bluuebunny/crossref_metadata_2025 huggingface dataset:
Spatial Gap-Filling of Himawari-8 Hourly AOD Products Using Machine Learning with Model-Based AOD and Meteorological Data: A Focus on the Korean Peninsula
Authors: Youn Youjeong , Kim Seoyeon , Kim Seung Hee , Lee Yangwon
Published: November 2024

Given the complex spatiotemporal variability of aerosols, high-frequency satellite observations are essential for accurately mapping their distribution. However, optical remote sensing encounters difficulties in detecting Aerosol Optical Depth (AOD) over cloud-covered regions, creating data gaps that limit comprehensive environmental analysis. This study introduces a spatial gap-filling method for Himawari-8/Advanced Himawari Imager (AHI) hourly AOD data, using a Random Forest (RF) model that integrates meteorological variables and model-based AOD data. Developed and validated over South Korea from 1 January to 31 December 2019, the model effectively improved data coverage from 6% to 100%. The approach demonstrated high performance in blind tests, achieving a root mean square error (RMSE) of 0.064 and a correlation coefficient (CC) of 0.966. Meteorological analysis indicated optimal model performance under cold, dry conditions (RMSE: 0.047, CC: 0.956), compared to humid conditions (RMSE: 0.105, CC: 0.921). Validation against Aerosol Robotic Network (AERONET) ground observations showed that, while the original Himawari-8 data exhibited higher accuracy (RMSE: 0.189, CC: 0.815, n = 346), the gap-filled dataset maintained reasonable precision (RMSE: 0.208, CC: 0.711) and significantly increased the number of valid data points (n = 4149). Furthermore, the gap-filled dataset successfully captured seasonal AOD patterns, with values ranging from 0.245–0.300 in winter to 0.381–0.391 in summer, providing a comprehensive view of aerosol dynamics across South Korea.
Contextual Attribution Maps-Guided Transferable Adversarial Attack for 3D Object Detection
Authors: Cai Mumuxin , Wang Xupeng , Sohel Ferdous , Lei Hang
Published: November 2024

The study of LiDAR-based 3D object detection and its robustness under adversarial attacks has achieved great progress. However, existing adversarial attack methods mainly focus on the targeted object, which destroys the integrity of the object and makes the attack easy to perceive. In this work, we propose a novel adversarial attack against deep 3D object detection models named the contextual attribution maps-guided attack (CAMGA). Based on the combinations of subregions in the context area and their impact on the prediction results, contextual attribution maps can be generated. An attribution map exposes the influence of individual subregions in the context area on the detection results and narrows down the scope of the adversarial attack. Subsequently, perturbations are generated under the guidance of a dual loss, which is proposed to suppress the detection results and maintain visual imperception simultaneously. The experimental results proved that the CAMGA method achieved an attack success rate of over 68% on three large-scale datasets and 83% on the KITTI dataset. Meanwhile, the CAMGA has a transfer attack success rate of at least 50% against all four victim detectors, as they all overly rely on contextual information.
ARSOD-YOLO: Enhancing Small Target Detection for Remote Sensing Images
Authors: Qiu Yijuan , Zheng Xiangyue , Hao Xuying , Zhang Gang , Lei Tao , Jiang Ping
Published: November 2024

Remote sensing images play a vital role in domains including environmental monitoring, agriculture, and autonomous driving. However, the detection of targets in remote sensing images remains a challenging task. This study introduces innovative methods to enhance feature extraction, feature fusion, and model optimization. The Adaptive Selective Feature Enhancement Module (AFEM) dynamically adjusts feature weights using GhostModule and sigmoid functions, thereby enhancing the accuracy of small target detection. Moreover, the Adaptive Multi-scale Convolution Kernel Feature Fusion Module (AKSFFM) enhances feature fusion through multi-scale convolution operations and attention weight learning mechanisms. Moreover, our proposed ARSOD-YOLO optimized the network architecture, component modules, and loss functions based on YOLOv8, enhancing outstanding small target detection capabilities while preserving model efficiency. We conducted experiments on the VEDAI and AI-TOD datasets, showcasing the excellent performance of ARSOD-YOLO. Our algorithm achieved an mAP50 of 74.3% on the VEDAI dataset, surpassing the YOLOv8 baseline by 3.1%. Similarly, on the AI-TOD dataset, the mAP50 reached 47.8%, exceeding the baseline network by 6.1%.
A High-Sensitivity Fiber Optic Soil Moisture Sensor Based on D-Shaped Fiber and Tin Oxide Thin Film Coatings
Authors: Tien Chuen-Lin , Shih Hsi-Fu , Tien Jia-Kai , Wang Ching-Chiun
Published: November 2024

We present a high-sensitivity fiber optic soil moisture sensor based on side-polished multimode fibers and lossy mode resonance (LMR). The multimode fibers (MMFs), after side-polishing to form a D-shaped structure, are coated with a single-layer SnO2 thin film by electron beam evaporation with ion-assisted deposition technology. The LMR effect can be obtained when the refractive index of the thin film is positive and greater than its extinction coefficient and the real part of the external medium permittivity. The D-shaped fiber optic soil moisture sensor was placed in soil, allowing moisture to penetrate into the thin film microstructure, and it observed the resonance wavelength shift in LMR spectra to measure the relative humidity change in soil. Meanwhile, an Arduino electronic soil moisture sensing module was used as the experimental control group, with soil relative humidity ranging from 10%RH to 90%RH. We found that the D-shaped fiber with a residual thickness of 93 μm and SnO2 thin film thickness of 450 nm had a maximum sensitivity of 2.29 nm/%RH, with relative humidity varying from 10%RH to 90%RH. The D-shaped fiber also demonstrates a fast response time and good reproducibility.
Femtosecond Laser Introduced Cantilever Beam on Optical Fiber for Vibration Sensing
Authors: Qiu Jin , Wang Zijie , Ke Zhihong , Tao Tianlong , Liu Shuhui , Deng Quanrong , Huang Wei , Tong Weijun
Published: November 2024

An all-fiber vibration sensor based on the Fabry-Perot interferometer (FPI) is proposed and experimentally evaluated in this study. The sensor is fabricated by introducing a Fabry-Perot cavity to the single-mode fiber using femtosecond laser ablation. The cavity and the tail act together as a cantilever beam, which can be used as a vibration receiver. When mechanical vibrations are applied, the cavity length of the Fabry-Perot interferometer changes accordingly, altering the interference fringes. Due to the low moment of inertia of the fiber optic cantilever beam, the sensor can achieve broadband frequency responses and high vibration sensitivity without an external vibration receiver structure. The frequency range of sensor detection is 70 Hz–110 kHz, and the sensitivity of the sensor is 60 mV/V. The sensor’s signal-to-noise ratio (SNR) can reach 56 dB. The influence of the sensor parameters (cavity depth and fiber tail length) on the sensing performance are also investigated in this study. The sensor has the advantages of compact structure, high sensitivity, and wideband frequency response, which could be a promising candidate for vibration sensing.
Advancements in Sensor Fusion for Underwater SLAM: A Review on Enhanced Navigation and Environmental Perception
Authors: Merveille Fomekong Fomekong Rachel , Jia Baozhu , Xu Zhizun , Fred Bissih
Published: November 2024

Underwater simultaneous localization and mapping (SLAM) has significant challenges due to the complexities of underwater environments, marked by limited visibility, variable conditions, and restricted global positioning system (GPS) availability. This study provides a comprehensive analysis of sensor fusion techniques in underwater SLAM, highlighting the amalgamation of proprioceptive and exteroceptive sensors to improve UUV navigational accuracy and system resilience. Essential sensor applications, including inertial measurement units (IMUs), Doppler velocity logs (DVLs), cameras, sonar, and LiDAR (light detection and ranging), are examined for their contributions to navigation and perception. Fusion methodologies, such as Kalman filters, particle filters, and graph-based SLAM, are evaluated for their benefits, limitations, and computational demands. Additionally, innovative technologies like quantum sensors and AI-driven filtering techniques are examined for their potential to enhance SLAM precision and adaptability. Case studies demonstrate practical applications, analyzing the compromises between accuracy, computational requirements, and adaptability to environmental changes. This paper proceeds to emphasize future directions, stressing the need for advanced filtering and machine learning to address sensor drift, noise, and environmental unpredictability, hence improving autonomous underwater navigation through reliable sensor fusion.
EC-WAMI: Event Camera-Based Pose Optimization in Remote Sensing and Wide-Area Motion Imagery
Authors: Nkrumah Isaac , Moshrefizadeh Maryam , Tahri Omar , Blasch Erik , Palaniappan Kannappan , AliAkbarpour Hadi
Published: November 2024

In this paper, we present EC-WAMI, the first successful application of neuromorphic event cameras (ECs) for Wide-Area Motion Imagery (WAMI) and Remote Sensing (RS), showcasing their potential for advancing Structure-from-Motion (SfM) and 3D reconstruction across diverse imaging scenarios. ECs, which detect asynchronous pixel-level brightness changes, offer key advantages over traditional frame-based sensors such as high temporal resolution, low power consumption, and resilience to dynamic lighting. These capabilities allow ECs to overcome challenges such as glare, uneven lighting, and low-light conditions that are common in aerial imaging and remote sensing, while also extending UAV flight endurance. To evaluate the effectiveness of ECs in WAMI, we simulate event data from RGB WAMI imagery and integrate them into SfM pipelines for camera pose optimization and 3D point cloud generation. Using two state-of-the-art SfM methods, namely, COLMAP and Bundle Adjustment for Sequential Imagery (BA4S), we show that although ECs do not capture scene content like traditional cameras, their spike-based events, which only measure illumination changes, allow for accurate camera pose recovery in WAMI scenarios even in low-framerate(5 fps) simulations. Our results indicate that while BA4S and COLMAP provide comparable accuracy, BA4S significantly outperforms COLMAP in terms of speed. Moreover, we evaluate different feature extraction methods, showing that the deep learning-based LIGHTGLUE descriptor consistently outperforms traditional handcrafted descriptors by providing improved reliability and accuracy of event-based SfM. These results highlight the broader potential of ECs in remote sensing, aerial imaging, and 3D reconstruction beyond conventional WAMI applications. Our dataset will be made available for public use.
Assisting Standing Balance Recovery for Parkinson’s Patients with a Lower-Extremity Exoskeleton Robot
Authors: Lee Chi-Shiuan , Yu Lo-Ping , Lee Si-Huei , Chen Yi-Chia , Chen Chun-Ta
Published: November 2024

Parkinson’s disease (PD) is a neurodegenerative disorder and always results in balance loss. Although studies in lower-extremity exoskeleton robots are ample, applications with a lower-extremity exoskeleton robot for PD patients are still challenging. This paper aims to develop an effective assistive control for PD patients with a lower-extremity exoskeleton robot to maintain standing balance while being subjected to external disturbances. When an external force is applied to participants to force them to lose balance, the hip strategy for balance recovery based on the zero moment point (ZMP) metrics is used to generate a reference trajectory of the hip joint, and then, a model-free linear extended state observer (LESO)-based fuzzy sliding mode control (FSMC) is synthesized to regulate the human body to recover balance. Balance recovery trials for healthy individuals and PD patients with and without exoskeleton assistance were conducted to evaluate the performance of the proposed exoskeleton robot and balance recovery strategy. Our experiments demonstrated the potential effectiveness of the proposed exoskeleton robot and controller for standing balance recovery control in PD patients.
A Linear Rehabilitative Motion Planning Method with a Multi-Posture Lower-Limb Rehabilitation Robot
Authors: Wang Xincheng , Lin Musong , Sang Lingfeng , Wang Hongbo , Feng Yongfei , Niu Jianye , Yu Hongfei , Cheng Bo
Published: November 2024

In rehabilitation, physicians plan lower-limb exercises via linear guidance. Ensuring efficacy and safety, they design patient-specific paths, carefully plotting smooth trajectories to minimize jerks. Replicating their precision in robotics is a major challenge. This study introduces a linear rehabilitation motion planning method designed for physicians to use a multi-posture lower-limb rehabilitation robot, encompassing both path and trajectory planning. By subdividing the lower limb’s action space into four distinct training sections and classifying this space, we articulate the correlation between linear trajectories and key joint rehabilitation metrics. Building upon this foundation, a rehabilitative path generation system is developed, anchored in joint rehabilitation indicators. Subsequently, high-order polynomial curves are employed to mimic the smooth continuity of traditional rehabilitation trajectories and joint motions. Furthermore, trajectory planning is refined through the resolution of a constrained quadratic optimization problem, aiming to minimize the abrupt jerks in the trajectory. The optimized trajectories derived from our experiments are compared with randomly generated trajectories, demonstrating the suitability of trajectory optimization for real-time rehabilitation trajectory planning. Additionally, we compare trajectories generated based on the two groups of joint rehabilitation indicators, indicating that the proposed path generation system effectively assists clinicians in executing efficient and precise robot-assisted rehabilitation path planning.
A Shortest Distance Priority UAV Path Planning Algorithm for Precision Agriculture
Authors: Zhang Guoqing , Liu Jiandong , Luo Wei , Zhao Yongxiang , Tang Ruiyin , Mei Keyu , Wang Penggang
Published: November 2024

Unmanned aerial vehicles (UAVs) have made significant advances in autonomous sensing, particularly in the field of precision agriculture. Effective path planning is critical for autonomous navigation in large orchards to ensure that UAVs are able to recognize the optimal route between the start and end points. When UAVs perform tasks such as crop protection, monitoring, and data collection in orchard environments, they must be able to adapt to dynamic conditions. To address these challenges, this study proposes an enhanced Q-learning algorithm designed to optimize UAV path planning by combining static and dynamic obstacle avoidance features. A shortest distance priority (SDP) strategy is integrated into the learning process to minimize the distance the UAV must travel to reach the target. In addition, the root mean square propagation (RMSP) method is used to dynamically adjust the learning rate according to gradient changes, which accelerates the learning process and improves path planning efficiency. In this study, firstly, the proposed method was compared with state-of-the-art path planning techniques (including A-star, Dijkstra, and traditional Q-learning) in terms of learning time and path length through a grid-based 2D simulation environment. The results showed that the proposed method significantly improved performance compared to existing methods. In addition, 3D simulation experiments were conducted in the AirSim virtual environment. Due to the complexity of the 3D state, a deep neural network was used to calculate the Q-value based on the proposed algorithm. The results indicate that the proposed method can achieve the shortest path planning and obstacle avoidance operations in an orchard 3D simulation environment. Therefore, drones equipped with this algorithm are expected to make outstanding contributions to the development of precision agriculture through intelligent navigation and obstacle avoidance.
The Adoption of Taxonomy Requirements to Facilitate the Green Transition of the Buildings Sector
Authors: Džiugaitė-Tumėnienė Rasa , Motuzienė Violeta , Mikučionienė Rūta
Published: November 2024

For direct investment towards activities that significantly contribute to the achievement of the European Green Deal objectives, the European Union has adopted “Taxonomy Regulation”, which also applies disclosure requirements to financial institutions that finance the construction, renovation, or acquisition of buildings. For this reason, the financial sector needs methodologies and guidelines, adapted to the national situation, to define the primary energy thresholds to be used when assessing sustainability and financing the acquisition of real estate. This paper presents the methodology developed to identify 15% and 30% of the most energy-efficient national building stock in Lithuania based on EPC data. As a result, functional primary energy indicator (FPEI) threshold values are set for 17 distinct categories of existing buildings built by 31 December 2020. The 15% FPEI thresholds range from 81 kWh/m2 for warehouse buildings to 228 kWh/m2 for swimming pool buildings. Similarly, the 30% FPEI thresholds span from 104 kWh/m2 for warehouses to 303 kWh/m2 for foodservice buildings. The methodologies and threshold values are compared to other countries’ practice and recommendations are provided.
Forecasting Urban Land Use Dynamics Through Patch-Generating Land Use Simulation and Markov Chain Integration: A Multi-Scenario Predictive Framework
Authors: Marey Ahmed , Wang Liangzhu (Leon) , Goubran Sherif , Gaur Abhishek , Lu Henry , Leroyer Sylvie , Belair Stephane
Published: November 2024

Rapid urbanization and changing land use dynamics require robust tools for projecting and analyzing future land use scenarios to support sustainable urban development. This study introduces an integrated modeling framework that combines the Patch-generating Land Use Simulation (PLUS) model with Markov Chain (MC) analysis to simulate land use and land cover (LULC) changes for Montreal Island, Canada. This framework leverages historical data, scenario-based adjustments, and spatial drivers, providing urban planners and policymakers with a tool to evaluate the potential impacts of land use policies. Three scenarios—sustainable, industrial, and baseline—are developed to illustrate distinct pathways for Montreal’s urban development, each reflecting different policy priorities and economic emphases. The integrated MC-PLUS model achieved a high accuracy level, with an overall accuracy of 0.970 and a Kappa coefficient of 0.963 when validated against actual land use data from 2020. The findings indicate that sustainable policies foster more contiguous green spaces, enhancing ecological connectivity, while industrial-focused policies promote the clustering of commercial and industrial zones, often at the expense of green spaces. This study underscores the model’s potential as a valuable decision-support tool in urban planning, allowing for the scenario-driven exploration of LULC dynamics with high spatial precision. Future applications and enhancements could expand its relevance across diverse urban contexts globally.
Optimized Irrigation Strategies for Saline Soil Remediation in Agricultural Lands Under Water-Limited Conditions
Authors: Li Chaokun , Yang Ye , Xi Hui , Chen Zhiwen , Dong Yingying , Liu Meiling , Liu Junsheng , Wang Wendong
Published: November 2024

Soil salinization is a prevalent global issue, significantly impacting crop cultivation and food production. This study investigates the potential of sustainably harnessing rainwater for the remediation of saline soils in water-scarce regions. Soil column leaching experiments were performed to evaluate the effectiveness of different methods for salt removal from the tillage layer. The findings demonstrated that intermittent leaching was more effective than continuous leaching in remediating NaCl-type saline soils. When continuous leaching with 27 cm of rainwater was applied, the salt removal in soil layers below 5 cm ranged from 12.28% to 26.86%. Intermittent leaching increased the salt removal rate to between 44.49% and 54.18%. This higher desalination efficiency of intermittent leaching is attributable to the extended soil–water contact time. When the leaching time in continuous leaching was increased from 1.5 h to over 4.5 h, comparable desalination effects were produced. The rainwater leaching demonstrated similar salt removal patterns in Na2SO4-type saline soils. However, due to the stronger affinity of SO42− for clay particles, their effluent concentration and removal were lower than Cl− under the same conditions. To optimize desalination efficiency, operational parameters can be adjusted to reduce the leaching depth of rainwater from 27 cm to 15 cm, and the interval between leaching events from 24 h to 4.5 h. The findings of this study may serve as a valuable reference for saline soil restoration and improvement efforts in water-scarce regions.
Development and Characterization of PVA Membranes Modified with In(BTC) Metal–Organic Framework for Sustainable Pervaporation Separation of Isopropanol/Water
Authors: Polyakov Vladimir , Dmitrenko Mariia , Kalmakhelidze Meri , Kuzminova Anna , Dubovenko Roman , Mukhanova Elizaveta , Soldatov Alexander , Penkova Anastasia
Published: November 2024

In this study, pervaporation membranes from synthetic biodegradable polyvinyl alcohol (PVA) with improved properties for isopropanol dehydration were developed through modification with a synthesized In(BTC) metal–organic framework. The improvement in the PVA membrane properties was achieved by varying the In(BTC) concentration (2.5–7 wt.%) in the PVA matrix to allow us to select the optimal concentration for the membrane, which was further chemically cross-linked with maleic acid to increase the resistance, and developing a cross-linked supported membrane from the optimal PVA/5%In(BTC) composite for promising industrial applications. The synthesized In(BTC) and membranes were characterized by using spectroscopic, microscopic, X-ray diffraction, and thermogravimetric analysis methods, as well as swelling degree, contact angle measurements, and the Brunauer–Emmett–Teller adsorption model. The obtained regularities were confirmed by quantum chemical calculations. The cross-linked supported membrane from PVA/5%In(BTC) had optimal transport properties for isopropanol dehydration (20–90 wt.% water), 99.9–89.0 wt.% water in the permeate, and 0.142–0.341 kg/(m2h) of permeation flux, the rate of which was four times higher compared to the PVA membrane in separating 20–30 wt.% water/isopropanol.
Urban Sustainability Through the Lens of Urban Fabric Typologies: A Case Study of Cuenca, Ecuador
Authors: Calderón Francisco , Orellana Daniel , Carrasco María Isabel , Astudillo Johnatan , Hermida María Augusta
Published: November 2024

Understanding the relationship between urban fabrics and sustainability is essential for addressing contemporary urban challenges, as urban fabrics provide critical morphological and socio-economic data that enhance our understanding of the performance and spatial variability of urban systems. This study focuses on Cuenca, spatially divided into a 150 m radius hexagonal grid with 30 sustainability indicators and 18 urban fabric typologies. Using spatial, statistical, and visual analysis, relationships and patterns between sustainability indicators and urban fabric types are explored. The results reveal significant variation in sustainability across different fabric types, with built environment indicators playing a central role. There is marked spatial heterogeneity: inner-core areas exhibit higher sustainability, fringe areas lag behind, and transitional zones are also identified. Spatial clustering reveals that fabric types are homogeneous in terms of sustainability at both the high and low extremes but heterogeneous in mid-range sustainability areas. This quantitative analysis of Cuenca’s urban fabric typologies highlights substantial differences in sustainability and distinct spatial patterns, offering valuable insights for evidence-based urban planning. The open-source data and tools provided facilitate customisation and replication in other urban contexts.
Determining the Applicability and Use of the Intrinsic Motivation Inventory in the Context of Water Conservation
Authors: Gibson Kristin E. , Erskine Olivia M. , Lamm Kevan W. , Lamm Alexa J. , Warner Laura A. , Holt Jessica
Published: November 2024

Urgent action is needed to educate the public and encourage water conservation behaviors amidst the global threat of water scarcity impacting billions by 2050. Crafting tailored educational programs about water conservation is crucial for incentivizing behavior change and achieving the Sustainable Development Goals. Intrinsic motivation, which is shaped by personal interests and values, plays a fundamental role in understanding individual behavior in various contexts, and therefore may aid in our understanding of water conservation behaviors. Despite the widespread application of the intrinsic motivation inventory (IMI) to measure intrinsic motivation, its adaptation and utilization in water conservation education remains largely unexplored, prompting this study to establish a relevant scale for intrinsic motivation in the context of water conservation education. Data were collected from 907 respondents in Florida, Georgia, and Alabama and subsequently analyzed using an exploratory factor analysis and confirmatory factor analysis to establish and confirm factor structures related to intrinsic motivation, perceived competence, perceived choice, and relatedness in water conservation. The study findings established the Water Conservation IMI as a reliable tool for measuring intrinsic motivation in water conservation education. Further validation of measures for extrinsic motivation in environmental literature are needed to enhance comprehensive education strategies for sustainability.
Research on the Optimization of Urban–Rural Passenger and Postal Integration Operation Scheduling Based on Uncertainty Theory
Authors: Xue Yunqiang , Liu Jiayu , Tu Haokai , Bao Guangfa , He Tong , Qiu Yang , Bi Yuhan , Guan Hongzhi
Published: November 2024

The integration of postal and passenger transport is an effective measure to enhance the utilization efficiency of passenger and freight transportation resources and to promote the sustainable development of urban–rural transit and logistics. This paper considers the uncertainty in passenger and freight demand as well as transit operation times, constructing an optimization model for integrated urban–rural transit and postal services based on uncertainty theory. Passenger and freight demand, along with the inverse uncertain distribution of events, serve as constraints, while minimizing passenger travel time and the cost for passenger transport companies are the optimization objectives. Taking into account the uncertainty of urban–rural bus travel time, the scheduling model is transformed into a robust form for scenarios involving single and multiple origin stations. The model is solved using an improved NSGA-II (Nondominated Sorting Genetic Algorithm II) to achieve effective coordinated scheduling of both passenger and freight services. Through a case study in Lotus County, Jiangxi Province, vehicle routing plans with varying levels of conservativeness were obtained. Comparing the results from different scenarios, it was found that when the total vehicle operating mileage increased from 1.96% to 62.26%, passenger transport costs rose from 2.95% to 62.66%, while the total passenger travel time decreased from 55.99% to 172.31%. In terms of optimizing costs and improving passenger travel efficiency, operations involving multiple starting stations for a single vehicle demonstrated greater advantages. Meanwhile, at a moderate level of robustness, it was easier to achieve a balance between operational costs and passenger travel time. The research findings provide theoretical support for improving travel conditions and resource utilization in rural areas, which not only helps enhance the operational efficiency of urban–rural transit but also contributes positively to promoting balanced urban–rural sustainable development and narrowing the urban–rural gap.
A Quantitative Evaluation Study on Return-to-Hometown Entrepreneurship Policies in 16 Provinces (Municipalities) and Autonomous Regions in China Under the Rural Revitalization Strategy
Authors: Li Mingqian , Zhang Lingping , Huang Junyan , Lu Yiyi
Published: November 2024

The Rural Revitalization Strategy is an important policy enacted by the Chinese government, aimed at bridging the disparity between urban and rural areas while enhancing economic, cultural, and social development levels in rural areas. Research evaluating return-to-hometown entrepreneurship policies is pivotal for facilitating the sustained flow and comprehensive sustainable development of capital, technology, and talent to rural areas within the Rural Revitalization Strategy framework. This study employs NVivo12 and the policy modeling consistency (PMC) index to analyze trends, characteristics, and policy tool utilization quantitatively, as well as regional differences or commonalities in 55 return-to-hometown entrepreneurship policies of 16 provinces and autonomous regions in China from 2013 to 2023. The content analysis of these policy texts reveals trends characterized by multistakeholder participation, an increasing emphasis on technology integration, and increased efforts toward talent retention. Overall, the results of the PMC model analysis indicate that the scores for return-to-hometown entrepreneurship policies across 16 provinces (municipalities and autonomous regions) are relatively high in terms of the indices of policy field, policy content, and policy tools, whereas the scores are comparatively low in terms of the indices of policy nature and policy timeliness. The remaining indices present moderate scores with slight differences. When examining the average scores between the eastern and central-western regions, it is observed that there is only a very slight difference in the index of policy timeliness and policy implementation. Additionally, the performance of eastern regions is superior in terms of the index of policy issuer, policy functions, and policy content. However, central-western regions demonstrate higher scores in the indices of policy nature, policy field, policy tools, and policy evaluation.
Toward Sustainable Fisheries: Assessing Catch per Unit Effort, Retained Bycatch, and Discard Ratios in the Red Sea Shrimp Trawl Fishery of the Kingdom of Saudi Arabia
Authors: Kadengal Sirajudheen Thayyil , Ceyhan Tevfik , Tosunoğlu Zafer , Gireesh Sheeja , Charles Santhosh Kumar , Santucci Ronald Grech , Adam Adel M. S. , Tıraşın Eyüp Mümtaz , Ünal Vahdet , Dimech Mark
Published: November 2024

Shrimp trawl fishery is of significant importance in Saudi Arabia’s marine capture fishery sector. Al Qunfudhah and Jizan are the only ports along the southern Saudi Arabian coast of the Red Sea dedicated to shrimp fishing. This study evaluates the catch per unit effort (CPUE) of targeted shrimp species along with the associated retained bycatch and discard ratios across these two trawling locations. Using daily catch data from commercial shrimp trawlers specific to each port’s vessel type, this research provides the first comprehensive analysis of CPUE dynamics and fishing practices in these fishing grounds. The CPUE of target shrimps exhibited a wide range (0.01 kg h−1 to 13.08 kg h−1) with an overall mean of 1.37 ± 0.143 kg h−1. Penaeus semisulcatus emerged as the predominant species (mean CPUE: 3.77 ± 0.28 kg h−1), followed by Metapenaeus monoceros (mean CPUE: 0.1 ± 0.02 kg h−1). Significant differences in CPUE were observed among species, including P. semisulcatus, Penaeus hathor, and Penaeus pulchricaudatus, although CPUE differences between the two fishing grounds were minor, likely due to their proximity and similar environmental conditions. The bycatch analysis identified a total of 72 species across 28 families, with significant variation in the relative ratios. Sepiidae species constituted 31.8% of the total bycatch, followed by Portunidae species (24.8%). Discard patterns also varied, with Gerres oyena being the most commonly discarded species (mean discard ratio: 11.37% ± 1.89%), followed by Equulites klunzingeri (mean discard ratio: 8.76% ± 2.00%). While CPUE differences between Al Qunfudhah and Jizan were modest, this study highlights the need for expanded spatial and temporal coverage to enhance future analyses. These findings underscore the importance of integrated data for a comprehensive understanding of fishing effort and bycatch dynamics, supporting sustainable fisheries management in the Red Sea. By addressing bycatch and discard practices, this research contributes to efforts to mitigate ecological impacts and promote sustainable resource use in Saudi Arabia’s fisheries sector.
Preparation of Lignin-Based Slow-Release Nitrogen Fertilizer
Authors: Zhang Yiru , Jiao Gaojie , Wang Jian , She Diao
Published: November 2024

Slow-release nitrogen fertilizer technology is essential for sustainable agriculture, reducing field pollution and enhancing fertilizer efficiency. Lignin, a natural polymer derived from agricultural and forestry waste, offers unique benefits for slow-release fertilizers due to its biocompatibility, biodegradability and low cost. Unlike conventional biochar-based fertilizers that often rely on simple pyrolysis, this study employs hydrothermal activation to create a lignin-based slow-release nitrogen fertilizer (LSRF) with enhanced nutrient retention and controlled release capabilities. By incorporating porous carbon derived from industrial alkaline lignin, this LSRF not only improves soil fertility, but also reduces nitrogen loss and environmental contamination, addressing key limitations in existing fertilizer technologies. We studied the hydrothermal carbonization and chemical activation of IAL, optimizing the conditions for producing LSRF by adjusting the ratios of PC, IAL and urea. Using BET, SEM and FT-IR analyses, we characterized the PC, finding a high specific surface area of 1935.5 m2/g. A selected PC sample with 1923.51 m2/g surface area and 0.82 cm3/g pore volume and yield (37.59%) was combined with urea via extrusion granulation to create the LSRF product. Soil column leaching experiments showed that LSRF effectively controls nutrient release, reducing nitrogen loss and groundwater contamination, ensuring long-term crop nutrition. This research demonstrates LSRF’s potential in improving fertilizer efficiency and promoting sustainable agriculture globally.
What Configurational Paths Enhance the High-Quality Construction of Cold Region Rural Landscapes? A Fuzzy-Set Qualitative Comparative Analysis of 66 Villages in Heilongjiang Province
Authors: Meng Jie , Yuan Qing , Leng Hong , Yan Tianjiao , Kong Fanqiu , Anwar Ayesha
Published: November 2024

This study addresses the construction of high-quality rural landscapes, crucial for China’s rural revitalization strategy, encompassing economic, social, cultural, and ecological dimensions. Focusing on 66 cold-region villages in Heilongjiang Province, it develops a dual-dimensional quality evaluation system that integrates both objective data and subjective perception indicators. It employs the entropy weight TOPSIS model to evaluate and grade the quality of rural landscapes and uses fuzzy-set Qualitative Comparative Analysis (fsQCA) to analyze the complex causal relationships influencing high-quality rural landscapes. The results show that (1) The TOPSIS model identifies four grades of rural landscape quality: “Excellent-Good-Average-Poor”, with “Excellent and Good” grades defined as high-quality rural landscape. (2) The fsQCA reveals eight configuration paths that influence high-quality rural landscapes, which are categorized into four models: natural ecology, efficient industry, cultural heritage, and comprehensive development. The main contribution of this study lies in its systematic analysis of the complex causal relationships affecting rural landscape quality, providing a theoretical and technological foundation for guiding the sustainable development of cold-region rural landscapes within the framework of rural revitalization strategy in China.
Guardians of the Green: Exploring Climate Advocacy, Data Privacy-Conscious Marketing, and Social Moral Licensing in Regenerative Tourism in Hawaii
Authors: Zaman Umer
Published: November 2024

While environmental consciousness has gained global momentum, the influence of climate advocacy on consumer behavior within tourism has rarely been examined. The present study investigated the impact of climate advocacy on regenerative tourism intentions while examining the moderating effects of data privacy-conscious marketing and social moral licensing. Drawing on theoretical frameworks focusing on social psychology and digital marketing ethics, the present study employed a survey-based quantitative approach to collect online data from environmentally conscious tourists in Hawaii (N = 526). The findings highlighted that the positive impact of climate advocacy on regenerative tourism intention is significantly reinforced by data privacy-conscious marketing and social moral licensing. The present study extends theoretical implications by underscoring the integration of behavior change theories in understanding consumer decision-making processes in regenerative tourism. Importantly, the practical implications of this study advocate for ethical digital marketing strategies and climate advocacy initiatives to support regenerative tourism practices. Hence, the present study offers ground-breaking evidence on the nuanced interplay between climate advocacy, privacy-conscious marketing, social moral licensing, and regenerative tourism intention. These strategic insights can aid tourism marketers and destination managers in fostering a more environmentally responsible tourism industry amidst global environmental challenges.
Biofuels and Their Blends—A Review of the Effect of Low Carbon Fuels on Engine Performance
Authors: Xiong Qian , Duan Yulong , Liang Dezhi , Li Tie , Luo Hongliang , Chen Run
Published: November 2024

Energy is an important aspect concerning global economic development and environmental conservation. Economic growth has been accompanied by extensive use of fossil fuels, resulting in significant emissions of greenhouse gases and other pollutants. Therefore, researchers have turned their attention to low/zero carbon fuels. Among these, biofuels have attracted wide attention due to their relatively low cost, clean combustion products and renewability. This article reviews the combustion, performance and emission characteristics of internal combustion (IC) engines fueled with biofuels categorized into three generations by their raw material sources. According to most research findings, biofuels generally exhibit poorer combustion performance in IC engines compared to fossil fuels due to their high viscosity and low lower heating value. However, these biofuels, characterized by a high oxygen content, facilitate more complete combustion and reduce emissions of CO, UHC and smoke, albeit increasing NOx emission and fuel consumption. Both thermal efficiency and brake power also tend to decrease, but various optimization strategies such as advanced combustion modes or injection control methods can partially compensate for these drawbacks. In conclusion, biofuels should be a promising low-carbon fuel for IC engines in the future.
Advancing Sustainability in Urban Planning by Measuring and Matching the Supply and Demand of Urban Green Space Ecosystem Services
Authors: Li Man , Chen Tao , Li Juan , Yang Guangbin , Zhao Linglin , Cao Qiangyang , Yang Li , Sun Yang
Published: November 2024

As global cities rapidly develop, the realization of sustainable urban development has become a pressing global concern. As a critical component of the urban built environment, urban green spaces are essential for maintaining the sustainability of urban ecosystems by offering a variety of ecological benefits. However, there are some problems in the design and construction of urban green framework, which often disregard the real needs of residents and hinder the sustainable development of urban areas. Therefore, urban planning that takes into account the supply and demand relationship of urban green space is considered to be a key technology that should help to improve residents’ happiness and promote urban sustainable development. This paper takes Zunyi City as an example; selects six indicators; combines UAV images, population, economic, and environmental data to evaluate ecosystem services valuation existing green space in the city; and analyzes its social and ecological needs. Affected by land utilization and development density, the supply value showed low spatial density characteristics in the central urban area and high spatial distribution characteristics in the surrounding urban area. The correlation between demand value and population density is the largest, and the distribution pattern is similar to that of population density. The supply-demand ratio in the study area is −0.54, indicating a severe imbalance between supply and demand. The correlation analysis between the comprehensive supply and demand ratio and each influencing factor shows that the correlation with population density is the largest. The combination of supply and demand maps addresses a major problem in planning, namely, ensuring that communities have priority in providing urban green space based on society’s need for ecosystem services. In addition, these maps will help develop the best green space planting plans to meet the specific needs of different communities. Taken together, the findings can help city planners and decision makers improve the layout of urban green areas, increase ecosystem service potential, and improve urban environmental quality and sustainability.
From Mine Waste to Construction Materials: A Bibliometric Analysis of Mining Waste Recovery and Tailing Utilization in Construction
Authors: Zetola Vicente , Keith Brian F. , Lam Elizabeth J. , Montofré Ítalo L. , Rojas Rodrigo J. , Marín Juan , Becerra Mathias
Published: November 2024

This article presents a comprehensive scientometric analysis of mining waste valorization, focusing on tailings utilization in construction materials from 2010 to 2024. Through examination of 1096 Web of Science publications and utilizing CiteSpace mapping and network analyses, we analyze the intellectual structure of this field. Subject category analysis reveals materials science, construction technology, and environmental engineering as the dominant disciplines, interconnected through 168 links across 64 thematic nodes. Our co-citation analysis identifies 12 major research clusters, with materials science and environmental engineering serving as primary disciplinary pillars. Keyword co-occurrence analysis of 532 nodes connected by 1181 links highlights the field’s emphasis on fly ash, concrete applications, and mechanical properties. Recent citation bursts indicate growing research focus on thermal stability, heavy metal treatment, and innovative processing methods. Through synthesizing these scientometric indicators, this review provides strategic insights for advancing sustainable construction practices through mining waste utilization. Research gaps identified include long-term durability assessment, standardization needs, and scalability challenges. By synthesizing these diverse scientometric indicators, this review provides strategic insights for researchers, industry practitioners, and policymakers, contributing to the advancement of sustainable construction practices through mining waste utilization.
Parametrized Half-Hyperbolic Tangent Function-Activated Complex-Valued Neural Network Approximation
Authors: Anastassiou George A. , Karateke Seda
Published: November 2024

In this paper, we create a family of neural network (NN) operators employing a parametrized and deformed half-hyperbolic tangent function as an activation function and a density function produced by the same activation function. Moreover, we consider the univariate quantitative approximations by complex-valued neural network (NN) operators of complex-valued functions on a compact domain. Pointwise and uniform convergence results on Banach spaces are acquired through trigonometric, hyperbolic, and hybrid-type hyperbolic–trigonometric approaches.
Finite-Time Control for Automatic Berthing of Pod-Driven Unmanned Surface Vessel with an Event-Triggering Mechanism
Authors: Piao Zaiji , Sun Shuang , Chen Yiling , Ju Moran
Published: November 2024

Coastal water accidents have occurred frequently in recent years, and human factors are still the main cause of these accidents. The purpose of this study is to provide a better and safer solution for the symmetry problem of unmanned surface vessels during automatic berthing in coastal waters. In automatic berthing, the symmetry problem refers to whether the USVs can maintain a stable state during motion and positioning, including dynamic symmetry and environmental symmetry. A finite-time controller based on a global nonsingular terminal sliding mode is designed to improve response speed and state consistency. Dynamic uncertainty and disturbance are considered in the design process, which can optimize the control law and effectively ensure the symmetry of the vessel in different states. Then, an event-triggering mechanism based on a dynamic threshold is adopted. In practice, there is excessive operation of the actuator. This mechanism ensures that the actuator is triggered only when the threshold is reached. USVs can adaptively adjust control strategies based on the real-time status, thereby improving symmetry during berthing. In simulation analysis, a pod-driven unmanned surface vessel with good maneuverability is taken as the research object. The results indicate that this control strategy can ensure rapid and consistent response of the vessel when subjected to external disturbances, which helps to maintain the symmetry of the automatic berthing motion under different conditions.