bluuebunny/crossref_metadata_2025 huggingface dataset:
Adaptive Equivalent Consumption Minimization Strategy with Enhanced Battery Life for Hybrid Trucks Using Constraint of Near-Optimal Equivalent Factor Bounds
Authors: Li Jiawei , Xia Zhenxing , Jiang Zhenhe , Dai Wei
Published: February 2025

The equivalent factor (EF) of adaptive equivalent consumption minimization strategy (A-ECMS) has a direct impact on the performance of hybrid electric trucks (HETs). Although EF on the state of charge (SoC) can effectively achieve fuel economy and SoC maintenance, battery life issues still need to be considered. Battery replacement costs are extremely high, directly affecting the operational costs of HETs. Thus, A-ECMS with enhanced battery life (A-ECMS-EBL) is proposed. Firstly, the near-optimal boundary of EF is determined to ensure the fuel economy of A-ECMS-EBL by analyzing the working mechanism of the HET powertrain. Secondly, a new EF calculation method is developed to enhance battery life. This method utilizes accelerator pedal opening (APO) feedback to optimize the power distribution between the engine and battery under high load conditions, thereby reducing the ratio of battery output power and number of battery cycle (NBC). Finally, the simulation results show that under typical cycle conditions, the equivalent fuel consumption (EFC) of A-ECMS-EBL increased by only 2.3% compared to the dynamic programming (DP), decreased by 1.1% compared to the A-ECMS, and the NBC significantly decreased by 6.12%. The results indicate that A-ECMS-EBL has significant advantages in improving fuel economy and enhancing battery life.
Semantic Communication on Digital Wireless Communication Systems
Authors: Huang Binhong , Chen Hao , Guo Cheng , Xu Xiaodong , Ma Nan , Zhang Ping
Published: February 2025

Semantic communication is an effective technological approach for the integration of intelligence and communication, enabling more efficient and context-aware data transmission. In this paper, we propose a bit-conversion-based semantic communication transmission framework to ensure compatibility with existing wireless systems. Specifically, a series of physical layer processing modules in end-to-end transmission are designed. Additionally, we develop a semantic communication simulator to implement and evaluate this framework. To optimize the performance of this framework, we introduce a novel physical layer metric, termed Integer Error Rate (IER), which provides a more suitable evaluation criterion for semantic communication compared to the conventional bit error rate (BER). On the basis of the IER, a minimum Manhattan distance constellation mapping scheme is proposed, which can improve the transmission quality of semantic communication under the same BER condition. Furthermore, we propose a hybrid joint source–channel coding (JSCC) and separate source–channel coding (SSCC) transmission scheme. This scheme decouples the semantic quantization output from the modulation order by segmenting the bits to be transmitted. Simulation results demonstrate that the hybrid JSCC/SSCC transmission scheme can improve the semantic performance, such as the Peak Signal-to-Noise Ratio (PSNR), in low Signal-to-Noise Ratio (SNR) environments while reducing bandwidth usage by up to 50% compared to the benchmark scheme.
A 64 dB-DR, 4.5 GHz-BW Logarithmic Amplifier for RSSI Measurement in 180 nm SiGe Process
Authors: Wang Yanhu , Ruan Wei , Zhou Yuanjie , Lu Mengchen , Teng Rui , Li Jiapeng
Published: February 2025

For RSSI measurement of RF systems, a wide band, large dynamic range (DR), parallel-summation logarithmic amplifier is presented in this paper. The circuit adopts an 8-stage DC-coupled cascaded limiting amplifier structure. The output voltage of the limiting amplifier is converted into current through a rectifier to realize parallel summation. In order to reduce offset, this paper introduces offset reduction circuits in the gain and output stage, respectively. In addition, a log slope adjuster is proposed, which can achieve log slope control of different frequency inputs. The post-simulation results show that at a power supply voltage of 5 V, the 3 dB gain bandwidth is 4.5 GHz, the dynamic range reaches 64 dB, and the log error is less than ±1 dB. The overall circuit consumes 21 mA of current.
Beam-Switching Antennas Using a Butler Matrix with a Five-Element Configuration
Authors: Peng Wei-Heng , Chen Yen-Sheng
Published: February 2025

Beam-switching technology is critical for fifth-generation (5G) Frequency Range 1 (FR1) base stations, yet existing odd-number Butler matrix designs often struggle to achieve compact size, low complexity, and efficient performance. Although a few studies have investigated 5 × 5 Butler matrices, their reliance on waveguide structures or multilayer implementations leads to larger footprints and greater fabrication complexity. This work introduces a novel 5 × 5 Butler matrix integrated with a five-element dipole antenna array for 3.3–3.7 GHz applications, offering notable improvements in beam-switching efficiency and overall system design. The proposed matrix generates five distinct beams at −144°, −72°, 0°, 72°, and 144° by employing precise phase progression, while eliminating crossovers and power dividers to simplify the layout. With a compact footprint of 2.67 × 0.80 × 0.02 cubic wavelength—94.4% smaller than waveguide-based designs—the matrix achieves a bandwidth of 3.32–3.62 GHz and consistently covers the target beams. The integrated system attains measured gains up to 11.4 dBi and half-power beamwidths ranging from 7.96° to 23.66°, with sidelobe levels comparable to those of state-of-the-art configurations. By employing a low-loss substrate, the gain can be further enhanced by as much as 6.81 dB, highlighting the potential for additional performance gains. These innovations establish the proposed design as a compact, low-complexity, and high-performance solution for 5G base station applications.
2D to 3D Human Skeleton Estimation Based on the Brown Camera Distortion Model and Constrained Optimization
Authors: Ma Lan , Huo Hua
Published: February 2025

In the rapidly evolving field of computer vision and machine learning, 3D skeleton estimation is critical for applications such as motion analysis and human–computer interaction. While stereo cameras are commonly used to acquire 3D skeletal data, monocular RGB systems attract attention due to benefits including cost-effectiveness and simple deployment. However, persistent challenges remain in accurately inferring depth from 2D images and reconstructing 3D structures using monocular approaches. The current 2D to 3D skeleton estimation methods overly rely on deep training of datasets, while neglecting the importance of human intrinsic structure and the principles of camera imaging. To address this, this paper introduces an innovative 2D to 3D gait skeleton estimation method that leverages the Brown camera distortion model and constrained optimization. Utilizing the Azure Kinect depth camera for capturing gait video, the Azure Kinect Body Tracking SDK was employed to effectively extract 2D and 3D joint positions. The camera’s distortion properties were analyzed, using the Brown camera distortion model which is suitable for this scenario, and iterative methods to compensate the distortion of 2D skeleton joints. By integrating the geometric constraints of the human skeleton, an optimization algorithm was analyzed to achieve precise 3D joint estimations. Finally, the framework was validated through comparisons between the estimated 3D joint coordinates and corresponding measurements captured by depth sensors. Experimental evaluations confirmed that this training-free approach achieved superior precision and stability compared to conventional methods.
FLPSHARD: A Flexible and Efficient Blockchain Sharding Solution for IIoT
Authors: Zhang Hongtao , Xue Jingfeng
Published: February 2025

The introduction of blockchain sharding technology in IIoT (Industrial Internet of Things) can leverage the scalability, decentralization, and immutability of blockchain to significantly enhance security. However, existing IIoT operations are highly correlated with business and geographical locations, as well as network conditions, making traditional random sharding schemes unsuitable for such scenarios. To address this issue, we propose the FLPShard model, a method that transforms the sharding problem into a single-source capacitated facility location problem (SSCFLP). FLPShard can handle multiple constraints simultaneously and allows multiple nodes to be bundled and allocated as a whole, meeting the demands of IIoT. By constructing neighborhoods during the dynamic adjustment process, we achieve dynamic incremental updates and automatic splitting of blockchain shards. We evaluated FLPShard by building a system prototype, and the results show that, compared to random sharding algorithms, FLPShard significantly increases system throughput and greatly reduces transaction latency.
Investigation of Text-Independent Speaker Verification by Support Vector Machine-Based Machine Learning Approaches
Authors: Kohler Odin , Imtiaz Masudul
Published: February 2025

Speaker verification is a common issue that has enumerable biomedical security applications. Speaker verification comes in two different forms: text-independent and text-dependent. Each of these forms can be implemented via many different machine learning and deep learning techniques. From our research, we found that there is significantly less work implementing text-independent speaker verification using machine learning techniques than there is using deep learning techniques. Because of this gap, we were motivated to build our own SVM and CNN model for text-independent speaker verification and compare them to other systems using SVMs or deep learning techniques. We limited ourselves to SVMs because they are commonly used for speech recognition and achieved very high accuracies. The main motivation behind this was two-fold. The first reason is to demonstrate that SVMs can and have been successfully used for text-independent speaker verification at a level comparable to deep learning techniques; the second reason is to make work using SVMs for text-independent speaker verification more accessible so it can be expanded upon easily. The analysis and comparison conducted in this paper will demonstrate how SVMs achieve results comparable to deep learning techniques and allow future researchers to more easily find SVMs used for text-independent speaker verification and derive a sense of what is being implemented in the field.
Generalized Chirp Spread Spectrum for Underwater Acoustic Communications
Authors: Kim Jinwon , Han Sangman , Seo Boguen , Kim Yongcheol , Lee Hojun
Published: February 2025

In this paper, we propose a generalized-chirp spread spectrum (G-CSS) that can have various modulation orders and offers superior bit error rate (BER) performance to improve the communication performance of existing chirp-based modulation/demodulation schemes. The proposed G-CSS sets frequency bins with different modulation orders at the start and end points of the symbol and selects frequency bins based on the bits to be transmitted, modulating the signal using corresponding chirps. Therefore, the proposed method allows for independent design of modulation orders at the start and end of the symbol, enabling signal design and flexible transmission tailored to the transmission rate required by the system. Through computer simulations and practical ocean experiments, we compared and analyzed the BER performances of the proposed G-CSS with existing up/down-CSS and long-range CSS (LoRa-CSS). The proposed G-CSS demonstrated a superior BER performance at the same transmission rate.
A Semi-Supervised Clustering Algorithm for Underground Disaster Monitoring and Early Warning
Authors: Gong Qiang , Ma Shilun , Zhang Ning , Liu Hongliang , Gao Huaxin , Zhao Yue , Jiang Xinbo , Tu Wenfeng , Chen Changyuan , Yang Fan
Published: February 2025

Due to complex geological conditions and external environmental factors, the structural safety of tunnels faces many challenges. In order to achieve real-time monitoring and early warning for tunnel safety, this paper proposes a semi-supervised clustering algorithm, named SSCME. First, in the traditional concept drift detection stage, this algorithm improves traditional methods by utilizing data distribution to calculate the concept deviation and accurately identify four different types of concept drift. Second, in the incremental update stage, the EM algorithm is further optimized to remove the outlier data used for incrementally updating the classifier, thus resolving the sensitivity issue of DBSCAN in parameter selection. Finally, a large number of sensors are installed in multiple tunnels to collect data and construct datasets. The experimental results on multiple datasets demonstrate that, compared with existing baseline methods, this algorithm has higher effectiveness and reliability.
Theoretical Design and Simulation of a Dual-Band Sheet Beam Extended Interaction Oscillator
Authors: Ling Jialang , Li Xiaofeng , Zhao Qixiang , Lu Ruiqi , Liu Xingpeng , Shi Shaoliang
Published: February 2025

Millimeter-wave devices have great application value and development prospects in military radar, satellite communication, and other fields. Extended interaction devices (EIDs) are widely used in various fields because of their small size, light weight, large bandwidth, and high output power, which are of great significance to the research of millimeter-wave sources. This article presents the design of a sheet beam, dual-beam, dual-cavity coupled extended interaction oscillator (EIO) that can operate separately at 94 GHz and 140 GHz. The coupling coefficient, characteristic impedance, and other parameters were analyzed to optimize the cavity structure and improve transmission performance. The results of the 3D particle-in-cell (PIC) simulation demonstrated that the designed EIO reached a peak output power of 6.3 kW and 41 kW, respectively, when driven by sheet electron beams of 3 A, 34 kV and 3 A, 56 kV.
PCRNet+: A Point Cloud Alignment Algorithm Introducing Dynamic Graph Convolutional Neural Networks
Authors: Qi Te , Li Yingchun , Tian Jing , Chen Hang
Published: February 2025

In this paper, an improved point cloud alignment network based on PCRNet is proposed. In the improved model, DGCNN is used as a feature extractor to capture the local and global geometric features of the point cloud, and a fully connected layer is used for feature fusion and rigid-body transformation parameter prediction. Compared with the original PCRNet, the improved network shows higher accuracy and robustness in the point cloud alignment task. In order to verify the performance of the improved network, two classical algorithms, ICP and FGR, are used as benchmarks in our experiment, and experimental comparisons of PCRNet and its improved version are performed under noise-free and noise-containing conditions, respectively. The experimental results show that the improved network (PCRNet+) proposed in our approach outperforms the original PCRNet under different test conditions, including experiments conducted in noise-free, noise-containing, and occlusion scenarios. Specifically, under noise-containing conditions, PCRNet+ surpasses the next-best algorithm, FGR, by over 95.9% across three key metrics. In occlusion scenarios, PCRNet+ achieves more than 100% improvement in all evaluated metrics compared to FGR.
A Novel Transfer Function Model Based on the Feature Selection Validation Method for Quadrotor Unmanned Aerial Vehicles in High-Intensity Radiated Field Environments
Authors: Ma Zhenyang , Wei Hongrui , Yuan Xufeng
Published: February 2025

The growing use of quadrotor unmanned aerial vehicles (UAVs), especially in low-altitude airspace, has raised concerns about their susceptibility to high-intensity radiated fields (HIRFs). These electromagnetic interferences can significantly affect UAV performance and safety. Therefore, understanding the electromagnetic behavior of quadrotor UAVs in HIRF environments and establishing robust airworthiness standards is crucial. In this paper, a novel transfer function model specifically designed for small quadrotor UAVs in HIRF environments is studied, covering the frequency range from 100 MHz to 6 GHz. The model characterizes the relationship between internal electric field strength and frequency in response to external excitation conditions. A comprehensive simulation of the interference with UAVs in the HIRF environment is conducted, followed by test validation in an anechoic chamber. To assess the consistency between simulation and test results, the feature selection validation (FSV) method is employed. This paper also compares the UAV transfer function with that of a conventional aircraft, revealing the unique electromagnetic characteristics of quadrotor UAVs and offering insights into their vulnerability in HIRF environments. The findings provide valuable contributions to the development of UAV airworthiness certification standards and their safe operation in complex electromagnetic environments.
Research on the Nonlinear Dynamic Characteristics of Fractional-Order Flyback Converter Based on Generalized Euler Method
Authors: Xie Lingling , Pang Lu
Published: February 2025

The nonlinear dynamic characteristics of a peak current regulation fractional-order (FO) flyback converter, considering the fractional nature of inductance and capacitance, are investigated in detail. First, the discrete iterative model of the fractional-order (FO) flyback converter under 10 kHz operating conditions is accomplished using the application of the Generalized Euler Method (GEM). On this basis, bifurcation diagrams, phase diagrams, and simulated time domain diagrams are used to describe the nonlinear dynamic behavior of the converter. The nonlinear dynamics of the converter are investigated through bifurcation and phase diagram analyses. A comprehensive examination is conducted to evaluate the impact of key parameters, including input voltage, reference current, and the fractional orders of inductance and capacitance, on the system’s stability. Furthermore, a comparative analysis is performed with conventional integer-order (IO) flyback converters to highlight the distinctive characteristics. The findings demonstrate that the FO converter manifests distinct nonlinear characteristics, including period-doubling bifurcation and chaotic behavior. Moreover, for identical parameter sets, the FO flyback converter is found to possess a smaller stability domain but a larger parameter region for bifurcation and chaos compared to its IO counterpart. This behavior allows the FO converter to more accurately capture the nonlinear dynamic characteristics of the flyback converter. Simulation results further substantiate the theoretical predictions.
FRU-Adapter: Frame Recalibration Unit Adapter for Dynamic Facial Expression Recognition
Authors: Her Myungbeom , Nabi Hamza Ghulam , Han Ji-Hyeong
Published: February 2025

Dynamic facial expression recognition (DFER) is one of the most important challenges in computer vision, as it plays a crucial role in human–computer interaction. Recently, adapter-based approaches have been introduced into DFER, and they have achieved remarkable success. However, the adapters still suffer from the following problems: overlooking irrelevant frames and interference with pre-trained information. In this paper, we propose a frame recalibration unit adapter (FRU-Adapter) which combines the strengths of a frame recalibration unit (FRU) and temporal self-attention (T-SA) to address the aforementioned issues. The FRU initially recalibrates the frames by emphasizing important frames and suppressing less relevant frames. The recalibrated frames are then fed into T-SA to capture the correlations between meaningful frames. As a result, the FRU-Adapter captures enhanced temporal dependencies by considering the irrelevant frames in a clip. Furthermore, we propose a method for attaching the FRU-Adapter to each encoder layer in parallel to reduce the loss of pre-trained information. Notably, the FRU-Adapter uses only 2% of the total training parameters per task while achieving an improved accuracy. Extended experiments on DFER tasks show that the proposed FRU-Adapter not only outperforms the state-of-the-art models but also exhibits parameter efficiency. The source code will be made publicly available.
Optimal Control Model of Electromagnetic Interference and Filter Design in Motor Drive System
Authors: Wang Shufen , Zhao Wei , Zong Xianming , Zhang Wenzhuo
Published: February 2025

Electromagnetic interference (EMI) is a key problem in the design of electric vehicle motor drive systems. Based on the system composition and conducted EMI mechanism, an equivalent circuit model including a motor, inverter, cable, and battery is established, and an optimized double closed-loop control strategy is proposed. Through the joint simulation platform of Simulink and Simplorer, the conduction EMI prediction model of a motor drive system is constructed. On this basis, a filter design method based on Π-type topology is proposed based on the 6 dB safety margin of reducing the interference limit. The simulation results show that the designed filter significantly suppresses the conducted EMI in the frequency band from 150 kHz to 30 MHz, and the interference peak is reduced by approximately 40 dBμV. The effectiveness of the model and filter is verified by experimental tests, and the electromagnetic compatibility (EMC) performance of the system is improved, which provides theoretical support and an engineering reference for the high-frequency interference suppression of the motor drive system.
MNv3-MFAE: A Lightweight Network for Video Action Recognition
Authors: Liu Jie , Liu Wenyue , Han Ke
Published: February 2025

Video action recognition aims to achieve the automatic classification of human behaviors by analyzing the actions in videos, with its core lying in accurately capturing the spatial detail features of images and the temporal dynamic features among video frames. In response to the problems of limited action recognition accuracy in videos containing complex temporal dynamics and large network model parameters, this paper proposes an innovative multi-feature fusion information modeling method. This paper designs a plug-and-play multi-feature action extraction (MFAE) module. The module adopts a multi-branch parallel processing strategy and integrates the functions of modeling and extracting temporal features, spatial features, and motion features to ensure the efficient modeling of the spatio-temporal information, inter-frame differences, and temporal dependencies of video actions. Meanwhile, the network employs a lightweight channel attention module (TiedSE), which reduces the complexity of the network model and decreases the number of network parameters. Finally, the effectiveness of the model is demonstrated on the Jester dataset, SomethingV2 dataset, and UCF101 dataset, achieving accuracies of 94.01%, 66.19%, and 96.74% with only 1.45 M parameters, significantly fewer than existing algorithms. The proposed method balances accuracy and computational efficiency in video action recognition, overcoming the shortcomings of traditional algorithms in temporal modeling and demonstrating its effectiveness in the task of video action recognition.
Robust Synchronization Error Estimation Under Multipath Fading in Distributed SAR
Authors: Xin Jihao , Liang Xingdong , Jiang Zhiyu , Li Hang , Dai Yujie , Wang Huan , Zhang Yuan , Bu Xiangxi
Published: February 2025

Unmanned Aerial Vehicle (UAV)-based distributed Synthetic Aperture Radar (SAR) is a current research focus. Phase synchronization is crucial for eliminating the non-coherence of distributed systems. However, as the number of UAVs increases, fast time-varying multipath effects caused by rotors can lead to multipath fading. This degrades the signal-to-noise ratio (SNR) of the synchronization link and distorts the synchronization waveform. It further breaks the reciprocity of the dual one-way synchronization link, ultimately degrading phase synchronization accuracy. We propose a robust method for spike detection and error propagation to improve phase synchronization precision. Using the Hampel filter, we detect pulse peak position jitter and remove observations from anomalous links. We then use data fusion based on minimum variance to recover synchronization errors in these links, leveraging the redundancy in synchronization phase matrices. The effectiveness of the proposed method is confirmed through flight test data from a four-UAV distributed TomoSAR experiment. Compared to the maximum-peak detection method, the phase accuracy is improved from 12.84 deg to 0.61 deg. This method supports the application of distributed SAR.
A Dual-Headed Teacher–Student Framework with an Uncertainty-Guided Mechanism for Semi-Supervised Skin Lesion Segmentation
Authors: Zou Changman , Jeon Wang-Su , Ju Hye-Rim , Rhee Sang-Yong
Published: February 2025

Medical image segmentation is a challenging task due to limited annotated data, complex lesion boundaries, and the inherent variability in medical images. These challenges make accurate and robust segmentation crucial for clinical applications. In this study, we propose the Uncertainty-Driven Auxiliary Mean Teacher (UDAMT) model, a novel semi-supervised framework specifically designed for skin lesion segmentation. Our approach employs a dual-headed teacher–student architecture with an uncertainty-guided mechanism, enhancing feature learning and boundary precision. Extensive experiments on the ISIC 2016, ISIC 2017, and ISIC 2018 datasets demonstrate that UDAMT achieves significant improvements over state-of-the-art methods, with increases of 1.17 percentage points in the Dice coefficient and 1.31 percentage points in mean Intersection over Union (mIoU) under low-label settings (5% labeled data). Furthermore, UDAMT requires 12.9 M parameters, which is slightly higher than the baseline model (12.5 M) but significantly lower than MT (14.8 M) and UAMT (15.2 M). It also achieves an inference time of 25.7 ms per image, ensuring computational efficiency. Ablation studies validate the contributions of each component, and cross-dataset evaluations on the PH2 benchmark confirm robustness to small lesions. This work provides a scalable and efficient solution for semi-supervised medical image segmentation, balancing accuracy, efficiency, and clinical applicability.
Communication Efficient Secure Three-Party Computation Using Lookup Tables for RNN Inference
Authors: Wu  Yulin , Liao Chuyi , Sun  Xiaozhen , Shen  Yuyun , Wu  Tong
Published: February 2025

Many leading technology companies currently offer Machine Learning as a Service Platform, enabling developers and organizations to access the inference capabilities of pre-trained models via API calls. However, due to concerns over user data privacy, inter-enterprise competition, and legal and regulatory constraints, directly utilizing pre-trained models in the cloud for inference faces security challenges. In this paper, we propose communication-efficient secure three-party protocols for recurrent neural network (RNN) inference. First, we design novel three-party secret-sharing protocols for digit decomposition, B2A conversion, enabling efficient transformation of secret shares between Boolean and arithmetic rings. Then, we propose the lookup table-based secure three-party protocol. Unlike the intuitive way of directly looking up tables to obtain results, we compute the results by utilizing the inherent mathematical properties of binary lookup tables, and the communication complexity of the lookup table protocol is only related to the output bit width. We also design secure three-party protocols for key functions in the RNN, including matrix multiplication, sigmoid function, and Tanh function. Our protocol divides the computation into online and offline phase, and places most of the computations locally. The theoretical analysis shows that the communication round of our work was reduced from four rounds to one round. The experiment results show that compared with the current SOTA-SIRNN, the online communication overhead of sigmoid and tanh functions decreased by 80.39% and 79.94%, respectively.
Tea Harvest Robot Navigation Path Generation Algorithm Based on Semantic Segmentation Using a Visual Sensor
Authors: Tao Houqi , Zhang Ruirui , Zhang Linhuan , Zhang Danzhu , Yi Tongchuan , Wu Mingqi
Published: February 2025

During the process of autonomous tea harvesting, it is essential for the tea-harvesting robots to navigate along the tea canopy while obtaining real-time and precise information about these tea canopies. Considering that most tea gardens are located in hilly and mountainous areas, GNSS signals often encounter disturbances, and laser sensors provide insufficient information, which fails to meet the navigation requirements of tea-harvesting robots. This study develops a vision-based semantic segmentation method for the identification of tea canopies and the generation of navigation paths. The proposed CDSC-Deeplabv3+ model integrates a Convnext backbone network with the DenseASP_SP module for feature fusion and a CFF module for enhanced semantic segmentation. The experimental results demonstrate that our proposed CDSC-Deeplabv3+ model achieves mAP, mIoU, F1-score, and FPS metrics of 96.99%, 94.71%, 98.66%, and 5.0, respectively; both the accuracy and speed performance indicators meet the practical requirements outlined in this study. Among the three compared methods for fitting the navigation central line, RANSAC shows superior performance, with minimum average angle deviations of 2.02°, 0.36°, and 0.46° at camera tilt angles of 50°, 45°, and 40°, respectively, validating the effectiveness of our approach in extracting stable tea canopy information and generating navigation paths.
Smart Grids in the Context of Smart Cities: A Literature Review and Gap Analysis
Authors: Silva Nuno Souza e , Castro Rui , Ferrão Paulo
Published: February 2025

Cities host over 50% of the world’s population and account for nearly 75% of the world’s energy consumption and 80% of the global greenhouse gas emissions. Consequently, ensuring a smart way to organize cities is paramount for the quality of life and efficiency of resource use, with emphasis on the use and management of energy, under the context of the energy trilemma, where the objectives of sustainability, security, and affordability need to be balanced. Electrification associated with the use of renewable energy generation is increasingly seen as the most efficient way to reduce the impact of energy use on GHG emissions and natural resource depletion. Electrification poses significant challenges to the development and management of the electrical infrastructure, requiring the deployment of Smart Grids, which emerge as a key development of Smart Cities. Our review targets the intersection between Smart Cities and Smart Grids. Several key components of a Smart City in the context of Smart Grids are reviewed, including elements such as metering, IoT, renewable energy sources and other distributed energy resources, grid monitoring, artificial intelligence, electric vehicles, or buildings. Case studies and pilots are reviewed, and metrics concerning existing deployments are identified. A portfolio of 16 solutions that may contribute to bringing Smart Grid solutions to the level of the city or urban settings is identified, as well as 11 gaps existing for effective and efficient deployment. We place these solutions in the context of the energy trilemma and of the Smart Grid Architecture Model. We posit that depending on the characteristics of the urban setting, including size, location, geography, a mix of economic activities, or topology, the most appropriate set of solutions can be identified, and an indicative roadmap can be built.
High-Fidelity Aeroelastic Analysis of a Wind Turbine Using a Nonlinear Frequency-Domain Solution Method
Authors: Win Naung Shine , Rahmati Mohammad , Shine Htet
Published: February 2025

This paper investigates the aeroelastic behaviour of a full wind turbine model with realistic blade vibration amplitude (9% span) using a nonlinear frequency-domain solution method. The primary objective is to demonstrate the computational efficiency of this method for an aeroelastic analysis compared to resource-intensive time-domain approaches. The underlying CFD model was validated against experimental data and benchmark simulations. The frequency-domain method was then validated against a conventional time-domain method, comparing aerodynamic damping and unsteady pressure distributions, with strong agreement observed. Results show a more complex unsteady pressure distribution at 324.5 RPM compared to 424.5 RPM, directly affecting aerodynamic damping. While aeroelastic stability was observed at both speeds, aerodynamic damping was significantly lower at 324.5 RPM. Flow field analysis reveals a clear relationship between relative velocity, static pressure, and blade vibration. Critically, the frequency-domain method achieved comparable accuracy to the time-domain method but with a significantly reduced computational cost (9 h versus 120 h), making it highly attractive for routine aeroelastic analyses and design optimisation.
Impact Assessment of Second-Life Batteries and Local Photovoltaics for Decarbonizing Enterprises Through System Digitalization and Energy Management
Authors: Borrego-Orpinell Gerard , Forero Jose-Fernando , Caprara Adriano , Díaz-González Francisco
Published: February 2025

This paper shows an impact assessment of second-life batteries (SLBs) and local photovoltaics (PV) for decarbonizing enterprises through system digitalization and energy management. SLBs from electric vehicles offer a cost-effective and environmentally sustainable energy storage solution for enterprises. These systems can significantly reduce fossil fuel dependence coupled with local PV installations. This paper proposes a methodology for developing the complete digital twin of an enterprise in combination with an optimization algorithm for energy management. This methodology can be applied to a wide range of enterprises across different sectors, both industrial and non-industrial, with diverse consumption patterns. A sensitivity analysis has been carried out to evaluate the potential of this methodology for enterprises in different contexts, where different battery sizes, PV installations, consumption types, and environmental prioritization policies are encountered. Findings indicate that combining SLBs and PV installation, supported by digital energy management, can substantially reduce carbon footprints and operational costs.
Navigating the Evolution of Cyprus’ Electricity Landscape: Drivers, Challenges and Future Prospects
Authors: Venizelou Venizelos , Poullikkas Andreas
Published: February 2025

The energy transition of Cyprus presents a distinctive case study influenced by its geographic isolation, regulatory evolution, and the imperative to integrate renewable energy sources (RESs). This paper critically examines the chronological progression of Cyprus’ energy transition, beginning with the formulation of a liberalized electricity market aligned with the European Union’s Target Model. The analysis explores key drivers underpinning increased RES investments, while addressing the transformative impacts of global disruptions on energy security and policy priorities. Furthermore, it assesses pivotal regulatory reforms and the advancement of enabling infrastructure, such as advanced metering systems and cross–border interconnections, which underpin the island’s energy modernization efforts. Finally, this paper identifies opportunities for Cyprus to position itself as a regional smart energy hub, offering valuable insights into the challenges and prospects faced by isolated energy systems within the context of the European energy transition.
Meteorological Drivers and Forest Structural Prevention of the Canker Disease in Betula alnoides—A Case Study in South China
Authors: Zhao Zhi-Gang , Li Zhao-Jia , Qiu Zhi-Xiong , Wang Chun-Sheng , He Yong-Jia , Chen Qi-Wu , Ma Hai-Bin
Published: February 2025

The risk of forest diseases is on the rise due to climate change and the consequential increase in extreme weather events, which disrupt the balance between pathogen, hosts, and the environment. This study analyzed two consecutive outbreaks of canker disease in Betula alnoides (Buch.-Ham. ex D. Don 1825) plantations and the temperature and precipitation changes in 2019 and 2020 in the northern Guangdong Province, China, to understand the impact of meteorological factors on disease outbreaks. We also examined the growth and mortality of B. alnoides with different gap sizes and reserved densities to explore how stand structure affects disease resistance in B. alnoides individuals. In both years, the disease outbreaks were preceded by periods of increasing heat and significant drops in humidity, as indicated by the z-score and relative similarity index. The mortality of B. alnoides due to canker disease was negatively correlated with seedling growth, which was optimized at a moderate reserved density of 225–300 trees per hectare in the upper layer and a gap size of 500–750 m2. The findings suggest that closely monitoring meteorological changes and implementing afforestation with a well-managed upper layer can help mitigate the impact of canker disease in subtropical regions, particularly in the context of climate change. Further long-term studies with a more systemic approach are needed to assess the effects of thinning and gap creation in forest management.
Detection of Spotted Lanternfly (Lycorma delicatula) by Bats: A qPCR Approach to Forest Pest Surveillance
Authors: McHale Erin , Kwait Robert , Kerwin Kathleen , Kyle Kathleen , Crosby Christian , Maslo Brooke
Published: February 2025

Invasive insect pests pose a significant threat to forest ecosystems. Effective pest management relies on detecting these pests, which can be challenging when populations are sparse, newly introduced, or not easily observable. The spotted lanternfly (Lycorma delicatula White), a recent invader to North America, has caused extensive damage across the eastern United States since its introduction in 2014. Conventional monitoring methods, such as traps or visual inspections, are limited in their spatial coverage and may not reliably attract or capture target species. In this study, we explored the potential of bat guano as an additional tool for invasive insect detection. We collected guano samples from five bat species across three forested sites in New Jersey, USA, between 2018 and 2022 and used species-specific quantitative PCR (qPCR) to detect spotted lanternfly DNA. Spotted lanternfly DNA was detected in guano from two bat species: big brown bats (Eptesicus fuscus) and eastern red bats (Lasiurus borealis). Detection probability was strongly influenced by spotted lanternfly phenology, with higher detection rates occurring during the adult life stage. The detection of spotted lanternfly DNA in bat guano demonstrates the feasibility of using guano analysis as a complementary tool for insect pest surveillance. Integrating guano-based monitoring with traditional methods could enhance insect pest detection efforts across diverse forested and agricultural landscapes.
Krüppel Homolog 1 Is Required for the Role of Methyl Farnesoate in Vitellogenesis in the Mud Crab Scylla paramamosain
Authors: Lai Yongqi , Lu Li , Gong Shaoming , Liu Fang , Ye Haihui
Published: February 2025

Methyl farnesoate (MF), a counterpart of insect juvenile hormones in crustaceans, plays essential roles in molting, metamorphosis, and reproduction. In this paper, we isolated a gene-encoding Krüppel homolog 1 from the mud crab Scylla paramamosain (Sp-Kr-h1) and investigated its involvement in MF-regulated vitellogenesis. RT-PCR showed that Sp-Kr-h1 had a wide expression in various tissues. qRT-PCR showed that its expression level in the ovary peaked at stage III. Both in vitro and in vivo experiments suggested that the expression of Sp-Kr-h1 and Vitellogenin (Vg) in the hepatopancreas was significantly induced by MF administration. Further, the RNA interference technique was employed to illustrate the precise role of Sp-Kr-h1 in ovarian development. This revealed that the in vitro silencing of Sp-Kr-h1 significantly decreased the level of Vg transcripts located in the hepatopancreas. Meanwhile, an in vivo experiment demonstrated that oocyte growth was inhibited after the knockdown of Sp-Kr-h1 in female S. paramamosain. In conclusion, this study identified a Kr-h1 gene in S. paramamosain and demonstrated that it is an essential factor for MF-regulated vitellogenesis. Our results provided a new insight into the mechanism underlying MF inducing ovarian development in mud crabs.
Impact of Advanced Impregnation Technologies on the Bioactivity, Bioaccessibility, and Quality of a Hydrolyzed Collagen-Enriched Apple Snack
Authors: Nuñez Helena , Retamal Rodrigo , Jaques Aldonza , Pinto Marlene , Valencia Pedro , Valdenegro Mónika , Ramirez Cristian , Almonacid Sergio , Córdova Andrés
Published: February 2025

The increasing demand for functional foods with added health benefits has driven the development of innovative food products. This study aimed to develop a functional snack made from Granny Smith apples enriched with hydrolyzed collagen using impregnation technologies, including vacuum impregnation (VI), ultrasound (US), and moderate electric field (MEF), and pretreatment with CO2 laser microperforations (MPs) combined with drying methods, including conventional drying (CD) and refractance window drying (RW). The collagen content increased significantly across treatments, with MP-I achieving the highest retention (79.86 g/100 g db). Compared with VI-CD (3.8 mg GAE/g db), MP-RW drying resulted in more total polyphenols (up to 7.2 mg GAE/g db), which was attributed to its shorter drying time (55 min vs. 160 min). The RW treatments also better-preserved color quality, with higher a* (red tones) and b* (yellow tones) values, especially in the MP-RW and US-RW treatments, highlighting their advantages in maintaining visual appeal. Texture analysis revealed that RW drying produced slices with reduced hardness and increased crispness, with MP-RW resulting in the highest sensory crispness score (8.3). In vitro digestion demonstrated that the (VI) treatment resulted in the highest degree of collagen bioaccessibility (~90%), underscoring the effectiveness of this method in improving nutrient delivery compared with the 65% MP, ~70% US, and ~74% methods. The ~90% bioaccessibility is particularly noteworthy, as it indicates that a significant portion of the impregnated collagen remains available for absorption, reinforcing the potential of VI as a strategy for developing functional foods with enhanced nutritional benefits.
Immunoglobulin Y-Based Lateral Flow Immunoassay Strip Test for Detecting Ciprofloxacin Antibiotic in Raw Pork Samples
Authors: Yadoung Sumed , Xu Huan-Yuan , Dokuta Sirikwan , Jeeno Peerapong , Yana Pichamon , Thongkham Marninphan , Sringarm Korawan , Ishimatsu Ryoichi , Xu Zhen-Lin , Hongsibsong Surat
Published: February 2025

Ciprofloxacin is metabolized from enrofloxacin for use in poultry to manage respiratory and gastrointestinal diseases, raising concerns due to its widespread tissue distribution and prolonged systemic persistence. This lateral flow immunoassay was designed to detect ciprofloxacin using an alternative IgY antibody binded with gold nanoparticles to detect ciprofloxacin residue in raw pork meat samples. The developed strip test achieved adequate sensitivity and specificity under the optimized conditions for pH, which is 7.8, and 20% of MeOH in 0.01 M phosphate buffer containing 1% Tween-20 was used for the buffer composition. An antibody concentration of 1.25 µg/mL was used to bind with gold nanoparticles as a probe for detection. The concentration of the test line (coating antigen) and control line (anti-IgY secondary antibody) was 0.5 mg/mL and 0.2 mg/mL, respectively. The efficiency of the developed strip test showed sensitivity with a 50% inhibitory concentration (IC50) of ciprofloxacin at 7.36 µg/mL, and the limit of detection was 0.2 µg/mL. The proposed strategy exhibited potential for monitoring ciprofloxacin in raw pork samples.
Royal Jelly Enhances the Social Status of Submissive Rats by Restoring Balance to the Disturbed Gut–Brain Communication
Authors: Zhu Feng , Xu Jinchun , Wang Tian , Yang Ruili , He Biao , Wang Hui-Li , Xu Yi
Published: February 2025

Royal jelly (RJ) has long been considered a crucial dietary component in dictating caste differentiation in honeybees. As a nutritional additive, royal jelly imparts a broad range of benefits to mammals and humans; however, its precise impact on the social hierarchy of these advanced animals is not yet fully understood. This study aims to determine whether the benefits of royal jelly can be transferred to rats to alter their social ranks and uncover the underlying mechanisms. A submissive model was established by inducing dysbiosis in rats, via the persistent exposure of vancomycin. Royal jelly at a dose of 2.5 g/kg was daily administered to the subject rats during postnatal weeks (PNW) 6 and 7. At the end of the intervention, animals were subjected to agonistic, water and tube competition tests, in order to assess their dominance status. As revealed by the results, the RJ treatment significantly improved the social rank of the dysbiotic rats, demonstrating that RJ can elicit positive effect on the social behaviors (caused by dysbiosis) of rats. All behavioral paradigms yielded consistent results, with no notable differences in body weight or anxiety levels. Regarding gut microbiome, vancomycin exposure caused the dysbiosis of the subject rats, which was partially reversed by treatment with royal jelly. Specifically, the intestinal presence of Proteobacteria was profoundly attenuated by the RJ supplementation, resulting in a comparable level with the intact/dominant rats. At the genus level, both Escherichia and Clostridium displayed similar dynamics in relation to Proteobacteria, implying their involvement with the RJ-mediated dominance switching. Transcriptomic analysis in the medial prefrontal context showed that the expression of a broad range of genes was influenced by RJ intake, embodying various pathways related to neuronal transmission such as neuroactive ligan–receptor interaction, the synaptic vesicle cycle, etc. By virtue of correlation analysis, Escherichia, Akkermansia and Clostridium were strongly associated with a set of gene modules around gastrin releasing peptide (Grp) and signaling pathways around Rps6ka3, establishing an intrinsic gut–brain communication. Furthermore, the infection trials of Escherichia significantly degraded the social ranks of the RJ-remedied rats in tube tests, while a series of cerebral genes like Grpr and Grpel1, as well as prefrontal spine density, were concordantly altered, underscoring the critical role of the gut–brain link in deciding the outcomes of the dyadic contests. In summary, this is an intriguing example of how royal jelly can influence the social ranks of mammals, emphasizing the importance of microbe–host interaction in mediating this species-spanning function of royal jelly in shaping social hierarchy.