bluuebunny/crossref_metadata_2025 huggingface dataset:
Optimal Mapping of Soil Erodibility in a Plateau Lake Watershed: Empirical Models Empowered by Machine Learning
Authors: Wang Jiaxue , Wei Yujiao , Sun Zheng , Gu Shixiang , Bai Shihan , Chen Jinming , Chen Jing , Hong Yongsheng , Chen Yiyun
Published: August 2024

Soil erodibility (K) refers to the inherent ability of soil to withstand erosion. Accurate estimation and spatial prediction of K values are vital for assessing soil erosion and managing land resources. However, as most K-value estimation models are empirical, they suffer from significant extrapolation uncertainty, and traditional studies on spatial prediction focusing on individual empirical K values have neglected to explore the spatial pattern differences between various empirical models. This work proposed a universal framework for selecting an optimal soil-erodibility map using empirical models enhanced by machine learning. Specifically, three empirical models, namely, the erosion-productivity impact calculator model (K_EPIC), the Shirazi model (K_Shirazi), and the Torri model (K_Torri) were used to estimate K values. Random Forest (RF) and Gradient-Boosting Decision Tree (GBDT) algorithms were employed to develop prediction models, which led to the creation of three K-value maps. The spatial distribution of K values and associated environmental covariates were also investigated across varying empirical models. Results showed that RF achieved the highest accuracy, with R2 of K_EPIC, K_Shirazi, and K_Torri increasing by 46%, 34%, and 22%, respectively, compared to GBDT. And distinctions among environmental variables that shape the spatial patterns of empirical models have been identified. The K_EPIC and K_Shirazi are influenced by soil porosity and soil moisture. The K_Torri is more sensitive to soil moisture conditions and terrain location. More importantly, our study has highlighted disparities in the spatial patterns across the three K-value maps. Considering the data distribution, spatial distribution, and measured K values, the K_Torri model outperformed others in estimating soil erodibility in the plateau lake watershed. This study proposed a framework that aimed to create optimal soil-erodibility maps and offered a scientific and accurate K-value estimation method for the assessment of soil erosion.
Rational Polynomial Coefficient Estimation via Adaptive Sparse PCA-Based Method
Authors: Yan Tianyu , Wang Yingqian , Wang Pu
Published: August 2024

The Rational Function Model (RFM) is composed of numerous highly correlated Rational Polynomial Coefficients (RPCs), establishing a mathematical relationship between two-dimensional images and three-dimensional spatial coordinates. Due to the existence of ill-posedness and overparameterization, the estimated RPCs are sensitive to any slight perturbations in the observation data, particularly when handling a limited number of Ground Control Points (GCPs). Recently, Principal Component Analysis (PCA) has demonstrated significant performance improvements in the RFM optimization problem. In the PCA-based RFM, each Principal Component (PC) is a linear combination of all variables in the design matrix. However, some original variables are noise related and have very small or almost zero contributions to the construction of PCs, which leads to the overparameterization problem and makes the RPC estimation process ill posed. To address this problem, in this paper, we propose an Adaptive Sparse Principal Component Analysis-based RFM method (ASPCA-RFM) for RPC estimation. In this method, the Elastic Net sparsity constraint is introduced to ensure that each PC contains only a small number of original variables, which automatically eliminates unnecessary variables during PC computation. Since the optimal regularization parameters of the Elastic Net vary significantly in different scenarios, an adaptive regularization parameter approach is proposed to dynamically adjust the regularization parameters according to the explained variance of PCs and degrees of freedom. By adopting the proposed method, the noise and error in the design matrix can be reduced, and the ill-posedness and overparameterization of the RPC estimation can be significantly mitigated. Additionally, we conduct extensive experiments to validate the effectiveness of our method. Compared to existing state-of-the-art methods, the proposed method yields markedly improved or competitive performance.
Calibration of Receiver-Dependent Pseudorange Bias and Its Impact on BDS Augmentation Positioning Accuracy
Authors: Liao Min , Tang Chengpan , Zhao Liqian , Zhou Shanshi , Hu Xiaogong , Chen Yilun , Li Kai , Gui Yubo
Published: August 2024

Pseudorange bias refers to the receiver-dependent and satellite-dependent constant bias in the pseudorange resulting from the nonideal characteristics of a signal. The impact of pseudorange bias on high-precision satellite navigation services has long been ignored. This paper proposes a pseudorange bias calibration method for two collocated receivers. Then, we calibrate pseudorange biases for two types of collocated receivers at a monitoring station within China and evaluate their impact on two high-precision services: BeiDou Navigation Satellite System 3 (BDS-3) dual-frequency pseudorange augmentation and precise point precision (PPP). Theoretical analysis reveals that the calibrated pseudorange biases contribute 17.2% and 7.7% to the user equivalent ranging error (UERE) of BDS-3 and Global Positioning System (GPS) dual-frequency pseudorange augmentation, respectively, and that the convergence time of the GPS static and kinematic PPP increases from 6 min and 26 min to 19 min and 58 min, respectively. The experimental results indicate that the calibrated pseudorange biases are consistent as the receiver location and time vary. The spatial distribution consistency is generally better than 0.1 m, and the temporal consistency is better than 0.15 m. The pseudorange biases for BDS-3 B1C and B2a are approximately 0.7 m and 0.1 m, respectively, whereas those for GPS L1C/A and L2P are both approximately 0.25 m. Furthermore, The results show that after correction of the pseudorange biases, the average convergence time for BDS-3/GPS static PPP decreases from 48.83/24.03 min to 38.54/21.12 min, respectively, a decrease of approximately 21%/12%. For BDS-3/GPS/BDS-3 + GPS kinematic PPP, the average convergence time decreases from 109.53/45.10/39.15 min to 62.99/40.83/22.94 min, respectively, a decrease of approximately 42%/41%/9%. Similarly, the three-dimensional positioning accuracy for BDS-3/GPS/BDS-3 + GPS dual-frequency pseudorange augmentation improves from 3.25/3.94/2.49 m to 2.65/3.69/2.16 m, respectively, increasing by approximately 6.3%, 18.5%, and 13.3%, respectively. The above analysis and experiments demonstrate that pseudorange bias is an important error source affecting both dual-frequency pseudorange augmentation and PPP services.
Leveraging Mixed Data Sources for Enhanced Road Segmentation in Synthetic Aperture Radar Images
Authors: Lan Tian , He Shuting , Qing Yuanyuan , Wen Bihan
Published: August 2024

In this study, we tackle the task of road segmentation from Synthetic Aperture Radar (SAR) imagery, which is vital for remote sensing applications including urban planning and disaster management. Despite its significance, SAR-based road segmentation is hindered by the scarcity of high-resolution, annotated SAR datasets and the distinct characteristics of SAR imagery, which differ significantly from more commonly used electro-optical (EO) imagery. To overcome these challenges, we introduce a multi-source data approach, creating the HybridSAR Road Dataset (HSRD). This dataset includes the SpaceNet 6 Road (SN6R) dataset, derived from high-resolution SAR images and OSM road data, as well as the DG-SAR and SN3-SAR datasets, synthesized from existing EO datasets. We adapt an off-the-shelf road segmentation network from the optical to the SAR domain through an enhanced training framework that integrates both real and synthetic data. Our results demonstrate that the HybridSAR Road Dataset and the adapted network significantly enhance the accuracy and robustness of SAR road segmentation, paving the way for future advancements in remote sensing.
Cognitive Computing Advancements: Improving Precision Crop Protection through UAV Imagery for Targeted Weed Monitoring
Authors: Mesías-Ruiz Gustavo A. , Peña José M. , de Castro Ana I. , Borra-Serrano Irene , Dorado José
Published: August 2024

Early detection of weeds is crucial to manage weeds effectively, support decision-making and prevent potential crop losses. This research presents an innovative approach to develop a specialized cognitive system for classifying and detecting early-stage weeds at the species level. The primary objective was to create an automated multiclass discrimination system using cognitive computing, regardless of the weed growth stage. Initially, the model was trained and tested on a dataset of 31,002 UAV images, including ten weed species manually identified by experts at the early phenological stages of maize (BBCH14) and tomato (BBCH501). The images were captured at 11 m above ground level. This resulted in a classification accuracy exceeding 99.1% using the vision transformer Swin-T model. Subsequently, generative modeling was employed for data augmentation, resulting in new classification models based on the Swin-T architecture. These models were evaluated on an unbalanced dataset of 36,556 UAV images captured at later phenological stages (maize BBCH17 and tomato BBCH509), achieving a weighted average F1-score ranging from 94.8% to 95.3%. This performance highlights the system’s adaptability to morphological variations and its robustness in diverse crop scenarios, suggesting that the system can be effectively implemented in real agricultural scenarios, significantly reducing the time and resources required for weed identification. The proposed data augmentation technique also proved to be effective in implementing the detection transformer architecture, significantly improving the generalization capability and enabling accurate detection of weeds at different growth stages. The research represents a significant advancement in weed monitoring across phenological stages, with potential applications in precision agriculture and sustainable crop management. Furthermore, the methodology showcases the versatility of the latest generation models for application in other knowledge domains, facilitating time-efficient model development. Future research could investigate the applicability of the model in different geographical regions and with different types of crops, as well as real-time implementation for continuous field monitoring.
An Ice Loss Evaluation of Lake-Terminating Glaciers Based on Lake Bathymetry—A Case Study of the Jiongpu Glacier
Authors: Li Da , Shangguan Donghui , Han Tianding , Butt Asim Qayyum , Pan Baotian , Cao Bo , Wang Meixia , Wang Rongjun , Li Yaojun
Published: August 2024

Lake-terminating glaciers are among the most severely retreating glacier types in high mountain areas. However, the characteristic of being covered by glacial lakes after retreat makes it hard to estimate their actual ice loss in recent years, as does the contribution of different parts in ice loss, which leads to significant obstacles not only in evaluating solid water resources but understanding inter-relationships between glacial ice and glacial lakes. This study presents a detailed investigation of Jiongpu Co, one of the biggest glacial lakes in the Tibetan Plateau, including its bathymetry and area evolution. The ice loss in the last two decades was analyzed using a multisource DEM dataset. The main results showed that from 1976 to 2021, Jiongpu Co had expanded from 1.19 ± 0.09 km2 to 5.34 ± 0.07 km2. The volume of Jiongpu Co showed a surprising increment from 0.09 ± 0.004 Gt to 0.66 ± 0.03 Gt from 1976 to 2021, leading to a subaqueous equivalent ice loss of 0.32 ± 0.01 Gt water from 2000 to 2020 and resulting in an underestimated ice loss of 0.06 Gt, 19% compared with previous evaluations. The total ice loss of the Jiongpu glacier was 1.52 ± 0.37 Gt from 2000 to 2020, and more than 1/3 ice loss was related to lake expansion (0.32 ± 0.01 Gt underwater, 0.19 ± 0.02 Gt above water). This study makes a further contribution to the understanding of ice loss in the complicated system of lake-terminating glaciers.
Comparison of the NASA Standard MODerate-Resolution Imaging Spectroradiometer and Visible Infrared Imaging Radiometer Suite Snow-Cover Products for Creation of a Climate Data Record: A Case Study in the Great Basin of the Western United States
Authors: Hall Dorothy K. , Riggs George A. , DiGirolamo Nicolo E.
Published: August 2024

A nearly continuous daily, global Environmental Science Data Record of NASA Standard MODerate-resolution Imaging Spectroradiometer (MODIS) snow-cover extent (SCE) data products has been available since 2000. When the MODIS record ends, the ‘moderate resolution’ SCE record will continue with NASA Standard Visible Infrared Imaging Radiometer Suite (VIIRS) SCE data products. The objective of this work is to evaluate and quantify the continuity between the MODIS and VIIRS SCE data products to enable the merging of the data product records. A climate data record (CDR) could be developed when 30 years of daily global moderate-resolution SCE become available if the continuity of the MODIS and VIIRS records can be established. Here, we focus on the daily cloud-gap-filled MODIS and VIIRS SCE NASA standard data products, MOD10A1F and VNP10A1F, respectively, for a case study in the Great Basin of the western United States during a period of sensor overlap. Using the methodologies described herein (daily percent of snow cover, duration of snow cover, average monthly number of days (Ndays) of snow cover, and trends in Ndays of snow cover, we show that the snow maps display excellent agreement. For example, the average monthly number of days of snow cover in the Great Basin calculated using MOD10A1F and VNP10A1F agrees with a Pearson’s correlation coefficient of r = 0.99 for our 11-year study period from WY 2013 to 2023. Additionally, the SCE derived from each data product agrees very well with meteorological station data, with a Pearson’s correlation coefficient of r = 0.91 and r = 0.92 for MOD10A1F and VNP10A1F, respectively. Our results support the eventual creation of a CDR.
Enhancing User Localization with an Integrated Sensing and Communication (ISAC) System: An Experimental UAV Search-and-Rescue Use Case
Authors: Moro Stefano , Linsalata Francesco , Manzoni Marco , Magarini Maurizio , Tebaldini Stefano
Published: August 2024

This paper explores the potential of an Integrated Sensing and Communication (ISAC) system to enhance search-and-rescue operations. While prior research has explored ISAC capabilities in Unmanned Aerial Vehicles (UAVs), our study focuses on addressing the specific challenges posed by modern communication standards (e.g., power, frequency, and bandwidth limitations) in the context of search-and-rescue missions. The paper details effective methods for processing echoed signals generated by downlink transmissions and evaluates key performance indicators, including Noise Equivalent Sigma Zero (NESZ) and channel capacity. Additionally, we utilize synchronization uplink signals transmitted by User Equipment (UE) to improve target detection and classification of possible victims by fusing SAR imagery with triangulation results from uplink signals. An experimental campaign validates the proposed setup by integrating SAR images of the environment with active localization results, both produced by a UAV equipped with a Software Defined Radio (SDR) payload. Our results demonstrate the system’s capability to detect and localize buried targets in avalanche scenarios, with localization errors ranging from centimeters to 10 m depending on environmental conditions. This successful integration highlights the practical applicability of our approach in challenging search-and-rescue missions.
A Novel Urban Heat Vulnerability Analysis: Integrating Machine Learning and Remote Sensing for Enhanced Insights
Authors: Li Fei , Yigitcanlar Tan , Nepal Madhav , Thanh Kien Nguyen , Dur Fatih
Published: August 2024

Rapid urbanization and climate change exacerbate the urban heat island effect, increasing the vulnerability of urban residents to extreme heat. Although many studies have assessed urban heat vulnerability, there is a significant lack of standardized criteria and references for selecting indicators, building models, and validating those models. Many existing approaches do not adequately meet urban planning needs due to insufficient spatial resolution, temporal coverage, and accuracy. To address this gap, this paper introduces the U-HEAT framework, a conceptual model for analyzing urban heat vulnerability. The primary objective is to outline the theoretical foundations and potential applications of U-HEAT, emphasizing its conceptual nature. This framework integrates machine learning (ML) with remote sensing (RS) to identify urban heat vulnerability at both long-term and detailed levels. It combines retrospective and forward-looking mapping for continuous monitoring and assessment, providing essential data for developing comprehensive strategies. With its active learning capacity, U-HEAT enables model refinement and the evaluation of policy impacts. The framework presented in this paper offers a standardized and sustainable approach, aiming to enhance practical analysis tools. It highlights the importance of interdisciplinary research in bolstering urban resilience and stresses the need for sustainable urban ecosystems capable of addressing the complex challenges posed by climate change and increased urban heat. This study provides valuable insights for researchers, urban administrators, and planners to effectively combat urban heat challenges.
MsFNet: Multi-Scale Fusion Network Based on Dynamic Spectral Features for Multi-Temporal Hyperspectral Image Change Detection
Authors: Feng Yining , Ni Weihan , Song Liyang , Wang Xianghai
Published: August 2024

With the development of satellite technology, the importance of multi-temporal remote sensing (RS) image change detection (CD) in urban planning, environmental monitoring, and other fields is increasingly prominent. Deep learning techniques enable a profound exploration of the intrinsic features within hyperspectral (HS) data, leading to substantial enhancements in CD accuracy while addressing several challenges posed by traditional methodologies. However, existing convolutional neural network (CNN)-based CD approaches frequently encounter issues during the feature extraction process, such as the loss of detailed information due to downsampling, which hampers a model’s ability to accurately capture complex spectral features. Additionally, these methods often neglect the integration of multi-scale information, resulting in suboptimal local feature extraction and, consequently, diminished model performance. To address these limitations, we propose a multi-scale fusion network (MsFNet) which leverages dynamic spectral features for effective multi-temporal HS-CD. Our approach incorporates a dynamic convolution module with spectral attention, which adaptively modulates the receptive field size according to the spectral characteristics of different bands. This flexibility enhances the model’s capacity to focus on critical bands, thereby improving its ability to identify and differentiate changes across spectral dimensions. Furthermore, we develop a multi-scale feature fusion module which extracts and integrates features from deep feature maps, enriching local information and augmenting the model’s sensitivity to local variations. Experimental evaluations conducted on three real-world HS-CD datasets demonstrate that the proposed MsFNet significantly outperforms contemporary advanced CD methods in terms of both efficacy and performance.
Regional Urban Shrinkage Can Enhance Ecosystem Services—Evidence from China’s Rust Belt
Authors: Xu Ziqi , Chang Jiang , Wang Ziyi , Li Zixuan , Liu Xiaoyi , Chen Yedong , Wei Zhongyin , Sun Jingyu
Published: August 2024

Rapid urbanization is universally acknowledged to degrade ecosystem services, posing significant threats to human well-being. However, the effects of urban shrinkage, a global phenomenon and a counterpart to urbanization, on ecosystem services (ESs) remain unclear. This study focuses on China’s Rust Belt during the period from 2000 to 2020, constructing a comprehensive analytical framework based on long-term remote sensing data to reveal the temporal and spatial patterns of ESs and their associations with cities experiencing varying degrees of shrinkage. It employs a random forest (RF) model and a Shapley additive explanation (SHAP) model to measure and visualize the significance and thresholds of socioeconomic factors influencing changes in ESs. Our findings highlight the following: (1) Since 2010, the three provinces of Northeast China (TPNC) have begun to shrink comprehensively, with the degree of shrinkage intensifying over time. Resource-based cities have all experienced contraction. (2) Regional urban shrinkage has been found to enhance the overall provision capacity of ESs, with the most significant improvements in cities undergoing continuous shrinkage. (3) The impact of the same socioeconomic drivers varies across cities with different levels of shrinkage; increasing green-space ratios and investing more in public welfare have been identified as effective measures to enhance ESs. (4) Threshold analysis indicates that the stability of the tertiary sector’s proportion is critically important for enhancing ESs in cities undergoing intermittent shrinkage. An increase of 10% to 15% in this sector can allow continuously shrinking cities to balance urban development with ecological improvements. This research highlights the positive aspects of urban shrinkage, demonstrating its ability to enhance the provision capacity of ESs. It offers new insights into the protection and management of regional ecosystems and the urban transformation of the three eastern provinces.
A Multi-Objective Intelligent Optimization Method for Sensor Array Optimization in Distributed SAR-GMTI Radar Systems
Authors: Li Xianghai , Wang Rong , Liang Gengchen , Yang Zhiwei
Published: August 2024

The design and optimization of sensor array configurations is a significant challenge for distributed SAR-GMTI radar systems because the system performance of distributed array radar is a comprehensive result of several conflicting evaluation indicators. This paper developed a multi-objective intelligent optimization method to solve the global optimal problem of array configurations in terms of achieving optimal GMTI performance. Firstly, to formulate the relationship between array configuration and GMTI performance, we established three objective functions derived from evaluating indicators of SAR-GMTI performance. Specifically, in the objective functions, we proposed a novel clutter covariance matrix model that added several typical non-ideal factors of the real-world detection environment. This provides a way to build a bridge between the array configuration, environment clutter, and GMTI performance. Then, we proposed an improved multi-objective snake optimization algorithm (IMOSOA) that combined the Pareto optimization mechanism with snake optimization to solve the multi-objective optimization problem while reconciling the conflicts between different objective functions. Meanwhile, some significant improvements were made to speed up convergence. That is, tent chaotic mapping-based initialization, multi-group coevolution, and individual mutation strategies were applied to solve the non-convergence problem of global searching. Finally, in the case of an airborne SAR-GMTI system, numerical experiments demonstrated that the proposed IMOSOA has superior performance than other contrast methods, especially in terms of GMTI applications.
An Enhanced Collaborative Localization Method Based on Belief Propagation Aided by 3D Terrain Modelling
Authors: Wang Rong , Zhao Weicheng , Xiong Zhi , Chen Xiaoyi
Published: August 2024

Navigation system performance degrades significantly in complex environments. It is important to analyze satellite visibility through 3D terrain modelling and separate the satellite signals propagated by NLOS to suppress the NLOS error. However, the traditional 3D terrain modelling visibility analysis method based on the pure terrain cover angle is only suitable for determining the visibility of GNSS satellites and may incorrectly separate LOS propagate measurement signals from members with low relative ranges and elevation angles under air–ground swarm conditions. To this end, this paper proposes a belief-propagating cooperative navigation method based on air–ground visibility analysis, which avoids mistakenly separating close-range LOS cooperative navigation signals by simultaneously considering the distances, elevation angles, and azimuths of the signal sources relative to the air–ground swarm members. The simulation shows that the cooperative navigation NLOS identification method based on air–ground visibility analysis proposed in this paper can more accurately realize the separation of NLOS signals under cooperative conditions than the traditional pure angular 3D terrain modelling visibility analysis method can, and the localization error of the members to be assisted is significantly reduced.
PointUR-RL: Unified Self-Supervised Learning Method Based on Variable Masked Autoencoder for Point Cloud Reconstruction and Representation Learning
Authors: Li Kang , Zhu Qiuquan , Wang Haoyu , Wang Shibo , Tian He , Zhou Ping , Cao Xin
Published: August 2024

Self-supervised learning has made significant progress in point cloud processing. Currently, the primary tasks of self-supervised learning, which include point cloud reconstruction and representation learning, are trained separately due to their structural differences. This separation inevitably leads to increased training costs and neglects the potential for mutual assistance between tasks. In this paper, a self-supervised method named PointUR-RL is introduced, which integrates point cloud reconstruction and representation learning. The method features two key components: a variable masked autoencoder (VMAE) and contrastive learning (CL). The VMAE is capable of processing input point cloud blocks with varying masking ratios, ensuring seamless adaptation to both tasks. Furthermore, CL is utilized to enhance the representation learning capabilities and improve the separability of the learned representations. Experimental results confirm the effectiveness of the method in training and its strong generalization ability for downstream tasks. Notably, high-accuracy classification and high-quality reconstruction have been achieved with the public datasets ModelNet and ShapeNet, with competitive results also obtained with the ScanObjectNN real-world dataset.
Sensitivity of Local Climate Zones and Urban Functional Zones to Multi-Scenario Surface Urban Heat Islands
Authors: Deng Haojian , Zhang Shiran , Chen Minghui , Feng Jiali , Liu Kai
Published: August 2024

Local climate zones (LCZs) and urban functional zones (UFZs) can intricately depict the multidimensional spatial elements of cities, offering a comprehensive perspective for understanding the surface urban heat island (SUHI) effect. In this study, we retrieved two types of land surface temperature (LST) data and constructed 12 SUHI scenarios over the Guangdong–Hong Kong–Macao Greater Bay Area Central region using six SUHI identification methods. It compared the SUHI sensitivity differences among different types of LCZ and UFZ to analyze the global and local sensitivity differences of influencing factors in the 12 SUHI scenarios by utilizing the spatial gradient boosting trees, geographically weighted regression, and the coefficient of variation model. Results showed the following: (1) The sensitivity of different LCZ and UFZ types to multi-scenario SUHI was significantly affected by differences in SUHI identification methods and non-urban references. (2) In the morning, the shading effect of building clusters reduced the surface urban heat island intensity (SUHII) of some built environment types (such as LCZ 1 (compact high-rise zone) to LCZ 5 (open midrise zone)). The SUHIIs of LCZ E (bare rock or paved zone) and LCZ 10 (industry zone) were 4.22 °C and 3.87 °C, respectively, and both are classified as highly sensitive to SUHI. (3) The sensitivity of SUHI influencing factors exhibited regional variability, with importance differences in the sensitivity of importance for factors such as the impervious surface ratio, elevation, average building height, vegetation coverage, and average building volume between LCZs and UFZs. Amongst the 12 SUHI scenarios, an average of 87.43% and 89.97% of areas in LCZs and UFZs, respectively, were found to have low spatial sensitivity types. Overall, this study helps urban planners and managers gain a more comprehensive understanding of the complexity of the SUHI effect in high-density cities, providing a scientific basis for future urban climate adaptability planning.
Thermo-Chemical Characterization of Organic Phase Change Materials (PCMs) Obtained from Lost Wax Casting Industry
Authors: Sarcinella Antonella , Cunha Sandra , Aguiar José , Frigione Mariaenrica
Published: August 2024

The high global energy demand drives the search for sustainable alternatives for energy production and storage. Among the most effective solutions are phase change materials (PCMs). In particular, organic PCMs offer a high capacity to store and release thermal energy in response to external thermal variations, even over a wide temperature range. They find profitable applications in various sectors, from construction to electronics, offering flexibility and considerable energy storage according to need. In the search for new and effective PCMs, reusing by-products from different industries would offer both economic and environmental benefits. With this goal in mind, several organic PCMs with different characteristics and origins were analyzed in the present study. Two of them were by-products of the lost wax casting industry. In fact, we wanted to verify whether this waste could be employed as an effective, low-cost PCM. For comparison purposes, two commercial PCMs were selected, namely a paraffin and a microencapsulated PCM. Finally, a PCM blend was produced by mixing a commercial PCM and a waxy by-product. The five selected or developed PCMs were subjected to different tests to investigate their chemical composition, thermal characteristics, and thermal stability before and after repeated (i.e., 100) cycles of melting and crystallization processes. The results demonstrated that the durability of the non-commercial PCMs with regard to thermal loads was not inferior, and was in some cases even superior, to commercial PCMs. This study therefore proposes an innovative path to reuse the by-products of different production processes to support the environment.
Remote Sensing Inversion of Soil Organic Matter Content in Straw-Returned Fields in China’s Black Soil Region
Authors: Qv Wei , Du Huishi , Wang Xiao
Published: August 2024

China’s black earth region is the country’s corn golden belt, and returning corn straw to the field not only helps improve the Soil Organic Matter (SOM) content and soil fertility, but also resolves environmental pollution caused by straw burning. To study the effects of different years and amounts of straw returned to the field on SOM content, this study used soil sampling data from a conservation tillage experimental base in Gaojia Village, Lishu County, combined with indoor measurements of imaging spectral data, to establish a prediction model of SOM content by applying partial least squares regression, and inverting the SOM content in the study area. The results showed that the PLSR model accuracy using indoor measured soil imaging spectral data as the independent variable was high. The accuracy coefficients of samples with different field return and different field return amounts, R2, were 0.9176 and 0.8901, respectively, which better predicted SOM content. In the 0–50 cm tillage layer, the highest average SOM content of 39.73 g/kg was found under the NT-1 treatment with different no-tillage straw return year treatments. The depth of the tillage layer in the typical black soil region of Northeast China is around 0–20 cm, and the most significant increase in SOM content was observed in the experimental samples under the NT-1 treatment. SOM content in NT-1 treatment increased by 31.83% compared with CK-1, 68.24% compared with CK-2, 72.18% compared with NT-0, 699.48% compared with NT-2, and 311.44% compared with NT-3, respectively. The highest SOM content of 31.9 g/kg was found in NT-100 under the different treatments of different years of field return. At the 0–20 cm soil layer, the SOM content increases most significantly under NT-100 treatment, which is the most suitable treatment method for straw return to the field. And NT-100 is 22.09% higher than CK-1, 55.36% higher than CK-2, 58.99% higher than NC-0, 115.95% higher than NT-33, and 48.72% higher than NT-67, respectively. This study provides data that can support the conservation of soil ecosystem diversity and sustainable soil use, and it also enriches the application of the PLSR model application.
Adapting to the Agricultural Labor Market Shaped by Robotization
Authors: Marinoudi Vasso , Benos Lefteris , Camacho Villa Carolina , Lampridi Maria , Kateris Dimitrios , Berruto Remigio , Pearson Simon , Sørensen Claus Grøn , Bochtis Dionysis
Published: August 2024

Agriculture is being transformed through automation and robotics to improve efficiency and reduce production costs. However, this transformation poses risks of job loss, particularly for low-skilled workers, as automation decreases the need for human labor. To adapt, the workforce must acquire new qualifications to collaborate with automated systems or shift to roles that leverage their unique human abilities. In this study, 15 agricultural occupations were methodically mapped in a cognitive/manual versus routine/non-routine two-dimensional space. Subsequently, each occupation’s susceptibility to robotization was assessed based on the readiness level of existing technologies that can automate specific tasks and the relative importance of these tasks in the occupation’s execution. The qualifications required for occupations less impacted by robotization were summarized, detailing the specific knowledge, skills, and work styles required to effectively integrate the emerging technologies. It was deduced that occupations involving primary manual routine tasks exhibited the highest susceptibility rate, whereas occupations with non-routine tasks showed lower susceptibility. To thrive in this evolving landscape, a strategic combination of STEM (science, technology, engineering, and mathematics) skills with essential management, soft skills, and interdisciplinary competences is imperative. Finally, this research stresses the importance of strategic preparation by policymakers and educational systems to cultivate key competencies, including digital literacy, that foster resilience, inclusivity, and sustainability in the sector.
Evaluation of Deep Learning Models for Predicting the Concentration of Air Pollutants in Urban Environments
Authors: Tello-Leal Edgar , Ramirez-Alcocer Ulises Manuel , Macías-Hernández Bárbara A. , Hernandez-Resendiz Jaciel David
Published: August 2024

Air pollution is an issue of great concern globally due to the risks to the health of humanity, animals, and ecosystems. On the one hand, air quality monitoring systems allow for determining the concentration level of air pollutants and health risks through an air quality index (AQI). On the other hand, accurate future predictions of air pollutant concentration levels can provide valuable information for data-driven decision-making to reduce health risks from short- and long-term exposure when indicators exceed permissible limits. In this paper, five deep learning architectures are evaluated to predict the concentration of particulate matter pollutants (in their fractions PM2.5 and PM10) and carbon monoxide (CO) in consecutive hours. The proposed prediction models are based on recurrent neural networks (RNNs), long short-term memory (LSTM), vanilla LSTM, Stacked LSTM, Bi-LSTM, and encoder–decoder LSTM networks. Moreover, a methodology is presented to guide the construction of the prediction model, encompassing raw data processing, model design and optimization, and neural network training, testing, and evaluation. The results underscore the precision and reliability of the Stacked LSTM model in predicting the hourly concentration level for PM2.5, with an RMSE of 3.4538 μg/m3. Similarly, the encoder–decoder LSTM model accurately predicts the concentration level for PM10 and CO, with an RMSE of 3.2606 μg/m3 and 2.1510 ppm, respectively. These evaluations, with their minimal differences in error metrics and coefficient of determination, validate the effectiveness and superiority of the deep learning models over other reference models, instilling confidence in their potential.
Optimal Machine Learning Model to Predict Demolition Waste Generation for a Circular Economy
Authors: Cha Gi-Wook , Park Choon-Wook , Kim Young-Chan
Published: August 2024

A suitable waste-management strategy is crucial for a sustainable and efficient circular economy in the construction sector, and it requires precise data on the volume of demolition waste (DW) generated. Therefore, we developed an optimal machine learning model to forecast the quantity of recycling and landfill waste based on the characteristics of DW. We constructed a dataset comprising information on the characteristics of 150 buildings, demolition equipment utilized, and volume of five waste types generated (i.e., recyclable mineral, recyclable combustible, landfill specified, landfill mix waste, and recyclable minerals). We applied an artificial neural network, decision tree, gradient boosting machine, k-nearest neighbors, linear regression, random forest, and support vector regression. Further, we derived the optimal model through data preprocessing, input variable selection, and hyperparameter tuning. In both the validation and test phases, the “recyclable mineral waste” and “recyclable combustible waste” models achieved accuracies (R2) of 0.987 and 0.972, respectively. The “recyclable metals” and “landfill specified waste” models achieved accuracies (R2) of 0.953 and 0.858 or higher, respectively. Moreover, the “landfill mix waste” model exhibited an accuracy of 0.984 or higher. This study confirmed through Shapley Additive exPlanations analysis that the floor area is the most important input variable in the four models (i.e., recyclable mineral waste, recyclable combustible waste, recyclable metals, and landfill mix waste). Additionally, the type of equipment employed in demolition emerged as another crucial input variable impacting the volume of recycling and landfill waste generated. The results of this study can provide more detailed information on the generation of recycling and landfill waste. The developed model can provide precise data on waste management, thereby facilitating the decision-making process for industry professionals.
A Prediction Method for City Traffic Noise Based on Traffic Simulation under a Mixed Distribution Probability
Authors: Wang Haibo , Wu Zhaolang , Chen Jincai
Published: August 2024

Predicting and assessing urban traffic noise is crucial for environmental management. This paper establishes a traffic noise simulation method based on microscopic traffic simulation, utilizing a traffic simulation under a mixed distribution probability combining normal and exponential distributions. This method integrates a single-vehicle noise prediction model to compute the spatial distribution of noise. Comparison with empirical data demonstrates that the proposed model effectively predicts the level of traffic noise. The accuracy of the model is validated through comparison with measured data, showing minimum and maximum errors of 3.60 dB(A) and 4.37 dB(A), respectively. Additionally, the noise spatial results under microscopic traffic models are compared with those under line source models, revealing that the proposed model provides a more detailed and realistic noise spatial distribution. Furthermore, the noise variation patterns between stable and time-varying traffic flows are investigated. Results indicate that noise levels fluctuate under stable traffic flow, whereas under time-varying traffic flow, noise values exhibit a stepped change.
Sustainability Constraints on Rural Road Infrastructure
Authors: Li Qin , Lv Shuangning , Cui Jingya , Hou Dongchen , Liu Yijun , Li Wenlong
Published: August 2024

Research on the sustainability of rural roads is of great significance to the integrated promotion of rural habitat improvement, the maintenance of regional ecological patterns, and the implementation of the rural revitalization strategy. This paper examines the constraints to ensuring the sustainability of road infrastructure in rural Shaanxi, China. Rural road infrastructure plays an important role in poverty alleviation. Rural Shaanxi Province is situated among a variety of terrains and spans a large area. Therefore, using the current situation of rural roads in the province as a starting point, the constraints and their rankings that impede the sustainability of road infrastructure are identified through the fuzzy Delphi method (FDM) and structural equation modeling (SEM), which in turn suggests governance measures that can be taken. The data in this paper come from 498 valid questionnaires obtained from 50 townships and 101 sample villages in Shaanxi Province. Due to the huge number of questionnaires, the questionnaires were tested with the help of SPSS 26.0 software, which showed that the questionnaires had high reliability and validity, and then the structural equation model was validated and modified according to the recommendations of goodness-of-fit statistics measurement and the modification index. Finally, the key factors affecting the sustainable development of rural road infrastructure were finally identified.
Strengthening AKIS for Sustainable Agricultural Features: Insights and Innovations from the European Union: A Literature Review
Authors: Kountios Georgios , Kanakaris Spyridon , Moulogianni Christina , Bournaris Thomas
Published: August 2024

The Agricultural Knowledge and Innovation System (AKIS) and the Farm Advisory Service (FAS) are important elements of the current Programming Period of the Common Agricultural Policy (2023–2027), as it is now deemed necessary to transition the European agricultural model to more sustainable forms, through the dissemination of agricultural knowledge, while simultaneously promoting innovative methods. An Agricultural Knowledge and Innovation System must be able to propose and develop ideas for the transmission of agricultural knowledge, promoting innovation and the exchange of ideas. The present study includes a significant number of investigations on the effectiveness of the implementation of the AKIS and the FAS so that the conclusions are representative of the scope of the work. The object in question is now the cornerstone of the modernization of agricultural production and an important way out towards more competitive models of agriculture. The purpose of this paper is to give an extensive overview of the degree of implementation of Agricultural Knowledge and Innovation Systems in the countries of the European Union (including the United Kingdom until 2019) from 2014 until today, essentially during the implementation of the second period of Common Agricultural Policy. In the context of this article, the main results of the research carried out in the EU countries during the period under study and the proposed improvements by the researchers, where they are mentioned, are recorded epigrammatically. Finally, the countries are categorized according to the degree of implementation of the AKIS and the contribution of the Agricultural Advisory System to more sustainable forms of agricultural practices.
Adapting Harvests: A Comprehensive Study of Farmers’ Perceptions, Adaptation Strategies, and Climatic Trends in Dera Ghazi Khan, Pakistan
Authors: Shah Syed Ali Asghar , Mehmood Muhammad Sajid , Muhammad Ihsan , Ahamad Muhammad Irfan , Wu Huixin
Published: August 2024

Understanding farmers’ perceptions, attitudes, and adaptation strategies toward climate change is important for developing effective policies that support agricultural development and food security in rural communities. This study aimed to understand climatic trends over the past two decades (2003–2022), farmers’ perceptions, and adaptation strategies regarding climate change in Dera Ghazi Khan, Pakistan. The Mann–Kendall trend test identified a significant decrease in average minimum temperature (τ = −0.357, p < 0.05) and an increase in rainfall patterns (τ = 0.337, p < 0.05). A mixed-method approach is employed, utilizing a survey of 180 randomly selected farmers, focus group discussions, and climate data analysis. Ordinal and binary logistic regression models were used to analyze the influence of sociodemographic factors on farmers’ perceptions of climate change and their choice of adaptation strategies. The results indicate that farmers primarily rely on religious beliefs/prayers (65.6%) to cope with climate change, followed by seeking off-farm jobs (50%) and changing fertilizer use (42.2%). The result of the binary logistic regression indicates that among the different demographic characteristics, education, and land size significantly influence farmers’ decisions in taking adaptation measures against climate change. This study emphasizes the need for climate policies that integrate farmer knowledge with meteorological data and sociodemographic analysis to ensure a sustainable agricultural sector in Pakistan. Further research is necessary to explore more effective adaptation methods.
Where Are We Going Now? The Current and Future Distributions of the Monk Parakeet (Myiopsitta monachus) and Eurasian Collared Dove (Streptopelia decaocto) in a Megalopolis
Authors: Ramírez-Albores Jorge E. , Sánchez-González Luis A. , Prieto-Torres David A. , Navarro-Sigüenza Adolfo G.
Published: August 2024

The monk parakeet (Myiopsitta monachus) and the Eurasian collared dove (Streptopelia decaocto) are two of the most prevalent invasive species globally due to their high dispersal ability. Since these birds were first recorded (1999 for the monk parakeet and 2013 for Eurasian collared dove) in the Mexico City Metropolitan Area (MCMA), both species have spread rapidly throughout the area. However, the impacts of global climate changes on the distribution patterns of these species remain poorly studied across the MCMA. Therefore, based on an ecological niche modeling approach, we assessed the expansion and potential invasion of both species in this megalopolis using current and future climate projections (year 2050). Our results estimated that the current suitable areas are 5564 km2 for the monk parakeet and 5489 km2 for the Eurasian collared dove, covering ~70% of the study area, suggesting a rapidly invading species, as expected. We observed a slight decrease (up to 24%) in both species in future climate scenarios, but our models estimated that the sizes of the suitable areas would remain stable. We found that the range expansion of these species in the megalopolis may be largely attributed to their propensity for jump dispersion and short-time niche expansion ability. Our findings allow for a better understanding of the factors contributing to the range expansion of the monk parakeet and the Eurasian collared dove in Mexico and can better inform the monitoring guidelines for and assessments of these invasive species.