bluuebunny/crossref_metadata_2025 huggingface dataset:
Gene-Silencing Therapeutic Approaches Targeting PI3K/Akt/mTOR Signaling in Degenerative Intervertebral Disk Cells: An In Vitro Comparative Study Between RNA Interference and CRISPR–Cas9
Authors: Ryu Masao , Yurube Takashi , Takeoka Yoshiki , Kanda Yutaro , Tsujimoto Takeru , Miyazaki Kunihiko , Ohnishi Hiroki , Matsuo Tomoya , Kumagai Naotoshi , Kuroshima Kohei , Hiranaka Yoshiaki , Kuroda Ryosuke , Kakutani Kenichiro
Published: December 2024

The mammalian target of rapamycin (mTOR), a serine/threonine kinase, promotes cell growth and inhibits autophagy. The following two complexes contain mTOR: mTORC1 with the regulatory associated protein of mTOR (RAPTOR) and mTORC2 with the rapamycin-insensitive companion of mTOR (RICTOR). The phosphatidylinositol 3-kinase (PI3K)/Akt/mTOR signaling pathway is important in the intervertebral disk, which is the largest avascular, hypoxic, low-nutrient organ in the body. To examine gene-silencing therapeutic approaches targeting PI3K/Akt/mTOR signaling in degenerative disk cells, an in vitro comparative study was designed between small interfering RNA (siRNA)-mediated RNA interference (RNAi) and clustered regularly interspaced short palindromic repeat (CRISPR)–CRISPR-associated protein 9 (Cas9) gene editing. Surgically obtained human disk nucleus pulposus cells were transfected with a siRNA or CRISPR–Cas9 plasmid targeting mTOR, RAPTOR, or RICTOR. Both of the approaches specifically suppressed target protein expression; however, the 24-h transfection efficiency differed by 53.8–60.3% for RNAi and 88.1–89.3% for CRISPR–Cas9 (p < 0.0001). Targeting mTOR, RAPTOR, and RICTOR all induced autophagy and inhibited apoptosis, senescence, pyroptosis, and matrix catabolism, with the most prominent effects observed with RAPTOR CRISPR–Cas9. In the time-course analysis, the 168-h suppression ratio of RAPTOR protein expression was 83.2% by CRISPR–Cas9 but only 8.8% by RNAi. While RNAi facilitates transient gene knockdown, CRISPR–Cas9 provides extensive gene knockout. Our findings suggest that RAPTOR/mTORC1 is a potential therapeutic target for degenerative disk disease.
The p.R66W Variant in RAC3 Causes Severe Fetopathy Through Variant-Specific Mechanisms
Authors: Sugawara Ryota , Ito Hidenori , Tabata Hidenori , Ueda Hiroshi , Scala Marcello , Nagata Koh-ichi
Published: December 2024

RAC3 encodes a small GTPase of the Rho family that plays a critical role in actin cytoskeleton remodeling and intracellular signaling regulation. Pathogenic variants in RAC3, all of which reported thus far affect conserved residues within its functional domains, have been linked to neurodevelopmental disorders characterized by diverse phenotypic features, including structural brain anomalies and facial dysmorphism (NEDBAF). Recently, a novel de novo RAC3 variant (NM_005052.3): c.196C>T, p.R66W was identified in a prenatal case with fetal akinesia deformation sequence (a spectrum of conditions that interfere with the fetus’s ability to move), and complex brain malformations featuring corpus callosum agenesis, diencephalosynapsis, kinked brainstem, and vermian hypoplasia. To investigate the mechanisms underlying the association between RAC3 deficiency and this unique, distinct clinical phenotype, we explored the pathophysiological significance of the p.R66W variant in brain development. Biochemical assays revealed a modest enhancement in intrinsic GDP/GTP exchange activity and an inhibitory effect on GTP hydrolysis. Transient expression studies in COS7 cells demonstrated that RAC3-R66W interacts with the downstream effectors PAK1, MLK2, and N-WASP but fails to activate SRF-, AP1-, and NFkB-mediated transcription. Additionally, overexpression of RAC3-R66W significantly impaired differentiation in primary cultured hippocampal neurons. Acute expression of RAC3-R66W in vivo by in utero electroporation resulted in impairments in cortical neuron migration and axonal elongation during corticogenesis. Collectively, these findings suggest that the p.R66W variant may function as an activated version in specific signaling pathways, leading to a distinctive and severe prenatal phenotype through variant-specific mechanisms.
Circular RNAs in Viral Infection and Antiviral Treatment
Authors: Yin Xiaocai , Li Hongjun , Zhou Yan
Published: December 2024

Circular RNAs (circRNAs) are a class of noncoding RNAs that lack the 5′-cap structure and the 3′ poly(A) tail. Their distinguishing feature is that the 3′ and 5′ ends are covalently linked to form a closed circular structure. CircRNAs have a longer half-life and stronger ribonuclease resistance compared with linear RNA. Viral infections lead to the production of circRNA molecules through the transcription and splicing mechanisms of host cells. circRNAs are produced from the transcription and splicing of the viral genome or from the splicing reactions of the host cell gene. They participate in regulating the replication of many viruses, including coronaviruses, human herpesviruses, human immunodeficiency virus, and cytomegalovirus. CircRNAs regulate the infection process by modulating circRNA expression in host cells and affect cellular biological processes. Some circRNAs have been proposed as diagnostic markers for viral infections. In this review, we discussed the properties of virus-derived circRNAs, the biological functions of diverse viruses-derived and host circRNAs during viral infections, and the critical role of circRNAs in the host’s antiviral immune defense. Extensive research on the applications of circRNAs can help us better understand gene regulatory networks and disease mechanisms.
Plantwide Control for the Separation of THF-H2O in an Azeotropic Distillation Process
Authors: Ramos-Martinez Moises , Ortiz-Torres Gerardo , Sorcia-Vázquez Felipe D. J. , Torres-Cantero Carlos Alberto , Calixto-Rodriguez Manuela , Mena-Enriquez Mayra G. , Valdez Martínez Jorge Salvador , Sarmiento-Bustos Estela , Cruz Rojas Alan , Rumbo-Morales Jesse Y.
Published: December 2024

This paper presents a plantwide control strategy for optimizing a pressure-swing azeotropic distillation process used in tetrahydrofuran dehydration. Leveraging Skogestad’s methodology, this strategy focused on two distillation columns: a low-pressure column for water recovery at 20 psia and a high-pressure column that achieved 0.99 molar fraction purity of tetrahydrofuran at 115 psia. This study identified critical control variables through plant analysis by implementing PI controllers in the regulatory control layer to stabilize flows and pressures. In the supervisory control layer, a PI controller combined with MIMO MPC effectively enhanced the product purity and reduced the energy consumption by 36%. Stable inlet and outlet flow conditions (100 lbmol/hr inlet, 29.59 lbmol/hr outlet) were maintained without compromising the equipment integrity. The operational ranges for the process included variations in the tetrahydrofuran mole fraction from 0.25 to 0.35 at the inlet, which demonstrated a robust performance across perturbations. These achievements signify significant advancements in process efficiency and sustainability, offering substantial reductions in energy usage while ensuring consistent high purity levels in tetrahydrofuran production. The developed control structure sets a new standard for efficient azeotropic distillation processes, with implications for enhancing operational performance across industrial applications.
Resilience of Chinese Ports to Tropical Cyclones: Operational Efficiency and Strategic Importance
Authors: Poo Mark Ching-Pong , Zhang Wen , Kamalian Leila , Wang Tianni , Lau Yui-yip , Xu Tina Ziting
Published: December 2024

This study evaluated the resilience of five major Chinese ports—Shanghai, Tsingtao, Shenzhen, Xiamen, and Qinzhou—against the impacts of tropical cyclones. These ports, as integral global maritime supply chain nodes, face rising vulnerabilities from climate-related disruptions such as typhoons, sea-level rise, and extreme temperature fluctuations. Employing a resilience assessment framework, this study integrated climate and operational data to gauge how cyclone-induced events affect port performance, infrastructure, and economic stability. Multi-centrality analysis and the Borda count method were applied to assess each port’s strategic importance and operational efficiency under cyclone exposure. The findings highlight variations in resilience across the ports, with Shanghai and Tsingtao showing heightened risk due to their critical roles within international logistics networks. This study suggests strategies like strengthening infrastructure, improving emergency responses, and adopting climate-resilient policies to make China’s ports more sustainable and resilient to climate threats. This research offers actionable insights for policymakers and port authorities, contributing to a more climate-resilient maritime logistics framework.
Study on the Surface Properties of Lavender Microcapsule Fragrance-Releasing Flooring
Authors: Wang Wei , Zhou Muyao , Liu Yifan , Gui Chengsheng , Wu Zhihui , Liu Xinyou
Published: December 2024

This study investigates the effect of incorporating lavender microcapsules into flooring coatings on the surface properties of the flooring. The experimental results indicate that after adding lavender microcapsules, the flooring’s color, gloss, wear resistance, and hydrophobicity experienced slight changes, with a color difference (ΔE) below 2 units, gloss decreasing by 8%, and wear resistance reducing by approximately 5%. However, these changes were within an acceptable range. Chemical composition analysis confirmed the fragrance-releasing effect of the lavender microcapsules. Furthermore, scanning electron microscopy (SEM) and Fourier transform infrared spectroscopy (FTIR) verified the structural integrity of the microcapsules and their effective fragrance release. This study demonstrates that the introduction of lavender microcapsules not only enhances the scent perception of the flooring but also achieves long-term fragrance release while maintaining its fundamental physical properties, highlighting its potential for practical applications.
High-Severity Wildfires Alter Ant (Hymenoptera: Formicidae) Foraging Assemblage Structure in Montane Coniferous Forests and Grasslands in the Jemez Mountains, New Mexico, USA
Authors: Knudsen Jonathan , Parmenter Robert , Sumnicht Theodore , Verble Robin
Published: December 2024

High-severity wildfires create heterogeneous patterns of vegetation across burned landscapes. While these spatial patterns are well-documented, less is known about the short- and long-term effects of large-scale high-severity wildfires on insect community assemblages and dynamics. Ants are bottom-up indicators of ecosystem health and function that are sensitive to disturbance and fill a variety of roles in their ecosystems, including altering soil chemistry, dispersing seeds, and serving as a key food resource for many species, including the federally endangered Jemez Mountain salamander (Plethodon neomexicanus). We examined the post-fire effects of the 2011 Las Conchas Wildfire on ant communities in the Valles Caldera National Preserve (Sandoval County, New Mexico, USA). We collected ants via pitfall traps in replicated burned and unburned sites across three habitats: ponderosa pine forests, mixed-conifer forests, and montane grassland. We analyzed trends in species richness, abundance, recruitment, loss, turnover, and composition over five sequential years of post-fire succession (2011–2015). Ant foraging assemblage was influenced by burn presence, season of sampling, and macrohabitat. We also found strong seasonal trends and decreases over time since fire in ant species richness and ant abundance. However, habitat and seasonal effects may be a stronger predictor of ant species richness than the presence of fire or post-fire successional patterns.
Comparative Analysis of Rapid and Less Invasive Methods for A2A2 Dairy Cattle Genotyping and A2 Milk Purity Detection
Authors: Albiero Leandra Oliveira Xavier , Ansiliero Rafaela , Kempka Aniela Pinto
Published: December 2024

In this study, two methods for assessing the purity of A2 fermented milk and β-casein genotypes in dairy cows were examined. The need for rapid and precise methods for herd screening and A2 milk quality control justified this study. Accordingly, an ELISA test was developed to identify β-casein A1 in fermented milk, and a commercial Lateral Flow Immunoassay (LFIA) was evaluated to determine A2A2 genotypes and the purity of A2 milk. The results demonstrated 100% sensitivity and specificity of the ELISA test in identifying β-casein A1. The LFIA test successfully identified A2A2 genotypes and confirmed the purity of A2 milk, with a minimum detectable contamination of 5% for raw milk and 10% for fermented milk. Both tests exhibited 100% sensitivity and specificity, resulting in positive and negative predictive values of 100%. The positive likelihood ratio was infinite, while the negative was zero, indicating a precise and reliable test with no false diagnoses. Compared to traditional genotyping, these methods proved to be more practical and showed potential for large-scale screening. It was concluded that ELISA and LFIA are valuable tools for ensuring the quality and authenticity of A2 milk, meeting the demands of producers and consumers for safe and healthy dairy products.
Multi-Modal Dataset of Human Activities of Daily Living with Ambient Audio, Vibration, and Environmental Data
Authors: Pfitzinger Thomas , Koch Marcel , Schlenke Fabian , Wöhrle Hendrik
Published: December 2024

The detection of human activities is an important step in automated systems to understand the context of given situations. It can be useful for applications like healthcare monitoring, smart homes, and energy management systems for buildings. To achieve this, a sufficient data basis is required. The presented dataset contains labeled recordings of 25 different activities of daily living performed individually by 14 participants. The data were captured by five multisensors in supervised sessions in which a participant repeated each activity several times. Flawed recordings were removed, and the different data types were synchronized to provide multi-modal data for each activity instance. Apart from this, the data are presented in raw form, and no further filtering was performed. The dataset comprises ambient audio and vibration, as well as infrared array data, light color and environmental measurements. Overall, 8615 activity instances are included, each captured by the five multisensor devices. These multi-modal and multi-channel data allow various machine learning approaches to the recognition of human activities, for example, federated learning and sensor fusion.
Definitions of Incidental [18F]FDG PET/CT Findings in the Literature: A Systematic Review and Definition Proposal
Authors: Mølstrøm Jacob Pilegaard , Lange Natascha , Pareek Manan , Thomassen Anders , Nielsen Anne Lerberg , Høilund-Carlsen Poul Flemming , Godballe Christian , Rohde Max
Published: December 2024

Objectives: The objectives of this study were (1) to systematically review the currently used definitions of incidental 2-deoxy-2-[18F]fluoro-D-glucose positron emission tomography/computed tomography findings (IPFs) in the literature and (2) to propose an IPF definition. Methods: A systematic search was conducted according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses statement. The search was guided by the question “How is IPF defined?” and was performed in MEDLINE, Embase, and the Cochrane Library. The retrieved studies were reviewed and analyzed. The definitions of IPFs in the included studies were compiled into two sets of categories based on the description of FDG uptake and the specification of clinical factors in defining IPFs. Results: The systematic literature search identified 4852 publications accessible for title–abstract screening, which yielded 395 studies for full-text assessment. Sixty-five studies met the eligibility criteria and were included. Sixty-two percent mentioned “FDG uptake” in their definition. In 40% of the definitions, “Focal FDG uptake” was specified, while “FDG uptake in the surrounding tissue” was included in 15%. Fifty-seven percent stated that IPFs were “Unrelated to PET/CT indication”. Thirty-four percent specified IPFs as “Present in other organ than PET/CT indication”, whereas 20% included “No known disease related to IPF”. Seventeen percent of the definitions comprised a “New finding”, while 15% and 11% encompassed a “Clinical asymptomatic patient” and “Not a metastasis”, respectively. Finally, 5% of the definitions included “Potential clinical significance”. Conclusions: No generally accepted definition of IPFs currently exists. We propose an IPF definition based on explicit FDG uptake and clinical patient-related factors.
Comprehensive Insights into Artificial Intelligence for Dental Lesion Detection: A Systematic Review
Authors: Demir Kubra , Sokmen Ozlem , Karabey Aksakalli Isil , Torenek-Agirman Kubra
Published: December 2024

Background/Objectives: The growing demand for artificial intelligence (AI) in healthcare is driven by the need for more robust and automated diagnostic systems. These methods not only provide accurate diagnoses but also promise to enhance operational efficiency and optimize resource utilization in clinical workflows. In the field of dental lesion detection, the application of deep learning models to various imaging techniques has gained significant prominence. This study presents a comprehensive systematic review of the utilization of deep learning methods for detecting dental lesions across different imaging modalities, including panoramic imaging, periapical radiographs, and cone-beam computed tomography (CBCT). A systematic search was conducted following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines to ensure a structured and transparent review process. Methods: This study addresses four key research questions related to the types of objects used for AI in dental images, state-of-the-art approaches for detecting lesions in dental images, data augmentation methods, and challenges and possible solutions to the existing AI-based dental lesion detection. Furthermore, this systematic review was performed on 29 primary studies identified from multiple electronic databases. This review focused on studies published between 2019 and 2024, sourced from IEEE, Web of Knowledge, Springer, ScienceDirect, PubMed, and Google Scholar. Results: We identified five types of lesions in dental images as periapical lesions, cyst lesions, jawbone lesions, dental caries, and apical lesions. Among the fourteen state-of-the-art deep learning approaches, the results demonstrate that deep learning models, such as U-Net, AlexNet, and You Only Look Once (YOLO) version 8 (YOLOv8) are commonly employed for dental lesion detection. These deep learning models have the potential to serve as integral components of decision-making processes by improving detection accuracy and supporting clinical workflows. Furthermore, we found that among twelve types of data augmentation techniques, flipping, rotation, and reflection methods played an important role in increasing the diversity of the datasets. We also identified six challenges for dental lesion detection, and the main issues were identified as data integration, poor data quality, limited model generalization, and overfitting. Proposed solutions against the aforementioned challenges include the integration of larger datasets, model optimization, and diversification of data sources. Conclusions: This study provides a comprehensive overview of current methodologies and potential advancements in dental lesion detection using deep learning. The findings indicate that possible solutions against the challenges of AI-based diagnostic methods in dental lesion detection need to be more generalizable regardless of image type, the number of data, and data quality.
UAVs-Based Visual Localization via Attention-Driven Image Registration Across Varying Texture Levels
Authors: Ren Yan , Dong Guohai , Zhang Tianbo , Zhang Meng , Chen Xinyu , Xue Mingliang
Published: December 2024

This study investigates the difficulties associated with image registration due to variations in perspective, lighting, and ground object details between images captured by drones and satellite imagery. This study proposes an image registration and drone visual localization algorithm based on an attention mechanism. Initially, an improved Oriented FAST and Rotated BRIEF (ORB) algorithm incorporating a quadtree-based feature point homogenization method is designed to extract image feature points, providing support for the initial motion estimation of UAVs. Following this, we combined a convolutional neural network with an attention mechanism and the inverse-combined Lucas-Kanade method to further extract image features. This integration facilitates the efficient registration of drone images with satellite tiles. Finally, we utilized the registration results to correct the initial motion of the drone and accurately determine its location. Our experimental findings indicate that the proposed algorithm achieves an average absolute positioning error of less than 40 m for low-texture flight paths and under 10 m for high-texture paths. This significantly mitigates the positioning challenges that arise from inconsistencies between drone images and satellite maps. Moreover, our method demonstrates a notable improvement in computational speed compared to existing algorithms.
Research on 3D Localization of Indoor UAV Based on Wasserstein GAN and Pseudo Fingerprint Map
Authors: Yang Junhua , Tian Jinhang , Qi Yang , Cheng Wei , Liu Yang , Han Gang , Wang Shanzhe , Li Yapeng , Cao Chenghu , Qin Santuan
Published: December 2024

In addition to outdoor environments, unmanned aerial vehicles (UAVs) also have a wide range of applications in indoor environments. The complex and changeable indoor environment and relatively small space make indoor localization of UAVs more difficult and urgent. An innovative 3D localization method for indoor UAVs using a Wasserstein generative adversarial network (WGAN) and a pseudo fingerprint map (PFM) is proposed in this paper. The primary aim is to enhance the localization accuracy and robustness in complex indoor environments. The proposed method integrates four classic matching localization algorithms with WGAN and PFM, demonstrating significant improvements in localization precision. Simulation results show that both the WGAN and PFM algorithms significantly reduce localization errors and enhance environmental adaptability and robustness in both small and large simulated indoor environments. The findings confirm the robustness and efficiency of the proposed method in real-world indoor localization scenarios. In the inertial measurement unit (IMU)-based tracking algorithm, using the fingerprint database of initial coarse particles and the fingerprint database processed by the WGAN algorithm to locate the UAV, the localization error of the four algorithms is reduced by 30.3% on average. After using the PFM algorithm for matching localization, the localization error of the UAV is reduced by 28% on average.
Strategic Learning Alliances and Cooperation: A Game Theory Perspective on Organizational Collaboration
Authors: Tlemsani Issam , Matthews Robin , Mohamed Hashim Mohamed Ashmel
Published: December 2024

This study explores the dynamics of international strategic learning alliances through the lens of game theory, incorporating complexity and cooperative game theories to develop a model of organizational evolution. Using simulations and network resources, we examine 1200 cases to assess the costs and benefits of inter-organizational cooperation, with a focus on mutual payoffs and strategic decision-making. Our research addresses key gaps in the literature by analyzing how game-theoretic structures impact the success of alliances, providing actionable insights for firms aiming to enhance strategic partnerships. The findings offer valuable guidance for international partners involved in learning alliances, emphasizing the importance of aligning institutional responses with perceived risks and opportunities. By identifying the motivations and success factors behind strategic alliances, organizations can better formulate optimal strategies for collaboration. This paper contributes to the discourse on inter-firm cooperation by highlighting the complexities of strategic learning alliances and offering new perspectives for future research.
An All-Digital Dual-Mode Clock and Data Recovery Circuit for Human Body Communication Systems
Authors: Heo Yoon , Lee Won-Young
Published: December 2024

This paper describes an all-digital clock and data recovery (CDR) circuit for implementing edge processing with a wireless body area network (WBAN). The CDR circuit performs delay-locked loop (DLL)-based and phase-locked loop (PLL)-based operations depending on the use of an external reference clock and is implemented using a digital method that is robust against external noise. The clock generator circuit shared by the two operation methods is described in detail, and the CDR circuit recovers 42 Mb/s input data and a 42 MHz clock, which are the specifications of human body communication (HBC). In DLL-based CDR operation, the clock generator operates as a digitally controlled delay line (DCDL) that delays the reference clock by more than one period. In PLL-based CDR operations, it operates as a digitally controlled oscillator (DCO) that oscillates the 42 MHz clock and adjusts the clock frequency. The proposed all-digital CDR is fabricated in 65 nm CMOS technology with an area of 0.091 mm2 and operates with a supply voltage of 1.0 V. Post-layout simulation results show that the lock time for DLL-based CDR operation is 1.6 μs, the clock peak-to-peak jitter is 0.38 ns, and the power consumption is 341.8 μW. For PLL-based CDR operations, the lock time is 6 μs, the clock peak-to-peak jitter is 2.92 ns, and the power consumption is 280.2 μW, respectively.
KG-EGV: A Framework for Question Answering with Integrated Knowledge Graphs and Large Language Models
Authors: Hou Kun , Li Jingyuan , Liu Yingying , Sun Shiqi , Zhang Haoliang , Jiang Haiyang
Published: December 2024

Despite the remarkable progress of large language models (LLMs) in understanding and generating unstructured text, their application in structured data domains and their multi-role capabilities remain underexplored. In particular, utilizing LLMs to perform complex reasoning tasks on knowledge graphs (KGs) is still an emerging area with limited research. To address this gap, we propose KG-EGV, a versatile framework leveraging LLMs to perform KG-based tasks. KG-EGV consists of four core steps: sentence segmentation, graph retrieval, EGV, and backward updating, each designed to segment sentences, retrieve relevant KG components, and derive logical conclusions. EGV, a novel integrated framework for LLM inference, enables comprehensive reasoning beyond retrieval by synthesizing diverse evidence, which is often unattainable via retrieval alone due to noise or hallucinations. The framework incorporates six key stages: generation expansion, expansion evaluation, document re-ranking, re-ranking evaluation, answer generation, and answer verification. Within this framework, LLMs take on various roles, such as generator, re-ranker, evaluator, and verifier, collaboratively enhancing answer precision and logical coherence. By combining the strengths of retrieval-based and generation-based evidence, KG-EGV achieves greater flexibility and accuracy in evidence gathering and answer formulation. Extensive experiments on widely used benchmarks, including FactKG, MetaQA, NQ, WebQ, and TriviaQA, demonstrate that KG-EGV achieves state-of-the-art performance in answer accuracy and evidence quality, showcasing its potential to advance QA research and applications.
SincNet-Based Speaker Identification for Robotic Environments with Varying Human–Robot Interaction Distance
Authors: Kim Seo-Hyun , Jo A-Hyeon , Kwak Keun-Chang
Published: December 2024

As human–robot interaction (HRI) becomes increasingly significant, various studies have focused on speaker recognition. However, few studies have explored this topic in the specific environment of home service robots. Notably, most existing research relies on databases composed of English-language data, while studies utilizing Korean speech data are exceedingly scarce. This gap underscores the need for research on speaker recognition in robotic environments, specifically using Korean data. In response, this paper conducts experiments using a speaker recognition database tailored to the Korean language and set in a robotic context. The database includes noise generated by robot movement as well as common environmental noise, accounting for variable distances between humans and robots, which are partitioned accordingly. The deep learning model employed is SincNet, with experiments conducted under two settings for the SincNet filter parameters: one with learnable parameters and the other with fixed values. After training the model with data collected at varying distances, performance was tested across these distances. Experimental results indicate that SincNet with learnable parameters achieved a peak accuracy of 99%.
Efficient Neural Network-Based Compact Modeling for Novel Device Structures Using a Multi-Fidelity Model and Active Learning
Authors: Jeong HyunJoon , Choi JinYoung , Kim Yohan , Kong Jeong-Taek , Kim SoYoung
Published: December 2024

Neural network (NN)-based compact modeling methodologies are gaining attention due to the challenges of device complexity, narrow model coverage, and SPICE simulation speed in advanced semiconductor technology nodes. As device complexity increases, the number of process and structural variables also increases, which significantly increases the amount of technology computer-aided design (TCAD) simulation data required for NN-based compact modeling. This study proposes a multi-fidelity model and active learning approach to predict global and local variations of nanosheet FETs (NSFETs) with less than 1.5% error, significantly reducing the number of required TCAD simulations by more than half compared with conventional modeling techniques. In addition, the simplified NN model with a smaller training dataset significantly reduces the SPICE simulation time.
Particle Swarm Optimization for k-Coverage and 1-Connectivity in Wireless Sensor Networks
Authors: Siamantas Georgios , Kandris Dionisis
Published: December 2024

Wireless Sensor Networks are used in an ever-increasing range of applications, thanks to their ability to monitor and transmit data related to ambient conditions in almost any area of interest. The optimization of coverage and the assurance of connectivity are fundamental for the efficiency and consistency of Wireless Sensor Networks. Optimal coverage guarantees that all points in the field of interest are monitored, while the assurance of the connectivity of the network nodes assures that the gathered data are reliably transferred among the nodes and the base station. In this research article, a novel algorithm based on Particle Swarm Optimization is proposed to ensure coverage and connectivity in Wireless Sensor Networks. The objective function is derived from energy function minimization methodologies commonly applied in bounded space circle packing problems. The performance of the novel algorithm is not only evaluated through both simulation and statistical tests that demonstrate the efficacy of the proposed methodology but also compared against that of relative algorithms. Finally, concluding remarks are drawn on the potential extensibility and actual use of the algorithm in real-world scenarios.
PMHR: Path-Based Multi-Hop Reasoning Incorporating Rule-Enhanced Reinforcement Learning and KG Embeddings
Authors: Ma Ang , Yu Yanhua , Shi Chuan , Zhen Shuai , Pang Liang , Chua Tat-Seng
Published: December 2024

Multi-hop reasoning provides a means for inferring indirect relationships and missing information from knowledge graphs (KGs). Reinforcement learning (RL) was recently employed for multi-hop reasoning. Although RL-based methods provide explainability, they face challenges such as sparse rewards, spurious paths, large action spaces, and long training and running times. In this study, we present a novel approach that combines KG embeddings and RL strategies for multi-hop reasoning called path-based multi-hop reasoning (PMHR). We address the issues of sparse rewards and spurious paths by incorporating a well-designed reward function that combines soft rewards with rule-based rewards. The rewards are adjusted based on the target entity and the path to it. Furthermore, we perform action filtering and utilize the vectors of entities and relations acquired through KG embeddings to initialize the environment, thereby significantly reducing the runtime. Experiments involving a comprehensive performance evaluation, efficiency analysis, ablation studies, and a case study were performed. The experimental results on benchmark datasets demonstrate the effectiveness of PMHR in improving KG reasoning accuracy while preserving interpretability. Compared to existing state-of-the-art models, PMHR achieved Hit@1 improvements of 0.63%, 2.02%, and 3.17% on the UMLS, Kinship, and NELL-995 datasets, respectively. PMHR provides not only improved reasoning accuracy and explainability but also optimized computational efficiency, thereby offering a robust solution for multi-hop reasoning.
A Review of Emerging Sensor Technologies for Tank Inspection: A Focus on LiDAR and Hyperspectral Imaging and Their Automation and Deployment
Authors: Pallas Enguita Sergio , Chen Chung-Hao , Kovacic Samuel
Published: December 2024

This paper reviews various sensor technologies for tank inspection, focusing on Light Detection and Ranging (LiDAR) and Hyperspectral Imaging (HSI) as advanced solutions for corrosion detection. These technologies are evaluated alongside traditional methods such as ultrasonic, electromagnetic, and thermographic inspections. This review highlights their potential to enhance inspection accuracy, reduce the limitations of manual inspection, and support integrated data analysis for comprehensive asset management. Additionally, this paper proposes a pathway for automating these techniques to streamline inspection processes and improve implementation in practical applications.
Research on Personalized Recommendation of Complementary Products Based on Demand Cross-Elasticity and Hypergraphs
Authors: Duan Ganglong , Du Yutong , Shang Yanying
Published: December 2024

To improve recommendation systems, it is essential to enhance both their practicality and accuracy, thereby supporting users in making informed shopping decisions. Incorporating two types of product relationships can effectively achieve these goals: first, the product relationships, like complements, and second, the social relationships among users. However, existing studies have paid little attention to user-side information or item-side information. This paper proposes a product recommendation model that utilizes cross-elasticity of demand and hypergraphs, referred to as Hg-CR. First, users and items build a hypergraph. The user–item interactions form the hyperedges. Also, users build a hypergraph between themselves based on their social relationships. Second, hypergraph attention networks (HANs) learn the relationships between nodes. They capture the key features of nodes and hyperedges with a high degree of adaptability. A community detection algorithm organizes users into groups for product recommendations by assessing their similarities. Within different communities, individuals seek complementary products based on the cross-elasticity theory of demand. Additionally, we provide recommendations for complementary products. Tests on real datasets show that the Hg-CR model is about 10% more accurate than the other baseline models.
Developing an Alert System for Agricultural Protection: Sika Deer Detection Using Raspberry Pi
Authors: Sharma Sandhya , Pansri Buchaputara , Timilsina Suresh , Gautam Bishnu Prasad , Okada Yoshifumi , Watanabe Shinya , Kondo Satoshi , Sato Kazuhiko
Published: December 2024

Agricultural loss due to the overpopulation of Sika deer poses a significant challenge in Japan, leading to frequent human–wildlife conflicts. We conducted a study in Muroran, Hokkaido (42°22′56.1″ N–141°01′51.5″ E), with the objective of monitoring Sika deer and notifying farmers and locals. We deployed a Sika deer detection model (YOLOv8-nano) on a Raspberry Pi, integrated with an infrared camera that captured images only when a PIR sensor was triggered. To further understand the timing of Sika deer visits and potential correlations with environmental temperature and humidity, respective sensors were installed on Raspberry Pi and the data were analyzed using an ANOVA test. In addition, a buzzer was deployed to deter Sika deer from the study area. The buzzer was deactivated in the first 10 days after deployment and was activated in the following 20 days. The Sika deer detection model demonstrated excellent performance, with precision and recall values approaching 1, and a bounding box creation latency of 0.82 frames per second. Once a bounding box was established after Sika deer detection, alert notifications were automatically sent via email and the LINE messaging application, with an average notification time of 0.32 s. Regarding the buzzer’s impact on Sika deer, 35% of the detected individuals reacted by standing upright with alert ears, while 65% immediately fled the area. Analysis revealed that the time of day for Sika deer visits was significantly correlated with humidity (F = 8.95, p < 0.05), but no significant association with temperature (F = 0.681, p > 0.05). These findings represent a significant step toward mitigating human–wildlife conflicts and reducing agricultural production losses through effective conservation measures.
Bearing Fault Diagnosis Method Based on Osprey–Cauchy–Sparrow Search Algorithm-Variational Mode Decomposition and Convolutional Neural Network-Bidirectional Long Short-Term Memory
Authors: Xiong Zhiyuan , Jiang Haochen , Wang Da , Wu Xu , Wu Kenan
Published: December 2024

To solve the problem of the low diagnosis rate of early weak faults of rolling bearings, a novel bearing fault diagnosis method based on Variational Mode Decomposition (VMD) and convolutional neural network (CNN)−Bidirectional Long Short-Term Memory (BiLSTM) was proposed. Based on the basic Sparrow Search Algorithm, the tent chaotic mapping, the Osprey Optimization Algorithm, and the Cauchy mutation were used to enhance the global search ability of the algorithm. To improve the accuracy of fault diagnosis, the BiLSTM layer is introduced into CNN to preserve the global and local features to the maximum extent. The experimental results show that VMD avoids the end effect problem in Empirical Mode Decomposition (EMD). The accuracy rate of the diagnosis model based on CNN-BILSTM reached 97.6667%, which was higher than that of the common diagnosis model.
Supporting the Characterization of Preeclampsia Patients Through Descriptive and Clustering Analysis
Authors: Parrales-Bravo Franklin , Caicedo-Quiroz Rosangela , Vasquez-Cevallos Leonel , Tolozano-Benites Elena , Charco-Aguirre Jorge , Barzola-Monteses Julio , Cevallos-Torres Lorenzo
Published: December 2024

One of the most common causes of maternal death during pregnancy is preeclampsia. A deeper understanding of the patient’s features can aid in the hospital’s clinical care distribution. However, at the IESS Los Ceibos Hospital, these types of studies have not been carried out for preeclampsia. Therefore, in this work, we describe the application of descriptive and clustering analysis to characterize preeclamptic patients. Preeclamptic patients treated at the IESS Los Ceibos Hospital in Guayaquil comprised the dataset used in this study. Descriptive and clustering analysis allowed us to find that severe preeclampsia (O141) is the most common diagnosis when preeclamptic patients arrive at the hospitalization unit, representing 79.5% of the cases. Moreover, women whose maternal age falls between 26 and 35 years have the highest prevalence of preeclampsia, representing 55.4% of the cases. Finally, adult patients in their late 30s or older are often diagnosed with severe preeclampsia (O141) and often require many hours of hospital care during the first two visits. These findings will help to generate care and prevention policies, such as the use of a low dose of aspirin, in these age groups to avoid the complications that preeclampsia can cause.
Morphological Alterations and Oxidative Stress Induction in Danio rerio Liver After Short-Term Exposure to the Strobilurin Fungicide Dimoxystrobin
Authors: Macirella Rachele , Ahmed Abdalmoiz I. M. , Talarico Federica , Gharbi Naouel , Mezzasalma Marcello , Brunelli Elvira
Published: December 2024

Unlike many other fungicides, strobilurins are applied several times during the growing season for prophylactic purposes, thus heightening the risk of environmental contamination. In the EU, the dimoxystrobin approval period lasted for 17 years. It has been classified as moderately toxic to birds and highly toxic to earthworms, and it is suspected to be carcinogenic to humans. However, it is still commercialized in several countries. The effects of dimoxystrobin are still largely underexplored, with only three studies reporting sublethal alterations in fish. Here, we evaluated for the first time the effects of dimoxystrobin on zebrafish liver after short-term exposure (96 h) to two sublethal and environmentally relevant concentrations (6.56 and 13.13 μg/L), providing evidence of morphological, functional, and ultrastructural modifications. We revealed severe alterations encompassing three reaction patterns: circulatory disturbance, regressive and progressive changes, which also showed a dose-dependent trend. Furthermore, we revealed that dimoxystrobin induced a significant increase in lipid content, a decrease in glycogen granules and affected the defensive response against oxidative stress through a significant downregulation of SOD and CAT. The information presented here demonstrates that the hazardous properties of dimoxystrobin may result from several pathological events involving multiple targets. Our results also emphasize the importance of the combined use of morphological, ultrastructural and functional investigation in ecotoxicological studies.
The Maturation of the International Health Crisis Response: The Polish Typhus Epidemic of 1916–1923 Compared to the African Ebola Virus Disease Epidemic of 2013–2016: Part I, the Polish Epidemic
Authors: Anstead Gregory M.
Published: December 2024

Poland suffered an epidemic of louse-borne typhus from 1916–1923, with 400,000 cases and more than 130,000 deaths. The causative factors were depressed economic conditions and a refugee crisis that engulfed Poland after World War I. The recognition of the epidemic in 1919 stimulated the creation of the League of Red Cross Societies (LRCS). However, the LCRS had limited resources, and the Poles requested help from other governments and the League of Nations (LoN). The United States sent the American–Polish Relief Expedition to conduct delousing. However, the Polish–Soviet War of 1920 disrupted typhus control and exacerbated the refugee situation. The LoN belatedly organized an underfunded Epidemic Commission. The LCRS sent a research team that did groundbreaking work on the pathology of typhus. Into 1921, the epidemic continued, driven by refugees from typhus-stricken Russia. By 1924, typhus cases were finally approaching pre-World War I levels. Multiple factors lead to the amelioration of the epidemic. The repatriation of prisoners of war and displaced civilians had concluded by 1923. Also, there had been a steady influx of sanitary, food, economic, and medical aid from various organizations into Poland since 1919. Administratively, within Poland, the anti-typhus campaign was also conducted more effectively by the Extraordinary Epidemic Commissariat.
Investigation of Climatic Factors Affecting the Amount of Foraged Matsutake Mushrooms in Korea
Authors: Choi Doo-Ho , Ko Cheol-Soon , Oh Youn-Lee , Im Ji-Hoon , Oh Minji , Lee Eun-Ji
Published: December 2024

Tricholoma matsutake is a valuable edible mushroom in Eastern Asia. Due to the challenges of artificial cultivation, T. matsutake cultivation has relied on foraging from pine forests. Changes in global climate variables, such as temperature and precipitation, could affect the amount of foraged T. matsutake. In this study, we investigated the correlation between the amount of foraged T. matsutake and climatic factors (average monthly temperature and precipitation) in Yangyang-gun using the augmented Dickey–Fuller test and the ordinary least squares method. Among the assessed correlations between the foraged T. matsutake and different climatic factors, the average temperature in August was significantly correlated with the amount of foraged T. matsutake, increasing by 1.5 tons when the temperature in August increased by 1 unit. Overall, this study identified a potentially strong positive correlation between the average temperature in August and amount of foraged T. matsutake.
Evaluating Variables’ Influence on Forwarder Performance and Fuel Efficiency in Mountain Salvage Logging Using an Automatic Work-Element Detection Method
Authors: Bacescu Narcis Mihail , Hueller Sebastiano , Marchi Luca , Grigolato Stefano
Published: December 2024

Extreme climate events are increasingly damaging forests, particularly in Europe’s Alps. These disturbances lead to more damaged timber, necessitating rapid salvage operations to preserve timber value and protect ecosystems. However, salvage logging, though essential, raises concerns about its environmental impact, especially on soil conservation and forest regeneration. To mitigate these effects, best practices such as leaving logging residues and avoiding wet soils are recommended. Nevertheless, fuel efficiency remains a critical concern. This study focuses on addressing gaps in understanding forwarder productivity in salvage logging, considering factors such as assortment number, extraction distance, and payload. Utilizing Automatic Work-Element Detection (AWED) for data collection, this study enhances fuel efficiency analysis. Findings show that the average cycle time was 27.4 min, with 4.9 L of fuel consumed per cycle. Each cycle covered 241.3 m, extracting 11.7 m3 of timber, yielding a productivity rate of 31.6 m3 per machine hour and a fuel efficiency of 0.4 L per m3 and per 100 m. Traveling was the most time- and fuel-intensive task. Assortment type significantly impacted loading time and fuel consumption, with short sawlogs requiring fewer crane cycles. Key factors influencing productivity and fuel efficiency were average log volume, distance, payload, and slope.