bluuebunny/crossref_metadata_2025 huggingface dataset:
SAT: Spectrum-Adaptive Transformer with Spatial Awareness for UAV Target Tracking
Authors: Xu Wanying , Sun Haijiang , Wang Shuai
Published: December 2024

With the increasing threat of Unmanned Aerial Vehicle (UAV) intrusions, enhancing the perceptual capabilities of counter-UAV systems has become essential. Although various advanced algorithms have been developed in the domain of object tracking, resources specifically dedicated to UAV target tracking remain scarce. To bridge this gap, we propose a novel tracking algorithm named the Spectrum-Adaptive Transformer with Spatial Awareness for UAV Target Tracking (SAT). This network incorporates a spatial location awareness module, which captures long-range dependencies while retaining precise positional information, thereby enriching target feature representation in complex environments. Additionally, we introduce a spectrum-adaptive attention module, allowing the network to adapt to UAV targets with dynamic scale variations. Experimental results demonstrate that our algorithm achieves a 9.4% increase in success rate and a 12.1% improvement in precision compared to baseline methods on the UAV target datasets, demonstrating superior performance in handling scale variations and complex tracking scenarios. This research provides a significant advancement in UAV tracking, offering a robust and efficient solution for counter-UAV systems, and has the potential to enhance real-world applications in security and surveillance.
Evaluating Different Crown Reconstruction Approaches from Airborne LiDAR for Quantifying APAR Distribution Using a 3D Radiative Transfer Model
Authors: Zhao Xun , Liu Can , Qi Jianbo , Yuan Lijuan , Yu Zhexiu , He Siying , Huang Huaguo
Published: December 2024

Accurately quantifying fine-scale forest canopy-absorbed photosynthetically active radiation (APAR) is essential for monitoring forest growth and understanding ecological processes. The development of 3D radiative transfer models (3D RTMs) enables the precise simulation of canopy–light interactions, facilitating better quantification of forest canopy radiation dynamics. However, the complex parameters of 3D RTMs, particularly detailed 3D scene structures, pose challenges to the simulation of radiative information. While high-resolution LiDAR offers precise 3D structural data, the effectiveness of different tree crown reconstruction methods for APAR quantification using airborne laser scanning (ALS) data has not been fully investigated. In this study, we employed three ALS-based tree crown reconstruction methods: alphashape, ellipsoid, and voxel-based combined with the 3D RTM LESS to assess their effectiveness in simulating and quantifying 3D APAR distribution. Specifically, we used two distinct 3D forest scenes from the RAMI-V dataset to simulate ALS data, reconstruct virtual forest scenes, and compare their simulated 3D APAR distributions with the benchmark reference scenes using the 3D RTM LESS. Furthermore, we simulated branchless scenes to evaluate the impact of branches on APAR distribution across different reconstruction methods. Our findings indicate that the alphashape-based tree crown reconstruction method depicts 3D APAR distributions that closely align with those of the benchmark scenes. Specifically, in scenarios with sparse (HET09) and dense (HET51) canopy distributions, the APAR values from scenes reconstructed using this method exhibit the smallest discrepancies when compared to the benchmark scenes. For HET09, the branched scenario yields RMSE, MAE, and MAPE values of 33.58 kW, 33.18 kW, and 40.19%, respectively, while for HET51, these metrics are 12.74 kW, 12.97 kW, and 10.27%. In the branchless scenario, HET09′s metrics are 10.65 kW, 10.22 kW, and 9.79%, and for HET51, they are 2.99 kW, 2.65 kW, and 2.11%. However, differences remain between the branched and branchless scenarios, with the extent of these differences being dependent on the canopy structure. Our conclusion demonstrated that among the three tree crown reconstruction methods tested, the alphashape-based method has the potential for simulating and quantifying fine-scale APAR at a regional scale. It provides a convenient technical support for obtaining fine-scale 3D APAR distributions in complex forest environments at a regional scale. However, the impact of branches in quantifying APAR using ALS-reconstructed scenes also needs to be further considered.
RS-FeatFuseNet: An Integrated Remote Sensing Object Detection Model with Enhanced Feature Extraction
Authors: Qiu Yijuan , Xue Jiefeng , Zhang Gang , Hao Xuying , Lei Tao , Jiang Ping
Published: December 2024

With the advancement of satellite and sensor technologies, remote sensing images are playing crucial roles in both civilian and military domains. This paper addresses challenges such as complex backgrounds and scale variations in remote sensing images by proposing a novel attention mechanism called ESHA. This mechanism effectively integrates multi-scale feature information and introduces a multi-head self-attention (MHSA) to better capture contextual information surrounding objects, enhancing the model’s ability to perceive complex scenes. Additionally, we optimized the C2f module of YOLOv8, which enhances the model’s representational capacity by introducing a parallel multi-branch structure to learn features at different levels, resolving feature scarcity issues. During training, we utilized focal loss to handle the issue of imbalanced target class distributions in remote sensing datasets, improving the detection accuracy of challenging objects. The final network model achieved training accuracies of 89.1%, 91.6%, and 73.2% on the DIOR, NWPU VHR-10, and VEDAI datasets, respectively.
Wireless Sensor Network Coverage Optimization Using a Modified Marine Predator Algorithm
Authors: Wang Guohao , Li Xun
Published: December 2024

To solve the coverage problem caused by the random deployment of wireless sensor network nodes in the forest fire-monitoring system, a modified marine predator algorithm (MMPA) is proposed. Four modifications have been made based on the standard marine predator algorithm (MPA). Firstly, tent mapping is integrated into the initialization step to improve the searching ability of the early stage. Secondly, a hybrid search strategy is used to enhance the ability to search and jump out of local optimum. Thirdly, the golden sine guiding mechanism is applied to accelerate the convergence of the algorithm. Finally, a stage-adjustment strategy is proposed to make the transition of stages more smoothly. Six specific test functions chosen from the CEC2017 function and the benchmark function are used to evaluate the performance of MMPA. It shows that this modified algorithm has good optimization capability and stability compared to MPA, grey wolf optimizer, sine cosine algorithm, and sea horse optimizer. The results of coverage tests show that MMPA has a better uniformity of node distribution compared to MPA. The average coverage rates of MMPA are the highest compared to the commonly used metaheuristic-based algorithms, which are 91.8% in scenario 1, 95.98% in scenario 2, and 93.88% in scenario 3, respectively. This demonstrates the superiority of this proposed algorithm in coverage optimization of the wireless sensor network.
Configurable Multi-Layer Perceptron-Based Soft Sensors on Embedded Field Programmable Gate Arrays: Targeting Diverse Deployment Goals in Fluid Flow Estimation
Authors: Ling Tianheng , Qian Chao , Klann Theodor Mario , Hoever Julian , Einhaus Lukas , Schiele Gregor
Published: December 2024

This study presents a comprehensive workflow for developing and deploying Multi-Layer Perceptron (MLP)-based soft sensors on embedded FPGAs, addressing diverse deployment objectives. The proposed workflow extends our prior research by introducing greater model adaptability. It supports various configurations—spanning layer counts, neuron counts, and quantization bitwidths—to accommodate the constraints and capabilities of different FPGA platforms. The workflow incorporates a custom-developed, open-source toolchain ElasticAI.Creator that facilitates quantization-aware training, integer-only inference, automated accelerator generation using VHDL templates, and synthesis alongside performance estimation. A case study on fluid flow estimation was conducted on two FPGA platforms: the AMD Spartan-7 XC7S15 and the Lattice iCE40UP5K. For precision-focused and latency-sensitive deployments, a six-layer, 60-neuron MLP accelerator quantized to 8 bits on the XC7S15 achieved an MSE of 56.56, an MAPE of 1.61%, and an inference latency of 23.87 μs. Moreover, for low-power and energy-constrained deployments, a five-layer, 30-neuron MLP accelerator quantized to 8 bits on the iCE40UP5K achieved an inference latency of 83.37 μs, a power consumption of 2.06 mW, and an energy consumption of just 0.172 μJ per inference. These results confirm the workflow’s ability to identify optimal FPGA accelerators tailored to specific deployment requirements, achieving a balanced trade-off between precision, inference latency, and energy efficiency.
An Improved Method for Enhancing the Accuracy and Speed of Dynamic Object Detection Based on YOLOv8s
Authors: Liu Zhiguo , Zhang Enzheng , Ding Qian , Liao Weijie , Wu Zixiang
Published: December 2024

Accurate detection and tracking of dynamic objects are critical for enabling skill demonstration and effective skill generalization in robotic skill learning and application scenarios. To further improve the detection accuracy and tracking speed of the YOLOv8s model in dynamic object tracking tasks, this paper proposes a method to enhance both detection precision and speed based on YOLOv8s architecture. Specifically, a Focused Linear Attention mechanism is introduced into the YOLOv8s backbone network to enhance dynamic object detection accuracy, while the Ghost module is incorporated into the neck network to improve the model’s tracking speed for dynamic objects. By mapping the motion of dynamic objects across frames, the proposed method achieves accurate trajectory tracking. This paper provides a detailed explanation of the improvements made to YOLOv8s for enhancing detection accuracy and speed in dynamic object detection tasks. Comparative experiments on the MS-COCO dataset and the custom dataset demonstrate that the proposed method has a clear advantage in terms of detection accuracy and processing speed. The dynamic object detection experiments further validate the effectiveness of the proposed method for detecting and tracking objects at different speeds. The proposed method offers a valuable reference for the field of dynamic object detection, providing actionable insights for applications such as robotic skill learning, generalization, and artificial intelligence-driven robotics.
Automatic Optical Path Alignment Method for Optical Biological Microscope
Authors: Peng Guojin , Yu Zhenming , Zhou Xinjian , Pang Guangyao , Wang Kuikui
Published: December 2024

A high-quality optical path alignment is essential for achieving superior image quality in optical biological microscope (OBM) systems. The traditional automatic alignment methods for OBMs rely heavily on complex masker-detection techniques. This paper introduces an innovative, image-sensor-based optical path alignment approach designed for low-power objective (specifically 4×) automatic OBMs. The proposed method encompasses reference objective (RO) identification and alignment processes. For identification, a model depicting spot movement with objective rotation near the optical axis is developed, elucidating the influence of optical path parameters on spot characteristics. This insight leads to the proposal of an RO identification method utilizing an edge gradient and edge position probability. In the alignment phase, a symmetry-based weight distribution scheme for concentric arcs is introduced. A significant observation is that the received energy stabilizes with improved alignment precision, prompting the design of an advanced alignment evaluation method that surpasses conventional energy-based assessments. The experimental results confirm that the proposed RO identification method can effectively differentiate between 4× and 10× objectives across diverse light intensities and exposure levels, with a significant numerical difference of up to 100. The error–radius ratio of the weighted circular fitting method is maintained below 1.16%, and the fine alignment stage’s evaluation curve is notably sharper. Moreover, tests under various imaging conditions in artificially saturated environments indicate that the alignment estimation method, predicated on critical saturation positions, achieves an average error of 0.875 pixels.
Sorghum Spike Detection Method Based on Gold Feature Pyramid Module and Improved YOLOv8s
Authors: Qiu Shujin , Gao Jian , Han Mengyao , Cui Qingliang , Yuan Xiangyang , Wu Cuiqing
Published: December 2024

In order to solve the problems of high planting density, similar color, and serious occlusion between spikes in sorghum fields, such as difficult identification and detection of sorghum spikes, low accuracy and high false detection, and missed detection rates, this study proposes an improved sorghum spike detection method based on YOLOv8s. The method involves augmenting the information fusion capability of the YOLOv8 model’s neck module by integrating the Gold feature pyramid module. Additionally, the SPPF module is refined with the LSKA attention mechanism to heighten focus on critical features. To tackle class imbalance in sorghum detection and expedite model convergence, a loss function incorporating Focal-EIOU is employed. Consequently, the YOLOv8s-Gold-LSKA model, based on the Gold module and LSKA attention mechanism, is developed. Experimental results demonstrate that this improved method significantly enhances sorghum spike detection accuracy in natural field settings. The improved model achieved a precision of 90.72%, recall of 76.81%, mean average precision (mAP) of 85.86%, and an F1-score of 81.19%. Comparing the improved model of this study with the three target detection models of YOLOv5s, SSD, and YOLOv8, respectively, the improved model of this study has better detection performance. This advancement provides technical support for the rapid and accurate recognition of multiple sorghum spike targets in natural field backgrounds, thereby improving sorghum yield estimation accuracy. It also contributes to increased sorghum production and harvest, as well as the enhancement of intelligent harvesting equipment for agricultural machinery.
Study of Positioning Accuracy Parameters in Selected Configurations of a Modular Industrial Robot—Part 1
Authors: Suszyński Marcin , Wiśniewski Marcin , Wojciechowicz Kajetan , Trączyński Marek , Butlewski Marcin , Cernohlavek Vit , Talar Rafał
Published: December 2024

This article presents the fundamental principles of robot accuracy. It characterizes a modular robot, describes the measurement setup, and outlines the methodology for evaluating positioning accuracy across different configurations of the modular robot (four, five, and six modules) under varying loads of 6, 10, and 16 kg. An analysis was conducted on the impact of load changes on four- and five-module configurations, as well as the effect of configuration changes on the robot’s performance with 6 and 10 kg loads. The findings indicate that both the number of modules and the load affect positioning accuracy. This article highlights the importance of selecting the optimal configuration based on planned industrial tasks to ensure the highest precision and operational efficiency.
Assessment of Optimal Seeding Rate for Fine and Coarse Rice Varieties Using the Direct Seeded Rice (DSR) Method
Authors: Naeem Atif , Ali Madad , Jawad Ahmad , Ameen Asif , Mehwish  , Liaqat Talha , Nazeer Samreen , Akram Muhammad Zubair , Hussain Shahbaz
Published: December 2024

Rice (Oryza sativa L.) is one of the most crucial cereal crops worldwide, serving as a staple food for a significant portion of the global population. Rice is the second most important staple food crop in Pakistan after wheat, and it is also a major export commodity. Concerning this, the current study aimed to evaluate the effects of different seed rates on the yield and yield-contributing parameters of rice varieties. The experiment was conducted over two consecutive kharif summer seasons, from 2020–21 and 2021–22, at the Pakistan Agricultural Research Council (PARC) Rice Program experimental area in Kala Shah Kaku, Lahore, Pakistan, by following a factorial randomized complete block design with three replications using coarse rice (KSK-133) and fine rice (Super Basmati) varieties. Different seed rates, including 27 kg/ha, 22 kg/ha, 17 kg/ha, and 12 kg/ha, were tested during the experiment. Different growth and yield-related attributes, such as plant height (cm), the number of productive tillers per plant, panicle length (cm), the number of grains per panicle, and grain yield (m−2), were recorded. The results showed that for KSK-133 and Super Basmati, the maximum grain yield was achieved at a sowing rate of 27 kg/ha in direct seed rice (DSR). The lowest yield was observed at a seeding rate of 12 kg/ha for KSK-133 and Super Basmati in DSR. Both basmati (Super Basmati) and coarse-grain (KSK-133) varieties exhibited similar responses to seed rate treatments, with the optimal performance observed at the highest seed rate of 27 kg/ha for both seasons. Grains per panicle and thousand grain weight emerged as critical determinants of yield, highlighting the need to balance vegetative growth with reproductive development. Breeding programs should focus on developing varieties that balance vegetative traits like tiller production and panicle length with reproductive traits to enhance overall yield. Based on these findings, it is concluded that using an optimal seeding rate of 27 kg/ha for direct-seeded fine and coarse rice varieties is beneficial in terms of tillers and higher yield.
Development of Energy Efficient Domestic Hot Water Loop System Integrated with a Chilled Water Plant in Commercial Building
Authors: Yoo Mooyoung
Published: December 2024

This study investigates a novel approach to reduce energy consumption in large commercial buildings by recovering waste heat from the condenser of a chiller and utilizing it to preheat domestic hot water (DHW). While numerous energy-saving strategies have been developed for building heating and cooling systems, the energy efficiency of DHW systems has lagged behind due to the increasing demand for hot water driven by improving living standards and hygiene concerns. By integrating a heat exchanger between the chiller and the DHW system, the proposed system effectively improves the chiller’s performance and significantly reduces the energy consumption of the DHW heater. Simulation results demonstrate that during the cooling season (June–August), the chiller achieved a 3% reduction in energy consumption, while the DHW heater experienced energy savings exceeding 70%. Additionally, the operating frequency of the DHW heater was substantially decreased. Notably, despite the significantly higher energy consumption of the chiller compared to the DHW heater, the reduction in greenhouse gas emissions from the DHW heater accounted for more than 50% of the total reduction. This finding highlights the significant contribution of energy savings in the natural gas-fired DHW heater to improving the building’s overall sustainability.
Nitrogen Level Impacting Fruit Yield and Quality of Mango in Northern Tropical Australia
Authors: Asis Constancio A. , Tilbrook Joanne , Anson Dallas , Niscioli Alan , Guinto Danilo , Bristow Mila , Rowlings David
Published: December 2024

Nitrogen (N) is vital for mango yield and fruit quality, but finding the optimal amount is crucial to avoid the ‘stay green’ problem, which diminishes both fruit quality and profitability. This study aimed to assess the impact of N levels on the fruit quality and yield of ‘Kensington Pride’ (‘KP’) mangoes and determine the amount of N that triggers the ‘stay green’ effect in fruit. A field trial was conducted in a commercial orchard with N treatments (0, 12.5, 25, and 50 kg ha−1) and four replications during the 2018 and 2019 cropping seasons. Fruit yield was quantified, and post-harvest quality (skin color during ripening, sugar content [°Brix], and texture) as well as ethylene effects were assessed. Fruit yields did not vary among N levels over the two cropping seasons but were significantly lower in 2018 (20.0 t ha−1) compared to 2019 (38.5 t ha−1), illustrating the alternate year-bearing habit of ‘KP’ mangoes. In the 2018 harvest, fruit from trees receiving 25 kg N ha−1 appeared yellow–green compared to those with less N, while fruit from trees with 50 kg N ha−1 exhibited ‘stay green’ skin, indicating that applications of 25 and 50 kg N ha−1 were excessive. There was no ‘stay green’ skin observed in the 2019 harvest, indicating that the environment may also be a contributing factor. The texture of ripe fruit from untreated control trees had the highest flesh resistance. Moreover, ethylene-treated fruit ripened in nine days post-harvest and had significantly lower sugar content than untreated fruit, which ripened in 14 days. This study provides valuable insights into the complex interactions among N application, fruit quality, and yield of ‘KP’ mangoes, highlighting the importance of appropriate N management for a sustainable and environmentally friendly commercial mango production system.
Shanghai as a Model: Research on the Journey of Transportation Electrification and Charging Infrastructure Development
Authors: Zhang Cong , Lian Jingchao , Min Haitao , Li Ming
Published: December 2024

As the world pivots to a greener paradigm, Shanghai emerges as an archetype in the sustainable urban transit narrative, particularly through the aggressive expansion and refinement of its electric vehicle (EV) charging infrastructure. This scholarly article provides a comprehensive examination of the current state of charging infrastructure in Shanghai, highlighting the challenges that the existing infrastructure may face in light of the burgeoning electric vehicle market. This paper delves into the strategic development approaches adopted by Shanghai to address these challenges, particularly emphasizing the expansion of high-power charging infrastructure to meet the anticipated increase in future electric vehicle charging demands. It also discusses the implementation of co-construction and sharing models, the enhancement of interconnectivity and standardized management of charging facilities, and the continuous improvement and strengthening of infrastructure construction and operations. Furthermore, this article explores the implementation of time-of-use electricity pricing policies and the ongoing conduct of demand response activities, which are instrumental in creating conditions for vehicle-to-grid interaction. The aim of our presentation is to foster a keen understanding among policymakers, industry stakeholders, and urban planners of the mechanisms necessary to effectively navigate the emerging electric vehicle market, thereby encouraging harmonious development between metropolises and transportation systems. Future research endeavors should delve into the realms of fast-charging technologies, intelligent operation and maintenance of charging infrastructure, and vehicle-to-grid interaction technologies. These areas of study are pivotal in fostering the harmonious development of electric vehicles (EVs) and their charging infrastructure, thereby aligning with the dual objectives of advancing urban transportation systems and sustainable green city development. The findings presented herein offer valuable insights for policymakers, urban planners, and industry leaders, guiding them in crafting informed strategies that not only address the immediate needs of the EV market but also lay the groundwork for a scalable and resilient charging infrastructure, poised to support the long-term vision of sustainable urban mobility.
Evaluation of the Effectiveness of High-Level Construction of Rural Living Environment in China Under the Incentive Policies
Authors: Wang Jiarui , Yang Shuoxin , Hu Siwei , Li Qian , Liu Chong , Gao Yi , Huang Jianyin , Chow Christopher W. K. , Liu Fang , Zheng Xiangqun
Published: December 2024

Improving the rural living environment is of great significance in enhancing the life quality of rural residents and promoting rural sustainable development. The Chinese government initiated a nationwide three-year action in 2018, followed by a five-year campaign starting in 2021, to improve the rural living environment. Despite these efforts, comprehensive assessment covering multiple facets of the rural living environment at the national level remained scarce. A novel evaluation method was proposed in this study that included seven aspects and nineteen indicators, applied AHP for weighting, and PLS-SEM to analyze the relationships between variables. Then, the completion of key tasks of rural living environment improvement in 37 counties, which were incentivized by the State Council on account of its excellent governance results, was comprehensively investigated and evaluated. The assessment of key tasks in the 37 incentivized counties revealed high completion in rural household solid waste disposal, domestic sewage treatment, and toilet improvement. However, the level of the rural living environment was uneven among different regions, and the progress varied in different key tasks. The funds input had a greater impact on the comprehensive level of rural living environment than social and economic factors. Accordingly, the government should provide more targeted financial and policy support to underdeveloped areas. And priority should be given to the sewage treatment and sanitation, especially in rural and remote areas. Furthermore, local governments should diversify funding sources to ensure the sustainability of rural living environment development. These findings provide a theoretical basis for developing policies and specific plans to address the challenges of financial investment and rural living environment management in China and other developing countries and regions worldwide.
Strategic Entry of Czech Construction Companies into the Bosnian Public Procurement Market: A Comparative Analysis of Legislative Frameworks and Market Conditions
Authors: Matějková Jitka , Dohnal Radek
Published: December 2024

This article explores the strategic market entry of Czech construction companies into Bosnia and Herzegovina’s public procurement sector, with a focus on the intricate legislative, economic, and cultural variables that define this emerging market. Employing Bosnia as a representative case study, this research synthesizes traditional marketing frameworks like the 4Ps model with contemporary paradigms such as relationship marketing and value co-creation, delivering a multidimensional understanding of market navigation in transitional economies. The study employs a robust methodological approach, incorporating comparative legislative analysis, SWOT and PEST assessments, and primary data collection through stakeholder interviews and surveys. Key strategies identified include fostering local partnerships, aligning with sustainability imperatives, and adopting tailored market entry methods such as joint ventures and strategic alliances. The findings bridge theoretical constructs with actionable insights, offering a dual contribution: generalizable frameworks for international market entry and targeted strategies for navigating Bosnia’s public procurement landscape. By advancing the discourse on project-based marketing in construction, this research provides a comprehensive toolkit for both academic and practical applications. The conclusions drawn emphasize the need for innovative partnerships, collaborative value creation, and alignment with evolving market trends, offering actionable recommendations for policymakers, practitioners, and researchers alike.
Rice Disease Classification Using a Stacked Ensemble of Deep Convolutional Neural Networks
Authors: Wang Zhibin , Wei Yana , Mu Cuixia , Zhang Yunhe , Qiao Xiaojun
Published: December 2024

Rice is a staple food for almost half of the world’s population, and the stability and sustainability of rice production plays a decisive role in food security. Diseases are a major cause of loss in rice crops. The timely discovery and control of diseases are important in reducing the use of pesticides, protecting the agricultural eco-environment, and improving the yield and quality of rice crops. Deep convolutional neural networks (DCNNs) have achieved great success in disease image classification. However, most models have complex network structures that frequently cause problems, such as redundant network parameters, low training efficiency, and high computational costs. To address this issue and improve the accuracy of rice disease classification, a lightweight deep convolutional neural network (DCNN) ensemble method for rice disease classification is proposed. First, a new lightweight DCNN model (called CG-EfficientNet), which is based on an attention mechanism and EfficientNet, was designed as the base learner. Second, CG-EfficientNet models with different optimization algorithms and network parameters were trained on rice disease datasets to generate seven different CG-EfficientNets, and a resampling strategy was used to enhance the diversity of the individual models. Then, the sequential least squares programming algorithm was used to calculate the weight of each base model. Finally, logistic regression was used as the meta-classifier for stacking. To verify the effectiveness, classification experiments were performed on five classes of rice tissue images: rice bacterial blight, rice kernel smut, rice false smut, rice brown spot, and healthy leaves. The accuracy of the proposed method was 96.10%, which is higher than the results of the classic CNN models VGG16, InceptionV3, ResNet101, and DenseNet201 and four integration methods. The experimental results show that the proposed method is not only capable of accurately identifying rice diseases but is also computationally efficient.
Study on the Impact of the Digital Economy on Employment Quality and the Mechanism of Action Based on China’s Interprovincial Panel Data from 2013 to 2022
Authors: Chen Jingjing , Xiong Bin
Published: December 2024

The digital economy is reshaping the employment landscape, playing a crucial role in promoting employment stability and fostering sustainable economic growth. Research has focused mainly on the impact of the digital economy on the employment structure, such as China’s changing employment structure under the influence of the digital economy and the relationships among the digital economy, employment structure and labor share. However, comprehensive research exploring how the digital economy affects employment quality and spatial dynamic evolution is lacking. This study aims to fill this gap. First, we construct a set of digital economy indicator systems containing multiple dimensions and use the entropy-weighted Topsis method to measure the weight of each indicator of the digital economy. Second, using 2013–2022 Chinese interprovincial panel data, we introduce a two-way fixed effects model to verify the hypotheses and cite multiple models to verify the robustness of the results. Additionally, we carry out spatial heterogeneity and dynamic evolution analysis and explore the spatial differences and dynamic evolution process of the impact of the digital economy on employment quality. This study revealed that the digital economy positively influences employment quality. Mechanistic analysis reveals this impact is achieved mainly through improving human capital and increasing salaries. Additionally, the effect of the digital economy on employment quality is regionally differentiated, with the eastern regions having a more developed digital economy and higher employment quality. This study contributes to the theoretical understanding of the digital economy and employment quality, offering empirical insights and policy recommendations for fostering digital economy development and improving employment quality.
Energy-Efficient Connectivity Algorithm for Directional Sensor Networks in Edge Intelligence Systems
Authors: Wu Dingcheng , Xu Xueyong , Lu Chang , Mu Dapeng
Published: December 2024

The proliferation of edge intelligence systems necessitates efficient and reliable connectivity for sensor networks deployed at the edge. This paper proposes a novel energy-efficient connectivity algorithm called Constrained Angle-aware Connectivity Optimization (CA-Opt), designed for directional sensor networks to address the challenges of limited resources and asymmetric network constraints in edge environments. CA-Opt constructs a hop-constrained, degree-bounded network topology while considering the directional coverage of sensor nodes. The algorithm incorporates an angle-aware child selection strategy to optimize the energy consumption by minimizing the number of active links and the total communication distance. Extensive simulations demonstrated that CA-Opt achieved comparable connectivity to the traditional Breadth-First Search (BFS) algorithms while significantly reducing the energy consumption. Furthermore, the impact of key parameters, such as the communication range, node density, maximum degree, and directional coverage angle, on CA-Opt’s performance was analyzed. The results underscore the potential of CA-Opt to balance asymmetry-driven connectivity control with energy-efficient operation, making it particularly suitable for resource-constrained edge applications, such as smart manufacturing, environmental monitoring, and intelligent transportation systems.
Moving Mesh Partial Differential Equation Modelling of a 5CB Nematic Liquid Crystal Confined in Symmetric and Asymmetric Pi-Cells: A Review
Authors: Amoddeo Antonino
Published: December 2024

The switching properties of nematic liquid crystals under electrical and mechanical stresses play a fundamental role in the design and fabrication of electro-optical devices. Depending on the stress applied to a nematic texture confined in a pi-cell, different nematic configurations are allowed inside the cell, while the induced distortion is relaxed by means of growing biaxial domains which can end with the order reconstruction phenomenon, a transition connecting two topologically different nematic textures which can occur in different regions of the pi-cell. Due to the different space and time scales involved, modelling in the frame of the Landau–de Gennes order tensor theory is mandatory to correctly describe the fast-switching mechanisms involved, while from a computational point of view, sophisticated numerical techniques are required to grasp tiny and fast features which can be predicted by the mathematical modelling. In this paper, we review the results obtained from the mathematical and numerical modelling of a 5CB liquid crystal confined in a pi-cell performed by using a numerical technique based on the equidistribution principle, tailored for the description of a complex physical system in which fast switching phenomena are coupled with strong distortions. After a recap on the underneath theory and on the numerical method, we focus on the switching properties of the nematic material when subjected to variable mechanical and electrical stresses in both symmetric and asymmetric conditions.
A Three-Echelon Healthcare Supply Chain Model for Blood Distribution During Crisis Times
Authors: Khiabani Amir , Rashidi Komijan Alireza , Karbassi Yazdi Amir , Tan Yong , Piñones Giuliani Coluccio , Ishizaka Alessio
Published: December 2024

We aim to implement a three-tiered healthcare supply chain model within the context of a portable blood hub during times of crisis to provide healthcare to the most vulnerable populations. Due to a lack of blood, the number of deaths related to this issue has significantly increased in recent years. A critical challenge for healthcare decision-makers is the efficient distribution of blood among different centers. This research proposes a new method to address this issue, simplifying future solutions to similar problems. As a result of our findings, we introduce a model that considers three tiers in healthcare blood supply: point one (blood collection centers), point two (blood hubs), and final destinations (hospitals and clinics) during disasters. For this study, we focused on six collection sites, five blood centers, and thirty-one medical facilities providing eight different blood types. Our model demonstrates that blood can be transported from collection sites to medical centers at a lower cost during crises. This approach requires considering the full three tiers of the healthcare supply chain rather than focusing solely on the supply chain level at blood hubs. Unlike previous studies, which focused only on individual levels of supply chain management, this study examined the entire value chain, from blood collection to delivery to the end recipient.
An Integrated Method for Selecting Architecture Alternatives and Reconfiguration Options Towards System-of-Systems Resilience
Authors: Fang Zhemei , Li Hang , Chen Dazhi
Published: December 2024

Delivering persistent values in a dynamic environment is a challenging but imperative capability for a system-of-systems (SoS). Practitioners in the SoS and defense domains are exploring the benefits of the operational-level reconfiguration strategies via new operational concepts such as mosaic warfare. However, an architecture design that allows reconfiguration is also a crucial task, but has not yet received adequate attention, not to mention accounting for the mutual impact between architecture design alternatives and reconfiguration options. Therefore, this paper proposes an integrated method that can select the architecture with a specific inherent structure in the design phase that supports dynamic reconfiguration during the operational phase. This method firstly builds a structural framework that connects architecture design and reconfiguration, and identifies the enablers for SoS architecture reconfiguration. After developing an SoS effectiveness evaluator, the method constructs an integrated multi-objective formulation for the initial architecture selection and reconfiguration process, and provides a solution algorithm based on a fast non-dominated sorting genetic algorithm. An application to an air and missile defense SoS illustrates the effectiveness of the proposed method. The generated Pareto optimal set of solutions that have non-dominated recoverability and survivability provide useful decision support for SoS composition and initial architecture configuration, based upon which an SoS can also respond effectively to disruptions by computing the reconfiguration decisions.
Effects of Illegal Solid Waste Dumping on the Structure of Soil Bacterial Communities: A Case Study in China
Authors: Chen Jialiang , Zhang Lulu , Song Lang , Ye Mai , Wang Lin , Fan Bin , Li Bin , Yang Zetao , Jin Rongzhou , Jia Pu
Published: December 2024

Illegal solid waste dumping is a significant factor contributing to environmental damage. In this study, 16S rRNA gene sequencing technology was used for the identification and assessment of environmental damage in an illegal dumping area in China, with the aim of confirming environmental damage through analyzing changes in the soil bacterial communities across slag, sewage sludge, and non-contaminated areas. The results indicate that the diversity of soil bacteria decreases with an increase in the degree of pollution. The illegal dumping of slag resulted in an increase in the relative abundance of Firmicutes and a decrease in the relative abundance of Acidobacteriota. Additionally, illegal dumping of sewage sludge resulted in an increase in the relative abundance of Proteobacteria and a decrease in the relative abundance of Acidobacteriota. The contents of Ni and Be in slag and Cu, Pb, and Cd in sewage sludge were key factors affecting bacterial community composition. The results reveal the effects of heavy metal pollution on the soil bacterial community structure and its environmental driving factors, thus expanding understanding in the context of management of the environmental damage caused by illegal dumping, as well as providing a perspective on the changes in the soil bacterial community, allowing for environmental damage confirmation.
Symmetry Restorations in the Singlet Scalar Yukawa Model Within the Auxiliary Field Method
Authors: Nogueira Anderson A. , Braghin Fábio L.
Published: December 2024

The aim of this work is to investigate the connection between thermal gap-coupled equations and the concept of symmetry restoration. For that, we consider the Yukawa model, a standard model for interactions between massless fermions mediated by a real self-interacting scalar field. To explore possible symmetry restoration, we study the thermal gap-coupled equations using the auxiliary field method (Hubbard–Stratonovich), and then we derive the effective action with thermal contributions through the background external fields method. With the thermal contributions for the effective action, we investigate the phase transitions and critical phenomena in an environment featuring mixing angles arising from the quantum description of composite states. Finally, we present the Dolan–Jackiw equations to determine the critical temperatures.
Identifying a Point-Symmetrical Morphology in the Core-Collapse Supernova Remnant W44
Authors: Soker Noam
Published: December 2024

I identify a point-symmetrical morphology in the core-collapse supernova remnant (CCSNR) W44 compatible with shaping by three or more pairs of jets in the jittering jets explosion mechanism (JJEM). Motivated by recent identifications of point-symmetrical morphologies in CCSNRs and their match to the JJEM, I revisit the morphological classification of CCSNR W44. I examine a radio map of W44 and find the outer bright rim of the radio map to possess a point-symmetric structure compatible with shaping by two energetic pairs of opposite jets rather than an S-shaped morphology shaped by a precessing pair of jets. An inner pair of filaments might hint at a third powerful pair of jets. More pairs of jets were involved in the explosion process. This study adds to the growing evidence that the JJEM is the primary explosion mechanism of core-collapse supernovae.
Can Effects of a Generalized Uncertainty Principle Appear in Compact Stars?
Authors: Gimenez João Gabriel Galli , Hadjimichef Dimiter , Hess Peter Otto , Netz-Marzola Marcelo , Zen Vasconcellos César A.
Published: December 2024

In the present contribution, a preliminary analysis of the effects of the Generalized Uncertainty Principle (GUP) with a minimum length, in the context of compact stars, is performed. On basis of a deformed Poisson canonical algebra with a parametrized minimum length scale that induces deviations from conventional Quantum Mechanics, fundamental questions involving the consistence, evidences and proofs of this approach as a possible cure for unbounded energy divergence are outlined. The incorporation of GUP effects into semiclassical 2N-dimensional systems is made by means of a time-invariant distortion transformation applied to their non-deformed counterparts. Assuming the quantum hadrodynamics σ−ω approach as a toy-model, due to its simplicity and structured description of neutron stars, we perform a preliminary analysis of GUP effects with a minimum spacetime length on these compact objects. The corresponding results for the equation of state and the mass-radius relation for neutron stars are in tune with recent observations with a maximum mass around 2.5 M⊙ and radius close to 12 km. Our results also indicate the smallness of the noncommutative scale.
The Addis Declaration on Immunization: Assessing the Effectiveness and Efficiency of Immunization Service Delivery Systems in Africa as of the End of 2023
Authors: Mboussou Franck , Wiysonge Charles Shey , Farham Bridget , Bwaka Ado , Wanyoike Sarah , Petu Amos , Ndiaye Sidy , Bita Fouda Andre , Ticha Johnson Muluh , Amani Adidja , Obiang Regis , Bagayoko Magaran Monzon , Impouma Benido
Published: December 2024

Background/Objectives: The Addis Declaration on Immunization (ADI) is a historic pledge aiming at increasing political will to achieve universal access to immunization services and includes ten commitments to shape the future of immunization in Africa. Methods: To analyze African countries’ performance in achieving the fourth ADI commitment, a cross-sectional retrospective study was conducted including the 54 African Member States of the World Health Organization (WHO) out of 55 African Union (AU) Member States. The fourth ADI commitment aims at increasing the effectiveness and efficiency of immunization delivery systems and has four performance indicators. Results: The median percentage of districts with less than 10% of dropout rate between the first dose of diphtheria–tetanus–pertussis-containing vaccine (DTP1) and the third dose (DTP3) was 86.5%, ranging from 22% to 100%. Thirty-four countries (63%) recorded 80% or above of districts with less than 10% dropout rate between DTP1 and DTP3. Eleven countries (20.3%) and ten countries (18.5%) sustained 90% or above coverage for DTP3 and first dose of measles-containing vaccine (MCV1), respectively, in the past three years (2021–2023). Four countries (7.4%) had 44.5 skilled health workers per 10,000 people. Out of the 54 WHO Member States, 7 achieved at least three of the four indicators of the fourth ADI commitment (13%). Conclusions: It is critical, as a follow up to this study, to document best practices from the seven countries that achieved the fourth ADI commitment. Additionally, a deeper analysis of factors associated with achieving the ADI commitments is required.
Regulating Immune Responses Induced by PEGylated Messenger RNA–Lipid Nanoparticle Vaccine
Authors: Jo Hyein , Jeoung Jaewhoon , Kim Wonho , Jeoung Dooil
Published: December 2024

Messenger RNA (mRNA)-based therapeutics have shown remarkable progress in the treatment and prevention of diseases. Lipid nanoparticles (LNPs) have shown great successes in delivering mRNAs. After an mRNA-LNP vaccine enters a cell via an endosome, mRNA is translated into an antigen, which can activate adaptive immunity. mRNAs can bind to various pattern recognition receptors (PRRs), including toll-like receptors (TLRs), and increase the production of inflammatory cytokines. This review summarizes mechanisms of innate immunity induced by mRNAs. Polyethylene glycol (PEG) has been employed as a component of the mRNA-LNP vaccine. PEGylated nanoparticles display enhanced stability by preventing aggregation of particles. However, PEGylation can cause adverse reactions, including blood clearance (ABC) of nanoparticles via complement activation and anaphylaxis. Mechanisms of PEG-induced ABC phenomenon and anaphylaxis are presented and discussed. There have been studies aimed at reducing immune responses associated with PEG to make safe and effective vaccines. Effects of modifying or replacing PEG in reducing immune responses associated with PEGylated nanoparticles are also discussed. Modifying mRNA can induce immune tolerance, which can prevent hypersensitivity reactions induced by PEGylated mRNA-LNP vaccines. Current progress of immune tolerance induction in association with mRNA-LNP is also summarized. This review might be helpful for developing safe and effective PEGylated mRNA-LNP vaccines.