bluuebunny/crossref_metadata_2025 huggingface dataset:
Effect of DEM Used for Terrain Correction on Forest Windthrow Detection Using COSMO SkyMed Data
Authors: Dalponte Michele , Marinelli Daniele , Solano-Correa Yady Tatiana
Published: November 2024

Preprocessing Synthetic Aperture Radar (SAR) data is a crucial initial stage in leveraging SAR data for remote sensing applications. Terrain correction, both radiometric and geometric, and the detection of layover/shadow areas hold significant importance when SAR data are collected over mountainous regions. This study aims at investigating the impact of the Digital Elevation Model (DEM) used for terrain correction (radiometric and geometric) and for mapping layover/shadow areas on windthrow detection using COSMO SkyMed SAR images. The terrain correction was done using a radiometric and geometric terrain correction algorithm. Specifically, we evaluated five different DEMs: (i–ii) a digital terrain model and a digital surface model derived from airborne LiDAR flights; (iii) the ALOS Global Digital Surface Model; (iv) the Copernicus global DEM; and (v) the Shuttle Radar Topography Mission (SRTM) DEM. All five DEMs were resampled at 2 m and 30 m pixel spacing, obtaining a total of 10 DEMs. The terrain-corrected COSMO SkyMed SAR images were employed for windthrow detection in a forested area in the north of Italy. The findings revealed significant variations in windthrow detection across the ten corrections. The detailed LiDAR-derived terrain model (i.e., DTM at 2 m pixel spacing) emerged as the optimal choice for both pixel spacings considered.
Generative Simplex Mapping: Non-Linear Endmember Extraction and Spectral Unmixing for Hyperspectral Imagery
Authors: Waczak John , Lary David J.
Published: November 2024

We introduce a new model for non-linear endmember extraction and spectral unmixing of hyperspectral imagery called Generative Simplex Mapping (GSM). The model represents endmember mixing using a latent space of points sampled within a (n−1)-simplex corresponding to n unique sources. Barycentric coordinates within this simplex are naturally interpreted as relative endmember abundances satisfying both the abundance sum-to-one and abundance non-negativity constraints. Points in this latent space are mapped to reflectance spectra via a flexible function combining linear and non-linear mixing. Due to the probabilistic formulation of the GSM, spectral variability is also estimated by a precision parameter describing the distribution of observed spectra. Model parameters are determined using a generalized expectation-maximization algorithm, which guarantees non-negativity for extracted endmembers. We first compare the GSM against three varieties of non-negative matrix factorization (NMF) on a synthetic data set of linearly mixed spectra from the USGS spectral database. Here, the GSM performed favorably for both endmember accuracy and abundance estimation with all non-linear contributions driven to zero by the fitting procedure. In a second experiment, we apply the GTM to model non-linear mixing in real hyperspectral imagery captured over a pond in North Texas. The model accurately identified spectral signatures corresponding to near-shore algae, water, and rhodamine tracer dye introduced into the pond to simulate water contamination by a localized source. Abundance maps generated using the GSM accurately track the evolution of the dye plume as it mixes into the surrounding water.
D-InSAR-Based Analysis of Slip Distribution and Coulomb Stress Implications from the 2024 Mw 7.01 Wushi Earthquake
Authors: Ding Yurong , Liu Xin , Dai Xiaofeng , Yin Gaoying , Yang Yang , Guo Jinyun
Published: November 2024

On 23 January 2024, an Mw 7.01 earthquake struck the Wushi County, Xinjiang Uygur Autonomous Region, China. The occurrence of this earthquake provides an opportunity to gain a deeper understanding of the rupture behavior and tectonic activity of the fault system in the Tianshan seismic belt. The coseismic deformation field of the Wushi earthquake was derived from Sentinel-1A ascending and descending track data using Differential Interferometric Synthetic Aperture Radar (D-InSAR) technology. The findings reveal a maximum line-of-sight (LOS) displacement of 81.1 cm in the uplift direction and 16 cm in subsidence. Source parameters were determined using an elastic half-space dislocation model. The slip distribution on the fault plane for the Mw 7.01 Wushi earthquake was further refined through a coseismic slip model, and Coulomb stress changes on nearby faults were calculated to evaluate seismic hazards in surrounding areas. Results indicate that the coseismic rupture in the Mw 7.01 Wushi earthquake sequence was mainly characterized by left-lateral strike-slip motion. The peak fault slip was 3.2 m, with a strike of 228.34° and a dip of 61.80°, concentrated primarily at depths between 5 and 25 km. The focal depth is 13 km. This is consistent with findings reported by organizations like the United States Geological Survey (USGS). The fault rupture extended to the surface, consistent with field investigations by the Xinjiang Uygur Autonomous Region Earthquake Bureau. Coulomb stress results suggest that several fault zones, including the Kuokesale, Dashixia, Piqiang North, Karaitike, southeastern sections of the Wensu, northwestern sections of the Tuoergan, and the Maidan-Sayram Fault Zone, are within regions of stress loading. These areas show an increased risk of future seismic activity and warrant close monitoring.
Enabling Design of Secure IoT Systems with Trade-Off-Aware Architectural Tactics
Authors: Orellana Cristian , Cereceda-Balic Francisco , Solar Mauricio , Astudillo Hernán
Published: November 2024

The increasing use of the Internet of Things (IoT) in homes and industry brings significant security and privacy challenges, while also considering trade-off for performance, energy consumption, and processing capabilities. Few explicit and specific guidelines exist to help architects in considering these trade-offs while designing secure IoT systems. This article proposes to address this situation by extending the well-known architectural tactics taxonomies with IoT-specific trade-offs; to preserving auditability, the trade-offs address the quality characteristics of the ISO 25010:2023 standard. The proposed technique and catalog are illustrated with the design of the Nunatak environmental monitoring system. The proposal was empirically validated with a controlled experiment, where a balanced mix of 12 novice and expert practitioners had to design a secure IoT Environmental Monitoring System; they used similar architectural tactics catalogs, with versus without trade-off information. Results suggest that having this information yield significant improvements in decision-making effectiveness (Precision) and usefulness (F1-Score), particularly benefiting less experienced designers. Wider adoption of trade-off-aware catalogs of architectural tactics will allow systematic, auditable design of secure IoT systems, and especially so by novice architects.
Coal and Gangue Detection Networks with Compact and High-Performance Design
Authors: Cao Xiangyu , Liu Huajie , Liu Yang , Li Junheng , Xu Ke
Published: November 2024

The efficient separation of coal and gangue remains a critical challenge in modern coal mining, directly impacting energy efficiency, environmental protection, and sustainable development. Current machine vision-based sorting methods face significant challenges in dense scenes, where label rewriting problems severely affect model performance, particularly when coal and gangue are closely distributed in conveyor belt images. This paper introduces CGDet (Coal and Gangue Detection), a novel compact convolutional neural network that addresses these challenges through two key innovations. First, we proposed an Object Distribution Density Measurement (ODDM) method to quantitatively analyze the distribution density of coal and gangue, enabling optimal selection of input and feature map resolutions to mitigate label rewriting issues. Second, we developed a Relative Resolution Object Scale Measurement (RROSM) method to assess object scales, guiding the design of a streamlined feature fusion structure that eliminates redundant components while maintaining detection accuracy. Experimental results demonstrate the effectiveness of our approach; CGDet achieved superior performance with AP50 and AR50 scores of 96.7% and 99.2% respectively, while reducing model parameters by 46.76%, computational cost by 47.94%, and inference time by 31.50% compared to traditional models. These improvements make CGDet particularly suitable for real-time coal and gangue sorting in underground mining environments, where computational resources are limited but high accuracy is essential. Our work provides a new perspective on designing compact yet high-performance object detection networks for dense scene applications.
Multi-Task Water Quality Colorimetric Detection Method Based on Deep Learning
Authors: Zhang Shenlan , Wu Shaojie , Chen Liqiang , Guo Pengxin , Jiang Xincheng , Pan Hongcheng , Li Yuhong
Published: November 2024

The colorimetric method, due to its rapid and low-cost characteristics, demonstrates a wide range of application prospects in on-site water quality testing. Current research on colorimetric detection using deep learning algorithms predominantly focuses on single-target classification. To address this limitation, we propose a multi-task water quality colorimetric detection method based on YOLOv8n, leveraging deep learning techniques to achieve a fully automated process of “image input and result output”. Initially, we constructed a dataset that encompasses colorimetric sensor data under varying lighting conditions to enhance model generalization. Subsequently, to effectively improve detection accuracy while reducing model parameters and computational load, we implemented several improvements to the deep learning algorithm, including the MGFF (Multi-Scale Grouped Feature Fusion) module, the LSKA-SPPF (Large Separable Kernel Attention-Spatial Pyramid Pooling-Fast) module, and the GNDCDH (Group Norm Detail Convolution Detection Head). Experimental results demonstrate that the optimized deep learning algorithm excels in precision (96.4%), recall (96.2%), and mAP50 (98.3), significantly outperforming other mainstream models. Furthermore, compared to YOLOv8n, the parameter count and computational load were reduced by 25.8% and 25.6%, respectively. Additionally, precision improved by 2.8%, recall increased by 3.5%, mAP50 enhanced by 2%, and mAP95 rose by 1.9%. These results affirm the substantial potential of our proposed method for rapid on-site water quality detection, offering new technological insights for future water quality monitoring.
MST-YOLO: Small Object Detection Model for Autonomous Driving
Authors: Li Mingjing , Liu Xinyang , Chen Shuang , Yang Le , Du Qingyu , Han Ziqing , Wang Junshuai
Published: November 2024

Autonomous vehicles operating in public transportation spaces must rapidly and accurately detect all potential hazards in their surroundings to execute appropriate actions such as yielding, lane changing, and overtaking. This capability is a prerequisite for achieving advanced autonomous driving. In autonomous driving scenarios, distant objects are often small, which increases the risk of detection failures. To address this challenge, the MST-YOLOv8 model, which incorporates the C2f-MLCA structure and the ST-P2Neck structure to enhance the model’s ability to detect small objects, is proposed. This paper introduces mixed local channel attention (MLCA) into the C2f structure, enabling the model to pay more attention to the region of small objects. A P2 detection layer is added to the neck part of the YOLOv8 model, and scale sequence feature fusion (SSFF) and triple feature encoding (TFE) modules are introduced to assist the model in better localizing small objects. Compared with the original YOLOv8 model, MST-YOLOv8 demonstrates a 3.43% improvement in precision (P), an 8.15% improvement in recall (R), an 8.42% increase in mAP_0.5, a reduction in missed detection rate by 18.47%, a 70.97% improvement in small object detection AP, and a 68.92% improvement in AR.
Triple Spectral Line Imaging of Whole-Body Human Skin: Equipment, Image Processing, and Clinical Data
Authors: Spigulis Janis , Rubins Uldis , Kviesis-Kipge Edgars , Saknite Inga , Oshina Ilze , Vasilisina Egija
Published: November 2024

Multispectral imaging can provide objective quantitative data on various clinical pathologies, e.g., abnormal content of bio-substances in human skin. Performance of diagnostics increases with decreased spectral bandwidths of imaging; from this point, ultra-narrowband laser spectral line imaging is well suited for diagnostic applications. In this study, 40 volunteers participated in clinical validation tests of a newly developed prototype device for triple laser line whole-body skin imaging. The device comprised a vertically movable high-resolution camera coupled with a specific illumination unit—a side-emitting optical fiber spiral that emits simultaneously three RGB laser spectral lines at the wavelengths 450 nm, 520 nm, and 628 nm. The prototype’s design details, skin spectral image processing, and the obtained first clinical data are reported and discussed.
Long-Tune Natural Logarithmic Wavelength Modulation Spectroscopy for Gas Sensing
Authors: Lan Lijuan , Zhang Changsheng , Wang Yibo , Xie Yu , Wang Luheng , Yang Chunhua
Published: November 2024

This article presents a gas sensing method based on long-tune natural logarithmic wavelength modulation spectroscopy (long-tune ln-WMS) and explores means to improve its accuracy. The long-tune spectrum can detect multiple gases with high precision. In ln-WMS, due to the natural logarithm algorithm, the harmonic magnitude which is related to gas concentration would not be affected by the light intensity fluctuations. However, the background signal of the harmonic will become strong and nonlinear in the long-tune spectrum. Three CO2 absorption lines and one H2O line near 2004 nm are applied to verify the proposed theory. The effects of light intensity, modulation depth, gas concentration, and phase shift on the harmonics are tested separately through both simulations and experiments. The results reveal that our proposed method can always keep the harmonics at their maximum which ensures high measurement precision. Moreover, the background signal only varies with the modulation depth, not the concentration and light intensity. Even the mechanical vibrations cannot disturb the harmonics, which enables the proposed method to be suitable for gas detection in harsh environments, especially for heavy dust and severe mechanical vibrations. The CO2 concentration detection results indicate that when the background is eliminated, the accuracy can be achieved with a relative error of below 0.5%, while the error would be greater than 5% with background presence. The proposed long-tune ln-WMS method is effective for trace gas detection (weak absorption) or over-modulation conditions and has potential applications in field inspection.
Silent Speech Eyewear Interface: Silent Speech Recognition Method Using Eyewear and an Ear-Mounted Microphone with Infrared Distance Sensors
Authors: Igarashi Yuya , Futami Kyosuke , Murao Kazuya
Published: November 2024

As eyewear devices such as smart glasses become more common, it is important to provide input methods that can be used at all times for such situations and people. Silent speech interaction (SSI) has the potential to be useful as a hands-free input method for various situations and people, including those who have difficulty with voiced speech. However, previous methods have involved sensor devices that are difficult to use anytime and anywhere. We propose a method for SSI that involves using an eyewear device equipped with infrared distance sensors. The proposed method measures facial skin movements associated with speech from the infrared distance sensor mounted on an eyewear device and recognizes silent speech commands by applying machine learning to time series sensor data. The proposed method was applied to a prototype system including a sensor device consisting of eyewear and ear-mounted microphones to measure the movements of the cheek, jaw joint, and jaw. Evaluations 1 and 2 showed that five speech commands could be recognized with an F value of 0.90 and ten longer speech commands with an F value of 0.83. Evaluation 3 showed how the recognition accuracy changes with the combination of sensor points. Evaluation 4 examined whether the proposed method can be used for a larger number of speech commands with 21 commands by using deep learning LSTM and a combination of DTW and kNN. Evaluation 5 examined the recognition accuracy in some situations affecting recognition accuracy such as re-attaching devices and walking. These results show the feasibility of the proposed method for a simple hands-free input interface, such as with media players and voice assistants. Our study provides the first wearable sensing method that can easily apply SSI functions to eyewear devices.
AI-Aided Gait Analysis with a Wearable Device Featuring a Hydrogel Sensor
Authors: Hasan Saima , D’auria Brent G. , Mahmud M. A. Parvez , Adams Scott D. , Long John M. , Kong Lingxue , Kouzani Abbas Z.
Published: November 2024

Wearable devices have revolutionized real-time health monitoring, yet challenges persist in enhancing their flexibility, weight, and accuracy. This paper presents the development of a wearable device employing a conductive polyacrylamide–lithium chloride–MXene (PLM) hydrogel sensor, an electronic circuit, and artificial intelligence (AI) for gait monitoring. The PLM sensor includes tribo-negative polydimethylsiloxane (PDMS) and tribo-positive polyurethane (PU) layers, exhibiting extraordinary stretchability (317% strain) and durability (1000 cycles) while consistently delivering stable electrical signals. The wearable device weighs just 23 g and is strategically affixed to a knee brace, harnessing mechanical energy generated during knee motion which is converted into electrical signals. These signals are digitized and then analyzed using a one-dimensional (1D) convolutional neural network (CNN), achieving an impressive accuracy of 100% for the classification of four distinct gait patterns: standing, walking, jogging, and running. The wearable device demonstrates the potential for lightweight and energy-efficient sensing combined with AI analysis for advanced biomechanical monitoring in sports and healthcare applications.
Solid-State Gas Sensors with Ni-Based Sensing Materials for Highly Selective Detecting NOx
Authors: Zhang Zhenghu , Yi Chenghan , Chen Tao , Zhao Yangbo , Zhang Yuyan , Jin Han
Published: November 2024

Precise monitoring of NOx concentrations in nitric oxide delivery systems is crucial to ensure the safety and well-being of patients undergoing inhaled nitric oxide (iNO) therapy for pulmonary arterial hypertension. Currently, NOx sensing in commercialized iNO instruments predominantly relies on chemiluminescence sensors, which not only drives up costs but also limits their portability. Herein, we developed solid-state gas sensors utilizing Ni-based sensing materials for effectively tracking the levels of NO and NO2 in the NO delivery system. These sensors comprised of NiO-SE or (NiFe2O4 + 30 wt.% Fe2O3)-SE vs. Mn-based RE demonstrated high selectivity toward 100 ppm NO under the interference of 10 ppm NO2 or 3 ppm NO2 under the interference of 100 ppm NO, respectively. Meanwhile, excellent stability, repeatability, and humidity resistance were also verified for the proposed sensors. Sensing mechanisms were thoroughly investigated through assessments of adsorption capabilities and electrochemical reactivity. It turns out that the superior electrochemical reactivity of NiO toward NO, alongside the NO2 favorable adsorption characteristics of (NiFe2O4 + 30 wt.% Fe2O3), is the primary reason for the high selectivity to NOx. These findings indicate a bright future for the application of these NOx sensors in innovative iNO treatment technologies.
A Novel Multi-Sensor Nonlinear Tightly-Coupled Framework for Composite Robot Localization and Mapping
Authors: Chen Lu , Hussain Amir , Liu Yu , Tan Jie , Li Yang , Yang Yuhao , Ma Haoyuan , Fu Shenbing , Li Gun
Published: November 2024

Composite robots often encounter difficulties due to changes in illumination, external disturbances, reflective surface effects, and cumulative errors. These challenges significantly hinder their capabilities in environmental perception and the accuracy and reliability of pose estimation. We propose a nonlinear optimization approach to overcome these issues to develop an integrated localization and navigation framework, IIVL-LM (IMU, Infrared, Vision, and LiDAR Fusion for Localization and Mapping). This framework achieves tightly coupled integration at the data level using inputs from an IMU (Inertial Measurement Unit), an infrared camera, an RGB (Red, Green and Blue) camera, and LiDAR. We propose a real-time luminance calculation model and verify its conversion accuracy. Additionally, we designed a fast approximation method for the nonlinear weighted fusion of features from infrared and RGB frames based on luminance values. Finally, we optimize the VIO (Visual-Inertial Odometry) module in the R3LIVE++ (Robust, Real-time, Radiance Reconstruction with LiDAR-Inertial-Visual state Estimation) framework based on the infrared camera’s capability to acquire depth information. In a controlled study, using a simulated indoor rescue scenario dataset, the IIVL-LM system demonstrated significant performance enhancements in challenging luminance conditions, particularly in low-light environments. Specifically, the average RMSE ATE (Root Mean Square Error of absolute trajectory Error) improved by 23% to 39%, with reductions from 0.006 to 0.013. At the same time, we conducted comparative experiments using the publicly available TUM-VI (Technical University of Munich Visual-Inertial Dataset) without the infrared image input. It was found that no leading results were achieved, which verifies the importance of infrared image fusion. By maintaining the active engagement of at least three sensors at all times, the IIVL-LM system significantly boosts its robustness in both unknown and expansive environments while ensuring high precision. This enhancement is particularly critical for applications in complex environments, such as indoor rescue operations.
Race/Ethnicity and Homeownership in an Emerging Immigrant Gateway of the US Southeast: A Neighborhood Scale Analysis
Authors: Sharma Madhuri
Published: November 2024

Owning a home has become a distant, often unattainable dream for many Americans since the 2007–2009 recession. The shortage of homes has decreased affordability, forcing 43 million U.S. households to become renters rather than owners. Racially targeted policies and widespread discrimination, coupled with neoliberal urban renewal policies, have forced communities of color, especially immigrants and the foreign-born, at the greatest disadvantage in homeownership. This paper examines tract-scale disparities in homeownership across major racial/ethnic groups. Using the U.S. Census Office of Management and Budget’s (OMB) 2019 definition of the 13-county-metropolitan statistical area (MSA) of Nashville, Tennessee, as the study area, I use five-year American Community Survey (ACS) (2015–2019) data estimates to examine the spatial disparity in homeownership and its predictors. Nashville MSA is one of the fastest-growing southern gateways, and it is also the largest, most diverse, and most intermixed metropolis in Tennessee. It contains higher than the state’s overall share of foreign-born, and during 2019–2040, its share of immigrants is projected to grow by 40.7%, making it the best-suited laboratory for race/immigrant-focused research on housing. This analysis finds significant differences in race-based mean per-capita income, with Whites ($32,522) and Asians ($32,556) at the top, whereas Blacks ($25,062) and Hispanics ($20,091) are at the lowest. The ratio of race-based per-capita-income-versus-median housing values is the highest for Whites (15.19) and Asians (15.07) and the lowest for Blacks (11.49) and Hispanics (9.27), putting these two groups as the most disadvantaged regarding their affordability. Regression models suggest lower White homeownership in higher diversity tracts among foreign-born-not-citizens (FBNCs), whereas Black and Hispanic homeownerships are higher in tracts with higher diversity among FBNCs. Interestingly, Asian homeownership is high in tracts with high-income Black tracts, pointing toward the increasing significance of class.
Design and Test of a Multi-Media Web Platform Prototype Based on People’s Preferences to Increase Cultural Heritage Awareness
Authors: Wang Benshuo , Dane Gamze , de Vries Bauke , Arentze Theo
Published: November 2024

Cultural heritage significantly influences the transmission of history from the past to the present and future. The core of protecting cultural heritage is inheritance, making sure the heritages can be sustainable forever. Various ICT methods facilitate the dissemination and preservation of cultural heritage information. However, traditional ICT platforms often focus on official authority perspectives, neglecting users’ preferences for retrieving cultural heritage information. To address this, a hypothetical media platform was developed to collect people’s preferences for cultural heritage content and media types through a questionnaire, leading to the creation of a new multi-media platform. For demonstration purposes, Strijp-S, an industrial cultural heritage site in Eindhoven, the Netherlands, was used as a case study. A questionnaire was administered to test the prototype’s effectiveness and compare it with a comprehensive search engine like Google. Independent and paired-samples t-tests were conducted to analyze the results, demonstrating that the dedicated multi-media platform prototype was more effective than Google in raising awareness of Strijp-S. These findings indicate that a platform designed based on users’ preferences can enhance public awareness of cultural heritage. This approach can assist policymakers in developing platforms to promote local cultural heritage effectively.
Shaping Entrepreneurial Intentions Through Education: An Empirical Study
Authors: Abbes Ikram
Published: November 2024

This study examines the impact of entrepreneurship education on shaping entrepreneurial intentions among business administration students at Tayma University College. Utilizing both the Entrepreneurial Intention Model (EIM) and the Entrepreneurship Education Framework (EEF), this research assesses how content, pedagogical approaches, and the learning environment influence students’ entrepreneurial aspirations. A survey of 135 male and female students was conducted, and Structural Equation Modeling (SEM) was used to analyze the relationships between educational factors and entrepreneurial intentions. The findings show that comprehensive course content, interactive teaching methods, and a supportive educational environment significantly enhance students’ perceived feasibility and desirability of entrepreneurship. Moreover, attitudes towards entrepreneurship mediate the relationship between education and entrepreneurial intentions. This study contributes to the growing literature by providing insights into how targeted entrepreneurship education can foster entrepreneurial intentions, particularly within the context of Saudi Arabia’s Vision 2030 initiative to promote economic diversification and youth empowerment. This study not only explores entrepreneurial education but also highlights its contribution to sustainability. By fostering a mindset that aligns with the socio-economic goals of Vision 2030, entrepreneurship education promotes sustainable economic growth and prepares students to contribute to a diversified and resilient economy.
Cadmium Contamination in Aquatic Environments: Detoxification Mechanisms and Phytoremediation Approach
Authors: Farias Josiane Pinheiro , Okeke Benedict C. , Demarco Carolina Faccio , Carlos Filipe Selau , da Silva Rodrigo Ferreira , da Silva Marcos Antonio , Quadro Maurízio Silveira , Pieniz Simone , Andreazza Robson
Published: November 2024

The increased presence of cadmium in the environment can lead to its increase in the food chain, particularly due to its accumulation in the consumable parts of plants. For humans, ingesting food containing high levels of Cd is a significant exposure pathway. Being a non-essential and non-metabolized element, it is harmful to microorganisms, animals, plants, and humans, even in minimal concentrations. As a result, there is a need for the remediation of both natural and urban environments. Bioremediation is a sustainable and eco-friendly technique for cleaning up the environment and reducing contamination of living organisms. This review explores the potential of phytoremediation, a bioremediation approach that utilizes plants as agents for decontamination, as a method to restore such areas. Certain plants, particularly macrophytes, are capable of remediating Cd. In response to induced stress, plants activate various tolerance mechanisms, including antioxidant enzyme systems (as peroxidase, catalase, ascorbate peroxidase, superoxide dismutase, and glutathione peroxidase) as well as non-enzymatic pathways (like phytochelatins). However, a thorough understanding of these tolerance mechanisms is essential for optimizing this method, especially for application in aquatic environments. This study will, therefore, review the existing tolerance and detoxification mechanisms for Cd, along with bioremediation strategies. The application of this eco-friendly approach is highly correlated with the three main areas required for sustainability: economic, environmental and social.
Evaluation of Sustainability and Cost Effectiveness of Using LCO2 as Cutting Fluid in Industrial Hard-Turning Installations
Authors: Llanos Iñigo , Urresti Espilla Iker , Bilbatua David , Zelaieta Oier
Published: November 2024

Conventional oil-based emulsions used in hard-turning processes present significant environmental and economic challenges, including high waste generation and hazardous disposal requirements. In response, cryogenic CO2 cooling has gained attention as a sustainable alternative, offering improved productivity, reduced tool wear and a diminished environmental footprint. While technical advances have been reported, the industrial adoption of cryogenic cooling is still limited due to the lack of clear data on its actual viability. This paper moves beyond the analysis of the technical performance of cryogenic CO2 cooling analyzed in previous works to conduct a detailed evaluation of its environmental and economic performance when machining roller bearing components with pCBN tools on a hard-turning installation. Utilizing Life Cycle Assessment (LCA) and Return-on-Investment (ROI) methodologies, this study compares cryogenic CO2 with traditional cooling methods, quantitatively assessing the environmental impact and economic viability across different manufacturing scenarios. The findings reveal that cryogenic cooling can outperform conventional cooling regarding both environmental impact and cost-effectiveness thanks to the tool life improvements provided by cryogenic cooling, specifically in cases where high tool consumption is generated during hard-turning operations. These results provide critical insights for selecting cooling strategies during the design phase of industrial turnkey projects, highlighting the potential of cryogenic CO2 as a superior solution for sustainable and efficient hard-turning operations.
Hybrid Reinforced Concrete Frames with Engineering Cementitious Composites: Experimental and Numerical Investigations
Authors: Metawa Abdulrahman , Leblouba Moussa , Barakat Samer
Published: November 2024

Reinforced concrete (RC) structures are vulnerable to damage under dynamic loads such as earthquakes, necessitating innovative solutions that enhance both performance and sustainability. This study investigates the integration of Engineered Cementitious Composites (ECC) in RC frames to improve ductility, durability, and energy dissipation while considering cost-effectiveness. To achieve this, the partial replacement of concrete with ECC at key structural locations, such as beam–column joints, was explored through experimental testing and numerical simulations. Small-scale beams with varying ECC replacements were tested for failure modes, load–deflection responses, and crack propagation patterns. Additionally, nonlinear quasi-static cyclic and modal analyses were performed on full RC frames, ECC-reinforced frames, and hybrid frames with ECC at the joints. The results demonstrate that ECC reduces the need for shear reinforcement due to its crack-bridging ability, enhances ductility by up to 25% in cyclic loading scenarios, and lowers the formation of plastic hinges, thereby contributing to improved structural resilience. These findings suggest that ECC is a viable, sustainable solution for achieving resilient infrastructure in seismic regions, with an optimal balance between performance and cost.
Influence of Digital Economy on Urban Energy Efficiency in China
Authors: Ma Haoyuan , Li Zhijiang , Dong Rui , Tang Decai
Published: November 2024

The digital economy (DE) is characterized by invention, low energy consumption, cross-sector integration, and open sharing. It can effectively enhance social production methods, influence consumer behavior, and provide new pathways to enhance total factor energy efficiency (TFEE). This paper studies 280 Chinese cities, employing the entropy method and data envelopment analysis (DEA) model to evaluate and analyze urban DE and TFEE. It also constructs a system generalized method of moments model (SGMM model) and a threshold regression model (TR model) to examine the impact of the DE on TFEE in China. The main study findings include the following: (1) The regression results of the SGMM model indicate that the effect of DE on TFEE in Chinese cities shows a U-shaped trend. (2) The regression results of the TR model further confirm a U-shaped association connecting DE and TFEE, with the threshold estimated at 0.304. (3) The economic factors and industrial structure have a major impact on inhibiting the improvement of TFEE, whereas technological advancements and environmental regulations significantly facilitate its improvement.
Initial Training of Primary Education Teachers in Nutrition: Review of Contents of Spanish University Curricula Based on ESD
Authors: Bermejo Patricia , Blanco Fontao Carolina , Arias-Gago Ana Rosa
Published: November 2024

In formal education, teachers play a fundamental role in teaching healthy nutritional habits, which are crucial for promoting balanced diets throughout life. This research aims to analyze the nutritional training of teachers by reviewing the presence of nutritional education content in their university education. The methodology employed is a descriptive documentary analysis incorporating both qualitative and quantitative components, utilizing a document analysis of the teaching guides (280 documents) from primary education degrees offered in Spain (67 universities). The results reveal significant deficiencies in the nutritional content of the teaching guides, highlighting gaps in the initial training of teachers. Among the inadequately addressed topics are food sovereignty, food security, diseases related to poor nutrition, guidelines for healthy eating, and types of diets that promote good health and respect the socioeconomic environment. Therefore, to meet some of the challenges proposed by the 2030 Agenda and the SDGs, it would be advisable to provide primary education degree students at Spanish universities with greater knowledge of nutrition-related aspects.
From Perception to Practices: Adoption of Ecosystem-Based Adaptation in Vietnam Upland Areas—A Case Study in Thua Thien Hue Province
Authors: Phuong Le Thi Hong , Khanh Ho Le Phi , Beckman Malin , Hoan Do Trong , Phung Le Dinh , Sen Le Thi Hoa
Published: November 2024

In the context of increasing interest in ecosystem-based adaptation (EbA), there remains a paucity of discussion regarding the transition from perception to practice in aiding farmer households to adapt to climate change (CC) while maintaining the provision of essential ecosystem services. Thus, this study aims to explore policymakers’ and local people’s perceptions, from thinking about the implementation of EbA strategies to responding to CC in current and future agricultural production and forestry in upland Thua Thien Hue province, Vietnam. This study has adopted the Model of Private Proactive Adaptation to CC to investigate the perceptions of EbA among various administrative and household levels through in-depth interviews and focus group discussion methods. Our findings indicate a significant relationship between the perceptions and understanding of EbA among policymakers and farmer households, and the adoption of EbA practices. Many EbA practices are already well-established and have demonstrated their ability to enhance ecosystem services provision, adaptation benefits, and livelihood and food security. These benefits are crucial for helping farmer households to adapt to CC. However, current financial, technical, and market constraints hinder the broader adoption of these practices. Therefore, to increase adaptive capacity to CC and upscale EbA practices, EbA interventions must consider technical, financial, and market aspects. Furthermore, it is essential to provide evidence from both scientific and practical perspectives and disseminate information on EbA practices to encourage broader adoption by local farmers. In addition, supportive policies from various departmental and agency levels are necessary for managers in the agricultural and forest sectors as well as households to recognize EbA as a vital strategy for developing agriculture and forestry in a manner that is sustainable and resilient to CC.
Agricultural Innovations and Adaptations to Climate Change in the Northern Cameroon Region
Authors: Tchuenga Gaitan Thierry Seutchueng , Tchindjang Mesmin , Ngome Precillia Ijang Tata , Degrande Ann , Basga Simon Djakba , Saha Frédéric
Published: November 2024

Adaptation to climate change has remained a major socio-ecological issue in the Northern Region of Cameroon since 1973. Presently, this region is subject to the severe chaos of drought, floods, and ecosystem degradation, causing harm and disrupting climatic patterns. Climate change results in the drying of surface water and crops, threatening food security and the well-being of households. It has a serious impact on the entire agricultural production system at global scale. Here, it is suggested that successive adjustments to deeper systemic and transformational adaptations through efforts from NGOs, the Government, and donors, as well as innovations, are necessary to offset the negative impact of climate change on the agricultural value chain. Therefore, this research aimed to identify adaptation strategies and practices for rural communities and households, who suffer from limited access to these agricultural innovations, for a transformative adaptation. Through surveys and focus group discussions carried out in several villages in the Northern Cameroon Region, this study provides empirical data on emerging agricultural innovations in contrasting socio-economic, agricultural, and ecological contexts. Our findings demonstrate that agricultural innovations fostered at the village level have several characteristics that contribute to adaptation and mitigation of the impact of climate change. To begin with, conservation agriculture is very interesting, because crop residues left on the soil protect it from rainfall and dry winds, and gradually add humus to the top soil. In addition, agroforestry plays an important role for the household regarding ecosystem services, including food supply, soil fertility, protection from erosion, regulation of water regime, and sociocultural value. Generally, heads of households (83%) were more involved in innovative initiatives than other social strata, resulting in unequal access and proximity to agricultural innovations. Furthermore, the results highlight a significant lack of coordination and poor visibility of permanent structures supporting agricultural innovations at local level, weakening the sustainable transformation of adaptation. From a scientific perspective, this study could help build a conceptual relationship between agricultural innovation and sustainability transformation, i.e., a climate-smart agriculture. In practice, it provides levers that can be used to multiply and expedite agricultural innovation processes, water conservation, and livestock sustainability, thus contributing to the sustainability of the whole agricultural system in Cameroon and within the Sahel region of Africa.
Impact of Management Strategies on Reducing of Mulching Film Residues Pollution in Arid Regions
Authors: Zhang Mei , Liu Jintong , Huang Jinlou , Fu Tonggang , Gao Hui
Published: November 2024

Plastic pollution caused by mulching film residues (MFRs) is escalating in arable lands, which affects the function of agricultural ecosystems, and poses a serious obstacle to agricultural sustainable development in arid regions. Internationally, increasing recycling rate of polyethylene (PE) film and adopting biodegradable films are recommended strategies to mitigate plastic pollution in farmland, aiming to increase agricultural sustainability and food security. However, impacts of the future of these strategies remain underexplored. This study estimated MFRs accumulation over the next 50 years under varying PE and polybutylene adipate terephthalate (PBAT) film recovery scenarios: no recovery, and recovery rates increased to 80%, 85%, 90%, and 95%. Additionally, cumulative ecological effects (CEEs) of MFR pollution were assessed based on historical MFRs accumulations of 75 kg hm−2, 160 kg hm−2, 220 kg hm−2, 300 kg hm−2, and 400 kg hm−2, by evaluating direct and indirect ecological effects. The findings revealed that (1) with no recovery, PE film residues could increase by 480 kg hm−2, whereas achieving a 95% recovery rate could limit residues increasing to below the national threshold of 75 kg hm−2, outperforming the 80%, 85%, and 90% recovery rates. On the other hand, using PBAT film would maintain the increasing MFRs below 75 kg hm−2 regardless of recovery rate. (2) Without PE film recovery, CEEs would intensify significantly, as both the direct and indirect effects increase notably, while the CEEs of MFRs could maintain the current status or decrease under the strategy of 95% recovery rate of PE film and using PBAT film, similar to the variation of direct effects. However, indirect effects would persist due to ongoing microplastics (MPs) and phthalate esters (PAEs) released from residual films. Overall, a 95% PE film recycling rate and PBAT film usage emerged as particularly effective strategies for minimizing MFRs accumulation and mitigating ecological impacts over the next 50 years. Further research should prioritize the indirect ecological effects of MFRs, given their persistence despite reduction efforts. The results could provide a theoretical support for agricultural sustainable development in arid regions.
Relationship Between Spatial Form, Functional Distribution, and Vitality of Railway Station Areas Under Station-City Synergetic Development: A Case Study of Four Special-Grade Stations in Beijing
Authors: Sun Yuhan , Wan Bo , Sheng Qiang
Published: November 2024

The integration of railway stations into urban environments necessitates a detailed examination of their vitality and influencing factors. This study assesses urban vitality around four major railway stations in Beijing utilizing a variety of analytical models including Ordinary Least Squares, Geographically Weighted Regression, Multi-Scale Geographically Weighted Regression, and machine learning approaches such as XGBoost 2.0.3, Random Forest 1.4.1.post1, and LightGBM 4.3.0. These analyses are grounded in Baidu heatmaps and examine relationships with spatial form, functional distribution, and spatial configuration. The results indicate significant associations between urban vitality and variables such as commercial density, average number of floors, integration, residential density, and housing prices, particularly in predicting weekday vitality. The MGWR model demonstrates enhanced fit and robustness, explaining 84.8% of the variability in vitality, while the Random Forest model displays the highest stability among the machine learning options, accounting for 76.9% of vitality variation. The integration of SHAP values with MGWR coefficients identifies commercial density as the most critical predictor, with the average number of floors and residential density also being key. These findings offer important insights for spatial planning in areas surrounding railway stations.
Data-Sharing System with Attribute-Based Encryption in Blockchain and Privacy Computing
Authors: Wu Hao , Liu Yu , Zhu Konglin , Zhang Lin
Published: November 2024

With the development of the data-sharing system in recent years, financial management systems and their privacy have sparked great interest. Existing financial data-sharing systems store metadata, which include a hash value and database index on the blockchain, and store high-capacity actual data in the center database. However, current data-sharing systems largely depend on centralized systems, which are susceptible to distributed denial-of-service (DDoS) attacks and present a centralized attack vector. Furthermore, storing data in a local center database has a high risk of information disclosure and tampering. In this paper, we propose the ChainMaker Privacy Computing (CPC) system, a new decentralized data-sharing system for secure financial data, to solve this problem. It provides a series of financial data information and a data structure rather than actual data on the blockchain to protect the privacy of data. We utilize a smart contract to establish a trusted platform for the local database to obtain encrypted data. We design a resource catalog to provide a trusted environment of data usage in the privacy computing system that is visible for members on the blockchain. Based on cipher-policy attribute-based encryption (CP-ABE), We design a CPC-CP-ABE algorithm to enable fine-grained access control through attribute based encryption. Finally, We propose an efficient scheme that allows authenticated data-sharing systems to perform Boolean searches on encrypted data information. The results of experiment show that the CPC system can finish trusted data sharing to all organizations on the blockchain.
The Impact of Climate Policy Uncertainty on the ESG Performance of Enterprises
Authors: Zhang Zhi , Feng Yanhong , Zhou Hongwei , Chen Liming , Liu Yi
Published: November 2024

In the context of addressing climate change, the uncertainty of climate policies has intensified the environmental and regulatory risks faced by enterprises, forcing them to adjust their strategies for fulfilling ESG responsibilities in pursuit of sustainable development. This paper uses panel data from listed non-financial enterprises on China’s Shanghai and Shenzhen A-share markets from 2011 to 2022, employing a fixed-effects panel model to examine the impact of climate policy uncertainty on corporate ESG performance. The findings indicate that climate policy uncertainty significantly hampers the ESG performance of enterprises. The mechanism analysis reveals that climate policy uncertainty negatively affects ESG performance by deepening corporate financing constraints and increasing short-term financial performance. The heterogeneity analysis shows that in terms of ownership structure, the negative impact of climate policy uncertainty on the ESG performance of state-owned enterprises is relatively weaker. In terms of industry heterogeneity, climate policy uncertainty suppresses the ESG performance of enterprises in technology-intensive industries. From a regional perspective, climate policy uncertainty has a stronger inhibitory effect on the ESG performance of enterprises in eastern China. This study provides valuable insights for both national climate policy formulation and corporate efforts to enhance ESG performance.