bluuebunny/crossref_metadata_2025 huggingface dataset:
Enhancing Grapevine Node Detection to Support Pruning Automation: Leveraging State-of-the-Art YOLO Detection Models for 2D Image Analysis
Authors: Oliveira Francisco , da Silva Daniel Queirós , Filipe Vítor , Pinho Tatiana M. , Cunha Mário , Cunha José Boaventura , Santos Filipe N.
Published: September 2024

Automating pruning tasks entails overcoming several challenges, encompassing not only robotic manipulation but also environment perception and detection. To achieve efficient pruning, robotic systems must accurately identify the correct cutting points. A possible method to define these points is to choose the cutting location based on the number of nodes present on the targeted cane. For this purpose, in grapevine pruning, it is required to correctly identify the nodes present on the primary canes of the grapevines. In this paper, a novel method of node detection in grapevines is proposed with four distinct state-of-the-art versions of the YOLO detection model: YOLOv7, YOLOv8, YOLOv9 and YOLOv10. These models were trained on a public dataset with images containing artificial backgrounds and afterwards validated on different cultivars of grapevines from two distinct Portuguese viticulture regions with cluttered backgrounds. This allowed to evaluate the robustness of the algorithms on the detection of nodes in diverse environments, compare the performance of the YOLO models used, as well as create a publicly available dataset of grapevines obtained in Portuguese vineyards for node detection. Overall, all used models were capable of achieving correct node detection on images of grapevines from the three distinct datasets. Considering the trade-off between accuracy and inference speed, the YOLOv7 model demonstrated to be the most robust in detecting nodes in 2D images of grapevines, achieving F1-Score values between 70 % and 86.5 % with inference times of around 89 ms for an input size of 1280×1280 px. Considering these results, this work contributes with an efficient approach for real-time node detection for further implementation on an autonomous robotic pruning system.
Molecular Composition of Exogenous Dissolved Organic Matter Regulates Dissimilatory Iron Reduction and Carbon Emissions in Paddy Soil
Authors: Wang Haibo , Liu XiPeng , Shu Yuchen , Li Gang , Sun ChengLiang , Jones Davey , Zhu YongGuan , Lin Xianyong
Published: September 2024

Soil carbon (C) cycling under anoxic conditions is mechanistically linked to dissimilatory iron (Fe) reduction, which could be regulated by exogenous dissolved organic matter (DOM). However, the impact of complex exogenous DOM on soil microbial activity and C-Fe coupling in paddy soils remains underexplored. With a 100-day microcosm experiment, we determined that the molecular composition of DOM derived from organic fertilizers and biochar on paddy soil affects soil microbial community, Fe reduction, and C emissions. Our results indicated that biochar-DOM significantly promoted Fe reduction and accelerated CH4 and CO2 emissions, and organic manure-DOM increased soil CO2 emissions, which was closely related to the molecular composition of exogenous DOM. DOM molecules with high aromaticity (AImod) and high DBE, including lignins-vascular plant-derived polyphenols, lignins-polycyclic aromatics (PA), and condensed aromatics-PA, promoted soil Fe reduction and CH4 emissions by enriching soil Fe-reducing bacteria (FeRB), reducing methanotrophs, and facilitating r-strategists at the early stage of incubation. Conversely, DOMs with low AImod, low DBE and high H/C, such as lignins-highly unsaturated and phenolic compounds and proteins-aliphatic compounds, enhanced CO2 emissions by facilitating recalcitrant C degradation and CH4 oxidation at the late stage of incubation. In conclusion, our study highlights the importance of the molecular composition of DOMs derived from organic amendments in regulating soil Fe reduction and C emissions. The findings offer novel insights into the effective utilization of agricultural resources and the mitigation of soil C emissions.
A Comprehensive Analysis of the Lipidomic Signatures in Rhizopus delemar
Authors: Ali Basharat , Chauhan Anshu , Kumar Mohit , Kumar Praveen , Carolus Hans , Lobo Romero Celia , Vergauwen Rudy , Singh Ashutosh , Rudramurthy Shivaprakash M. , Van Dijck Patrick , Ibrahim Ashraf S , Prasad Rajendra
Published: September 2024

Certain species of Mucorales have been identified as causative agents of mucormycosis, a rare yet often lethal fungal infection. Notably, these fungi exhibit intrinsic resistance to common azole drugs, which target lipids. Given the pivotal role of lipids in drug resistance and their contribution to the innate resistance to azoles, this study provides a comprehensive overview of key lipid classes, including sphingolipids (SLs), glycerophospholipids(GPLs), and sterols, in Rhizopus delemar 99-880, a well-characterized reference strain among Mucorales. Through high-throughput analysis involving the identification and quantification of intermediates, we have elucidated the biosynthetic pathways of SLs, PGLs, and sterols. Notably, the acidic SLs were not found probably because the acidic branch of the SL biosynthesis pathway terminates at α-hydroxy phytoceramides as evident by their high abundance. Intermediates in the neutral SL pathway incorporated higher levels of 16:0 fatty acid compared to other pathogenic fungi. A strikingly high phosphatidylethanolamine (PE)/phosphatidylcholine (PC) ratio was observed among GPLs. Ergosterol remains the major sterol, similar to other fungi, and our analysis confirms the existence of alternate ergosterol biosynthesis pathways. The total lipidomic profile of R. delemar 99-880 offers insights into its lipid metabolism and potential implications for studying pathogenesis and drug resistance mechanisms.
Electrolyzed Water Treatment for the Control of the Zoonotic Pathogen Vibrio vulnificus in Aquaculture: A One Health Per-Spective
Authors: Ibányez-Payá Pablo , Blasco Adolfo , Ros-Lis José V. , Fouz Belén , Amaro Carmen
Published: September 2024

Vibrio vulnificus (Vv) is a bacterial pathogen native to warm and brackish water ecosystems that can cause fatal septicemia (Vv-vibriosis) in humans and various farmed fish species. From a One Health perspective, controlling Vv-vibriosis outbreaks on farms is essential not only for animal but also for human health, as it reduces the risk of Vv transmission to humans. Electrolyzed water (EW) is a sustainable control method, exhibiting transient disinfectant properties due to the formation of hypochlorous acid (HOCl). We hypothesized that EW could effectively reduce Vv populations in aquaculture facilities, preventing outbreak emergence. To test this hypothesis, survival assays in EW were conducted under varying conditions of salinity, pH, and free available chlorine (FAC). The results indicated that an intermediate concentration of FAC had a significant bactericidal effect on Vv populations regardless of the condition and tested strain. Consequently, the strategic use of EW could serve as an eco-friendly preventive and control measure against Vv-vibriosis by significantly decreasing the bacterial load in farm water.
Domain Adaptation of Deep Learning Segmentation Model of Small Agricultural Burn Area Detection Using Hi-Resolution Sentinel-2 Observations: A Case Study of Punjab, India
Authors: Anand Anamika , Imasu Ryoichi , Dhaka Surendra Kumar , Patra Prabir
Published: September 2024

This study investigates the capabilities of high-resolution Sentinel-2 observations and deep learning (DL) segmentation models, specifically convolutional neural networks (CNNs), for accurate mapping of small and fragmented agricultural burn areas in Punjab, India. Initially, the model was trained using ICNF burn area data from Portugal to capture large fire and burn area delineation, achieving moderate accuracy. Subsequent fine-tuning using annotated data from Punjab improved the model’s ability to detect small burn patches, demonstrating higher accuracy and precision than the baseline Normalised Burn Ratio (NBR) Index method. On-ground validation using buffer zone analysis and crop field images confirms the DL approach effectiveness. Cloud interference and temporal gaps in satellite data posed some challenges. Despite these limitations, the study highlights the methodological advancements and potential of using DL models for small burn area detection in agricultural settings. The model achieved overall testing accuracy of 0.64 in Dice score assessment. This report presents a promising approach for improved mapping of the burn area, which might engender better estimation of emissions and the management practices in regions with little available ground truth data.
Environmental Degradation, Neoliberal Development, and Deforestation in the Chittagong Hill Tracts of Bangladesh: A Critical Analysis in the Context of Ongoing Climate Crisis
Authors: Nadiruzzaman Md. , Shewly Hosna J , Rashid Bazlur , Dutta Orchisman , Mukul Sharif A.
Published: September 2024

Though Bangladesh is often labelled as "climate change ground zero," the Chittagong Hill Tracts (CHT) were never considered a significant climate change hotspot until heavy rainfall and consequent landslides in 2017. This shift in problem narrative seemingly overlooks myriad environmentally unsustainable 'development' initiatives along with (il)legal deforestation. This paper examines climate change trends and impacts on the lives and livelihoods of diverse local groups in CHT. By analysing changing patterns in meteorological data and qualitative information on changes and challenges in everyday life and the livelihoods of local people, we show a shortfall of captured climate data on CHT to make a strong case for climate change. Yet, this study finds that this region is exposed to a substantial threat of land degradation, flash floods, water scarcity, landslides, and other slow and quick onsets due to a combined effect of deforestation, neoliberalised development, and slight changes in temperature and precipitation patterns. This paper calls for proactive measures to address climate change impacts by endorsing sustainable development strategies prioritizing environmental stewardship, social justice, and economic prosperity.
Exploring Floristic Diversity, Propagation Patterns and Plant Functions in Domestic Gardens Across Urban Planning Gradient in Lubumbashi, DR Congo
Authors: Useni Sikuzani Yannick , Kisangani Kalonda Bernard , Mpanda Mukenza Médard , Yona Mleci Jonas , Mpibwe Kalenga Alex , Malaisse François , Bogaert Jan
Published: September 2024

Urbanization degrades natural habitats and creates new urban ecosystems, like domestic gardens. The plant composition of these gardens varies with socio-economic factors and urban planning levels. However, the diversity and impact of introduced species are often poorly assessed, causing potential ecological imbalances, particularly in Lubumbashi (DR Congo). The objective was to analyze the spatial structure, plant diversity, propagation strategies, and ecological functions of domestic gardens. Three distinct neighborhoods were selected: planned, unplanned and residential neighborhood. 20 avenues (with 5 plots per avenue) were chosen to represent the diversity within each neighborhood, and stratified random sampling of plots was conducted to analyze gardening practices. Gardens were classified into types, and their vegetation was evaluated based on species origin and ecological impact. The analysis of domestic gardens in Lubumbashi reveals significant variations across different neighborhood types. Residential neighborhoods exhibit larger average garden sizes (315.1 m²), higher species richness (22 species), and larger plot sizes (1032 m²) compared to unplanned and planned neighborhoods, where garden areas and species richness are notably lower. Rectangular gardens dominate in unplanned areas, while planned neighborhoods feature more intentional landscaping elements, such as flowerbeds and hedges. The use of gardens for food production is prominent in planned areas (40.7%), whereas residential neighborhoods prioritize ornamentation (51.4%). The study identifies 232 species across 68 families, with a predominance of exotic species (80%) in all neighborhoods, particularly in unplanned areas (82.25%). The data reveals that Mangifera indica and Persea americana are abundant in all neighborhoods, illustrating their adaptability to different urban contexts. Herbaceous species are most common, followed by woody plants, with vines being sparse. Species dispersal is primarily driven by human activities (anthropochory), accounting for over 85% in all neighborhoods. These findings highlight the strong human influence on the composition and structure of domestic gardens in Lubumbashi, emphasizing the dominance of exotic species and the importance of anthropogenic factors in shaping urban green spaces. Urban policies should incorporate strategies to minimize the negative impacts of exotic species on native flora.
Decentralized Finance (DeFi) and Its Impact on Traditional Banking Systems: Opportunities, Challenges, and Future Directions
Authors: Ali Arif , Abdul-sobur  Dembo Sardauna
Published: September 2024

The rapid emergence of Decentralized Finance (DeFi), driven by blockchain technology and smart contracts, is transforming the landscape of the financial industry by enabling peer-to-peer financial services without traditional intermediaries. This paper examines the impact of DeFi on traditional banking systems, focusing on the opportunities, challenges, and future directions of this revolutionary financial model. We explore how DeFi disrupts traditional financial intermediation by offering faster, cheaper, and more transparent financial services, potentially reducing the dominance of conventional banks. The study addresses critical issues such as regulatory challenges, including compliance with Anti-Money Laundering (AML) and Know Your Customer (KYC) requirements, and the importance of consumer protection in a decentralized environment. We also investigate collaboration opportunities between traditional banks and DeFi platforms, which could lead to innovative financial products and services. To provide a comprehensive analysis, the paper employs a mixed-method approach, combining a review of recent literature, empirical data, case studies, and expert interviews. The findings highlight significant gaps in current regulatory frameworks and emphasize the need for a balanced approach that fosters innovation while ensuring financial stability and consumer safety. This study concludes by offering recommendations for policymakers, financial institutions, and DeFi developers to harness the potential of DeFi while addressing its inherent risks, paving the way for the sustainable growth of decentralized finance in the broader financial ecosystem.
Navigating Uncertainty: A Tripartite Creative Knowledge Exchange Among Artists, Researchers, Carers and People with Lived Experience of Dementia, A co‐Created Space for Living Uncertainty
Authors: Estarellas Garcia Mar , Giovannucci Tatiana A , Steggals Lucy
Published: September 2024

In a world filled with uncertainties, Ebb&Flow emerged as a story of convergence, intertwining dementia research, caregiving, and artistic exploration into a tripartite creative knowledge exchange. This study delves into the complexities of uncertainty in dementia care and research through a collaborative project involving artists, researchers, carers, and individuals with lived experience of dementia. Initiated by the Trellis:Arbor program, Ebb&Flow sought to co-created a human space addressing the multifaceted nature of uncertainty. The sea, with its unpredictable nature, served as a guiding metaphor for the project, reflecting the unpredictability of dementia and life. Over three months, through paintings, writings, and digital mediums, participants documented the changing emotions, challenges, and discoveries they encountered daily. The project fostered an environment where uncertainties were openly discussed, building supportive and resilient communities through participatory co-creation and methodological exchange. Key activities included a digital logbook, allowing participants to document and share their experiences of uncertainty, and the creation of the Ebb&Flow card deck—a compilation of personal stories and insights that serve as a legacy of shared experiences. Each card facilitated dialogue, understanding, and connection, bridging gaps between those affected by dementia and the broader community. The project culminated in a final exhibition and sharing event, where participants, researchers, and the public celebrated the journey's outcomes through stories and artworks. Throughout this journey, we observed that embracing uncertainty and fostering empathy through creative collaboration profoundly impacted participants, enhancing empathy and resilience. This project underscores the transformative potential of integrating artistic practices with scientific research to address the emotional and psychological burdens of dementia and research environments. Ebb&Flow demonstrates that even in the face of the unknown, a united community can find strength, solace, and understanding, highlighting the power and beauty of unity in exploring and expressing the multifaceted experiences of dementia and life.
Adapting to the Evolution of Cyber Threats: Insights for 2024 and Beyond
Authors: Biplob Md. Badiuzzaman , Akther Mili , Farabi Al Mohaimin , Ramisha Jannatul Ferdous
Published: September 2024

This paper provides a thorough examination of important themes critical to understanding and tackling modern cyber threats. It explores the revolutionary significance of AI and machine learning in cybersecurity, emphasizing their dynamic influence on defense systems. It also addresses how zero-trust designs have caused a paradigm change in network security, highlighting the significance of trust verification in current cybersecurity frameworks. Furthermore, the article investigates the integration of cybersecurity with corporate strategy, arguing for a collaborative approach to efficiently mitigating risks while minimizing operational impact. Furthermore, it investigates the idea of cyber resilience, highlighting an organization's ability to recover from assaults and adapt to changing threats. Furthermore, the paper discusses the issues faced by deepfakes in the synthetic media revolution and emphasizes the rising worry about data privacy in the digital era. Finally, it looks into the introduction of behavioral biometrics as a possible way to improve authentication and security methods. This paper contributes to the discussion of effective tactics for mitigating cyber threats in today's digital ecosystem by providing a thorough overview of these subjects.
Novel Accelerated Cyclic Iterative Approximation for Hierarchical Variational Inequalities Constrained by Multiple-Set Split Common Fixed Point Problems
Authors: Ye Yao , Lan Hengyou
Published: September 2024

In this paper, we investigate a class of hierarchical variational inequalities (HVIPs, i.e., strongly monotone variational inequality problems defined on the solution set of multiple-set split common fixed point problems) with quasi-pseudocontractive mappings in real Hilbert spaces, which special cases can be found in many important engineering practical applications such as image recognizing, signal processing and machine learning. In order to solve HVIPs of potential application value, inspired by the primal-dual algorithm, we propose a novel accelerated cyclic iterative algorithm that combines the inertial method with a correction term and a self-adaptive step-size technique. Our approach eliminates the need for prior knowledge of the bounded linear operator norm. Under appropriate assumptions, we establish strong convergence of the algorithm. Finally, we apply our novel iterative approximation to solve multiple-set split feasibility problems and verify the effectiveness of the proposed iterative algorithm through numerical results.
A Commentary on U.S. Sovereign Debt Persistence and Non-Linear Fiscal Adjustment
Authors: Andric Vladimir , Bodroza Dusko , Djukic Mihajlo
Published: September 2024

The purpose of this paper is to show how the self-exciting threshold autoregressive (SETAR) model could be an appropriate econometric framework for characterizing the dynamics of the U.S. public debt/GDP ratio after the Bretton Woods collapse. Our preferred SETAR specifications are capable in capturing the main stylized facts of the U.S. public debt/GDP ratio between 1974 and 2024. In addition, the estimated SETAR models are consistent with several theoretical frameworks that seek to explain the behavior of the U.S. public debt/GDP ratio before and after the Global Financial Crisis (GFC). Finally, the paper provides some arguments on why the previous studies that use the exponential smooth threshold autoregressive (ESTAR) models, or SETAR-type models for the first differences of the U.S. public debt/GDP ratio, are potentially mis-specified, both on econometric and economic grounds.
Characterization of Fermented Grains in the Upper and Lower Layers during NongXiangXing Baijiu Fermentation
Authors: Deng Jie , Ren Zhiqiang , He Chaojiu , Wei Chunhui , Xie Jun , Huang Zhiguo
Published: September 2024

NongXiangXing Baijiu (NBJ) is produced using a solid-state fermentation system, but the impact of stratified fermented grains on fermentation remains unclear. Therefore, in a typical distillery plant, we evaluated the physical and chemical composition, volatile compound profile, and microbial community of fermented grains in NBJ both above (FG-A) and below (FG-B) the Huangshui line. Significant differences in fermentation parameters between FG-A and FG-B were observed after 30 days of fermentation (P < 0.05). Additionally, the partial least squares projection to latent structure discriminant analysis (PLS-DA) revealed distinct differences in volatile compounds between FG-B and FG-A, identifying 26 discriminant markers. The diversity of short-chain fatty acids (SCFAs) and their esters was higher in FG-B compared to FG-A. Furthermore, microbial diversity and abundance differed significantly between the two layers of fermented grains (P < 0.05). Correlation and pathway enrichment analyses indicated that the higher SCFA content in FG-B could be attributed to the greater abundance of acid-producing microorganisms compared to FG-A. This study highlights the differences between the two layers of fermented grains in NBJ fermentation, offering new insights into solid-state fermentation and expanding the current understanding of the traditional NBJ fermentation process.
Tricuspid Regurgitation Associated with Implantable Cardiac Devices: A Double-Edged Sword
Authors: Iliescu Stefan , Voroneanu Luminita , Covic Alexandra Maria , Scripcariu Dragos Viorel , Stătescu Cristian , Covic Adrian C.
Published: September 2024

The use of cardiac implantable electronic devices (CIEDs) has increased considerably, becoming a cornerstone of management for patients with brady- or tachyarrhythmia or for the prevention of sudden cardiac death. On the other hand, tricuspid regurgitation (TR) associated with CIEDs is progressively accepted as a serious clinical issue; prognostic impact of TR is profound, as it is independently associated with increased mortality and a higher risk of heart failure hospitalization. Additionally, management of established CIED-related TR continues to be challenging, with limited options for intervention once significant TR has developed. The balance between the lifesaving benefits of CIEDs and the risk of TR underlines the necessity for cautious patient selection and innovative approaches to device implantation and management. This review highlighting the clinical importance, underlying mechanisms, and challenges associated with lead-related tricuspid regurgitation in patients with CIEDs.
Finding a Research Paper Which Meaningfully Averages Unbounded Sets
Authors: Krishnan Bharath
Published: September 2024

Suppose n ∈ N. We wish to meaningfully average ‘sophisticated’ unbounded sets (i.e., sets with positive n-d Hausdorff measure, in any n-d box of the n-d plane, where the measures don’t equal the area of the boxes). We do this by taking the most generalized, satisfying extension of the expected value, w.r.t the Hausdorff measure in its dimension, on bounded sets which takes finite values only. As of now, I’m unable to solve this due to limited knowledge of advanced math and most people are too busy to help. Therefore, I’m wondering if anyone knows a research paper which solves my doubts.
Adaptation and Validation of the Pet Bereavement Questionnaire (PBQ) for Chinese Population
Authors: Yiu W.Y. Winnie , Cheung H.N. Amy , Wong Paul W.C.
Published: September 2024

Despite the increasing prevalence of pet ownership in Chinese societies, standardized tools to assess grief from pet loss remain lacking. Research predominantly focuses on Western populations, creating a gap in understanding pet bereavement in Chinese cultural settings. This study aimed to adapt and validate the Pet Bereavement Questionnaire (PBQ-C) for a Chinese context to create a culturally appropriate assessment tool. A total of 245 participants with companion animal loss experiences were recruited through the university of the research team. They were invited to complete an online survey including the Chinese PBQ, the Depression subscale of the Depression Anxiety Stress Scales (DASS-21), and the Inventory of Complicated Grief (ICG). Both Exploratory Factor Analysis and Confirmatory Factor Analysis were conducted to examine the psychometric properties of the PBQ-C and the findings supported a three-factor structure—grief, anger, and guilt—aligned with the original PBQ, with three items were reassigned to different factors. Despite these adjustments, the PBQ-C showed strong internal consistency, split-half reliability, and concurrent validity. The validated PBQ-C provides a culturally sensitive tool for assessing pet bereavement in Chinese society that can promote research and counselling support for this under-researched and under-recognized type of loss of human-animal relationships.
An Advanced Software Tool for Precision Laser Beam Propagation Analysis
Authors: Bell Lebohang
Published: September 2024

This paper presents an advanced software tool for precise laser beam propagation analysis, focusing on the calculation of the M2 value, a key measure of beam quality. Developed in Python, the software integrates libraries like NumPy, Matplotlib, and SciPy for robust data handling and accurate polynomial fitting. It features a user-friendly interface for easy data input, analysis, and visualization, making it accessible to both professionals and novices. The tool has been rigorously validated against theoretical models (M2=1) and experimental data (showing M2=1.2548 and 1.3166), ensuring high accuracy and reliability. It visualizes beam width data and fitted polynomial curves, aiding in assessing beam propagation characteristics and optimizing laser systems. Future enhancements include additional beam quality metrics, machine learning integration, and cloud-based functionality. This tool significantly advances laser beam analysis, benefiting research and industrial applications by improving performance and efficiency. The software is available at DOI: 10.5281/zenodo.13325911
An Explainable AI-based Modified YOLOv8 Model for Efficient Fire Detection
Authors: Hasan Md. Waliul , Shanto Shahria , Nayeema Jannatun , Rahman Rashik , Helaly Tanjina , Rahman Ziaur , Mehedi Sk. Tanzir
Published: September 2024

Fire detection is crucial for ensuring human safety and minimizing property damage. By utilizing advanced technologies, we can identify fires early, before they escalate. Autonomous fire detection systems are particularly vital in high-risk areas with minimal human presence. To address this need, we propose an automated fire-detection model based on YOLOv8. We modified the architecture of YOLOv8 and achieved impressive performances. Through comprehensive analysis of different YOLO models and their predecessors, we identified and addressed substantial gaps in this field. Also, by augmenting the framework with additional capabilities, we surpassed previous models, enabling real-time fire detection. Our proposed model achieves impressive performances, with 98% accuracy in fire detection and 97.8% in smoke detection. Moreover, recall rates for fire and smoke are 97.1% and 97.4%, respectively, with a mean average precision (mAP) accuracy of 99.1% for both cases. Finally, we applied an explainable artificial intelligence technique to interpret our proposed model and its results. This study lays the foundation for future research aimed at enhancing fire detection efficiency. The reliability of our modified model presents promising opportunities for future advancements, ensuring increased effectiveness and precision in efficient fire detection.
Comorbidity of Hypothyroidism and Low-Alanine Aminotransferase Associated Sarcopenia is Common and Associated with Shortened Survival. A Retrospective Study of 16,827 patients Over a Period of 21 Years
Authors: Segal Omer , Khoury Rabia , Vaisman Adva , Segal Gad
Published: September 2024

Background. Hypothyroidism is very common worldwide. It is known to be associated with frailty which in turn, is associated with increased morbidity and mortality in the elderlies. Low ALT blood activity is an established marker for sarcopenia and frailty. The incidence and outcomes of the association of low ALT values and hypothyroidism, as manifested in elevated blood TSH levels, is unknown.
Methods. This was a retrospective analysis of hospitalized patients in a large, tertiary hospital.
Results. Over a period of 21 years, an overall population of 16,827 patients were identified as eligible to participate in this study. Within the study population 726 (4.3%) were classified as suffering from hypothyroidism (TSH values > 6 MIU/L) and 2,184 (13%) were classified as patients with sarcopenia (ALT < 12 IU/L). Within our patients’ population, hypothyroidism was associated with sarcopenia in a statistically significant manner (p = 0.011). Patients classified as suffering from both hypothyroidism and sarcopenia had significantly shorter survival: A multivariate analysis showed that the frail and hypothyroid group of patients had a statistically significant risk of mortality in the for coming 5 years (HR = 3.6; CI 2.75-4.71; p < 0.001).
Conclusions. Sarcopenia and frailty are common comorbidities, bearing negative long-term clinical outcomes. Low ALT values could serve as a useful biomarker for screening of patients already diagnosed with hypothyroidism.
Evaluation of C-C Motif Chemokine Receptor 5 (CCR5) as a Sample Adequacy Control in HPV Molecular Diagnostics
Authors: Njoku Ruth Chinyere , Martinelli Marianna , Giubbi Chiara , De Marco Sofia , Torsello Barbara , D'Avenia Morena , Sironi Manuela , Bianchi Cristina , Cocuzza Clementina Elvezia
Published: September 2024

Background: Reliable HPV testing and genotyping are essential for quality assurance in HPV-based primary screening, disease management and for monitoring the impact of HPV vaccination. The clinical validation of HPV molecular diagnostic assays has significantly contributed to these objectives, however little emphasis has been placed in assuring sample quality. This study aims to assess the accuracy of sample cellularity assessment using C-C Motif Chemokine Receptor 5 (CCR5) gene target as a marker of sample adequacy in molecular diagnostics. Methods: Jurkat cell line samples were counted both using Thoma cell-counting chamber and Fluorescent-Activated Cell Sorting (FACS). Jurkat cell line samples at three different concentrations were subsequently evaluated using the OncoPredict HPV Quality Control (QC) real-time PCR assay, employing CCR5 for molecular cellularity quantification. Results: Cellularity values obtained were comparable across the 3 different methods for all dilutions of the cell line tested. Conclusions: The results obtained from this study show that CCR5 represents a promising molecular marker for the accurate quantification of sample cellularity, confirming its use as a reliable sample adequacy control, thus reducing the risk of “false negative” results.
Host-Pathogen Interactions during STEC Adherence and Colonization in the Bovine Gut: A Comprehensive Review
Authors: Edison Lekshmi K. , Kudva Indira T , Kariyawasam Subhashinie
Published: September 2024

Shiga toxin-producing Escherichia coli (STEC) is a significant public health threat due to its ability to cause severe gastrointestinal diseases in humans, ranging from diarrhea to life-threatening conditions such as hemorrhagic colitis and hemolytic uremic syndrome (HUS). As the primary reservoir of STEC, cattle play a crucial role in its transmission through contaminated food and water, posing a considerable risk to human health. This comprehensive review explores host-pathogen interactions during STEC colonization of the bovine gut, focusing on the role of gut microbiota in modulating these interactions and influencing disease outcomes. We integrated findings from published transcriptomics, proteomics, and genomics studies to provide a thorough understanding of how STEC adheres to and colonizes the bovine gastrointestinal tract. The insights from this review offer potential avenues for the development of novel preventative and therapeutic strategies aimed at controlling STEC colonization in cattle threby reducing the risk of zoonotic transmission.
Oman's Green Horizon: Steering Towards Sustainability through Decarbonization and Energy transition
Authors: Abdel- Gadir Sufian Eltayeb , Mohammed Mwahib Gasmelsed
Published: September 2024

This paper examines the determinants of CO2 emissions in Oman from 1990 to 2022, focusing on the impacts of energy consumption, economic growth, urbanization, financial development, and foreign direct investment. Utilizing stepwise regression, robust least squares, Fully Modified Least Squares (FMOLS), Canonical Cointegrating Regression (CCR), and various cointegration tests including the Johansen cointegration test, Engle-Granger cointegration test, and Vector Error Correction (VEC) model, the study reveals several key findings. Energy use per capita (ENGY) and real GDP per capita (RGDPC) are consistently significant positive predictors of CO2 emissions. Urbanization (URB) also significantly contributes to higher emissions. Conversely, Financial Development Index (FDX) and foreign direct investment (FDI) do not show significant effects on CO2 levels. The high R-squared values across models indicate that these variables explain a substantial portion of the variation in emissions. Cointegration tests confirm long-term equilibrium relationships among the variables, with the Johansen test identifying two cointegrating equations and the Engle-Granger test showing significant tau-statistics for FDX, ENGY, and URB. The VEC model further highlights the short-term dynamics and adjustment mechanisms. These findings underscore the importance of energy policy, economic development, and urban planning in Oman's efforts towards sustainable development and decarbonization.
Identifying Oversold Levels and Developing Low-Frequency Trading Algorithms for the S&P 500: An Analysis Using Stochastic Oscillator, Williams %R, and Trading Volume
Authors: Paik Chan Kyu , Choi Jinhee , Vaquero Ivan Ureta
Published: September 2024

Recent algorithmic-based research in the stock market has predominantly focused on ultra-high-frequency trading and index estimation. We developed an improved algorithm to address the need for a low-frequency, real-world trading model to build on the existing high hit-ratio and low maximum drawdown. We utilized established price indicators, Stochastic and Williams %R, and introduced a volume factor to increase the model's safety to enhance the model's performance. This improved algorithm demonstrated superior returns while maintaining high hit-ratio and low maximum drawdown. Specifically, we leveraged the existing algorithm to trade using 2X and 3X signals, incorporating volume data, the 52-week average, standard deviation, and other factors. The data set comprised SPY ETF price and volume from 2010 to 2023, covering over 13 years. Our enhanced algorithmic trading model outperformed both the benchmark and previous results, achieving a hit rate of over 90%, a maximum drawdown of less than 1%, an average of 1.5 trades per year, a total return of +519.3%, and an annualized return (AnnR) of +15.1%. The analysis demonstrates that the model's simplicity, ease of application, and comprehensibility can assist investors in making informed investment decisions albeit the past performance does not guarantee future returns.
Treatment of Refractory Osteochondral Lesions of the Talus with a Novel Transmalleolar Drilling Technique
Authors: Mullin Edmund P. , Edmonds Eric W.
Published: September 2024

Case:

A 15-year-old adolescent girl underwent revision surgery for a posteromedial osteochondral lesion of the talus (OLT). To provide anatomic reduction of her lesion, fluoroscopy and ankle arthroscopy facilitated a transmalleolar tunnel for screw fixation. Now, 8 years after surgery, the patient reports high patient satisfaction and pain-free functional use of her ankle.

Conclusion:

A transmalleolar tunnel technique provides an alternative method to treat posteromedial OLT. This minimally invasive procedure allows patients to be spared potential complications from a medial malleolar osteotomy or a posteromedial incision. Most importantly, this case report demonstrates a positive long-term patient outcome.
ChatGPT-4 Knows Its A B C D E but Cannot Cite Its Source
Authors: Ghanem Diane , Zhu Alexander R. , Kagabo Whitney , Osgood Greg , Shafiq Babar
Published: September 2024

Introduction:

The artificial intelligence language model Chat Generative Pretrained Transformer (ChatGPT) has shown potential as a reliable and accessible educational resource in orthopaedic surgery. Yet, the accuracy of the references behind the provided information remains elusive, which poses a concern for maintaining the integrity of medical content. This study aims to examine the accuracy of the references provided by ChatGPT-4 concerning the Airway, Breathing, Circulation, Disability, Exposure (ABCDE) approach in trauma surgery.

Methods:

Two independent reviewers critically assessed 30 ChatGPT-4–generated references supporting the well-established ABCDE approach to trauma protocol, grading them as 0 (nonexistent), 1 (inaccurate), or 2 (accurate). All discrepancies between the ChatGPT-4 and PubMed references were carefully reviewed and bolded. Cohen's Kappa coefficient was used to examine the agreement of the accuracy scores of the ChatGPT-4–generated references between reviewers. Descriptive statistics were used to summarize the mean reference accuracy scores. To compare the variance of the means across the 5 categories, one-way analysis of variance was used.

Results:

ChatGPT-4 had an average reference accuracy score of 66.7%. Of the 30 references, only 43.3% were accurate and deemed “true” while 56.7% were categorized as “false” (43.3% inaccurate and 13.3% nonexistent). The accuracy was consistent across the 5 trauma protocol categories, with no significant statistical difference (p = 0.437).

Discussion:

With 57% of references being inaccurate or nonexistent, ChatGPT-4 has fallen short in providing reliable and reproducible references—a concerning finding for the safety of using ChatGPT-4 for professional medical decision making without thorough verification. Only if used cautiously, with cross-referencing, can this language model act as an adjunct learning tool that can enhance comprehensiveness as well as knowledge rehearsal and manipulation.
FAKTOR PENYEBAB TERJADI TINDAK PIDANA KEKERASAN TERHADAP ANAK DI KABUPATEN LEMBATA
Authors: Subhan Rabil , Michael Tomy
Published: September 2024

This research examines the factors that cause criminal acts of violence against children in Lembata Regency. Data from the Lembata District Court shows a drastic increase in cases of child violence, from 8 cases in 2019 to 24 cases in 2023. The method used is empirical legal research with sociological and legal anthropology approaches, including observation, interviews with perpetrators, and study of court documents. The research results revealed that the internal factor that causes violence is the psychological condition of the perpetrator. Meanwhile, external factors include economic difficulties, low levels of education, and the social environment. The form of violence that often occurs is sexual violence such as child molestation. These findings show the importance of stricter law enforcement with appropriate punishment for perpetrators in accordance with applicable regulations to provide a deterrent effect. Public education about child protection, as well as aiding services for victims are also needed to overcome the problem of violence against children. This research concludes that efforts from various parties are needed to prevent and handle cases of child violence in Lembata Regency.
PERBANDINGAN SISTEM PEMILIHAN UNTUK INDONESIA YANG MAJEMUK (KOMPARASI DENGAN SISTEM CHILE DAN AMERIKA SERIKAT)
Authors:   Universitas Jember, Cipta Yulvia Chandra , Indrayati Rosita , Putri Intan Permata ,   Fakultas Hukum Universitas Jember,   Mahkamah Konstitusi RI
Published: September 2024

General elections, including the election of president and vice president, legislative members and other regional election, are a manifestation of democracy and the basic principles of the nation in Pancasila and the 1945 Constitution of the Republic of Indonesia. Indonesia currently adheres to an open proportionality election system. As time goes by and many changes occur, the electoral system adopted by Indonesia has many shortcomings, and various problems are also faced, such as identity politics and kinship politics. Based on the many differences in electoral systems in the world, the author examines two other countries, namely Chile and the United States, to see the differences and advantages of each of the electoral systems adopted. This is because the two countries are pluralistic countries. This research uses a normative method with a conceptual approach and a comparative approach which is configured into a systematic answer. Researchers found that Indonesia was right to use an open proportional electoral system, noting that Indonesia could adopt and combine several things to develop a better electoral system.
Spatial Quantification of Marine Litter Using Satellite and Drone Data through Empirical and Deep Learning Techniques – A Case Study from India
Authors: P Thanabalan , K Gayathrri , Hidaka Mitsuko , Matsuoka Daisuke , Mishra Pravakar , Dierssen Heidi , Dash S K , Usha Tune , V Ramanamurthy M
Published: September 2024

Marine Litter is a major contaminant in the world's oceans. The mismanaged land-based garbage reaches the marine environment via rivers and creeks. Remote sensing techniques have the potential for detecting, classifying, and quantifying litter patches in the coastal zone. A case study for Chennai metropolitan city beaches attempted to quantify debris using different satellite sensors with specific spectral bands. Equally, the drone can detect and quantify macro litters (> 5 mm) at a relatively better resolution (2 to 2.5 cm). This study was carried out to evaluate the suitability of the drone images for detecting macro litter in the coastal segment. Image-based semantic segmentation deep learning techniques are applied. Estimating litter abundance with the spatial extent of natural and artificial litter on the beaches agrees with ground truth data.
The Impact of Diesel and Various Blends of Biodiesel on Performance, Emissions, and Combustion in DI Diesel Engines
Authors: Veerasamy Arunachalam , Pancharam Naveenchandran , Pandian Balu
Published: September 2024

The rise in consumption, environmental emissions, and the cost of gasoline make energy recovery from trash a top priority. Not only can biodiesel meet energy demands, but it can also reduce greenhouse gas emissions, making it an important source of renewable energy. An experimental study of diesel and many different biodiesel blends was conducted to analyse their performance, emissions, and combustion characteristics. To prepare the emulsified fuel and go on with further experimental work, this effort aimed to identify the optimal biodiesel blends. The results of this paper showed that among all other biodiesel blends with various compositions, Syzygy Cumini (SC) biodiesel, 20% by volume with diesel (SC20), was the best mix. Based on the outcomes of performance, combustion, and emission metrics, it was selected. In comparison to other biodiesel blends, the SC20 biodiesel produced brake thermal efficiency that was nearly as high as that of diesel. With the exception of nitrogen oxide (NOx) emissions, the emission parameters were likewise determined to be lower than those of other biodiesel blends.
Sustainable Processing of Zea Mays peel ash (Methylene adsorbed) biosorbent as a novel green heterogeneous catalyst for biodiesel production
Authors: Prakash Akshay , Manuel Jerold
Published: September 2024

The escalating energy crisis, propelled by extensive fossil fuel consumption, necessitates sustainable and environmentally friendly alternatives. Biodiesel, derived from renewable sources, has emerged as a promising solution. This study explores using methylene blue (M.B., which is a significant water pollutant in several parts of the world) dye-adsorbed biochar, a waste-derived green catalyst, for biodiesel production. The catalyst was synthesized from Zea Mays peels, demonstrating a circular economy approach. The optimization of transesterification reactions is achieved using the Taguchi method, considering factors including reaction time, temperature, catalyst load, and methanol-to-oil ratio. The resulting biodiesel was purified and characterized through various analyses, including Gas Chromatography and Nuclear Magnetic Resonance. Adsorption studies reveal the catalyst's potential, and structural analyses (FESEM, XRD, FTIR) provide insights into its composition. The synthesized biodiesel, identified through GC-MS, exhibited qualities that align with the findings of the literature. Overall, the study presents a sustainable and economically viable pathway for biodiesel production using a novel green catalyst derived from waste resources.