bluuebunny/crossref_metadata_2025 huggingface dataset:
Three-Dimensional Reconstruction and Visualization of Underwater Bridge Piers Using Sonar Imaging
Authors: Luo Jianbin , Jiang Shaofei , Zeng Yamian , Lai Changqin
Published: July 2024

The quality of underwater bridge piers significantly impacts bridge safety and long-term usability. To address limitations in conventional inspection methods, this paper presents a sonar-based technique for the three-dimensional (3D) reconstruction and visualization of underwater bridge piers. Advanced MS1000 scanning sonar is employed to detect and image bridge piers. Automated image preprocessing, including filtering, denoising, binarization, filling, and morphological operations, introduces an enhanced wavelet denoising method to accurately extract the foundation contour coordinates of bridge piers from sonar images. Using these coordinates, along with undamaged pier dimensions and sonar distances, a model-driven approach for a 3D pier reconstruction algorithm is developed. This algorithm leverages multiple sonar data points to reconstruct damaged piers through multiplication. The Visualization Toolkit (VTK) and surface contour methodology are utilized for 3D visualization, enabling interactive manipulation for enhanced observation and analysis. Experimental results indicate a relative error of 13.56% for the hole volume and 10.65% for the spalling volume, demonstrating accurate replication of bridge pier defect volumes by the reconstructed models. Experimental validation confirms the method’s accuracy and effectiveness in reconstructing underwater bridge piers in three dimensions, providing robust support for safety assessments and contributing significantly to bridge stability and long-term safety assurance.
Inversion Method for Transformer Winding Hot Spot Temperature Based on Gated Recurrent Unit and Self-Attention and Temperature Lag
Authors: Hao Yuefeng , Zhang Zhanlong , Liu Xueli , Yang Yu , Liu Jun
Published: July 2024

The hot spot temperature of transformer windings is an important indicator for measuring insulation performance, and its accurate inversion is crucial to ensure the timely and accurate fault prediction of transformers. However, existing studies mostly directly input obtained experimental or operational data into networks to construct data-driven models, without considering the lag between temperatures, which may lead to the insufficient accuracy of the inversion model. In this paper, a method for inverting the hot spot temperature of transformer windings based on the SA-GRU model is proposed. Firstly, temperature rise experiments are designed to collect the temperatures of the entire side and top of the transformer tank, top oil temperature, ambient temperature, the cooling inlet and outlet temperatures, and winding hot spot temperature. Secondly, experimental data are integrated, considering the lag of the data, to obtain candidate input feature parameters. Then, a feature selection algorithm based on mutual information (MI) is used to analyze the correlation of the data and construct the optimal feature subset to ensure the maximum information gain. Finally, Self-Attention (SA) is applied to optimize the Gate Recurrent Unit (GRU) network, establishing the GRU-SA model to perceive the potential patterns between output feature parameters and input feature parameters, achieving the precise inversion of the hot spot temperature of the transformer windings. The experimental results show that considering the lag of the data can more accurately invert the hot spot temperature of the windings. The inversion method proposed in this paper can reduce redundant input features, lower the complexity of the model, accurately invert the changing trend of the hot spot temperature, and achieve higher inversion accuracy than other classical models, thereby obtaining better inversion results.
Open-Vocabulary Predictive World Models from Sensor Observations
Authors: Karlsson Robin , Asfandiyarov Ruslan , Carballo Alexander , Fujii Keisuke , Ohtani Kento , Takeda Kazuya
Published: July 2024

Cognitive scientists believe that adaptable intelligent agents like humans perform spatial reasoning tasks by learned causal mental simulation. The problem of learning these simulations is called predictive world modeling. We present the first framework for a learning open-vocabulary predictive world model (OV-PWM) from sensor observations. The model is implemented through a hierarchical variational autoencoder (HVAE) capable of predicting diverse and accurate fully observed environments from accumulated partial observations. We show that the OV-PWM can model high-dimensional embedding maps of latent compositional embeddings representing sets of overlapping semantics inferable by sufficient similarity inference. The OV-PWM simplifies the prior two-stage closed-set PWM approach to the single-stage end-to-end learning method. CARLA simulator experiments show that the OV-PWM can learn compact latent representations and generate diverse and accurate worlds with fine details like road markings, achieving 69 mIoU over six query semantics on an urban evaluation sequence. We propose the OV-PWM as a versatile continual learning paradigm for providing spatio-semantic memory and learned internal simulation capabilities to future general-purpose mobile robots.
An Optimal Spatio-Temporal Hybrid Model Based on Wavelet Transform for Early Fault Detection
Authors: Xing Jingyang , Li Fangfang , Ma Xiaoyu , Qin Qiuyue
Published: July 2024

An optimal spatio-temporal hybrid model (STHM) based on wavelet transform (WT) is proposed to improve the sensitivity and accuracy of detecting slowly evolving faults that occur in the early stage and easily submerge with noise in complex industrial production systems. Specifically, a WT is performed to denoise the original data, thus reducing the influence of background noise. Then, a principal component analysis (PCA) and the sliding window algorithm are used to acquire the nearest neighbors in both spatial and time dimensions. Subsequently, the cumulative sum (CUSUM) and the mahalanobis distance (MD) are used to reconstruct the hybrid statistic with spatial and temporal sequences. It helps to enhance the correlation between high-frequency temporal dynamics and space and improves fault detection precision. Moreover, the kernel density estimation (KDE) method is used to estimate the upper threshold of the hybrid statistic so as to optimize the fault detection process. Finally, simulations are conducted by applying the WT-based optimal STHM in the early fault detection of the Tennessee Eastman (TE) process, with the aim of proving that the fault detection method proposed has a high fault detection rate (FDR) and a low false alarm rate (FAR), and it can improve both production safety and product quality.
Integrating the Capsule-like Smart Aggregate-Based EMI Technique with Deep Learning for Stress Assessment in Concrete
Authors: Ta Quoc-Bao , Pham Quang-Quang , Pham Ngoc-Lan , Kim Jeong-Tae
Published: July 2024

This study presents a concrete stress monitoring method utilizing 1D CNN deep learning of raw electromechanical impedance (EMI) signals measured with a capsule-like smart aggregate (CSA) sensor. Firstly, the CSA-based EMI measurement technique is presented by depicting a prototype of the CSA sensor and a 2 degrees of freedom (2 DOFs) EMI model for the CSA sensor embedded in a concrete cylinder. Secondly, the 1D CNN deep regression model is designed to adapt raw EMI responses from the CSA sensor for estimating concrete stresses. Thirdly, a CSA-embedded cylindrical concrete structure is experimented with to acquire EMI responses under various compressive loading levels. Finally, the feasibility and robustness of the 1D CNN model are evaluated for noise-contaminated EMI data and untrained stress EMI cases.
RETRACTED: Syah et al. A New Hybrid Algorithm for Multi-Objective Reactive Power Planning via FACTS Devices and Renewable Wind Resources. Sensors 2021, 21, 5246
Authors: Syah Rahmad , Khorshidian Mianaei Peyman , Elveny Marischa , Ahmadian Naeim , Ramdan Dadan , Habibifar Reza , Davarpanah Afshin
Published: July 2024

The Sensors Editorial Office retracts the article, “A New Hybrid Algorithm for Multi-Objective Reactive Power Planning via FACTS Devices and Renewable Wind Resources” [...]
A Miniaturized Dual-Band Circularly Polarized Implantable Antenna for Use in Hemodialysis
Authors: Song Zhiwei , Wang Yuchao , Shi Youwei , Zheng Xianren
Published: July 2024

Hemodialysis is achieved by implanting a smart arteriovenous graft (AVG) to build a vascular pathway, but reliability and stability in data transmission cannot be guaranteed. To address this issue, a miniaturized dual-band circularly polarized implantable antenna operating at 1.4 GHz (for energy transmission) and 2.45 GHz (for wireless telemetry), implanted in a wireless arteriovenous graft monitoring device (WAGMD), has been designed. The antenna design incorporates a rectangular serpentine structure on the radiation surface to reduce its volume to 9.144 mm3. Furthermore, matching rectangular slots on the radiation surface and the ground plane enhance the antenna’s circular polarization performance. The simulated effective 3 dB axial ratio (AR) bandwidths are 11.43% (1.4 GHz) and 12.65% (2.45 GHz). The simulated peak gains of the antenna are −19.55 dBi and −22.85 dBi at 1.4 GHz and 2.45 GHz, respectively. The designed antenna is implanted in a WAGMD both in the simulation and the experiment. The performance of the system is simulated in homogeneous human tissue models of skin, fat, and muscle layers, as well as a realistic adult male forearm model. The measurement results in a minced pork environment align closely with the simulation results.
An Intelligent Hand-Assisted Diagnosis System Based on Information Fusion
Authors: Li Haonan , Zhou Yitong
Published: July 2024

This research proposes an innovative, intelligent hand-assisted diagnostic system aiming to achieve a comprehensive assessment of hand function through information fusion technology. Based on the single-vision algorithm we designed, the system can perceive and analyze the morphology and motion posture of the patient’s hands in real time. This visual perception can provide an objective data foundation and capture the continuous changes in the patient’s hand movement, thereby providing more detailed information for the assessment and providing a scientific basis for subsequent treatment plans. By introducing medical knowledge graph technology, the system integrates and analyzes medical knowledge information and combines it with a voice question-answering system, allowing patients to communicate and obtain information effectively even with limited hand function. Voice question-answering, as a subjective and convenient interaction method, greatly improves the interactivity and communication efficiency between patients and the system. In conclusion, this system holds immense potential as a highly efficient and accurate hand-assisted assessment tool, delivering enhanced diagnostic services and rehabilitation support for patients.
DeMambaNet: Deformable Convolution and Mamba Integration Network for High-Precision Segmentation of Ambiguously Defined Dental Radicular Boundaries
Authors: Zou Binfeng , Huang Xingru , Jiang Yitao , Jin Kai , Sun Yaoqi
Published: July 2024

The incorporation of automatic segmentation methodologies into dental X-ray images refined the paradigms of clinical diagnostics and therapeutic planning by facilitating meticulous, pixel-level articulation of both dental structures and proximate tissues. This underpins the pillars of early pathological detection and meticulous disease progression monitoring. Nonetheless, conventional segmentation frameworks often encounter significant setbacks attributable to the intrinsic limitations of X-ray imaging, including compromised image fidelity, obscured delineation of structural boundaries, and the intricate anatomical structures of dental constituents such as pulp, enamel, and dentin. To surmount these impediments, we propose the Deformable Convolution and Mamba Integration Network, an innovative 2D dental X-ray image segmentation architecture, which amalgamates a Coalescent Structural Deformable Encoder, a Cognitively-Optimized Semantic Enhance Module, and a Hierarchical Convergence Decoder. Collectively, these components bolster the management of multi-scale global features, fortify the stability of feature representation, and refine the amalgamation of feature vectors. A comparative assessment against 14 baselines underscores its efficacy, registering a 0.95% enhancement in the Dice Coefficient and a diminution of the 95th percentile Hausdorff Distance to 7.494.
Autonomous Crack Detection for Mountainous Roads Using UAV Inspection System
Authors: Chen Xinbao , Wang Chenxi , Liu Chang , Zhu Xiaodong , Zhang Yaohui , Luo Tianxiang , Zhang Junhao
Published: July 2024

Road cracks significantly affect the serviceability and safety of roadways, especially in mountainous terrain. Traditional inspection methods, such as manual detection, are excessively time-consuming, labor-intensive, and inefficient. Additionally, multi-function detection vehicles equipped with diverse sensors are costly and unsuitable for mountainous roads, primarily because of the challenging terrain conditions characterized by frequent bends in the road. To address these challenges, this study proposes a customized Unmanned Aerial Vehicle (UAV) inspection system designed for automatic crack detection. This system focuses on enhancing autonomous capabilities in mountainous terrains by incorporating embedded algorithms for route planning, autonomous navigation, and automatic crack detection. The slide window method (SWM) is proposed to enhance the autonomous navigation of UAV flights by generating path planning on mountainous roads. This method compensates for GPS/IMU positioning errors, particularly in GPS-denied or GPS-drift scenarios. Moreover, the improved MRC-YOLOv8 algorithm is presented to conduct autonomous crack detection from UAV imagery in an on/offboard module. To validate the performance of our UAV inspection system, we conducted multiple experiments to evaluate its accuracy, robustness, and efficiency. The results of the experiments on automatic navigation demonstrate that our fusion method, in conjunction with SWM, effectively enables real-time route planning in GPS-denied mountainous terrains. The proposed system displays an average localization drift of 2.75% and a per-point local scanning error of 0.33 m over a distance of 1.5 km. Moreover, the experimental results on the road crack detection reveal that the MRC-YOLOv8 algorithm achieves an F1-Score of 87.4% and a mAP of 92.3%, thus surpassing other state-of-the-art models like YOLOv5s, YOLOv8n, and YOLOv9 by 1.2%, 1.3%, and 3.0% in terms of mAP, respectively. Furthermore, the parameters of the MRC-YOLOv8 algorithm indicate a volume reduction of 0.19(×106) compared to the original YOLOv8 model, thus enhancing its lightweight nature. The UAV inspection system proposed in this study serves as a valuable tool and technological guidance for the routine inspection of mountainous roads.
Constrained Flooding Based on Time Series Prediction and Lightweight GBN in BLE Mesh
Authors: Li Junxiang , Li Mingxia , Wang Li
Published: July 2024

Bluetooth Low Energy Mesh (BLE Mesh) enables Bluetooth flexibility and coverage by introducing Low-Power Nodes (LPNs) and enhanced networking protocol. It is also a commonly used communication method in sensor networks. In BLE Mesh, LPNs are periodically woken to exchange messages in a stop-and-wait way, where the tradeoff between energy and efficiency is a hard problem. Related works have reduced the energy consumption of LPNs mainly in the direction of changing the bearer layer, improving time synchronization and broadcast channel utilization. These algorithms improve communication efficiency; however, they cause energy loss, especially for the LPNs. In this paper, we propose a constrained flooding algorithm based on time series prediction and lightweight GBN (Go-Back-N). On the one hand, the wake-up cycle of the LPNs is determined by the time series prediction of the surrounding load. On the other, LPNs exchange messages through lightweight GBN, which improves the window and ACK mechanisms. Simulation results validate the effectiveness of the Time series Prediction and LlightWeight GBN (TP-LW) algorithm in energy consumption and throughput. Compared with the original algorithm of BLE Mesh, when fewer packets are transmitted, the throughput is increased by 214.71%, and the energy consumption is reduced by 65.14%.
Robot-Assisted Augmented Reality (AR)-Guided Surgical Navigation for Periacetabular Osteotomy
Authors: Ding Haoyan , Sun Wenyuan , Zheng Guoyan
Published: July 2024

Periacetabular osteotomy (PAO) is an effective approach for the surgical treatment of developmental dysplasia of the hip (DDH). However, due to the complex anatomical structure around the hip joint and the limited field of view (FoV) during the surgery, it is challenging for surgeons to perform a PAO surgery. To solve this challenge, we propose a robot-assisted, augmented reality (AR)-guided surgical navigation system for PAO. The system mainly consists of a robot arm, an optical tracker, and a Microsoft HoloLens 2 headset, which is a state-of-the-art (SOTA) optical see-through (OST) head-mounted display (HMD). For AR guidance, we propose an optical marker-based AR registration method to estimate a transformation from the optical tracker coordinate system (COS) to the virtual space COS such that the virtual models can be superimposed on the corresponding physical counterparts. Furthermore, to guide the osteotomy, the developed system automatically aligns a bone saw with osteotomy planes planned in preoperative images. Then, it provides surgeons with not only virtual constraints to restrict movement of the bone saw but also AR guidance for visual feedback without sight diversion, leading to higher surgical accuracy and improved surgical safety. Comprehensive experiments were conducted to evaluate both the AR registration accuracy and osteotomy accuracy of the developed navigation system. The proposed AR registration method achieved an average mean absolute distance error (mADE) of 1.96 ± 0.43 mm. The robotic system achieved an average center translation error of 0.96 ± 0.23 mm, an average maximum distance of 1.31 ± 0.20 mm, and an average angular deviation of 3.77 ± 0.85°. Experimental results demonstrated both the AR registration accuracy and the osteotomy accuracy of the developed system.
Sensor-Fused Nighttime System for Enhanced Pedestrian Detection in ADAS and Autonomous Vehicles
Authors: Park Jungme , Thota Bharath Kumar , Somashekar Karthik
Published: July 2024

Ensuring a safe nighttime environmental perception system relies on the early detection of vulnerable road users with minimal delay and high precision. This paper presents a sensor-fused nighttime environmental perception system by integrating data from thermal and RGB cameras. A new alignment algorithm is proposed to fuse the data from the two camera sensors. The proposed alignment procedure is crucial for effective sensor fusion. To develop a robust Deep Neural Network (DNN) system, nighttime thermal and RGB images were collected under various scenarios, creating a labeled dataset of 32,000 image pairs. Three fusion techniques were explored using transfer learning, alongside two single-sensor models using only RGB or thermal data. Five DNN models were developed and evaluated, with experimental results showing superior performance of fused models over non-fusion counterparts. The late-fusion system was selected for its optimal balance of accuracy and response time. For real-time inferencing, the best model was further optimized, achieving 33 fps on the embedded edge computing device, an 83.33% improvement in inference speed over the system without optimization. These findings are valuable for advancing Advanced Driver Assistance Systems (ADASs) and autonomous vehicle technologies, enhancing pedestrian detection during nighttime to improve road safety and reduce accidents.
Online Scene Semantic Understanding Based on Sparsely Correlated Network for AR
Authors: Wang Qianqian , Song Junhao , Du Chenxi , Wang Chen
Published: July 2024

Real-world understanding serves as a medium that bridges the information world and the physical world, enabling the realization of virtual–real mapping and interaction. However, scene understanding based solely on 2D images faces problems such as a lack of geometric information and limited robustness against occlusion. The depth sensor brings new opportunities, but there are still challenges in fusing depth with geometric and semantic priors. To address these concerns, our method considers the repeatability of video stream data and the sparsity of newly generated data. We introduce a sparsely correlated network architecture (SCN) designed explicitly for online RGBD instance segmentation. Additionally, we leverage the power of object-level RGB-D SLAM systems, thereby transcending the limitations of conventional approaches that solely emphasize geometry or semantics. We establish correlation over time and leverage this correlation to develop rules and generate sparse data. We thoroughly evaluate the system’s performance on the NYU Depth V2 and ScanNet V2 datasets, demonstrating that incorporating frame-to-frame correlation leads to significantly improved accuracy and consistency in instance segmentation compared to existing state-of-the-art alternatives. Moreover, using sparse data reduces data complexity while ensuring the real-time requirement of 18 fps. Furthermore, by utilizing prior knowledge of object layout understanding, we showcase a promising application of augmented reality, showcasing its potential and practicality.
Improving Rebar Twist Prediction Exploiting Unified-Channel Attention-Based Image Restoration and Regression Techniques
Authors: Park Jong-Chan , Kim Gun-Woo
Published: July 2024

Recent research has made significant progress in automated unmanned systems utilizing Artificial Intelligence (AI)-based image processing to optimize the rebar manufacturing process and minimize defects such as twisting during production. Despite various studies, including those employing data augmentation through Generative Adversarial Networks (GANs), the performance of rebar twist prediction has been limited due to image quality degradation caused by environmental noise, such as insufficient image quality and inconsistent lighting conditions in rebar processing environments. To address these challenges, we propose a novel approach for real-time rebar twist prediction in manufacturing processes. Our method involves restoring low-quality grayscale images to high resolution and employing an object detection model to identify and track rebar endpoints. We then apply regression analysis to the coordinates obtained from the bounding boxes to estimate the error rate of the rebar endpoint positions, thereby determining the occurrence of twisting. To achieve this, we first developed a Unified-Channel Attention (UCA) module that is robust to changes in intensity and contrast for grayscale images. The UCA can be integrated into image restoration models to more accurately detect rebar endpoint characteristics in object detection models. Furthermore, we introduce a method for predicting the future positions of rebar endpoints using various linear and non-linear regression models. The predicted positions are used to calculate the error rate in rebar endpoint locations, determined by the distance between the actual and predicted positions, which is then used to classify the presence of rebar twisting. Our experimental results demonstrate that integrating the UCA module with our image restoration model significantly improved existing models in Peak Signal-to-Noise Ratio (PSNR) and Structural Similarity Index Measure (SSIM) metrics. Moreover, employing regression models to predict future rebar endpoint positions enhances the F1 score for twist prediction. As a result, our approach offers a practical solution for rapid defect detection in rebar manufacturing processes.
Acute Response of Different High-Intensity Interval Training Protocols on Cardiac Auto-Regulation Using Wearable Device
Authors: Seo Myong-Won
Published: July 2024

The purpose of this study was to compare different high-intensity interval training (HIIT) protocols with different lengths of work and rest times for a single session (all three had identical work-to-rest ratios and exercise intensities) for cardiac auto-regulation using a wearable device. With a randomized counter-balanced crossover, 13 physically active young male adults (age: 19.4 years, BMI: 21.9 kg/m2) were included. The HIIT included a warm-up of at least 5 min and three protocols of 10 s/50 s (20 sets), 20 s/100 s (10 sets), and 40 s/200 s (5 sets), with intensities ranging from 115 to 130% Wattmax. Cardiac auto-regulation was measured using a non-invasive method and a wearable device, including HRV and vascular function. Immediately after the HIIT session, the 40 s/200 s protocol produced the most intense stimulation in R-R interval (Δ-33.5%), ln low-frequency domain (Δ-42.6%), ln high-frequency domain (Δ-73.4%), and ln LF/HF ratio (Δ416.7%, all p < 0.05) compared to other protocols of 10 s/50 s and 20 s/100 s. The post-exercise hypotension in the bilateral ankle area was observed in the 40 s/200 s protocol only at 5 min after HIIT (right: Δ-12.2%, left: Δ-12.6%, all p < 0.05). This study confirmed that a longer work time might be more effective in stimulating cardiac auto-regulation using a wearable device, despite identical work-to-rest ratios and exercise intensity. Additional studies with 24 h measurements of cardiac autoregulation using wearable devices in response to various HIIT protocols are warranted.
A Dual-Branch Fusion of a Graph Convolutional Network and a Convolutional Neural Network for Hyperspectral Image Classification
Authors: Yang Pan , Zhang Xinxin
Published: July 2024

Semi-supervised graph convolutional networks (SSGCNs) have been proven to be effective in hyperspectral image classification (HSIC). However, limited training data and spectral uncertainty restrict the classification performance, and the computational demands of a graph convolution network (GCN) present challenges for real-time applications. To overcome these issues, a dual-branch fusion of a GCN and convolutional neural network (DFGCN) is proposed for HSIC tasks. The GCN branch uses an adaptive multi-scale superpixel segmentation method to build fusion adjacency matrices at various scales, which improves the graph convolution efficiency and node representations. Additionally, a spectral feature enhancement module (SFEM) enhances the transmission of crucial channel information between the two graph convolutions. Meanwhile, the CNN branch uses a convolutional network with an attention mechanism to focus on detailed features of local areas. By combining the multi-scale superpixel features from the GCN branch and the local pixel features from the CNN branch, this method leverages complementary features to fully learn rich spatial–spectral information. Our experimental results demonstrate that the proposed method outperforms existing advanced approaches in terms of classification efficiency and accuracy across three benchmark data sets.
Rural Development Projects in Latin America: The Need to Integrate Socio-Economic, Political, and Empowerment Lenses for Sustained Impact
Authors: Leao Raphael , Goulao Luis F.
Published: July 2024

Latin America has witnessed a significant influx of foreign aid aimed at fostering development, particularly in rural areas, over recent decades. Despite these considerable investments, persistently high levels of poverty endure, and rural communities have not consistently witnessed improvements in livelihoods. Rural development initiatives frequently achieve their inherent objectives at the output level but less frequently attain the intended outcomes and are seldom successful in establishing the prerequisites for enduring impact over the long term. The existing literature on this topic is extensive but often fragmented, with some scholars examining the context through socio-economic and political lenses, others scrutinizing the intrinsic characteristics of projects, and still others emphasizing levels of empowerment and local participation. This paper synthesizes and critically evaluates key aspects that influence the sustained impact of rural development projects across these three analytical streams. Through an integrative compilation of insights from these disparate perspectives, this work lays the foundation for a comprehensive analysis, shedding light on crucial considerations that may have previously “fallen into the cracks” through an examination of the segmented discourse.
Static Task Allocation Method for Multi-Machines in Cooperative Operations Combining OGFR-GA and MLW-Prim
Authors: Wu Yuezhong , Wen Ya , Wu Yingbo , Li Yungang , Zheng Xiangming , Chen Lingjiao
Published: July 2024

Aiming at the operational efficiency of small farm machinery groups in cooperative operations in hilly areas, this study proposes a static task allocation model. This method combines the optimal gene fragment retention method based on a genetic algorithm (OGFR-GA) and the method for generating multi-loop weighted connected graphs based on Prim’s algorithm (MLW-Prim). The collaborative objective function for the harvester group was established by considering factors such as operation time, fuel consumption, and distance. The OGFR-GA was designed and applied multiple times to obtain several optimal gene fragments corresponding to the number of farm machines. These fragments were used as critical paths in the weighted connected graph generated based on farm machinery performance parameters and task parameters. The MLW-Prim method was proposed to construct this weighted connected graph and realize the static task allocation model for multi-machine cooperative operations. Simulation experiments showed that the model combining OGFR-GA and MLW-Prim achieved optimal values with fewer iterations, and reduced both group cost and cost variance compared to traditional algorithms. This method meets the static task allocation needs for multi-machine cooperative operations in agricultural production and can be integrated with intelligent operations in mountainous and hilly regions, laying a theoretical foundation for improving efficiency.
Unpacking Consumer Preferences: Perceptions and Sustainability of Packaging Material for Orange Juice
Authors: Dlamini Nomzamo N. , Mayhew Emily J. , Nolden Alissa A.
Published: July 2024

Understanding the motivation behind consumers’ packaging choices is crucial to providing insights for achieving environmental sustainability outcomes. Here, we examined the influence of product attributes (packaging type, packaging claim, product claim, and price) and consumers factors (e.g., diet type, demographic information) driving orange juice selection. Participants residing in the USA (n = 847) responded to an online survey including: (1) a check-all-that-apply (CATA) to valued beverage characteristics question; (2) a choice-based conjoint task with packaging type, packaging claim, product claim, and price as the attributes; (3) a question tasking respondents to rank packaging material from their perception of the least to most sustainable; and (4) demographic questions. The conjoint analysis revealed that price was the most important attribute, particularly the lowest price. This study revealed that the most ideal orange juice option was packaged in glass, labelled as 100% recyclable, locally produced, and priced at $1.10 per 12 fl. oz. Not only was glass the most preferred packaging type, but it was also incorrectly perceived as the most sustainable. The intention to purchase sustainable packaging was the most important predictor of attribute relative importance (RI) and packaging utilities, followed by effectiveness perception, which only predicted the RI of price. Thus, for consumers to make more sustainable choices, education initiatives need to direct consumers to more sustainable, yet affordable, choices, while considering that purchase intention and effectiveness perception are key attitudinal drivers.
Manufacturer’s Channel Strategy and Demand Information Sharing in a Retailer-Led Green Supply Chain
Authors: Wang Ruiping , Lou Zhenkai , Lou Xuming
Published: July 2024

In the rapidly evolving landscape of e-commerce, companies are increasingly focusing on their channel strategies to gain sustainable development. However, asymmetric demand information poses challenges to these decisions. This paper explores the interplay between a retailer’s information sharing strategy and a manufacturer’s channel strategy in a retailer-led green supply chain, where the manufacturer may establish an online channel to sell its green products directly. The dominant retailer has private demand information about the market and decides whether to share private information with the manufacturer. By establishing a game model, we analyze the impacts of information sharing and the manufacturer’s channel strategy on the payoffs for all the supply chain members, considering sustainability aspects such as the environmental benefits of green products and the efficiency of supply chain operations. The results show that information sharing benefits both the retailer and the manufacturer, irrespective of the establishment of an online channel. When the retailer shares demand information, opening an online channel benefits the manufacturer but benefits the retailer under certain conditions. Furthermore, through a numerical approach, we examine the strategic preferences of the firms and derive the equilibrium strategy. Interestingly, the manufacturer consistently prefers the scenario involving both an online channel and information sharing. The retailer’s preference, however, depends on the direct selling cost; it favors information sharing with or without an online channel based on this cost. Ultimately, our findings suggest that the equilibrium strategy can either be sharing information with an online channel or not sharing information without an online channel, which is contingent upon the direct selling cost and the forecast signal precision. These insights provide actionable strategies for enhancing the sustainability of supply chain operations.
A Sustainable Dynamic Capacity Estimation Method Based on Bike-Sharing E-Fences
Authors: Deng Chen , Ma Houqiang
Published: July 2024

Increasing urban traffic congestion and environmental pollution have led to the embrace of bike-sharing for its low-carbon convenience. This study enhances the operational efficiency and environmental benefits of bike-sharing systems by optimizing electronic fences (e-fences). Using bike-sharing order data from Shenzhen, China, a data-driven multi-objective optimization approach is proposed to design the sustainable dynamic capacity of e-fences. A dynamic planning model, solved with an improved Non-dominated Sorting Genetic Algorithm II (NSGA-II), adjusts e-fence capacities to match fluctuating user demand, optimizing resource utilization. The results show that an initial placement of 20 bicycles per e-fence provided a balance between cost efficiency and user convenience, with the enterprise cost being approximately 76,000 CNY and an extra walking distance for users of 15.1 m. The optimal number of e-fence sites was determined to be 40 based on the solution algorithm constructed in the study. These sites are strategically located in high-demand areas, such as residential zones, commercial districts, educational institutions, subway stations, and parks. This strategic placement enhances urban mobility and reduces disorderly parking.
Smell the Perfume: Can Blockchain Guarantee the Provenance of Key Product Ingredients in the Fragrance Industry?
Authors: MacCarthy Bart L. , Das Surajit , Ahmed Wafaa A. H.
Published: July 2024

Although many studies have proclaimed the potential benefits of blockchain technology for supply chain traceability, there are very few examples of successfully scaled blockchain traceability solutions. Here, we examine the challenges in adopting blockchain-based traceability solutions using the case of a complex globally fragmented industry—the fragrance sector. Fragrances are used in a vast number of products. Leading brands make substantial claims about the origin, production methods, certifications and environmental and social sustainability of the fragrances in their products. We categorize the types of traceability-related claims made and examine the potential use of blockchain to support them. The industry exemplifies the challenges in ensuring supply chain traceability in globally dispersed supply chains. The research highlights the potential of blockchain-based traceability solutions but identifies four major challenges—bringing together supply chain partners for a blockchain initiative, disclosing and sharing more information than is common in buyer-supplier relationships, ensuring information encoded on a blockchain provides an accurate record of a product’s journey through the supply chain and involving parties such as regulatory and industry bodies in blockchain initiatives. The challenges identified are reflected in supply chains that are fragmented and globally dispersed, showing why the uptake of blockchain-based traceability has been limited in such sectors.
Efficiency of Agricultural Insurance in Facilitating Modern Agriculture Development: From the Perspective of Production Factor Allocation
Authors: Fu Li-Sha , Qin Tao , Li Gan-Qiong , Wang San-Gui
Published: July 2024

Agricultural insurance is instrumental in consolidating the gains of poverty alleviation and advancing rural revitalization. It significantly aids in the efficient allocation of agricultural production factors, which in turn enhances agricultural output and bolsters the evolution of modern agriculture. Therefore, utilizing data from 583 household surveys and employing endogenous transformation and intermediary effect models, this paper analyzes the production factor allocation effect and specific mechanism of agricultural insurance. It focuses on small-scale farmers and new agricultural operators, exploring how insurance contributes to the advancement of modern agricultural practices. The results show the following: (1) Agriculture insurance can significantly affect the agricultural scale input behavior of farmers such as land input scale and input scale, agricultural machinery application behavior such as the degree of mechanization and water conservancy application, agricultural technology adoption behavior, and planting structure selection behavior, thereby helping to modernize agriculture. (2) There is heterogeneity in the impact of agriculture insurance on the allocation of production factors for small farmers and new agricultural operators. For small farmers, agriculture insurance has a significant promoting effect on their agricultural machinery application behavior, agricultural technology adoption behavior, and planting structure selection behavior. For new agricultural operators, agriculture insurance significantly promotes their agricultural scale input behavior, agricultural machinery application behavior, and agricultural technology adoption behavior. (3) In terms of the mechanism of action, agriculture insurance mainly promotes agricultural scale input behavior through land transfer, facilitates agricultural machinery application behavior by purchasing agricultural machinery equipment and services, encourages agricultural technology adoption behavior by strengthening agricultural technology training, and enhances professional production levels by increasing the scale of insured planting, thereby contributing to the development of modern agriculture. Based on this, several policy suggestions have been proposed. These include enhancing the directionality of agriculture insurance policies, improving the collaborative interaction mechanism between agriculture insurance and agricultural credit financing, and adopting certain reward and punishment measures to curb moral hazard.
Multi-Scenario Simulation of Land Use Change and Ecosystem Service Value Based on the Markov–FLUS Model in Ezhou City, China
Authors: Zhang Maomao , Chen Enqing , Zhang Cheng , Liu Chen , Li Jianxing
Published: July 2024

Changes in land use patterns, types, and intensities significantly impact ecosystem services. This study follows the time series logic from history to the expected future to investigate the spatial and temporal characteristics of land use changes in Ezhou and their potential impacts on the ecosystem services value (ESV). The results show that the Markov–FLUS model has strong applicability in predicting the spatial pattern of land use, with a Kappa coefficient of 0.9433 and a FoM value of 0.1080. Between 2000 and 2020, construction land expanded continuously, while water area remained relatively stable, and other land types experienced varying degrees of contraction. Notably, the area of construction land expanded significantly compared to 2000, and it expanded by 70.99% in 2020. Moreover, the watershed area expanded by 9.30% from 2000 to 2010, but there was very little change in the following 10 years. Under the three scenarios, significant differences in land use changes were observed in Ezhou City, driven by human activities, particularly the strong expansion of construction land. In the inertial development scenario, construction land expanded to 313.39 km2 by 2030, representing a 38.30% increase from 2020. Conversely, under the farmland protection scenario, construction land increased to 237.66 km2, a 4.89% rise from 2020. However, in the ecological priority development scenario, the construction land area expanded to 253.59 km2, a 10.13% increase from 2020. Compared to 2020, the ESV losses in the inertia development and farmland protection scenarios were USD 4497.71 and USD 1072.23, respectively, by 2030. Conversely, the ESV under the ecological protection scenario increased by USD 2749.09, emphasizing the importance of prioritizing ecological protection in Ezhou City’s development. This study may provide new clues for the formulation of regional strategies for sustainable land use and ecosystem restoration.
Reducing Washout of Proteins from Defatted Soybean Flakes by Alkaline Extraction: Fractioning and Characterization
Authors: Wittmann Giovana , Duarte Lovaine Silva , Ayub Marco Antônio Záchia , Rossi Daniele Misturini
Published: July 2024

Human health, sustainable development, numerous environmental issues, and animal welfare are increasingly driving research and development of plant-based protein products that can serve as meat substitutes. This trend is expected to continue in the coming years due to growing consumer awareness, with people gradually shifting from animal-based foods to more sustainable plant-based options. Soy proteins are a valuable source of plant proteins and are widely used in human and animal diets due to their nutritional value and health benefits. In this study, soybean protein extraction by two methods was compared: water extraction (lower salt content) and Tris-HCl extraction (higher salt content), aiming to characterize the resulting protein fractions. These fractions were studied using differential precipitation based on the isoelectric point. Protein identification by SDS-PAGE, scanning electron microscopy (SEM) for cellular structure assessment, and Fourier-transform infrared spectroscopy (FTIR) were used to determine residual protein left in the solid fraction after extraction using the two methods. Electrophoresis assays revealed the presence of the four main protein fractions (2S, 7S, 11S, and soy whey proteins) in the defatted soybean flakes, establishing the protein profile of Brazilian soybeans and for the two main waste streams of the production process—spent flakes and whey. The separation of fractions was carried out by differential precipitation. FTIR analysis indicated higher residual protein levels in solid residues after the water extraction method compared to the Tris-HCl extraction method. SEM analysis revealed the removal of protein bodies in both extraction methods and the presence of residual oil-containing bodies. Both methodologies are viable alternatives for the industrial separation of soybean protein fractions. Differential precipitation could be implemented to produce isolated products and improve the nutritional profile, increase process yield thus generating less industrial waste and driving the process towards environmental sustainability.
Sustainable Integration of Autonomous Vehicles into Road Networks: Ecological and Passenger Comfort Considerations
Authors: Hosseinian Seyed Mohsen , Mirzahossein Hamid , Guzik Robert
Published: July 2024

Autonomous vehicle (AV) technology is rapidly advancing, leading to a sustainable evolution in transportation. AVs offer the promise of enhanced safety, reduced emissions, improved traffic flow, and increased mobility for passengers. However, the integration of AVs into existing traffic networks presents complex challenges related to ecological sustainability and passenger comfort. This review aims to bridge the gap in the literature by providing a comprehensive evaluation of the influence of AVs on both ecology and passenger comfort within traffic networks. The study synthesizes existing knowledge on AV technology, ecological impacts, and passenger comfort to offer a comprehensive understanding of the challenges and opportunities associated with AVs’ integration. The main objectives of this research are to review the current literature on the environmental impact of AVs, analyze studies on passenger comfort in AVs, identify key challenges and opportunities, and propose future research directions. The results highlight the need for a holistic, sustainable approach that considers both ecological and passenger-centric factors in the design and deployment of AVs. Future research directions are proposed to further our understanding of the complex interplay between AVs, the environment, and passenger well-being, and inform policy decisions and technological advancements that promote sustainable and comfortable transportation solutions.
The Impact of Financial Efficiency and Renewable Energy Consumption on CO2 Emission Reduction in GCC Economies: A Panel Data Quantile Regression Approach
Authors: Elmonshid Lena Bedawi Elfadli , Sayed Omer Ahmed , Awad Yousif Ghadda Mohamed , Eldaw Kamal Eldin Hassan Ibrahim , Hussein Muawya Ahmed
Published: July 2024

As prominent oil producers, Gulf Cooperation Council (GCC) countries have played a significant role in the global energy market. However, as the world’s attention increasingly shifts towards environmental sustainability, understanding the implications of the GCC’s economic activities on CO2 emissions becomes indispensable. This research paper investigates the relationship between specific economic indicators and their impact on CO2 emissions in the GCC from 2001 to 2021. This study employs quantile regression, a robust statistical method that estimates the conditional quantiles of a response variable given a set of predictor variables. The findings reveal several essential insights: Financial institution efficiency is significant and negative at a 1% level at the lower (10th, −83,537.3) and higher quantiles (90th, −549,002.3). The relationship between the GDP per capita and CO2 emissions varies across quantiles, highlighting the complexity of the growth–environment nexus. Total patents exhibit a positive and significant relationship with emissions, underscoring the importance of directing innovation towards environmentally sustainable solutions. Renewable energy consumption displays a nuanced relationship with CO2 emissions, with a more substantial negative impact observed at higher consumption levels. This underscores the potential of renewable energy to mitigate emissions when integrated at scale. This study’s outcomes hold crucial policy implications for GCC countries as they seek to align economic growth with environmental sustainability. The findings emphasize the importance of fostering financial institution efficiency, promoting green innovation, and expanding renewable energy sources to reduce emissions.