bluuebunny/crossref_metadata_2025 huggingface dataset:
Weighted Variable Optimization-Based Method for Estimating Soil Salinity Using Multi-Source Remote Sensing Data: A Case Study in the Weiku Oasis, Xinjiang, China
Authors: Jiang Zhuohan , Hao Zhe , Ding Jianli , Miao Zhiguo , Zhang Yukun , Alimu Alimira , Jin Xin , Cheng Huiling , Ma Wen
Published: August 2024

Soil salinization is a significant global threat to sustainable agricultural development, with soil salinity serving as a crucial indicator for evaluating soil salinization. Remote sensing technology enables large-scale inversion of soil salinity, facilitating the monitoring and assessment of soil salinization levels, thus supporting the prevention and management of soil salinization. This study employs multi-source remote sensing data, selecting 8 radar polarization combinations, 10 spectral indices, and 3 topographic factors to form a feature variable dataset. By applying a normalized weighted variable optimization method, highly important feature variables are identified. AdaBoost, LightGBM, and CatBoost machine learning methods are then used to develop soil salinity inversion models and evaluate their performance. The results indicate the following: (1) There is generally a strong correlation between radar polarization combinations and vegetation indices, and a very high correlation between various vegetation indices and the salinity index S3. (2) The top five feature variables, in order of importance, are Aspect, VH2, Normalized Difference Moisture Index (NDMI), VH, and Vegetation Moisture Index (VMI). (3) The method of normalized weighted importance scoring effectively screens important variables, reducing the number of input feature variables while enhancing the model’s inversion accuracy. (4) Among the three machine learning models, CatBoost performs best overall in soil salt content (SSC) prediction. Combined with the top five feature variables, CatBoost achieves the highest prediction accuracy (R2 = 0.831, RMSE = 2.653, MAE = 1.034) in the prediction phase. This study provides insights for the further development and application of methods for collaborative inversion of soil salinity using multi-source remote sensing data.
MSPV3D: Multi-Scale Point-Voxels 3D Object Detection Net
Authors: Zhang Zheng , Bao Zhiping , Wei Yun , Zhou Yongsheng , Li Ming , Tian Qing
Published: August 2024

Autonomous vehicle technology is advancing, with 3D object detection based on point clouds being crucial. However, point clouds’ irregularity, sparsity, and large data volume, coupled with irrelevant background points, hinder detection accuracy. We propose a two-stage multi-scale 3D object detection network. Firstly, considering that a large number of useless background points are usually generated by the ground during detection, we propose a new ground filtering algorithm to increase the proportion of foreground points and enhance the accuracy and efficiency of the two-stage detection. Secondly, given that different types of targets to be detected vary in size, and the use of a single-scale voxelization may result in excessive loss of detailed information, the voxels of different scales are introduced to extract relevant features of objects of different scales in the point clouds and integrate them into the second-stage detection. Lastly, a multi-scale feature fusion module is proposed, which simultaneously enhances and integrates features extracted from voxels of different scales. This module fully utilizes the valuable information present in the point cloud across various scales, ultimately leading to more precise 3D object detection. The experiment is conducted on the KITTI dataset and the nuScenes dataset. Compared with our baseline, “Pedestrian” detection improved by 3.37–2.72% and “Cyclist” detection by 3.79–1.32% across difficulty levels on KITTI, and was boosted by 2.4% in NDS and 3.6% in mAP on nuScenes.
Smart Classrooms: How Sensors and AI Are Shaping Educational Paradigms
Authors: Zhang Xiaochen , Ding Yiran , Huang Xiaoyu , Li Wujing , Long Liumei , Ding Shiyao
Published: August 2024

The integration of advanced technologies is revolutionizing classrooms, significantly enhancing their intelligence, interactivity, and personalization. Central to this transformation are sensor technologies, which play pivotal roles. While numerous surveys summarize research progress in classrooms, few studies focus on the integration of sensor and AI technologies in developing smart classrooms. This systematic review classifies sensors used in smart classrooms and explores their current applications from both hardware and software perspectives. It delineates how different sensors enhance educational outcomes and the crucial role AI technologies play. The review highlights how sensor technology improves the physical classroom environment, monitors physiological and behavioral data, and is widely used to boost student engagements, manage attendance, and provide personalized learning experiences. Additionally, it shows that combining sensor software algorithms with AI technology not only enhances the data processing and analysis efficiency but also expands sensor capabilities, enriching their role in smart classrooms. The article also addresses challenges such as data privacy protection, cost, and algorithm optimization associated with emerging sensor technologies, proposing future research directions to advance educational sensor technologies.
Efficiency Analysis of Powertrain for Internal Combustion Engine and Hydrogen Fuel Cell Tractor According to Agricultural Operations
Authors: Jeon Hyeon-Ho , Baek Seung-Yun , Baek Seung-Min , Choi Jang-Young , Kim Yeon-Soo , Kim Wan-Soo , Kim Yong-Joo
Published: August 2024

As interest in eco-friendly work vehicles grows, research on the powertrains of eco-friendly tractors has increased, including research on the development of eco-friendly vehicles (tractors) using hydrogen fuel cell power packs and batteries. However, batteries require a long time to charge and have a short operating time due to their low energy efficiency compared with hydrogen fuel cell power packs. Therefore, recent studies have focused on the development of tractors using hydrogen fuel cell power packs; however, there is a lack of research on powertrain performance analysis considering actual working conditions. To evaluate vehicle performance, an actual load measurement during agricultural operation must be conducted. The objective of this study was to conduct an efficiency analysis of powertrains according to their power source using data measured during agricultural operations. A performance evaluation with respect to efficiency was performed through comparison and an analysis with internal combustion engine tractors of the same level. The specifications of the transmission for hydrogen fuel cell and engine tractors were used in this study. The power loss and efficiency of the transmission were calculated using ISO 14179-1 equations, as shown below. Plow tillage and rotary tillage operations were conducted for data measurement. The measurement system consists of four components. The engine data load measurement was calculated using the vehicle’s controller area network (CAN) data, the axle load was measured using an axle torque meter and proximity sensors, and fuel consumption was measured using the sensor installed on the fuel line. The calculated capacities, considering the engine’s fuel efficiency for plow and rotary tillage operations, were 131.2 and 175.1 kWh, respectively. The capacity of the required power, considering the powertrain’s efficiency for hydrogen fuel cell tractors with respect to plow and rotary tillage operations, was calculated using the efficiency of the motor, inverter, and power pack, and 51.3 and 62.9 kWh were the values obtained, respectively. Considering these factors, the engine exhibited an efficiency of about 47.9% compared with the power pack in the case of plow tillage operations, and the engine exhibited an efficiency of about 29.3% in the case of rotary tillage operations. A hydrogen fuel cell tractor is considered suitable for high-efficiency and eco-friendly vehicles because it can operate on eco-friendly power sources while providing the advantages of a motor.
Design of a Compact Multiband Monopole Antenna with MIMO Mutual Coupling Reduction
Authors: Lin Chang-Keng , Lin Ding-Bing , Lin Han-Chang , Lin Chang-Ching
Published: August 2024

In this article, the authors present the design of a compact multiband monopole antenna measuring 30 × 10 × 1.6 mm3, which is aimed at optimizing performance across various communication bands, with a particular focus on Wi-Fi and sub-6G bands. These bands include the 2.4 GHz band, the 3.5 GHz band, and the 5–6 GHz band, ensuring versatility in practical applications. Another important point is that this paper demonstrates effective methods for reducing mutual coupling through two meander slits on the common ground, resembling a defected ground structure (DGS) between two antenna elements. This approach achieves mutual coupling suppression from −6.5 dB and −9 dB to −26 dB and −13 dB at 2.46 GHz and 3.47 GHz, respectively. Simulated and measured results are in good agreement, demonstrating significant improvements in isolation and overall multiple-input multiple-output (MIMO) antenna system performance. This research proposes a compact multiband monopole antenna and demonstrates a method to suppress coupling in multiband antennas, making them suitable for internet of things (IoT) sensor devices and Wi-Fi infrastructure systems.
A Deep Learning Approach to Distance Map Generation Applied to Automatic Fiber Diameter Computation from Digital Micrographs
Authors: Alejo Huarachi Alain M. , Beltrán Castañón César A.
Published: August 2024

Precise measurement of fiber diameter in animal and synthetic textiles is crucial for quality assessment and pricing; however, traditional methods often struggle with accuracy, particularly when fibers are densely packed or overlapping. Current computer vision techniques, while useful, have limitations in addressing these challenges. This paper introduces a novel deep-learning-based method to automatically generate distance maps of fiber micrographs, enabling more accurate fiber segmentation and diameter calculation. Our approach utilizes a modified U-Net architecture, trained on both real and simulated micrographs, to regress distance maps. This allows for the effective separation of individual fibers, even in complex scenarios. The model achieves a mean absolute error (MAE) of 0.1094 and a mean square error (MSE) of 0.0711, demonstrating its effectiveness in accurately measuring fiber diameters. This research highlights the potential of deep learning to revolutionize fiber analysis in the textile industry, offering a more precise and automated solution for quality control and pricing.
Energy Minimization for IRS-Assisted SWIPT-MEC System
Authors: Zhang Shuai , Zhu Yujun , Mei Meng , He Xin , Xu Yong
Published: August 2024

With the rapid development of the internet of things (IoT) era, IoT devices may face limitations in battery capacity and computational capability. Simultaneous wireless information and power transfer (SWIPT) and mobile edge computing (MEC) have emerged as promising technologies to address these challenges. Due to wireless channel fading and susceptibility to obstacles, this paper introduces intelligent reflecting surfaces (IRS) to enhance the spectral and energy efficiency of wireless networks. We propose a system model for IRS-assisted uplink offloading computation, downlink offloading computation results, and simultaneous energy transfer. Considering constraints such as IRS phase shifts, latency, energy harvesting, and offloading transmit power, we jointly optimize the CPU frequency of IoT devices, offloading transmit power, local computation workload, power splitting (PS) ratio, and IRS phase shifts. This establishes a multi-variate coupled nonlinear problem aimed at minimizing IoT devices energy consumption. We design an effective alternating optimization (AO) iterative algorithm based on block coordinate descent, and utilize closed-form solutions, Dinkelbach-based Lagrange dual method, and semidefinite relaxation (SDR) method to minimize IoT devices energy consumption. Simulation results demonstrate that the proposed scheme achieves lower energy consumption compared to other resource allocation strategies.
Short-Term Wind Power Prediction Based on Encoder–Decoder Network and Multi-Point Focused Linear Attention Mechanism
Authors: Mei Jinlong , Wang Chengqun , Luo Shuyun , Xu Weiqiang , Deng Zhijiang
Published: August 2024

Wind energy is a clean energy source that is characterised by significant uncertainty. The electricity generated from wind power also exhibits strong unpredictability, which when integrated can have a substantial impact on the security of the power grid. In the context of integrating wind power into the grid, accurate prediction of wind power generation is crucial in order to minimise damage to the grid system. This paper proposes a novel composite model (MLL-MPFLA) that combines a multilayer perceptron (MLP) and an LSTM-based encoder–decoder network for short-term prediction of wind power generation. In this model, the MLP first extracts multidimensional features from wind power data. Subsequently, an LSTM-based encoder-decoder network explores the temporal characteristics of the data in depth, combining multidimensional features and temporal features for effective prediction. During decoding, an improved focused linear attention mechanism called multi-point focused linear attention is employed. This mechanism enhances prediction accuracy by weighting predictions from different subspaces. A comparative analysis against the MLP, LSTM, LSTM–Attention–LSTM, LSTM–Self_Attention–LSTM, and CNN–LSTM–Attention models demonstrates that the proposed MLL-MPFLA model outperforms the others in terms of MAE, RMSE, MAPE, and R2, thereby validating its predictive performance.
Feature Interaction-Based Face De-Morphing Factor Prediction for Restoring Accomplice’s Facial Image
Authors: Cai Juan , Duan Qiangqiang , Long Min , Zhang Le-Bing , Ding Xiangling
Published: August 2024

Face morphing attacks disrupt the essential correlation between a face image and its identity information, posing a significant challenge to face recognition systems. Despite advancements in face morphing attack detection methods, these techniques cannot reconstruct the face images of accomplices. Existing deep learning-based face de-morphing techniques have mainly focused on identity disentanglement, overlooking the morphing factors inherent in the morphed images. This paper introduces a novel face de-morphing method to restore the identity information of accomplices by predicting the corresponding de-morphing factor. To obtain reasonable de-morphing factors, a channel-wise attention mechanism is employed to perform feature interaction, and the correlation between the morphed image and the real-time captured reference image is integrated to promote the prediction of the de-morphing factor. Furthermore, the identity information of the accomplice is restored by mapping the morphed and reference images into the StyleGAN latent space and performing inverse linear interpolation using the predicted de-morphing factor. Experimental results demonstrate the superiority of this method in restoring accomplice facial images, achieving improved restoration accuracy and image quality compared to existing techniques.
MelTrans: Mel-Spectrogram Relationship-Learning for Speech Emotion Recognition via Transformers
Authors: Li Hui , Li Jiawen , Liu Hai , Liu Tingting , Chen Qiang , You Xinge
Published: August 2024

Speech emotion recognition (SER) is not only a ubiquitous aspect of everyday communication, but also a central focus in the field of human–computer interaction. However, SER faces several challenges, including difficulties in detecting subtle emotional nuances and the complicated task of recognizing speech emotions in noisy environments. To effectively address these challenges, we introduce a Transformer-based model called MelTrans, which is designed to distill critical clues from speech data by learning core features and long-range dependencies. At the heart of our approach is a dual-stream framework. Using the Transformer architecture as its foundation, MelTrans deciphers broad dependencies within speech mel-spectrograms, facilitating a nuanced understanding of emotional cues embedded in speech signals. Comprehensive experimental evaluations on the EmoDB (92.52%) and IEMOCAP (76.54%) datasets demonstrate the effectiveness of MelTrans. These results highlight MelTrans’s ability to capture critical cues and long-range dependencies in speech data, setting a new benchmark within the context of these specific datasets. These results highlight the effectiveness of the proposed model in addressing the complex challenges posed by SER tasks.
A Novel Adversarial Example Detection Method Based on Frequency Domain Reconstruction for Image Sensors
Authors: Huang Shuaina , Zhang Zhiyong , Song Bin
Published: August 2024

Convolutional neural networks (CNNs) have been extensively used in numerous remote sensing image detection tasks owing to their exceptional performance. Nevertheless, CNNs are often vulnerable to adversarial examples, limiting the uses in different safety-critical scenarios. Recently, how to efficiently detect adversarial examples and improve the robustness of CNNs has drawn considerable focus. The existing adversarial example detection methods require modifying CNNs, which not only affects the model performance but also greatly enhances training cost. With the purpose of solving these problems, this study proposes a detection algorithm for adversarial examples that does not need modification of the CNN models and can simultaneously retain the classification accuracy of normal examples. Specifically, we design a method to detect adversarial examples using frequency domain reconstruction. After converting the input adversarial examples into the frequency domain by Fourier transform, the adversarial disturbance from adversarial attacks can be eliminated by modifying the frequency of the example. The inverse Fourier transform is then used to maximize the recovery of the original example. Firstly, we train a CNN to reconstruct input examples. Then, we insert Fourier transform, convolution operation, and inverse Fourier transform into the features of the input examples to automatically filter out adversarial frequencies. We refer to our proposed method as FDR (frequency domain reconstruction), which removes adversarial interference by converting input samples into frequency and reconstructing them back into the spatial domain to restore the image. In addition, we also introduce gradient masking into the proposed FDR method to enhance the detection accuracy of the model for complex adversarial examples. We conduct extensive experiments on five mainstream adversarial attacks on three benchmark datasets, and the experimental results show that FDR can outperform state-of-the-art solutions in detecting adversarial examples. Additionally, FDR does not require any modifications to the detector and can be integrated with other adversarial example detection methods to be installed in sensing devices to ensure detection safety.
Low-Resource Time-to-Digital Converters for Field Programmable Gate Arrays: A Review
Authors: Real Diego , Calvo David
Published: August 2024

A fundamental aspect in the evolution of Time-to-Digital Converters (TDCs) implemented within Field-Programmable Gate Arrays (FPGAs), given the increasing demand for detection channels, is the optimization of resource utilization. This study reviews the principal methodologies employed for implementing low-resource TDCs in FPGAs. It outlines the foundational architectures and interpolation techniques utilized to bolster TDC performances without unduly burdening resource consumption. Low-resource Tapped Delay Line, Vernier Ring Oscillator, and Multi-Phase Shift Counter TDCs, including the use of SerDes, are reviewed. Additionally, novel low-resource architectures are scrutinized, including Counter Gray Oscillator TDCs and interpolation expansions using Process–Voltage–Temperature stable IODELAYs. Furthermore, the advantages and limitations of each approach are critically assessed, with particular emphasis on resolution, precision, non-linearities, and especially resource utilization. A comprehensive summary table encapsulating existing works on low-resource TDCs is provided, offering a comprehensive overview of the advancements in the field.
Malware Identification Method in Industrial Control Systems Based on Opcode2vec and CVAE-GAN
Authors: Huang Yuchen , Liu Jingwen , Xiang Xuanyi , Wen Pan , Wen Shiyuan , Chen Yanru , Chen Liangyin , Zhang Yuanyuan
Published: August 2024

Industrial Control Systems (ICSs) have faced a significant increase in malware threats since their integration with the Internet. However, existing machine learning-based malware identification methods are not specifically optimized for ICS environments, resulting in suboptimal identification performance. In this work, we propose an innovative method explicitly tailored for ICSs to enhance the performance of malware classifiers within these systems. Our method integrates the opcode2vec method based on preprocessed features with a conditional variational autoencoder–generative adversarial network, enabling classifiers based on Convolutional Neural Networks to identify malware more effectively and with some degree of increased stability and robustness. Extensive experiments validate the efficacy of our method, demonstrating the improved performance of malware classifiers in ICSs. Our method achieved an accuracy of 97.30%, precision of 92.34%, recall of 97.44%, and F1-score of 94.82%, which are the highest reported values in the experiment.
Analyzing Wav2Vec 1.0 Embeddings for Cross-Database Parkinson’s Disease Detection and Speech Features Extraction
Authors: Klempíř Ondřej , Krupička Radim
Published: August 2024

Advancements in deep learning speech representations have facilitated the effective use of extensive unlabeled speech datasets for Parkinson’s disease (PD) modeling with minimal annotated data. This study employs the non-fine-tuned wav2vec 1.0 architecture to develop machine learning models for PD speech diagnosis tasks, such as cross-database classification and regression to predict demographic and articulation characteristics. The primary aim is to analyze overlapping components within the embeddings on both classification and regression tasks, investigating whether latent speech representations in PD are shared across models, particularly for related tasks. Firstly, evaluation using three multi-language PD datasets showed that wav2vec accurately detected PD based on speech, outperforming feature extraction using mel-frequency cepstral coefficients in the proposed cross-database classification scenarios. In cross-database scenarios using Italian and English-read texts, wav2vec demonstrated performance comparable to intra-dataset evaluations. We also compared our cross-database findings against those of other related studies. Secondly, wav2vec proved effective in regression, modeling various quantitative speech characteristics related to articulation and aging. Ultimately, subsequent analysis of important features examined the presence of significant overlaps between classification and regression models. The feature importance experiments discovered shared features across trained models, with increased sharing for related tasks, further suggesting that wav2vec contributes to improved generalizability. The study proposes wav2vec embeddings as a next promising step toward a speech-based universal model to assist in the evaluation of PD.
A New Approach to Recording Rumination Behavior in Dairy Cows
Authors: Hoffmann Gundula , Strutzke Saskia , Fiske Daniel , Heinicke Julia , Mylostyvyi Roman
Published: August 2024

Rumination behavior in cattle can provide valuable information for monitoring health status and animal welfare, but continuous monitoring is essential to detect changes in rumination behavior. In a previous study validating the use of a respiration rate sensor equipped with a triaxial accelerometer, the regurgitation process was also clearly visible in the pressure and accelerometer data. The aim of the present study, therefore, was to measure the individual lengths of rumination cycles and to validate whether the sensor data showed the same number of regurgitations as those counted visually (video or direct observation). For this purpose, 19 Holstein Friesian cows equipped with a respiration rate sensor were observed for two years, with a focus on rumination behavior. The results showed a mean duration of 59.27 ± 9.01 s (mean ± SD) per rumination cycle and good agreement (sensitivity: 99.1–100%, specificity: 87.8–95%) between the two methods (sensor and visual observations). However, the frequency of data streaming (continuously or every 30 s) from the sensor to the data storage system strongly influenced the classification performance. In the future, an algorithm and a data cache will be integrated into the sensor to provide rumination time as an additional output.
Permeability Measurement of Glass-Fiber Textiles Used in Composites Industry Using Radial Flow Experimental Setup and Comparison with Image-Based Numerical Methods
Authors: Boubaker Mouadh , Wijaya Willsen , Cantarel Arthur , Debenest Gérald , Bickerton Simon
Published: August 2024

Permeability measurement of engineering textiles is a key step in preparing composite manufacturing processes. A radial flow experimental setup was used in this work to measure the unsaturated and saturated in-plane permeabilities of five different types of E-glass textiles and their ratios. In parallel, delayed tow saturation during the oil injection stage was visually observed to identify fabrics that exhibited a significant dual-scale effect. A numerical approach to determine the saturated permeability of a given fabric geometry at the mesoscale was tested and validated against analytical models found in the literature. It was then applied to a realistic geometry acquired from an E-glass plain weave textile using an X-ray microtomography scanner (μCT). Two numerical methods were adopted: the single-scale method, where the tows are considered impermeable, and the dual-scale method, where the permeability of the tows is taken into account. The numerical results from both methods were then compared with the experimental values and showed good agreement, especially with the second method.
Exploration of the Historical and Social Significance of One of the First Cinematographic Devices Based on Gender Roles in the Andalusian Environment
Authors: Rodriguez-Cunill Inmaculada , Martín-Leal María del Mar , Domínguez-López Juan José
Published: August 2024

In 1914, El Noticiero Sevillano and other Spanish newspapers published a piece about the Cinémhymen, a cinematographic device designed to capture and sell images of prospective wives. This article explores why this advertisement was not considered derogatory and examines the construction of a patriarchy during a time when the term “feminist” was already appearing in the Spanish press. In our methodology, we analyzed the name of the device and the business, both based on the word Hymen, used a bibliographic review of Spanish feminism of those years, and researched the film technology of the time. The Manzano’s pyramid of oppression served us in establishing the control operations underlying the advertisement. Our study reveals the patriarchal principles of Cinémhymen, which stigmatized women once they conformed to the expected role. The objectifying gaze present in Cinémhymen provides insight into the progression of patriarchy in a visual world that subjugates women. The camera could see through the female masquerade (as Joan Rivière explained) and explore the “true” body underneath, the very core of the female (or what is considered to be). In some ways, Cinémhymen serves as a precursor to the current subjugation seen in online pornography and represents a distorted evolution of the panopticon principle as applied to women.
Factors Influencing Consumption of Animal-Based Dairy and Plant-Based Milk Alternatives in Australia
Authors: Tulysewski Grace , Hendrie Gilly A. , Baird Danielle L. , Umberger Wendy , Malek Lenka
Published: August 2024

In the transition to more environmentally sustainable food systems, the production and consumption of resource-intensive animal-based dairy foods (ABD) remains a talking point. Given the power of consumer choice in transforming food systems, understanding consumer priorities surrounding ABD consumption and their plant-based counterparts is fundamental. Using data from 3271 unique survey respondents, the objectives of this study were to examine the consumption frequencies of ABD and plant-based milk (PBM) in Australia over time (2019–2022) and determine the psychosocial and sociodemographic factors associated with distinct consumer segments. Multivariate analysis identified sociodemographic and food choice factors associated with three consumer segments: ‘exclusive PBM consumers’, ‘exclusive ABD consumers’, and ‘mixed consumers’ (consumers of both product types). Despite the growing availability of plant-based milk alternatives, exclusive PBM consumption remains relatively uncommon compared to mixed plant and animal dairy consumption. ‘Exclusive ABD consumers’ had a higher probability of being older and less likely to prioritise health and nutrition than ‘mixed consumers’. ‘Exclusive PBM consumers’ were more likely to prioritise food tolerance and restrictions and less likely to prioritise product taste than ‘exclusive ABD consumers’. We report sociodemographic and motivational factors influencing animal-based dairy and plant-based milk choices. The outcomes are of interest to sustainable food systems development stakeholders, with potential applications in the public health and commercial food promotion sectors.
Data Industry Green Development Promoted by Public Policy and Law in China
Authors: Chen Bing , Liu Yongji
Published: August 2024

It is difficult to fully match the current green development initiatives in the context of public policy and industrial economy to the new infrastructure process driven by massive data. Its connotation and extension and the level of the rule of law need to be deepened to ensure that data production, processing, circulation, elimination, and other activities can achieve high-quality and sustainable development within the collaborative construction of hard facilities and the soft environment in the new era. Based on the practical needs of data industry green development in key areas, we can resolve the legal risk of data industry green development from three aspects: improving the legal level of green public policy, clarifying the green legal benchmark of data industry development, and improving the green legal system of digital society. Then, this paper considers the design of an environmental protection system based on hardware facilities and the structural and readable clean data system innovation of the soft data industry development framework mode so as to clarify the legalization scheme of data industry green development under the new infrastructure. Overall, achieving the green development of the data industry requires not only active attempts by China but also joint efforts by China and other countries.
Research on the Sustainable Evolution Mechanism of Dual-Dimensional Convergence Innovation in Digital Products
Authors: Weng Zhigang , Cai Yubao , Weng Siqi , Zuo Chun
Published: August 2024

The complex characteristics of cross-disciplinarity, dynamic expansion, ambiguity, diversity, and the rapid changes in demand significantly amplify the uncertainties in digital product innovation. The existing innovation theories, such as “stage-gate”, open innovation, agile development, and data-driven decision making, are insufficient for fully and effectively addressing these uncertainties. Based on a case study of a fintech app, we reveal that digital product innovation is similar to biological evolution, exhibiting dual life-like features of “inheritance” and “mutation” within “dual-dimensional convergence”. However, unlike natural evolution, the evolutionary process of digital product innovation can augment its use of the digital ecosystem and capabilities, establish a data-driven rapid proactive selection mechanism for the main three stages, and quickly enhance product competitiveness. The complexity of knowledge in the innovation process can be partially solved through the use of a micro-knowledge integration learning mechanism formed by the interactions of social and cognitive translation. This study also discovers that market competition and policy regulation are two unique innovation-driven characteristics in digital product innovation. This mechanism can achieve the earlier clarification of product evolution’s direction, reduce the three major uncertainties of innovation, and improve efficiency in the utilization of innovation resources to achieve sustainable development.
Optimal Electric Vehicle Battery Management Using Q-learning for Sustainability
Authors: Suanpang Pannee , Jamjuntr Pitchaya
Published: August 2024

This paper presents a comprehensive study on the optimization of electric vehicle (EV) battery management using Q-learning, a powerful reinforcement learning technique. As the demand for electric vehicles continues to grow, there is an increasing need for efficient battery-management strategies to extend battery life, enhance performance, and minimize operating costs. The primary objective of this research is to develop and assess a Q-learning-based approach to address the intricate challenges associated with EV battery management. This paper starts by elucidating the key challenges inherent in EV battery management and discusses the potential advantages of incorporating Q-learning into the optimization process. Leveraging Q-learning’s capacity to make dynamic decisions based on past experiences, we introduce a framework that considers state-of-charge, state-of-health, charging infrastructure, and driving patterns as critical state variables. The methodology is detailed, encompassing the selection of state, action, reward, and policy, with the training process informed by real-world data. Our experimental results underscore the efficacy of the Q-learning approach in optimizing battery management. Through the utilization of Q-learning, we achieve substantial enhancements in battery performance, energy efficiency, and overall EV sustainability. A comparative analysis with traditional battery-management strategies is presented to highlight the superior performance of our approach. A comparative analysis with traditional battery-management strategies is presented to highlight the superior performance of our approach, demonstrating compelling results. Our Q-learning-based method achieves a significant 15% improvement in energy efficiency compared to conventional methods, translating into substantial savings in operational costs and reduced environmental impact. Moreover, we observe a remarkable 20% increase in battery lifespan, showcasing the effectiveness of our approach in enhancing long-term sustainability and user satisfaction. This paper significantly enriches the body of knowledge on EV battery management by introducing an innovative, data-driven approach. It provides a comprehensive comparative analysis and applies novel methodologies for practical implementation. The implications of this research extend beyond the academic sphere to practical applications, fostering the broader adoption of electric vehicles and contributing to a reduction in environmental impact while enhancing user satisfaction.
A Tale of Two Cities: How Can We Use Social Media to Engage Young People with an Environmentally Friendly Lifestyle in Hong Kong and Guam?
Authors: Ho Kevin K. W. , Chen Yaoqin , Sayama Kristina L. C. , Chiu Dickson K. W.
Published: August 2024

There has recently been a growing concern about developing an environmentally friendly lifestyle (EFL) through suitable education arrangements on different media types. In this study, we propose a research model to compare the effectiveness of using traditional media and social media in promoting and engaging young people with such educational information. We collected survey data from 400 participants from Hong Kong and Guam to test the proposed model using structural equation modeling. Our findings show the effectiveness of social media in engaging young people to practice an EFL through environmental involvement and other practicing concerns, as well as through value co-creation on social media. Yet, traditional media is a more effective tool for disseminating such information to young people than social media. The relatively consistent results obtained from data collected from Hong Kong and Guam show the generalizability of the research model, showing the importance of using both traditional media and social media to engage with young people with EFL information. Our findings, therefore, provide insights for stakeholders as foundations for further research in using social media to disseminate EFL information to society.
Forecasting Maximum Temperature Trends with SARIMAX: A Case Study from Ahmedabad, India
Authors: Shah Vyom , Patel Nishil , Shah Dhruvin , Swain Debabrata , Mohanty Manorama , Acharya Biswaranjan , Gerogiannis Vassilis C. , Kanavos Andreas
Published: August 2024

Globalization and industrialization have significantly disturbed the environmental ecosystem, leading to critical challenges such as global warming, extreme weather events, and water scarcity. Forecasting temperature trends is crucial for enhancing the resilience and quality of life in smart sustainable cities, enabling informed decision-making and proactive urban planning. This research specifically targeted Ahmedabad city in India and employed the seasonal autoregressive integrated moving average with exogenous factors (SARIMAX) model to forecast temperatures over a ten-year horizon using two decades of real-time temperature data. The stationarity of the dataset was confirmed using an augmented Dickey–Fuller test, and the Akaike information criterion (AIC) method helped identify the optimal seasonal parameters of the model, ensuring a balance between fidelity and prediction accuracy. The model achieved an RMSE of 1.0265, indicating a high accuracy within the typical range for urban temperature forecasting. This robust measure of error underscores the model’s precision in predicting temperature deviations, which is particularly relevant for urban planning and environmental management. The findings provide city planners and policymakers with valuable insights and tools for preempting adverse environmental impacts, marking a significant step towards operational efficiency and enhanced governance in future smart urban ecosystems. Future work may extend the model’s applicability to broader geographical areas and incorporate additional environmental variables to refine predictive accuracy further.
A New Approach to Vertical Plant Cultivation Maximises Crop Efficiency
Authors: Ptak Mariusz , Wasieńko Sebastian , Makuła Piotr
Published: August 2024

This publication presents an innovative tower cultivation device designed to significantly increase vertical farming’s efficiency. The device divides the cultivation system into separate chambers. One division corresponds to the different growth phases of the plants, while another reflects the daily variation in conditions. Each chamber presents slightly different conditions and cultivation patterns from the others. For the early stages, crops are grown horizontally in trays; once they mature, they are transplanted into mobile cultivation towers. The closed circulation of ventilation and irrigation reduces water consumption by up to 95%. A unique separate day–night division optimises light, temperature, and humidity conditions, mimicking natural growth patterns. This approach not only saves water and energy but also improves cultivation in a three-dimensional space. The presented solution focuses on the often-overlooked aspects of cultivating in vertical farms and makes this method of growing much more cost-effective and feasible to implement on a large scale. Our comparative analysis with other vertical farming solutions is based on publicly available data and provides valuable insights, while acknowledging the potential limitations at play.
Towards 2050: Evaluating the Role of Energy Transformation for Sustainable Energy Growth in Serbia
Authors: Backović Nemanja , Ilić Bojan , Radaković Jelena Andreja , Mitrović Dušan , Milenković Nemanja , Ćirović Marko , Rakićević Zoran , Petrović Nataša
Published: August 2024

This paper aims to investigate the outlook of energy generation by means of transformation within the context of sustainable energy development. An analysis is conducted to assess the stability of energy systems so to implement cutting-edge energy production models at the national level, with a focus on a contemporary approach to energy modeling. Considering the energy transition and the existing constraints within the energy industry, the model assesses the feasibility of the practical advancement of renewable energy sources. The bottom-up energy model was used to determine how the components of energy development sustainability can be applied until the year 2050. To perform comparison testing with the reference state scenario, the LEAP energy model was used. This instrument was selected because of its ability to provide flexible and advanced options for selecting suitable parameters for energy transformation prediction. A progressive reduction in environmental pollution can be achieved by the deployment of current methods of energy generation by transformation until the year 2050 in Serbia, as indicated by the findings. The research highlights the significance of utilizing green energy sources for the continuing development of energy and the gradual reduction in environmental pollution through value co-creation.
Achieving SOC Conservation without Land-Use Changes between Agriculture and Forests
Authors: Pandey Hari Prasad , Maraseni Tek Narayan , Apan Armando , Bhusal Shreejana
Published: August 2024

Global land-use changes impact soil’s ability to perform essential functions. This study investigates whether soil organic carbon (SOC) can be conserved without altering land use in traditional farming systems and degraded natural forests, focusing on ‘disturbed’ agricultural soils and ‘undisturbed’ forest soils. We also examine the influence of dominant crops on SOC within the top 30 cm of soil in data-deficient regions of Nepal. Using a multi-stage cluster sampling design, we tested 12 regression models to identify the best relationships among variables such as SOC, soil bulk density (BD), pH, dominant crops, climate, topography, and management practices. Our analysis revealed similar SOC levels in both disturbed and undisturbed soils, indicating significant degradation in forested areas, whereas traditional farming systems could support SOC and preserve farm-based indigenous knowledge alongside food security. Further, SOC stocks varied significantly (p < 0.05) across different cropping systems, suggesting that managing dominant crops could be a strategy to optimize SOC, with these crops serving as indicators. Additionally, our results show that the weak linear correlation between SOC and BD in regularly disturbed soils, such as farmlands, where anthropogenic activities frequently alter soil bulk density, may be misleading when estimating bulk density-dependent SOC. This finding suggests the need for further research into varying degrees of anthropogenic disturbance in soil to confirm these results. While the site-specific nature of the findings warrants caution with respect to generalization, they provide valuable insights for carbon monitoring, climate actions, ecosystem health, and land-use management in similar traditional farming systems and degraded forests, particularly in data-poor regions.
Predictive Analysis of Adaptation to Drought of Farmers in the Central Zone of Colombia
Authors: Hernández-López Jorge Armando , Puerta-Cortés Diana Ximena , Andrade Hernán J.
Published: August 2024

Drought constitutes one of the natural phenomena that causes the greatest socio-economic, and environmental losses in both the short and long term worldwide. Each year, these events are related to the presence of “El Niño—Southern Oscillation” (ENSO), which occurs throughout Colombia and has serious consequences in the agricultural and food sectors, as well as in most of the country’s population. Farmers have adopted a number of strategies to mitigate the negative impact of droughts on food production. Certainly, when implementing future strategies, such strategies will be less effective if farmers’ insights on ENSO are not considered. Consequently, this study was carried out to analyze the variables that predict adaptation to droughts in the dry zones of the department of Tolima. Three questionnaires were designed: socioeconomic vulnerability (SVT), risk perception (SRPT) and drought adaptation (SAT). A non-probability sample of 538 farmers was surveyed. Socio-economic vulnerability and drought perception were found to be predictive of drought adaptation in the study sample, and older people were found to be resilient to adaptation. The results of this research provide empirical evidence to analyze and formulate public policies about the impact of droughts on the most vulnerable populations.
Solar Photovoltaic Module End-of-Life Waste Management Regulations: International Practices and Implications for the Kingdom of Saudi Arabia
Authors: Ali Amjad , Islam Md Tasbirul , Rehman Shafiqur , Qadir Sikandar Abdul , Shahid Muhammad , Khan Muhammad Waseem , Zahir Md. Hasan , Islam Asif , Khalid Muhammad
Published: August 2024

This paper examines the end-of-life (EOL) waste management regulations and guidelines of five leading countries—China, USA, India, Japan, and Germany—to identify best practices and lessons that can enhance Saudi Arabia’s EOL waste management strategies. The study delves into China’s regulatory framework, highlighting its import bans on certain wastes, the USA’s manufacturer responsibility requirements for product disposal, India’s engagement of the informal sector in waste collection and recycling, Japan’s take-back system mandating manufacturer responsibility for product recovery and recycling, and Germany’s advanced system promoting the separate collection of EOL products and stringent hazardous waste regulations. By evaluating these diverse regulatory approaches and integrating insights from recent literature, this paper aims to provide a comprehensive perspective to assist Saudi Arabia in developing an effective EOL waste management system. Given the current state and deployment of solar PV systems in Saudi Arabia, the generation of PV solar panel waste is currently minimal. However, future projections indicate a significant increase, and it is worrisome that the KSA currently lacks the necessary systems and infrastructure to handle this waste effectively. To address this challenge sustainably, it is essential to introduce and implement an Extended Producer Responsibility (EPR) policy, develop robust recycling infrastructure, enhance public awareness and education, and foster public–private partnerships. These measures will provide a strong foundation for managing end-of-life PV solar panel waste in Saudi Arabia. Such a system would ensure environmental protection, public health, and economic growth. Moreover, the research findings could serve as a valuable resource for other countries seeking to improve their EOL waste management practices. This study underscores the importance of learning from successful international waste management practices to enhance EOL waste management systems globally.