bluuebunny/crossref_metadata_2025 huggingface dataset:
Moving from Principles to Practice: A scoping Review of Value-Based Healthcare (VBHC) Implementation Strategies
Authors: de Mattia Egidio , Angioletti Carmen , D'Agostino Melissa , Paoletti Filippo , de Belvis Antonio Giulio
Published: November 2024

The principles of value-based Healthcare (VBHC) have received widespread endorsement, lead-ing healthcare organizations worldwide to shift their strategies towards them. However, despite growing recognition and acceptance, the actual implementation of value-based approaches varies widely. This research aims to identify studies that address the implementation of VBHC at dif-ferent levels (healthcare policymakers, hospital administrators, and healthcare providers), fo-cusing on each level’s relative strategies; Methods: To this end, a scoping review was conducted in accordance with the PRISMA extension for Scoping Reviews checklist. The electronic databases of Web of Science, PubMed, MEDLINE and Scopus were searched to identify relevant publica-tions in English from January 2006 to 31 July 2023; Results: We identified 30 eligible studies. Findings are organized into four main macro strategic levels, each comprising specific dimen-sions and operational approaches. Fourteen articles analyzed the role of government commitment in VBHC implementation, while six articles focused on regional integrated care systems. The role of hospitals was described in sixteen records; Conclusions: Our study suggests that a compre-hensive approach is necessary for the successful implementation of VBHC. Hospitals emerge as pivotal in this shift, requiring organizational and attitudinal changes among healthcare profes-sionals. However, a complete transition towards VBHC that ensures a seamless patient manage-ment throughout the entire care delivery value chain, necessitates government involvement in terms of state legislation, reimbursement methods, and hospital networking.
Declining Prey Size in the Southern African Pleistocene: Evaluating the Human Impact
Authors: Ben-Dor Miki , Barkai Ran
Published: November 2024

Megafauna extinctions are known from the Late Quaternary. This study analyzes trends in prey size from 184 contexts across 49 archaeological sites in southern Africa to assess changes in prey size during the Pleistocene, including the pre-Late Quaternary transition between the Early Stone Age (ESA) and the Middle Stone Age (MSA). Very large prey (>950kg) accounted for over 34% of the biomass in the ESA, declining to 22% in MSA and 11% in LSA, with a compensatory increase in the contribution of smaller (<295 kg) prey that increased from 7% in the ESA to 37% in the MSA and to 48% in the LSA. These trends persisted even when only non-cave sites were considered. We also hypothesize that targeting fat in prey because of a constraint on protein consumption by humans could have been a causal factor in the decline.
Advanced Predictive Modeling of Physical Trajectories and Cascading Events, Dual-State Feedback and Synthetic Insula
Authors: Watchus Berend
Published: November 2024

This paper explores the possibility of achieving self-awareness in artificial intelligence (AI) through the integration of embodied feedback loops, inspired by the role of the insula in human consciousness. Building on prior work (Watchus, 2024), which established the framework of sensory feedback and embodiment as core elements of sentience, we propose a model for AI systems capable of simulating self-awareness through dual embodiment and sensory integration. Specifically, we explore how feedback loops can be implemented in AI systems to facilitate the emergence of intuitive behaviors, allowing them to predict the trajectories of physical objects and the cascading effects of events in their environment. This paper focuses on the potential of these models for practical applications in autonomous systems, including robots, home care robotics for elderly assistance, traffic prediction, competitive sports, construction safety, and disaster recovery.
Diversity of Rhizosphere Microbial Communities in Different Rice Varieties and Diverse Adaptive Responses to Saline and Alkaline Stress Conditions
Authors: Zhong Yu , Chi Hai , Wu Tao , Fan Wenbo , Su Haoyu , Li Ruoyu , Jiang Wenzhu , Du Xinglin , Ma Ziming
Published: November 2024

Rice rhizosphere microbiota plays a crucial role in crop yield and abiotic stress tolerance. However, little is known about how the composition and function of rhizosphere soil microbial communities respond to soil salinity, alkalinity, and rice variety in rice paddy ecosystems. In this study, we analyzed the composition and function of rhizosphere soil microbial communities associated with two rice varieties (Jida177 and Tongxi933) cultivated in soils with different levels of salinity-alkalinity in Northeast China using a metagenomics approach. Our results indicate that the rhizospheres of Jida177 and Tongxi933 rice varieties harbor distinct microbial communities, and these microbial communities are differentiated based on both soil salinity-alkalinity and rice varieties. Furthermore, the observed differences in rice yield and grain quality between the Jida177 and Tongxi933 rice varieties suggest that these changes may be attributed to alterations in the rhizosphere microbiome under varying salinity conditions. These findings may pave the way for more efficient soil management and deeper understanding of the potential effects of soil salinization on the rice rhizosphere system.
The Impact of COVID-19 on Learning Loss in Elementary School Students: A Comparative Study of Academic Performance Across Grades
Authors: Nappo Raffaele , Simeoli Roberta , Cerasuolo Mariangela , Ciaramella Francesco , Rega Angelo
Published: November 2024

The COVID-19 pandemic led to extensive school closures and an accelerated shift to remote learning, which had substantial consequences for students' academic development. This study seeks to examine the impact of COVID-19 on learning loss among elementary students in southern Italy, with a focus on grade-level variations across key academic domains: spelling, reading comprehension, reading speed and accuracy, and mathematical abilities. A sample of 332 students from grades II through V participated, with their performance evaluated using standardized assessments for each academic skill. Findings revealed that students in grade III encountered greater difficulties in spelling and reading comprehension compared to both older and younger cohorts, indicating that remote learning may have disproportionately affected the initial stages of skill acquisition. These results highlight the necessity for targeted interventions, especially for younger students and those with preexisting academic challenges, to address the lasting effects of pandemic-related educational disruptions.
Early Transpyloric Tube Feeding in Preventing Adverse Respiratory Events in Extremely Low Birth Weight Infants
Authors: Tanaka Shinya , Namba Fumihiko , Nagaya Ken , Yonemoto Naohiro , Hirano Shinya , Yanagihara Itaru , Kitajima Hiroyuki , Fujimura Masanori
Published: November 2024

Background: This study aims to compare the safety and efficacy of early transpyloric (TP) tube feeding with that of nasogastric (NG) tube feeding in relation to bronchopulmonary dysplasia (BPD). Methods: The study population consisted of 39 ELBWIs, which were randomly assigned to different groups based on the method of tube feeding. The primary outcome was the incidence of adverse events. This trial was registered at the UMIN Clinical Trials Registry (www.umin.ac.jp) (UMIN000001728). Results: The hazard ratio for primary adverse events was significantly lower in the TP group. The TP group had a median time of 34 days (range 24–85), and the NG group 24 days (range 13–70). In general, neither group exhibited severe intestinal complications or poor growth. Conclusions: Early TP tube feeding may be a safer alternative method of NG tube feeding for intubated ELBWIs and has been shown to reduce the frequency of adverse respiratory events.
Flunarizine As a Candidate for Drug Repurposing Against Human Pathogenic Mammarenaviruses
Authors: Ofodile Chukwudi A. , Uzochukwu Ikemefuna C. , Ezebuo Fortunatus C. , Ejiofor InnocentMary , Adebola Mercy Adeola , Okpoli Innocent , Cubitt Beatrice , Witwit Haydar , Okwuanaso Chetachi B. , Onyemelukwe Ngozi , de la Torre Juan C.
Published: November 2024

Lassa fever (LF), a viral hemorrhagic fever disease with a case fatality rate that can be over 20% among hospitalized LF patients, is endemic to many West African countries. Currently, no vaccines or therapies are specifically licensed to prevent or treat LF. Hence, the significance of developing therapeutics against the mammarenavirus Lassa virus (LASV), the causative agent of LF. We used in silico docking approaches to investigate the binding affinities of 2015 existing drugs to LASV proteins known to play critical roles in the formation and activity of the virus ribonucleoprotein complex (vRNP) responsible for directing replication and transcription of the viral genome. Validation of docking protocols were achieved with reference inhibitors of the respective targets. Our in silico docking screen identified five drugs (dexamethasone, tadalafil, mefloquine, ergocalciferol, and flunarizine) with strong predicted binding affinity to LASV proteins involved in the formation of the vRNP. We used cell-based functional assays to evaluate the antiviral activity of the five selected drugs. We found that flunarizine, a calcium-entry blocker, inhibited the vRNP activity of LASV and LCMV, and virus surface glycoprotein fusion activity required for mammarenavirus cell entry. Consistent with these findings, flunarizine significantly reduced peak titers of LCMV in a multi-step growth kinetics assay in human A549 cells. Flunarizine is being used in several countries worldwide to treat vertigo and migraine, supporting the interest in exploring its repurposing as a candidate drug to treat LASV infections.
Enhancing Educational Content Matching Using Transformer Models and InfoNCE Loss
Authors: Long Yujian , Gu Dian , Li Xinrui , Lu Peiqing , Cao Jing
Published: November 2024

The task of matching educational content to specific topics within curricula is crucial for enhancing learning outcomes and ensuring that students receive relevant and coherent learning experiences. This research seeks to develop a precise and efficient model, leveraging a dataset that spans various languages and numerous STEM fields. Our proposed solution integrates a Transformer model with InfoNCE Loss and model distillation techniques to effectively address noise from related content and improve matching accuracy. By incorporating advanced deep learning methods, the model is designed to handle the complexity and variability of educational datasets, outperforming existing models in terms of accuracy and efficiency. This approach provides a robust framework for educational content alignment, significantly benefiting educational practitioners by streamlining the content matching process and enhancing the quality of education delivered to students.
Toxic Memes Recognition Through with Multimodal Bidirectional Cross-Attention
Authors: Hashim Alex , Coleman Natalie , Roy Jannat
Published: November 2024

Despite the considerable advancements achieved with machine learning techniques in identifying hate speech, numerous technical obstacles persist that hinder these models from reaching human-level accuracy. Challenges such as understanding nuanced context, detecting sarcasm, and effectively interpreting the interplay between visual and textual elements in memes complicate the detection process. This study delves into the comprehensive evaluation of several cutting-edge visual-linguistic Transformer architectures, including VL-BERT, VLP, UNITER, and LXMERT, to assess their capabilities and limitations in handling the multifaceted nature of hateful content within memes. Building upon these evaluations, we introduce significant enhancements aimed at boosting their efficacy in this domain by developing a novel bidirectional cross-attention mechanism. This mechanism facilitates a more seamless integration of visual and textual information, enabling the model to better capture the subtle cues that distinguish hateful memes from benign ones. In addition to the architectural improvements, we leverage deep ensemble strategies to aggregate predictions from multiple model instances, thereby enhancing the robustness and reliability of the detection system. By combining the strengths of diverse models, the ensemble approach mitigates individual weaknesses and reduces the likelihood of false positives and negatives. Our proposed framework not only addresses the existing shortcomings of single-model approaches but also markedly surpasses existing baseline performances by a large margin, achieving higher AUROC and accuracy scores. The refined model demonstrates superior capability in discerning hateful content within multimodal memes, offering a more robust and reliable tool for mitigating the proliferation of harmful online material. Furthermore, the scalability of our approach ensures its applicability to evolving online threats, providing a sustainable solution for automated hate speech detection. These advancements signify a meaningful step towards enhancing the effectiveness of machine learning models in creating a safer and more respectful online environment.
Bichromatic Splicing Detector Allows Quantification of THRA1 and THRA2 Splicing Isoforms in Single Cells by Fluorescent Live-Cell Imaging
Authors: Graceffo Eugenio , Pedersen Elisa , Rosário Marta , Krude Heiko , Schuelke Markus
Published: November 2024

Thyroid hormone receptor alpha (THRα) is a nuclear hormone receptor that binds triiodothyronine (T3) and acts as an important transcription factor in development, metabolism, and reproduction. The coding gene, THRA, has two major splicing isoforms in mammals, THRA1 and THRA2, which encode THRα1 and THRα1, respectively. The better characterized isoform, THRα1, is a transcriptional stimulator of genes involved in cell metabolism and growth. The less well characterized isoform, THRα2, lacks the Ligand Binding Domain (LBD) and may act as an inhibitor of THRα1 activity. Thus, the ratio of THRα1 to THRα2 isoforms is critical for transcriptional regulation in various tissues and during development and may be abnormal in a number of thyroid hormone resistance syndromes. However, the complete characterization of the THRα isoform expression pattern in healthy human tissues, and especially the study of changes in the ratio of THRα1 to THRα2 in patient cell cultures has been hampered by the lack of suitable tools to detect the isoform-specific expression patterns. Therefore, we developed a plasmid splicing detector that allows the visualization and quantification of the differential expression of THRA1 and THRA2 splicing isoforms in living single cells during time-lapse and perturbation experiments. This tool enables experiments to further characterize the role of THRα2 and to perform high-throughput drug screening. Molecules that modify THRA splicing may be developed into drugs for the treatment of thyroid hormone resistance syndromes.
Tool Wear Prediction Using Informer Architecture for Multi-Source Sensor Data Fusion
Authors: Dazhi Liu
Published: November 2024

In the field of machining, precise monitoring of tool wear status can fully utilize tool life. On the one hand, it prevents tool replacement before the tool reaches its service life, which increases costs. On the other hand, it is necessary to monitor the tool wear status during the cutting process in order to avoid tool replacement due to severe tool wear, which affects the machining accuracy of the workpiece. In addition, based on monitoring the current tool wear status, predicting future wear values can enable machine tools to issue corresponding warnings and replace tools in a timely manner before tool wear and damage occurs, while ensuring machining quality and improving production efficiency. Therefore, this article has conducted research on the monitoring and prediction algorithms of tool wear status, and the main research contents are summarized as follows:
（1）Based on the theoretical foundation of tool wear related technologies, the variation trends of various sensor signal values and wear values in experimental data were analyzed. Preprocessing of sensor signal data was carried out, including removing invalid values, modifying outliers, etc., and optimizing the signal through wavelet threshold denoising, laying the data foundation for the establishment of subsequent tool wear models.
（2）Propose a comprehensive model for multi-step prediction of tool wear. Establish a monitoring model based on Informer to monitor the wear value at the current time point through multi-sensor information. Considering that in actual machining, the tools and machining states at two closer time points are closer, that is, the short-term information at the time point closer to the predicted point has a greater impact on the predicted point than the long-term information at the time point farther away, an Informer prediction model with Attention mechanism is introduced. The encoding decoding structure can achieve multi-step prediction of tool wear values to obtain more sufficient time for tool control. Verify the effectiveness and advantages of the comprehensive model through experiments.
Predicting Axillary Pathologic Complete Response to Neoadjuvant Chemotherapy in Clinically Node-Positive HER2+ Breast Cancer Patients
Authors: Raimondi Lucrezia , Cuzzocrea Marco , Paone Gaetano , Peradze Nickolas , Gasparri Maria Luisa , Harder Yves , Schmauss Daniel , Veronese Barbara , Graffeo Galbiati Rossella , Roselli Mario , Rossi Lorenzo
Published: November 2024

Background: The prognostic significance of achieving an axillary pathologic complete response (AxpCR) following neoadjuvant chemotherapy (NAT) in clinically node-positive (cN+) breast cancer (BC) is well established; however, there is currently no consensus regarding the optimal strategy for axillary management, particularly in patients with HER2-positive BC. This study, which seeks to assess the utility of the maximum standardized uptake value of axillary lymph nodes (N-SUVmax) obtained from baseline 18F-FDG PET/CT scans, in conjunction with the absolute monocyte count (AMC), as predictors of AxpCR to NAT in patients with cN+ HER2-positive BC, promises to bring significant benefits to the field of breast cancer treatment. Methods: The clinical, pathological, and imaging data of HER2-positive BC patients with cN+, who were eligible for NAT at the time of diagnosis, were analyzed retrospectively. Individual receiver operating characteristic curves were employed to establish the optimal cut-off values for both baseline N-SUVmax and AMC. Results: Involving 117 patients diagnosed with HER2+BC, 86 initially presented with cN+ disease. Among this group, 56 patients, representing 66.3%, achieved AxpCR following NAT. Patients with an N-SUVmax of 3.5 or higher exhibited a 4.6-fold increased probability of achieving AxpCR (p=0.039, odds ratio [OR]=4.638 [1.081; 1.904]). Conversely, those with an AMC exceeding 340/mm³ experienced a 90.4% decrease in the likelihood of achieving AxpCR (p<0.001, OR=0.096 [0.028; 0.324]). The interaction between N-SUVmax and AMC was thoroughly investigated, revealing that an increase in AMC mitigated the significance of N-SUVmax on the probability of attaining AxpCR after treatment (p=0.006, OR=0.999 [0.997; 1.000]). Patients were categorized into three groups based on their N-SUVmax and AMC levels: 1. High N-SUVmax – Low AMC group (Group 1, n=38) 2. High N-SUVmax – High AMC group or Low N-SUVmax – Low AMC group (Group 2, n=37) 3. Low N-SUVmax – High AMC group (Group 3, n=11) Individuals in Group 2 (p<0.001; OR=0.047 [0.009; 0.237]) and those in Group 3 (p<0.001, OR=0.017 [0.002; 0.137]) demonstrated a significantly lower probability of achieving AxpCR following NAT compared to patients in Group 1. Conclusions: Evaluating axillary treatment response in the neoadjuvant setting presents a valuable opportunity for HER2-positive breast cancer patients. By combining baseline N-SUVmax and AMC, clinicians can enhance their ability to determine the most appropriate axillary surgical approach for each patient, paving the way for enhanced treatment outcomes.
The Effect of Plastic Pollution on Human Health in Coastal Area: A Comparative Analysis of Bangladesh and India
Authors: Emu MD Rezanur Rahman Howlader
Published: November 2024

Plastic pollution has emerged one of main problem of the twenty first century as well as critically environmentally issue on the Costal area. Plastic debris or scrapes accumulates in enormous quantity severely impacting not only the marine but also the human ecosystems. Bangladesh and Indians who lives in the Coastal are highly vulnerable to the adverse effects of plastic pollution due to their close proximity to large water bodies, reliance on marine resources, and often improper knowledge of inadequate waste management systems. The study survey the pathways through which type plastic pollution affects human health in these regions, also emphasizing the severity of the problem and the need for urgent solutions. In additions, the paper also inspected some policy responses as well as waste management practices, and community-level efforts to mitigate the health risks associated with the plastic related pollution.
Designing Robotic Lifting Systems: Calculating Power Needs for Wall-Mounted Robots and Quad-Rotors
Authors: Ozgeziji Khamza , Isaev Ruslan , Ermakov Andrei
Published: November 2024

Robotic systems are becoming increasingly sophisticated, with diverse applications that often demand precise calculations to achieve optimal functionality. This study focuses on designing and calculating the power requirements for two common robotic lifting scenarios: wall-mounted robots and quad-rotor drones. For wall-mounted robots, which typically rely on dual motor systems to lift and support loads vertically, we provide a detailed analysis of motor power calculations necessary to handle payloads of 300 grams and above. By incorporating various factors such as torque, gravitational force, and motor efficiency, we propose formulas and present tables that guide motor selection to ensure adequate lift. In the case of quad-rotor drones, thrust calculations become crucial as these systems require balanced power distribution across four rotors to lift payloads of 400 grams and potentially heavier loads. This article explores different approaches for calculating thrust, examining variables such as rotor speed, blade size, and air density to optimize lift capability. Through comparative analysis, we suggest methods for improving payload capacity and energy efficiency in each system. The findings offer practical insights for engineers and researchers working on robotics applications that demand precision in motor selection and thrust optimization. This article ultimately aims to contribute to the field by providing a foundation for designing efficient and reliable robotic lifting systems.
Improving the Processing Efficiency of Redundant Galvanometer Scanner-Based Systems by Optimising the Processing Order of Geometries in a CAM Tool
Authors: Kurth Daniel , Reiff Colin Yannick , Jiang Yujiao , Verl Alexander
Published: November 2024

The combination of highly dynamic systems with a limited work envelope, with a less dynamic system with a larger working envelope, promises to combine the advantages of both systems while eliminating the disadvantages. For these systems separation algorithms determine the trajectories based on the target geometries. However, arbitrary processing orders of these result in inefficient trajectories because successive geometries maybe geometrically far apart. This causes the dynamic system to operate below its potential. Current planning tools do not optimise the processing order for such redundant systems. The aim is to design and implement a planning tool, for the application of laser marking. The tool considers the processing order of the 2D geometries from a geometric point of view. The resulting sequenced path data can then be used by trajectory generation algorithms to make full use of the potential of redundant systems. The approach analyses literature on Travelling Salesman Problems (TSP), which is then transferred to the given application. A heuristic and a genetic algorithm are developed and integrated into a planning tool. The results show the heuristic algorithm being faster, while still producing solutions whose total path length is similar to that of the genetic algorithm. Even though, the solutions don’t meet any optimality standards, the presented automated approaches are superior to manual approaches and are to be seen as a starting point for further research.
An Integrated Multimodal System for Identifying Offensive Memes
Authors: Cohen Ella , Levy Jaden , Roy Jannat
Published: November 2024

The manifestation of online hate speech has increasingly adopted a multimodal format, prominently featuring \textit{memes} that combine both visual imagery and textual content. Developing automated systems capable of accurately identifying such hateful material is crucial for reducing its negative impact on society. Detecting hate speech within memes presents a complex and unresolved challenge, as it necessitates the simultaneous analysis of images and text, thereby requiring sophisticated multimodal reasoning and a unified understanding of both visual and linguistic elements. In this study, we aim to push the boundaries of current research by introducing an integrated multimodal system, MemeGuardNet, designed specifically for the identification of offensive memes. Our MemeGuardNet enhances the efficacy of existing multimodal detection techniques beyond mere fine-tuning. Notably, we demonstrate the benefits of augmenting contrastive examples through upsampling to promote multimodal integration and employ ensemble learning techniques grounded in cross-validation to bolster the system's robustness. Additionally, we conduct a thorough examination of instances where the model fails to classify correctly, leading us to propose several hypothesis-driven data augmentations and evaluate their impact on overall performance. These insights are intended to guide and inspire future investigations in this domain. Our most effective model configuration utilizes an ensemble architectures and achieves an Area Under the Receiver Operating Characteristic curve (AUROC) score of 80.53. Furthermore, we introduce the following performance metric formula to quantify the model's effectiveness. This metric provides a comprehensive measure of the model's discriminative ability across all classification thresholds.
MDD-Net: Medical Segmentation with Parallel Denoising Transformation
Authors: Brown Sophia , Davis James , Roy Jannat
Published: November 2024

The automated segmentation of brain gliomas from multimodal MRI scans is pivotal in both clinical trials and everyday medical practice. Manual segmentation, however, presents significant challenges due to its labor-intensive nature, high costs, and susceptibility to inaccuracies. These difficulties arise despite the involvement of skilled human experts, primarily because of the substantial variability and uncertainty inherent in human annotations. Addressing these issues, our study introduces MDD-Net, an advanced end-to-end deep learning-based segmentation framework. This framework leverages a novel multi-decoder architecture that concurrently addresses three distinct sub-tasks through a partially shared encoder, enhancing the model's ability to generalize across different segmentation challenges. Additionally, we incorporate sophisticated smoothing techniques applied to the input MRI images, producing denoised versions that serve as supplementary inputs to the network. This dual-input strategy not only mitigates noise-related distortions but also enriches the feature representation, leading to more precise segmentation outcomes. Comprehensive validation results demonstrate a significant improvement in segmentation performance when employing the proposed MDD-Net method compared to existing approaches. This advancement holds promise for enhancing diagnostic accuracy and streamlining workflows in clinical settings. Future work will explore the integration of additional imaging modalities and the application of MDD-Net to other types of tumors, potentially broadening its clinical applicability and impact.
New Insights on the Seismic Activity of Ostuni (Apulia Region, Southern Italy) Offshore
Authors: Pierri Pierpaolo , Filippucci Marilena , Del Gaudio Vincenzo , Tallarico Andrea , Venisti Nicola , Festa Vincenzo
Published: November 2024

On March 23, 2018, an event of magnitude Mw 3.7 occurred about 10 km from the town of Ostuni, in the Adriatic offshore. It was the most energetic earthquake in south-central Apulia ever recorded instrumentally. On February 13, 2019, in the same area, a second ML 3.3 event was recorded. The analysis of the 2018 event shows that the ambiguity of the solution of the fault plane reported by INGV (Istituto Nazionale di Geofisica e Vulcanologia) on the Italian National Earthquake Centre website can be solved considering existing seismic profiles, exploration well logs and the Quaternary activity of faults in the epicentral area. A seismogenic source is identified in the rupture of a small portion of a 40 km length structure with strike NW-SE, dipping at high angle towards south. In this work, we have relocated the recent earthquakes by using the seismic stations managed by the University of Bari (UniBa), one of which quite close to the event epicenter (about 20 kilometers), together with data coming from the RSN (Rete Sismica Nazionale); furthermore, we have determined the focal mechanism of some events, with implication on stress field of the area.
Multi-Granularity User Anomalous Behavior Detection
Authors: Feng Wenying , Cao Yu , Chen Yilu , Wang Ye , Hu Ning , Jia Yan , Gu Zhaoquan
Published: November 2024

Insider threats pose significant risks to organizational security, often going undetected due to their familiarity with the systems. Detection of insider threats faces challenges of imbalanced data distributions and difficulties in fine-grained detection. Specifically, anomalous users and anomalous behaviors take up a very small fraction of all insider behavior data, making precise detection of anomalous users challenging. Moreover, not all behaviors of anomalous users are anomalous, so it is difficult to detect their behaviors standardizing with single rules or models. To address these challenges, This paper presents a novel approach for insider threat detection, leveraging machine learning techniques to analyze diverse behavior patterns within an organization. We introduce the Multi-Granularity User Anomalous Behavior Detection (MG-UABD) system, which combines coarse-grained and fine-grained anomaly detection to improve the accuracy and effectiveness of detecting anomalous behaviors. The coarse-grained module screens all of the user activities to identify potential anomalies, while the fine-grained module focuses on specific anomalous users to refine the detection process. Besides, MG-UABD employs a combination of over-sampling and under-sampling techniques to address the imbalance in the datasets, ensuring robust model performance. Through extensive experimentation on the commonly used dataset CERT R4.2, we demonstrate that the MG-UABD system achieves superior detection rate and precision, particularly in identifying anomalies in Internet usage and file operations. Our findings suggest that a multi-granularity approach for anomaly detection, combined with tailored sampling strategies, is highly effective in addressing insider threats.
Optimizing Solar Radiation Prediction: A Meta-Learning-Based VotingRegressor Approach
Authors: Alameen Abdalla
Published: November 2024

The growing global reliance on renewable energy underscores the need for accurate solar radiation forecasting, a key component in optimizing solar power generation, energy management, and climate policy. Traditional statistical and machine learning models, though effective under certain conditions, often struggle with the complexity and variability inherent in solar radiation data. This study proposes a meta-learning-optimized VotingRegressor model, designed to overcome these challenges by dynamically adjusting base model weights—including Linear Regression, Random Forest, XGBoost, and CatBoost—through meta-learning. This adaptive ensemble approach enhances model performance by balancing linear and non-linear data patterns, leading to improved accuracy and robustness. Leveraging three years of data from the National Solar Radiation Database (NSRDB), the model demonstrated substantial RMSE and MAE reductions and maintained high R² values across diverse meteorological conditions, confirming its robustness. The findings highlight the model's practicality for real-world applications, including solar farm optimization, smart grid management, and renewable energy trading. Future research could focus on improving computational efficiency through advanced optimization techniques, integrating additional atmospheric variables, and exploring hybrid architectures to support real-time adaptability.
Assessing Code Reasoning in Large Language Models: A Literature Review of Benchmarks and Future Directions
Authors: Dehghan Saman
Published: November 2024

The growing role of Large Language Models (LLMs) in software development calls for robust methods to evaluate their code reasoning capabilities. Although LLMs perform well in code generation, accurately assessing their deeper understanding of programming logic, execution behavior, and semantic analysis remains a key challenge. This paper presents a comprehensive review of benchmarks and evaluation frameworks designed to measure code reasoning in LLMs. We explore key terminology, concepts, and frameworks for evaluating code reasoning in LLMs and then analyze the limitations of traditional benchmarks. Emerging evaluation frameworks that assess deeper comprehension through code interpretation, execution behavior, and semantic understanding are also examined. By identifying current gaps and challenges, we offer insights into the state of code reasoning evaluation and propose directions for future research to develop more robust, unbiased benchmarks. This review aims to guide researchers and practitioners in selecting effective evaluation frameworks and advancing the development of LLMs with enhanced code reasoning capabilities.
Optimizing the Human-Computer Interaction Interface of Warehouse Management Systems Using Automatic Speech Recognition Technology
Authors: Song Xue
Published: November 2024

The exponential growth of the e-commerce and logistics industries in recent years has underscored the necessity for a warehouse management system (WMS) that is more efficient and intelligent. The prevailing WMSs are dependent on manual input and the use of handheld devices, which can result in inefficiencies and the potential for human error. In this work, we propose a human-computer interaction model for speech recognition optimized for the warehouse environment. This model integrates noise suppression technology based on an adaptive filter, which can dynamically detect and filter background noise, such as forklift operation sounds, human voices, and mechanical operation sounds. Furthermore, to address the variability in pronunciation, speech rate, and accent among different users, the system incorporates a speech enhancement model based on the variational autoencoder (VAE) technique. This approach enables the system to adaptively adjust the input speech features, thereby enhancing the robustness of recognition. In regard to natural language processing, a natural language understanding module based on bidirectional encoder representations from transformers (BERT) has been developed. The module is capable of semantic parsing of instructions in the user's voice and converting them into executable operation commands. Semantic slot filling technology enables the system to automatically identify the key entities in a task and perform linkage operations with the backend WMS database. The experimental analysis demonstrates that the proposed system is effective in an actual warehouse scenario. Compared with the traditional method, the task completion speed and accuracy are significantly improved.
Functional Food Supplement Enriched with Vegetable Proteins and Probiotics: A Hyperproteic and Probiotic-Formulated Product
Authors: Colla Elielma Cristiane Xavier , Meneguzzi Daiane , Mota Thaís Fernandes Mendonça , Dutra Joyce , Carpes Solange Teresinha , Appelt Patrícia , Batista Trentin Alex , Marchioro Marcelo Luis Kuhn , Dekker Robert F.H. , Alves da Cunha Mário Antônio
Published: November 2024

Functional foods enriched with probiotics can contribute to maintaining health and improving mental and cognitive states. This research involved developing and characterizing a hyperproteic dietary supplement containing a blend of plant proteins and probiotic cells (Lactobacillus reuteri LRE 02 ID 1774). The supplement exhibited high nutritional quality, particularly in protein content and amino acid profile, containing all essential amino acids and branched-chain amino acids (BCAA). Omega-3 fatty acids (150 mg/100g), omega-6 (1420 mg/100g), and omega-9 (1180 mg/100g, cis-11-eicosenoic acid 20 mg/100g) were also detected. The supplement is a source of dietary fiber and probiotics (108 CFU/mL) and demonstrates moderate antioxidant activity. It can be considered innovative as it contains essential amino acids and oryzatein, an immuno-stimulating peptide. Its relevance lies in its hypoallergenic properties, low concentration of anti-nutritional factors, good digestibility, and being gluten- and lactose-free. Additionally, it includes Lactobacillus reuteri, a probiotic with potential immunomodulatory and neuro-cognitive benefits.
Targeting Shikimate Kinase in Acinetobacter baumannii: Gallic Acid as a Potent Inhibitory Metabolite from FDA-Approved Drug Screening
Authors: Alturki Mansour S. , Al Khzem Abdulaziz H. , Gomaa Mohamed , Tawfeeq Nada , Alhamadah Marwah H. , M. Alshehri Futun , Alzahrani Raghad , Alghamdi Hanin , Rants’o Thankhoe A. , Ayil Khaled A.G. , Al Mouslem Abdulaziz K. , Almaghrabi Mohammed
Published: November 2024

The current study identified certain FDA-approved drugs that could be repurposed as selective inhibitors against Acinetobacter baumannii shikimate kinase. Structure- and ligand-based drug design approaches have been integrated herein with extensive in-silico screening, molecular docking, and molecular dynamics studies, focusing on binding affinity and stability. Gallic acid, a plant-derived metabolite, has emerged as the most promising candidate, with significant binding interactions that are well supported by both computational analyses and experimental validation. MIC and MBC tests were conducted to examine their effectiveness against Acinetobacter baumannii and Enterococcus faecalis. Gallic acid demonstrated remarkable activity against the test microbial strains, presenting a promising therapeutic agent, especially given the high expression of shikimate kinase in Acinetobacter baumannii.
Multi-Ligament Reconstruction in an Adolescent Female Affected by Congenital Femoral Deficiency and Complete Anterior and Posterior Cruciate Ligament Agenesis: A Case Report
Authors: Giusti Simone , Bocchi Maria Beatrice , De Fenu Edoardo , Palmacci Osvaldo , Adriani Ezio
Published: November 2024

Background Multi-ligament reconstruction in adolescent patients affected by congenital femoral deficiency is an extremely rare and delicate surgical procedure. There are very few reported cases of complete anterior and posterior cruciate ligament agenesis in these patients. Methods We present a complex case of a 16-year-old girl affected by congenital femoral deficiency and ipsilateral tibial hypoplasia who was treated successfully for a complete agenesis of the anterior (ACL) and posterior (PCL) cruciate ligament with single-sitting ACL and PCL reconstruction. Results (Not Applicable) Conclusions Knee MRI (Magnetic Resonance Imaging) should be requested in all patients affected by congenital femoral deficiency to exclude ligamentous agenesis. Where present, these should be reconstructed at an early stage as soon as limb-lengthening procedures are completed. If still skeletally immature, physeal-sparing surgical techniques should be implemented.
Molecular Epidemiology and In-Depth Characterization of Klebsiella pneumoniae Clinical Isolates from Armenia
Authors: Sedrakyan Anahit , Gevorgyan Zaruhi , Zakharyan Magdalina , Arakelova Karine , Hakobyan Shoghik , Hovhannisyan Alvard , Aminov Rustam
Published: November 2024

The global dissemination of Klebsiella pneumoniae pathotypes with multidrug resistant (MDR) and hypervirulent traits poses a threat to public health. The situation in Armenia is unclear, and we performed a comprehensive characterisation of 48 clinical isolates of K. pneumoniae, collected from 2018 to 2024. The majority of the isolates (64.58%) were extensively drug-resistant (XDR) and MDR. Genomic analysis of 21 isolates revealed the presence of in-ternational high-risk MDR clones (ST395, ST15, and ST307). The ST395 strains were isolated from children and resisted the first-line drugs such as beta-lactams. These isolates harboured a range of virulence determinants from capsule polysaccharides to siderophores to regulators of the mucoid phenotype. The ST395 strains are enriched by ICEs, plasmids and prophages, on which antimicrobial resistance (AMR) and virulence genes are located and which may lead to the convergence of MDR and hypervirulent traits. There is a widespread non-specific AMR mechanisms among our K. pneumoniae strains. These are mutations in the porin genes, which reduce permeability to antimicrobials, and mutations in the regulators of efflux pumps, which lead to overexpression of drug efflux pumps such as AcrAB. These mechanisms may contribute to the elevated MICs and confer AMR to strains with no specific AMR genes.
Innovative Mitochondrial Isolation for Osteoarthritis Transplantation: Safety Profiles and Localization Outcomes In Vitro and In Vivo
Authors: Vaamonde García Carlos , Hermida Gómez Tamara , Paniagua Barro Sara , Burguera Elena F. , Blanco Francisco J. , Fernández Moreno Mercedes
Published: November 2024

Osteoarthritis (OA) is the most common rheumatologic disease and a major cause of pain and disability in older adults. No efficient treatment is currently available. Mitochondrial dysfunc-tion in chondrocytes promotes the molecular dysregulation involved in OA pathogenesis, making transplant of mitochondria as a potential therapeutic targets for treating OA. Mitochondria were isolated from liver using a combining the procedure described by Preble and coworkers with magnetic beads coupled to anti-TOM22 antibodies. The organelles obtained were analyzed to determine their purity and viability. The safety and viability of administration isolated mitochondria into articular tissues as well as the integration and distribution of transplanted mitochondria within joint tissues were analyzed using both in vitro and in vivo models. We estab-lished an efficient, reproducible, effective, and rapid protocol for isolating mitochondria from liver. We obtained mitochondria with high viability, yield, and purity. The isolated mitochondria were injected into joint tissue using both in vitro and in vivo models. Functional mitochondria were detected in the extracellular matrix of the cartilage, menisci and synovium. Our results indicate that mitochondrial transplantation is safe for treating joint alterations. The procedure and data described here support further studies on mitochondrial transplantation as an efficient treatment for OA.
Evaluation of Urban Thermal Environment Variation and Stress Using Remote Sensing Imagery and QGIS for Sustainable Urban Development over Adama City, Ethiopia
Authors: Abera Bereket
Published: November 2024

The urban environments are facing a rapid thermal increase and heat-related stresses driven by an interplay of several factors. To address these challenges, it is crucial to identify space-time thermal state fluctuations of an urban area. The objective of this study is to explore the long-term variation in the thermal state of Adama City, Ethiopia. The study utilized multi-temporal Landsat imagery and QGIS workspace and employed a Mono-window algorithm for Land Surface Temperature (LST) retrieval. The minimum, maximum, and mean LST of the city increased by 0.91, 5.26, and 4.4oc from 1995 to 2010 and 2.23, 6.08, and 3.28oc from 2010 to 2023. The highest urban heat island (UHI) value was 3.25°C in 1995; it varied to 2.44°C in 2010 and 2.84°C in 2023. Furthermore, in 1995, 2010, and 2023, the minimum UHI values were -4.9°C, -4.6°C, and -4.26°C, respectively. The maximum, minimum, and average values of urban thermal field variance (UTFVI) increased from 1995 (0.1685, -0.44588, -0.00368) to 2010 (0.1678, -0.6179, -0.08533) and 2023 (0.20, -0.601, -0.00765). The findings give an overall conclusion that Adama City’s thermal state levels have fluctuated and escalated significantly over time. The study findings emphasize the relevance of increased green areas-focused urban planning to achieve sustainable urban development and SDG-11.
Towards Advanced Speech Signal Processing: A Statistical Perspective on Convolution-Based Architectures and it’s Applications
Authors: Kapu Nirmal Joshua , Karan Raghav
Published: November 2024

This article surveys convolution-based models convolutional neural networks (CNNs), Conformers, ResNets, and CRNNs-as speech signal processing models and provide their statistical backgrounds and speech recognition, speaker identification, emotion recognition, and speech enhancement applications. Through comparative training cost assessment, model size, accuracy and speed assessment, we compare the strengths and weaknesses of each model, identify potential errors and propose avenues for further research, emphasising the central role it plays in advancing applications of speech technologies.
Design and Evaluation of a Low-Cost Mount for Attaching a Laser Tracker’s SMR to a Robot Flange
Authors: Stöckl Florian , Müller Silvan , Strand Marcus , Gardill Markus
Published: November 2024

Robot measurements for calibration and modelling are typically performed using a laser tracker. To measure three-dimensional positions, a Spherically-Mounted Retroreflector (SMR) is attached to the robot. Some researchers use adhesive bonds to attach the SMR, but this makes the positioning of the SMR inaccurate, unstable and unrepeatable, which affects the measurement itself and the traceability of the research results. We therefore investigated ways of attaching a SMR to the robot’s flange for accurate and repeatable measurements. At the same time, we observed measurement errors that occur when using a tool to attach a SMR to the flange. The solution that we developed is a 3D printed mount that can be attached to the flange. We evaluated the accuracy of the mount by measuring its eccentricity and the repeatability of placing the SMR in the mount. Our experiments showed that with an eccentricity radius of 0.35 mm and a repeatability inaccuracy of X=0.075 mm, Y=0.328 mm and Z=0.485 mm the mount has a sufficient accuracy to support calibration processes, traceability of research results and to replace adhesive bond.
Functional Delsarte-Goethals-Seidel-Kabatianskii-Levenshtein-Pfender Bound
Authors: KRISHNA K. MAHESH
Published: November 2024

Pfender \textit{[J. Combin. Theory Ser. A, 2007]} provided a one-line proof for a variant of the Delsarte-Goethals-Seidel-Kabatianskii-Levenshtein upper bound for spherical codes, which offers an upper bound for the celebrated (Newton-Gregory) kissing number problem. Motivated by this proof, we introduce the notion of codes in pointed metric spaces (in particular on Banach spaces) and derive a nonlinear (functional) Delsarte-Goethals-Seidel-Kabatianskii-Levenshtein-Pfender upper bound for spherical codes. We also introduce nonlinear (functional) Kissing Number Problem.