bluuebunny/crossref_metadata_2025 huggingface dataset:
Accurate Paddy Rice Mapping Based on Phenology-Based Features and Object-Based Classification
Authors: Zhang Jiayi , Gao Lixin , Liu Miao , Dong Yingying , Liu Chongwen , Casa Raffaele , Pignatti Stefano , Huang Wenjiang , Li Zhenhai , Tian Tingting , Hu Richa
Published: November 2024

Highly accurate rice cultivation distribution and area extraction are essential to food security. Moreover, Inner Mongolia, whose slogan is “from scientific rice to world rice”, is an essential national rice production base. However, high-quality rice mapping products at high resolutions are still scarce around the Inner Mongolia Autonomous Region. This condition is not conducive to rational planning of farmland resources, maintaining food security, and promoting sustainable growth of the local agricultural economy. In this study, the rice backscattering intensity difference index from the vertically polarized backscatter intensity of Sentinel-1 and the phenology differential index from the spectral indices of two critical rice phenological phases of Sentinel-2 images were constructed. Other spectral features, including spectral indices, tasseled cap, and texture features, were computed using simple non-iterative clustering (SNIC) to achieve image segmentation. These variables served as input features for the random forest (RF) algorithm. Results reveal that employing the RF with the SNIC segmentation algorithm and combining it with optical and synthetic aperture radar data is an effective way to extract data on rice in mid-latitude regions. The overall accuracy and kappa coefficient are 0.98 and 0.967, correspondingly. The accuracy for rice is 0.99, as proven by empirical data. These results meet the requirements of regional rice cultivation assessment and area monitoring. Furthermore, owing to its resilience against longitude-associated influences, the model discerns rice across diverse regions and multiple years, achieving an R2 of 0.99. This capability significantly bolsters efforts to improve regional food security and the pursuit of sustainable development.
Potential Impact of Sea Surface Temperature Variability on the 2007 Sudden Bloom of Ulva prolifera in the Southern Yellow Sea
Authors: Pan Yufeng , Li Pin , Sun Jiaxuan , Liu Siyu , Xing Lvyang , Yu Di , Feng Qi
Published: November 2024

Since 2007, Ulva prolifera (U. prolifera) originating in northern Jiangsu (NJ) has consistently expanded to the southern coast of the Shandong Peninsula. However, the underlying reasons for the 2007 sudden bloom of U. prolifera on a large scale remain unknown. This study uses remote sensing data from MODIS/AQUA spanning the period 2003–2022 to investigate the sea surface temperature (SST) structure changes in the southern Yellow Sea (SYS) over the past 20 years. The results demonstrate the following. (1) Since 2007, the NJ northward current and the Yangtze estuary warm current have exhibited higher temperatures, earlier northward intrusions, and larger influence areas, leading to a faster warming rate in NJ before mid-May. This rapid increase in SST to a level suitable for early U. prolifera growth triggers large-scale blooms. (2) The change in temperature structure is primarily induced by a prolonged and intense La Niña event in 2007–2008. However, since 2016, under stable global climate conditions, the temperature structure of the SYS has returned to the pre-2007 state, corresponding to a decrease in the scale of U. prolifera blooms.
A Framework for Separating Climate and Anthropogenic Contributions to Evapotranspiration Changes in Natural to Agricultural Regions of Watersheds Based on Machine Learning
Authors: Liang Zixin , Li Fengping , Li Hongyan , Zhang Guangxin , Qi Peng
Published: November 2024

Evapotranspiration is a crucial component of the water cycle and is significantly influenced by climate change and human activities. Agricultural expansion, as a major aspect of human activity, together with climate change, profoundly affects regional ET variations. This study proposes a quantification framework to assess the impacts of climate change (ETm) and agricultural development (ETh) on regional ET variations based on the Random Forest algorithm. The framework was applied in a large-scale agricultural expansion area in China, specifically, the Songhua River Basin. Meteorological, topographic, and ET remote sensing data for the years of 1980 and 2015 were selected. The Random Forest model effectively simulates ET in the natural areas (i.e., forest, grassland, marshland, and saline-alkali land) in the Songhua River Basin, with R2 values of around 0.99. The quantification results showed that climate change has altered ET by −8.9 to 24.9 mm and −3.4 to 29.7 mm, respectively, in the natural areas converted to irrigated and rainfed agricultural areas. Deducting the impact of climate change on the ET variation, the development of irrigated and rainfed agriculture resulted in increases of 2.9 mm to 55.9 mm and 0.9 mm to 53.4 mm in ET, respectively, compared to natural vegetation types. Finally, the Self-Organizing Map method was employed to explore the spatial heterogeneity of ETh and ETm. In the natural–agriculture areas, ETm is primarily influenced by moisture conditions. When moisture levels are adequate, energy conditions become the predominant factor. ETh is intricately linked not only to meteorological conditions but also to the types of original vegetation. This study provides theoretical support for quantifying the effects of climate change and farmland development on ET, and the findings have important implications for water resource management, productivity enhancement, and environmental protection as climate change and agricultural expansion persist.
Spatiotemporal Variation in Aerosol Optical Depth and Its Potential Effects on Snowmelt in High Mountain Asia from 2004 to 2023
Authors: Yin Lichen , Wang Xin , Du Wentao , Chen Jizu , Jiang Youyan , Sun Weijun , Yang Chengde , Li Bowen , Xue Xingyu , Lu Changsheng
Published: November 2024

Light-absorbing particles, which are vital components of aerosols, can cause significant snow albedo darkening and accelerate melting. However, restricted by the poor quality of remote sensing-based aerosol products in High Mountain Asia (HMA), previous studies have seldom reported the long-term pattern of aerosols. In this study, we analyzed the spatial and temporal distribution characteristics of AOD in HMA and surrounding areas using Moderate Resolution Imaging Spectroradiometer and Ozone Monitoring Instrument data from 2004 to 2023. The Mann-Kendall test was applied to analyze the temporal trend and abrupt changes in AOD, while Rotated Empirical Orthogonal Function was used to identify subregions and investigate spatiotemporal variations. Moreover, random forest and XGBoost-Shap models were employed to quantify the contributions of the aerosols to changes in snow albedo and melting. The results indicate that the annual (monthly) average highest and lowest AOD occurred in 2021 (April) and 2022 (September) between 2004 and 2023, respectively. The AOD first increased and then decreased during our study period and an abrupt decline was detected in 2013. The REOF model revealed three regions in HMA (northern, southwestern, and southeastern parts) with strong variations in AOD load, which are strongly correlated with atmospheric circulation and monsoon driving. Specifically, REOF1, REOF2, and REOF3 are primarily associated with frequent dust events during springtime atmospheric circulation and anthropogenic emission transport during the monsoon season. Aerosol types were divided into four types, BC aerosol, DUST aerosol, MIX aerosol, and clean conditions, whose proportions were 16.7%, 16.1%, 6.6%, and 60.6%, respectively. The clean conditions constituted the main aerosol type in the region. The AOD notably decreased snow albedo (17.8%) and increased snowmelt (9.0%); we highlight the contribution of AOD to the intensification of snowmelt. These results could provide guidance for further studies on the relationship between snowmelt and AOD.
HSD2Former: Hybrid-Scale Dual-Domain Transformer with Crisscrossed Interaction for Hyperspectral Image Classification
Authors: Luo Binxin , Li Meihui , Wei Yuxing , Zuo Haorui , Zhang Jianlin , Liu Dongxu
Published: November 2024

An unescapable trend of hyperspectral image (HSI) has been toward classification with high accuracy and splendid performance. In recent years, Transformers have made remarkable progress in the HSI classification task. However, Transformer-based methods still encounter two main challenges. First, they concentrate on extracting spectral information and are incapable of using spatial information to a great extent. Second, they lack the utilization of multiscale features and do not sufficiently combine the advantages of the Transformer’s global feature extraction and multiscale feature extraction. To tackle these challenges, this article proposes a new solution named the hybrid-scale dual-domain Transformer with crisscrossed interaction (HSD2Former) for HSI classification. HSD2Former consists of three functional modules: dual-dimension multiscale convolutional embedding (D2MSCE), mixed domainFormer (MDFormer), and pyramid scale fusion block (PSFB). D2MSCE supersedes conventional patch embedding to generate spectral and spatial tokens at different scales, effectively enriching the diversity of spectral-spatial features. MDFormer is designed to facilitate self-enhancement and information interaction between the spectral domain and spatial domain, alleviating the heterogeneity of the spatial domain and spectral domain. PSFB introduces a straightforward fusion manner to achieve advanced semantic information for classification. Extensive experiments conducted on four datasets demonstrate the robustness and significance of HSD2Former. The classification evaluation indicators of OA, AA, and Kappa on four datasets almost exceed 98%, reaching state-of-the-art performance.
Study on the Evolution of the Mountain Ecological Environment Based on Remote Sensing and Water Footprints: Case Study of Northwestern Sichuan, Southwest China
Authors: Huang Lei , Xiao Jiangtao , Zhou Zhiquan , Ren Ping
Published: November 2024

The unique geographical conditions in southwestern China lead to a fragile ecological balance and uneven geographical distribution of water resources. This study takes the Aba Tibetan and Qiang Autonomous Prefecture as its research subject, investigating the connection between water resources and the ecological environment in the Aba Tibetan and Qiang Autonomous Prefecture, which is of paramount importance. Given this, the current study constructs the remote sensing ecological index and water footprint for the period spanning from 2000 through 2020, analyses the coordination status of the two by using the coupling coordination degree, and then combines it with a detector to determine the primary drivers impacting the state of the ecological environment. The outcomes reveal that: (1) The ecological environment of the Aba Tibetan and Qiang Autonomous Prefecture gradually improved from 2000 through 2020, and the excellent ecological environment area observed within the study’s scope is primarily focused in the eastern part. The poor ecological environment area is focused mainly in the northwestern part within the study’s scope. (2) The total water footprint of the Aba Tibetan and Qiang Autonomous Prefecture has increased year by year, with agricultural water consumption comprising an immense 78% proportion, dominating the total water footprint. (3) The coupled coordination degree of the remote sensing ecological index and the water footprint shows a rising trend in general, turning from a barely coordinated stage to an intermediate coordinated stage. (4) The geodetector results show that all factor interactions were dual-factor enhancement or nonlinear enhancement, and the WET, NDBSI and NDVI factors contributed the most. The above results can provide important guidance for utilizing water resources and protecting the ecological environment in the Aba Tibetan and Qiang Autonomous Prefecture.
Multiple-Input Multiple-Output Synthetic Aperture Radar Waveform and Filter Design in the Presence of Uncertain Interference Environment
Authors: Xu Ke , Sun Guohao , Ji Yuandong , Ding Zhiquan , Chen Wenhao
Published: November 2024

Multiple-input multiple-output synthetic aperture radar (MIMO-SAR) anti-jamming waveform design relies on accurate prior information about the interference. However, it is difficult to obtain accurate prior knowledge about uncertain intermittent sampling repeater jamming (ISRJ), leading to a severe decline in the detection performance of MIMO-SAR systems. Therefore, this article studies the robust joint design problem of MIMO radar transmit waveform and filter against uncertain ISRJ. We characterize two categories of uncertain interference, including sample length uncertainty and sample-time uncertainty, modeled as Gaussian distribution in different range bins. Based on the uncertain interference model, we formulate the maximizing SINR as a figure of merit, which is a non-convex quadratic optimization problem under specific waveform constraints. Based on the alternating direction method of multipliers (ADMM) framework, a novel joint design algorithm of waveform and filter is proposed. In order to improve the convergence performance of ADMM, the difference in convex functions (DC) programming is applied to the ADMM iterations framework to solve the problem of waveform energy inequality constraint. Finally, numerical results demonstrate the effectiveness and robustness of the proposed method, compared to the existing methods that utilize deterministic interference models in the uncertain ISRJ environment. Moreover, the spaceborne SAR real scene imaging simulations are conducted to evaluate the anti-ISRJ performance.
Combining the SHAP Method and Machine Learning Algorithm for Desert Type Extraction and Change Analysis on the Qinghai–Tibetan Plateau
Authors: Lu Ruijie , Liu Shulin , Duan Hanchen , Kang Wenping , Zhi Ying
Published: November 2024

For regional desertification control and sustainable development, it is critical to quickly and accurately understand the distribution pattern and spatial and temporal changes of deserts. In this work, five different machine learning algorithms are used to classify different desert types on the Qinghai–Tibetan Plateau (QTP), and their classification performance is evaluated on the basis of their classification results and classification accuracy. Then, on the basis of the best classification model, the Shapely Additive Explanations (SHAP) method is used to clarify the contribution of each classification feature to the identification of desert types during the machine learning classification process, both globally and locally. Finally, the independent and interactive effects of each factor on desert change on the Qinghai-Tibetan Plateau during the study period are quantitatively analyzed via geodetector. The main results are as follows: (1) Compared with other classification algorithms (GTB, CART, KNN, and SVM), the RF classifier achieves the best performance in classifying QTP desert types, with an overall accuracy (OA) of 87.11% and a kappa coefficient of 0.83. (2) From the perspective of the overall classification of deserts, the five features, namely, elevation, slope, VV, VH, and GLCM, contribute most significantly to the features. In terms of the influence of each classification feature on the extraction of different types of deserts, the radar backscattering coefficient VV serves the most important role in distinguishing sandy deserts; the VH is helpful in distinguishing the four types of deserts: rocky desert, alpine cold desert, sandy deserts, and loamy desert; slope is more effective in distinguishing between the two desert types (rocky desert and alpine cold desert) and other types of deserts; and elevation has a significant role in the identification of alpine cold deserts; and the short-wave infrared band SR_B7 has an important role in the identification of salt crusts and saline deserts. (3) During the study period, the QTP deserts exhibited a reversing trend, and the proportion of desert area decreased from 28.62% to 26.20%. (4) Compared with other factors, slope, precipitation, elevation, vegetation type, and the human footprint have greater effects on changes in the QTP desert area, and the interactions among the factors affecting changes in the desert area all show bidirectional enhancement or nonlinear enhancement effects.
An Efficient Anomalous Sound Detection System for Microcontrollers
Authors: Lo Yi-Cheng , Tsai Tsung-Lin , Yang Chieh-Wen , Wu An-Yeu
Published: November 2024

Anomalous Sound Detection (ASD) systems are pivotal in the Industrial Internet of Things (IIoT). Through the early detection of machines’ anomalies, these systems facilitate proactive maintenance, thereby mitigating potential losses. Although prior studies have improved system accuracy using various advanced machine learning technologies, they frequently neglect the associated substantial computing and storage demands, which are crucial in resource-constrained IIoT environments. In this paper, we propose an ASD system that is efficiently optimized for both software and hardware considerations regarding edge intelligence. For the software aspect, we identify signal variation as a critical issue for ASD. Hence, we introduce a suite of lightweight yet robust processing techniques that enhance accuracy while minimizing resource consumption. As for the hardware aspect, we find that memory constraints may be a significant challenge for deploying ASD systems on microcontrollers (MCUs). Therefore, we propose a memory-aware pruning algorithm specialized for ASD to fit into MCUs’ constraints. Finally, we evaluate our method on the DCASE dataset, and the results show that our system achieves favorable outcomes in both accuracy and resource efficiency, marking our contribution to ASD system practice.
Internet of Things-Based Automated Solutions Utilizing Machine Learning for Smart and Real-Time Irrigation Management: A Review
Authors: Nsoh Bryan , Katimbo Abia , Guo Hongzhi , Heeren Derek M. , Nakabuye Hope Njuki , Qiao Xin , Ge Yufeng , Rudnick Daran R. , Wanyama Joshua , Bwambale Erion , Kiraga Shafik
Published: November 2024

This systematic review critically evaluates the current state and future potential of real-time, end-to-end smart, and automated irrigation management systems, focusing on integrating the Internet of Things (IoTs) and machine learning technologies for enhanced agricultural water use efficiency and crop productivity. In this review, the automation of each component is examined in the irrigation management pipeline from data collection to application while analyzing its effectiveness, efficiency, and integration with various precision agriculture technologies. It also investigates the role of the interoperability, standardization, and cybersecurity of IoT-based automated solutions for irrigation applications. Furthermore, in this review, the existing gaps are identified and solutions are proposed for seamless integration across multiple sensor suites for automated systems, aiming to achieve fully autonomous and scalable irrigation management. The findings highlight the transformative potential of automated irrigation systems to address global food challenges by optimizing water use and maximizing crop yields.
Resilient Multi-Robot Coverage Path Redistribution Using Boustrophedon Decomposition for Environmental Monitoring
Authors: Gong Junghwan , Kim Hyunbin , Lee Seunghwan
Published: November 2024

This study introduces a resilient and adaptive multi-robot coverage path planning approach based on the Boustrophedon Cell Decomposition algorithm, designed to dynamically redistribute coverage tasks in the event of robot failures. The proposed method ensures minimal disruption and maintains a balanced workload across operational robots through a propagation-based redistribution strategy. By iteratively reallocating the failed robot’s coverage path to neighboring robots, the method prevents any single robot from becoming overburdened, ensuring efficient task distribution and continuous environmental monitoring. Simulations conducted in five distinct environments, ranging from simple open areas to complex, obstacle-rich terrains, demonstrate the method’s robustness and adaptability. A key strength of the proposed approach is its fast and efficient task reallocation process, achieved with minimal propagation cycles, making it suitable for real-time applications even in complex scenarios. The approach reduces task variance and maintains balanced coverage throughout the mission.
Smart Autism Spectrum Disorder Learning System Based on Remote Edge Healthcare Clinics and Internet of Medical Things
Authors: Mohammed Mazin Abed , Alyahya Saleh , Mukhlif Abdulrahman Abbas , Abdulkareem Karrar Hameed , Hamouda Hassen , Lakhan Abdullah
Published: November 2024

Autism spectrum disorder (ASD) is a brain disorder causing issues among many young children. For children suffering from ASD, their learning ability is typically slower when compared to normal children. Therefore, many technologies aiming to teach ASD children with optimized learning approaches have emerged. With this motivation, this study presents a smart autism spectrum disorder learning system based on remote edge healthcare clinics and the Internet of Medical Things, the objective of which is to offer an online education and healthcare environment for autistic children. Concave and convex optimization constraints, such as accuracy, learning score, total processing time with deadline, and resource failure, are considered in the proposed system, with a focus on different autism education learning applications (e.g., speaking, reading, writing, and listening), while respecting the system’s quality of service (QoS) requirements. All of the autism applications are executed on smartwatches, mobile devices, and edge healthcare nodes during their training and analysis in the system. This study presents the smartwatch autism spectrum data learning scheme (SM-ASDS), which consists of different offloading approaches, training analyses, and schemes. The SM-ASDS algorithm methodology includes partitioning offloading and deep convolutional neural network (DCNN)- and adaptive long short-term memory (ALSTM)-based schemes, which are used to train autism-related data on different nodes. The simulation results show that SM-ASDS improved the learning score by 30%, accuracy by 98%, and minimized the total processing time by 33%, when compared to baseline methods. Overall, this study presents an education learning system based on smartwatches for autistic patients, which facilitates educational training for autistic patients based on the use of artificial intelligence techniques.
The Development of an Electron Pulse Dilation Photomultiplier Tube Diagnostic Instrument
Authors: Fu Wenyong , Hu Chenman , Chen Ping , Zhou Rongyan , Li Ling
Published: November 2024

A new pulse-dilated photomultiplier tube (PD-PMT) with sub-20 ps temporal resolution and associated drivers have been developed for use detection and signal amplification in the inertial confinement fusion (ICF) community. The PD-PMT is coupled to a transmission line output in order to provide a continuous time history of the input signal. Electron pulse dilation provides high-speed detection capabilities by converting incoming signals into a free-electron cloud and manipulating the electron signal with electric and magnetic fields. This velocity dispersion is translated into temporal separation after the electrons transit into a drift space. The free electrons are then detected by using conventional time-resolved methods and the effective temporal resolution is improved about 12 times. In order to accurately obtain the actual device input signal, we experimentally investigated the relationship between microchannel plate (MCP) gain and electron energy during the first collision. We report the measurements with the PD-PMT, and the error source of the amplitude of the compressed signal is analyzed, which provides a reference for subsequent accurate construction.
Seasonal and Temporal Ensemble Models for Accurate Near-Surface Air Temperature Estimation
Authors: Jalbuena Rey , Yee Jurng-Jae
Published: November 2024

The near-surface air temperature (NSAT) is crucial for understanding thermal and urban environments. Traditional estimation methods using general remote sensing images often focus on the types of spatial data or machine learning models used, neglecting the importance of seasonal and temporal variations, limiting their accuracy. This study introduces a novel ensemble model that incorporates both seasonal and temporal information integrated with satellite-derived land surface temperature (LST) data to enhance NSAT estimation, along with a rigorous feature importance analysis to identify the most impactful parameters. Data from 2022, collected from 147 South Korean weather stations, were used to develop and evaluate the models. Thirteen initial variables, including the LST and other auxiliary data, were considered. Random forest regression was employed to build separate models for each season. This novel approach of separating data by season allowed optimized feature selection tailored to each season, improving the model efficiency and capturing finer seasonal and daily temperature variations. These seasonal models were then combined to form an ensemble model. The seasonal models demonstrated varying accuracy, with the R2 values indicating a strong correlation between the predicted and actual NSAT, particularly high in spring and fall and lower in summer and winter. The ensemble model showed improved performance, achieving an MAE of 0.534, an RMSE of 0.391, an R2 of 0.996, and a cross-validated R2 of 0.968. These findings highlight the effectiveness of incorporating seasonal and temporal information into NSAT estimation models, offering significant improvements over traditional approaches. The developed models support precise temperature monitoring and forecasting, aiding environmental and urban management.
LPC-SonoNet: A Lightweight Network Based on SonoNet and Light Pyramid Convolution for Fetal Ultrasound Standard Plane Detection
Authors: Yu Tianxiang , Tsui Po-Hsiang , Leonov Denis , Wu Shuicai , Bin Guangyu , Zhou Zhuhuang
Published: November 2024

The detection of fetal ultrasound standard planes (FUSPs) is important for the diagnosis of fetal malformation and the prevention of perinatal death. As a promising deep-learning technique in FUSP detection, SonoNet’s network parameters have a large size. In this paper, we introduced a light pyramid convolution (LPC) block into SonoNet and proposed LPC-SonoNet with reduced network parameters for FUSP detection. The LPC block used pyramid convolution architecture inspired by SimSPPF from YOLOv6 and was able to extract features from various scales with a small parameter size. Using SonoNet64 as the backbone, the proposed network removed one of the convolutional blocks in SonoNet64 and replaced the others with LPC blocks. The proposed LPC-SonoNet model was trained and tested on a publicly available dataset with 12,400 ultrasound images. The dataset with six categories was further divided into nine categories. The images were randomly divided into a training set, a validation set, and a test set in a ratio of 8:1:1. Data augmentation was conducted on the training set to address the data imbalance issue. In the classification of six categories and nine categories, LPC-SonoNet obtained the accuracy of 97.0% and 91.9% on the test set, respectively, slightly higher than the accuracy of 96.60% and 91.70% by SonoNet64. Compared with SonoNet64 with 14.9 million parameters, LPC-SonoNet had a much smaller parameter size (4.3 million). This study pioneered the deep-learning classification of nine categories of FUSPs. The proposed LPC-SonoNet may be used as a lightweight network for FUSP detection.
Fano Resonance-Associated Plasmonic Circular Dichroism in a Multiple-Dipole Interaction Born–Kuhn Model
Authors: Bian Wanlu , Zhu Guodong , Ma Fengcai , Zhu Tongtong , Fang Yurui
Published: November 2024

Plasmon chirality has garnered significant interest in sensing application due to its strong electromagnetic field localization and highly tunable optical properties. Understanding the effects of mode coupling in chiral structures on chiral optical activity is particularly important for advancing this field. In this work, we numerically investigate the circular dichroism (CD) of elliptical nanodisk dimers arranged in an up-and-down configuration with a specific rotation angle. By adjusting the inter-particle distance and geometric parameters, we introduce the coupling between dipole and electric hexapole modes, forming an extended Born–Kuhn model that achieves strong CD. Our findings show that the coupling of dipole modes with electric hexapole modes in elliptical nanodisks can also show obvious Fano resonance and a strong CD effect, and the structure with the largest Fano asymmetry factor shows the highest CD. In addition, CD spectroscopy is highly sensitive to changes in the refractive index of the surrounding medium, especially in the visible and near-infrared regions, highlighting its potential for application in high-sensitivity refractive index sensors.
Exploring Greek Students’ Attitudes Toward Artificial Intelligence: Relationships with AI Ethics, Media, and Digital Literacy
Authors: Saklaki Asimina , Gardikiotis Antonis
Published: November 2024

This exploratory study (N = 310) investigates the relationship between students’ attitudes toward artificial intelligence (AI), their attitudes toward AI ethics, and their media and digital literacy levels. This study’s specific objectives were to examine students’ (a) general attitudes toward AI, (b) attitudes toward AI ethics, (c) the relationship between the two, and (d) whether attitudes toward AI are associated with media and digital literacy. Participants, drawn from a convenience sample of university students, completed an online survey including four scales: (a) a general attitude toward AI scale (including two subscales, positive and negative attitudes), (b) an attitude toward AI ethics scale (including two subscales, attitudes toward accountable and non-accountable AI use), (c) a media literacy scale, and (d) a digital literacy scale, alongside demographic information. The findings revealed that students held moderate positive attitudes toward AI and strong attitudes favoring accountable AI use. Interestingly, media literacy was positively related to accountable AI use and negatively to positive attitudes toward AI, whereas digital literacy was positively related to positive attitudes, and negatively to negative attitudes toward AI. These findings carry significant theoretical implications by highlighting the unique relationship of distinct literacies (digital and media) with students’ attitudes. They also offer practical insights for educators, technology designers, and administrators, emphasizing the need to address ethical considerations in AI deployment.
Direct Anatomical Reconstruction of the Achilles Tendon and Its Application for Surgical Treatment of Acute Achilles Tendon Ruptures
Authors: Pisano Alessandro , Caruso Gaetano
Published: November 2024

Background/Objectives: Acute ruptures of the Achilles Tendon (AT) are common injuries in the active population, in particular among men aged 30 to 50. Full functional recovery after this kind of injury is long and challenging and nowadays there is no universal “gold standard” strategy when dealing with them. Methods: When it comes to surgical treatment, various techniques have been described: in case of a typical lesion at the midportion of the tendon (which is the most common type), the basic principle of surgical repair is the end-to-end suture of the tendon stumps. The AT (“calcaneal tendon” according to the International Anatomical Terminology) is the strongest tendon of the human body, it is the conjunct tendon of the two Gastrocnemii Muscles and the Soleus Muscle and has a well-recognizable twisted structure: the subtendon from the Medial Head of the Gastrocnemius attaches postero-laterally on the calcaneal tendon footprint, the subtendon from the Lateral Head of the Gastrocnemius attaches antero-laterally and the subtendon from the Soleus attaches medially, therefore creating a 90° twist of the tendon structure. Results: the twisted structure of the human AT is of central importance to its biomechanics, since it gives the tendon a higher resistance to deformation and concurs in supination of the subtalar joint during gait. Conclusions: given the abovementioned anatomical and biomechanical premises, we believe that the restoration of the subtendons anatomy can lead to a better functional recovery of the Triceps Surae—Achilles Tendon complex, therefore we recently decided to adopt for open surgical repair the Direct Anatomical Reconstruction of the Achilles Tendon, which we describe in the present article.
Analysis of the Morphological Characteristics of PM2.5 and Its Microbiological Composition in a Fattening Pig House
Authors: Tang Mingfeng , Jian Yue , Zhu Jiaming , Tian Kun , Tan Qiong , Zhao Run
Published: November 2024

Particulate matter (PM2.5) in pig houses and the microorganisms in PM2.5 restrict the sustainable development of the pig industry and have a negative influence on environmental sustainability. This study aimed to investigate the morphological characteristics and diel microbial composition of PM2.5 in fattening pig sheds and explore how changes in the diel microbial composition of PM2.5 correlate with environmental factors and sources. To this end, environmental data from a fattening pig house were monitored, and PM2.5, feed, and faecal particles were examined using electron microscopy. Additionally, the bacterial and fungal assemblages contained in PM2.5 were analysed using 16S and 18S rRNA gene sequencing, respectively. The results showed that NH3, CO2, temperature, and relative humidity were significantly higher at night than during the day. PM2.5 particles from the fattening pig house exhibited different morphologies such as spherical, flocculent, and chain structures. The microbial diversity and bacterial assemblage showed significant variations, which were related to diel environmental factors in the fattening house. In addition, faeces may be the main source of airborne bacteria and feed may be the main source of airborne fungi in fattening houses. These findings provide a scientific basis for exploring the potential risks of the morphological characteristics of PM2.5 and its microbial composition to human and animal health. Additionally, they contribute to the sustainable development of the pig industry and the protection of the environment.
Color as a Key Factor in Creating Sustainable Living Spaces for Seniors
Authors: Jaglarz Anna
Published: November 2024

Color as a feature of interior spaces is a fundamental component of human interaction with the built environment. Therefore, planning the design concept of an interior space with attention to color choices is extremely important, especially when the users of a given space include individuals who require additional support and care, such as the elderly. The colors used in architecture and interiors play a crucial role in the well-being of older adults, influencing their spatial perception, behavior, and activity. With age, color perception changes due to the effects of the aging process on vision. Visual deficits lead to shifts in sensory perception, which can, in turn, affect the sense of well-being, often creating a heightened sense of uncertainty. These changes in color perception due to aging may impact the color preferences of older people within their living environments. To create supportive environments for seniors, it is necessary to investigate which colors are most easily recognized, which colors are easiest to distinguish, and which colors are preferred by older adults. Based on a review of the literature and research findings on age-related changes in chromatic and spatial perception, design recommendations and guidelines have been developed. To supplement this knowledge, a survey was conducted among seniors to gather insights on their color preferences. Observations and analyzes of the survey results point to strategies for color use and underscore the importance of color in designing sustainable living spaces for seniors.
Leader–Member Exchange (LMX) and Adjustment to the Work Mode as Protective Factors to Counteract Exhaustion and Turnover Intention: A Chain Mediation Model
Authors: Petrilli Sara , Giunchi Marianna , Vonthron Anne-Marie
Published: November 2024

In the context of the New Normal and the VUCA (volatile, uncertain, complex, ambiguous) era, organisations face adjustments to the changes brought by the COVID-19 pandemic, especially the shifting to new work modes and configurations with their consequences on employees’ wellbeing, in terms of exhaustion, resignation, and quitting tendencies. This study, rooted in the psychology of sustainability and sustainable development, employs a primary prevention perspective to examine a relational factor, the leader–member exchange (LMX), which might shield employees from exhaustion and turnover intention. Specifically, we propose a double-chained mediation model to investigate how high-quality LMX fosters a positive adjustment to employees’ specific work modes, in-person or hybrid work, subsequently reducing employees’ feelings of exhaustion and their intentions to leave the organisation. A convenience sample of 257 Italian employees participated in this study by completing an online self-report survey. Hypotheses were tested using the PROCESS macro in SPSS 25.0 (Model 6). The results indicate that positive LMX and adjustment to the work mode reduce exhaustion and turnover intention; furthermore, they highlight the existence of a more complex dynamic linking LMX to turnover intention through a double-chained mediation of adjustment to the work mode and exhaustion. Indeed, higher LMX quality favours adjustment to the work mode, decreasing employees’ feelings of exhaustion and, in turn, their turnover intention. Specifically, the findings of this study add a novel contribution to the literature on the psychology of sustainability and sustainable development by emphasising the significance of positive LMX in becoming a factor of wellbeing and sustainability in the workplace through the promotion of the adjustment to both in-person and hybrid work modes. Organisations may benefit of this approach to LMX that, through the consideration of employees’ needs, may favour their adjustment to different work modes, thus becoming a sustainable LMX, and a promoter of employees’ wellbeing and retention.
The Digital Economy, Integration of Productive Services and Manufacturing, and High-Quality Development of the Manufacturing Sector: Evidence from China
Authors: Zhang Aiqin , Zhang Wenjie , Guo Xiaoqiang
Published: November 2024

The rapid penetration of digital technologies into the manufacturing sector has profoundly impacted the development of China’s manufacturing industry. This study aims to explore the role of the digital economy (DE) in promoting high-quality development in China’s manufacturing sector and to reveal the specific mechanisms through which it enhances the sustainability of manufacturing. Using panel data from 30 provinces across China for 2012–2022, this study employs an econometric model to conduct an empirical analysis. The results indicate that the DE significantly fosters high-quality manufacturing development (HQMD), particularly in eastern and central regions. Furthermore, by promoting the integration of productive services with manufacturing (IPSIM), the DE accelerates advancements in HQMD. The threshold effect analysis unveils a nonlinear relationship between the DE and HQMD. Based on these findings, policy recommendations are proposed to encourage balanced regional DE growth, strengthen digital innovation capacities, and catalyze the IPSIM. These insights provide critical implications for advancing high-quality development in China’s manufacturing sector.
Rapid, Non-Destructive Prediction of Ripeness of Pink Lady Apples by Using Near-Infrared Spectroscopic Methods to Monitor Firmness, Sugar Content, Juiciness and Acidity
Authors: Arnal Amandine , Volmerange Léa , Brustel Jean , Verdier Céline , Gerbaud Sylvain , Pages Marielle , Levasseur-Garcia Cecile
Published: November 2024

Apples are one of the most widely consumed fruits in the world and are available year- round. In France, Pink Lady production has increased despite stable global production in recent years. To meet consumer expectations in terms of quality, apples must be at optimum ripeness. Traditional destructive methods are currently used to measure physicochemical parameters. To avoid such destructive measurements, it has been shown in the literature that near-infrared (NIR) spectroscopy can predict fructose and glucose content in apple juice, as well as firmness, titratable acidity and sugar content in Fuji. The present study demonstrates the relevance of the MicroNIR spectrometer to address agricultural sustainability concerns. This compact device is easy to use in the field and allows non-destructive monitoring of the physicochemical characteristics of Pink Lady. The device acquires NIR spectra from different areas of apples, followed by standard analyses to assess these characteristics. Results indicate no impact on measurements across different quarters of the apple, though there is a slight impact between the median zone and the poles. Firmness is predictable with a 77 N threshold (using partial least square regression), and juiciness prediction is reliable, though a larger database could improve the model. Predictions for sugar content and acidity still need improvement, which would confirm the MicroNIR device’s potential for assessing Pink Lady apple ripeness in the field.
Organic Mulching: A Sustainable Technique to Improve Soil Quality
Authors: Rossi Gabriella , Beni Claudio , Neri Ulderico
Published: November 2024

Organic mulching is a promising technique for sustainable weed control and soil management, as it enhances crop growth, soil quality, water retention, and erosion control. This research evaluated the effects of organic mulches—wheat straw, wood chips, spray cellulose pulp, compost, and a cover crop mixture—on the physical–mechanical properties of organic garden soil transitioning to natural farming. The controlled soil received no mulch. The soil was fertilized with mature bovine manure prior to a three-year crop rotation of tomato, lettuce, and savoy cabbage. Mulching occurred after the second harrowing and before transplanting. Soil analyses were conducted to assess changes after three years. Soil organic carbon levels increased significantly in soils treated with compost, cover crops, or chipped wood mulching (6.81, 3.17, and 2.07%, respectively) compared to other treatments (1.24% in the control plot). Different kinds of mulch had a significant impact on soil’s physical–mechanical parameters. Compost, compared to the control, decreased the bulk density (from 1.22 to 0.89 Mg m−3), increased the infiltration rate (from 8.53 to 21.07 L m−2), and reduced compressive deformation (from 37.08 to 18.23%). The composition of mulch materials, specifically their nitrogen and carbon concentrations, C/N ratio, and moisture content, plays a significant role in influencing changes in soil properties.
Dynamics of Zinder’s Urban Landscape: Implications for Sustainable Land Use Management and Environmental Conservation
Authors: Doulay Seydou Kadiza , Morenikeji Wole , Diouf Abdoulaye , Dicko Kagou , Erdanaev Elbek , Loewner Ralf , Okhimamhe Appollonia Aimiosino
Published: November 2024

Unplanned urban expansion poses significant challenges to environmental sustainability and urban planning. This study analyzes the spatiotemporal dynamics of Zinder’s urban landscape using Landsat satellite imagery from 1988, 2000, 2011, and 2022. The study applied remote sensing (RS), geographic information system (GIS) techniques, and urban growth models. The random forest classifier, a machine learning algorithm, was used to classify three land use/land cover categories: “vegetation”, “built-up”, and “others”. Zinder’s arid environment is characterized by sparse vegetation, which constitutes a limited but vital component of its landscape. Despite the already sparse vegetation in the area, the findings reveal a 3.5% reduction in vegetation cover between 1988 and 2022, alongside an 11.5% increase in “built-up” areas and an 8% decrease in the “others” category. This loss of already minimal vegetation raises significant concerns about environmental degradation and the exacerbation of desertification risks. Interestingly, urban expansion showed no significant correlation with population growth (r = 0.29, p > 0.5), suggesting that other factors, such as economic activities, infrastructure development, and land use policies, drive land conversion. Edge expansion emerged as the dominant growth type, with a significant directional preference (Chi-Square = 2334.41, p < 0.001) toward major roads and areas with higher accessibility to public services. These findings emphasize the need for strategic urban planning and land management policies to address the drivers of unplanned expansion. Prioritizing sustainable infrastructure development, enforcing land use regulations, and conserving natural landscapes are critical to balancing urban growth with environmental preservation, ensuring resilience and sustainability in Zinder.
Impact of Energy Intensity and CO2 Emissions on Economic Growth in Gulf Cooperation Council Countries
Authors: Abid Ihsen , Hechmi Soumaya , Chaabouni Ines
Published: November 2024

This study investigates the impact of energy intensity and CO2 emissions on economic growth in Gulf Cooperation Council (GCC) countries, aiming to understand the interplay between energy consumption, environmental sustainability, and economic performance. We analyze data from 1990 to 2023 across six GCC countries. The study employs the fixed effects model, random effects model, and pooled regression model to examine the relationships between energy intensity, CO2 emissions, and GDP growth, controlling for factors such as foreign direct investment, trade openness, population, unemployment, and urbanization. Our findings reveal a significant negative impact of energy intensity on economic growth, and an increase in energy intensity is associated with a decrease of approximately 0.2969 units in GDP, indicating that higher energy consumption per unit of output hinders economic performance. While CO2 emissions positively affect growth in GCC countries, a one-unit increase in CO2 emissions is associated with an increase of approximately 0.3961 units in GDP. The study emphasizes the necessity for GCC countries to adopt sustainable energy practices to reduce energy intensity and boost economic growth. By aligning economic strategies with environmental sustainability goals, these nations can achieve long-term growth while effectively addressing the challenges of climate change. This research contributes to the ongoing discourse on sustainable development in the region and underscores the importance of harmonizing economic growth strategies with environmental objectives.