bluuebunny/crossref_metadata_2025 huggingface dataset:
Study on Basic Pavement Performance of High-Elasticity Asphalt Concrete
Authors: Wang Juan , Huo Taixu , Wang Dahui , Zhang Peng
Published: July 2024

In order to improve the basic pavement performance of high-elastic asphalt concrete filled in the expansion longitudinal joints of seamless bridges, rubber particles and polyester fibers were added to optimize the mix proportion of elastic asphalt concrete, and the optimal asphalt–aggregate ratio was determined. The influence of rubber particles and polyester fibers on the basic pavement performance of high-elastic asphalt concrete was studied. The results show that when the dosage of polyester fiber is not more than 0.6%, the optimal asphalt–aggregate ratio is 1:5, and when it exceeds 0.6%, the optimal asphalt–aggregate ratio is 1:4. The incorporation of rubber particles reduces the compressive strength of high-elastic asphalt concrete but enhances its high-temperature stability, fracture performance, and deformation recovery ability. The incorporation of polyester fibers improves its compressive strength, high-temperature stability, fracture performance, and deformation recovery ability. In addition, the incorporation of rubber granules and polyester fibers promotes the use of green building materials and provides strong support for sustainable building practices.
Optimizing Fish Feeding with FFAUNet Segmentation and Adaptive Fuzzy Inference System
Authors: Huang Yo-Ping , Vadloori Spandana
Published: July 2024

Efficient and optimized fish-feeding practices are crucial for enhancing productivity and sustainability in aquaculture. While many studies have focused on classifying fish-feeding intensity, there is a lack of research on optimizing feeding, necessitating a precise and automated model. This study fills this gap with a hybrid solution for precision aquaculture feeding management involving segmentation and optimization phases. In the segmentation phase, we used the novel feature fusion attention U-Net (FFAUNet) to accurately segment fish-feeding intensity areas. The FFAUNet achieved impressive metrics: a mean intersection over union (mIoU) of 89.39%, a mean precision of 95.07%, a mean recall of 95.08%, a mean pixel accuracy of 95.12%, and an overall accuracy of 95.61%. In the optimization phase, we employed an adaptive neuro-fuzzy inference system (ANFIS) with a particle swarm optimizer (PSO) to optimize feeding. Extracting feeding intensity percentages from the segmented output, the ANFIS with PSO achieved an accuracy of 98.57%, a sensitivity of 99.41%, and a specificity of 99.53%. This model offers fish farmers a robust, automated tool for precise feeding management, reducing feed wastage and improving overall productivity and sustainability in aquaculture.
Special Issue: Advances in Renewable Energy Systems
Authors: Salata Ferdinando , Ciancio Virgilio
Published: July 2024

The greatest challenge that the industrialized world faces today is converting its energy production systems into those that utilize renewable sources, which are more environmentally friendly compared to traditional systems [...]
Holistic Review of UAV-Centric Situational Awareness: Applications, Limitations, and Algorithmic Challenges
Authors: MahmoudZadeh Somaiyeh , Yazdani Amirmehdi , Kalantari Yashar , Ciftler Bekir , Aidarus Fathi , Al Kadri Mhd Omar
Published: July 2024

This paper presents a comprehensive survey of UAV-centric situational awareness (SA), delineating its applications, limitations, and underlying algorithmic challenges. It highlights the pivotal role of advanced algorithmic and strategic insights, including sensor integration, robust communication frameworks, and sophisticated data processing methodologies. The paper critically analyzes multifaceted challenges such as real-time data processing demands, adaptability in dynamic environments, and complexities introduced by advanced AI and machine learning techniques. Key contributions include a detailed exploration of UAV-centric SA’s transformative potential in industries such as precision agriculture, disaster management, and urban infrastructure monitoring, supported by case studies. In addition, the paper delves into algorithmic approaches for path planning and control, as well as strategies for multi-agent cooperative SA, addressing their respective challenges and future directions. Moreover, this paper discusses forthcoming technological advancements, such as energy-efficient AI solutions, aimed at overcoming current limitations. This holistic review provides valuable insights into the UAV-centric SA, establishing a foundation for future research and practical applications in this domain.
InstLane Dataset and Geometry-Aware Network for Instance Segmentation of Lane Line Detection
Authors: Cheng Qimin , Ling Jiajun , Yang Yunfei , Liu Kaiji , Li Huanying , Huang Xiao
Published: July 2024

Despite impressive progress, obtaining appropriate data for instance-level lane segmentation remains a significant challenge. This limitation hinders the refinement of granular lane-related applications such as lane line crossing surveillance, pavement maintenance, and management. To address this gap, we introduce a benchmark for lane instance segmentation called InstLane. To the best of our knowledge, InstLane constitutes the first publicly accessible instance-level segmentation standard for lane line detection. The complexity of InstLane emanates from the fact that the original data are procured using cameras mounted laterally, as opposed to traditional front-mounted sensors. InstLane encapsulates a range of challenging scenarios, enhancing the generalization and robustness of the lane line instance segmentation algorithms. In addition, we propose GeoLaneNet, a real-time, geometry-aware lane instance segmentation network. Within GeoLaneNet, we design a finer localization of lane proto-instances based on geometric features to counteract the prevalent omission or multiple detections in dense lane scenarios resulting from non-maximum suppression (NMS). Furthermore, we present a scheme that employs a larger receptive field to achieve profound perceptual lane structural learning, thereby improving detection accuracy. We introduce an architecture based on partial feature transformation to expedite the detection process. Comprehensive experiments on InstLane demonstrate that GeoLaneNet can achieve up to twice the speed of current State-Of-The-Artmethods, reaching 139 FPS on an RTX3090 and a mask AP of 73.55%, with a permissible trade-off in AP, while maintaining comparable accuracy. These results underscore the effectiveness, robustness, and efficiency of GeoLaneNet in autonomous driving.
AI-Driven Computer Vision Detection of Cotton in Corn Fields Using UAS Remote Sensing Data and Spot-Spray Application
Authors: Yadav Pappu Kumar , Thomasson J. Alex , Hardin Robert , Searcy Stephen W. , Braga-Neto Ulisses , Popescu Sorin C. , Rodriguez Roberto , Martin Daniel E. , Enciso Juan
Published: July 2024

To effectively combat the re-infestation of boll weevils (Anthonomus grandis L.) in cotton fields, it is necessary to address the detection of volunteer cotton (VC) plants (Gossypium hirsutum L.) in rotation crops such as corn (Zea mays L.) and sorghum (Sorghum bicolor L.). The current practice involves manual field scouting at the field edges, which often leads to the oversight of VC plants growing in the middle of fields alongside corn and sorghum. As these VC plants reach the pinhead squaring stage (5–6 leaves), they can become hosts for boll weevil pests. Consequently, it becomes crucial to detect, locate, and accurately spot-spray these plants with appropriate chemicals. This paper focuses on the application of YOLOv5m to detect and locate VC plants during the tasseling (VT) growth stage of cornfields. Our results demonstrate that VC plants can be detected with a mean average precision (mAP) of 79% at an Intersection over Union (IoU) of 50% and a classification accuracy of 78% on images sized 1207 × 923 pixels. The average detection inference speed is 47 frames per second (FPS) on the NVIDIA Tesla P100 GPU-16 GB and 0.4 FPS on the NVIDIA Jetson TX2 GPU, which underscores the relevance and impact of detection speed on the feasibility of real-time applications. Additionally, we show the application of a customized unmanned aircraft system (UAS) for spot-spray applications through simulation based on the developed computer vision (CV) algorithm. This UAS-based approach enables the near-real-time detection and mitigation of VC plants in corn fields, with near-real-time defined as approximately 0.02 s per frame on the NVIDIA Tesla P100 GPU and 2.5 s per frame on the NVIDIA Jetson TX2 GPU, thereby offering an efficient management solution for controlling boll weevil pests.
A New Combination Approach for Gibbs Phenomenon Suppression in Regional Validation of Global Gravity Field Model: A Case Study in North China
Authors: Shen Yingchun , Feng Wei , Yang Meng , Zhong Min , Tian Wei , Xiong Yuhao , Jiang Zhongshan
Published: July 2024

A global gravity field model (GGM) is essential to be validated with ground-based or airborne observational data for the accurate application of the GGM at a regional scale. Furthermore, accurately understanding the commission errors between the GGM and observational data are crucial for improving regional gravity fields. Taking the North China region as an example, to circumvent the omission errors, it is necessary to unify the spatial resolutions of the EIGEN-6C4 model and terrestrial gravity observational data to 110 km (determined by the distribution of gravity stations) by employing the spherical harmonic function for the EIGEN-6C4 model and the Slepian basis function for the gravity data, respectively. However, the application of spherical harmonic function expansions in the gravity model results in the Gibbs phenomenon, which may be a primary factor contributing to commission errors and impedes the accurate validation of the EIGEN-6C4 model with terrestrial gravity data. To effectively mitigate this issue, this study proposes a combination approach of window function filtering and regional eigenvalue constraint (based on the Slepian basis). Utilizing the EIGEN-6C4 gravity model to derive the gravity disturbance field at a resolution of 110 km (with spherical harmonic expansion up to the 180th degree and order), the combination approach effectively suppresses over 90% of high-degree (above the 120th degree) Gibbs phenomena. This approach also reduces signal leakage outside the region, thus enhancing the spatial accuracy of the regional gravity disturbance field. A subsequent comparison of the regional gravity disturbance field derived from the true model and terrestrial gravity data in North China indicates excellent consistency, with a root mean squared error (RMSE) of 0.80 mGal. This validation confirms that the combined approach of window function filtering and regional eigenvalue constraints effectively mitigates the Gibbs phenomenon and yields precise regional gravity fields. This approach is anticipated to significantly benefit scientific applications such as improving the accuracy of regional elevation benchmarks and accurately inverting the Earth’s internal structure.
The Preparation Phase of the 2023 Kahramanmaraş (Turkey) Major Earthquakes from a Multidisciplinary and Comparative Perspective
Authors: Cianchini Gianfranco , Calcara Massimo , De Santis Angelo , Piscini Alessandro , D’Arcangelo Serena , Fidani Cristiano , Sabbagh Dario , Orlando Martina , Perrone Loredana , Campuzano Saioa A. , De Caro Mariagrazia , Nardi Adriano , Soldani Maurizio
Published: July 2024

On 6 February 2023, Turkey experienced its most powerful earthquake in over 80 years, with a moment magnitude (Mw) of 7.7. This was then followed by a second earthquake of Mw 7.6 just nine hours later. According to the lithosphere–atmosphere–ionosphere coupling (LAIC) models, such a significant seismic activity is expected to cause anomalies across various observables, from the Earth’s surface to the ionosphere. This multidisciplinary study investigates the preparatory phase of these two major earthquakes by identifying potential precursors across the lithosphere, atmosphere, and ionosphere. Our comprehensive analysis successfully identified and collected various anomalies, revealing that their cumulative occurrence follows an accelerating trend, either exponential or power-law. Most anomalies appeared to progress from the lithosphere upward through the atmosphere to the ionosphere, suggesting a sequential chain of processes across these geospheres. Notably, some anomalies deviated from this overall trend, manifesting as oscillating variations. We propose that these anomalies support a two-way coupling model preceding major earthquakes, highlighting the potential role of fluid chemistry in facilitating these processes.
Land-Use and Land-Cover Changes in Cottbus City and Spree-Neisse District, Germany, in the Last Two Decades: A Study Using Remote Sensing Data and Google Earth Engine
Authors: Ahmed Rezwan , Zafor Md. Abu , Trachte Katja
Published: July 2024

Regular detection of land-use and land-cover (LULC) changes with high accuracy is necessary for natural resources management and sustainable urban planning. The produced LULC maps from Google Earth Engine (GEE) also illustrate the transformation of the LULC for the respective landscape over time. The selected study area, Cottbus City and the Spree-Neisse district in northeastern Germany, has undergone significant development over the past decades due to various factors, including urbanization and industrialization; also, the landscape has been converted in some areas for post-mining activities. Detection of LULC changes that have taken place over the last few decades thus plays a vital role in quantifying the impact of these factors while improving the knowledge of these developments and supporting the city planners or urban management officials before implementing further long-term development initiatives for the future. Therefore, the study aims to (i) detect LULC changes for the time slices 2002 and 2022, testing machine learning (ML) algorithms in supervised and unsupervised classification for Landsat satellite imageries, and (ii) validate the newly produced LULC maps with the available regional database (RDB) from the federal and state statistical offices, Germany, and the Dynamic World (DW) near real-time 10 m global LULC data set powered by artificial intelligence (AI). The results of the Random Forest (RF) and the Smilecart classifiers of supervised classification using Landsat 9 OLI-2/TIRS-2 in 2022 demonstrated a validation accuracy of 88% for both, with Kappa Index (KI) of 83% and 84%, respectively. Moreover, the Training Overall Accuracy (TOA) was 100% for both years. The wekaKMeans cluster of the unsupervised classification also illustrated a similar transformation pattern in the LULC maps. Overall, the produced LULC maps offered an improved representation of the selected region’s various land-cover classes (i.e., vegetation, waterbodies, built areas, and bare ground) in the last two decades (20022 to 2022).
Improvements to a Crucial Budyko-Fu Parameter and Evapotranspiration Estimates via Vegetation Optical Depth over the Yellow River Basin
Authors: Wang Xingyi , Jin Jiaxin
Published: July 2024

Against the backdrop of global warming and vegetation restoration, research on the evapotranspiration mechanism of the Yellow River basin has become a hot topic. The Budyko-Fu model is widely used to estimate basin-scale evapotranspiration, and its crucial parameter ω is used to characterize the underlying surface and climate characteristics of different basins. However, most studies only use factors such as the normalized difference vegetation index (NDVI), which represents the greenness of vegetation, to quantify the relationship between ω and the underlying surface, thereby neglecting richer vegetation information. In this study, we used long time-series multi-source remote sensing data from 1988 to 2015 and stepwise regression to establish dynamic estimation models of parameter ω for three subwatersheds of the upper Yellow River and quantify the contribution of underlying surface factors and climate factors to this parameter. In particular, vegetation optical depth (VOD) was introduced to represent plant biomass to improve the applicability of the model. The results showed that the dynamic estimation models of parameter ω established for the three subwatersheds were reasonable (R2 = 0.60, 0.80, and 0.40), and parameter ω was significantly correlated with the VOD and standardized precipitation evapotranspiration index (SPEI) in all watersheds. The dominant factors affecting the parameter in the different subwatersheds also differed, with underlying surface factors mainly affecting the parameter in the watershed before Longyang Gorge (BLG) (contributing 64% to 76%) and the watershed from Lanzhou to Hekou Town (LHT) (contributing 63% to 83%) and climate factors mainly affecting the parameter in the watershed from Longyang Gorge to Lanzhou (LGL) (contributing 75% to 93%). The results of this study reveal the changing mechanism of evapotranspiration in the Yellow River watershed and provide an important scientific basis for regional water balance assessment, global change response, and sustainable development.
Investigation of the Zero-Frequency Component of Nonlinear Lamb Waves in a Symmetrical Undulated Plate
Authors: Sun Xiaoqiang , Shui Guoshuang
Published: July 2024

When an ultrasonic pulse propagates in a thin plate, nonlinear Lamb waves with higher harmonics and a zero-frequency component (ZFC) will be generated because of the nonlinearity of materials. The ZFC, also known as the static displacement or static component, has its unique application on the evaluation of early-stage damages in the elastic symmetrical undulated plate. In this study, analysis of the excitation mechanism of the ZFC and the second harmonic component (SHC) was theoretically and numerically investigated, and the material early-stage damage of a symmetrical undulated was characterized by studying the propagation of nonlinear Lamb waves. Both the ZFC and SHC can be effectively employed in monitoring the material damages of the undulated plate in its early stage. However, several factors must be considered for the propagation of the SHC in an undulated plate because of the geometric curvature and interference between the second harmonics during propagation, preventing efficient application of this technique. If the fundamental wave can propagate in the plate regardless of the plate boundary conditions, an accumulative effect always exists for the ZFC in a thin plate, indicating that the ZFC is independent of the structural geometry. This study reveals that the ZFC-based inspection technique is more efficient and powerful in characterizing the damages of a symmetrical undulated plate in the early stage of service compared to the second harmonic method.
GTR: GAN-Based Trusted Routing Algorithm for Underwater Wireless Sensor Networks
Authors: Wang Bin , Ben Kerong
Published: July 2024

The transmission environment of underwater wireless sensor networks is open, and important transmission data can be easily intercepted, interfered with, and tampered with by malicious nodes. Malicious nodes can be mixed in the network and are difficult to distinguish, especially in time-varying underwater environments. To address this issue, this article proposes a GAN-based trusted routing algorithm (GTR). GTR defines the trust feature attributes and trust evaluation matrix of underwater network nodes, constructs the trust evaluation model based on a generative adversarial network (GAN), and achieves malicious node detection by establishing a trust feature profile of a trusted node, which improves the detection performance for malicious nodes in underwater networks under unlabeled and imbalanced training data conditions. GTR combines the trust evaluation algorithm with the adaptive routing algorithm based on Q-Learning to provide an optimal trusted data forwarding route for underwater network applications, improving the security, reliability, and efficiency of data forwarding in underwater networks. GTR relies on the trust feature profile of trusted nodes to distinguish malicious nodes and can adaptively select the forwarding route based on the status of trusted candidate next-hop nodes, which enables GTR to better cope with the changing underwater transmission environment and more accurately detect malicious nodes, especially unknown malicious node intrusions, compared to baseline algorithms. Simulation experiments showed that, compared to baseline algorithms, GTR can provide a better malicious node detection performance and data forwarding performance. Under the condition of 15% malicious nodes and 10% unknown malicious nodes mixed in, the detection rate of malicious nodes by the underwater network configured with GTR increased by 5.4%, the error detection rate decreased by 36.4%, the packet delivery rate increased by 11.0%, the energy tax decreased by 11.4%, and the network throughput increased by 20.4%.
Calibrating the Discrete Boundary Conditions of a Dynamic Simulation: A Combinatorial Approximate Bayesian Computation Sequential Monte Carlo (ABC-SMC) Approach
Authors: Shamas Jah , Rogers Tim , Krynkin Anton , Prisutova Jevgenija , Gardner Paul , Horoshenkov Kirill V. , Shelley Samuel R. , Dickenson Paul
Published: July 2024

This paper presents a novel adaptation of the conventional approximate Bayesian computation sequential Monte Carlo (ABC-SMC) sampling algorithm for parameter estimation in the presence of uncertainties, coined combinatorial ABC-SMC. Inference of this type is used in situations where there does not exist a closed form of the associated likelihood function, which is replaced by a simulating model capable of producing artificial data. In the literature, conventional ABC-SMC is utilised to perform inference on continuous parameters. The novel scheme presented here has been developed to perform inference on parameters that are high-dimensional binary, rather than continuous. By altering the form of the proposal distribution from which to sample candidates in subsequent iterations (referred to as waves), high-dimensional binary variables may be targeted and inferred by the scheme. The efficacy of the proposed scheme is demonstrated through application to vibration data obtained in a structural dynamics experiment on a fibre-optic sensor simulated as a finite plate with uncertain boundary conditions at its edges. Results indicate that the method provides sound inference on the plate boundary conditions, which is validated through subsequent application of the method to multiple vibration datasets. Comparisons between appropriate forms of the metric function used in the scheme are also developed to highlight the effect of this element in the schemes convergence.
Combining Optimization and Simulation for Next-Generation Off-Road Vehicle E/E Architectural Design
Authors: Bianchi Cristian , Merlino Rosario , Passerone Roberto
Published: July 2024

The automotive industry, with particular reference to the off-road sector, is facing several challenges, including the integration of Advanced Driver Assistance Systems (ADASs), the introduction of autonomous driving capabilities, and system-specific requirements that are different from the traditional car market. Current vehicular electrical–electronic (E/E) architectures are unable to support the amount of data for new vehicle functionalities, requiring the transition to zonal architectures, new communication standards, and the adoption of Drive-by-Wire technologies. In this work, we propose an automated methodology for next-generation off-road vehicle E/E architectural design. Starting from the regulatory requirements, we use a MILP-based optimizer to find candidate solutions, a discrete event simulator to validate their feasibility, and an ascent-based gradient method to reformulate the constraints for the optimizer in order to converge to the final architectural solution. We evaluate the results in terms of latency, jitter, and network load, as well as provide a Pareto analysis that includes power consumption, cost, and system weight.
Ensemble Learning Improves the Efficiency of Microseismic Signal Classification in Landslide Seismic Monitoring
Authors: Xin Bingyu , Huang Zhiyong , Huang Shijie , Feng Liang
Published: July 2024

A deep-seated landslide could release numerous microseismic signals from creep-slip movement, which includes a rock-soil slip from the slope surface and a rock-soil shear rupture in the subsurface. Machine learning can effectively enhance the classification of microseismic signals in landslide seismic monitoring and interpret the mechanical processes of landslide motion. In this paper, eight sets of triaxial seismic sensors were deployed inside the deep-seated landslide, Jiuxianping, China, and a large number of microseismic signals related to the slope movement were obtained through 1-year-long continuous monitoring. All the data were passed through the seismic event identification mode, the ratio of the long-time average and short-time average. We selected 11 days of data, manually classified 4131 data into eight categories, and created a microseismic event database. Classical machine learning algorithms and ensemble learning algorithms were tested in this paper. In order to evaluate the seismic event classification performance of each algorithmic model, we evaluated the proposed algorithms through the dimensions of the accuracy, precision, and recall of each model. The validation results demonstrated that the best performing decision tree algorithm among the classical machine learning algorithms had an accuracy of 88.75%, while the ensemble algorithms, including random forest, Gradient Boosting Trees, Extreme Gradient Boosting, and Light Gradient Boosting Machine, had an accuracy range from 93.5% to 94.2% and also achieved better results in the combined evaluation of the precision, recall, and F1 score. The specific classification tests for each microseismic event category showed the same results. The results suggested that the ensemble learning algorithms show better results compared to the classical machine learning algorithms.
Characterizing the Sensing Response of Carbon Nanocomposite-Based Wearable Sensors on Elbow Joint Using an End Point Robot and Virtual Reality
Authors: Chaudhari Amit , Lokesh Rakshith , Chheang Vuthea , Doshi Sagar M. , Barmaki Roghayeh Leila , Cashaback Joshua G. A. , Thostenson Erik T.
Published: July 2024

Physical therapy is often essential for complete recovery after injury. However, a significant population of patients fail to adhere to prescribed exercise regimens. Lack of motivation and inconsistent in-person visits to physical therapy are major contributing factors to suboptimal exercise adherence, slowing the recovery process. With the advancement of virtual reality (VR), researchers have developed remote virtual rehabilitation systems with sensors such as inertial measurement units. A functional garment with an integrated wearable sensor can also be used for real-time sensory feedback in VR-based therapeutic exercise and offers affordable remote rehabilitation to patients. Sensors integrated into wearable garments offer the potential for a quantitative range of motion measurements during VR rehabilitation. In this research, we developed and validated a carbon nanocomposite-coated knit fabric-based sensor worn on a compression sleeve that can be integrated with upper-extremity virtual rehabilitation systems. The sensor was created by coating a commercially available weft knitted fabric consisting of polyester, nylon, and elastane fibers. A thin carbon nanotube composite coating applied to the fibers makes the fabric electrically conductive and functions as a piezoresistive sensor. The nanocomposite sensor, which is soft to the touch and breathable, demonstrated high sensitivity to stretching deformations, with an average gauge factor of ~35 in the warp direction of the fabric sensor. Multiple tests are performed with a Kinarm end point robot to validate the sensor for repeatable response with a change in elbow joint angle. A task was also created in a VR environment and replicated by the Kinarm. The wearable sensor can measure the change in elbow angle with more than 90% accuracy while performing these tasks, and the sensor shows a proportional resistance change with varying joint angles while performing different exercises. The potential use of wearable sensors in at-home virtual therapy/exercise was demonstrated using a Meta Quest 2 VR system with a virtual exercise program to show the potential for at-home measurements.
Multi-Focus Images Fusion for Fluorescence Imaging Based on Local Maximum Luminosity and Intensity Variance
Authors: Cheng Hao , Wu Kaijie , Gu Chaochen , Ma Dingrui
Published: July 2024

Due to the limitations on the depth of field of high-resolution fluorescence microscope, it is difficult to obtain an image with all objects in focus. The existing image fusion methods suffer from blocking effects or out-of-focus fluorescence. The proposed multi-focus image fusion method based on local maximum luminosity, intensity variance and the information filling method can reconstruct the all-in-focus image. Moreover, the depth of tissue’s surface can be estimated to reconstruct the 3D surface model.
Using Video Technology and AI within Parkinson’s Disease Free-Living Fall Risk Assessment
Authors: Moore Jason , Celik Yunus , Stuart Samuel , McMeekin Peter , Walker Richard , Hetherington Victoria , Godfrey Alan
Published: July 2024

Falls are a major concern for people with Parkinson’s disease (PwPD), but accurately assessing real-world fall risk beyond the clinic is challenging. Contemporary technologies could enable the capture of objective and high-resolution data to better inform fall risk through measurement of everyday factors (e.g., obstacles) that contribute to falls. Wearable inertial measurement units (IMUs) capture objective high-resolution walking/gait data in all environments but are limited by not providing absolute clarity on contextual information (i.e., obstacles) that could greatly influence how gait is interpreted. Video-based data could compliment IMU-based data for a comprehensive free-living fall risk assessment. The objective of this study was twofold. First, pilot work was conducted to propose a novel artificial intelligence (AI) algorithm for use with wearable video-based eye-tracking glasses to compliment IMU gait data in order to better inform free-living fall risk in PwPD. The suggested approach (based on a fine-tuned You Only Look Once version 8 (YOLOv8) object detection algorithm) can accurately detect and contextualize objects (mAP50 = 0.81) in the environment while also providing insights into where the PwPD is looking, which could better inform fall risk. Second, we investigated the perceptions of PwPD via a focus group discussion regarding the adoption of video technologies and AI during their everyday lives to better inform their own fall risk. This second aspect of the study is important as, traditionally, there may be clinical and patient apprehension due to ethical and privacy concerns on the use of wearable cameras to capture real-world video. Thematic content analysis was used to analyse transcripts and develop core themes and categories. Here, PwPD agreed on ergonomically designed wearable video-based glasses as an optimal mode of video data capture, ensuring discreteness and negating any public stigma on the use of research-style equipment. PwPD also emphasized the need for control in AI-assisted data processing to uphold privacy, which could overcome concerns with the adoption of video to better inform IMU-based gait and free-living fall risk. Contemporary technologies (wearable video glasses and AI) can provide a holistic approach to fall risk that PwPD recognise as helpful and safe to use.
The Impacts of Regulatory Approaches to Carbon Quotas on Third-Party Logistics Low-Carbon Financing Strategies and Emission Reduction Effects
Authors: Wang Huipo , Fu Xiaozhen
Published: July 2024

Carbon emission reduction is an important issue for sustainable development. The logistics industry is a key area for carbon emission reduction. The modern logistics supply chain includes logistics parks (fourth-party logistics, 4PL) and small, medium and micro logistics enterprises settled in the parks (third-party logistics, 3PL). Facing the pressure of the need for sustainable development, 3PL enterprises need to invest a lot of money in green transformation. However, 3PL enterprises are subject to serious financial constraints. In order to break the capital constraints, 3PL enterprises can raise funds from banks or from 4PL financing. Under the carbon quota policy, the government can regulate the 4PL or the 3PL. Therefore, this paper uses the Stackelberg game model to build a green financing strategy model of small and medium-sized logistics enterprises considering different supervision methods under carbon quotas, explores the optimal emission reduction decision-making process of small and medium-sized logistics enterprises, and provides solutions to the financing problems of small and medium-sized logistics enterprises in realizing sustainable development. The study found that the decisions of enterprises under different governmental supervision methods are affected by carbon quotas, and the government’s supervision of 3PL is more conducive to carbon emission reduction; in this scenario, the 4PL financing strategy is more likely to be adopted compared with bank financing, because 4PL charge lower service fees, thus encouraging 3PL to increase their low-carbon investment. Finally, this paper puts forward two different carbon emission supervision methods and considers the green financing services of 4PL; this provides references for government supervision and the sustainable development strategies of logistics enterprises.
Bridging the Gap between Biowaste and Biomethane Production: A Systematic Review Meta-Analysis Methodological Approach
Authors: Toufexis Charalampos , Makris Dimitrios-Orfeas , Vlachokostas Christos , Michailidou Alexandra V. , Mertzanakis Christos , Vachtsiavanou Athanasia
Published: July 2024

Anaerobic digestion (AD) is a promising biowaste valorization technology for sustainable energy, circular economy, local energy community growth, and supporting local authorities’ environmental goals. This paper presents a systematic review meta-analysis methodology for biomethane estimation, using over 600 values of volatile solids (VS) content and biochemical methane potential (BMP) of six different waste streams, collected from 240 scientific studies. The waste streams include cow manure (CM), sheep/goat manure (SGM), wheat straw (WS), household waste (HW), organic fraction of municipal solid waste (OFMSW), and sewage sludge (SS). The statistical analysis showed a mean VS content of 11.9% (CM), 37.3% (SGM), 83.1% (WS), 20.8% (HW), 19.4% (OFMSW), and 10.6% (SS), with BMP values of 204.6, 184.1, 305.1, 361.7, 308.3, and 273.1 L CH4/kg VS, respectively. The case study of Kozani, Greece, demonstrated the methodology’s applicability, revealing a potential annual CH4 production of 15,429,102 m3 (corresponding to 551 TJ of energy), with SGM, WS, and CM as key substrates. Kozani, aiming for climate neutrality by 2030, currently employs conventional waste management, like composting, while many local business residual streams remain unused. The proposed model facilitates the design and implementation of AD units for a sustainable, climate-neutral future.
Building a Resilient City through Sustainable Flood Risk Management: The Flood-Prone Area of Phra Nakhon Sri Ayutthaya, Thailand
Authors: Munpa Phichet , Dubsok Atima , Phetrak Athit , Sirichokchatchawan Wandee , Taneepanichskul Nutta , Lohwacharin Jenyuk , Kittipongvises Suthirat , Polprasert Chongrak
Published: July 2024

Resilience has gained significant prominence in the management of climate-related shocks and the minimization of disaster risks. Assessing flood resilience is, therefore, crucial in identifying areas lacking support and opportunities for development. Comprehensive risk assessments are urgently required, especially in areas prone to floods. By applying the Analytical Hierarchy Process (AHP), stakeholder meetings, and keyword analysis, this study aims to assess the flood resilience of Phra Nakhon Sri Ayutthaya Province (PNSAP) in Thailand, an area at high risk of flooding and home to a UNESCO World Heritage Site. According to the Arup and Rockefeller Foundation’s City Resilience indicators, the key elements for building resilience in PNSAP include effective emergency response services, comprehensive hazard monitoring and risk assessment, and emergency medical care. In addition, the focus group discussions and keyword analysis identified the following critical components for strengthening flood resilience: “financial aspects”, “community awareness and preparedness”, “multi-stakeholder collaboration”, “citizen engagement”, and “urban flood monitoring and data management.” Moreover, to gain insight into the perceptions of people in local communities, the results of a household survey (n = 552) indicated that approximately one-third of the respondents had never engaged in flood preparedness activities. Neither the gender nor the education level of the respondents significantly influenced their engagement in flood preparedness. The respondents also perceived that infrastructure flexibility, affordable transport networks, flood monitoring and data management, the adequate provision of critical services, and reliable communication systems in building resilience were important during a flood disaster. These results provide valuable insight into the community’s perception of the effectiveness of disaster risk reduction mechanisms for building flood resilience in PNSAP and can serve as a useful guide for future resilience-building initiatives. By addressing these factors, PNSAP could enhance its resilience to the impacts of flooding and contribute to the global field of flood management and resilience building.
Optimization Analysis of Yellowtail Kingfish (Seriola aureovittata) Land–Sea Relay Farming Based on Life Cycle Environment and Cost Assessment in Dalian, China
Authors: Yu Lixingbo , Ren Anqi , Han Fengfan , Jia Fei , Li Shijia , Guan Jiaqi , Hou Haochen , Liu Ying
Published: July 2024

The farming mode of yellowtail kingfish (Seriola aureovittata) in China tends to be mature. However, there are some problems with environmental impact and economic benefits that cannot be ignored in the sustainable farming process. This study focused on a yellowtail kingfish aquaculture company in Dalian, China, and carried out a life cycle environmental and cost assessment (LCA and LCC) study to evaluate the environmental impact and economic benefits of the yellowtail kingfish farming process. According to the LCA and LCC results, the environmental impact is significantly influenced by fossil energy consumption and feed production. Moreover, five improvement scenarios were proposed and discussed, and the results show that replacing coal and thermal power generation with wind power generation will comprehensively (scenario 5) reduce environmental impact by 82.14% and decrease costs by 24.25%. The results of this study can provide effective improvement scenarios for yellowtail kingfish aquaculture enterprises and enrich the international aquaculture LCA basic database with data support.
Sustainably Produced but Unsustainably Destroyed: Effective Price Promotion for the Sustainable Management of Unsold Inventory in Korea
Authors: Jung Sojin , Lee Stacy H. , Kim Min Jung
Published: July 2024

Focusing on sustainable fashion brands’ effective price promotion, this experimental study developed two sets of stimuli, ‘discount’ and ‘disclosure’ strategies, and tested consumers’ evaluations of price fairness, product attractiveness, quality, and brand trust based on each strategy. Subsequently, this study compared consumers’ evaluations of the discount and disclosure strategies. An analysis of 961 Korean samples revealed that a high discount rate increased price fairness and product attractiveness, and the highly promotion-focused consumers were more likely to perceive product attractiveness and quality when positive framing was presented. In the disclosure strategy, the reference point effect was prominent; when the conventional markup rate was provided, consumers showed greater price fairness, product attractiveness, and brand trust. Furthermore, it was noteworthy that disclosing conventional markup along with the firm’s markup showed the same price fairness perceptions as that of a high markup rate. Built on reference point and regulatory focus fit, this study empirically proved the effectiveness of the price promotions of sustainable fashion brands to whom quality and trustworthiness are greatly important, extending the academic originality of this study. Practically, effective use of price promotion strategies can help fashion management handle inventory problems in a sustainable way without massive investment in technologies.
Blockchain-Driven Supply Chain Analytics and Sustainable Performance: Analysis Using PLS-SEM and ANFIS
Authors: Espahbod Shervin , Tashakkori Arash , Mohsenibeigzadeh Mahsa , Zarei Mehrnaz , Arani Ghasem Golshan , Dzikuć Maria , Dzikuć Maciej
Published: July 2024

This study investigated the impact of blockchain-driven supply chain analytics on the dimensions of lean, agile, resilient, green, and sustainable (LARGS) supply chain management, as well as supply chain innovation (SCI) and sustainable supply chain performance (SSCP). The research involved 262 managers and vice presidents of supply chains from large- and medium-sized manufacturing companies listed in the Tehran Stock Exchange. A hybrid approach utilizing structural equations modelling with partial least squares-structural equation modeling (PLS-SEM) and the adaptive neuro-fuzzy inference systems (ANFIS) technique was employed for data analysis. The findings demonstrated a significantly positive effect of blockchain-driven supply chain analytics on SCI, the LARGS supply chain, and SSCP. Additionally, SCI exhibited a significantly positive impact on the LARGS supply chain and SSCP. Moreover, the LARGS supply chain was shown to have a significantly positive influence on SSCP. Both SCI and the LARGS supply chain played positive and significant mediating roles in the impact of blockchain-driven supply chain analytics on SSCP. Furthermore, the LARGS supply chain also acted as a significant mediator in the effect of SCI on SSCP. Lastly, SCI had a positive and significant mediating role in the impact of blockchain-driven supply chain analytics on the LARGS supply chain. In conclusion, it can be inferred that blockchain-driven supply chain analytics contributes to the enhancement of SSCP through the facilitation of SCI and the promotion of LARGS supply chain principles.
Subsurface Drainage and Nitrogen Fertilizer Management Affect Fertilizer Fate in Claypan Soils
Authors: Kaur Harpreet , Nelson Kelly A.
Published: July 2024

Sustainable nitrogen (N) fertilizer management practices in the Midwest U.S. strive to optimize crop production while minimizing N gas emission losses and nitrate-N (NO3-N) losses in subsurface drainage water. A replicated site in upstate Missouri from 2018 to 2020 investigated the influence of different N fertilizer management practices on nutrient concentrations in drainage water, nitrous oxide (N2O) emissions, and ammonia (NH3) volatilization losses in a corn (Zea mays, 2018, 2020)–soybean (Glyince max, 2019) rotation. Four N treatments applied to corn included fall anhydrous ammonia with nitrapyrin (fall AA + NI), spring anhydrous ammonia (spring AA), top dressed SuperU and ESN as a 25:75% granular blend (TD urea), and non-treated control (NTC). All treatments were applied to subsurface-drained (SD) and non-drained (ND) replicated plots, except TD urea, which was only applied with SD. Across the years, NO3-N concentration in subsurface drainage water was similar for fall AA + NI and spring AA treatments. The NO3-N concentration in subsurface drainage water was statistically (p < 0.0001) lower with TD urea (9.1 mg L−1) and NTC (8.9 mg L−1) compared to fall AA + NI (14.6 mg L−1) and spring AA (13.8 mg L−1) in corn growing years. During corn years (2018 and 2020), cumulative N2O emissions were significantly (p < 0.05) higher with spring AA compared to other fertilizer treatments with SD and ND. Reduced corn growth and plant N uptake in 2018 caused greater N2O loss with TD urea and spring AA compared to the NTC and fall AA + NI in 2019. Cumulative NH3 volatilization was ranked as TD urea > spring AA > fall AA + NI. Due to seasonal variability in soil moisture and temperature, gas losses were higher in 2018 compared to 2020. There were no environmental benefits to applying AA in the spring compared to AA + NI in the fall on claypan soils. Fall AA with a nitrification inhibitor is a viable alternative to spring AA, which maintains flexible N application timings for farmers.
Development of Power-Delay Product Optimized ASIC-Based Computational Unit for Medical Image Compression
Authors: Mendez Tanya , Parupudi Tejasvi , Kedlaya K Vishnumurthy , Nayak Subramanya G.
Published: July 2024

The proliferation of battery-operated end-user electronic devices due to technological advancements, especially in medical image processing applications, demands low power consumption, high-speed operation, and efficient coding. The design of these devices is centered on the Application-Specific Integrated Circuits (ASIC), General Purpose Processors (GPP), and Field Programmable Gate Array (FPGA) frameworks. The need for low-power functional blocks arises from the growing demand for high-performance computational units that are part of high-speed processors operating at high clock frequencies. The operational speed of the processor is determined by the computational unit, which is the workhorse of high-speed processors. A novel approach to integrating Very Large-Scale Integration (VLSI) ASIC design and the concepts of low-power VLSI compatible with medical image compression was embraced in this research. The focus of this study was the design, development, and implementation of a Power Delay Product (PDP) optimized computational unit targeted for medical image compression using ASIC design flow. This stimulates the research community’s quest to develop an ideal architecture, emphasizing on minimizing power consumption and enhancing device performance for medical image processing applications. The study uses area, delay, power, PDP, and Peak Signal-to-Noise Ratio (PSNR) as performance metrics. The research work takes inspiration from this and aims to enhance the efficiency of the computational unit through minor design modifications that significantly impact performance. This research proposes to explore the trade-off of high-performance adder and multiplier designs to design an ASIC-based computational unit using low-power techniques to enhance the efficiency in power and delay. The computational unit utilized for the digital image compression process was synthesized and implemented using gpdk 45 nm standard libraries with the Genus tool of Cadence. A reduced PDP of 46.87% was observed when the image compression was performed on a medical image, along with an improved PSNR of 5.89% for the reconstructed image.