bluuebunny/crossref_metadata_2025 huggingface dataset:
Data on the Land Cover Transition, Subsequent Landscape Degradation, and Improvement in Semi-Arid Rainfed Agricultural Land in North–West Tunisia
Authors: Shiri Zahra , Frija Aymen , Rejeb Hichem , Ouerghemmi Hassen , Le Quang Bao
Published: August 2024

Understanding past landscape changes is crucial to promote agroecological landscape transitions. This study analyzes past land cover changes (LCCs) alongside subsequent degradation and improvements in the study area. The input land cover (LC) data were taken from ESRI’s ArcGIS Living Atlas of the World and then assessed for accuracy using ground truth data points randomly selected from high-resolution images on the Google Earth Engine. The LCC analyses were performed on QGIS 3.28.15 using the Semi-Automatic Classification Plugin (SCP) to generate LCC data. The degradation or improvement derived from the analyzed data was subsequently assessed using the UNCCD Good Practice Guidance to generate land cover degradation data. Using the Landscape Ecology Statistics (LecoS) plugin in QGIS, the input LC data were processed to provide landscape metrics. The data presented in this article show that the studied landscape is not static, even over a short-term time horizon (2017–2022). The transition from one LC class to another had an impact on the ecosystem and induced different states of degradation. For the three main LC classes (forest, crops, and rangeland) representing 98.9% of the total area in 2022, the landscape metrics, especially the number of patches, reflected a 105% increase in landscape fragmentation between 2017 and 2022.
Genomic Insights into Bacillus thuringiensis V-CO3.3: Unveiling Its Genetic Potential against Nematodes
Authors: Palma Leopoldo , Bel Yolanda , Escriche Baltasar
Published: August 2024

Bacillus thuringiensis (Bt) is a Gram-positive, spore-forming, and ubiquitous bacterium harboring plasmids encoding a variety of proteins with insecticidal activity, but also with activity against nematodes. The aim of this work was to perform the genome sequencing and analysis of a native Bt strain showing bipyramidal parasporal crystals and designated V-CO3.3, which was isolated from the dust of a grain storehouse in Córdoba (Spain). Its genome comprised 99 high-quality assembled contigs accounting for a total size of 5.2 Mb and 35.1% G + C. Phylogenetic analyses suggested that this strain should be renamed as Bacillus cereus s.s. biovar Thuringiensis. Gene annotation revealed a total of 5495 genes, among which, 1 was identified as encoding a Cry5Ba homolog protein with well-documented toxicity against nematodes. These results suggest that this Bt strain has interesting potential for nematode biocontrol.
Arabic Lexical Substitution: AraLexSubD Dataset and AraLexSub Pipeline
Authors: Naser-Karajah Eman , Arman Nabil
Published: August 2024

Lexical substitution aims to generate a list of equivalent substitutions (i.e., synonyms) to a sentence’s target word or phrase while preserving the sentence’s meaning to improve writing, enhance language understanding, improve natural language processing models, and handle ambiguity. This task has recently attracted much attention in many languages. Despite the richness of Arabic vocabulary, limited research has been performed on the lexical substitution task due to the lack of annotated data. To bridge this gap, we present the first Arabic lexical substitution benchmark dataset AraLexSubD for benchmarking lexical substitution pipelines. AraLexSubD is manually built by eight native Arabic speakers and linguists (six linguist annotators, a doctor, and an economist) who annotate the 630 sentences. AraLexSubD covers three domains: general, finance, and medical. It encompasses 2476 substitution candidates ranked according to their semantic relatedness. We also present the first Arabic lexical substitution pipeline, AraLexSub, which uses the AraBERT pre-trained language model. The pipeline consists of several modules: substitute generation, substitute filtering, and candidate ranking. The filtering step shows its effectiveness by achieving an increase of 1.6 in the F1 score on the entire AraLexSubD dataset. Additionally, an error analysis of the experiment is reported. To our knowledge, this is the first study on Arabic lexical substitution.
Baseline Normative and Test–Retest Reliability Data for Sideline Concussion Assessment Measures in Youth
Authors: Wethe Jennifer V. , Bogle Jamie , Dodick David W. , Howard Marci D. , Gould Amanda Rach , Butterfield Richard J. , Buras Matthew R. , Adler Jennifer , Talaber Alexandra , Soma David , Starling Amaal J.
Published: August 2024

Tools used for the identification, evaluation, and monitoring of concussion have not been sufficiently studied in youth or real-world settings. Normative and reliability data on sideline concussion assessment measures in the youth athlete population is needed. Pre-season normative data for 515 athletes (93.5% male) aged 5 to 16 on the Standardized Assessment of Concussion (SAC/SAC-Child), modified Balance Errors Scoring System (mBESS), Timed Tandem Gait (TTG), and the King–Devick Test (KDT) are provided. A total of 212 non-injured athletes repeated the measures post-season to assess test–retest reliability. Mean performance on the SAC-C, mBESS, TTG, and KDT tended to improve with age. KDT was the only measure that demonstrated good to excellent stability across age ranges (ICC = 0.758 to 0.941). Concentration was the only SAC/SAC-C subtest to demonstrate moderate test–retest stability (ICC = 0.503 to 0.706). TTG demonstrated moderate to good (ICC = 0.666 to 0.811) reliability. mBESS demonstrated poor to moderate reliability (ICC = −0.309 to 0.651). Commonly used measures of concussion vary regarding test–retest reliability in youth. The data support the use of at least annual sport concussion baseline assessments in the pediatric population to account for the evolution in performance as the child ages. Understanding the variation in the stability and the evolution of baseline performance will enable improved identification of possible injury.
Utilizing Next-Generation Sequencing: Advancements in the Diagnosis of Fungal Infections
Authors: Naik Sheetal , Kashyap Dharambir , Deep Jashan , Darwish Saif , Cross Joseph , Mansoor Edmond , Garg Vivek Kumar , Honnavar Prasanna
Published: August 2024

Next-generation sequencing (NGS) has emerged as a promising tool for diagnosing fungal infections. It enables the identification of a wide range of fungal species and provides more accurate and rapid results than traditional diagnostic methods. NGS-based approaches involve the sequencing of DNA or RNA from clinical samples, which can be used to detect and identify fungal pathogens in complex clinical samples. The development of targeted gene panels and whole-genome sequencing has allowed for identifying genetic markers associated with antifungal drug resistance, enabling clinicians to tailor patient treatment options. NGS can also provide insights into the pathogenesis of fungal infections and aid in discovering novel drug targets. Although NGS has some limitations, such as cost and data analysis, it can potentially revolutionize the future diagnosis and treatment of fungal infections.
Utility and Micro-Costing Framework for TERT Promoter Mutation Analysis in Melanocytic Lesions of Uncertain Malignant Potential: A Retrospective Study in Dutch Local Clinical Practice
Authors: Barrutia Leire , Schuuring Ed , Rácz Emõke , Diercks Gilles F. H. , van Kempen Léon C.
Published: August 2024

The 2018 WHO edition on the classification of cutaneous melanocytic tumors recognizes eight evolutionary pathways of melanoma and describes tumors of uncertain malignant potential for each. When histology and immunohistochemistry do not support a confident conclusion about its malignant potential, a window of diagnostic uncertainty is created. Mutations in the telomerase reverse transcriptase gene promoter (TERTp) are highly specific for melanoma and can be used as an ancillary technique to acquire a higher level of confidence in the diagnosis. However, little is known about the cost-effectiveness of testing for TERTp mutations. The aims of this study were to determine how often knowledge of the TERTp mutation status contributed to the final diagnosis and to develop a micro-costing framework to calculate cost-effectiveness. A retrospective analysis of all cutaneous melanocytic lesions that were discussed in the Noord-Nederland Melanoma Panel from January 2021 to October 2022 was performed to identify the cases in which the preliminary histopathological diagnosis was uncertain regarding malignancy (ambiguous, likely benign, or likely malignant). For cases in which a TERTp mutation analysis was performed, the final diagnoses were collected, and it was determined whether this impacted the overall conclusion. A micro-costing framework was established to model the financial impact of introducing TERTp mutation analyses and subsequent clinical procedures. The study included 367 cases, of which 175 diagnoses of uncertain malignant potential were initially reported. TERTp mutation analysis was performed for 151/175 (86%). In 38% of these cases, a higher level of confidence regarding malignant potential was obtained. The implementation of TERTp mutation analyses for cutaneous melanocytic proliferations with uncertain malignant potential can narrow the window of diagnostic uncertainty. For the patient group with an initial uncertain diagnosis, the increased cost for molecular testing (86.145 €) was compensated by a reduced overall treatment cost (−122.304 €). A microsimulation model to determine the cost-effectiveness of TERTp mutation analysis projected an overall saving for the healthcare system.
Audiovestibular Dysfunction in Systemic Lupus Erythematosus Patients: A Systematic Review
Authors: Chen Jiann-Jy , Hsu Chih-Wei , Chen Yen-Wen , Chen Tien-Yu , Zeng Bing-Syuan , Tseng Ping-Tao
Published: August 2024

Audiovestibular dysfunction in patients with systemic lupus erythematosus has been underestimated for decades. Systemic lupus erythematosus can affect both the auditory and vestibular systems simultaneously. Several potential pathophysiological mechanisms behind systemic lupus erythematosus-related audiovestibular dysfunction have been proposed, including antibody-mediated immune responses, cell-mediated cytotoxicity, immune complex deposition in microvessels, central involvement in the audiovestibular pathway, and ototoxicity from medications used in systemic lupus erythematosus treatment. Currently available tests to evaluate audiovestibular function in systemic lupus erythematosus patients are neither specific nor sensitive. Nevertheless, there is no consensus regarding the efficacy of treatments for audiovestibular dysfunction in such patients. In this systematic review, we electronically searched the PubMed, Embase, ClinicalKey, Web of Science, and ScienceDirect platforms to find eligible articles. The first inspection date was on 29 December 2023 and the final update search date was on 11 June 2024. Further, we rated the quality of the included articles with Newcastle–Ottawa Scale. Based upon the aforementioned systematic review process, we have summarized the currently available evidence on the characteristics, pathophysiology, examination, and treatment of audiovestibular dysfunction related to systemic lupus erythematosus. Furthermore, we have proposed a specific steroid treatment protocol to manage audiovestibular dysfunction related to systemic lupus erythematosus. Audiovestibular dysfunction related to systemic lupus erythematosus may be responsive to adequate treatments, potentially allowing for reversibility if the disease is recognized and managed in a timely manner. Therefore, to provide clinically relevant evidence to clinicians, we have organized this literature review article to summarize the available evidence on the characteristics, pathophysiology, examination, and treatment of audiovestibular dysfunction in patients with systemic lupus erythematosus. Finally, based on our modified steroid treatment protocol, we would like to provide a new treatment strategy to clinicians to manage systemic lupus erythematosus-related audiovestibular dysfunction.
Computed Tomography Angiography Identified High-Risk Coronary Plaques: From Diagnosis to Prognosis and Future Management
Authors: Dimitriadis Kyriakos , Pyrpyris Nikolaos , Theofilis Panagiotis , Mantzouranis Emmanouil , Beneki Eirini , Kostakis Panagiotis , Koutsopoulos George , Aznaouridis Konstantinos , Aggeli Konstantina , Tsioufis Konstantinos
Published: August 2024

CT angiography has become, in recent years, a main evaluating modality for patients with coronary artery disease (CAD). Recent advancements in the field have allowed us to identity not only the presence of obstructive disease but also the characteristics of identified lesions. High-risk coronary atherosclerotic plaques are identified in CT angiographies via a number of specific characteristics and may provide prognostic and therapeutic implications, aiming to prevent future ischemic events via optimizing medical treatment or providing coronary interventions. In light of new evidence evaluating the safety and efficacy of intervening in high-risk plaques, even in non-flow-limiting disease, we aim to provide a comprehensive review of the diagnostic algorithms and implications of plaque vulnerability in CT angiography, identify any differences with invasive imaging, analyze prognostic factors and potential future therapeutic options in such patients, as well as discuss new frontiers, including intervening in non-flow-limiting stenoses and the role of CT angiography in patient stratification.
Well-Being and Support Network Affiliations for Black and Indigenous College Students during the COVID-19 Pandemic
Authors: Wicker Paris D.
Published: August 2024

While much of the research suggests that quality relationships and supportive campus environments shape well-being in college, racialized experiences can moderate the effort students put into their academic and well-being endeavors. However, our understanding of how relationships and networks support student well-being is understudied. This descriptive study employs a critical-relational well-being framework to analyze (n = 1200) survey responses from the Healthy Minds survey to determine perceived institutional and personal well-being support connections for Black and Indigenous college students in the United States before and during the COVID-19 pandemic. A descriptive two-mode social network analysis suggests a slight decrease in support network diversity and network differences in perceived well-being support by the level of well-being and gender. Faculty and advisors were structurally central in Black and Indigenous men’s well-being support and for those with higher well-being, but less central for Black and Indigenous women, and those with lower well-being. While family and friends provided vital social support, campus actors such as professors from class and academic advisors also served central structural roles for students with more diverse networks. Teaching assistants, student affairs staff, and religious affiliations served unique roles for students with fewer support role categories.
Using Role Models and Game-Based Learning to Attract Adolescent Girls to STEM
Authors: Vekiri Ioanna , Meletiou-Mavrotheris Maria , Mannay Oliver
Published: August 2024

Various pedagogical approaches have been proposed to attract more female students to STEM (science, technology, engineering, and mathematics), targeting student beliefs and perceptions that are linked to STEM study intentions. The current study, which took place in a secondary school in Cyprus and employed a quasi-experimental design, aims at contributing to this literature. Responses to pre- and post-questionnaires by 69 experimental and 27 control students show that students in the experimental group, who participated in an intervention in which they learned about the lives and accomplishments of STEM/STEAM role models via a game-based learning approach, improved their STEM ability perceptions compared to control group students, who reported similar pre-post survey levels of STEM ability perceptions. Female students benefited more compared to their male counterparts, and using a game-based learning approach contributed significantly to the effectiveness of students’ exposure to the role models. Taken together, study findings support the use of role models and learning games as tools to attract more female students to STEM.
Energy-Efficient Neural Network Acceleration Using Most Significant Bit-Guided Approximate Multiplier
Authors: Huang Pengfei , Gong Bin , Chen Ke , Wang Chenghua
Published: August 2024

The escalating computational demands of deep learning and large-scale models have led to a significant increase in energy consumption, highlighting the urgent need for more energy-efficient hardware designs. This study presents a novel weight approximation strategy specifically designed for quantized neural networks (NNs), resulting in the development of an efficient approximate multiplier leveraging most significant one (MSO) shifting. Compared to both energy-efficient logarithmic approximate multipliers and accuracy-prioritized non-logarithmic approximate multipliers, our proposed logarithmic-like design achieves an unparalleled balance between accuracy and hardware costs. When compared with the baseline exact multiplier, our innovative design exhibits remarkable reductions, encompassing a decrease of up to 28.31% in area, a notable 57.84% reduction in power consumption, and a diminution of 11.86% in delay. Experimental outcomes reveal that the proposed multiplier, when applied in neural networks, can conserve approximately 60% of energy without compromising task accuracy. Concurrently, experiments focused on the transformer accelerator and image processing illustrate the substantial energy savings that can be obtained for Large Language Models (LLMs) and image processing tasks through the implementation of our proposed design, further validating its efficacy and practicality.
FPGA Implementation of Pillar-Based Object Classification for Autonomous Mobile Robot
Authors: Park Chaewoon , Lee Seongjoo , Jung Yunho
Published: August 2024

With the advancement in artificial intelligence technology, autonomous mobile robots have been utilized in various applications. In autonomous driving scenarios, object classification is essential for robot navigation. To perform this task, light detection and ranging (LiDAR) sensors, which can obtain depth and height information and have higher resolution than radio detection and ranging (radar) sensors, are preferred over camera sensors. The pillar-based method employs a pillar feature encoder (PFE) to encode 3D LiDAR point clouds into 2D images, enabling high-speed inference using 2D convolutional neural networks. Although the pillar-based method is employed to ensure real-time responsiveness of autonomous driving systems, research on accelerating the PFE is not actively being conducted, although the PFE consumes a significant amount of computation time within the system. Therefore, this paper proposes a PFE hardware accelerator and pillar-based object classification model for autonomous mobile robots. The proposed object classification model was trained and tested using 2971 datasets comprising eight classes, achieving a classification accuracy of 94.3%. The PFE hardware accelerator was implemented in a field-programmable gate array (FPGA) through a register-transfer level design, which achieved a 40 times speedup compared with the firmware for the ARM Cortex-A53 microprocessor unit; the object classification network was implemented in the FPGA using the FINN framework. By integrating the PFE and object classification network, we implemented a real-time pillar-based object classification acceleration system on an FPGA with a latency of 6.41 ms.
Efficient Sparse Bayesian Learning Model for Image Reconstruction Based on Laplacian Hierarchical Priors and GAMP
Authors: Jin Wenzhe , Lyu Wentao , Chen Yingrou , Guo Qing , Deng Zhijiang , Xu Weiqiang
Published: August 2024

In this paper, we present a novel sparse Bayesian learning (SBL) method for image reconstruction. We integrate the generalized approximate message passing (GAMP) algorithm and Laplacian hierarchical priors (LHP) into a basic SBL model (called LHP-GAMP-SBL) to improve the reconstruction efficiency. In our SBL model, the GAMP structure is used to estimate the mean and variance without matrix inversion in the E-step, while LHP is used to update the hyperparameters in the M-step.The combination of these two structures further deepens the hierarchical structures of the model. The representation ability of the model is enhanced so that the reconstruction accuracy can be improved. Moreover, the introduction of LHP accelerates the convergence of GAMP, which shortens the reconstruction time of the model. Experimental results verify the effectiveness of our method.
A Survey: Security Vulnerabilities and Protective Strategies for Graphical Passwords
Authors: Saadi Zena Mohammad , Sadiq Ahmed T. , Akif Omar Z. , Farhan Alaa K.
Published: August 2024

As technology advances and develops, the need for strong and simple authentication mechanisms that can help protect data intensifies. The contemporary approach to giving access control is through graphical passwords comprising images, patterns, or graphical items. The objective of this review was to determine the documented security risks that are related to the use of graphical passwords, together with the measures that have been taken to prevent them. The review was intended to present an extensive literature review of the subject matter on graphical password protection and to point toward potential future research directions. Many attacks, such as shoulder surfing attacks, SQL injection attacks, and spyware attacks, can easily exploit the graphical password scheme, which is one of the most widely used. To counter these security threats, several measures have been suggested, but none of the security attacks can be completely overcome. Each of the proposed measures has its pros and cons. This study begins by elucidating some of the graphical password schemes studied between 2012 and 2023, delving into potential threats and defense mechanisms associated with these schemes. Following a thorough identification and selection process, five of the reviewed papers explain the threat of shoulder surfing and spyware attacks on graphical password schemes, while two explain the threat of brute force attacks. One paper focuses on dictionary attacks, while four other papers address social engineering, SQL injection attacks, and guessing attacks as potential threats to graphical password schemes. In addition, the papers recognize other forms of attacks, such as video recording attacks, filtering attacks, reverse engineering attacks, multiple observation attacks, key/mouse logger attacks, insider attacks, computer vision attacks, image gallery attacks, sonar attacks, reply attacks, data interception attacks, and histogram manipulation attacks. These attacks are examined in three, three, eight, one, four, one, one, one, one, one, one, and one papers, respectively. Moreover, out of all such countermeasures, most of them are based on three categories—randomization, obfuscation, and password space complexity—which are the most commonly employed strategies for improving graphical password schemes.
A Prosumer Hydro Plant Network as a Sustainable Distributed Energy Depot
Authors: Morawski Michał , Ignaciuk Przemysław
Published: August 2024

The shortage of efficient, low-cost storage depots inhibits the large-scale adoption of volatile-by-nature, renewable sources of energy (RSEs). In this paper, we outline how to utilize prosumer-owned hydro plants of a few to several kW as a distributed, short-term energy storage solution that is deployable with little investment and a low operational expenditure. The proposed solution is a system of interconnected hydro depots with an active water-flow control algorithm that reduces the grid’s load variability and benefits prosumers. According to the tests conducted, prosumer revenue grows from several percent to over 30 percent, depending on weather conditions, in comparison to the free-flow case. In turn, the cushioning effect of the distributed energy buffer balances the fluctuations introduced by other RSEs, e.g., photovoltaic- or wind-based ones. Hence, while benefitting the involved parties, it also facilitates the inclusion of RSEs within the power distribution system.
Job Recommendations: Benchmarking of Collaborative Filtering Methods for Classifieds
Authors: Kwieciński Robert , Górecki Tomasz , Filipowska Agata , Dubrov Viacheslav
Published: August 2024

Classifieds pose numerous challenges for recommendation methods, including the temporary visibility of ads, the anonymity of most users, and the fact that typically only one user can consume an advertised item. In this work, we address these challenges by choosing models and evaluation procedures that are considered accurate, diverse, and efficient (in terms of memory and time consumption during training and prediction). This paper aims to benchmark various recommendation methods for job classifieds, using OLX Jobs as an example, to enhance the conversion rate of advertisements and user satisfaction. In our research, we implement scalable methods and represent different approaches to the recommendations: Alternating Least Square (ALS), LightFM, Prod2Vec, RP3Beta, and Sparse Linear Methods (SLIM). We conducted A/B tests by sending millions of messages with recommendations to perform online evaluations of selected methods. In addition, we have published the dataset created for our research. To the best of our knowledge, this is the first dataset of its kind. It contains 65,502,201 events performed on OLX Jobs by 3,295,942 users who interacted with (displayed, replied to, or bookmarked) 185,395 job ads over two weeks in 2020. We demonstrate that RP3Beta, SLIM, and ALS perform significantly better than Prod2Vec and LightFM when tested in a laboratory setting. Online A/B tests also show that sending messages with recommendations generated by the ALS and RP3Beta models increases the number of users contacting advertisers. Additionally, RP3Beta had a 20% more significant impact on this metric than ALS.
SQnet: An Enhanced Multi-Objective Detection Algorithm in Subaquatic Environments
Authors: Zhu Yutao , Shan Bochen , Wang Yinglong , Yin Hua
Published: August 2024

With the development of smart aquaculture, the demand for accuracy for underwater target detection has increased. However, traditional target detection methods have proven to be inefficient and imprecise due to the complexity of underwater environments and the obfuscation of biological features against the underwater environmental background. To address these issues, we proposed a novel algorithm for underwater multi-target detection based on the YOLOv8 architecture, named SQnet. A Dynamic Snake Convolution Network (DSConvNet) module was introduced for tackling the overlap between target organisms and the underwater environmental background. To reduce computational complexity and parameter overhead while maintaining precision, we employed a lightweight context-guided semantic segmentation network (CGNet) model. Furthermore, the information loss and degradation issues arising from indirect interactions between non-adjacent layers were handled by integrating an Asymptotic Feature Pyramid Network (AFPN) model. Experimental results demonstrate that SQnet achieves an mAP@0.5 of 83.3% and 98.9% on the public datasets URPC2020, Aquarium, and the self-compiled dataset ZytLn, respectively. Additionally, its mAP@0.5–0.95 reaches 49.1%, 85.4%, and 84.6%, respectively, surpassing other classical algorithms such as YOLOv7-tiny, YOLOv5s, and YOLOv3-tiny. Compared to the original YOLOv8 model, SQnet boasts a PARM of 2.25 M and consistent GFLOPs of 6.4 G. This article presents a novel approach for the real-time monitoring of fish using mobile devices, paving the way for the further development of intelligent aquaculture in the domain of fisheries.
Deep Reinforcement Learning-Based Multipath Routing for LEO Megaconstellation Networks
Authors: Han Chi , Xiong Wei , Yu Ronghuan
Published: August 2024

The expansion of megaconstellation networks (MCNs) represents a promising solution for achieving global Internet coverage. To meet the growing demand for satellite services, multipath routing allows the simultaneous establishment of multiple transmission paths, enabling the transmission of flows in parallel. Nevertheless, the mobility of satellites and time-varying link states presents a challenge for the discovery of optimal paths and traffic scheduling in multipath routing. Given the inflexibility of traditional static deep reinforcement learning (DRL)-based routing algorithms in dealing with time-varying constellation topologies, DRL-based multipath routing (DMR) enabled by a graph neural network (GNN) is proposed as a means of enhancing the transmission performance of MCNs. DMR decouples the stochastic optimization problem of multipath routing under traffic and bandwidth constraints into two subproblems: multipath routing discovery and multipath traffic scheduling. Firstly, the minimum hop count-based multipath route discovery algorithm (MHMRD) is proposed for the computation of multiple available paths between all source and destination nodes. Secondly, the GNN-based multipath traffic scheduling scheme (GMTS) is proposed as a means of dynamically scheduling the traffic on each available path for each data stream, based on the state information of ISLs and traffic demand. Simulation results demonstrate that the proposed scheme can be scaled to constellations with different configurations without the necessity for repeated training and enhance the throughput, completion ratio, and delay by 42.64%, 17.39%, and 3.66% in comparison with the shortest path first algorithm (SPF), respectively.
A Physics-Based Equivalent Circuit Model and State of Charge Estimation for Lithium-Ion Batteries
Authors: Li Yigang , Qi Hongzhong , Shi Xinglei , Jian Qifei , Lan Fengchong , Chen Jiqing
Published: August 2024

This paper proposes a novel physics-based equivalent circuit model of the lithium-ion battery for electric vehicle applications that has comprehensive electrochemical significance and an acceptable level of complexity. Initially, the physics-based extended single particle (ESP) model is improved by adding a correction term to mitigate its voltage bias. Then, the equivalent circuit model based on the improved extended single particle (ECMIESP) model is derived. In this model, the surface state of charge (SOC) of solid particles is approximated using a capacity and multi first-order resistance-capacity equivalent circuits with only two lumped parameters. The overpotential of electrolyte diffusion is approximated using a first-order resistance-capacitance equivalent circuit. The electrochemical reaction overpotential is characterized by a nonlinear resistance. The voltage accuracies of ECMIESP and conventional 2RC equivalent circuit model (ECM2RC) are compared across the entire SOC range under various load profiles. The results demonstrate that the ECMIESP model outperforms ECM2RC model, particularly at low SOC or when the electrochemical reaction overpotential exceeds 50 mV. For instance, the ECMIESP model shows an 820.4 mV reduction in voltage error compared to the ECM2RC model at the endpoint during a 2C constant current discharge test. Lastly, the ECMIESP model was used for SOC estimation with extended Kalman filter, resulting in significantly improved accuracy compared to the conventional ECM2RC model. Therefore, the ECMIESP model has great potential for real-time applications in enhancing voltage and SOC estimation precision.
Electric Vehicle (EV) Review: Bibliometric Analysis of Electric Vehicle Trend, Policy, Lithium-Ion Battery, Battery Management, Charging Infrastructure, Smart Charging, and Electric Vehicle-to-Everything (V2X)
Authors: Veza Ibham , Syaifuddin Mohd , Idris Muhammad , Herawan Safarudin Gazali , Yusuf Abdulfatah Abdu , Fattah Islam Md Rizwanul
Published: August 2024

Electric vehicles (EVs) have seen significant growth due to the increasing awareness about environmental concerns and the negative impacts of internal combustion engine vehicles (ICEVs). The electric vehicle landscape is rapidly evolving, with EV policies, battery, and charging infrastructure and electric vehicle-to-everything (V2X) at its forefront. This review study used a bibliometric analysis of the Scopus database to investigate the development of EV technology. This bibliometric study specifically focuses on analyzing electric vehicle trends, policy implications, lithium-ion batteries, EV battery management systems, charging infrastructure, EV smart charging technologies, and V2X. Through this detailed bibliometric analysis discussion, we aim to provide a better understanding of holistic EV technology and inspire further research in electric vehicles. The analysis covers the period from 1990 to 2022. This bibliometric analysis underscores the interplay of electric vehicle policies, technology, and infrastructure, specifically focusing on developments in battery management and the possibility of V2X technology. In addition, this bibliometric analysis suggests the synchronization of international electric vehicle policy, advancement of battery technology, and promotion of the use of EV smart charging and V2X systems. This bibliometric analysis emphasizes that the expansion of EVs and sustainable mobility relies on a comprehensive strategy that encompasses policy, technology, and infrastructure. This bibliometric analysis recommends fostering collaboration between different sectors to drive innovation and advancements in electric vehicle technology.
Inverter Multi-Machine Grid Integration Resonance Suppression Strategy by Active Damping
Authors: Hou Tianhao , Jiang Yunhao , Cai Zishuo
Published: August 2024

The current inverter control strategies have limitations in suppressing grid resonance, especially in complex grid environments with high penetration of renewable energy sources. These strate-gies often focus on suppressing resonance at a single frequency point, but their effectiveness is constrained when dealing with multi-band resonance or dynamically changing grid conditions. The study investigates the application of parallel-operated inverters in the grid, particularly their impact on grid stability. A novel active damping strategy is developed to enhance the grid’s dynamic response and suppress grid resonance. The effectiveness of the control strategy is verified through simulation by establishing Norton equivalent circuit models for multiple in-verters. Fast Through simulation, this study comprehensively evaluates the performance and adaptability of the strategy under various conditions. Results demonstrate that implementing the active damping strategy increases the inverter output power from 9.5 kW to 10 kW, an im-provement of 5.26%. System response time is reduced from 50 ms to 30 ms, and post-stabilization fluctuations decrease to 1.5%. These data conclusively prove the effectiveness of the control strategy in enhancing grid stability and reducing resonance effects. The findings underscore the potential of active damping strategies in improving grid performance and in-verter efficiency. However, further research and optimization are necessary to assess the adapt-ability of these strategies under different grid conditions.
Natural Factors Rather Than Anthropogenic Factors Control the Greenness Pattern of the Stable Tropical Forests on Hainan Island during 2000–2019
Authors: Zheng Binbin , Yu Rui
Published: August 2024

Vegetation, being a core component of ecosystems, is known to be influenced by natural and anthropogenic factors. This study used the annual mean Normalized Difference Vegetation Index (NDVI) as the vegetation greenness indicator. The variation in NDVI on Hainan Island was analyzed using the Theil–Sen median trend analysis and Mann–Kendall test during 2000–2019. The influence of natural and anthropogenic factors on the driving mechanism of the spatial pattern of NDVI was explored by the Multiscale Weighted Regression (MGWR) model. Additionally, we employed the Boosted Regression Tree (BRT) model to explore their contribution to NDVI. Then, the MGWR model was utilized to predict future greenness patterns based on precipitation and temperature data from different Shared Socioeconomic Pathway (SSP) scenarios for the period 2021–2100. The results showed that: (1) the NDVI of Hainan Island forests significantly increased from 2000 to 2019, with an average increase rate of 0.0026/year. (2) the R2 of the MGWR model was 0.93, which is more effective than the OLS model (R2 = 0.42) in explaining the spatial relationship. The spatial regression coefficients of the NDVI with temperature ranged from −10.05 to 0.8 (p < 0.05). Similarly, the coefficients of Gross Domestic Product (GDP) with the NDVI varied between −5.98 and 3.28 (p < 0.05); (3) The natural factors played the most dominant role in influencing vegetation activities as a result of the relative contributions of 83.2% of forest NDVI changes (16.8% contributed by anthropogenic activities). (4) under SSP119, SSP245, and SSP585 from 2021 to 2100, the NDVI is projected to have an overall decreasing pattern under all scenarios. This study reveals the trend of greenness change and the spatial relationship with natural and anthropogenic factors, which can guide the medium and long-term dynamic monitoring and evaluation of tropical forests on Hainan Island.
A Survey of Organic Carbon Stocks in Mineral Soils of Eucalyptus globulus Labill. Plantations under Mediterranean Climate Conditions
Authors: Quintela Ana , Ferreira Daniela , Fabres Sérgio , Coutinho João
Published: August 2024

The main aim of this study was to assess the amount of carbon (C) stored in the upper 30 cm layer of mineral soils in eucalypt plantations in Portugal, with a Mediterranean-type climate. Soil sampling data (2468 samples), field evaluations (soil profile description) and relevant information on the particle size distribution, climate, bedrock and reference soil group were accomplished. Bulk density per sample was assessed using pedo-transfer functions and soil C stock was estimated. The results showed an average of 41.2 t C ha−1 stored in the soil. In the northern regions of Portugal, the coldest and wettest areas of the country with better stand productivity, a higher soil organic carbon (SOC) is achieved (median SOC of 39.2 g kg−1 and soil C stock of 55 t ha−1) than in southern and inland regions, with a warmer and drier climate (median SOC of 15.2 g kg−1 and soil C stock of 28 t ha−1). The assessment of mean soil C stock per bedrock type revealed higher C stored in granites followed by conglomerates, coal shales and clay shales. Regarding soil type, the results showed a higher C stock in Cambisols, Leptosols and Fluvisols (>50 t C ha−1), whereas Regosols and Luvisols stored less, following the same trend presented for reference soil groups in Europe. Comparing the geographic distribution of the C stock in the upper layer of the mineral soils with the amount of C in eucalyptus stands (root and aboveground biomass—data from national forest inventory), the mineral soil pool can represent more than two-thirds of the total C stored in eucalyptus plantations in Portugal. Further studies should focus on the evolution of C stocks in eucalypt plantations during different stages of stand growth and under different management practices.
The Impact of Forestland Tenure Security on Rural Household Income: Analysis of Mediating Effects Based on Labor Migration
Authors: Luo Xin , Li Lishan , Zhang Ling , Ning Caiwang , Liu Xiaojin
Published: August 2024

Although collective forest tenure reform (CFTR) has improved the legal tenure security of forestland, its impact on increasing farmers’ income is unsustainable. This study used a multiple linear regression model to empirically analyze data from 505 farmers in Jiangxi Province, examining the impact of legal, actual, and perceived tenure security on rural household income, and incorporating migration into the framework. The findings indicate that both actual and perceived tenure security have a substantial positive impact on the total rural household income and forestry income. However, it is worth noting that legal tenure security only has a positive effect on forestry income. Furthermore, outside-of-county labor migration can serve as a mediator for the income effects of actual and perceived tenure security. However, the mediating effect of intra-county labor migration is not considerable. The study found that the increase in income due to the security of actual tenure security is significant for the group of people who own less than 50 mu of forestland. However, both actual and perceived tenure security have a significant impact on income for the group of people who own more than 50 mu of forestland. The aforementioned findings indicate that, in the ongoing extensive advancement of collective forest right reform, it is crucial to prioritize the execution of forest reform policies at the local level and enhance farmers’ awareness and comprehension of said policies. In addition, the government should enhance the monitoring system for policy implementation and intensify efforts in publicizing these policies, in order to fully utilize the benefits of CFTR.
Variety and Site Drive Salix Mixture Effects on Soil Organic Matter Chemistry and Soil Carbon Accumulation
Authors: Jensen Joel , Fransson Petra , Baum Christel , Leinweber Peter , Eckhardt Kai-Uwe , Weih Martin
Published: August 2024

Soil organic matter (SOM) is essential for nutrient cycling and soil carbon (C) accumulation, both of which are heavily influenced by the quality and quantity of plant litter. Since SOM dynamics in relation to plant diversity are poorly understood, we investigated the effects of willow variety and mixture, and site on the soil C stocks, SOM chemical composition and thermal stability. Using pyrolysis-field ionization mass spectrometry (Py-FIMS), a method of stepwise thermal degradation in ultrahigh vacuum combined with soft ionization in a high electric field, followed by mass-spectrometric separation and detection of molecular ions, we analyzed SOM in the top 10 cm of soil from two 7-year-old experimental sites in Germany and Sweden. Monocultures and mixtures of two willow varieties (Salix spp.) belonging to different species were grown at the experimental plots. Overall, site had the strongest effect on SOM quality. The results showed significant variability across sites for willow identity and mixture effects on C accumulation and SOM chemistry. In the German site (Rostock), yearly soil C accumulation was higher (p < 0.05) for variety ‘Loden’ (1.0 Mg C ha−1 year−1) compared to ‘Tora’ (0.5 Mg C ha−1 year−1), whilst in the Swedish site (Uppsala), both varieties exhibited similar soil C accumulation rates of around 0.6 Mg C ha−1 year−1. Willow variety identity significantly affected SOM quality at both sites, while mixing had minor effects. Our findings emphasize the significance of site-specific context and variety and species identity in shaping soil C accumulation in willow plantations.
Dynamic Storage Optimization for Communication between AI Agents
Authors: Tara Andrei , Turesson Hjalmar K. , Natea Nicolae
Published: August 2024

Today, AI is primarily narrow, meaning that each model or agent can only perform one task or a narrow range of tasks. However, systems with broad capabilities can be built by connecting multiple narrow AIs. Connecting various AI agents in an open, multi-organizational environment requires a new communication model. Here, we develop a multi-layered ontology-based communication framework. Ontology concepts provide semantic definitions for the agents’ inputs and outputs, enabling them to dynamically identify communication requirements and build processing pipelines. Critical is that the ontology concepts are stored on a decentralized storage medium, allowing fast reading and writing. The multi-layered design offers flexibility by dividing a monolithic ontology model into semantic layers, allowing for the optimization of read and write latencies. We investigate the impact of this optimization by benchmarking experiments on three decentralized storage mediums—IPFS, Tendermint Cosmos, and Hyperledger Fabric—across a wide range of configurations. The increased read-write speeds allow AI agents to communicate efficiently in a decentralized environment utilizing ontology principles, making it easier for AI to be used widely in various applications.
RAS-Designed Diets Result in Lower Accumulation of Nitrogen, Phosphorus, and Zinc in Recirculating Aquaculture System Compared with Traditional Flow-Through Designed Diets
Authors: Flo Vegard Øvstetun , Cavrois-Rogacki Thomas , Hansen Jon Øvrum , Vigen Jannicke , Gitlesen Thomas , Lekang Odd-Ivar
Published: August 2024

A four-week trial was conducted to compare the effects of a conventional flow-through system diet (FTS) and an experimental RAS diet (ERAS) on fish performance, water quality and general system implication in a replicated recirculation aquaculture system (RAS). Six identical RAS, each with a total system water volume of 1500 L and cylindrical rearing tanks of 1000 L were stocked with Atlantic salmon (Salmo salar) post-smolts with an average weight of 199.7 ± 28 g, to an average stocking density of 30 kg/m3 and reaching approximately 49 kg/m3 at the trial end. The ERAS diet were composed to inhabit typical RAS feed characteristics compared with the FTS diet, such as a higher fecal stability and reduced protein levels (−12%), but at the same time increased fat content (+8%) to secure similar gross energy levels (22–23 MJ kg−1) between the two diets. Water quality parameters were measured individually. The total accumulation of minerals and metals was analyzed in water from different parts of the system at the start and end of the trial period for both diets. No differences in growth, condition factor, feed conversion rate (FCR), or survival of fish fed the two dietary adaptations were observed. The system using the ERAS diet showed significantly higher pH (+1.2%) and alkalinity (+17%) and lower total ammonia nitrogen (TAN) (−18%) and NO2− (−46%) compared with the FTS diet. The count of drum filter activations was also significantly lower (−13%) with the ERAS diet. Compared with the FTS diet, the ERAS diet had a lower probability (−4%) of generating particles smaller than 50 μm, and that the RAS was also more effective in removing particles from the drum filter, prompting a lower daily activation of the filter of 22.1 ± 3.0 counts compared with 25.5 ± 3.5 for the FTS diet. Mineral analysis showed a significantly lower accumulation of total phosphorus (TP) (−90%) and dissolved phosphorus (DP) (−92%) in the RAS units using the ERAS diet compared with those using the FTS diet. Compared with a traditional flow-through diet, these results highlight the benefits of using an RAS-adapted diet that matches the energy requirement of flow-through diets regarding water quality, system performance, satisfactory growth, and condition.