bluuebunny/crossref_metadata_2025 huggingface dataset:
Classification of Small Targets on Sea Surface Based on Improved Residual Fusion Network and Complex Time–Frequency Spectra
Authors: Xu Shuwen , Niu Xiaoqing , Ru Hongtao , Chen Xiaolong
Published: September 2024

To address the problem that conventional neural networks trained on radar echo data cannot handle the phase of the echoes, resulting in insufficient information utilization and limited performance in detection and classification, we extend neural networks from the real-valued neural networks to the complex-valued neural networks, presenting a novel algorithm for classifying small sea surface targets. The proposed algorithm leverages an improved residual fusion network and complex time–frequency spectra. Specifically, we augment the Deep Residual Network-50 (ResNet50) with a spatial pyramid pooling (SPP) module to fuse feature maps from different receptive fields. Additionally, we enhance the feature extraction and fusion capabilities by replacing the conventional residual block layer with a multi-branch residual fusion (MBRF) module. Furthermore, we construct a complex time–frequency spectrum dataset based on radar echo data from four different types of sea surface targets. We employ a complex-valued improved residual fusion network for learning and training, ultimately yielding the result of small target classification. By incorporating both the real and imaginary parts of the echoes, the proposed complex-valued improved residual fusion network has the potential to extract more comprehensive features and enhance classification performance. Experimental results demonstrate that the proposed method achieves superior classification performance across various evaluation metrics.
Predicting Carbohydrate Concentrations in Avocado and Macadamia Leaves Using Hyperspectral Imaging with Partial Least Squares Regressions and Artificial Neural Networks
Authors: Bai Shahla Hosseini , Tootoonchy Mahshid , Kämper Wiebke , Tahmasbian Iman , Farrar Michael B. , Boldingh Helen , Pereira Trisha , Jonson Hannah , Nichols Joel , Wallace Helen M. , Trueman Stephen J.
Published: September 2024

Carbohydrate levels are important regulators of the growth and yield of tree crops. Current methods for measuring foliar carbohydrate concentrations are time consuming and laborious, but rapid imaging technologies have emerged with the potential to improve the effectiveness of tree nutrient management. Carbohydrate concentrations were predicted using hyperspectral imaging (400–1000 nm) of leaves of the evergreen tree crops, avocado, and macadamia. Models were developed using partial least squares regression (PLSR) and artificial neural network (ANN) algorithms to predict carbohydrate concentrations. PLSR models had R2 values of 0.51, 0.82, 0.86, and 0.85, and ANN models had R2 values of 0.83, 0.83, 0.78, and 0.86, in predicting starch, sucrose, glucose, and fructose concentrations, respectively, in avocado leaves. PLSR models had R2 values of 0.60, 0.64, 0.91, and 0.95, and ANN models had R2 values of 0.67, 0.82, 0.98, and 0.98, in predicting the same concentrations, respectively, in macadamia leaves. ANN only outperformed PLSR when predicting starch concentrations in avocado leaves and sucrose concentrations in macadamia leaves. Performance differences were possibly associated with nonlinear relationships between carbohydrate concentrations and reflectance values. This study demonstrates that PLSR and ANN models perform well in predicting carbohydrate concentrations in evergreen tree-crop leaves.
CRAUnet++: A New Convolutional Neural Network for Land Surface Water Extraction from Sentinel-2 Imagery by Combining RWI with Improved Unet++
Authors: Li Nan , Xu Xiaohua , Huang Shifeng , Sun Yayong , Ma Jianwei , Zhu He , Hu Mengcheng
Published: September 2024

Accurately mapping the surface water bodies through remote sensing technology is of great significance for water resources management, flood monitoring, and drought monitoring. At present, many scholars at home and abroad carry out research on deep learning image recognition algorithms based on convolutional neural networks, and a variety of variant-based convolutional neural networks are proposed to be applied to extract water bodies from remote sensing images. However, due to the low depth of convolutional layers employed and underutilization of water spectral feature information, most of the water body extraction methods based on convolutional neural networks (CNNs) for remote sensing images are limited in accuracy. In this study, we propose a novel surface water automatic extraction method based on the convolutional neural network (CRAUnet++) for Sentinel-2 images. The proposed method includes three parts: (1) substituting the feature extractor of the original Unet++ with ResNet34 to enhance the network’s complexity by increasing its depth; (2) Embedding the Spatial and Channel ‘Squeeze and Excitation’ (SCSE) module into the up-sampling stage of the network to suppress background features and amplify water body features; (3) adding the vegetation red edge-based water index (RWI) into the input data to maximize the utilization of water body spectral information of Sentinel-2 images without increasing the data processing time. To verify the performance and accuracy of the proposed algorithm, the ablation experiment under four different strategies and comparison experiment with different algorithms of RWI, FCN, SegNet, Unet, and DeepLab v3+ were conducted on Sentinel-2 images of the Poyang Lake. The experimental result shows that the precision, recall, F1, and IoU of CRAUnet++ are 95.99%, 96.41%, 96.19%, and 92.67%, respectively. CRAUnet++ has a good performance in extracting various types of water bodies and suppressing noises because it introduces SCSE attention mechanisms and combines surface water spectral features from RWI, exceeding that of the other five algorithms. The result demonstrates that CRAUnet++ has high validity and reliability in extracting surface water bodies based on Sentinel-2 images.
Use of SLSTR Sea Surface Temperature Data in OSTIA as a Reference Sensor: Implementation and Validation
Authors: Mao Chongyuan , Good Simon , Worsfold Mark
Published: September 2024

Sea surface temperature (SST) data from the Sea and Land Surface Temperature Radiometer (SLSTR) onboard the Sentinel-3 satellites have been used in the Met Office’s Operational Sea Surface Temperature and Sea Ice Analysis (OSTIA) since 2019 (Sentinel-3A SST data since March 2019 and Sentinel-3B data since December 2019). The impacts of using SLSTR SSTs and the SLSTR as the reference sensor for the bias correction of other satellite data have been assessed using independent Argo float data. Combining Sentinel-3A and -3B SLSTRs with two Visible Infrared Imaging Radiometer Suite (VIIRS) sensors (onboard the joint NASA/NOAA Suomi National Polar-orbiting Partnership and National Oceanic and Atmospheric Administration-20 satellites) in the reference dataset has also been investigated. The results indicate that when using the SLSTR as the only reference satellite sensor, the OSTIA system becomes warmer overall, although there are mixed impacts in different parts of the global ocean. Using both the VIIRS and the SLSTR in the reference dataset leads to moderate but more consistent improvements globally. Numerical weather prediction (NWP) results also indicate a better performance when using both the VIIRS and the SLSTR in the reference dataset compared to only using the SLSTR at night. Combining the VIIRS and the SLSTR with latitudinal weighting shows the best validation results against Argo, but further investigation is required to refine this method.
Research on Lightweight Method of Insulator Target Detection Based on Improved SSD
Authors: Zeng Bing , Zhou Yu , He Dilin , Zhou Zhihao , Hao Shitao , Yi Kexin , Li Zhilong , Zhang Wenhua , Xie Yunmin
Published: September 2024

Aiming at the problems of a large volume, slow processing speed, and difficult deployment in the edge terminal, this paper proposes a lightweight insulator detection algorithm based on an improved SSD. Firstly, the original feature extraction network VGG-16 is replaced by a lightweight Ghost Module network to initially achieve the lightweight model. A Feature Pyramid structure and Feature Pyramid Network (FPN+PAN) are integrated into the Neck part and a Simplified Spatial Pyramid Pooling Fast (SimSPPF) module is introduced to realize the integration of local features and global features. Secondly, multiple Spatial and Channel Squeeze-and-Excitation (scSE) attention mechanisms are introduced in the Neck part to make the model pay more attention to the channels containing important feature information. The original six detection heads are reduced to four to improve the inference speed of the network. In order to improve the recognition performance of occluded and overlapping targets, DIoU-NMS was used to replace the original non-maximum suppression (NMS). Furthermore, the channel pruning strategy is used to reduce the unimportant weight matrix of the model, and the knowledge distillation strategy is used to fine-adjust the network model after pruning, so as to ensure the detection accuracy. The experimental results show that the parameter number of the proposed model is reduced from 26.15 M to 0.61 M, the computational load is reduced from 118.95 G to 1.49 G, and the mAP is increased from 96.8% to 98%. Compared with other models, the proposed model not only guarantees the detection accuracy of the algorithm, but also greatly reduces the model volume, which provides support for the realization of visible light insulator target detection based on edge intelligence.
The Zero-Velocity Correction Method for Pipe Jacking Automatic Guidance System Based on Fiber Optic Gyroscope
Authors: Zhang Wenbo , Wang Lu , Zu Yutong
Published: September 2024

The pipe jacking guidance system based on a fiber optic gyroscope (FOG) has gained extensive attention due to its high degree of safety and autonomy. However, all inertial guidance systems have accumulative errors over time. The zero-velocity update (ZUPT) algorithm is an effective error compensation method, but accurately distinguishing between moving and stationary states in slow pipe jacking operations is a major challenge. To address this challenge, a “MV + ARE + SHOE” three-conditional zero-velocity detection (TCZVD) algorithm for the fiber optic gyroscope inertial navigation system (FOG-INS) is designed. Firstly, a Kalman filter model based on ZUPT is established. Secondly, the TCZVD algorithm, which combines the moving variance of acceleration (MV), angular rate energy (ARE), and stance hypothesis optimal estimation (SHOE), is proposed. Finally, experiments are conducted, and the results indicate that the proposed algorithm achieves a zero-velocity detection accuracy of 99.18% and can reduce positioning error to less than 2% of the total distance. Furthermore, the applicability of the proposed algorithm in the practical working environment is confirmed through on-site experiments. The results demonstrate that this method can effectively suppress the accumulated error of the inertial guidance system and improve the positioning accuracy of pipe jacking. It provides a robust and reliable solution for practical engineering challenges. Therefore, this study will contribute to the development of pipe jacking automatic guidance technology.
Analysis of Factors Influencing the Precision of Body Tracking Outcomes in Industrial Gesture Control
Authors: Weber Aleksej , Wilhelm Markus , Schmitt Jan
Published: September 2024

The body tracking systems on the current market offer a wide range of options for tracking the movements of objects, people, or extremities. The precision of this technology is often limited and determines its field of application. This work aimed to identify relevant technical and environmental factors that influence the performance of body tracking in industrial environments. The influence of light intensity, range of motion, speed of movement and direction of hand movement was analyzed individually and in combination. The hand movement of a test person was recorded with an Azure Kinect at a distance of 1.3 m. The joints in the center of the hand showed the highest accuracy compared to other joints. The best results were achieved at a luminous intensity of 500 lx, and movements in the x-axis direction were more precise than in the other directions. The greatest inaccuracy was found in the z-axis direction. A larger range of motion resulted in higher inaccuracy, with the lowest data scatter at a 100 mm range of motion. No significant difference was found at hand velocity of 370 mm/s, 670 mm/s and 1140 mm/s. This study emphasizes the potential of RGB-D camera technology for gesture control of industrial robots in industrial environments to increase efficiency and ease of use.
Learning Autonomous Navigation in Unmapped and Unknown Environments
Authors: He Naifeng , Yang Zhong , Bu Chunguang , Fan Xiaoliang , Wu Jiying , Sui Yaoyu , Que Wenqiang
Published: September 2024

Autonomous decision-making is a hallmark of intelligent mobile robots and an essential element of autonomous navigation. The challenge is to enable mobile robots to complete autonomous navigation tasks in environments with mapless or low-precision maps, relying solely on low-precision sensors. To address this, we have proposed an innovative autonomous navigation algorithm called PEEMEF-DARC. This algorithm consists of three parts: Double Actors Regularized Critics (DARC), a priority-based excellence experience data collection mechanism, and a multi-source experience fusion strategy mechanism. The algorithm is capable of performing autonomous navigation tasks in unmapped and unknown environments without maps or prior knowledge. This algorithm enables autonomous navigation in unmapped and unknown environments without the need for maps or prior knowledge. Our enhanced algorithm improves the agent’s exploration capabilities and utilizes regularization to mitigate the overestimation of state-action values. Additionally, the priority-based excellence experience data collection module and the multi-source experience fusion strategy module significantly reduce training time. Experimental results demonstrate that the proposed method excels in navigating the unmapped and unknown, achieving effective navigation without relying on maps or precise localization.
Research on the Optimization Method of Visual Sensor Calibration Combining Convex Lens Imaging with the Bionic Algorithm of Wolf Pack Predation
Authors: Wu Qingdong , Miao Jijun , Liu Zhaohui , Chang Jiaxiu
Published: September 2024

To improve the accuracy of camera calibration, a novel optimization method is proposed in this paper, which combines convex lens imaging with the bionic algorithm of Wolf Pack Predation (CLI-WPP). During the optimization process, the internal parameters and radial distortion parameters of the camera are regarded as the search targets of the bionic algorithm of Wolf Pack Predation, and the reprojection error of the calibration results is used as the fitness evaluation criterion of the bionic algorithm of Wolf Pack Predation. The goal of optimizing camera calibration parameters is achieved by iteratively searching for a solution that minimizes the fitness value. To overcome the drawback that the bionic algorithm of Wolf Pack Predation is prone to fall into local optimal, a reverse learning strategy based on convex lens imaging is introduced to transform the current optimal individual and generate a series of new individuals with potential better solutions that are different from the original individual, helping the algorithm out of the local optimum dilemma. The comparative experimental results show that the average reprojection errors of the simulated annealing algorithm, Zhang’s calibration method, the sparrow search algorithm, the particle swarm optimization algorithm, bionic algorithm of Wolf Pack Predation, and the algorithm proposed in this paper (CLI-WPP) are 0.42986500, 0.28847656, 0.23543161, 0.219342495, 0.10637477, and 0.06615037, respectively. The results indicate that calibration accuracy, stability, and robustness are significantly improved with the optimization method based on the CLI-WPP, in comparison to the existing commonly used optimization algorithms.
New, Optimized Skin Calorimeter Version for Measuring Thermal Responses of Localized Skin Areas during Physical Activity
Authors: Rodríguez de Rivera Miriam , Rodríguez de Rivera Pedro Jesús
Published: September 2024

We present an optimized version of the skin calorimeter for measuring localized skin thermal responses during physical activity. Enhancements include a new holding system, more sensitive thermopiles, and an upgraded spiked heat sink for improved efficiency. In addition, we used a new, improved calorimetric model that takes into account all the variables that influence the measurement process. Resolution in power measurement is 1 mW. Performance tests under air currents and movement disturbances showed that the device maintains high accuracy; the deviation produced by these significant disturbances is less than 5%. Human subject tests, both at rest and during exercise, confirmed its ability to accurately measure localized skin heat flux, heat capacity, and thermal resistance (less than 5% uncertainty). These findings highlight the calorimeter’s potential for applications in sports medicine and physiological studies.
Standard-Deviation-Based Adaptive Median Filter for Elimination of Batwing Effects in Step Microstructure Measurement Using Digital Holography
Authors: Wei Jiasi , Wu Junjie , Wang Chen
Published: September 2024

Digital holography has transformative potential for the measurement of stacked-chip microstructures due to its non-invasive, single-shot, full-field characteristics. However, significant light scattering and diffraction at steep edges in step microstructures cause the batwing effect, leading to measurement errors. Herein, we propose a standard-deviation-based adaptive median filter to eliminate batwing effects in step microstructure measurement using digital holography. The standard deviation determines the positions of the steps and the range of the batwing effect. During filtering, the filter window size varies: it adjusts according to the center’s position within the batwing effect range and reduces outside this range to prevent distortion in other regions. Filtering weights are set to maintain information integrity while using larger filter windows. Experiments on the Standard Resolution Target USAF 1951 and the standard step height target show that our method successfully eliminates batwings while preserving the integrity of the remaining profile.
A Step towards Sustainable Education: Does an Entrepreneurial Teacher Nurture Creativity?
Authors: Saygın Muhammet , Say Serkan , Öztürk İsmail Yavuz , Gülden Bahadır , Kaplan Kadir
Published: September 2024

This study explores the relationship between teachers’ entrepreneurial behaviors and their creativity-nurturing behaviors, with a particular emphasis on sustainability in education. While previous studies have typically examined entrepreneurship and creativity as separate concepts, often focusing on their individual dimensions, this study underscores the significance of integrating entrepreneurial and creative competencies to promote sustainable educational practices. By highlighting how teachers’ entrepreneurial and creative skills contribute to sustainable education, this study addresses the broader impact these competencies have on meeting the evolving needs and expectations of students, families, and society. This approach supports the development of a more resilient and adaptive education system that can thrive over time. The research involved 1396 classroom teachers from the Mersin province of Turkey. Analysis of the data revealed a strong positive correlation between teachers’ entrepreneurial behaviors and their creativity-nurturing behaviors, indicating that teachers who exhibit higher levels of entrepreneurial traits are more likely to engage in practices that foster creativity or that teachers who exhibit higher levels of creativity nurturing behaviors are more likely to engage in entrepreneurial practices among their students. Thus, the importance of the high-level relationship between entrepreneurial behaviors and creativity-enhancing behaviors in improving sustainability in education has been revealed. This study highlights the need to integrate entrepreneurial skills into teaching practices to support a sustainable educational framework.
The Scene Logic of Innovative Talent Agglomeration: An Empirical Study Based on 54 Cities in China
Authors: Wu Jun , Wu Xuan , Zheng Hao , Wang Tong
Published: September 2024

In recent years, China has been steadily implementing its innovation-driven development strategy, underscoring the vital importance of attracting innovative talents to cities. Major cities have come to realize that securing such talent is essential for maintaining sustainable urban competitiveness in the future. This article takes a novel perspective by focusing on the role of urban scenes, with a particular emphasis on the cultural, lifestyle, and quality-of-life factors that are crucial for attracting and retaining innovative talent, which is essential for sustainable urban growth. Utilizing ridge regression analysis, this study scrutinizes the scores across various sub-dimensions of urban ambiance and the location quotient of innovative talent in 54 cities nationwide. We report several findings. Firstly, urban scenes play a pivotal role in talent agglomeration, a critical factor for sustainable development. Secondly, both rational and transgressive scenes positively impact the gathering of scientific and financial talents, with transgressive scenes having a more pronounced effect. Thirdly, self-expressive scenes may counterintuitively impede the clustering of scientific and cultural talents, a finding that contrasts with international research outcomes. In conclusion, this study sheds light on how urban scenes drive the sustainable concentration of innovative talents, thus contributing to the enrichment of theoretical understanding of sustainable talent development and practical insights for policymakers aiming to create urban environments that foster innovation and sustainability.
Socioeconomic and Cultural Impacts of Native Cotton Cultivation in the Amazonian Communities of Alto Urubamba, La Convencion-Cusco Province, Peru
Authors: Morales-Aranibar Luis , Soto César Augusto Masgo , Sanchez Angel Ramiro Yupanqui , Morales-Aranibar Carlos Genaro , Apaza-Canqui Abrahan Erasmo , Saenz Manuel Antonio Canto , Aguilera Jorge González , Oliveira Bruno Rodrigues de
Published: September 2024

In the native Amazonian communities, there is a significant biodiversity of cotton varieties, where traditional agricultural practices are preserved, crucial for cultural identity and local economic livelihood. This study examines the socioeconomic and cultural impacts of native cotton cultivation in the Amazonian communities of Alto Urubamba, La Convención-Cusco, Peru. Through a structured survey encompassing eight dimensions—general data, family and household, housing and services, education, economy and work, perspectives and challenges, and community participation—data was collected from residents of the Koribeni, Poyentimari, and Chacopishiato communities, particularly artisans involved in cotton-related activities. The analysis revealed agriculture as the primary income source, with 94.1% of Chacopishiato, 100% of Koribeni, and 61.1% of Poyentimari respondents depending on it. Participation in native cotton activities varied, with 52.9% in Chacopishiato, 88.2% in Koribeni, and 33.3% in Poyentimari. Common challenges include limited access to quality seeds (68.8% in Chacopishiato) and a need for technical knowledge (100% in Koribeni and Poyentimari). Correlation analysis showed significant associations between the study’s dimensions. Variations in community perceptions and knowledge about cotton cultivation suggest the need for targeted interventions. This research underscores the importance of sustainable development strategies that integrate traditional agricultural practices, preserve biodiversity, and enhance community resilience in the region.
Agricultural Service Trade and Green Development: A Perspective Based on China’s Agricultural Total Factor Productivity
Authors: Wang Xiaocheng , Yang Chenxi , Qiao Cuixia
Published: September 2024

Agricultural service trade is closely related to the promotion of the sustainable development of China’s agriculture and is necessary for comprehensive rural revitalization. To clarify the relationship between trade in agricultural services and agricultural green total factor productivity (AGTFP) from a macroscopic perspective and then analyze the acting path and threshold effect, can provide an important reference for improving agriculture’s green level and realizing the sustainable development of agriculture. This paper uses the provincial panel data of China from 2007 to 2022 to measure agricultural green total factor productivity (AGTFP) using the SBM-GML model and explores the impact of agricultural service trade on AGTFP with the help of the fixed effect model, the mediation effect model, and the threshold model. According to this study: (1) Trade in agricultural services can significantly increase AGTFP. (2) Mechanism analysis shows that trade in agricultural services can promote AGTFP through promoting industrial agglomeration, enhancing technological innovation, and improving factor allocation. (3) Heterogeneity analysis shows that trade in agricultural services has a more obvious role in promoting AGTFP in the eastern region and the main grain marketing area. (4) The threshold effect finds that the promotion of agricultural services trade on AGTFP will gradually increase as the level of government support rises. Therefore, China should actively promote the development of agricultural service trade, implement the concept of sustainable development, improve the level of government support, and promote the improvement in agricultural total factor productivity and sustainable development.
Leveraging Food Security and Environmental Sustainability in Achieving Sustainable Development Goals: Evidence from a Global Perspective
Authors: Ajeigbe Kola Benson , Ganda Fortune
Published: September 2024

This study investigated the nexus between food security, environmental sustainability, and sustainable growth from a global perspective of 63 economies spanning 2010–2021. Different econometric strategies including the Generalized Method of Moments (GMMs), the Fully Modified Ordinary Least Squares (FMOLSs), and the Dynamic Ordinary Least Squares (DOLSs) methods were employed to accomplish the investigation. The empirical outcomes indicate that the coefficients of food export, agricultural production, fertilizer consumption, FDI, population growth, and employment are positively and statistically associated with economic growth but have negative relationships with poverty and unemployment, except for population growth and unemployment, which revealed insignificant results. Conversely, the coefficient of food import revealed a positive association with poverty and unemployment but is negatively associated with economic growth. Additionally, the Environmental Kuznets Curve (EKC) hypothesis is also established in the considered countries. Nations, governments, and policymakers must prioritize environmentally friendly economic and green policies that can support sustainable agriculture. International policies to enhance food security collaboration because of nations’ diverse natural endowments to achieve all-level inclusive growth and development must be highly prioritized to reduce global inequality. Innovativeness and the sustainable use of land and processing of food must be encouraged to reduce emissions and other forms of pollution to support eco-fishing, aquaculture, and agriculture in order to ensure food security and achievement of the SDGs.
Prediction of Potential Suitable Areas and Distribution Evolution of Phoebe zhennan under Different Climate Scenarios
Authors: Mao Bo , Zhu Yingfang
Published: September 2024

As climatic and geographical conditions change in the future, species’ habitats will also change. Phoebe zhennan is a national second-level key protected wild plant in China with extremely high economic value and landscape value. In order to better protect the resources of P. zhennan and achieve the goal of the sustainable development of P. zhennan resources, we predict potential suitable areas for P. zhennan under different scenarios in the future. We collect the current distribution data of P. zhennan, and in combination with nine climate factors and three geographical factors, use the MaxEnt ecological niche model and ArcGIS geographic information system software to predict and analyze potential suitable areas for P. zhennan in different climate scenarios in the future. The result shows that more accurate prediction results can be obtained by using China’s climatic and geographical data before clipping as environmental variables. The precipitation of the warmest quarter and the slope are the main influencing factors in the prediction of potential suitable areas for P. zhennan. The future potential suitable areas for P. zhennan are mainly distributed in the central–southern and southern regions of China, with a tendency to expand towards the Tibet Autonomous Region and the northeast. The suitable habitat area will increase significantly, and the highly suitable habitat area will be more concentrated. These research results can provide valuable references for the effective protection of existing P. zhennan populations, the cultivation of P. zhennan within suitable habitats in the future, the establishment of a P. zhennan reserve, and the promotion of the sustainable utilization of P. zhennan resources.
Comparative Tests on Failure Characteristics and Mechanisms of Mine Waste Dumps with Different Material Layouts
Authors: Ding Hui , Sun Shuwei , Li Qianhui , Hu Jiabing , Gong Runjie , Zhang Boyu
Published: September 2024

Numerous landslides occur in waste dumps due to unscientific placement. The behavior of waste dumps with different material layouts has yet to be understood when it comes to deformation and failure. In this research, several tests of base friction were carried out on mine waste dumps by considering various material layouts under self-weight loading conditions. The waste dump’s displacement was detected by the system composed with image-capture equipment. The findings indicate that the layout of waste materials greatly affected the way a waste dump fails and could be categorized into three types: crest-arced slip, bottom-arced slip, and composite overall slip. Sliding failure caused a gradual development of the slip surface from the dominant fine-gravel material stage to the adjacent waste dump stage. Three phases can be assigned to the waste dump’s failure process: the constant deformation phase, strain localization phase, and failed phase. The layout of waste materials exerts a significant influence on the moment that the waste dump’s deformation enters the three phases. The waste dump’s failure mechanism was explained by describing how maximum shear and volumetric strain developed and transmitted. To determine the most dominant impact during the tests by analyzing the relative degree of volumetric and shear influences on the waste dump, an effect coefficient was proposed. The stability of the waste dump was illustrated and comparatively analyzed based on a simplified approach, in which the moments of initial cracking and local failure were used as a failure index for the model tests. The design and stability analysis of mine waste dumps is strengthened by this study’s strong support, thereby minimizing the risk of landslides and promoting the sustainability of the mining industry.
Spatio-Temporal Dynamics of Soil Organic Carbon Stock in Greek Croplands: A Long-Term Assessment
Authors: Triantakonstantis Dimitrios , Batsalia Maria , Lolos Nikolaos
Published: September 2024

This study examines the soil organic carbon (SOC) within Greek croplands, offering a comprehensive understanding of its dynamics. SOC, a cornerstone in soil health, nutrient cycling, and global carbon dynamics, assumes critical significance in sustainable agriculture and climate change mitigation. Drawing on diverse soil properties, including pH, soil texture, and different drainage and slope categories, this research explores the nuanced relationships shaping SOC dynamics in the diverse agroecological landscape of Greece. The investigation transcends local boundaries, emphasizing SOC’s global role in climate change mitigation by sequestering carbon dioxide. Two maps were used as data sources: (1) the SOC stock baseline map (2010) by JRC, (2) and the SOC stock map (2021) by the Institute of Soil and Water Resources, Hellenic Agricultural Organization—DIMITRA in collaboration with FAO. Greek croplands emerge as a mosaic of agroecological diversity, where anthropogenic activities wield transformative influences on SOC stock, demanding a delicate balance between agricultural productivity and soil health. This study unveils the influence of soil order, weaving a tapestry of SOC variability. Factors, from soil texture to cation exchange capacity, further shape SOC dynamics, emphasizing the role of clayey soils and coarse materials in carbon retention. Although soil organic carbon decreased from 2010 to 2021, the degree of carbon loss varied. This scientific endeavor synthesizes existing knowledge and unveils novel insights. More specifically, understanding SOC dynamics depends on multiple factors, including soil texture, pH, and landscape characteristics like slope. These variables collectively influence SOC retention, stabilization, and loss rates, highlighting the need for an integrated approach to studying SOC behavior across different environments. These findings contribute valuable insights for sustainable land management practices and climate change mitigation strategies, underscoring the importance of region-specific approaches in addressing global challenges.
Inundation: A Gaming App for a Sustainable Approach to Sea Level Rise
Authors: Solarino Stefano , Musacchio Gemma , Eva Elena , Anzidei Marco , De Lucia Maddalena
Published: September 2024

Over the past few decades, communication has evolved significantly, driven by new technologies and digital connections, with the Internet and mobile phones transforming traditional communication methods. This shift has also impacted disaster risk awareness-raising, requiring messages to adapt to modern digital platforms. This article describes an effort to engage younger generations with the issue of sea level rise, critical yet often overlooked despite its significant impact on global coastal areas, through the serious digital game “Inundation”. Presented for the first time, the game offers an engaging experience where players protect territories from coastal flooding while understanding rising seas’ causes, effects, and impacts. Feedback from student beta testers highlighted the game’s effectiveness in conveying scientific concepts and increasing awareness about this pressing issue. The game’s innovative design, particularly its visual representation of sea level rise at a pace more relatable to human perception, fills a gap in environmental education by making complex topics accessible and engaging. While evaluating the impact of such tools is challenging, initial feedback suggests that “Inundation” has significant potential to foster disaster preparedness and proactive safeguarding actions.
Changes in the Buffer Properties of the Restored Lake Complex
Authors: Grochowska Jolanta Katarzyna , Goździejewska Anna Maria , Augustyniak-Tunowska Renata
Published: September 2024

The objects of this study were lakes Mielenko (LM) (7.8 ha, 1.9 m), Klasztorne Małe (LKM) (13.7 ha, 20.0 m), and Klasztorne Duże (LKD) (57.5 ha, 8.5 m), located in north Poland (Kashubian Lake District). These lakes received raw sanitary and storm wastewater, leading to extreme degradation of these water bodies. This study aimed to present changes in buffer indicators such as reaction, alkalinity, total hardness, and Ca and Mg content that occurred as an effect of their restoration using the sequential application of coagulants as an innovative approach to the phosphorus inactivation method. Sustainable phosphorus inactivation in the study lakes made it possible to achieve nutrient concentrations in the range of values typical for low-trophic lakes. A radical decrease in P concentration in the water resulted in a limitation of production processes. Moreover, after a, it was noticed that the water parameters determining its buffering properties did not show a clear vertical stratification with increasing values toward the bottom. For example, before restoration, the mean e value of alkalinity in LM fluctuated around 2.10 mval/L, and after restoration activities, the average value was 1.97 mval/L. In LKM, the mean alkalinity value in surface water layers was 2.53 mval/L, and in bottom water layers, it was 4.92 mval/L; after restoration treatments, the average alkalinity of surface water was 2.18 mval/L, and in bottom water, it was 2.99 mval/L. In the last LKD, under the influence of the applied restoration, the average alkalinity of surface waters changed from 2.21 to 2.07 mval/L, and in bottom water, it changed from 2.86 to 2.12 mval/L. The values of parameters determining the buffering properties of lake water after restoration activities allow their water to be classified as slightly or medium hard. The changes obtained can be considered beneficial because water that is too hard does not support the development of aquatic organisms, and the values obtained in the study lakes after sustainable restoration are optimal for the construction of plant walls, shells, and fish bones.
Reactive Dye Wash-Off Processing of Cotton Fabrics Using Polymer Dye Transfer Inhibitors for Sustainable Dyeing
Authors: Islam Muhammad Nazrul , Toprak-Cavdur Tuba , Islam Shafiqul , Tarannum Fatema , Walters Keisha B.
Published: September 2024

Cotton fiber consumption is higher than any other natural fiber due to its superior mechanical and physical properties. In the conventional reactive dyeing process, the dye undergoes hydrolysis due to hydrogen bonding and weak Van der Waals forces, yielding poor washing color fastness. Additionally, the post-dyeing wash-off process consumes significant amounts of water, energy, and time in order to remove the unreacted and hydrolyzed dye. Therefore, reactive dyes are predominantly utilized to color cotton fabrics as the covalent bond formed between dye and fiber results in excellent washing fastness for a wide range of colors, and improved ease of use. To support more efficient, economical, and sustainable reactive dyeing, polymers can be used as effective dye transfer inhibitors (DTIs) in the reactive wash-off process. In this study, poly(vinylpyrrolidone) (PVP) homopolymer and poly(vinylpyrrolidone)-co-poly(vinylimidazole) (PVP-co-PVI) were synthesized at different molecular weights. These polymers were then employed as DTIs to evaluate their performance during the wash-off process for dyed cotton fabrics treated with three reactive dyes (Reactive Red 195, Reactive Blue 221, and Reactive Yellow 145). It is noteworthy to mention that the alternative wash-off procedure involved only three stages, in contrast to the series of washing steps necessary in the conventional method. Evaluation of the DTIs was focused on parameters such as color strength, washing fastness, chemical oxygen demand (COD), as well as energy, water, and time consumption. The findings revealed that while the polymer DTIs generated minor differences in dyed fabric colors in comparison to conventional dyeing, they significantly improved washing fastness. The polymer DTIs studied display excellent potential to provide a much more sustainable dyeing process by saving 90% energy, 40% water, and 50% time versus conventional wash-off processing. The wastewater COD values from the polymer-assisted reactive dye wash-off process were higher for all reactive dyes tested than those from conventional processing. While the higher COD values are attributed to the lower water volumes, this finding highlights the need for further studies. In summary, PVP polymers successfully served as DTIs in the reactive dyeing washing-off process, providing massive water-energy-time efficiency improvements and, subsequently, a more sustainable process for cotton fabric dyeing.
Financial Statement Comparability and Environmental, Social, and Governance (ESG) Performance
Authors: Choi Seung Uk , Lee Woo Jae
Published: September 2024

This study explores the effect of financial statement comparability on environmental, social, and governance (ESG) performance. Comparability is a qualitative characteristic that enhances the usefulness of accounting information, as emphasized in the conceptual framework of accounting standards. Reporting accounting information with high comparability reduces the cost of obtaining information for users of financial statements, thereby enabling them to make decisions more efficiently. However, increased comparability of financial statements may lead managers to avoid investments that do not directly and rapidly enhance corporate value. In the context of ESG, this behavior could lead to a reduction in ESG investments, thereby lowering overall ESG performance. This study empirically investigates this hypothesis by analyzing data from listed firms in Korea. The analysis reveals a negative relationship between the comparability of financial statements and ESG performance. When breaking down ESG performance into individual components, the environmental (E) and social (S) factors show a negative relationship with comparability, while no significant relationship is observed with the governance (G) factor. The results remain consistent across various additional tests, including the use of alternative comparability and ESG proxies, as well as when employing firm fixed effects models. The findings of this study highlight the potential adverse effects of financial statement comparability on ESG performance. These results suggest that while comparability facilitates better decision-making through enhanced information efficiency, it may inadvertently discourage managers from engaging in ESG investments, thereby negatively impacting a company’s sustainability.
The Analysis of Mutual Relations and Dependencies of Intellectual Capital Components in Polish Enterprises in the Context of Sustainability Goals
Authors: Prusak Rafał , Kardas Edyta
Published: September 2024

Intellectual capital (IC) has been an important element of business management for some time. Understanding the role of IC and consciously incorporating it into organisational and management processes is one of the key aspects of building unique competitive advantages in the context of sustainable development. The basic goal of the research was to determine which IC components are given greater importance by the management staff of selected Polish enterprises when shaping their operating strategy and which elements are the object of greater concern during everyday functioning. A statistical analysis of the survey results obtained was carried out, allowing for the characterisation of both the phenomena and trends in the entire surveyed population and taking into account the division criteria related to the size of enterprises and the specificity of their activities. The basic research instrument consisted of a questionnaire. This study covered the territory of southern Poland. The results showed that there are certain elements of IC that managers focus on in particular, and there are differences in the approach that is related to the selected criteria of analysis. This study adds to the existing knowledge of the key elements of strategy for companies managing strategic capital in the context of sustainable development.
Dealing with Stationary Sinusoidal Responses of Seven Types of Multi-Fractional Vibrators Using Multi-Fractional Phasor
Authors: Li Ming
Published: September 2024

The novelty and main contributions of this paper are reflected in four aspects. First, we introduce multi-fractional phasor in Theorem 1. Second, we propose the motion phasor equations of seven types of multi-fractional vibrators in Theorems 2, 12, 22, 32, 43, 54, and 65, respectively. Third, we present the analytical expressions of response phasors of seven types of multi-fractional vibrators in Theorems 10, 20, 30, 41, 52, 63, and 74, respectively. Fourth, we bring forward the analytical expressions of stationary sinusoidal responses of seven types of multi-fractional vibrators in Theorems 11, 21, 31, 42, 53, 64, and 75, respectively. In addition, by using multi-fractional phasor, we put forward the analytical expressions of vibration parameters (equivalent mass, equivalent damping, equivalent stiffness, equivalent damping ratio, equivalent damping free natural angular frequency, equivalent damped natural angular frequency, equivalent frequency ratio) and frequency transfer functions of seven types of multi-fractional vibrators. Demonstrations exhibit that the effects of multi-fractional orders on stationary sinusoidal responses of those multi-fractional vibrators are considerable.
A Preliminary Assessment of the Usability of Magnetoplasma Compressors in Scientific and Technical Applications
Authors: Kuzenov Victor V. , Varaksin Aleksey Yu. , Ryzhkov Sergei V.
Published: September 2024

This paper presents a preliminary analysis of the plasma dynamic modes of operation of end-type magnetoplasma compressor (MPC) discharges. The characteristic methods used to organize the optical pumping of a photodissociation gas laser using an MPC discharge are briefly described. The kinetic and energy characteristics of photodissociation gas optical quantum generators (OQGs) with optical pumping by an MPC discharge were evaluated. Based on the numerical calculations, an analysis of the radiation–plasma dynamic structures and the spectral brightness characteristics of the MPC discharge in the ohmic mode of plasma heating was carried out.
A Survey of Semantic Parsing Techniques
Authors: Jiang Peng , Cai Xiaodong
Published: September 2024

In the information age, semantic parsing technology drives efficiency improvement and accelerates the process of intelligence. However, it faces complex understanding, data inflation, inappropriate evaluation, and difficult application of advanced large models. This study analyses the current challenges and looks forward to the development trend of the technology. Specific approaches include: this study adopts a systematic review method and strictly follows the PRISMA framework, deeply analyzes the key ideas, methods, problems, and solutions of traditional and neural network methods, and explores the model performance, API application, dataset, and evaluation mechanism. Through literature analysis, the technology is classified according to its application scenarios. Then, the practical application contributions are summarized, current limitations such as data size, model performance, and resource requirements are analyzed, and future directions such as dataset expansion, real-time performance enhancement, and industrial applications are envisioned. The results of the study show significant advances in semantic parsing technology with far-reaching impacts. Traditional and neural network methods complement each other to promote theoretical and practical innovation. In the future, with the continuous progress and in-depth application of machine learning technology, semantic parsing technology needs to further deepen the research on logical reasoning and evaluation, to better cope with technical challenges and lead the new development of natural language processing and AI.