bluuebunny/crossref_metadata_2025 huggingface dataset:
Improving Transmission in Integrated Unmanned Aerial Vehicle–Intelligent Connected Vehicle Networks with Selfish Nodes Using Opportunistic Approaches
Authors: Ye Meixin , Zhou Zhenfeng , Zhu Lijun , Huang Fanghui , Li Tao , Wang Dawei , Jin Yi , He Yixin
Published: December 2024

The integration of unmanned aerial vehicles (UAVs) into vehicular networks offers numerous advantages in enhancing communication and coverage performance. With the ability to move flexibly in three-dimensional space, UAVs can effectively bridge the communication gap between intelligent connected vehicles (ICVs) and infrastructure. However, the rapid movement of UAVs and ICVs poses significant challenges to the stability and reliability of communication links. Motivated by these challenges, integrated UAV–ICV networks can be viewed as vehicular delay-tolerant networks (VDTNs), where data delivery is accomplished through the “store-carry-forward” transmission mechanism. Since VDTNs exhibit social attributes, this paper first investigates the opportunistic transmission problem in the presence of selfish nodes. Then, by enabling node cooperation, this paper proposes an opportunistic transmission scheme for integrated UAV–ICV networks. To address the issue of node selfishness in practical scenarios, the proposed scheme classifies the degree of cooperation and analyzes the encounter probability between nodes. Based on this, information is initially flooded, and the UAV is selected for data distribution by jointly considering the node centrality, energy consumption, and cache size. Finally, simulation results demonstrate that the proposed scheme can effectively improve the delivery ratio and reduce the average delivery delay compared to state-of-the-art schemes.
Assessment of LiDAR-Based Sensing Technologies in Bird–Drone Collision Scenarios
Authors: Seoane Paula , Aldao Enrique , Veiga-López Fernando , González-Jorge Higinio
Published: December 2024

The deployment of Advanced Air Mobility requires the continued development of technologies to ensure operational safety. One of the key aspects to consider here is the availability of robust solutions to avoid tactical conflicts between drones and other flying elements, such as other drones or birds. Bird detection is a relatively underexplored area, but due to the large number of birds, their shared airspace with drones, and the fact that they are non-cooperative elements within an air traffic management system, it is of interest to study how their detection can be improved and how collisions with them can be avoided. This work demonstrates how a LiDAR sensor mounted on a drone can detect birds of various sizes. A LiDAR simulator, previously developed by the Aerolab research group, is employed in this study. Six different collision trajectories and three different bird sizes (pigeon, falcon, and seagull) are tested. The results show that the LiDAR can detect any of these birds at about 30 m; bird detection improves when the bird gets closer and has a larger size. The detection accuracy is higher than 1 m in most of the cases under study. The errors grow with increasing drone-bird relative speed.
UAV Localization Method with Keypoints on the Edges of Semantic Objects for Low-Altitude Economy
Authors: Li Yineng , Zeng Qinghua , Shao Chen , Zhuo Peng , Li Bowen , Sun Kecheng
Published: December 2024

The low-altitude economy heavily relies on new carriers represented by unmanned aerial vehicles (UAVs). The localization accuracy of UAVs highly relies on the Global Navigation Satellite System (GNSS), which can be easily affected in low-altitude urban environments, making it difficult to maintain effective localization accuracy. To solve this problem, this paper proposes a UAV autonomous localization method with keypoints on the edges of semantic objects (KESO). Firstly, semantic objects within the working area are selected, and then the latitude, longitude, and altitude of these semantic objects’ keypoints are measured to construct a database. By identifying the semantic objects from aerial images and detecting the edge of the semantic objects, the keypoints of the semantic objects are obtained. Finally, by matching the detected keypoints in the aerial images with the keypoints in the database, the UAV’s position can achieve a high-precision position when satellite signals are blocked in low-altitude urban environments. As verified by real flight data, the results show that the localization error is less than 5 m, and the edges of objects can obtain more accurate keypoints to help UAVs locate more accurately. This paper can provide a reference for UAV localization in the urban environments of the low-altitude economy.
A Lightweight Network with Domain Adaptation for Motor Imagery Recognition
Authors: Ding Xinmin , Zhang Zenghui , Wang Kun , Xiao Xiaolin , Xu Minpeng
Published: December 2024

Brain–computer interfaces (BCI) are an effective tool for recognizing motor imagery and have been widely applied in the motor control and assistive operation domains. However, traditional intention-recognition methods face several challenges, such as prolonged training times and limited cross-subject adaptability, which restrict their practical application. This paper proposes an innovative method that combines a lightweight convolutional neural network (CNN) with domain adaptation. A lightweight feature extraction module is designed to extract key features from both the source and target domains, effectively reducing the model’s parameters and improving the real-time performance and computational efficiency. To address differences in sample distributions, a domain adaptation strategy is introduced to optimize the feature alignment. Furthermore, domain adversarial training is employed to promote the learning of domain-invariant features, significantly enhancing the model’s cross-subject generalization ability. The proposed method was evaluated on an fNIRS motor imagery dataset, achieving an average accuracy of 87.76% in a three-class classification task. Additionally, lightweight experiments were conducted from two perspectives: model structure optimization and data feature selection. The results demonstrated the potential advantages of this method for practical applications in motor imagery recognition systems.
Self-Regulated Learning Strategies Among Graduate Students and Their Relationship with Statistics Anxiety
Authors: Youssef Nasser H. , Alibraheim Essa A.
Published: December 2024

The research set out to investigate Saudi Arabian graduate students’ levels of anxiety related to statistics and their use of self-regulated learning strategies. Research tools were created, such as a measure for measuring statistics anxiety and a measure for measuring the usage of self-regulated learning strategies, in order to meet the research objectives. Sixty-six master’s students from Imam Abdulrahman Bin Faisal University’s College of Education received these instruments. The findings showed that participants’ levels of anxiety related to statistics were moderate, and their use of self-regulated learning strategies was quite high. The results additionally demonstrated an inverse correlation between graduate students’ use of self-regulated learning strategies and statistics anxiety. Regression analysis revealed that self-regulated learning strategies significantly predicted levels of statistics anxiety. This suggests that enhancing self-regulated learning strategies could potentially mitigate statistics anxiety among graduate students. The research recommends the development of a comprehensive guide for self-regulated learning strategies for master’s students, including multiple examples for each strategy, the creation of training programs to enhance self-regulated learning strategies, and raising awareness about statistics anxiety among graduate students.
DRL-Based Dynamic SFC Orchestration Algorithm for LEO Satellite Networks
Authors: Zhang Ziyi , Hu Hefei , Wu You
Published: December 2024

Low-Earth-Orbit (LEO) satellite networks face unique challenges in service function chain (SFC) orchestration due to their dynamic topology and resource constraints, making traditional terrestrial network solutions inadequate. This study addresses the challenge of maximizing service provider benefits through efficient SFC deployment and readjustment in LEO satellite networks. We propose PPOSFC, a novel dynamic SFC orchestration algorithm based on Proximal Policy Optimization (PPO), which incorporates future topology information into the decision-making process. The algorithm models the orchestration problem as a Markov decision process and employs a dual-objective optimization approach considering both deployment success rate and readjustment costs. Simulation results demonstrate that PPOSFC achieves a 13.07% increase in cumulative profit and improves deployment success rates by 6.78% compared to existing algorithms. The algorithm exhibits superior performance in both high and low user service request intensity, effectively balancing service quality and operational efficiency. Furthermore, our analysis reveals that incorporating predicted topology information significantly enhances orchestration performance.
Efficiency Analysis of NIST-Standardized Post-Quantum Cryptographic Algorithms for Digital Signatures in Various Environments
Authors: Dziechciarz Dominik , Niemiec Marcin
Published: December 2024

The advent of quantum computing presents a significant threat to the security of asymmetric cryptographic algorithms, necessitating the adoption of new cryptographic mechanisms resilient to quantum-based attacks. This need is particularly critical for applications that rely exclusively on public-key cryptography, such as digital signatures. This paper presents a comprehensive analysis of the performance of various post-quantum cryptographic algorithms, focusing specifically on NIST-standardized digital signature algorithms—SPHINCS+ and Dilithium—and their practical implementations. The study evaluates these algorithms across different programming languages to identify optimal environments for diverse applications. Comparative analyses with the widely used RSA algorithm reveal that the computational cost of adopting post-quantum cryptographic systems is relatively low. Notably, some post-quantum algorithms demonstrate performance advantages over classical RSA in specific scenarios.
Preemptive-Level-Based Cooperative Autonomous Vehicle Trajectory Optimization for Unsignalized Intersection with Mixed Traffic
Authors: Li Pengrui , Liu Miaomiao , Zhu Mingyue , Yao Minkun
Published: December 2024

Buses constitute a crucial component of public transportation systems in numerous urban centers. Integrating autonomous driving technology into the bus transportation ecosystem has the potential to enhance overall urban mobility. The management of mixed traffic at intersections, involving both private vehicles and buses, particularly in the presence of bus lanes, presents several formidable challenges. This study proposes a preemptive-level-based cooperative autonomous vehicle (AV) trajectory optimization for intersections with mixed traffic. It takes into account dynamic changes in the intersection’s passing sequence, trajectory selection, and adherence to traffic regulations, including the different status of bus lanes. Based on the spatio–temporal coupling constraints of each vehicle trajectory at intersections, a preemptive-level-based AV passing order optimization method is proposed. Subsequently, a speed control mechanism is introduced to decouple these constraints, thereby preventing vehicle conflicts and reducing unnecessary braking. Ultimately, trajectory routes for multi-exit roads are selected, prioritizing traffic efficiency. In simulated validations, two representative types of intersections from the actual road network were selected, and eight typical scenarios established, including the operation status of bus lanes and different percentages of buses. The results indicate that the proposed method improves intersection traffic efficiency by a minimum of 12.55%, accompanied significantly by reduction of fuel consumption by 8.93%. This study verified that the proposed method significantly enhances intersection efficiency and reduces energy consumption while ensuring safety.
On Adaptation of Resources in New Radio Vehicle-to-Everything Mode 1 Dynamic Resource Allocation
Authors: Sabeeh Saif , Wesołowski Krzysztof
Published: December 2024

Vehicle-to-Everything (V2X) communication is one of the essential technologies in 5G systems and will certainly play an important role in incoming 6G communications. Two modes of 5G New Radio V2X communication (NR-V2X) have been defined to standardize the direct exchange of messages between vehicles. This paper concentrates on Mode 1, in which message exchange takes place with the support of the cellular infrastructure. In this mode, each vehicle uses a fixed number of subchannels with pre-configured subchannel sizes to transmit packet messages. However, if the packet sizes vary in each transmission, some resource blocks (RBs) assigned to V2X links are wasted. This paper presents the results of investigations on more efficient use of resource blocks, intending to minimize their waste and limit the delay in resource selection. In this paper, two new algorithms for radio resource block assignment are proposed and analyzed. The algorithms are characterized by a lower waste of RBs and a shorter delay in resource assignment compared to current solutions. The first algorithm uses adjacent RBs, whereas the second one can assign non-adjacent RBs, resulting in an even lower waste of radio resources and a shorter delay in their assignment. The simulation results presented confirm the quality of the proposed algorithms.
Development of a Hydrogen Fuel Cell Hybrid Urban Air Mobility System Model Using a Hydrogen Metal Hydride Tank
Authors: Yun Sanghyun , Im Seok Yeon , Han Jaeyoung
Published: December 2024

Hydrogen fuel cell-based UAM (urban air mobility) systems are gaining significant attention due to their advantages of higher energy density and longer flight durations compared to conventional battery-based UAM systems. To further improve the flight times of current UAM systems, various hydrogen storage methods, such as liquid hydrogen and hydrogen metal hydrides, are being utilized. Among these, hydrogen metal hydrides offer the advantage of high safety, as they do not require the additional technologies needed for high-pressure gaseous hydrogen storage or the maintenance of cryogenic temperatures for liquid hydrogen. Furthermore, because of the relatively slower dynamic response of hydrogen fuel cell systems compared to batteries, they are often integrated into hybrid configurations with batteries, necessitating an efficient power management system. In this study, a UAM system was developed by integrating a hydrogen fuel cell system with hydrogen metal hydrides and batteries in a hybrid configuration. Additionally, a state machine control approach was applied to a distribution valve for the endothermic reaction required for hydrogen desorption from the hydrogen metal hydrides. This design utilized waste heat generated by the fuel cell stack to facilitate hydrogen release. Furthermore, a fuzzy logic control-based power management system was implemented to ensure efficient power distribution during flight. The results show that approximately 43% of the waste heat generated by the stack was recovered through the tank system.
A Survey on Using Second-Life Batteries in Stationary Energy Storage Applications
Authors: Gharebaghi Majid , Rezaei Omid , Li Changyao , Wang Zhanle , Tang Yili
Published: December 2024

As a key component of transportation decarbonization, the adoption of electric vehicles (EVs) is rapidly increasing. However, EV batteries are typically retired once their state of health drops to around 80%, usually after 10 to 20 years of use. Despite this decline, retired EV batteries still retain 70–80% of their original capacity. Reusing these retired batteries as second-life batteries (SLBs) for battery energy storage systems can offer significant economic and environmental benefits. This article provides a comprehensive analysis of the technical challenges and solutions, economic feasibility, environmental impacts, and case studies of existing projects. It also explores the current battery recycling process. The article concludes with an overview of the feasibility assessment, future development trends, market potential, and policy recommendations for the battery energy storage market.
Optimizing Autonomous Multi-UAV Path Planning for Inspection Missions: A Comparative Study of Genetic and Stochastic Hill Climbing Algorithms
Authors: Aljalaud Faten , Alohali Yousef
Published: December 2024

Efficient path planning is vital for multi-UAV inspection missions, yet the comparative effectiveness of different optimization strategies has not received much attention. This paper introduces the first application of the Genetic Algorithm (GA) and Hill Climbing (HC) to multi-UAV inspection of indoor pipelines, providing a unique comparative analysis. GA exemplifies the global search strategy, while HC illustrates an enhanced stochastic local search. This comparison is impactful as it highlights the trade-offs between exploration and exploitation—two key challenges in multi-UAV path optimization. It also addresses practical concerns such as workload balancing and energy efficiency, which are crucial for the successful implementation of UAV missions. To tackle common challenges in multi-UAV operations, we have developed a novel repair mechanism. This mechanism utilizes problem-specific repair heuristics to ensure feasible and valid solutions by resolving redundant or missed inspection points. Additionally, we have introduced a penalty-based approach in HC to balance UAV workloads. Using the Crazyswarm simulation platform, we evaluated GA and HC across key performance metrics: energy consumption, travel distance, running time, and maximum tour length. The results demonstrate that GA achieves a 22% reduction in travel distance and a 23% reduction in energy consumption compared to HC, which often converges to suboptimal solutions. Additionally, GA outperforms HC, Greedy, and Random strategies, delivering at least a 13% improvement in workload balancing and other metrics. These findings establish a novel and impactful benchmark for comparing global and local optimization strategies in multi-UAV tasks, offering researchers and practitioners critical insights for selecting efficient and sustainable approaches to UAV operations in complex inspection environments.
Analytical Modeling of Filling Times for Metal Hydride–Hydrogen Storage Tanks With and Without Heat Reaction Recovery
Authors: Bouzgarrou Fatma , Mellouli Sofiene , Faqihi Abdullah A.
Published: December 2024

The analysis of metal hydride (MH) tanks requires numerical modeling, which can be complemented by analytical studies. These analytical studies are valuable for swiftly sizing efficient reservoirs intended for hydrogen or thermal energy storage systems. This study aims to develop an analytical model for estimating the filling time of various metal hydride–hydrogen storage tanks under two conditions, with and without heat reaction recovery, utilizing phase change material (PCM). Four scenarios of the metal hydride tank are considered: (i) one with an external electrical drum heater, (ii) one with an external heat transfer fluid, (iii) one with a PCM jacket, and (iv) one with a sandwiched MH-PCM configuration. Furthermore, this study investigates the influence of the MH tank design, geometric parameters (dimensions, geometry), and operational conditions (pressure and temperature) on the filling time. Overall, this investigation offers a basis for calculating the filling times of various metal hydride–hydrogen storage tank types, enabling well-informed design and system optimization decisions.
An Integrated Electricity–Gas–Heat Energy System Based on the Synergy of Long- and Short-Term Energy Storage
Authors: Zhang Min , Wang Jinhao , Zhi Huiqiang , Zhao Jun , Chang Xiao , Zhang Shifeng , Guo Xiangyu , Wang Tengxin
Published: December 2024

New energy sources, such as wind and solar energy, have been widely adopted; however, their volatility and instability have become the key issues restricting their utilization. To cope with this challenge, hybrid energy storage systems, as flexible regulation schemes, are capable of balancing the supply and demand of the power system according to different timescales and power demands, and enhancing the efficiency and utilization of new energy sources. Therefore, this paper proposes an integrated energy system planning and optimization method based on hybrid energy storage. Firstly, an adaptive noise integration empirical modal decomposition method based on the optimization improvement of the grey wolf algorithm is designed for the power allocation strategy of the hybrid energy storage system; secondly, for the electric–gas system, an energy management strategy for the hybrid electric–gas energy storage system, taking into account the operating characteristics of the alkaline electrolyzer, is proposed in order to strengthen the complementary mechanism between electric energy storage and gas energy storage. Finally, a multi-objective planning and optimization model for a comprehensive energy system based on a hybrid energy storage system is constructed. The combined configuration of long-term and short-term energy equipment can flexibly adjust energy supply and storage strategies according to demand changes on different timescales, achieve optimal resource allocation, and ensure the stability, economy, and reliability of the system. This paper uses a park in Shanxi, China, as a case study to validate the effectiveness of the methodology proposed in this paper. The example shows that the configuration of the electrical–thermal hybrid energy storage system proposed in this paper leads to a significant improvement in the economy, with an increase in annual profit of CNY 3.78 million, or 22.96%. At the same time, environmental protection is significantly enhanced, and total annual carbon emissions are reduced by 7.4 tons, with a reduction of 19.23%.
Wind Turbine SCADA Data Imbalance: A Review of Its Impact on Health Condition Analyses and Mitigation Strategies
Authors: Oliveira-Filho Adaiton , Comeau Monelle , Cave James , Nasr Charbel , Côté Pavel , Tahan Antoine
Published: December 2024

The rapidly increasing installed capacity of Wind Turbines (WTs) worldwide emphasizes the need for Operation and Maintenance (O&M) strategies favoring high availability, reliability, and cost-effective operation. Optimal decision-making and planning are supported by WT health condition analyses based on data from the Supervisory Control and Data Acquisition (SCADA) system. However, SCADA data are highly imbalanced, with a predominance of healthy condition samples. Although this imbalance can negatively impact analyses such as detection, Condition Monitoring (CM), diagnosis, and prognosis, it is often overlooked in the literature. This review specifically addresses the problem of SCADA data imbalance, focusing on strategies to mitigate this condition. Five categories of such strategies were identified: Normal Behavior Models (NBMs), data-level strategies, algorithm-level strategies, cost-sensitive learning, and data augmentation techniques. This review evidenced that the choice among these strategies is mainly dictated by the availability of data and the intended analysis. Moreover, algorithm-level strategies are predominant in analyzing SCADA data because these strategies do not require the costly and time-consuming task of data labeling. An extensive public SCADA database could ease the problem of abnormal data scarcity and help handle the problem of data imbalance. However, long-dated requests to create such a database are still unaddressed.
Multi-Objective Optimization Design for Cold-Region Office Buildings Balancing Outdoor Thermal Comfort and Building Energy Consumption
Authors: Guo Fei , Miao Shiyu , Xu Sheng , Luo Mingxuan , Dong Jing , Zhang Hongchi
Published: December 2024

Performance parameters and generative design applications have redefined the human–machine collaborative relationship, challenging traditional architectural design paradigms and guiding the architectural design process toward a performance-based design transformation. This study proposes a multi-objective optimization (MOO) design approach based on performance simulation, utilizing the Grasshopper-EvoMass multi-objective optimization platform. The Non-dominated Sorting Genetic Algorithm II (NSGA-II) is applied to coordinate two performance metrics—outdoor thermal comfort and building energy loads—for the multi-objective optimization of architectural design. The results indicate that (1) a performance-based multi-objective optimization design workflow is established. Compared to the baseline design, the optimized building form shows a significant improvement in performance. The Pareto optimal solutions, under 2022 meteorological conditions, demonstrate an annual energy efficiency improvement of 16.55%, and the outdoor thermal neutrality ratio increases by 1.11%. These results suggest that the optimization approach effectively balances building energy loads and outdoor thermal comfort. (2) A total of 1500 building form solutions were generated, from which 16 optimal solutions were selected through the Pareto front method. The resulting Pareto optimal building layouts provide multiple feasible form configurations for the early-stage design phase.
Challenges and Recommendations on Digital Health Sources in Pediatric Chronic Suppurative Lung Diseases
Authors: Kortianou Eleni A. , Mavronasou Aspasia , Sapouna Vaia , Moriki Dafni , Douros Konstantinos
Published: December 2024

In the context of digitalizing healthcare systems, digital health sources (DHSs) aim to enhance the efficiency, accessibility, and quality of healthcare services by leveraging technology. Multiple types of DHSs are increasingly established in healthcare, providing direct and wide communication between pediatric patients, parents, and healthcare professionals. Therefore, they are considered promising key tools to improve pediatric disease monitoring and management. At the same time, DHSs have been associated with several issues and risks, such as those related to data sharing, privacy, and the cultural readiness of the users. Yet, discussions in the literature have mostly focused on technical considerations and a user-friendly design. In contrast, the contribution of DHSs to treatment engagement in pediatric populations and data sharing has only partially been discussed. On this basis, we provide an overview of the available digital health technologies and their application in the pediatric population with chronic suppurative lung diseases; we describe the effectiveness in health-related outcomes, discuss possible challenges, and propose some recommendations that may overcome barriers in their everyday use.
Using Species Distribution Models to Assess the Status of the Declining Western Bumble Bee (Hymenoptera: Apidae: Bombus occidentalis) in Wyoming, USA
Authors: Tronstad Lusha M. , Bell Christine , Cook Katrina , Dillon Michael E.
Published: December 2024

Monitoring declining species is crucial to inform conservation but is challenging for rare species with limited information. The Western Bumble Bee (Bombus occidentalis) was previously common in the western United States but has drastically declined. Despite documented populations in the Intermountain West, many areas remain under-sampled. Species distribution models (SDM) can guide sampling efforts in large areas by predicting where the highest probability of suitable habitat may occur. We developed a sampling SDM using historical observations (1910–2010) in Wyoming to predict suitable habitat in the past. Using the model, we selected sampling sites that ranged from low to high predicted habitat suitability and we revisited historical locations where B. occidentalis were observed. Using all data (historical and current), we selected the predictors that explained the most variance, and created separate historical and current (2017–2018) SDM using the same variables to assess how predicted habitat suitability changed. We detected B. occidentalis at 30% of the revisited historical sites and 25% of all sites sampled. Areas predicted to be highly suitable for B. occidentalis in Wyoming declined by 5%; a small decrease compared to declines in the western portion of their range. Predicted habitat suitability increased the most in foothill areas. Creating SDM with landscape and climatic variables can bolster models and identify highly contributing variables. Regional SDM complement range-wide SDM by focusing on a portion of their range and assessing how predicted habitat changed.
Monitoring Aquatic Debris in a Water Environment Using a Remotely Operated Vehicle (ROV): A Comparative Study with Implications of Algal Detection in Lake Como (Northern Italy)
Authors: Lawrence Jassica , Castelnuovo Nicola , Bettinetti Roberta
Published: December 2024

This study investigates underwater debris in a freshwater lake using remotely operated vehicles (ROVs) during two distinct survey periods: 2019 and 2024. The primary objective was to count and document visible debris (metal and plastic) on the lakebed based on ROV video recordings. A total of 356 debris items were observed in 2019, while only 39 items were recorded in 2024. The notable decrease in debris visibility in 2024 is likely attributed to dense algal growth during the survey months, which hindered the visual identification of objects on the lakebed. The study highlights the challenges of monitoring underwater debris in freshwater systems, particularly during periods of high algal activity, which can significantly impact visibility and detection efforts. While ROVs have proven effective in identifying submerged debris in clear water, this research underscores their limitations under reduced visibility conditions caused by algal blooms, turbidity diminishing the video quality. The results provide valuable insights into the temporal variation in debris visibility and contribute to ongoing efforts to improve freshwater debris monitoring techniques.
Evaluation of Lonicera caerulea Cultivar Diversity Based on Phenotypic Traits and Nutrient Composition
Authors: Qi Yu , Li Chunming , Liang Xueying , Chen Anqi , Zhao Guimei , Bai Hui , Li Haixia , Wang Zhaoning , Han Wenzhe , Ma Yuandong , Tian Linping , Wang Yanmin , Liu Huanzhen
Published: December 2024

Lonicera caerulea L. has high nutritional and health value, and it is an important emerging small berry tree species. In this study, the morphology and nutrient composition of 60 cultivars were used to analyze and evaluate the diversity of the genus. Morphological analysis showed that the phenotypic traits of different cultivars had significant differences (p < 0.01). The phenotypic coefficient of variation (PCV) of each trait was 12.42%~84.06%, and the coefficient of genetic variation (GCV) was between 7.07%~71.72%. The analysis of nutrient content showed significant differences among the cultivars (p < 0.01). The PCV of each trait was 3.95%~96.10%, and the GCV was 0.13%~32.83%. Based on breeding objectives, cultivars with excellent growth and leaf quantitative traits, fruit quantitative traits and nutrient contents were selected through the method of comprehensive analysis of multiple characters. Traits of the selected varieties were all above average, and specific genetic gain was higher. At the same time, the selection of varieties was carried out according to flowering and fruiting phenology, which provided an indication for the breeding of improved varieties. In this study, growth, leaf and fruit quantitative traits, phenological period and nutrient components of different cultivars provided valuable information for the breeding of improved varieties.
Unfolding the Roles of Particulate- and Mineral-Associated Organic Carbon in Soil Microbial Communities
Authors: Sun Haiyan , Sun Fei , Deng Xiaoli , Storn Naleen , Wan Shubo
Published: December 2024

Forest succession is a rapid approach that can be used to increase soil carbon (C) stocks. It is crucial to understand how forest succession influences microbial community assembly and soil carbon fractions to improve carbon sequestration strategies. This present work analyzed microbial communities in forest succession, and the effects of particulate-associated organic C (POC) and mineral-associated organic C (MAOC) on microbial community structure and assembly in forest succession in Changbai Mountains, China. Compared to cropland, primary forest increased MAOC by 35% and POC by 43%, suggesting the importance of POC for microbial assembly processes, offering insights into forest restoration practices to enhance soil carbon sequestration. As succession proceeds, weak environmental selection facilitated the reduced deterministic processes, whereas local ecological and dispersal drift were elevated. Such shifts in fungal and bacterial communities could be mostly triggered by soil pH. Considering that POC was important, shifts in assembly processes can be determined by resource availability rather than succession sequences. Such findings conform to the neutral hypothesis, suggesting that POC exerts a negligible effect on analyzing microbial community assembly in forest succession. Overall, this present work sheds more light on the important effects of POC and MAOC on modeling different microbial communities and community assembly in forest succession.
The Contribution of Robo-Advisors as a Key Factor in Commercial Banks’ Performance After the Global Financial Crisis
Authors: Zogning Félix , Turcotte Pascal
Published: December 2024

In several countries, digital financial advisory services, particularly those supported by robo-advisors, are becoming increasingly popular in retail banking. These tools assist users with financial decisions such as risk assessment, portfolio selection, and rebalancing—all at a reduced cost. Recent studies suggest that, over time, robo-advisors could complement human financial advisors. Building on this research, which evaluates robo-advisors’ effectiveness in asset allocation, this study aims to assess the impact of this strategic shift on retail banks’ profitability. It compares the Canadian and French banking sectors, where robo-advisors were introduced in the 2010s. Results indicate that implementing robo-advisors enhances profitability in non-interest activities, with this effect being more pronounced in France than in Canada.
An Examination of the Leadership and Management Traits and Style in the Forest Fire Incident Command System: The Cyprus Forest Fire Service
Authors: Eliades Nicolas-George Homer , Karayiannis Achilleas , Tsantopoulos Georgios , Galatsidas Spyros
Published: December 2024

Since the early 21st century, wildlands have witnessed an effusion of wildfires, with climate and social changes resulting in unanticipated wildfire activity and impact. For forest fires to be prevented and suppressed effectively, forest firefighting forces have adopted a specific administrative system for organizing and managing the fighting force. Under the administrative system, a debate on desired “leadership and management qualities” arises, and hence, this study sought to identify the leadership and management traits that should distinguish individuals in the forest fire incident command system (FFICS) applied by the Department of Forests (Cyprus). The research subject was addressed using mixed method research, employing quantitative and qualitative data. Both datasets were used to distinguish the purposes of the applied triangulation, enabling the examination of differentiation between the trends/positions recorded in terms of the object of study. These findings point to ideal forms of transformational leadership and neoclassical management. The outcomes suggest that at the individual level, the leaders of each of the operating structures should develop leadership qualities related to emotional intelligence, empathy, judgment, critical thinking, and especially self-awareness of strengths and weaknesses. At the stage of pre-suppression, a democratic leadership style (or guiding style) is supported, while during the operational progress stage of the FFICS, a “hybrid” leadership style is suggested, borrowing elements from the democratic and authoritarian (or managerial) leadership styles. The administrative skills of FFICS leaders should include the moral and psychological rewards of subordinates, job satisfaction and recognition, and two-way communication. The current study illustrates the need for divergent leadership and management traits and styles among the different hierarchical structures of the FFICS.
Machine Learning Model Reveals Land Use and Climate’s Role in Caatinga Wildfires: Present and Future Scenarios
Authors: Vasconcelos Rodrigo N. , de Santana Mariana M. M. , Costa Diego P. , Duverger Soltan G. , Ferreira-Ferreira Jefferson , Oliveira Mariana , Barbosa Leonardo da Silva , Cordeiro Carlos Leandro , Franca Rocha Washington J. S.
Published: December 2024

Wildfires significantly impact ecosystems, economies, and biodiversity, particularly in fire-prone regions like the Caatinga biome in Northeastern Brazil. This study integrates machine learning with climate and land use data to model current and future fire dynamics in the Caatinga. Using MaxEnt, fire probability maps were generated based on historical fire scars from Landsat imagery and environmental predictors, including bioclimatic variables and human influences. Future projections under SSP1-2.6 (low-emission) and SSP5-8.5 (high-emission) scenarios were also analyzed. The baseline model achieved an AUC of 0.825, indicating a strong predictive performance. Key drivers of fire risk included the mean temperature of the driest quarter (with an importance of 14.1%) and isothermality (12.5%). Temperature-related factors were more influential than precipitation, which played a secondary role in shaping fire dynamics. Anthropogenic factors, such as proximity to farming and urban areas, also contributed to fire susceptibility. Under the optimistic scenario, low-fire-probability areas expanded to 29.129 Mha, suggesting a reduced fire risk with climate mitigation. However, high-risk zones persisted in the Western Caatinga. The pessimistic scenario projected an alarming expansion of very-high-risk areas to 12.448 Mha, emphasizing the vulnerability of the region under severe climate conditions. These findings underline the importance of temperature dynamics and human activities in shaping fire regimes. Future research should incorporate additional variables, such as vegetation recovery and socio-economic factors, to refine predictions. This study provides critical insights for targeted fire management and land use planning, promoting the sustainable conservation of the Caatinga under changing climatic conditions.
RIOK1/2 Negatively Regulates the Antiviral Response by Targeting TBK1 in Yellow Catfish (Pelteobagrus fulvidraco)
Authors: Liu Kejun , Huang Jiayang , Gui Yuting , Li Qian , Zhang Lei , Xiong Shuting
Published: December 2024

The yellow catfish (Pelteobagrus fulvidraco) industry has expanded to a certain scale and is an important part of aquaculture in China, but frequent diseases have caused huge economic losses. Comprehending the fish’s immune mechanisms, particularly the regulation of the interferon (IFN) response, is of paramount importance for future drug development and disease-resistant molecular breeding. Notably, the role of atypical protein kinases, such as the RIO kinase family, in immune regulation is poorly defined. Here, we investigated the roles of yellow catfish RIO kinases, PfRIOK1 and PfRIOK2, in modulating the IFN response through their interaction with PfTBK1, a key player in the RLR signaling pathway. Mechanically, PfRIOK1 and PfRIOK2 negatively regulate the IFN response by interacting with the RIO domains to target and degrade PfTBK1. Our findings reveal that the overexpression of PfRIOK1 and PfRIOK2 led to the decreased expression of IFN-related genes and enhanced viral replication in vitro. Additionally, PfRIOK1 and PfRIOK2 could inhibit PfTBK1-mediated antiviral responses in infected cells. These results suggest that PfRIOK1 and PfRIOK2 act as negative regulators of the IFN response in yellow catfish, providing new insights into the regulatory mechanisms of fish innate immunity and offering target molecules for molecular design breeding in aquaculture.
Damage on a Solid–Liquid Interface Induced by the Dynamical Behavior of Injected Gas Bubbles in Flowing Mercury
Authors: Kogawa Hiroyuki , Wakui Takashi , Futakawa Masatoshi
Published: December 2024

Microbubbles have been applied in various fields. In the mercury targets of spallation neutron sources, where cavitation damage is a crucial issue for life estimation, microbubbles are injected into the mercury to absorb the thermal expansion of the mercury caused by the pulsed proton beam injection and reduce the macroscopic pressure waves, which results in reducing the damage. Recently, when the proton beam power was increased and the number of injected gas bubbles was increased, unique damage morphologies were observed on the solid–liquid interface. Detailed observation and numerical analyses revealed that the microscopic pressure emitted from the gas bubbles contracting is sufficient to form pit damage, i.e., the directions of streak-like defects which are formed by connecting the pit damage coincides with the direction of the gas bubble trajectories, and the distances between the pits was understandable when taking the natural period of gas bubble vibration into account. This indicates that gas microbubbles, used to reduce macroscopic pressure waves, have the potential to be inceptions of cavitation damage due to the microscopic pressure emitted from these gas bubbles. To completely mitigate the damage, we have to consider the two effects of injecting gas bubbles: reducing macroscopic pressure waves and reducing the microscopic pressure due to bubble dynamics.
Intra-Cardiac Kinetic Energy and Ventricular Flow Analysis in Bicuspid Aortic Valve: Impact on Left Ventricular Function, Dilation Severity, and Surgical Referral
Authors: Fatehi Hassanabad Ali , Garcia Julio
Published: December 2024

Intra-cardiac kinetic energy (KE) and ventricular flow analysis (VFA), as derived from 4D-flow MRI, can be used to understand the physiological burden placed on the left ventricle (LV) due to bicuspid aortic valve (BAV). Our hypothesis was that the KE of each VFA component would impact the surgical referral outcome depending on LV function decrement, BAV phenotype, and aortic dilation severity. A total of 11 healthy controls and 49 BAV patients were recruited. All subjects underwent cardiac magnetic resonance imaging (MRI) examination. The LV mass was inferior in the controls than in the BAV patients (90 ± 26 g vs. 45 ± 17 g, p = 0.025), as well as the inferior ascending aorta diameter indexed (15.8 ± 2.5 mm/m2 vs. 19.3 ± 3.5 mm/m2, p = 0.005). The VFA KE was higher in the BAV group; significant increments were found for the maximum KE and mean KE in the VFA components (p < 0.05). A total of 14 BAV subjects underwent surgery after the scans. When comparing BAV nonsurgery vs. surgery-referred cohorts, the maximum KE and mean KE were elevated (p < 0.05). The maximum and mean KE were also associated with surgical referral (r = 0.438, p = 0.002 and r = 0.371, p = 0.009, respectively). In conclusion, the KE from VFA components significantly increased in BAV patients, including in BAV patients undergoing surgery.
Control of Vibrio parahaemolyticus in Seafood Using the Combination of Lytic Phages and Citric Acid
Authors: Zheng Xiaoshuang , Gao Lu , Yuan Lei , Chen Caowei , Yang Zhenquan
Published: December 2024

Vibrio parahaemolyticus is a key foodborne pathogen in seafood that poses health risks to consumers. The application of phages and organic acids is considered an alternative strategy for controlling bacterial contamination in foods. In the present study, the genome features of five previously isolated virulent V. parahaemolyticus phages (VPpYZU64, VPpYZU68, VPpYZU81, VPpYZU92, and VPpYZU110) were characterized, and their bacteriostatic effects in combination with citric acid were analyzed. Genome sequencing of the five phages showed a total genome length of 76,153–144,768 bp. No virulent or drug-resistant genes were detected in the five phages. Bacterial inhibition testing of salmon fillets stored at 25 °C for 12 h showed that the number of V. parahaemolyticus decreased by 2.02 and 3.84 log CFU/g after treatment with a phage mixture, VPpMIx, and the combination of phage mixture VPpMIx and citric acid. In addition, phage VPpYZU64 combined with 600 μg/mL citric acid exhibited the highest biofilm reduction rate for V. parahaemolyticus. Collectively, our results show that combining phages and citric acid is a natural and efficient method of controlling V. parahaemolyticus growth in seafood.
A New Approach to Recover Bioactive Compounds from Apple Pomace: Healthy Jelly Candies
Authors: Ciurlă Liliana , Enache Iuliana-Maria , Buțerchi Ioana , Mihalache Gabriela , Lipșa Florin Daniel , Patraș Antoanela
Published: December 2024

Rich in bioactive compounds, carbohydrates, fibers, minerals, and trace elements, apple pomace (AP) is a significant agro-industrial by-product, which pollutes and brings high management costs. The current study investigates the possibility of using an aqueous AP extract (APE) as the main ingredient in a jelly candy recipe, replacing artificial colors and flavors and improving its nutritional value. APE and formulated jelly candies were analyzed in terms of their phytochemical profile, antioxidant capacity, and color parameters. In addition, the microbiological and sensory properties of the jelly candies, as well as their behavior during storage, were analyzed. An HPLC analysis of AP revealed the presence of 9 individual phenolic compounds, with a high content of protocatechuic (375.21 ± 18.76 µg/g DW) and p-hydroxybenzoic (164.96 ± 13.83 µg/g DW) acids. The results of this study prove the presence of bioactive compounds with antioxidant and antidiabetic properties in both APE and its candies. Investigation on jelly candies with APE revealed an antioxidant capacity of 142.03 ± 1.08 mmol TE/g DW and a total polyphenolic content of 8.25 ± 0.17 mg GAE/g DW. Additionally, a sensory analysis highly appreciated the proposed jelly with APE, with scores higher than 4.70/5.00 for all evaluated attributes. Thus, this study succeeded in developing a new approach to recovering bioactive compounds from AP, demonstrating the potential of this by-product to improve jelly candies’ attributes while promoting sustainability through waste reduction and the effective use of natural resources.