bluuebunny/crossref_metadata_2025 huggingface dataset:
Leadership Strategies in Addressing Out-of-School Children: A Comparative Study of Heads of Government and Public–Private-Managed Schools in Sindh, Pakistan
Authors: Rind Irfan Ahmed
Published: November 2024

The issue of Out-of-School Children (OOSC) presents a significant policy challenge in Sindh, Pakistan, where 44% of 5–16-year-olds are not attending school. This study examines how different leadership strategies in government and Public-Private Partnership (PPP) schools address this crisis. Drawing on transformational leadership theory and institutional theory, this research investigates the influence of school governance structures—specifically government schools, Education Management Organizations (EMOs), and Sindh Education Foundation (SEF) schools—on school leaders’ abilities to increase student enrollment and retention. Through a multiple case study design, this study collects qualitative data from three school heads, augmented by perspectives from vice principals and teachers. The findings indicate that leadership agency is constrained in government schools due to centralized decision-making, while PPP schools, particularly SEF schools, offer more autonomy, enabling more adaptive and community-centered leadership strategies. This study concludes that decentralized governance in SEF schools is more effective in addressing the OOSC issue, particularly in marginalized areas. These findings offer valuable insights for policymakers seeking to enhance educational access in regions with similar socio-economic challenges. The implications of this research underscore the need for flexible, context-specific leadership models to combat the OOSC crisis.
The Impact Mechanism of Negative Academic Emotions on Academic Procrastination: The Mediating and Moderating Roles of Self-Efficacy and Goal Orientation
Authors: Chen Bowen , Zhang Hongfeng , Li Sigan
Published: November 2024

Academic procrastination is a widespread phenomenon among college students, significantly affecting their academic performance and mental health. Although previous studies have suggested a relationship between negative academic emotions and academic procrastination, the underlying mechanisms of this relationship remain insufficiently explored. Based on theoretical analysis and a review of the literature, this study utilizes structural equation modeling to examine the effects of negative academic emotions, self-efficacy, and goal orientation on academic procrastination. The results indicate a significant positive correlation between negative emotions and academic procrastination. Furthermore, self-efficacy fully mediates the relationship between negative academic emotions and academic procrastination, while goal orientation plays a moderating role in this relationship. This study reveals the intricate relationships between negative academic emotions and academic procrastination among Chinese college students, emphasizing the importance of enhancing self-efficacy and goal orientation to prevent procrastination. It provides theoretical and empirical support for improving learning efficiency and academic achievement, as well as for designing interventions to address academic procrastination.
Utilization of Generative Artificial Intelligence in Nursing Education: A Topic Modeling Analysis
Authors: Seo Won Jin , Kim Mihui
Published: November 2024

The advent of artificial intelligence (AI) has prompted the introduction of novel digital technologies, including mobile learning and metaverse learning, into nursing students’ learning environments. This study used text network and topic modeling analyses to identify the research trends in generative AI in nursing education for students and patients in schools, hospitals, and community settings. Additionally, an ego network analysis using strengths, weaknesses, opportunities, and threats (SWOT) words was performed to develop a comprehensive understanding of factors that impact the integration of generative AI in nursing education. The literature was searched from five databases published until July 2024. After excluding studies whose abstracts were not available and removing duplicates, 139 articles were identified. The seven derived topics were labeled as usability in future scientific applications, application and integration of technology, simulation education, utility in image and text analysis, performance in exams, utility in assignments, and patient education. The ego network analysis focusing on the SWOT keywords revealed “healthcare”, “use”, and “risk” were common keywords. The limited emphasis on “threats”, “strengths”, and “weaknesses” compared to “opportunities” in the SWOT analysis indicated that these areas are relatively underexplored in nursing education. To integrate generative AI technology into education such as simulation training, teaching activities, and the development of personalized learning, it is necessary to identify relevant internal strengths and weaknesses of schools, hospitals, and communities that apply it, and plan practical application strategies aligned with clear institutional guidelines.
Examining Teachers’ Professional Learning in an Online Asynchronous System: Personalized Supports for Growth and Engagement in Learning to Teach Statistics and Data Science
Authors: Lee Hollylynne S. , Thrasher Emily , Mojica Gemma F. , Graham Bruce M. , Lee J. Todd , Kuhlman Adrian
Published: November 2024

Teachers’ professional learning often includes online components. This study examined how a case of 37 teachers utilized a specific online asynchronous professional learning platform designed to support teachers’ growth in learning to teach statistics and data science in secondary schools in the United States. The platform’s features and learning materials were designed based on effective online learning designs, supports for self-guided learning, and research on the teaching and learning of statistics and data science. We paid particular attention to the features we designed into the platform to support self-regulation and personalizing the experiences to meet their preferred learning goals such as allowing for free choice of learning materials, flexibility of when and how long to engage, providing personal recommendations based on user input, internal systems to track progress, and generating certificates of completion. In this study, we used a case study with both quantitative and qualitative data to examine whether teachers had gains in meeting learning goals related to their development in teaching statistics and data science, had sustained engagement, and found the features for personalization supportive for their learning. Results showed, overall, positive growth towards meeting learning goals and making small changes towards improved classroom practice. Most teachers were generally engaged in sustained ways across the study period, though we found six different patterns of completion that highlight ways in which teachers’ goal-directed and self-regulated learning occurred within the busy schedules of educators. Several personalized features, especially the recommendations and tracking system, were highly utilized and perceived as supportive of teachers’ learning.
Lower Energy Consumption in Multi-CPU Cell-Free Massive MIMO Systems
Authors: Zhang Heng , Li Hui , Wang Xin
Published: November 2024

Under the ideal assumption of deploying only one central processing unit (CPU) in the entire system, cell-free (CF) systems can achieve significant macro-diversity gain, thereby providing uniformly reliable service to each user equipment (UE). However, due to limitations in system scalability and the feasibility of strict phase synchronization, CF systems require a multi-CPU setup and perform coherent transmission at a smaller scale. Moreover, conventional CF systems typically operate in time-division duplex (TDD) mode and utilize statistical channel state information (CSI) for downlink (DL) decoding, but the channel hardening effect is not significant. These factors reduce downlink spectral efficiency (SE) and increase DL transmission time, leading to higher energy consumption in CF systems. To address these issues, we introduce downlink channel estimation (DLCE) in multi-CPU CF systems and derive the approximate achievable DL SE. To reduce DL pilot overhead, we propose an uplink–pilot-reuse-constrained DL pilot allocation principle. Based on this principle, we develop a farthest distance pilot allocation (FDPA) algorithm to mitigate pilot contamination. In addition, leveraging the characteristics of the heuristic distributed power allocation algorithm, we propose two access point (AP) clustering algorithms: one based on CSI (BCSI) and the other based on coherent group size (BCGS). Simulation results indicate that the introduction of DLCE significantly improves DL SE in multi-CPU CF massive MIMO systems, while the proposed FDPA algorithm further enhances DL SE. The BCSI and BCGS algorithms also effectively improve DL SE and help reduce energy consumption. By combining DLCE, the FDPA algorithm, and the proposed AP clustering algorithms, the energy consumption of multi-CPU CF systems can be significantly reduced.
Speech Enhancement Algorithm Based on Microphone Array and Lightweight CRN for Hearing Aid
Authors: Xi Ji , Xu Zhe , Zhang Weiqi , Zhao Li , Xie Yue
Published: November 2024

To address the performance and computational complexity issues in speech enhancement for hearing aids, a speech enhancement algorithm based on a microphone array and a lightweight two-stage convolutional recurrent network (CRN) is proposed. The algorithm consists of two main modules: a beamforming module and a post-filtering module. The beamforming module utilizes directional features and a complex time-frequency long short-term memory (CFT-LSTM) network to extract local representations and perform spatial filtering. The post-filtering module uses analogous encoding and two symmetric decoding structures, with stacked CFT-LSTM blocks in between. It further reduces residual noise and improves filtering performance by passing spatial information through an inter-channel masking module. Experimental results show that this algorithm outperforms existing methods on the generated hearing aid dataset and the CHIME-3 dataset, with fewer parameters and lower model complexity, making it suitable for hearing aid scenarios with limited computational resources.
LFL-COBC: Lightweight Federated Learning on Blockchain-Based Device Contribution Allocation
Authors: Li Qiaoyang , Sun Yanan , Gao Ke , Xi Ning , Zhou Xiaolin , Wang Mingyan , Fan Kefeng
Published: November 2024

In the distributed cyber-physical systems (CPSs) within the industrial domain, the volume of data produced by interconnected devices is escalating at an unprecedented pace, presenting novel opportunities to enhance service quality through data sharing. Nevertheless, data privacy protection emerges as a significant challenge for data providers in wireless networks. This paper puts forward a solution integrating blockchain and lightweight federated learning, designated as LFL-COBC, which aims to tackle the issues related to data privacy and device performance optimization. We initially analyze multiple dimensions influencing the performance of computing devices, such as mining capacity, data quality, computational efficiency and local device deviation, which are crucial for augmenting user engagement. Based on these dimensions, we deduce a set of cooperation strategies for selecting the optimal committee members and rewarding the contributions of node devices equitably, thereby stimulating cooperation between users and servers. To intelligently and automatically detect device anomalies and alleviate the operational burden, a convolutional neural network (CNN) model is employed. Additionally, to address the escalating cost of customer participation and the potential data explosion issue, a near-optimal model pruning algorithm is designed. This algorithm can make the model obtained from the training of node equipment lightweight, thereby reducing the load of federated learning and the blockchain, as well as enhancing the overall efficiency of the system. The efficacy of our approach is demonstrated through numerical experiments on the HDFS and BGL public data sets. Experimental results indicate that the LFL-COBC scheme can effectively safeguard data privacy and optimize device performance concurrently, providing an effective solution for device anomaly detection in CPSs.
Research on an Algorithm for High-Speed Train Positioning and Speed Measurement Based on Orthogonal Time Frequency Space Modulation and Integrated Sensing and Communication
Authors: Xie Jianli , Hao Yong , Li Cuiran , Wang Huiqin
Published: November 2024

The Doppler effect caused by the rapid movement of high-speed rail services has a great impact on the accuracy of train positioning and speed measurement. Existing train positioning algorithms require a large number of trackside equipment and sensors, resulting in high construction and maintenance costs. Aiming to solve the above two problems, this article proposes a train positioning algorithm based on orthogonal time–frequency space (OTFS) modulation and integrated sensing and communication (ISAC). Firstly, based on the OTFS, the positioning and speed measurement architecture of communication awareness integration is constructed. Secondly, a two-stage estimation (TSE) algorithm is proposed to estimate the delay Doppler parameters of HST. In the first stage, a low-complexity coarse grid search is used, and in the second stage, a refined off-grid search is used to obtain the delay Doppler parameters. Then, the time difference of arrival/frequency difference of arrival (TDOA/FDOA) algorithm based on multiple base stations is used to locate the target, the weighted least square method is used to calculate the location, and the Cramér–Rao lower bound (CRLB) for positioning and speed measurement is derived. The simulation results demonstrate that, compared to GNSS/INS and OFDM radars, the algorithm exhibits enhanced positioning and speed measurement accuracy.
Resource Allocation in UAV-D2D Networks: A Scalable Heterogeneous Multi-Agent Deep Reinforcement Learning Approach
Authors: Wang Huayuan , Li Hui , Wang Xin , Xia Shilin , Liu Tao , Wang Ruonan
Published: November 2024

In unmanned aerial vehicle (UAV)-assisted device-to-device (D2D) caching networks, the uncertainty from unpredictable content demands and variable user positions poses a significant challenge for traditional optimization methods, often making them impractical. Multi-agent deep reinforcement learning (MADRL) offers significant advantages in optimizing multi-agent system decisions and serves as an effective and practical alternative. However, its application in large-scale dynamic environments is severely limited by the curse of dimensionality and communication overhead. To resolve this problem, we develop a scalable heterogeneous multi-agent mean-field actor-critic (SH-MAMFAC) framework. The framework treats ground users (GUs) and UAVs as distinct agents and designs cooperative rewards to convert the resource allocation problem into a fully cooperative game, enhancing global network performance. We also implement a mixed-action mapping strategy to handle discrete and continuous action spaces. A mean-field MADRL framework is introduced to minimize individual agent training loads while enhancing total cache hit probability (CHP). The simulation results show that our algorithm improves CHP and reduces transmission delay. A comparative analysis with existing mainstream deep reinforcement learning (DRL) algorithms shows that SH-MAMFAC significantly reduces training time and maintains high CHP as GU count grows. Additionally, by comparing with SH-MAMFAC variants that do not include trajectory optimization or power control, the proposed joint design scheme significantly reduces transmission delay.
Operating Key Factor Analysis of a Rotary Kiln Using a Predictive Model and Shapley Additive Explanations
Authors: Mun Seongil , Yoo Jehyeung
Published: November 2024

The global smelting business of nickel using rotary kilns and electric furnaces is expanding due to the growth of the secondary battery market. Efficient operation of electric furnaces requires consistent calcine temperature in rotary kilns. Direct measurement of calcine temperature in rotary kilns presents challenges due to inaccuracies and operational limitations, and while AI predictions are feasible, reliance on them without understanding influencing factors is risky. To address this challenge, various algorithms including XGBoost, LightGBM, CatBoost, and GRU were employed for calcine temperature prediction, with CatBoost achieving the best performance in terms of MAPE and MLSE. The influential factors on calcine temperature were identified using SHAP from XAI in the context of the CatBoost model. SHAP effectively assesses model impacts, accounting for variable interdependencies, and offers visualization in high-dimensional contexts. Given the correlation and dimensionality of variables predicting calcine temperature, SHAP was preferred over Feature Importance or PDP for the analysis. By incorporating seven out of twenty operational factors like burner fuel and reductant feed rate, combustion conditions inside of the rotary kiln and RPM, the calcine temperature increased from 840 °C in 2023 to 910 °C by October 2024, concurrently reducing the electricity unit consumption of the electric furnace by 7.8%. Enhancements to the CatBoost algorithm will enable the provision of guidance values after optimizing key variables. It is expected that managing the rotary kiln’s calcine temperature according to the predictive model’s guidance values will allow for autonomous operation of the rotary kiln through inputting guidance values to the PLC.
Video Anomaly Detection Based on Global–Local Convolutional Autoencoder
Authors: Sun Fusheng , Zhang Jiahao , Wu Xiaodong , Zheng Zhong , Yang Xiaowen
Published: November 2024

Video anomaly detection (VAD) plays a crucial role in fields such as security, production, and transportation. To address the issue of overgeneralization in anomaly behavior prediction by deep neural networks, we propose a network called AMFCFBMem-Net (appearance and motion feature cross-fusion block memory network), which combines appearance and motion feature cross-fusion blocks. Firstly, dual encoders for appearance and motion are employed to separately extract these features, which are then integrated into the skip connection layer to mitigate the model’s tendency to predict abnormal behavior, ultimately enhancing the prediction accuracy for abnormal samples. Secondly, a motion foreground extraction module is integrated into the network to generate a foreground mask map based on speed differences, thereby widening the prediction error margin between normal and abnormal behaviors. To capture the latent features of various models for normal samples, a memory module is introduced at the bottleneck of the encoder and decoder structures. This further enhances the model’s anomaly detection capabilities and diminishes its predictive generalization towards abnormal samples. The experimental results on the UCSD Pedestrian dataset 2 (UCSD Ped2) and CUHK Avenue anomaly detection dataset (CUHK Avenue) demonstrate that, compared to current cutting-edge video anomaly detection algorithms, our proposed method achieves frame-level AUCs of 97.5% and 88.8%, respectively, effectively enhancing anomaly detection capabilities.
Computationally Efficient Inference via Time-Aware Modular Control Systems
Authors: Shchyrba Dmytro , Zarzycki Hubert
Published: November 2024

Control in multi-agent decision-making systems is an important issue with a wide variety of existing approaches. In this work, we offer a new comprehensive framework for distributed control. The main contributions of this paper are summarized as follows. First, we propose PHIMEC (physics-informed meta control)—an architecture for learning optimal control by employing a physics-informed neural network when the state space is too large for reward-based learning. Second, we offer a way to leverage impulse response as a tool for system modeling and control. We propose IMPULSTM, a novel approach for incorporating time awareness into recurrent neural networks designed to accommodate irregular sampling rates in the signal. Third, we propose DIMAS, a modular approach to increasing computational efficiency in distributed control systems via domain-knowledge integration. We analyze the performance of the first two contributions on a set of corresponding benchmarks and then showcase their combined performance as a domain-informed distributed control system. The proposed approaches show satisfactory performance both individually in their respective applications and as a connected system.
Accurate Medical Vial Identification Through Mixed Reality: A HoloLens 2 Implementation
Authors: Mahmud Bahar Uddin , Hong Guan Yue , Sharmin Afsana , Asher Zachary D. , Hoyle John D.
Published: November 2024

The accurate identification of medicine vials is crucial for emergency medical services, especially for vials that resemble one another but have different labels, volumes, and concentrations. This study introduces a method to detect vials in real-time using mixed reality technology through Microsoft HoloLens 2. The system is also equipped with an SQL server to manage barcode and vial information. We conducted a comparative analysis of the barcode detection capabilities of the HoloLens 2 camera and an external scanner. The HoloLens 2 effectively identified larger barcodes when they were 20–25 cm away in normal lighting conditions. However, it faced difficulties in detecting smaller barcodes that were consistently detected by the external scanner. The frame rate investigation revealed performance fluctuations: an average of 10.54 frames per second (fps) under standard lighting conditions, decreasing to 10.10 fps in low light and further reducing to 10.05 fps when faced with high barcode density. Resolution tests demonstrated that a screen resolution of 1920 × 1080 yielded the best level of accuracy, with a precision rate of 98%. On the other hand, a resolution of 1280 × 720 achieved a good balance between accuracy 93% and speed. The HoloLens 2 demonstrates satisfactory performance under ideal circumstances; however, enhancements in detecting algorithms and camera resolution are required to accommodate diverse surroundings. This approach seeks to help paramedics make quick and accurate decisions during critical situations and tackle common obstacles such as reliance on networks and human mistakes. Our new approach of a hybrid method that integrates an external Bluetooth scanner with the MR device gives optimal results compared to the scanner-only approach.
Evaluation of an Infinite-Level Inverter Operation Powered by a DC–DC Converter in Open and Closed Loop
Authors: Valencia Pavón Nataly Gabriela , Aguila Téllez Alexander , Rojas Urbano Javier , Taramuel Obando Víctor , Guanga Edwin
Published: November 2024

This paper evaluates the open- and closed-loop DC–DC converter operation within a DC coupling multilevel inverter architecture to obtain an infinite-level stepped sinusoidal voltage. Adding a cascade controller to the DC–DC converter should reduce the settling time and increase the number of levels in the output voltage waveform; it could decrease the speed error and phase shift concerning the sinusoidal reference signal. The proposed methodology consists of implementing an experimental multilevel inverter with DC coupling through a single-phase bridge inverter energized from a BUCK converter. Trigger signals for the two converters are obtained from a control circuit based in an ATMEGA644P microcontroller to explore its capabilities in power electronics applications. A digital controller is also implemented to evaluate the operation of the BUCK converter in open and closed loop and observe its influence in the stepped sinusoidal output voltage. The evaluation is performed to energize a resistive load with common output voltage in multilevel inverters, i.e., 3, 5, 7, 11, and infinity levels. Results show that during the design stage, fast dynamic elements, like the storage capacitor, can be used to obtain a minimum THD because the settling time is sufficiently fast, the speed error remains small, and there is no need for a controller. A digital controller requires processing time, and although in theory it can reduce the settling time to a minimum, the processor introduces latency in the control signals generation, producing the opposite effect. Controller complexity of the digital controller must be considered because it increases processing time and influences the efficiency of the closed-loop operation.
A Comparative Analysis of Efficiency and Losses in a 5 kW Hybrid and Full-SiC Converter, for PV Applications in Austria
Authors: Chakraborty Rupak , Eskilson Troy , Biswas Sumanta , Makoschitz Markus
Published: November 2024

Wide Bandgap (WBG) devices like SiC-MOSFETs have become quite popular in recent times due to their superior switching characteristics, high current carrying capability and temperature stability. They are being adopted for many different applications and for a wide range of power levels. For the case of PV applications, manufacturers are considering moving to SiC-based topologies due to higher converter efficiencies and improved power density. However, the present industry largely uses hybrid approaches (IGBT + SiC-diode) to optimize system cost. The aim of this paper is to present a fair comparison of an industry-grade hybrid converter with another similar counterpart where only the Si device has been replaced with the SiC device. The effects of such a direct replacement on the efficiency and losses of the converter are studied under various power ratings. Both converters consist of two stages—a boost converter and a three-phase three-level DC to AC converter. Simulation and experimental results comprehensively indicate a higher efficiency (improvements of up to 8 percent points) for the full-SiC converter, and this is more prominent at low input voltages, where the boost converter is active. However, the gains in efficiency are moderate for high input voltages (1 percent point at nominal voltage), where the boost converter is bypassed, and the losses are almost entirely attributed to the inverter. When set in the backdrop of the Austrian inverter market, the use of SiC devices in PV inverters has the potential for an estimated savings of 37.5 GWh/year in terms of loss reduction.
Design and Research of Dual-Mode Power Generation Device Based on Improved Oscillating Buoy
Authors: Chen Fanglin , Wang Xiaori , Li Qiufen , Chen Deyv , Gao Zhiquan , Lai Jiachan , Cai Kaiwu
Published: November 2024

In response to the national “dual-carbon” strategic goals, a dual-mode ocean energy generation device was researched. First, improvements were made to the power generation system and mechanical structure of an oscillating buoy with a radius of 2.5 m, designing a power generation device that utilizes both wave energy and tidal energy. Subsequently, wave generation was carried out using the Realizable k-ε turbulence model, and the buoy’s strength was analyzed using the Ansys Fluent simulation platform with finite element analysis, with simulation results indicating a rational design. Then, the Froude–Krylov hypothesis method and the oscillating buoy body decomposition method were used to calculate the combined force in the vertical direction of the buoy, and combined with other forces, the maximum wave force on the buoy was determined. It was verified that the buoyancy of the buoy met the requirements of the hydraulic system. Finally, the hydraulic power generation system model was simulated and analyzed using AMEsim, yielding a power generation of 55.2284 kW. Using a formula, the maximum power generation of a single buoy was calculated to be approximately 15.5 kW, and the ideal maximum power generation of the entire device was able to reach 101.7284 kW.
Multirod Pumping Approach with Fresnel Lens and Ce:Nd:YAG Media for Enhancing the Solar Laser Efficiency
Authors: Almeida Joana , Costa Hugo , Vistas Cláudia R. , Tibúrcio Bruno D. , Matos Ana , Liang Dawei
Published: November 2024

A multirod Ce:Nd:YAG solar laser approach, using a Fresnel lens as a primary concentrator, is here proposed with the aim of considerably increasing the efficiency of solar-pumped lasers. Fresnel lenses are cost-effective, rendering solar lasers more economically competitive. In this work, solar-pumped radiation collected and concentrated using the Fresnel lens is received by a secondary three-dimensional compound parabolic concentrator which transmits and funnels the light toward the Ce:Nd:YAG laser rods within a water-cooled tertiary conical concentrator that enables efficient multipass pumping of the rods. To explore the full potential of the proposed approach, the performance of various multirod configurations is numerically evaluated. Through this study, configurations with three and seven Ce:Nd:YAG rods are identified as being the most efficient. A maximum continuous wave total laser power of 122.8 W is reached with the three-rod configuration, marking the highest value from a Ce:Nd:YAG solar laser, leading to solar-to-laser conversion and collection efficiencies of 7.31% and 69.50 W/m2, respectively. These results represent enhancements of 1.88 times and 1.79 times, respectively, over the previous experimental records from a Ce:Nd:YAG/YAG single-rod solar laser with a Fresnel lens. Furthermore, the above results are also 1.58 times and 1.68 times, respectively, greater than those associated with the most effective three-rod Ce:Nd:YAG solar laser utilizing a parabolic mirror as the main concentrator. The present study also shows the great usefulness of the simultaneous pumping of multiple laser rods in terms of reducing the thermal stress effects in active media, being the seven-rod configuration the one that offered the best compromise between maximum efficiency and thermal performance. This is crucial for the applicability of this sustainable technology, especially if we wish to scale our system to higher power laser levels.
A Fast Converter Synchronization Speed Method Based on the Frequency Difference on Both Sides of the Grid Connection Point
Authors: Lu Jiangang , Li Haobin , Liao Feng , Huang Yuhui , Li Xialin
Published: November 2024

The challenge of achieving a reliable and safe synchronization process for microgrids under weak communication conditions is a significant issue in distributed grid-connected energy storage. This is also the core motivation of this study. First, the concept of weak communication is introduced, and weak communication conditions are simulated by limiting the number of communications. Additionally, a fast synchronization method based on the frequency difference at the grid connection point is proposed, which allows for the rapid synchronization of converters under weak communication conditions. This method also includes an analysis of the range for key parameters, providing practical and feasible guidance for its real-world application. Finally, the validity of the theory is verified through PSCAD/EMTDC simulations and physical experiments.
A Forest Fire Prediction Model Based on Meteorological Factors and the Multi-Model Ensemble Method
Authors: Choi Seungcheol , Son Minwoo , Kim Changgyun , Kim Byungsik
Published: November 2024

More than half of South Korea’s land area is covered by forests, which significantly increases the potential for extensive damage in the event of a forest fire. The majority of forest fires in South Korea are caused by humans. Over the past decade, more than half of these types of fires occurred during the spring season. Although human activities are the primary cause of forest fires, the fact that they are concentrated in the spring underscores the strong association between forest fires and meteorological factors. When meteorological conditions favor the occurrence of forest fires, certain triggering factors can lead to their ignition more easily. The purpose of this study is to analyze the meteorological factors influencing forest fires and to develop a machine learning-based prediction model for forest fire occurrence, focusing on meteorological data. The study focuses on four regions within Gangwon province in South Korea, which have experienced substantial damage from forest fires. To construct the model, historical meteorological data were collected, surrogate variables were calculated, and a variable selection process was applied to identify relevant meteorological factors. Five machine learning models were then used to predict forest fire occurrence and ensemble techniques were employed to enhance the model’s performance. The performance of the developed forest fire prediction model was evaluated using evaluation metrics. The results indicate that the ensemble model outperformed the individual models, with a higher F1-score and a notable reduction in false positives compared to the individual models. This suggests that the model developed in this study, when combined with meteorological forecast data, can potentially predict forest fire occurrence and provide insights into the expected severity of fires. This information could support decision-making for forest fire management, aiding in the development of more effective fire response plans.
The Future Migration Direction of Deer and Japanese Yew Is Consistent Under Climate Change
Authors: Wang Xianzhe , Feng Jianan , Hong Yang , Du Hairong , Zhang Minghai , Zhang Weiqi
Published: November 2024

Climate change is becoming an important driver of biodiversity loss by altering the habitat, distribution and interspecific relationships of species. Japanese yew (Taxus cuspidata) is a first class protected plant in China, which has important ecological significance and occupies a certain position in the feeding habit of wapiti (Cervus elaphus) and Siberian roe deer (Capreolus pygargus). Due to human and animal damage, the number of Japanese yew has gradually decreased. Therefore, understanding the potential distribution of Japanese yew and the suitable areas for deer to browse on it under climate change will help to further protect these three species in Northeast China, especially migrate to more suitable areas in different scenarios in the future. From July 2021 to July 2024, we collected the information of species distribution and the variables associated with the species’ ecological limits in Muling National Nature Reserve to cross-reflect the current and future distribution and feeding area of the two species to assess each other’s impacts with Maximum entropy model (MaxEnt). The results showed that under the SSP2-4.5 and SSP5-8.5 scenarios, feeding pressure, driest quarter precipitation (BIO17) and seasonal temperature variation coefficient (BIO4) were the main variables affecting the distribution of Japanese yew, and the driest quarter precipitation (BIO17) and annual precipitation (BIO12) were the main variables affecting wapiti and Siberian roe deer foraging them. Under SSP2-4.5 and SSP5-8.5 scenarios, the suitable area of Japanese yew and the feeding area of the two species of deer gradually decreased from 2041 to 2100. Compared with wapiti, Siberian roe deer has a greater impact on the distribution range of Japanese yew, and the suitable feeding area is wider. It is expected that the potential centroid of Japanese yew, wapiti and Siberian roe deer will migrate to higher latitudes in the future. These findings provide a scientific basis for the reserve to develop relevant measures and plans and effectively protect the three species.
The Impact of Bamboo (Phyllostachys edulis) Expansion on the Water Use Patterns of Broadleaf Trees
Authors: Zeng Xiaoxia , Gong Chao , Lu Jian , Huang Guohai , Fu Chengjie , Yu Yanhua , Ran Huan , Song Qingni , Huang Dongmei , Liu Jun , Yang Qingpei
Published: November 2024

The expansion of bamboo (Phyllostachys edulis) affects the growth status of trees in colonized forests, but there has been insufficient research on changes in tree water physiology. In this study, we used stable δ2H, δ18O, and 13C isotope ratios to analyze the water sources and water use efficiency (WUE) of bamboo, deciduous broadleaf trees (Alniphyllum fortunei), and evergreen broadleaf trees (Machilus pauhoi and Castanopsis eyrei) in a bamboo-expended broadleaf forest (BEBF), a bamboo-absent broadleaf forest (BABF), and a bamboo forest (BF). We found that the expansion of bamboo had no significant effect on the water sources and WUE of deciduous broadleaf trees, but altered the water sources of evergreen broadleaf trees. During the growing season, evergreen broadleaf trees decrease their uptake fractions of surface soil water by 7.1% to 9.6% and increased their uptake fractions of middle soil water by 5.8%~9.4%. Conversely, during the non-growing season, they increased their uptake fractions of surface soil water by 11.9% and decreased their uptake fractions of deeper soil water by 5.6%~12.9%. Additionally, after expanding into broadleaf forests, bamboo increased its uptake proportion of surface and shallow soil water by 20.0% and 9.4% during the growing season. Its WUE also improved, increasing by 20.0 μmol/mol and 13.0 μmol/mol during the growing and non-growing seasons, respectively. These results indicate that as bamboo expands into broadleaf forests, it enhances its competitiveness for water resources by changing its water use strategy. Compared to deciduous broadleaf trees, evergreen broadleaf trees exhibit more flexible water use strategies under the conditions of bamboo expansion. Our research reveals, for the first time, how broadleaf trees adjust their water use strategies in response to bamboo expansion, and uncovers the mechanisms behind bamboo expansion into evergreen broadleaf forests from the perspective of water use strategies. This will aid future forest management under the conditions of bamboo expansion.
Nitrogen Addition Increased the Greenhouse Gas Emissions of Permafrost Peatland Due to the Abundance of Soil Microbial Functional Genes Increasing in the Great Khingan Mountains, Northeast China
Authors: Lu Boquan , Wu Xiaodong , Song Liquan , Sun Li , Xie Ruifeng , Zang Shuying
Published: November 2024

Permafrost peatlands are sensitive to changes in nitrogen levels because they are largely nitrogen-limited ecosystems. However, the microbial mechanisms by which the addition of nitrogen increases the emission of greenhouse gasses from permafrost peatlands remain unclear. This study was conducted to decipher the relationship between greenhouse gas emissions and soil microorganisms under nitrogen addition. Here, we performed a 154-day experimental investigation in order to assess the release of greenhouse gasses such as CO2, CH4, and N2O from the soils. Additionally, we examined the correlation between the rates of these gas emissions and the presence of crucial microbial functional genes in the soil. The results showed that the addition of low (0.01 g kg−1), medium (0.02 g kg−1), and high (0.04 g kg−1) levels of nitrogen increased the cumulative CO2 emissions by 2.35%–90.42%, respectively. The cumulative emissions of CH4 increased by 17.29%, 25.55% and 21.77%, respectively. The cumulative emissions of N2O increased 2.97, 7.49 and 7.72-fold. The addition of nitrogen increased the abundance of functional genes in the bacteria, fungi, methanogens, denitrifying bacteria, and nitrogen-fixing bacteria in soil by modifying abiotic soil variables and providing sufficient substrates for microorganisms. The results indicated that the addition of nitrogen can significantly promote the emission of greenhouse gasses by increasing the abundance of functional microbial genes in the soil of permafrost peatlands. These findings highlight the importance of considering nitrogen deposition and the nitrogen released from thawing permafrost when predicting the future greenhouse gasses emitted from permafrost peatlands.
Genome-Wide Identification and Expression Analysis of the NF-Y Transcription Factor Family in Prunus armeniaca
Authors: Wu Jiangting , He Yanguang , Wang Lin , Zhao Han , Jiang Nan , Wuyun Tana , Liu Huimin
Published: November 2024

The nuclear factor Y (NF-Y) gene family plays important roles in regulating many of the biological processes of plants, including oil accumulation. The apricot (Prunus armeniaca) is one of the most commercially traded plants, and apricot kernel oil has a high nutritional value owing to its richness in fatty acids and bioactive compounds. However, the systematic characterization of the PaNF-Y family in the apricot and the underlying regulatory mechanisms involved in oil biosynthesis remain unclear. In this study, a total of 28 PaNF-Y members from the apricot genome were identified and divided into three subfamilies (6 PaNF-YAs, 15 PaNF-YBs, and 7 PaNF-YCs) based on phylogenetic analysis results. The types and distributions of the gene structures and conserved motifs were similar in the clustered PaNF-Ys of the same subfamily. Gene duplication analysis results revealed that segmental duplication events were important for the expansion of the PaNF-Y family. Importantly, transcriptome data analysis results showed that most genes of the PaNF-YA subfamily and PaNF-YB4 of the PaNF-YB subfamily were specifically expressed in the apricot kernel. Furthermore, highly positive correlations were observed between apricot oil content and the transcript levels of PaNF-YA2, PaNF-YA6, and PaNF-YB4. In conclusion, our results provide insights into the molecular mechanisms of the key PaNF-Y genes regulating apricot oil biosynthesis.
Characterization of Novel Multifunctional Xylanase from Rumen Metagenome and Its Effects on In Vitro Microbial Fermentation of Wheat Straw
Authors: Zhang Moguang , Qiu Qinghua , Zhao Xianghui , Ouyang Kehui , Liu Chanjuan
Published: November 2024

This study investigated the characterization of a novel multifunctional enzyme, RuXyn394, derived from the metagenome of beef cattle rumen, and its impact on the in vitro microbial fermentation of wheat straw. RuXyn394, a member of the glycosyl hydrolase 11 family, displayed optimal activity under diverse pH and temperature conditions: xylanase at pH 5.5 and 50 °C, acetyl esterase at pH 6.5 and 60 °C, exoglucanase at pH 7.0 and 50 °C, and endoglucanase at pH 6.0 and 50 °C. The enzyme’s xylanase, endoglucanase, and exoglucanase activities exhibited remarkable pH stability across the range of pH 3–8 and maintained a relatively stable performance at temperatures from 20 to 50 °C, 20 to 60 °C, and 20 to 70 °C, respectively. The xylanase function, with the highest kcat/Km ratio, was identified as the predominant activity of RuXyn394. The enzyme’s various functions responded uniquely to metal ions; notably, the addition of 5 mM K+ significantly boosted the activities of xylanase, exoglucanase, and endoglucanase by 55.5%, 53.5%, and 16.4%, respectively, without affecting its acetyl esterase activity. Over the course of three time points (30 min, 60 min, 120 min), the degradation products of wheat straw xylan, including xylopentaose, xylotetraose, xylotriose, xylobiose, xylose, and total xylooligosaccharides, constituted an average of 18.4%, 33.7%, 20.6%, 22.9%, 4.3%, and 95.7% of the total products, respectively. RuXyn394 effectively hydrolyzed wheat straw, resulting in augmented volatile fatty acid production and ammonia-N levels during in vitro microbial fermentation. These findings indicate the potential of RuXyn394 as a novel and highly efficient enzyme preparation, offering promising prospects for the valorization of wheat straw, an agricultural by-product, in ruminant diets.
Biomass Accumulation, Contaminant Removal, and Settling Performance of Chlorella sp. in Unsterilized and Diluted Anaerobic Digestion Effluent
Authors: Wang Canbo , Zhang Qi , Gu Zhiqiang , Zhang Longfei , Lu Rumeng , Liu Cuixia , Liu Yuhuan
Published: November 2024

Microalgae demonstrate significant efficacy in wastewater treatment. Anaerobic digestion effluent (ADE) is regarded as an underutilized resource, abundant in carbon, nitrogen, phosphorus, and other nutrients; however, the presence of inhibitory factors restricts microalgal growth, thereby preventing its direct treatment via microalgae. The purpose of this study was to dilute ADE using various dilution media and subsequently cultivate Chlorella sp. to identify optimal culture conditions that enhance microalgal biomass and water quality. The effects of various dilution conditions were assessed by evaluating the biomass, sedimentation properties, and nutrient removal efficiencies of microalgae. The results demonstrate that microalgal biomass increases as the dilution ratio increased. The microalgae biomass in the treatments diluted with simulated wastewater was significantly higher than that with deionized water, but their effluent quality failed to meet discharge standards. The treatment diluted with deionized water for 10 times exhibited abundant microbial biomass with strong antioxidant properties. The corresponding total phosphorus concentration in the effluent (6.96 mg/L) adhered to emission limits under the Livestock and Poultry Industry Pollutant Emission Standards (8 mg/L), while ammonia nitrogen concentration (90 mg/L) was near compliance (80 mg/L). The corresponding microbial biomass, with a sludge volume index (SVI30) of 72.72 mL/g, can be recovered economically and efficiently by simple precipitation. Its high protein (52.07%) and carbohydrate (27.05%) content, coupled with low ash (10.75%), makes it a promising candidate for animal feed and fermentation. This study will aid in understanding microalgal growth in unsterilized ADE and establish a theoretical foundation for cost-effective ADE purification and microalgal biomass production.
Recent Advancements in Federated Learning: State of the Art, Fundamentals, Principles, IoT Applications and Future Trends
Authors: Papadopoulos Christos , Kollias Konstantinos-Filippos , Fragulis George F.
Published: November 2024

Federated learning (FL) is creating a paradigm shift in machine learning by directing the focus of model training to where the data actually exist. Instead of drawing all data into a central location, which raises concerns about privacy, costs, and delays, FL allows learning to take place directly on the device, keeping the data safe and minimizing the need for transfer. This approach is especially important in areas like healthcare, where protecting patient privacy is critical, and in industrial IoT settings, where moving large numbers of data is not practical. What makes FL even more compelling is its ability to reduce the bias that can occur when all data are centralized, leading to fairer and more inclusive machine learning outcomes. However, it is not without its challenges—particularly with regard to keeping the models secure from attacks. Nonetheless, the potential benefits are clear: FL can lower the costs associated with data storage and processing, while also helping organizations to meet strict privacy regulations like GDPR. As edge computing continues to grow, FL’s decentralized approach could play a key role in shaping how we handle data in the future, moving toward a more privacy-conscious world. This study identifies ongoing challenges in ensuring model security against adversarial attacks, pointing to the need for further research in this area.