methods. Therefore, in this paper, we propose an unsupervised multi-scale hybrid feature extraction network based on the CNN-Transformer architecture, referred to as MSHFE-Net. The MSHFE-Net consists of three main modules: a Multi-Scale Pixel-Guided CNN Encoder, a Multi-Scale Aggregation Transformer Encoder, and a Parallel Attention Fusion Module. The Multi-Scale Pixel-Guided CNN Encoder is designed for multi-scale, fine-grained feature extraction in unsupervised tasks, efficiently recovering local spatial information in images. Meanwhile, the Multi-Scale Aggregation Transformer Encoder introduces a multi-scale aggregation module, which further enhances the unsupervised acquisition of multi-scale contextual information, obtaining global features with stronger feature representation. The Parallel Attention Fusion Module employs an attention mechanism to fuse global and local features in both channel and spatial dimensions in parallel, enriching the semantic relations extracted during unsupervised training and improving the performance of unsupervised semantic segmentation. K-means clustering is then performed on the fused features to achieve high-precision unsupervised semantic segmentation. Experiments with MSHFE-Net on the Potsdam and Vaihingen datasets demonstrate its effectiveness in significantly improving the accuracy of unsupervised semantic segmentation.
Predicting Rock Hardness and Abrasivity Using Hyperspectral Imaging Data and Random Forest Regressor Model
Authors: Ghadernejad Saleh , Esmaeili Kamran
Published: October 2024

This study aimed to develop predictive models for rock hardness and abrasivity based on hyperspectral imaging data, providing valuable information without interrupting the mining processes. The data collection stage first involved scanning 159 rock samples collected from 6 different blasted rock piles using visible and near-infrared (VNIR) and short-wave infrared (SWIR) sensors. The hardness and abrasivity of the samples were then determined through Leeb rebound hardness (LRH) and Cerchar abrasivity index (CAI) tests, respectively. The data preprocessing involved radiometric correction, background removal, and staking VNIR and SWIR images. An integrated approach based on K-means clustering and the band ratio concept was employed for feature extraction, resulting in 28 band-ratio-based features. Afterward, the random forest regressor (RFR) algorithm was employed to develop predictive models for rock hardness and abrasivity separately. The performance assessment showed that the developed models can estimate rock hardness and abrasivity of unseen data with R2 scores of 0.74 and 0.79, respectively, with the most influential features located mainly within the SWIR region. The results indicate that integrated hyperspectral data and RFR technique have strong potential for practical and efficient rock hardness and abrasivity characterization during mining processes.
Relationship between Tibetan Plateau Surface Heat Fluxes and Daily Heavy Precipitation in the Middle and Lower Yangtze River Basins (1980–2022)
Authors: Li Lu , Dong Xiaohua , Ma Yaoming , Jin Hanyu , Wei Chong , Su Bob
Published: October 2024

Variable heat fluxes over the Tibetan Plateau (TP) interact thermally with the atmosphere, affecting weather in surrounding areas, particularly in the Middle and Lower Yangtze River (MLYR). However, the circulation patterns and time-lag effects between TP heat fluxes and MLYR precipitation remain unclear. This study identified 577 large-scale daily heavy precipitation events (LSDHPEs) in the MLYR from 1980 to 2022. We analyzed the weather causation and spatiotemporal correlations between the TP surface heat fluxes and MLYR LSDHPEs using self-organizing map clustering, singular value decomposition, and harmonic analysis of time series. The results found two dominant synoptic patterns of LSDHPEs at 500 hPa: one, driven by anticyclonic and cyclonic circulations coinciding with shifts in the West Pacific subtropical high and South Asian high, increased from 2000 to 2022; the other, influenced by MLYR cyclonic circulation, showed a significant decrease. For the first time, we revealed lagged effects of the latent heat anomalies (with a lag time of 1–10 d and 130–200 d) and sensible heat anomalies (with a lag time of 2–4 months) over the TP during LSDHPEs in the MLYR. The results may enhance our understanding of TP heat flux anomalies as precursor signals for early warning of heavy rainfall and flooding in the MLYR.
EHNet: Efficient Hybrid Network with Dual Attention for Image Deblurring
Authors: Ho Quoc-Thien , Duong Minh-Thien , Lee Seongsoo , Hong Min-Cheol
Published: October 2024

The motion of an object or camera platform makes the acquired image blurred. This degradation is a major reason to obtain a poor-quality image from an imaging sensor. Therefore, developing an efficient deep-learning-based image processing method to remove the blur artifact is desirable. Deep learning has recently demonstrated significant efficacy in image deblurring, primarily through convolutional neural networks (CNNs) and Transformers. However, the limited receptive fields of CNNs restrict their ability to capture long-range structural dependencies. In contrast, Transformers excel at modeling these dependencies, but they are computationally expensive for high-resolution inputs and lack the appropriate inductive bias. To overcome these challenges, we propose an Efficient Hybrid Network (EHNet) that employs CNN encoders for local feature extraction and Transformer decoders with a dual-attention module to capture spatial and channel-wise dependencies. This synergy facilitates the acquisition of rich contextual information for high-quality image deblurring. Additionally, we introduce the Simple Feature-Embedding Module (SFEM) to replace the pointwise and depthwise convolutions to generate simplified embedding features in the self-attention mechanism. This innovation substantially reduces computational complexity and memory usage while maintaining overall performance. Finally, through comprehensive experiments, our compact model yields promising quantitative and qualitative results for image deblurring on various benchmark datasets.
Integrated Pricing and Inventory Decisions for Product Quality-Driven Extended Warranty Services
Authors: Zha Wendi , Wu Zhenyong , Tan Jianxin , Chen Yiming , Fu Yaping , Xu Zhitao
Published: October 2024

Extended warranty services have become increasingly important for both manufacturers and retailers, offering avenues for new profit sources and growth opportunities. Focusing on the multiple effects of product quality, this study develops a two-period supply chain decision model to analyze the effects of product quality, pricing, and inventory management in the context of extended warranty services. Using a Stackelberg dynamic game model, this study examines the interaction between a manufacturer and a dominant retailer who provides extended warranties. The results indicate significant differences in optimal decisions between centralized and decentralized supply chains, especially concerning pricing and inventory control. Introducing a “quality cost-sharing” contract enhances product quality and improves coordination, leading to increased profits for both the manufacturer and the retailer. Numerical simulations confirm that the cost-sharing contract effectively balances product quality improvements with supply chain profitability.
Positive Strategies for Enhancing Elderly Interaction Experience in Smart Healthcare through Optimized Design Methods: An INPD-Based Research Approach
Authors: Luo Jiacheng , Zhang Ru , Xu Junping , Pan Younghwan
Published: October 2024

The breakthrough in artificial intelligence technology and the development of smart healthcare models have significantly improved modern healthcare services. However, the elderly population still faces numerous challenges. Therefore, the aim of this study is to enhance the interactive experience of elderly users and to propose effective design strategies through optimized design methods. Based on the INPD research methodology, the design process is divided into four stages. First, in the SET phase, product opportunity gaps are identified, followed by in-depth interviews and surveys to gather user needs. Second, the AHP method is used to establish a hierarchical model and judgment matrix to determine the subjective weights of each need, while the EWM method, based on survey data, determines the objective weights of each need. To ensure the scientific nature of the overall weight, a combined weighting approach is used, followed by a final prioritization of needs. Third, after translating user needs into design requirements, three design schemes are produced, and the TOPSIS method is used to calculate the weights and evaluate the optimal scheme. Fourth, the product opportunities are implemented and tested. The research results indicate that the proposed optimization design method is effective and not only reduces the barriers and challenges elderly users face when interacting with intelligent products but also enhances their overall experience. Moreover, it provides a practical approach to the sustainable development of smart healthcare. As an essential component of future healthcare services, the sustainability of smart healthcare will depend on a deep understanding of user needs and continuous optimization. The design strategy proposed in this study offers practical application value, improving elderly users’ satisfaction while also providing insights that may be useful for other smart services.
Using the Theory of Planned Behavior for Explaining Mobile Phone Recycling: The Role of Subjective Norms
Authors: Botetzagias Iosif , Grigoraki Eirini , Kokkoris Giorgos D.
Published: October 2024

This study examines the extent to which standard Theory of Planned Behavior (TPB) predictors (Attitude; Subjective Norms; and Perceived Behavioral Control) explain the intention to recycle an end-of-use mobile phone. Our data originate from empirical research on Greek citizens conducted in the summer of 2022 (N = 258). Through ordinal regression modeling, we found that all the TPB predictors positively influence one’s intention to recycle. In contrast to most previous studies, we found that Subjective Norms is the most influential predictor. However, its influence is dependent on the type of norm (descriptive vs. injunctive), the type of social referent (family vs. close friends), and, most importantly, one’s level of identification with the social referent.
Barriers to Pro-Environmental Behavior Change: A Review of Qualitative Research
Authors: Dioba Albina , Kroker Valentina , Dewitte Siegfried , Lange Florian
Published: October 2024

In recent decades, reports of deforestation, mass extinctions, biodiversity loss, and emergencies like floods and droughts have clearly highlighted the impact of human activities on climate change. This underscores the need for significant lifestyle changes to mitigate this impact. Pro-environmental behavior (PEB) change involves adopting actions that protect the environment and reducing environmentally harmful activities to promote sustainability. While qualitative research may provide unique insights into PEB barriers that are challenging to capture through quantitative methods, reports of qualitative studies are scattered across different literatures with little integration. With the present review, we bring together qualitative studies on PEB barriers and examine whether those studies reveal barriers compatible with existing barrier frameworks from environmental psychology. We identified 38 qualitative studies on barriers to PEB change. Our analysis revealed that most of the barriers to individual pro-environmental action identified in qualitative studies align with environmental psychology frameworks. These barriers were experienced not only by individuals in their role as consumer or citizen, but also by community-level actors (e.g., seeking to implement pro-environmental policies) and industrial decision-makers (e.g., aiming to reduce the ecological footprint of their business activities). Our findings underscore the multifaceted nature of barriers to PEB change and emphasize the importance of addressing structural and institutional factors alongside individual-level interventions.
Taxonomy Regulation as a New Instrument for the Sustainable Management of the Forest Environment in Europe
Authors: Brożek Jarosław , Kożuch Anna , Wieruszewski Marek , Jaszczak Roman , Adamowicz Krzysztof
Published: October 2024

Regulation (EU) 2020/852 of the European Parliament, also known as the Taxonomy Regulation, facilitates environmentally sustainable investments. It is part of the concept of the European Green Deal and a ‘tool’ for financial institutions, enterprises, and investors, facilitating the assessment of the environmental impact of a particular project. The Regulation contains the criteria an activity must meet to be considered environmentally sustainable. The role of the Taxonomy Regulation is to enable the flow of public and private capital towards ecological and sustainable activities. The document does not need to be implemented into the legal order of individual EU member-states, which results in its direct application. The main financial instruments enabling the achievement of the goals of the Taxonomy Regulation may be green bonds and other forms of capital raising by entrepreneurs and forest ownership structures. The assumption of the Regulation is to achieve the principles of sustainable environmental activity when spending funds obtained from private investors. It is an issue of key significance to identify the areas of management and financial accounting in the operational activities of forest enterprises that can be qualified for the Taxonomy Regulation. Forestry activities, including the processes mentioned therein, the objectives of the New EU Forest Strategy, and the LULUCF Regulation, are to play an essential role in reducing greenhouse gas emissions. The role of forestry in the supply chain in its broad sense is also considered. Forestry and forest management can receive capital for sustainable development due to the threat resulting from exclusions that strengthen the protective function of the forest (the protection of biodiversity). These processes will occur at the expense of production and numerous social functions.
Artificial Intelligence and the Sustainability of the Signaling and Human Capital Roles of Higher Education
Authors: Alexander W. Robert J. , Belloni Raffaella
Published: October 2024

Over the last several decades, there has been an arms race to acquire credentials as higher education has shifted from an elitist system to mass education. From an individual perspective, given the higher education system and labor market conditions, it is rational to pursue advanced qualifications. However, whether the education system delivers improvements in human capital or is principally a signaling mechanism is questionable. Estimates of the proportion of labor market rewards due to signaling range as high as 80%, suggesting that education is not only expensive but inefficient. In an increasingly transactional environment in which education providers are highly motivated by financial considerations, this situation is only likely to be exacerbated by the rapid developments in artificial intelligence (AI). The use of AI has the potential to make learning more effective, but given that many students see credential acquisition as transactional, it may reduce both human capital and the value of the signaling effect. If the credibility of the credentials offered is further damaged, the higher education sector in its present form and scale may well be unsustainable. We examine the evidence on credential inflation, returns to education, and mismatch of graduates to jobs before analyzing how AI is likely to affect these trends. We then suggest possible responses of prospective students, education providers, and employers to the growing adoption of AI in both education and the workplace. We conclude that the current offerings of generalist degrees, as opposed to vocational qualifications, are not sustainable and that to survive, even in a downsized form, the sector must respond to this disruptive technology by changing both the nature of its offerings and its methods of ensuring that the credentials they offer reflect genuine student learning.
Integrating Machine Learning, Land Cover, and Hydrological Modeling to Contribute Parameters for Climate Impacts on Water Resource Management
Authors: Heo Joonghyeok , Lee Jeongho , Hyun Yunjung , Park Joonkyu
Published: October 2024

The purpose of this study is to establish basic policies for managing the impacts of climate change on water resources using the integration of machine learning and land cover modeling. We predicted future changes in land cover within the water management and assessed its vulnerability to climate change. After confirming this vulnerability, we considered measures to improve climate resilience and presented future water resource parameters. We reviewed the finances available to promote climate projects, noting the major river management funds. The future project will serve as a stepping stone to promote climate resilience projects addressing water resource challenges exacerbated by future climate change. The study examined the results of analyzing changes in land cover maps due to climate change and assessed vulnerability in water management areas until 2050. According to the analysis results, the regulations for our study areas were set lower than those for other water management zones, resulting in a high rate of urbanization. Therefore, the climate resilience project in the water management area should be implemented first, despite the need for a long-term view in adapting to climate change.
Multi-Sensory Interaction and Spatial Perception in Urban Microgreen Spaces: A Focus on Vision, Auditory, and Olfaction
Authors: Zheng Haohua , Luo Man , Wang Yihan , Wei Yangyang
Published: October 2024

As important recreational spaces for urban residents, urban microgreen parks enhance the urban living environment and alleviate psychological pressure on residents. The visual, auditory, and olfactory senses are crucial forms of perception in human interaction with nature, and the sustainable perceptual design of miniature green parks under their interaction has become a recent research hotspot. This study aimed to investigate the effects of the visual, acoustic, and olfactory environments (e.g., aromatic green vegetation) on human perception in miniature green parks. Participants were evenly divided into eight groups, including single-sensory groups, multi-sensory interaction groups, and a control group. Eye-tracking technology, blood pressure monitoring, and the Semantic Differential (SD) scales and Profile of Mood State (POMS) were used to assess the effectiveness of physical and mental perception recovery in each group. The results revealed that in an urban microgreen space environment with relatively low ambient noise, visual–auditory, visual–olfactory, and visual–auditory–olfactory interactive stimuli were more effective in promoting the recovery of visual attention than single visual stimuli. Additionally, visual–auditory–olfactory interactive stimuli were able to optimize the quality of spatial perception by using positive sensory inputs to effectively mask negative experiences. Simultaneously, environments with a high proportion of natural sounds had the strongest stimuli, and in the visual–auditory group, systolic blood pressure at S7 and heart rate at S9 significantly decreased (p < 0.05), with reductions of 18.60 mmHg and 20.15 BPM, respectively. Aromatic olfactory sources were more effective in promoting physical and mental relaxation compared to other olfactory sources, with systolic blood pressure reductions of 24.40 mmHg (p < 0.01) for marigolds, 23.35 mmHg (p < 0.01) for small-leaved boxwood, and 27.25 mmHg (p < 0.05) for camphor trees. Specific auditory and olfactory conditions could guide visual focus, such as birdsong directing attention to trees, insect sounds drawing attention to herbaceous plants, floral scents attracting focus to flowers, and leaf scents prompting observation of a wider range of natural vegetation. In summary, significant differences exist between single-sensory experiences and multi-sensory modes of spatial perception and interaction in urban microgreen parks. Compared to a silent and odorless environment, the integration of acoustic and olfactory elements broadened the scope of visual attention, and In the visual–auditory–olfactory interactive perception, the combination of natural sounds and aromatic camphor tree scents had the best effect on attention recovery, thereby improving the quality of spatial perception in urban microgreen parks.
Identifying Key Indicators for Successful Foreign Direct Investment through Asymmetric Optimization Using Machine Learning
Authors: Kemiveš Aleksandar , Ranđelović Milan , Barjaktarović Lidija , Đikanović Predrag , Čabarkapa Milan , Ranđelović Dragan
Published: October 2024

The advancement of technology has led humanity into the era of the information society, where information drives progress and knowledge is the most valuable resource. This era involves vast amounts of data, from which stored knowledge should be effectively extracted for use. In this context, machine learning is a growing trend used to address various challenges across different fields of human activity. This paper proposes an ensemble model that leverages multiple machine learning algorithms to determine the key factors for successful foreign direct investment, which simultaneously enables the prediction of this process using data from the World Bank, covering 60 countries. This innovative model, which adds to scientific and research knowledge, employs two sets of methods—binary regression and feature selection—combined in a stacking ensemble using a classification algorithm as the combiner to enable asymmetric optimization. The proposed predictive ensemble model has been tested in a case study using a dataset compiled from World Bank data across countries worldwide. The model demonstrates better performance than each of the individual algorithms integrated into it, which are considered state-of-the-art in these methodologies. Additionally, the findings highlight three key factors for foreign direct investment from the dataset, leading to the development of an optimized prediction formula.
Frequency and Factors Associated with Disabilities among Leprosy Patients Admitted to the Kindia Disability Prevention and Physical Rehabilitation Centre (Pirp) in Guinea from 2017 to 2021
Authors: Sy Savané Ibrahima Sory , Sidibé Sidikiba , Kolié Delphin , Camara Mamadou , Sakho Fatoumata , Sidibé Sadan , Chérif Mahamoud Sama , Doumbouya Sékou , Nabé Abdoul Karim , Delamou Alexandre
Published: October 2024

This study aims to estimate the prevalence and analyze the factors associated with leprosy-related disabilities at the Kindia Disability Prevention and Physical Rehabilitation Centre (PIRP) in Guinea. It is a cross-sectional study using routine data from the centre from 2017 to 2021. Of 115 patients, 76% had a disability, 49% of which were grade II and 27% grade I. The age range of 15 to 30 years was the most represented (43.5%), with the average age (standard deviation) being 38 (16.5) years. Children under 14 years of age represented 3.5% of the total. Most (89%) patients had newly diagnosed leprosy. The majority (66.1%) had never come in contact with people with leprosy symptoms. Almost all (99.1%) patients had type 1 reactions on admission. Patients with multibacillary leprosy were in the majority (83.5%), and those with symptoms lasting 7–12 months represented 56.5% of the sample. In total, 79.1% of the patients received corticosteroid therapy, and 92.1% were reported cured at discharge. This neglected tropical disease continues to be a challenge in Guinea, even though leprosy care is free.
Protoporphyrin IX-Dependent Antiviral Effects of 5-Aminolevulinic Acid against Feline Coronavirus Type II
Authors: Doki Tomoyoshi , Shimada Junna , Tokunaga Misa , To Kaito , Orino Koichi , Takano Tomomi
Published: October 2024

5-Aminolevulinic acid (5-ALA), a non-proteinogenic amino acid, is an intermediate in the biosynthesis of heme and exerts antiviral effects against feline coronavirus (FCoV); however, the underlying mechanisms remain unclear. In the biosynthesis of heme, 5-ALA is condensed and converted to protoporphyrin IX (PpIX), which is then transformed into heme by the insertion of ferrous iron. Previous research has suggested that the metabolites generated during heme biosynthesis contribute to the antiviral effects of 5-ALA. Therefore, the present study investigated the in vitro mechanisms responsible for the antiviral effects of 5-ALA. The results obtained revealed that 5-ALA and PpIX both effectively reduced the viral titer in the supernatant of FCoV-infected fcwf-4 cells. Moreover, PpIX exerted virucidal effects against FCoV. We also confirmed that 5-ALA increased PpIX levels in cells. While hemin induced heme oxygenase-1 gene expression, it did not reduce the viral titer in the supernatant. Sodium ferrous citrate decreased PpIX levels and suppressed the antiviral effects of 5-ALA. Collectively, these results suggest that the antiviral effects of 5-ALA against FCoV are dependent on PpIX.
Structure and Antigenicity of the Porcine Astrovirus 4 Capsid Spike
Authors: Haley Danielle J. , Lanning Sarah , Henricson Kyle E. , Mardirossian Andre A. , Cirillo Iyan , Rahe Michael C. , DuBois Rebecca M.
Published: October 2024

Porcine astrovirus 4 (PoAstV4) has been recently associated with respiratory disease in pigs. In order to understand the scope of PoAstV4 infections and to support the development of a vaccine to combat PoAstV4 disease in pigs, we designed and produced a recombinant PoAstV4 capsid spike protein for use as an antigen in serological assays and for potential future use as a vaccine antigen. Structural prediction of the full-length PoAstV4 capsid protein guided the design of the recombinant PoAstV4 capsid spike domain expression plasmid. The recombinant PoAstV4 capsid spike was expressed in Escherichia coli, purified by affinity and size-exclusion chromatography, and its crystal structure was determined at 1.85 Å resolution, enabling structural comparisons to other animal and human astrovirus capsid spike structures. The recombinant PoAstV4 capsid spike protein was also used as an antigen for the successful development of a serological assay to detect PoAstV4 antibodies, demonstrating that the recombinant PoAstV4 capsid spike retains antigenic epitopes found on the native PoAstV4 capsid. These studies lay a foundation for seroprevalence studies and the development of a PoAstV4 vaccine for swine.
Preparation and Application of Polyclonal Antibodies for the Rapid Detection of Actinidia Chlorotic Ringspot-Associated Virus
Authors: Shang Jing , Feng Hongping , Wang Yuxuan , Wang Yunan , Zhang Xiao , Zhang Zhouyu
Published: October 2024

Actinidia chlorotic ringspot-associated virus (AcCRaV, Emaravirus actinidiae) is prevalent in Chinese kiwifruit, leading to substantial yield reduction. The intricate nature of symptoms presents diagnostic challenges, underscoring the necessity for a rapid and accurate detection method that facilitates effective control. In this investigation, AcCRaV isolates from key kiwi-producing regions in Sichuan province were collected and analyzed, with representative strains chosen as experimental materials. Primers targeting the nucleoprotein gene of AcCRaV were designed, and their codon usage was optimized to enhance performance. Various serological methods utilizing polyclonal antibodies were developed, including ELISA, dot immunobinding assay, and AcCRaV-specific gold immunochromatographic bands (AcCRaV-GICS). Field samples exhibited high specificity and sensitivity when tested using these methods. Furthermore, the results obtained from a large number of field samples are consistent with those derived from RT-PCR analysis, further validating the applicability of our approach. A detection method capable of handling a large volume of field samples infected with AcCRaV is currently lacking; thus, our system construction provides an important reference for addressing this gap.
Did We Overreact? Insights on COVID-19 Disease and Vaccination in a Large Cohort of Immune-Mediated Inflammatory Disease Patients during Sequential Phases of the Pandemic (The BELCOMID Study)
Authors: Geldof Jeroen , Truyens Marie , Sabino João , Ferrante Marc , Lambert Jo , Lapeere Hilde , Hillary Tom , Van Laethem An , de Vlam Kurt , Verschueren Patrick , Lobaton Triana , Padalko Elizaveta , Vermeire Séverine
Published: October 2024

Introduction: As the COVID-19 pandemic becomes an endemic state, still many questions remain regarding the risks and impact of SARS-CoV-2 infection and vaccination in patients with immune-mediated inflammatory diseases (IMIDs) who were excluded from the phase 3 COVID-19 vaccination trials. Methods: The BELCOMID study collected patient data and serological samples from a large, multicentric IMID patient cohort that was prospectively followed during sequential stages of the pandemic. Patients were stratified according to vaccination status into five groups across three sampling periods. Interactions between SARS-CoV-2 infection, COVID-19 vaccination status, IMID-treatment modalities and IMID course were explored. Results: In total, 2165 patients with IBD, a dermatological or rheumatological IMID participated. SARS-CoV-2 infection rates increased over the course of the pandemic and were highest in IMID patients that had refused every vaccine. After baseline COVID-19 vaccination, serologic spike (S)-antibody responses were attenuated by particular types of immune-modulating treatment: anti-TNF, rituximab, JAKi, systemic steroids, combined biologic/immunomodulator treatment. Nonetheless, S-antibody concentration increased progressively in patients who received a booster vaccination, reaching 100% seroconversion rate in patients who had received two booster vaccines. Previous SARS-CoV-2 infection was found as a predictor of higher S-antibody response. Patients who had refused every vaccine showed the lowest rates of S-seroconversion (53.8%). Multiple logistic regression did not identify previous SARS-CoV-2 infection as a risk factor for IMID flare-up. Furthermore, no increased risk of IMID flare-up was found with booster vaccination. Conclusions: Altogether, the BELCOMID study provides evidence for the efficacy and safety of COVID-19 vaccination and confirms the importance of repeated booster vaccination in IMID patients.
Numerical Investigation of the Sediment Load Exchange between a Coastal Mud Bank and Its Neighbouring Estuary
Authors: Abascal-Zorrilla Noelia , Huybrechts Nicolas , Orseau Sylvain , Vantrepotte Vincent , Anthony Edward , Gardel Antoine
Published: October 2024

Muddy coastlines cover much of the world’s shores, yet studies on the interaction between mud-affected coasts and estuaries are limited. This study focuses on the Mahury River estuary and its interaction with the muddy coast of the Guianas, primarily fed by the Amazon. A coupled wave–current–sediment transport model is developed to analyze the sediment exchange in an environment with strong interactions between the waves and the fluid mud. Simulations explore how seasonal changes in waves, mud availability, and tides affect sediment fluxes. The main processes influencing suspended particulate matter (SPM) and sediment transport are well emulated, notwithstanding the complexity of the ambient muddy environment. The results show that during the rainy season, strong wave damping and wave refraction zones cause high SPM resuspension in shallow waters (<5 m). In contrast, during the dry season, wave influence shifts to the estuary mouth. Erosion and sedimentation patterns indicate that ebb currents associated with neap tides during the rainy season represent the most favourable conditions for the alongshore migration of mud banks. Neaptide ebb currents also contribute to sedimentation during the dry season but only in the estuary mouth and the nearby coastal area. The abundance of mud leads to an extension of the estuary’s intertidal area during the dry season.
Exploring Deep Learning Methods for Short-Term Tide Gauge Water Level Predictions
Authors: Vicens-Miquel Marina , Tissot Philippe E. , Medrano F. Antonio
Published: October 2024

Accurate and timely water level predictions are essential for effective shoreline and coastal ecosystem management. As sea levels rise, the frequency and severity of coastal inundation events are increasing, causing significant societal and economic impacts. Predicting these events with sufficient lead time is essential for decision-makers to mitigate economic losses and protect coastal communities. While machine learning methods have been developed to predict water levels at specific sites, there remains a need for more generalized models that perform well across diverse locations. This study presents a robust deep learning model for predicting water levels at multiple tide gauge locations along the Gulf of Mexico, including the open coast, embayments, and ship channels, all near major ports. The selected architecture, Seq2Seq, achieves significant improvements over the existing literature. It meets the National Oceanic and Atmospheric Administration’s (NOAA) operational criterion, with the percentage of predictions within 15 cm for lead times up to 108 h at the tide gauges of Port Isabel (92.2%) and Rockport (90.4%). These results represent a significant advancement over current models typically failing to meet NOAA’s standard beyond 48 h. This highlights the potential of deep learning models to improve water level predictions, offering crucial support for coastal management and flood mitigation.
Heterogeneous Activation of Persulfate by Petal-Shaped Co3O4@BiOI to Degrade Bisphenol AF
Authors: Zhang Jian , Liu Changling , Lin Zheng , Chen Qiang
Published: October 2024

In catalytic tests, the results have shown that almost all the BPAF was removed within 30 min when the dosage of Co3O4@BiOI and sodium persulfate (PS) was 0.15 g and 0.1 mM, respectively. Acid conditions inhibited BPAF degradation, but the inclusion of a precise concentration of bicarbonate ions (HCO3−) promoted degradation. The presence of chloride (Cl−), sulfate ions (SO42−), and a high concentration of HCO3− inhibited the degradation process, whereas the addition of nitrate ions (NO3−) had a minor effect on the catalytic process. The presence of free radicals (sulfate (SO4•−), hydroxyl (•OH), and superoxide (O2•−)) and the non-free radical singlet oxygen (1O2) in the Co3O4@BiOI/PS system was determined by electron paramagnetic resonance (EPR) and quenching tests. We propose that the Co(II)/Co(III) and Bi(III)/Bi(V) redox pairs simultaneously activate PS where the Co3O4 and BiOI components work synergistically to promote the rapid oxidative degradation of BPAF in water.
Mechanism of Enhanced Fluoride Adsorption Using Amino-Functionalized Aluminum-Based Metal–Organic Frameworks
Authors: Luo Yiting , Liu Zhao , Ye Mingqiang , Zhou Yihui , Su Rongkui , Huang Shunhong , Chen Yonghua , Dai Xiangrong
Published: October 2024

Due to the increasing fluoride concentrations in water bodies, significant environmental concerns have arisen. This study focuses on aluminum-based materials with a high affinity for fluorine, specifically enhancing metal–organic frameworks (MOFs) with amino groups to improve their adsorption and defluorination performance. We systematically investigate the factors influencing and mechanisms governing the adsorption and defluorination behavior of amino-functionalized aluminum-based MOF materials in aqueous environments. An SEM, XRD, and FT-IR characterization confirms the successful preparation of NH2-MIL-101 (Al). In a 10 mg/L fluoride ion solution at pH 7.0, fluoride ion removal efficiency increases with the dosage of NH2-MIL-101 (Al), although the marginal improvement decreases beyond 0.015 g/L. Under identical conditions, the fluoride adsorption capacity of NH2-MIL-101 (Al) is seven times greater than that of NH2-MIL-101 (Fe). NH2-MIL-101 (Al) demonstrates effective fluoride ion adsorption across a broad pH range, with superior fluoride uptake in acidic conditions. At a fluoride ion concentration of 7 mg/L, with 0.015 g of NH2-MIL-101 (Al) at pH 3.0, adsorption equilibrium is achieved within 60 min, with a capacity of 31.2 mg/g. An analysis using adsorption isotherm models reveals that the fluoride ion adsorption on NH2-MIL-101 (Al) follows a monolayer adsorption model, while kinetic studies indicate that the predominant adsorption mechanism is chemical adsorption. This research provides a scientific basis for the advanced treatment of fluoride-containing wastewater, offering significant theoretical and practical contributions.
An Innovative Deep Neural Network Model for Precise Calorie Burn Prediction from Physical Activity Data
Authors: Ahmed Ayah M , Mohammed Chira N. , Ali Sardar Hasen
Published: October 2024

Accurate prediction of calories burned during physical activities is crucial for various applications in health monitoring, fitness tracking, and personalized nutrition. Traditional methods often lack the precision needed for individualized estimates, which has increased interest in advanced machine learning approaches. This research introduces a deep learning model designed to predict calories burned with enhanced accuracy by capturing complex, non-linear relationships in the data. The model employs a multilayer perceptron neural network, Leaky ReLU activations, dropout regularization, and the Adam optimizer to improve generalizability and prevent overfitting. The evaluation of training and validation loss over epochs demonstrated the model's robustness and capacity to generalize effectively to novel data. The model's performance was evaluated using various metrics, achieving superior results with a remarkable Mean Absolute Error (MAE) of 0.27% and an accuracy of 99.73%, outperforming other models discussed in the literature. These findings indicate that deep learning offers significant potential for improving calorie prediction models, providing more reliable fitness and health management tools.
Prediksi Kelulusan Mata Kuliah Mahasiswa Teknologi Informasi Menggunakan Algoritma K-Nearest Neighbor
Authors: Ahmad Nazaruddin , Hafizh Saifan , Sulthanah Rana
Published: October 2024

This research aims to develop a predictive model using the K-Nearest Neighbor (KNN) method to forecast the course completion of students in the Information Technology program. The issue at hand is the uncertainty in predicting student success based on historical data and specific attributes. This study focuses on the importance of understanding the factors that influence student success in the Database Management Systems course to provide accurate predictions and help improve student pass rates in this course. The objective of this research is to build a predictive model using the KNN algorithm and to implement this model using the PHP programming language. The study aims to offer valuable insights for educational institutions to enhance teaching and learning processes and to expand understanding of data mining concepts in specific case studies. The prediction aims to determine whether a student will pass or fail the Database Management Systems course based on predetermined training and testing data. By calculating the nearest distance between the training data and the test data. The results showed an accuracy rate of 90% for predicting course completion using k=5, with a dataset consisting of 40 training data points and 20 testing data points.