bluuebunny/crossref_metadata_2025 huggingface dataset:
Research on an Aerial Docking Strategy for Meta-UAVs Using Aerodynamic Data Surrogate Models
Authors: Sun Kangwen , Gao Yixiang , Wang Zhiyao , Liang Haoquan , Zhao Chenxuan , Ji Xinzhe
Published: December 2024

Meta-aircraft, in High-Altitude, Long-Endurance (HALE) unmanned aerial vehicle (UAV) applications, utilize a strategy of formation flying in the stratosphere and aerial docking in the troposphere to enhance flight range and gust resistance. This paper explores an aerial docking strategy for unmanned meta-aircraft using a surrogate model based on aerodynamic data. The study begins with an analysis of the aerodynamic characteristics and the establishment of a dynamic model, followed by the development of a surrogate model using the vortex lattice method and a BP neural network. This model accurately simulates aerodynamic changes near the wingtip. Optimization of the docking process, focusing on impulse and moment of impulse, is achieved using a greedy algorithm. The results show a reduction in drag impulse and rolling moment by 10.89% and 15.76%, respectively, thereby easing the burden on the control system of UAVs.
UAV Collision Avoidance in Unknown Scenarios with Causal Representation Disentanglement
Authors: Fan Zhun , Xia Zihao , Lin Che , Han Gaofei , Li Wenji , Wang Dongliang , Chen Yindong , Hao Zhifeng , Cai Ruichu , Zhuang Jiafan
Published: December 2024

Deep reinforcement learning (DRL) has significantly advanced online path planning for unmanned aerial vehicles (UAVs). Nonetheless, DRL-based methods often encounter reduced performance when dealing with unfamiliar scenarios. This decline is mainly caused by the presence of non-causal and domain-specific elements within visual representations, which negatively impact the learning of policies. Present techniques generally depend on predefined augmentation or regularization methods intended to direct the model toward identifying causal and domain-invariant components, thereby enhancing the model’s ability to generalize. However, these manually crafted approaches are intrinsically constrained in their coverage and do not consider the entire spectrum of possible scenarios, resulting in less effective performance in new environments. Unlike prior studies, this work prioritizes representation learning and presents a novel method for causal representation disentanglement. The approach successfully distinguishes between causal and non-causal elements in visual data. By concentrating on causal aspects during the policy learning phase, the impact of non-causal factors is minimized, thereby improving the generalizability of DRL models. Experimental results demonstrate that our technique achieves reliable navigation and effective collision avoidance in unseen scenarios, surpassing state-of-the-art deep reinforcement learning algorithms.
Multi-Agent Hierarchical Graph Attention Actor–Critic Reinforcement Learning
Authors: Li Tongyue , Shi Dianxi , Jin Songchang , Wang Zhen , Yang Huanhuan , Chen Yang
Published: December 2024

Multi-agent systems often face challenges such as elevated communication demands, intricate interactions, and difficulties in transferability. To address the issues of complex information interaction and model scalability, we propose an innovative hierarchical graph attention actor–critic reinforcement learning method. This method naturally models the interactions within a multi-agent system as a graph, employing hierarchical graph attention to capture the complex cooperative and competitive relationships among agents, thereby enhancing their adaptability to dynamic environments. Specifically, graph neural networks encode agent observations as single feature-embedding vectors, maintaining a constant dimensionality irrespective of the number of agents, which improves model scalability. Through the “inter-agent” and “inter-group” attention layers, the embedding vector of each agent is updated into an information-condensed and contextualized state representation, which extracts state-dependent relationships between agents and model interactions at both individual and group levels. We conducted experiments across several multi-agent tasks to assess our proposed method’s effectiveness, stability, and scalability. Furthermore, to enhance the applicability of our method in large-scale tasks, we tested and validated its performance within a curriculum learning training framework, thereby enhancing its transferability.
Spatio-Temporal Analysis of Acute Myocardial Ischaemia Based on Entropy–Complexity Plane
Authors: Valverde Esteban R. , Vampa Victoria , Rosso Osvaldo A. , Arini Pedro D.
Published: December 2024

Myocardial ischaemia is a decompensation of the oxygen supply and demand ratio, often caused by coronary atherosclerosis. During the initial stage of ischaemia, the electrical activity of the heart is disrupted, increasing the risk of malignant arrhythmias. The aim of this study is to understand the differential behaviour of the ECG during occlusion of both the left anterior descending (LAD) and right anterior coronary artery (RCA), respectively, using spatio-temporal quantifiers from information theory. A standard 12-lead ECG was recorded for each patient in the database. The control condition was obtained initially. Then, a percutaneous transluminal coronary angioplasty procedure (PTCA), which encompassed the occlusion/reperfusion period, was performed. To evaluate information quantifiers, the Bandt and Pompe permutation method was used to estimate the probability distribution associated with the electrocardiographic vector modulus. Subsequently, we analysed the positioning in the H×C causal plane for the control and ischaemia. In LAD occlusion, decreased entropy and increased complexity can be seen, i.e., the behaviour is more predictable with an increase in the degree of complexity of the system. RCA occlusion had the opposite effects, i.e., the phenomenon is less predictable and exhibits a lower degree of organisation. Finally, both entropy and complexity decrease during the reperfusion phase in LAD and RCA cases.
Teacher Growth Through Professional Development Centered on the Teaching for Robust Understanding Framework
Authors: Murray Eileen , Leonard Helene S. , Bonaccorso Victoria D.
Published: December 2024

Research on teacher learning has continually defined tenants of effective professional development. However, sweeping overhauls and mandates have not yielded the intended results. Instead, transforming approaches to teaching can and should be achieved through innovative and incremental changes that build upon teachers’ existing expertise. Informed by Communities of Practice theory, we employed case study methodology to describe, explain, and assess the experiences of one middle school mathematics teacher’s longitudinal participation in an innovative and incremental continuous PD model focused on the Teaching for Robust Understanding (TRU) framework. Based on classroom observations and interview data, our findings show evidence of TRU-aligned changes in teaching practice as a result of years of participation in the PD model. These findings strengthen the call for innovative and incremental PD programs.
WePred: Edge Weight-Guided Contrastive Learning for Bipartite Link Prediction
Authors: Ding Linlin , Han Yiming , Li Mo , Gu Yinghao , Liu Tingting , Yu Shidong
Published: December 2024

Bipartite networks are common in real-world applications, where link prediction helps understand network evolution and make recommendations. Traditional methods have two major limitations; they often ignore edge weight information by treating links as binary connections, and they struggle to capture complex interaction patterns in sparse networks. We propose WePred, a weight-guided contrastive learning method for link prediction which addresses both challenges through three key components: (1) a weight-guided edge attention mechanism that incorporates edge weights into neighbor aggregation using dynamic attention scores, enabling the fine-grained capture of interaction strengths; (2) a dual-level contrastive learning approach that combines edge-level and node-level contrasts to capture both local weighted patterns and global structural dependencies, which is particularly effective in sparse regions; and (3) a unified learning framework that integrates classification and contrastive objectives. We evaluate WePred on five real-world datasets. The experimental results show that WePred consistently outperformed state-of-the-art methods, achieving improvements of 7.27% in the AUC on ML-1M and 3.18% in the AUC on the sparsest dataset of Amazon-Book (density: 0.001%). Ablation studies confirm the effectiveness of each component, while parameter analyses provide deployment guidance.
A Scalable Fog Computing Solution for Industrial Predictive Maintenance and Customization
Authors: D’Agostino Pietro , Violante Massimo , Macario Gianpaolo
Published: December 2024

This study presents a predictive maintenance system designed for industrial Internet of Things (IoT) environments, focusing on resource efficiency and adaptability. The system utilizes Nicla Sense ME sensors, a Raspberry Pi-based concentrator for real-time monitoring, and a Long Short-Term Memory (LSTM) machine-learning model for predictive analysis. Notably, the LSTM algorithm is an example of how the system’s sandbox environment can be used, allowing external users to easily integrate custom models without altering the core platform. In the laboratory, the system achieved a Root Mean Squared Error (RMSE) of 0.0156, with high accuracy across all sensors, detecting intentional anomalies with a 99.81% accuracy rate. In the real-world phase, the system maintained robust performance, with sensors recording a maximum Mean Absolute Error (MAE) of 0.1821, an R-squared value of 0.8898, and a Mean Absolute Percentage Error (MAPE) of 0.72%, demonstrating precision even in the presence of environmental interferences. Additionally, the architecture supports scalability, accommodating up to 64 sensor nodes without compromising performance. The sandbox environment enhances the platform’s versatility, enabling customization for diverse industrial applications. The results highlight the significant benefits of predictive maintenance in industrial contexts, including reduced downtime, optimized resource use, and improved operational efficiency. These findings underscore the potential of integrating Artificial Intelligence (AI) driven predictive maintenance into constrained environments, offering a reliable solution for dynamic, real-time industrial operations.
Latent Diffusion Models for Image Watermarking: A Review of Recent Trends and Future Directions
Authors: Hur Hongjun , Kang Minjae , Seo Sanghyeok , Hou Jong-Uk
Published: December 2024

Recent advancements in deep learning-based generative models have simplified image generation, increasing the need for improved source tracing and copyright protection, especially with the efficient, high-quality output of latent diffusion models (LDMs) raising concerns about unauthorized use. This paper provides a comprehensive review of watermarking techniques applied to latent diffusion models, focusing on recent trends and the potential utility of these approaches. Watermarking using latent diffusion models offers the potential to overcome these limitations by embedding watermarks in the latent space during the image generation process. This represents a new paradigm of watermarking that leverages a degree of freedom unavailable in traditional watermarking techniques and underscores the need to explore the potential advancements in watermark technology. LDM-based watermarking allows for the natural internalization of watermarks within the content generation process, enabling robust watermarking without compromising image quality. We categorize the methods based on embedding strategies and analyze their effectiveness in achieving key functionalities—source tracing, copyright protection, and AI-generated content identification. The review highlights the strengths and limitations of current techniques and discusses future directions for enhancing the robustness and applicability of watermarking in the evolving landscape of generative AI.
Research on the Integrated Converter and Its Control for Fuel Cell Hybrid Electric Vehicles with Three Power Sources
Authors: Ma Yuang , Luo Wenguang
Published: December 2024

Separate DC-DC converters for each energy source are typically configured in fuel-cell hybrid vehicles. This results in a complex control structure of the powertrain system, low energy density of the converter, and high cost due to the large number of components. Conducting research on DC-DC converters with good energy flow management and high integration is a trend to solve such problems. Based on the analysis of the basic functional structure of the converter, this paper designs a buffering unit circuit with energy collection and distribution functions and appropriately connects it with the pulse unit circuit of the converter. Through device optimization reuse and power transmission path integration, a class of non-isolated four-port DC-DC converters is constructed, which consists of an auxiliary energy charging module, input energy source control module, braking energy feedback module and forward bootstrap boost circuit. This converter has two bi-directional ports, a uni-directional input and a bi-directional output, for separate connection to the power batteries, supercapacitors, fuel cells and DC bus. It can adapt to the fluctuation of the vehicle’s driving condition while achieving dynamic and flexible regulation of power flow and can flexibly allocate power according to the load current and voltage level of energy. It can realize a total of 14 operation modes, including six output power supply operation modes, five auxiliary power charging operation modes, and three braking energy regeneration operation modes. Furthermore, the mathematical model of this converter is constructed using the state-average method and the small-signal modeling method in order to achieve the responsiveness and stability of switching multiple operating modalities. The PI control parameters are optimized using the particle swarm optimization algorithm to achieve optimized control of the converter. The simulation system is set up using MATLAB R2024a to verify that the proposed converter topology and algorithm can dynamically allocate appropriate current paths to manipulate the power flow under various operating conditions, effectively improving the utilization rate and efficiency of energy. The converter has the characteristics of high gain and high power density, which is suitable for three-energy fuel cell hybrid electric vehicles.
Improving Efficiency of AB-Class Audio Power Amplifier Using Thermoelectric Generators
Authors: Marinović Ivan , Škalic Ivan
Published: December 2024

Since circuit-based solutions for increasing the efficiency of Class AB audio amplifiers have reached their peak, this article presents a non-circuit-based approach for the same purpose. The output transistors of this class of amplifiers dissipate a significant amount of thermal energy, resulting in relatively low amplifier efficiency and requiring the transistors to be mounted on large heatsinks. This study was conducted with the aim of capturing the waste heat energy dissipated by the output transistors and converting it into electrical energy using thermoelectric generators (TEGs), which can then be used to increase the efficiency of the amplifier system. In addition to improving efficiency, this study aims to determine the extent to which heatsinks with smaller dimensions can be used in combination with the amplifier through the use of TEGs, potentially leading to a wider commercial application of Class AB audio amplifiers. The experimental results show that the thermoelectric conversion efficiency of TEGs reached 1.097% in the best case, indicating a potential increase of 0.275% in the overall efficiency of the amplifier system. These results show that the low thermoelectric efficiency of TEGs is a limiting factor that prevents a breakthrough in improving the efficiency of the power amplifier system with the proposed non-circuit-based approach.
Enhancing VANET Security: An Unsupervised Learning Approach for Mitigating False Information Attacks in VANETs
Authors: Borah Abinash , Paranjothi Anirudh
Published: December 2024

Vehicular ad hoc networks (VANETs) enable communication among vehicles and between vehicles and infrastructure to provide safety and comfort to the users. Malicious nodes in VANETs may broadcast false information to create the impression of a fake event or road congestion. In addition, several malicious nodes may collude to collectively launch a false information attack to increase the credibility of the attack. Detection of these attacks is critical to mitigate the potential risks they bring to the safety of users. Existing techniques for detecting false information attacks in VANETs use different approaches such as machine learning, blockchain, trust scores, statistical methods, etc. These techniques rely on historical information about vehicles, artificial data used to train the technique, or coordination among vehicles. To address these limitations, we propose a false information attack detection technique for VANETs using an unsupervised anomaly detection approach. The objective of the proposed technique is to detect false information attacks based on only real-time characteristics of the network, achieving high accuracy and low processing delay. The performance evaluation results show that our proposed technique offers 30% lower data processing delay and a 17% lower false positive rate compared to existing approaches in scenarios with high proportions of malicious nodes.
Dual-Band Gysel Filtering Power Divider with a Frequency Transform Resonator and Microstrip/Slotline Phase Inverter
Authors: Xu Yongping , Sun Chaoyi , Chen Zhe , Sun Huayan , Huang Zeyu , Tang Runfeng , Yang Jinxiao , Li Weilin
Published: December 2024

This paper presents a novel dual-band Gysel filtering power divider (FPD) with an excellent isolation performance and a significantly wide isolation bandwidth. Although Gysel power dividers have been extensively studied in the field of radio frequency (RF), the integration of filtering functionality and the expansion of isolation bandwidth remain challenging. The proposed design addresses these challenges by incorporating frequency transform resonators (FTRs) and a microstrip/slotline (M/S) phase inverter into the classic Gysel topology. The FTR is directly connected to the output port to provide a dual-band response, enabling the Gysel FPD to operate without external coupling between the resonator and the port. The M/S phase inverter is a dual-layer 180° phase shifter, designed to replace the conventional 180° transmission lines loaded between the two isolation resistors of the Gysel FPD, achieving a wide isolation bandwidth. To validate the proposed design method, a dual-band Gysel FPD with center frequencies of 1.4 GHz and 1.7 GHz is designed, fabricated, and measured. The measured results show that the in-band return loss is greater than 20 dB, and the in-band insertion loss is about 0.6 dB, and the amplitude and phase imbalance characteristics are good. In addition, the 20 dB-isolation fractional bandwidth achieves 97% (0.78–2.25 GHz). The measured results show excellent agreement with the simulation results, validating the effectiveness of the proposed design methodology.
Effectiveness and Characteristics of Virtual Antennas in the Multiple Signal Classification Algorithm
Authors: Takemoto Riku , Cha Jaesang , Jeong Incheol , Ahn Chang-Jun
Published: December 2024

This paper proposes a novel direction of arrival (DoA) estimation method based on the Multiple Signal Classification (MUSIC) algorithm using virtual antennas. The MUSIC method is a widely used DoA estimation technique known for its high accuracy and high resolution. However, it has a fundamental limitation: when the necessary condition of having more antenna elements at the base station than users is not met, DoA estimation becomes infeasible. To address this limitation, we introduce the concept of virtual antennas, allowing DoA estimation, even under restricted conditions. Virtual antennas are not physically present but can be virtually arranged, enabling the generation of virtual-received signals similar to those of real array antennas. Through simulation experiments, we demonstrate that our approach enables DoA estimation with the MUSIC algorithm even when its necessary conditions are not satisfied. Additionally, this paper explores the characteristics of virtual antennas in detail. We conduct simulation experiments to examine the differences in estimation accuracy between real and virtual antennas, as well as the impact of virtual antenna arrangement and count on estimation accuracy. The results show that, although virtual antennas provide lower estimation accuracy compared to real antennas, their flexible arrangement allows for improved resolution when signal sources are closely spaced by increasing the spacing between virtual antennas. Furthermore, under Additive White Gaussian Noise (AWGN) conditions, increasing the number of virtual antennas enhances estimation accuracy.
Detection of Domain Name Server Amplification Distributed Reflection Denial of Service Attacks Using Convolutional Neural Network-Based Image Deep Learning
Authors: Shin Hoon , Jeong Jaeyeong , Cho Kyumin , Lee Jaeil , Kwon Ohjin , Shin Dongkyoo
Published: December 2024

Domain Name Server (DNS) amplification Distributed Reflection Denial of Service (DRDoS) attacks are a Distributed Denial of Service (DDoS) attack technique in which multiple IT systems forge the original IP of the target system, send a request to the DNS server, and then send a large number of response packets to the target system. In this attack, it is difficult to identify the attacker because of its ability to deceive the source, and unlike TCP-based DDoS attacks, it usually uses the UDP protocol, which has a fast communication speed and amplifies network traffic by simple manipulating options, making it one of the most widely used DDoS techniques. In this study, we propose a simple convolutional neural network (CNN) model that is designed to detect DNS amplification DRDoS attack traffic and has hyperparameters adjusted through experiments. As a result of evaluating the accuracy of the proposed CNN model for detecting DNS amplification DRDoS attacks, the average accuracy of the experiment was 0.9995, which was significantly better than several machine learning (ML) models in terms of performance. It also showed good performance compared to other deep learning (DL) models, and, in particular, it was confirmed that this simple CNN had the fastest time in terms of execution compared to other deep learning models by experimentation.
Sustainable Energy Development and Agriculture in European Union Member States—Territorial Differentiation and Interrelationships
Authors: Wyrwa Joanna , Barska Anetta , Jędrzejczak-Gas Janina
Published: December 2024

Energy is regarded as a crucial facilitator of social and economic advancement, and, consequently, sustainable development. The concept of sustainable energy development has evolved to become a significant political objective addressed by governments worldwide. All sectors of the economy, including agriculture, require stimulation, which should be triggered by linkages and structural changes contributing to qualitative and quantitative progress. Agriculture has a substantial impact on the environmental footprint, playing a pivotal role in causing climate change. The objective of this article is to address the research gap concerning the monitoring of sustainable development in the energy and agricultural sectors within the European Union. The assessment of sustainability is a complex process since it is a multidisciplinary field of study with practical implications. The progress of sustainable development in the energy and agricultural sectors was evaluated through a comprehensive analysis of relevant literature and statistical data obtained from the Eurostat database and the Research Institute of Organic Agriculture FiBL. The analysis encompasses the period between 2015 and 2022. The article employs the taxonomic method, specifically the linear ordering method (standardised sum method), which enables the construction of a synthetic measure. This approach enabled the creation of rankings and comparisons between European Union countries. The studies show that in the European Union, sustainable energy development and sustainable agriculture show significant territorial differentiation. The sustainable energy measures in the countries with the highest scores (Estonia, Sweden, Finland, Austria) were 2–3 times higher than in the countries with the lowest scores (Cyprus, Belgium, Greece, Bulgaria). A similar situation was observed for sustainable agriculture measures—the maximum measures (Estonia, Bulgaria, Slovakia, Lithuania) were about two times higher than the minimum measures (Poland, Malta, Slovenia, Luxembourg). The findings indicate that, on the one hand, there have been favourable developments at the national level in Europe, but also that there are a number of existing disparities, together with evidence of potentially significant deviations from the targets set out in the 2030 Agenda.
Ammonia Combustion: Internal Combustion Engines and Gas Turbines
Authors: Eyisse Edith Flora , Nadimi Ebrahim , Wu Dawei
Published: December 2024

The quest for renewable energy sources has resulted in alternative fuels like ammonia, which offer promising carbon-free fuel for combustion engines. Ammonia has been demonstrated to be a potential fuel for decarbonizing power generator, marine, and heavy-duty transport sectors. Ammonia’s infrastructure for transportation has been established due to its widespread primary use in the agriculture sector. Ammonia has the potential to serve as a zero-carbon alternative fuel for internal combustion engines and gas turbines, given successful carbon-free synthesis and necessary modifications to legacy heat engines. While its storage characteristics surpass those of hydrogen, the intrinsic properties of ammonia pose challenges in ignition, flame propagation, and the emissions of nitrogen oxides (NOx) and nitrous oxide (N2O) during combustion in heat engines. Recent noteworthy efforts in academia and industry have been dedicated to developing innovative combustion strategies and enabling technologies for heat engines, aiming to enhance efficiency, fuel economy, and emissions. This paper provides an overview of the latest advancements in the combustion of neat or high-percentage ammonia, offering perspectives on the most promising technical solutions for gas turbines, spark ignition, and compression ignition engines.
Charging Optimization with an Improved Dynamic Programming for Electro-Gasoline Hybrid Powered Compound-Wing Unmanned Aerial Vehicle
Authors: An Siqi , Gan Yuantao , Peng Xu , Dian Songyi
Published: December 2024

For a longer endurance of vertical and level cruise flight, an electro-gasoline hybrid power system is introduced on a compound-wing unmanned aerial vehicle (UAV). After discharging during vertical flight, the battery pack is charged by a piston engine-driven generator, which simultaneously powers the UAV for level cruise flight. A charging model is established based on the configuration of the hybrid power system. Considering fuel consumption and battery attenuation within the typical flight profile of a compound-wing UAV, an optimized charging plan is developed using dynamic programming to determine the trajectory of the generated power sequence. To address deviations between ideal and practical flight conditions in terms of charging performance, a feedforward compensation is introduced to improve optimal tracking control within the dynamic programming framework. Simulations validate the effectiveness of the optimized charging plan, while testbench experiments confirm improvements achieved through compensation enhancement. The results demonstrate practicality with minimal overall cost compared to other conventional control plans.
Blending-Based Ensemble Learning Low-Voltage Station Area Theft Detection
Authors: Chen Dunchu , Li Wenwu , Fang Jie
Published: December 2024

In order to improve the efficiency of electricity theft detection, the power theft detection area and users should be better integrated, we proposed a Blending ensemble learning electricity theft detection model based on the Base Learner Selection Strategy (BLSS). Firstly, the adaptive synthetic (ADASYN) sampling method is used to process the unbalanced power consumption data, and the sample distribution of training data is balanced. Secondly, the BLSS selection method is used to screen the optimal base learner combination and construct the Blending ensemble learning model. Then, based on the historical data, the model makes a short-term prediction of the power consumption of the station area the next day, and focuses on the verification of the suspected energy-stealing station area where the Root Mean Square Percentage Error (RSPE) exceeds the threshold, so as to lock in the potential energy stealing users. Finally, through the comparison and verification of real examples, the search scope for electricity theft inspections was reduced by 79.17%, greatly improving the detection efficiency of the power supply company. At the same time, the model’s electricity theft detection and recognition accuracy rate can be as high as 97.50%. The Blending ensemble learning electricity stealing detection model based on the BLSS base learner selection method has strong electricity stealing detection and recognition ability.
Improved Genetic Algorithm-Based Harmonic Mitigation Control of an Asymmetrical Dual-Source 13-Level Switched-Capacitor Multilevel Inverter
Authors: Iqbal Hasan , Sarwat Arif
Published: December 2024

A single-phase multilevel inverter with a switched-capacitor multilevel (SC-MLI) configuration is developed to provide 13-level output voltages. An improved genetic algorithm (GA) with adaptive mutation and crossover rates is employed to achieve robust harmonic mitigation by avoiding local optima and ensuring optimal performance. The topology introduces an SC-MLI that generates AC output voltage at desired levels using only two capacitors, two asymmetrical DC sources, one diode, and 11 switches. This allows the inverter to use fewer gate drivers and, hence, increases the power density of the converter. A significant challenge in the normal operation of SC-MLI circuits relates to the self-voltage balance of the capacitors, which easily becomes unstable, particularly at low modulation indices. The proposed design addresses this issue without the need for ancillary devices or complex control schemes, ensuring stable self-balanced operation across the entire spectrum of the modulation index. In this context, the harmonic mitigation technique optimized through GA applied in this inverter ensures low harmonic distortion, achieving a total harmonic distortion (THD) of 6.73%, thereby enhancing power quality even at low modulation indices. The performance of this SC-MLI is modeled under various loading scenarios using MATLAB/Simulink® 2023b with validation performed through an Opal-RT real-time emulator. Additionally, the inverter’s overall power losses and individual switch losses, along with the efficiency, are analyzed using the simulation tool PLEXIM-PLECS. Efficiency is found to be 96.62%.
Performance Analysis of Novel Lightweight Photovoltaic Curtain Wall Modules Under Different Climatic Conditions
Authors: Tao Guofeng , Zhu Qunzhi , Chen Yonggang , Shi Xiaolong , Guan Jun
Published: December 2024

Due to limited roof area, photovoltaic (PV) has gradually been installed on other facades of buildings. This research investigates the practical application of a lightweight PV curtain wall. We use EnergyPlus to build a base office building model of fit with a lightweight PV curtain wall. The performance of two typical lightweight PV curtain wall modules is evaluated in five sample Chinese cities of different climates. Simulations were carried out to determine the power generation of faux architectural material PV curtain wall modules (FAM PVCWMs) for the best cavity distance per facade in various cities. We discovered that, in Harbin, Beijing, and Shanghai, the capacity of PV curtain wall modules installed on the south facade is the best, while in Chengdu and Guangzhou, it is the west facade. We also analyzed the power generation and the impact on the indoor environment when installing semi-transparent PV curtain wall modules (ST PVCWMs). Compared with glass, the ST PVCWM’s power generation increased by at least 50%, while the glare index setpoint exceeded time reduced by at least 30.19%. Furthermore, when installed on the north facade of Chengdu and similar cities, it can ensure more than 50% of daylight indexed time and create a more favorable indoor environment.
A Comprehensive Review of Flamelet Methods: Future Directions and Emerging Challenges
Authors: Nejaamtheen Mohammed Niyasdeen , Choi Jeong-Yeol
Published: December 2024

Understanding and accurately modeling combustion processes in engines across a wide range of operating conditions is critical for advancing both subsonic and supersonic propulsion technologies. These engines, characterized by highly complex flow fields, varying degrees of compressibility, and intricate chemical reaction mechanisms, present unique challenges for computational combustion models. Among the various approaches, flamelet models have gained prominence due to their efficiency and intuitive nature. However, traditional flamelet models, which often assume fixed boundary conditions, face significant difficulties. This review article provides a comprehensive overview of the current state of incompressible flamelet modeling, with a focus on recent advancements and their implications for turbulent combustion simulations. The discussion extends to advanced topics such as the modeling of partially premixed combustion, the definition of reaction progress variables, efficient temperature computation, and the handling of mixture fraction variance. Despite the inherent challenges and limitations of flamelet modeling, particularly in 1D applications, the approach remains an attractive option due to its computational efficiency and applicability across a wide range of combustion scenarios. The review also highlights ongoing debates within the research community regarding the validity of the flamelet approach, particularly in high-speed flows, and suggests that while alternative methods may offer more detailed modeling, they often come with prohibitive computational costs. By synthesizing historical context, recent developments, and future directions, this article serves as a valuable resource for both novice and experienced combustion modelers.
A Bibliometric Analysis of Carbon Allowances in the Carbon Emissions Trading Market
Authors: Li Ziyu , Wang Bangjun
Published: December 2024

The carbon emissions trading market is an important policy tool for the implementation of the “double carbon” goal, and the study of carbon emission quotas is an important topic for promoting green transformation, energy savings, and emission reduction in enterprises. This paper surveys the development and construction history of China’s carbon trading market, uses the VOS-viewer measurement tool to analyze the keywords co-occurrence and evolution trend of the literature about the carbon trading market from 2005 to 2024, analyzes the research hotspots, and reviews the principles of the initial carbon quota allocation, carbon quota distribution methods, and the carbon trading market carbon quota mechanism under the model construction, etc. The following conclusions can be drawn: (1) The most commonly used principles for allocating initial carbon quota are the principle of equity, the principle of efficiency, and the principle of synthesis. The equity principle focuses on the capacities and burdens of different participants; the efficiency principle maximizes incentives for participants to reduce carbon emissions; the comprehensive principle allocates carbon allowances from the perspective of enterprises, with less consideration for social responsibility and economic benefits. (2) In terms of carbon quota allocation, the initial quota should be gradually tightened, and the proportion of paid quotas should be increased. (3) The types of participants in the carbon emission reduction supply chain model are relatively simple. This paper analyzes the current situation of the research on carbon emission quota, discusses its development rules and problems, and puts forward theoretical and practical suggestions for the better development and construction of China’s unified carbon market in the future.
Post-Processing Thermal Activation of Thermoelectric Materials Based on Germanium
Authors: Markowski Piotr Marek , Prociów Eugeniusz
Published: December 2024

After the deposition process, the lattice structure of doped germanium remains low. Post-processing annealing reorders the structure and increases the output parameters. Thin films of germanium doped with gold (Ge:Au) and vanadium (Ge:V) were magnetron-sputtered on glass substrates. The course of the activation process was monitored in situ. Two different methods of post-processing thermal activation of the films were studied. The first method was to place the structure at an elevated temperature for a specified period of time. The second method involved placing the structure on a heating table and cycling the heating and cooling several times from room temperature to about 823 K. Both methods fulfill their function well. The differences come down to research aspects. The best thermoelectric parameters were achieved for germanium doped with 0.95 at.% vanadium. The Seebeck coefficient of 212 μV/K and the power factor of 1.24 mW·m−1·K−2 were obtained at 500 K.
Wayfinding Strategies for Non-Emergency Services in Australian Hospitals
Authors: Geng Shiran , Yan Se , Chau Hing-Wah , Zhang Wenyu , Zhang Chunyang
Published: December 2024

Wayfinding refers to the process of guiding individuals through built spaces, particularly in environments where navigation may be challenging due to complex layouts. In hospital settings, efficient wayfinding is essential as it directly impacts the experiences of patients, visitors, and staff. This entry focuses on wayfinding strategies in Australian hospitals, where research on this topic is limited. The entry uses a comparative case study approach to analyse various wayfinding techniques for non-emergency services, including physical signage, digital navigation systems, and spatial design elements across six hospitals in Australia. The findings indicate that combining visual cues, digital tools, and spatial planning improves navigation efficiency. However, the hospital size and layout significantly influence the effectiveness of these systems. This entry provides insights into the current wayfinding strategies and challenges in Australian hospitals and suggests further research on global case studies using the comparative framework and definitions provided here.
A Dual Branch Time-Frequency Multi-Dilated Dense Network for Wood-Boring Pest Activity Signal Enhancement in the Larval Stage
Authors: Zhang Chaoyan , Chen Zhibo , Zhang Haiyan , Li Juhu
Published: December 2024

The early identification of forest wood-boring pests is essential for effective pest management. However, detecting infestation in the early stages is difficult, as larvae, such as the emerald ash borer (EAB), Agrilus planipennis Fairmaire (Coleoptera: Buprestidae), usually feed inside the trees. Acoustic sensors can detect the pulse signals generated by larval feeding or movement, but these sounds are often weak and easily masked by background noise. To address this, we propose a dual-branch time-frequency multi-dilated dense network (DBMDNet) for noise reduction. Our model decouples two denoising training objectives: a magnitude masking decoder for coarse denoising and a complex spectral decoder for further magnitude repair and phase correction. Additionally, to enhance global time-frequency modeling, we use three different multi-dilated dense blocks to effectively separate clean signals from noisy data. Given the difficult acquisition of clean larval activity signals, we describe a self-supervised training procedure that utilizes only noisy larval activity signals directly collected from the wild, without the need for paired clean signals. Experimental results demonstrate that our proposed approach achieves the optimal performance on various evaluation metrics while requiring fewer parameters (only 98.62 k) compared to competitive models, achieving an average signal-to-noise ratio (SNR) improvement of 17.45 dB and a log-likelihood ratio (LLR) of 0.14. Furthermore, using the larval activity signals enhanced by DBMDNet, most of the noise is suppressed, and the accuracy of the recognition model is also significantly improved.
EdgeGuard: Decentralized Medical Resource Orchestration via Blockchain-Secured Federated Learning in IoMT Networks
Authors: Patni Sakshi , Lee Joohyung
Published: December 2024

The development of medical data and resources has become essential for enhancing patient outcomes and operational efficiency in an age when digital innovation in healthcare is becoming more important. The rapid growth of the Internet of Medical Things (IoMT) is changing healthcare data management, but it also brings serious issues like data privacy, malicious attacks, and service quality. In this study, we present EdgeGuard, a novel decentralized architecture that combines blockchain technology, federated learning, and edge computing to address those challenges and coordinate medical resources across IoMT networks. EdgeGuard uses a privacy-preserving federated learning approach to keep sensitive medical data local and to promote collaborative model training, solving essential issues. To prevent data modification and unauthorized access, it uses a blockchain-based access control and integrity verification system. EdgeGuard uses edge computing to improve system scalability and efficiency by offloading computational tasks from IoMT devices with limited resources. We have made several technological advances, including a lightweight blockchain consensus mechanism designed for IoMT networks, an adaptive edge resource allocation method based on reinforcement learning, and a federated learning algorithm optimized for medical data with differential privacy. We also create an access control system based on smart contracts and a secure multi-party computing protocol for model updates. EdgeGuard outperforms existing solutions in terms of computational performance, data value, and privacy protection across a wide range of real-world medical datasets. This work enhances safe, effective, and privacy-preserving medical data management in IoMT ecosystems while maintaining outstanding standards for data security and resource efficiency, enabling large-scale collaborative learning in healthcare.
Development and Experimental Evaluation of an Investment Policy Framework for Enhancing Green Finance in Qatar
Authors: Boumaiza Ameni
Published: December 2024

The shift toward a sustainable future demands substantial investments in green technologies and infrastructure, with green finance emerging as a pivotal driver for mobilizing such investments. This paper introduces a novel framework for green investment models and platforms tailored specifically to Qatar’s unique economic landscape. Through an extensive literature review, we identify essential policy levers and principles that can enhance the effectiveness of green finance initiatives. An experimental assessment utilizing a simulation model evaluates the potential impact of various policy scenarios on key metrics such as green investment volume, job creation, and environmental impact reduction. This study advocates for a comprehensive investment policy framework that includes alignment with Qatar’s national development objectives, targeted incentives for diverse economic sectors, collaborative stakeholder engagement, and robust monitoring and evaluation mechanisms. Our findings demonstrate that implementing these design principles can dramatically accelerate green finance in Qatar, aligning initiatives with the country’s National Vision 2030 and broader sustainability goals. This paper emphasizes the critical role of fiscal incentives tailored to specific sectors, the importance of collaboration among financial institutions and governmental bodies, and the necessity of continuous performance evaluations to inform adaptive policy adjustments. Ultimately, we propose a dynamic platform that not only facilitates green investments but also fosters innovation and mitigates the risks associated with sustainable projects in Qatar.
Large-Eddy Simulation of the Flow Past a Circular Cylinder at Re = 130,000: Effects of Numerical Platforms and Single- and Double-Precision Arithmetic
Authors: Lysenko Dmitry A.
Published: December 2024

Numerical simulations of sub-critical flow past a circular cylinder at Reynolds number Re = 130,000 are performed using two numerical platforms: the commercial, Ansys Fluent, and the open-source, OpenFOAM (finite volume method and large-eddy simulation based on a differential equation for the sub-grid kinetic energy). An overview of the available experimental data and similar large-eddy simulation studies is presented. A detailed analysis of all accumulated data demonstrates satisfactory agreement between them with a dispersion of approximately 20% for the main integral flow parameters. A detailed comparison of the results obtained using single- and double-precision numerical methods in Ansys Fluent did not reveal any noticeable discrepancies in the integral and local flow parameters as well as the spectral characteristics. It is shown that the behavior of the dynamic system of the fluid dynamic equations computed with single precision is stable by Lyapunov and does not lead to any loss of accuracy. The reconstructed attractors of the dynamic systems in phase space are limited by an ellipsoid.
The Impact of Cooking on Antioxidant and Enzyme Activities in Ruichang Yam Polyphenols
Authors: Liu Haoping , Zhang Hua , Geng Mengting , Shi Dingxin , Liu Dongsheng , Jiao Yanxiao , Lei Zhiqiang , Peng You
Published: December 2024

In this study, the total polyphenol content (TPC), total flavonoid content (TFC), and biological activity of yam polyphenols (including free phenolics, conjugated phenolics, and bound phenolics) were investigated during home cooking. Polyphenol components were preliminary detected in raw yam by HPLC, including 2, 4-dihydroxybenzoic acid, syringic acid, vanillic acid, 4-coumaric acid, and sinapic acid. TPC and TFC of soluble conjugated polyphenols were the main phenolic compounds in Ruichang yam. Compared with uncooked yam, cooking times of 80 min and 40 min increased the TPC and TFC of multiple types of polyphenols, while cooking reduced the TPC and TFC of AHP (acid-hydrolyzed soluble conjugated polyphenols). All yam polyphenols exhibited good α-Glucosidase inhibitory activity; α-Glucosidase inhibitory activity was significantly higher for a cooking time of 120 min. Only some types of polyphenols had lower pancreatic lipase half-inhibition concentrations than orlistat when cooked. The pancreatic lipase of FPs (free polyphenols), BHPs (alkali-hydrolyzed soluble conjugated polyphenols), and ABPs (acid-hydrolyzed insoluble bound polyphenols) was the stronges when cooking for 80 min, and the pancreatic lipase inhibitory activity of AHPs and BBPs (alkali-hydrolyzed insoluble bound polyphenols) was strongest when cooking for 40 min. Pearson’s correlation coefficient analysis revealed that the TPC was positively correlated with the TFC, the IC50 value of α-Glucosidase was negatively correlated with the IC50 value of pancreatic lipase, and redox activity was positively correlated with the TPC and TFC, respectively.