bluuebunny/crossref_metadata_2025 huggingface dataset:
Low-Temperature Chlorination-Roasting–Acid-Leaching Uranium Process of Uranium Tailings: Comparison Between Microwave Roasting and Conventional Roasting
Authors: Hu Jinming , Song Jianwei , Hu Tu , Zhang Libo , Wang Yue , Zou Fa
Published: January 2025

A new and efficient low-temperature chlorination-roasting–acid-leaching uranium process was proposed to solve the problems of low leaching efficiency, since the leaching residue does not meet the discharge standard in the traditional nitric acid leaching-uranium tailings process, compared with conventional chlorination roasting. XRD, SEM, particle size analysis, and other analytical methods were used to characterize and analyze the phase transformation and structural changes in the roasting process of uranium-containing tailings. An investigation was conducted to elucidate the influence of roasting temperature, NaCl addition, and roasting time on the leaching efficiency of uranium. Compared with conventional chlorination roasting, microwave chlorination roasting can effectively destroy the gangue mineral structure of dissolved slag; the surface cracks of uranium tailings increase, and the particle size is smaller, so that the uranium element is fully exposed, thereby improving the leaching effect. Because microwave heating has the characteristics of selective heating and rapid heating, when the microwave-roasting power is 2000 W, the sample only needs 12 min to be heated to optimal roasting temperature, which is 8 min shorter than the conventional heating time, and the leaching rate of uranium is further improved. In the microwave roasting experiment, the roasting temperature is set to 250 °C, roasting time is 90 min, and addition amount of NaCl is 25 wt % of the tailings mass. Under the optimal roasting conditions, the leaching rate of uranium is 94.84%.
Mild Temperature Conditions Applied to Carrot (Daucus carota L.) Waste Using Different Drying Methods: Effect on the Kinetics and Some Chemical Parameters
Authors: Vega-Galvez Antonio , Orellana-Palma Patricio , Pasten Alexis , Uribe Elsa , Cortes Daniela , Carvajal Manuel
Published: January 2025

The effects of different mild drying conditions using various drying methods [freeze drying (FD), vacuum drying at 15 kPa pressure (VD15), vacuum drying at 1 kPa pressure (VD1), convective drying (CD), and infrared drying (IRD)] on drying kinetics, proximate composition, yield of extracted pectin, methoxyl content, sugar content, total carotenoids content, antioxidant potential, and color parameters of carrot wastes were examined experimentally. CD was the shortest drying treatment compared to the other drying processes, at 270 min, followed by IRD, VD1, FD, and VD15. The results showed a higher retention of pectin and carotenoids in CD-dried samples. Moreover, along with VD1, CD was able to maintain sucrose and antioxidant potential to a greater extent than other methods. Based on color parameters, FD and IRD had the most significant changes in relation to CIELab values, with ∆E* values close to 33 and 34 units, whereas VD15, VD1, and CD had values (without significant differences) close to 16, 18, and 21 units, respectively. Therefore, the current findings suggest that a short period of exposure of the waste to mild drying temperature conditions is essential for obtaining high-quality waste with potential for use in the food industry.
Multi-Domain Assessment of Thermomechanical Recycling Based on Bio-Based and Petroleum-Based Additively Manufactured Components
Authors: Nagengast Niko , Mandel Nicolas , Bay Christian , Döpper Frank , Neuber Christian , Schmidt Hans-Werner , Usma-Mansfield Clara , Fuss Franz Konstantin
Published: January 2025

The surge in global population growth and the escalating demand for social and economic prosperity present formidable challenges in the 21st century. However, asserting the sustainability of some ecological impact reduction initiatives, such as recycling, requires a comprehensive evaluation within various domains, including performance, ecology, and economics, and contemporary advancements in integrating quantitative assessments of material and manufacturing properties, coupled with mathematical decision-making approaches, contribute to mitigating subjectivity in determining the efficiency of recycling. This paper implements a robust multi-criteria decision-making (MCDM) approach to address the complexities of recycling, validating its implementation and effectiveness through a case study. The focus is set on the application of bio-based polylactic acid (PLA) and petroleum-based polypropylene (PP) additively manufactured (AM) parts produced through Fused Filament Fabrication (an approach to ecology/performance domains). The work introduces a cost analysis focusing on calculating thermomechanical recycling within the economic domain. The well-known Analytical Hierarchical Process (AHP) provides a structured framework for decision-making (the ecological impact domain) with the focus being on application. The assessment or recycling viability, encompassing AHP calculations, preprocessing, and supplementary tools, is provided by developing an open-source software tool for practitioners in the field of material science and manufacturing. The results indicate a preference for industrial-scaled recycling over virgin or lab-recycled manufacturing, particularly for petroleum-based polypropylene. The versatility and simple utilization of the software tool allow seamless integration for diverse use cases involving different materials and processes.
Ensemble Learning for Urban Flood Segmentation Through the Fusion of Multi-Spectral Satellite Data with Water Spectral Indices Using Row-Wise Cross Attention
Authors: Xu Han , Woodley Alan
Published: January 2025

In post-flood disaster analysis, accurate flood mapping in complex riverine urban areas is critical for effective flood risk management. Recent studies have explored the use of water-related spectral indices derived from satellite imagery combined with machine learning (ML) models to achieve this purpose. However, relying solely on spectral indices can lead these models to overlook crucial urban contextual features, making it difficult to distinguish inundated areas from other similar features like shadows or wet roads. To address this, our research explores a novel approach to improve flood segmentation by integrating a row-wise cross attention (CA) module with ML ensemble learning. We apply this method to the analysis of the Brisbane Floods of 2022, utilizing 4-band satellite imagery from PlanetScope and derived spectral indices. Applied as a pre-processing step, the CA module fuses a spectral band index into each band of a peak-flood satellite image using a row-wise operation. This process amplifies subtle differences between floodwater and other urban characteristics while preserving complete landscape information. The CA-fused datasets are then fed into our proposed ensemble model, which is constructed using four classic ML models. A soft voting strategy averages their binary predictions to determine the final classification for each pixel. Our research demonstrates that CA datasets can enhance the sensitivity of individual ML models to floodwater in complex riverine urban areas, generally improving flood mapping accuracy. The experimental results reveal that the ensemble model achieves high accuracy (approaching 100%) on each CA dataset. However, this may be affected by overfitting, which indicates that evaluating the model on additional datasets may lead to reduced accuracy. This study encourages further research to optimize the model and validate its generalizability in various urban contexts.
Multi-Spectral Point Cloud Constructed with Advanced UAV Technique for Anisotropic Reflectance Analysis of Maize Leaves
Authors: Bi Kaiyi , Niu Yifang , Yang Hao , Niu Zheng , Hao Yishuo , Wang Li
Published: January 2025

Reflectance anisotropy in remote sensing images can complicate the interpretation of spectral signature, and extracting precise structural information under these pixels is a promising approach. Low-altitude unmanned aerial vehicle (UAV) systems can capture high-resolution imagery even to centimeter-level detail, potentially simplifying the characterization of leaf anisotropic reflectance. We proposed a novel maize point cloud generation method that combines an advanced UAV cross-circling oblique (CCO) photography route with the Structure from the Motion-Multi-View Stereo (SfM-MVS) algorithm. A multi-spectral point cloud was then generated by fusing multi-spectral imagery with the point cloud using a DSM-based approach. The Rahman–Pinty–Verstraete (RPV) model was finally applied to establish maize leaf-level anisotropic reflectance models. Our results indicated a high degree of similarity between measured and estimated maize structural parameters (R2 = 0.89 for leaf length and 0.96 for plant height) based on accurate point cloud data obtained from the CCO route. Most data points clustered around the principal plane due to a constant angle between the sun and view vectors, resulting in a limited range of view azimuths. Leaf reflectance anisotropy was characterized by the RPV model with R2 ranging from 0.38 to 0.75 for five wavelength bands. These findings hold significant promise for promoting the decoupling of plant structural information and leaf optical characteristics within remote sensing data.
Precision Analysis of Multi-Parameter Multi-Epoch Emitter Localization Radar in Three-Satellite Formation
Authors: Lian Yiming , Wu Yuxuan , Chen Yaowen , Liu Xian , Jiang Liming
Published: January 2025

Emitter localization offers significant advantages such as high concealment, long detection range, and low cost, making it indispensable in target positioning. The utilization of low earth orbit satellite formation with AOA (Angle of Arrival) and TDOA (Time Difference of Arrival) measurements is a key technology for achieving emitter localization. To address the issues of requiring numerous cooperative platforms and the poor accuracy of single-epoch solutions with single-parameter closed-form algorithms, this paper proposes a multi-parameter multi-epoch positioning method based on a three-satellite formation. Simulation data are used to analyze the positioning accuracy under various epochs and different TDOA and AOA noise conditions. The experimental results demonstrate that, compared to the traditional single-parameter single-epoch localization method, utilizing a three-satellite formation with combined AOA and TDOA parameters, along with a multi-epoch solution approach, significantly improves localization accuracy to within an order of ten meters. This method enhances robustness and provides a viable strategy for addressing localization challenges caused by underdetermined systems of equations. Additionally, the results verify that an accumulated almanac element duration of 20 s ensures high positioning accuracy while maintaining a low computational cost. The combined multi-parameter multi-epoch method shows substantial advantages in improving both accuracy and robustness, providing valuable insights for future satellite-based emitter localization technologies.
Detection of Invasive Species (Siam Weed) Using Drone-Based Imaging and YOLO Deep Learning Model
Authors: Gautam Deepak , Mawardi Zulfadli , Elliott Louis , Loewensteiner David , Whiteside Timothy , Brooks Simon
Published: January 2025

This study explores the efficacy of drone-acquired RGB images and the YOLO model in detecting the invasive species Siam weed (Chromolaena odorata) in natural environments. Siam weed is a perennial scrambling shrub from tropical and sub-tropical America that is invasive outside its native range, causing substantial environmental and economic impacts across Asia, Africa, and Oceania. First detected in Australia in northern Queensland in 1994 and later in the Northern Territory in 2019, there is an urgent need to determine the extent of its incursion across vast, rugged areas of both jurisdictions and a need for distribution mapping at a catchment scale. This study tests drone-based RGB imaging to train a deep learning model that contributes to the goal of surveying non-native vegetation at a catchment scale. We specifically examined the effects of input training images, solar illumination, and model complexity on the model’s detection performance and investigated the sources of false positives. Drone-based RGB images were acquired from four sites in the Townsville region of Queensland to train and test a deep learning model (YOLOv5). Validation was performed through expert visual interpretation of the detection results in image tiles. The YOLOv5 model demonstrated over 0.85 in its F1-Score, which improved to over 0.95 with improved exposure to the images. A reliable detection model was found to be sufficiently trained with approximately 1000 image tiles, with additional images offering marginal improvement. Increased model complexity did not notably enhance model performance, indicating that a smaller model was adequate. False positives often originated from foliage and bark under high solar illumination, and low exposure images reduced these errors considerably. The study demonstrates the feasibility of using YOLO models to detect invasive species in natural landscapes, providing a safe alternative to the current method involving human spotters in helicopters. Future research will focus on developing tools to merge duplicates, gather georeference data, and report detections from large image datasets more efficiently, providing valuable insights for practical applications in environmental management at the catchment scale.
Characterising the Thematic Content of Image Pixels with Topologically Structured Clustering
Authors: Foody Giles M.
Published: January 2025

The location of a pixel in feature space is a function of its thematic composition. The latter is central to an image classification analysis, notably as an input (e.g., training data for a supervised classifier) and/or an output (e.g., predicted class label). Whether as an input to or output from a classification, little if any information beyond a class label is typically available for a pixel. The Kohonen self-organising feature map (SOFM) neural network however offers a means to both cluster together spectrally similar pixels that can be allocated suitable class labels and indicate relative thematic similarity of the clusters generated. Here, the thematic composition of pixels allocated to clusters represented by individual SOFM output units was explored with two remotely sensed data sets. It is shown that much of the spectral information of the input image data is maintained in the production of the SOFM output. This output provides a topologically structured representation of the image data, allowing spectrally similar pixels to be grouped together and the similarity of different clusters to be assessed. In particular, it is shown that the thematic composition of both pure and mixed pixels can be characterised by a SOFM. The location of the output unit in the output layer of the SOFM associated with a pixel conveys information on its thematic composition. Pixels in spatially close output units are more similar spectrally and thematically than those in more distant units. This situation also enables specific sub-areas of interest in the SOFM output space and/or feature space to be identified. This may, for example, provide a means to target efforts in training data acquisition for supervised classification as the most useful training cases may have a tendency to lie within specific sub-areas of feature space.
First Retrieval of Sea Surface Currents Using L-Band SAR in Satellite Formation
Authors: Pan Bo , Yuan Xinzhe , Li Tao , Lai Tao , Wang Xiaoqing , Xu Chengji , Huang Haifeng
Published: January 2025

The inversion of ocean currents is a significant challenge and area of interest in ocean remote sensing. Spaceborne along-track interferometric synthetic aperture radar (ATI-SAR) has several advantages and benefits, including precise observations, extensive swath coverage, and high resolution. However, a limited number of spaceborne interferometric synthetic aperture radar (InSAR) systems are operating in orbit. Among these, the along-track baseline length is generally suboptimal, resulting in low inversion accuracy and difficulty in achieving operational stability. One of the approaches involves employing lower-frequency bands such as the L band to increase the baseline length to achieve the optimal baseline for a satellite formation. The LuTan-1 mission, the world’s first L-band distributed spaceborne InSAR system, was successfully launched on 27 February 2022. L-band distributed formation operation provides insight into the development of future spaceborne ATI systems with application to new exploration regimes and under optimal baseline conditions. There are two novel aspects of this investigation: (1) We described the ocean current inversion process and results based on LuTan-1 SAR data for the first time. (2) A cross-track baseline component phase removal method based on parameterized modeling was proposed for distributed InSAR systems. Both qualitative and quantitative comparisons validated the effectiveness and accuracy of the inversion results.
An Autonomous Positioning Method for Drones in GNSS Denial Scenarios Driven by Real-Scene 3D Models
Authors: Cui Yongqiang , Gao Xue , Yu Rui , Chen Xi , Wang Dingwen , Bai Di
Published: January 2025

Drones are extensively utilized in both military and social development processes. Eliminating the reliance of drone positioning systems on GNSS and enhancing the accuracy of the positioning systems is of significant research value. This paper presents a novel approach that employs a real-scene 3D model and image point cloud reconstruction technology for the autonomous positioning of drones and attains high positioning accuracy. Firstly, the real-scene 3D model constructed in this paper is segmented in accordance with the predetermined format to obtain the image dataset and the 3D point cloud dataset. Subsequently, real-time image capture is performed using the monocular camera mounted on the drone, followed by a preliminary position estimation conducted through image matching algorithms and subsequent 3D point cloud reconstruction utilizing the acquired images. Next, the corresponding real-scene 3D point cloud data within the point cloud dataset is extracted in accordance with the image-matching results. Finally, the point cloud data obtained through image reconstruction is matched with the 3D point cloud of the real scene, and the positioning coordinates of the drone are acquired by applying the pose estimation algorithm. The experimental results demonstrate that the proposed approach in this paper enables precise autonomous positioning of drones in complex urban environments, achieving a remarkable positioning accuracy of up to 0.4 m.
Analysis of Autonomous Penetration Testing Through Reinforcement Learning and Recommender Systems
Authors: Moreno Ariadna Claudia , Hernandez-Suarez Aldo , Sanchez-Perez Gabriel , Toscano-Medina Linda Karina , Perez-Meana Hector , Portillo-Portillo Jose , Olivares-Mercado Jesus , García Villalba Luis Javier
Published: January 2025

Conducting penetration testing (pentesting) in cybersecurity is a crucial turning point for identifying vulnerabilities within the framework of Information Technology (IT), where real malicious offensive behavior is simulated to identify potential weaknesses and strengthen preventive controls. Given the complexity of the tests, time constraints, and the specialized level of expertise required for pentesting, analysis and exploitation tools are commonly used. Although useful, these tools often introduce uncertainty in findings, resulting in high rates of false positives. To enhance the effectiveness of these tests, Machine Learning (ML) has been integrated, showing significant potential for identifying anomalies across various security areas through detailed detection of underlying malicious patterns. However, pentesting environments are unpredictable and intricate, requiring analysts to make extensive efforts to understand, explore, and exploit them. This study considers these challenges, proposing a recommendation system based on a context-rich, vocabulary-aware transformer capable of processing questions related to the target environment and offering responses based on necessary pentest batteries evaluated by a Reinforcement Learning (RL) estimator. This RL component assesses optimal attack strategies based on previously learned data and dynamically explores additional attack vectors. The system achieved an F1 score and an Exact Match rate over 97.0%, demonstrating its accuracy and effectiveness in selecting relevant pentesting strategies.
End-to-End Multi-Scale Adaptive Remote Sensing Image Dehazing Network
Authors: Wang Xinhua , Yuan Botao , Dong Haoran , Hao Qiankun , Li Zhuang
Published: January 2025

Satellites frequently encounter atmospheric haze during imaging, leading to the loss of detailed information in remote sensing images and significantly compromising image quality. This detailed information is crucial for applications such as Earth observation and environmental monitoring. In response to the above issues, this paper proposes an end-to-end multi-scale adaptive feature extraction method for remote sensing image dehazing (MSD-Net). In our network model, we introduce a dilated convolution adaptive module to extract global and local detail features of remote sensing images. The design of this module can extract important image features at different scales. By expanding convolution, the receptive field is expanded to capture broader contextual information, thereby obtaining a more global feature representation. At the same time, a self-adaptive attention mechanism is also used, allowing the module to automatically adjust the size of its receptive field based on image content. In this way, important features suitable for different scales can be flexibly extracted to better adapt to the changes in details in remote sensing images. To fully utilize the features at different scales, we also adopted feature fusion technology. By fusing features from different scales and integrating information from different scales, more accurate and rich feature representations can be obtained. This process aids in retrieving lost detailed information from remote sensing images, thereby enhancing the overall image quality. A large number of experiments were conducted on the HRRSD and RICE datasets, and the results showed that our proposed method can better restore the original details and texture information of remote sensing images in the field of dehazing and is superior to current state-of-the-art methods.
Impact of Vanadium and Zirconium Contents on Properties of Novel Lightweight Ti3ZryNbVx Refractory High-Entropy Alloys
Authors: Al-Zoubi Noura , Almahmoud Amer , Obeidat Abdalla
Published: January 2025

This research explores the physical properties of refractory high-entropy alloys Ti3ZryNbVx (0.5 ≤ x ≤ 3.5; 1 ≤ y ≤ 2), utilizing the first-principles exact muffin-tin orbitals method, in addition to the coherent potential approximation. We examine the atomic size difference (δ), the valence electron concentration (VEC) and the total energy of the body-centered cubic (bcc), the face-centered cubic (fcc) and the hexagonal close-packed (hcp) lattices, revealing a disordered solid solution with a bcc lattice as the stable phase of these alloys. The stability of the bcc Ti3ZryNbVx alloys increases with the addition of vanadium, and slightly decreases with increasing Zr concentration. All the investigated RHEAs have densities less than 6.2 g/cm3. Adding V to the Ti-Zr-Nb-V system reduces the volume and slightly enhances the density of the studied alloys. Our results show that increasing V content increases the tetragonal shear modulus C′, which assures that V enhances the mechanical stability of the bcc phase, and also increases the elastic moduli. Moreover, all the examined alloys are ductile. Vickers hardness and bond strength increase as V concentration increases. In contrast, decreasing Zr content reduces the density and increases the hardness and the bond strength of the present RHEAs, potentially resulting in systems with desirable mechanical properties and lower densities. These findings provide theoretical insights into the behavior of RHEAs, and emphasize the necessity for additional experimental investigations.
Validating Questionnaires for Lower Limb Rehabilitation Systems and Devices: A Scoping Review
Authors: Moscoso Angie D. , Pérez Vera Z. , Betancur Manuel J.
Published: January 2025

This article aims to make a scoping review of Validating Questionnaires used in the field of lower limb (LL) rehabilitation in which systems, devices or exergames are used. Its main objective is to provide a more comprehensive understanding of the results obtained in the validation of questionnaires, as well as to identify specific criteria for evaluating systems, devices or exergames in the area of LL rehabilitation, through the analysis of validating instruments and their application in different associated contexts. The article details the methodology employed, a PRISMA ScR method review which included database research and an evaluation of the selected studies. Inclusion and exclusion criteria were applied to select all relevant studies, resulting in 81 studies after initial review based on titles and abstracts. Subsequently, the criteria were again applied to read the full text, resulting in 58 final studies. The document distinguishes between standardized and non-standardized validating questionnaires, emphasizing that standardized validating questionnaires have undergone rigorous statistical processes to ensure their validity, reliability and consistency. The information compiled in the tables provides a solid basis for identifying and evaluating validation questionnaires in the above-mentioned context. This resource constitutes an accurate and reliable reference for selecting the most appropriate instruments for future research and comparisons with similar work. This article is a valuable resource for those interested in the validation of questionnaires used in the field of lower limb rehabilitation systems/devices/exergames.
Machine Learning Approaches for Predicting Maize Biomass Yield: Leveraging Feature Engineering and Comprehensive Data Integration
Authors: Abbasi Maryam , Váz Paulo , Silva José , Martins Pedro
Published: January 2025

The efficient prediction of corn biomass yield is critical for optimizing crop production and addressing global challenges in sustainable agriculture and renewable energy. This study employs advanced machine learning techniques, including Gradient Boosting Machines (GBMs), Random Forests (RFs), Support Vector Machines (SVMs), and Artificial Neural Networks (ANNs), integrated with comprehensive environmental, soil, and crop management data from key agricultural regions in the United States. A novel framework combines feature engineering, such as the creation of a Soil Fertility Index (SFI) and Growing Degree Days (GDDs), and the incorporation of interaction terms to address complex non-linear relationships between input variables and biomass yield. We conduct extensive sensitivity analysis and employ SHAP (SHapley Additive exPlanations) values to enhance model interpretability, identifying SFI, GDDs, and cumulative rainfall as the most influential features driving yield outcomes. Our findings highlight significant synergies among these variables, emphasizing their critical role in rural environmental governance and precision agriculture. Furthermore, an ensemble approach combining GBMs, RFs, and ANNs outperformed individual models, achieving an RMSE of 0.80 t/ha and R2 of 0.89. These results underscore the potential of hybrid modeling for real-world applications in sustainable farming practices. Addressing the concerns of passive farmer participation, we propose targeted incentives, education, and institutional support mechanisms to enhance stakeholder collaboration in rural environmental governance. While the models assume rational decision-making, the inclusion of cultural and political factors warrants further investigation to improve the robustness of the framework. Additionally, a map of the study region and improved visualizations of feature importance enhance the clarity and relevance of our findings. This research contributes to the growing body of knowledge on predictive modeling in agriculture, combining theoretical rigor with practical insights to support policymakers and stakeholders in optimizing resource use and addressing environmental challenges. By improving the interpretability and applicability of machine learning models, this study provides actionable strategies for enhancing crop yield predictions and advancing rural environmental governance.
Confronting Public Health Challenges in a World on the Brink
Authors: Goniewicz Krzysztof
Published: January 2025

As we look around the globe today, it is impossible to ignore the mounting crises that threaten our very existence [...]
Research on Increasing the Building’s Energy Efficiency by Using the Ground Beneath It for Thermo-Accumulation
Authors: Zdankus Tadas , Bandarwadkar Sandeep , Vaiciunas Juozas , Stelmokaitis Gediminas , Vaicaitis Arnas
Published: January 2025

A whole series of factors influence the temperature of the soil surface and surface layers. The soil surface is heated by solar radiation during the day. It radiates some of the obtained heat at night. The heat exchange between the soil and the atmosphere depends on the air and soil temperatures and the speed of air movement. Precipitation may also affect surface soil layers, but this was not considered in this study. In the mentioned interaction, a specific temperature field of the surface layers of the soil is established. To increase the building’s energy efficiency, the aim is to optimize the operation of its heating and cooling systems and to reduce heat loss to the environment as much as possible. Heat loss through the floor of the building or the walls of the recessed part into the ground changes the established temperature field of the ground. The heat spreads in the soil and is given to the atmospheric air. During the research, to validate the numerical model, the heat flow density was analysed to determine how it changes while maintaining a constant temperature of the heating surface at a certain depth of the soil. It was found that the new thermodynamic equilibrium, depending on the seasonality, can be reached in a time interval of up to a week. The temperature change in the artificially limited volume of the ground under the building or next to it can be treated as the work of the ground thermo-accumulator: its charge or discharge by heat. This makes it possible to reduce the annual energy costs of the building by more than ten percent.
Scheduling the Just-in-Time Delivery of Parts for Mixed-Model Assembly Lines Considering the Electrical Energy Consumption of an Automated Guided Vehicle Trolley
Authors: Peng Yunfang , Li Xuejiao , Liao Shiyu , Liu Wangchao , Xia Beixin
Published: January 2025

This paper studies the scheduling of just-in-time part delivery for a mixed-model assembly line with material supermarkets under an unfixed path. According to the problem description and related assumptions, a mixed-integer programming model with the goal of minimizing the line-side inventory and delivery energy consumption of automated guided vehicles (AGVs) was constructed. Based on the material demand calculated according to the assembly plan, the model decides the parts to be delivered, the schedule of each delivery round, and the route of the AGV trolley. For small-scale problems, the epsilon constraint method is used to solve the Pareto frontier accurately. For large-scale problems, an improved version of the NSGA-II (Non-dominated Sorting Genetic Algorithm II) is designed to solve the problem more efficiently. Numerical analyses of different scales demonstrated the efficiency of the proposed algorithm. Furthermore, a sensitivity analysis showed that using a smaller AGV capacity with more frequent delivery can achieve a lower inventory level but higher energy consumption. Therefore, enterprises need to choose reasonable delivery schemes according to the assembly line layout and station occupation to save assembly space and achieve the target of carbon emissions reduction.
Digital Economy and Low-Carbon Trade Competitiveness: A Multidimensional Analysis of China’s Manufacturing Sector
Authors: He Youshi , Wang Min , Yuan Chuang
Published: January 2025

This study examines the mechanism of the digital economy on low-carbon trade competitiveness in China’s manufacturing sector using panel data from 30 provinces from 2011 to 2022, employing dynamic panel and moderated threshold model. The findings indicate that the digital economy significantly enhances manufacturing low-carbon trade competitiveness. Although green patents contribute to the overall effect, they do not serve as the primary indirect pathway through which the digital economy impacts manufacturing low-carbon trade competitiveness. Among these patents, utility model patents demonstrate the strongest mediating effect, followed by invention patents, design patents, and green invention patents, while green utility model patents show a weaker and non-significant effect. Furthermore, the digital economy facilitates a low-carbon transition in energy consumption structures, indirectly boosting manufacturing low-carbon trade competitiveness. This study also uncovers heterogeneous moderating effects of environmental regulations: market-based and voluntary regulations positively moderate the relationship, while command-and-control regulations show non-significant moderation. Environmental regulation exhibits a ‘U-shaped’ non-linear moderating effect, transitioning from non-significant negative moderation below a threshold to significant positive moderation beyond a critical value.
Prospects of Attaining Thailand’s Carbon Neutrality Target Through Carbon Capture and Storage by Public Power Utility
Authors: Thepsaskul Waranya , Wongsapai Wongkot , Jaitiang Tassawan , Jaekhajad Panuwich
Published: January 2025

Thailand has committed to achieving carbon neutrality by 2050, targeting the power generation sector, which contributes 35% of the country’s CO2 emissions, as a critical area for intervention. This study explores the transition toward carbon neutrality in power generation, focusing on fossil-fuel-based plants, particularly lignite and natural gas, which remain central to Thailand’s electricity production. A key strategy adopted by the Electricity Generating Authority of Thailand (EGAT) is “Sink Co-creation”, which includes the deployment of Carbon Capture and Storage (CCS) technologies in existing and future lignite power plants, leveraging favorable storage conditions. Additionally, natural gas power plants exhibit significant CCS potential through source–sink matching mechanisms. This study finds that the total greenhouse gas (GHG) emission reductions from fossil-fuel-based power plants could reach 17.07 MtCO2. Of this total, lignite power plants are projected to achieve a reduction of 3.79 MtCO2 by 2036, while natural gas power plants are expected to contribute an additional 13.28 MtCO2 in reductions by 2050. However, the realization of these reductions faces significant challenges, including the high costs associated with CCS implementation and limited investor interest, underscoring the critical need for sustained government support and policy incentives to facilitate progress toward carbon neutrality.
Enhancement and Evolutionary Mechanism of Ethnic Rural Tourism Resilience Based on the Actor Network Theory: A Case Study of Hala New Village in Northeast China
Authors: Wang Ping , Tang Wei , Li Yue , Chen Xiaohong , Pan Wei , Wang Ying , Li Qicai , Tian Chen
Published: January 2025

In the context of global climate change and urbanization, enhancing the resilience of rural tourism is crucial for achieving rural revitalization and fostering sustainable development. This study uses Hala New Village in Heilongjiang Province, China, as a case study to analyze the dynamic stages of tourism resilience development and its evolutionary mechanisms, applying the Actor Network Theory to provide strategies for enhancing rural tourism resilience. The study finds the following: (1) The evolution of tourism resilience in Hala New Village has progressed through three phases: the budding and vulnerability phase, the groping and resistance phase, and the optimization and adaptation phase. (2) The turnover of key actors, the change of actor types, the complexity of translation processes, and the integration and innovation of business models are the surface mechanisms driving the evolution of rural tourism resilience. (3) The deep mechanisms underlying this evolution are shaped by the synergistic effects of various internal and external factors, such as the national rural development strategy, environmental emergencies, market-driven changes in tourist demand following COVID-19, external capital interventions, and the inherent potential of Hala New Village’s natural and cultural environment. This study not only advances theoretical research on rural tourism resilience and actor networks but also offers practical insights for regions worldwide where rural tourism remains underdeveloped.
Evolution of Spatial Pattern and Configurational Path of Ecotourism Comfort in Chengdu City
Authors: Zhang Na , Yang Xia , Xu Yaowen , Zhao Wusheng
Published: January 2025

Ecotourism comfort is an important way to promote the harmonious coexistence between humans and nature, and it is the concrete implementation of the concept of harmonious coexistence and the theory of “Two Mountains”. Based on the urban comfort theory, the ecotourism comfort level of Chengdu city from 2010 to 2020 was measured. The MCR model and the gravity model were used to extract the potential corridor of ecotourism comfort to identify the core area of ecotourism comfort, and its configuration path was analyzed through a qualitative comparison of fsQCA fuzzy sets. The findings are as follows: (1) The overall level of ecotourism comfort in Chengdu has been improved, and the spatial distribution has a “core-edge” structure, which has obvious characteristics of “polarization leading and common development”. Among them, Wuhou District, Qingyang District, and Longquanyi District are the core areas, and the driving effect on marginal areas is particularly significant. (2) The potential ecological corridors in Chengdu are more densely distributed in the east than in the west, with significant north–south variations within both regions. (3) The core corridors of ecotourism comfort originate from Xinjin District and Longquanyi District, characterized by a banded distribution and primarily concentrated along the southwest–northeast axis. (4) The high level of ecotourism comfort in Chengdu is primarily manifested in five distinct types: “comprehensive drive, resource–transportation synergy, technology–transportation integration, government–led investment, and tourism market–technology linkage”. The research results offer a theoretical basis and scientific reference for enhancing the comfort level of urban ecotourism and comprehensively promoting the sustainable development of ecotourism.
How Do Multidimensional Relational Networks Affect Large-Scale Grain Producers’ Adoption of Low-Carbon Fertilization Technology?
Authors: Luo Xiaojuan , Ye Qingqing , Huang Xinzao , Zhao Bo , Liu Hongbin
Published: January 2025

Fertilizer carbon emissions contribute the largest proportion to agricultural carbon emissions in China, while the extension of low-carbon fertilization technologies (LCFTs) is an effective measure to address this issue. Research suggests that the relational networks surrounding farmers significantly influence their carbon reduction behavior. This study conducted a field survey of 239 large-scale grain producers in August 2022 on China’s Poyang Lake Basin, which is the nation’s largest freshwater lake and a vital agricultural production area. Using cross-sectional data, probit and ordered probit models were employed to analyze the impacts of multidimensional relational networks (market, government, and social networks) on the adoption of LCFTs by large-scale grain producers. Additionally, a mediating-effect model was used to examine the pathways through which relational networks influence LCFT adoption. The findings indicated that relational networks not only increased the likelihood of large-scale grain producers adopting LCFTs but also enhanced the intensity of adoption. However, the effects of different relational networks on low-carbon behavior varied. The market network exerted the most prominent influence on LCFT adoption, followed by the social and government networks. A mediation analysis identified information sharing, demonstration effects, and resource guarantees as the mediating pathways between multidimensional relational networks and LCFT adoption by large-scale grain producers. Furthermore, a heterogeneity analysis revealed that the effects of multidimensional relational networks on LCFT adoption differed across generations and carbon intensity levels. The impact was greater among older grain producers than the younger generation, and those in the high-carbon-intensity group exhibited a stronger incentive compared to the medium- and low-carbon-intensity groups.
Exploring Key Considerations for Artificial Intelligence Robots in Home Healthcare Using the Unified Theory of Acceptance and Use of Technology and the Fuzzy Analytical Hierarchy Process Method
Authors: Lin Keng-Yu , Chang Kuei-Hu , Lin Yu-Wen , Wu Mei-Jin
Published: January 2025

Most countries face declining birth rates and an aging population, which makes the persistent healthcare labor shortage a pressing challenge. Introducing artificial intelligence (AI) robots into home healthcare could help address these issues. Exploring the primary considerations for integrating AI robots in home healthcare has become an urgent topic. However, previous studies have not systematically examined the factors influencing elderly individuals’ adoption of home healthcare AI robots, hindering an understanding of their acceptance and adoption. Furthermore, traditional methods overlook the relative importance of each consideration and cannot manage the ambiguity inherent in subjective human cognition, potentially leading to biased decision-making. To address these limitations, this study employs the unified theory of acceptance and use of technology (UTAUT) as a theoretical framework, integrating the modified Delphi method (MDM) and the fuzzy analytical hierarchy process (FAHP) to identify the key considerations. The research determined the order of importance of four evaluation criteria and fourteen evaluation sub-criteria, revealing that customization, accompany, and subjective norms are key factors that influence elderly individuals’ adoption of home healthcare AI robots.
Optimization of Energy Consumption in Voice Assistants Through AI-Enabled Cache Implementation: Development and Evaluation of a Metric
Authors: Montoya Benitez Alber Oswaldo , Suárez Sarmiento Álvaro , López Elsa María Macías , Herrera-Ramirez Jorge
Published: January 2025

Intelligent systems developed under the Internet of Things (IoT) paradigm offer solutions for various social and productive scenarios. Voice assistants (VAs), as part of IoT-based systems, facilitate task execution in a simple and automated manner, from entertainment to critical activities. Lithium batteries often power these devices. However, their energy consumption can be high due to the need to remain in continuous listening mode and the time it takes to search for and deliver responses from the Internet. This work proposes the implementation of a VA through Artificial Intelligence (AI) training and using cache memory to minimize response time and reduce energy consumption. First, the difference in energy consumption between VAs in active and passive states is experimentally verified. Subsequently, a communication architecture and a model representing the behavior of VAs are presented, from which a metric is developed to evaluate the energy consumption of these devices. The cache-enabled prototype shows a reduction in response time and energy expenditure (comparing the results of cloud-based VA and cache-based VA), several times lower according to the developed metric, demonstrating the effectiveness of the proposed system. This development could be a viable solution for areas with limited power sources, low coverage, and mobility situations that affect internet connectivity.
Organic–Inorganic Hybrid Dielectric Layers for Low-Temperature Thin-Film Transistors Applications: Recent Developments and Perspectives
Authors: Meza-Arroyo Javier , Ramírez-Bon Rafael
Published: January 2025

This paper reviews the recent development of organic–inorganic hybrid dielectric materials for application as gate dielectrics in thin-film transistors (TFTs). These hybrid materials consist of the blending of high-k inorganic dielectrics with polymers, and their resulting properties depend on the amount and type of interactions between the organic and inorganic phases. The resulting amorphous networks, characterized by crosslinked organic and inorganic phases, can be tailored for specific applications, including gate dielectrics in TFTs. As dielectric materials, they offer a synergistic combination of high dielectric constants, low leakage currents, and mechanical flexibility, crucial for next-generation flexible electronics. Furthermore, organic–inorganic hybrid materials are easily processed in solution, allowing for low-temperature deposition compatible with flexible substrates. Various configurations of these hybrid gate dielectrics, such as bilayer structures and polymer nanocomposites, are discussed, with an emphasis on their potential to enhance device performance. Despite the significant advancements, challenges remain in optimizing the performance and stability of these hybrid materials. This review summarizes recent progress and highlights the advantages and emerging applications of low-temperature, solution-processed hybrid dielectrics, with a focus on their integration into flexible, stretchable, and wearable electronic devices.
Effective Dose Estimation in Computed Tomography by Machine Learning
Authors: Ferrante Matteo , De Marco Paolo , Rampado Osvaldo , Gianusso Laura , Origgi Daniela
Published: January 2025

Background: Computed tomography scans are widely used in everyday medical practice due to speed, image reliability, and detectability of a wide range of pathologies. Each scan exposes the patient to a radiation dose, and performing a fast estimation of the effective dose (E) is an important step for radiological safety. The aim of this work is to estimate E from patient and CT acquisition parameters in the absence of a dose-tracking software exploiting machine learning. Methods: In total, 69,037 CT acquisitions were collected with the dose-tracking software (DTS) available at our institution. E calculated by DTS was chosen as the target value for prediction. Different machine learning algorithms were selected, optimizing parameters to achieve the best performance for each algorithm. Effective dose was also estimated using DLP and k-factors, and with multiple linear regression. Mean absolute error (MAE, mean absolute percentage error (MAPE), and R2 were used to evaluate predictions in the test set and in an external dataset of 3800 acquisitions. Results: The random forest regressor (MAE: 0.416 mSv; MAPE: 7%; and R2: 0.98) showed best performances over the neural network and the support vector machine. However, all three machine learning algorithms outperformed effective dose estimation using k-factors (MAE: 2.06; MAPE: 26%) or multiple linear regression (MAE: 0.98; MAPE: 44.4%). The random forest regressor on the external dataset showed an MAE of 0.215 mSv and an MAPE of 7.1%. Conclusions: Our work demonstrated that machine learning models trained with data calculated by a dose-tracking software can provide good estimates of the effective dose just from patient and scanner parameters, without the need for a Monte Carlo approach.