bluuebunny/crossref_metadata_2025 huggingface dataset:
Computation Theory of Large-Scale Partially Coherent Imaging by the Modified Modal Expansion Method
Authors: Li Jiaqi , Yang Huaijiang
Published: July 2024

The numerical calculation of partially coherent imaging involves a fourfold integral, which is numerically complex and impracticable to be calculated directly. The use of coherent-mode decomposition (CMD) can make this problem more manageable but finding the coherent-modes for complicated partial coherent fields (without already-known coherent-mode expansion) are rather computationally intensive. In this letter, a modified modal expansion method is proposed, which significantly reduces the requirement of computational resources. The propagation of partial coherence in imaging systems with extremely large sampling number could be handled by an ordinary computer. A comparison between the new method and the traditional method in terms of memory resource requirements and computational time consumption is also detailed in this article. We will also show that this method could deal with the anisoplanatic imaging cases while maintaining the same computational efficiency.
The Potential of Two Phytoseiid Mites as Predators of the Grape Erineum Mite, Colomerus vitis
Authors: Al-Azzazy Mahmoud M. , Alhewairini Saleh S.
Published: July 2024

Phytoseius plumifer (Canestrini and Fanzago) and Euseius scutalis (Athias-Henriot) (Phytoseiidae) are generalist predatory mites important in controlling phytophagous mites on some agricultural crops. The biology of both species as potential biological control agents of the grape erineum mite, Colomerus vitis (Pagenstecher) (Eriophyidae) on grape leaf disks was studied in the laboratory at 33 ± 1 °C, 60%RH, 12:12 h L:D. The developmental time, survival, and reproductive parameters of P. plumifer and E. scutalis on C. vitis, date palm pollen as well as C. vitis plus date palm pollen were investigated. Both predators, P. plumifer and E. scutalis, thrived on the mixed diet of C. vitis and date palm pollen resulting in a shorter developmental time (6.16 and 6.69 days, respectively), higher oviposition rate (2.11 and 1.96 eggs/female/day, respectively), and higher intrinsic rate of increase (0.251 and 0.229 per female/day, respectively) than on any other diet. Date palm pollen was an adequate alternative food source for P. plumifer and E. scutalis. The results suggest that both predators have good potential to suppress C. vitis populations and that date palm pollen can support the population establishment of both predators in the absence or scarcity of the main prey in the environment. We discuss the relevance of our results for the biocontrol of C. vitis.
The Influence of Technological Factors on the Structure and Chemical Composition of Tuberous Dahlia Roots Determined Using Vibrational Spectroscopy
Authors: Moldovan Ioana , Cotoz Alex-Péter , Rózsa Sándor , Magyari Klara , Lehel Lukács , Baia Monica , Cantor Maria
Published: July 2024

This research investigated the structural and chemical modifications of Dahlia ‘Kennemerland’ across different technological conditions and throughout the vegetation period. Using FT-IR imaging, this study focused on the changes in the inulin, lignin, and suberin contents of tuberous roots. FT-IR maps were generated to visualize the distribution of these compounds across scanned areas, highlighting variations across cultivation methods and seasonal stages. The key compounds analyzed included inulin, lignin, and suberin, which were identified in different root zones. The results showed that inulin was distributed in all analyzed areas, predominantly in zone 1 (periderm), with a distribution that increased with forced cultivation, while lignin and suberin distributions varied with zone and season. Forced tuberous root lignin was detected in all four areas analyzed, in the fall accumulating mainly in area 4 and in suberin starting from summer until autumn. Based on the evaluation of the maps obtained by representing the area ratios of specific bands (inulin/lignin and inulin/suberin), we established where the inulin was present in the highest quantity and concluded that suberin was the constituent with the lowest concentration in tuberous Dahlia roots. These findings emphasize the influence of technological factors and seasonal changes on the biochemical makeup of tuberous Dahlia roots. This detailed biochemical mapping provides insights for optimizing Dahlia cultivation and storage for various industrial applications. This study concludes that FT-IR spectroscopy is an effective tool for monitoring and understanding the biochemical dynamics of Dahlia roots, aiding their agricultural and industrial utilization.
Double-Closed-Loop Model Predictive Control Based on a Linear Induction Motor
Authors: Ma Shuhang , Zhao Jinghong , Xiong Yiyong , Ran Guangpu , Yao Xing
Published: July 2024

The conventional PI control, which has been extensively employed in the high-performance control of linear induction motors, has the benefit of a straightforward concept, quick dynamic response, and simple control. Nevertheless, the linear induction motor is more susceptible to outside disturbances and mismatched parameters since it is a highly linked, high-order, nonlinear, time-varying, complex system. Because of this, this paper proposes a double-closed-loop control of the linear induction motor, whose speed and current loops are the model-predictive speed controller and the model-predictive current controller, respectively, to solve the problems of speed overshoot and oscillation when the linear induction motor is operated under PI control. The findings demonstrate the benefits of the suggested control strategy, which significantly enhances the linear induction motor’s speed control performance. These benefits include improved dynamic performance, limited overshooting, and robust resistance to load disturbance.
Special Issue: Biochemical Processes for Sustainability
Authors: Hernández-Fernández Francisco J. , de los Ríos Antonia Pérez
Published: July 2024

Currently, we are faced with the need to develop solution that are sustainable in terms of the energy and material resources used, which implies environmental sustainability [...]
Productivity Model Study of Water-Bearing Tight Gas Reservoirs Considering Micro- to Nano-Scale Effects
Authors: Chen Feifei , Duan Yonggang , Wang Kun
Published: July 2024

Tight sandstone is rich in micron- and nano-scale pores, making the two-phase flow of gas and water complex. Establishing reliable relative permeability and productivity models is an urgent issue. In this study, we first used a slip model to correct the gas phase’s no-slip Hagen–Poiseuille equation for nano- and micropores. Then, combined with the fractal theory of porous media and the tortuous capillary bundle model, we established two-phase relative permeability models for nanopores and micropores. These relative permeability models comprehensively consider the gas slippage effect, the initiation pressure gradient, the pores’ fractal characteristics, and water film mechanisms. Based on these models, we developed a three-region coupling productivity model for water-bearing tight gas reservoirs with multi-stage fractured horizontal wells. This productivity model considered the micro- and nano-scale effects and the heterogeneity of fracture networks. Then, the model was solved and validated with a field case. The results indicated that the three-region composite unsteady productivity model for water-bearing tight gas reservoirs, which incorporated micro- and nano-scale effects (with consideration of micro-scale and nano-scale phenomena in the fluid flow), could accurately predict a gas well’s productivity. An analysis of the factors influencing productivity showed that ignoring the micro- and nano-scale effects in water-bearing tight gas reservoirs will underestimate the reservoir’s productivity. The initial water saturation, the two-phase flow’s initiation pressure gradient, and capillary force are all negatively correlated with the productivity of gas wells, while the conductivity of the fractures is positively correlated with gas well productivity.
Catalytic Biomass Transformation to Hydrocarbons under Supercritical Conditions over Nickel Supported on Schungite
Authors: Schipanskaya Elena O. , Stepacheva Antonina A. , Markova Mariia E. , Bykov Alexey V. , Sidorov Alexander I. , Matveeva Valentina G. , Sulman Mikhail G. , Kiwi-Minsker Lioubov
Published: July 2024

Liquid fuel production from biomass-derived molecules has received great attention due to the diminished fossil fuel reserves, growing energy demand, and the necessity of CO2 emission reduction. The deoxygenation of oils and fatty acids is a promising process to obtain “green” diesel. Herein, we report the results of the study of the deoxygenation of stearic acid to alkanes as a model reaction. Series of Ni-supported on schungite were obtained by precipitation in subcritical water (hydrothermal deposition) and for comparison via wetness impregnation followed, in both cases, by calcination at 500 °C and a reduction in H2 at 300 °C. The catalyst obtained via hydrothermal synthesis showed a three-fold higher specific surface area with a four-fold higher active phase dispersion compared to the catalysts synthesized via conventional impregnation. The catalysts were tested in stearic acid deoxygenation in supercritical n-hexane as the solvent. Under optimized process conditions (temperature of 280 °C, hydrogen partial pressure of 1.5 MPa, and 13.2 mol of stearic acid per mol of Ni), a close to 100% yield of C10–C18 alkanes, containing over 70 wt.% of targeted n-heptadecane, was obtained after 60 min of reaction.
Classification Model for Real-Time Monitoring of Machining Status of Turned Workpieces
Authors: Wu Fei , Yuan Lai , Wu Aonan , Zhang Zhengrui
Published: July 2024

The occurrence of tool chatter can have a detrimental impact on the quality of the workpiece. In order to improve surface quality, machining stability, and reduce tool wear cycles, it is essential to monitor the workpiece machining process in real time during the turning process. This paper presents a tool chatter state recognition model based on a denoising autoencoder (DAE) for feature dimensionality reduction and a bidirectional long short-term memory (BiLSTM) network. This study examines the feature dimensionality reduction method of the DAE, whereby the reduced-dimensional data are concatenated and input into the BiLSTM model for training. This approach reduces the learning difficulty of the network and enhances its anti-interference capability. Turning experiments were conducted on a SK50P lathe to collect the dataset for model performance validation. The experimental results and analysis indicate that the proposed DAE-BiLSTM model outperforms other models in terms of prediction and classification accuracy in distinguishing between stable machining, over-machining, and severe chatter stages in turning chatter state recognition. The overall classification accuracy reached 96.28%.
Early Detection of Southern Pine Beetle Attack by UAV-Collected Multispectral Imagery
Authors: Kanaskie Caroline R. , Routhier Michael R. , Fraser Benjamin T. , Congalton Russell G. , Ayres Matthew P. , Garnas Jeff R.
Published: July 2024

Effective management of bark beetle infestations requires prompt detection of attacked trees. Early attack is also called green attack, since tree foliage does not yet show any visible signs of tree decline. In several bark beetle systems, including mountain pine beetle and European spruce bark beetle, unpiloted aerial vehicle (UAV)-based remote sensing has successfully detected early attack. We explore the utility of remote sensing for early attack detection of southern pine beetle (SPB; Dendroctonus frontalis Zimm.), paired with detailed ground surveys to link tree decline symptoms with SPB life stages within the tree. In three of the northernmost SPB outbreaks in 2022 (Long Island, New York), we conducted ground surveys every two weeks throughout the growing season and collected UAV-based multispectral imagery in July 2022. Ground data revealed that SPB-attacked pitch pines (Pinus rigida Mill.) generally maintained green foliage until SPB pupation occurred within the bole. This tree decline behavior illustrates the need for early attack detection tools, like multispectral imagery, in the beetle’s northern range. Balanced random forest classification achieved, on average, 78.8% overall accuracy and identified our class of interest, SPB early attack, with 68.3% producer’s accuracy and 72.1% user’s accuracy. After removing the deciduous trees and just mapping the pine, the overall accuracy, on average, was 76.9% while the producer’s accuracy and the user’s accuracy both increased for the SPB early attack class. Our results demonstrate the utility of multispectral remote sensing in assessing SPB outbreaks, and we discuss possible improvements to our protocol. This is the first remote sensing study of SPB early attack in almost 60 years, and the first using a UAV in the SPB literature.
JointNet: Multitask Learning Framework for Denoising and Detecting Anomalies in Hyperspectral Remote Sensing
Authors: Shao Yingzhao , Li Shuhan , Yang Pengfei , Cheng Fei , Ding Yueli , Sun Jianguo
Published: July 2024

One of the significant challenges with traditional single-task learning-based anomaly detection using noisy hyperspectral images (HSIs) is the loss of anomaly targets during denoising, especially when the noise and anomaly targets are similar. This issue significantly affects the detection accuracy. To address this problem, this paper proposes a multitask learning (MTL)-based method for detecting anomalies in noisy HSIs. Firstly, a preliminary detection approach based on the JointNet model, which decomposes the noisy HSI into a pure background and a noise–anomaly target mixing component, is introduced. This approach integrates the minimum noise fraction rotation (MNF) algorithm into an autoencoder (AE), effectively isolating the noise while retaining critical features for anomaly detection. Building upon this, the JointNet model is further optimized to ensure that the noise information is shared between the denoising and anomaly detection subtasks, preserving the integrity of the training data during the anomaly detection process and resolving the issue of losing anomaly targets during denoising. A novel loss function is designed to enable the joint learning of both subtasks under the multitask learning model. In addition, a noise score evaluation metric is introduced to calculate the probability of a pixel being an anomaly target, allowing for a clear distinction between noise and anomaly targets, thus providing the final anomaly detection results. The effectiveness of the proposed model and method is validated via testing on the HYDICE and San Diego datasets. The denoising metric results of the PSNR, SSIM, and SAM are 41.79, 0.91, and 4.350 and 42.83, 0.93, and 3.558 on the HYDICE and San Diego datasets, respectively. The anomaly detection ACU is 0.943 and 0.959, respectively. The proposed method outperforms the other algorithms, demonstrating that the reconstructed images using this method exhibited lower noise levels and more complete image information, and the JointNet model outperforms the mainstream HSI anomaly detection algorithms in both the quantitative evaluation and visual effect, showcasing its improved detection capabilities.
Piezoelectric Behaviour in Biodegradable Carrageenan and Iron (III) Oxide Based Sensor
Authors: Bučinskas Vytautas , Udris Dainius , Dzedzickis Andrius , Petronienė Jūratė Jolanta
Published: July 2024

This paper is dedicated to the research of phenomena noticed during tests of biodegradable carrageenan-based force and pressure sensors. Peculiar voltage characteristics were noticed during the impact tests. Therefore, the sensors’ responses to impact were researched more thoroughly, defining time-dependent sensor output signals from calibrated energy impact. The research was performed using experimental methods when a free-falling steel ball impacted the sensor material to create relatively definable impact energy. The sensor’s output signal, which is analogue voltage, was registered using an oscilloscope and transmitted to the PC for further analysis. The obtained results showed a very interesting outcome, where the sensor, which was intended to be piezoresistive, demonstrated a combination of behaviour typical for galvanic cells and piezoelectric material. It provides a stable DC output that is sensitive to the applied statical pressure, and in case of a sudden impact, like a hit, it demonstrates piezoelectric behaviour with some particular effects, which are described in the paper as proton transfer in the sensor-sensitive material. Such phenomena and sensor design are a matter of further development and research.
Sensor-Enhanced Smart Gripper Development for Automated Meat Processing
Authors: Takács Kristóf , Takács Bence , Garamvölgyi Tivadar , Tarsoly Sándor , Alexy Márta , Móga Kristóf , Rudas Imre J. , Galambos Péter , Haidegger Tamás
Published: July 2024

Grasping and object manipulation have been considered key domains of Cyber-Physical Systems (CPS) since the beginning of automation, as they are the most common interactions between systems, or a system and its environment. As the demand for automation is spreading to increasingly complex fields of industry, smart tools with sensors and internal decision-making become necessities. CPS, such as robots and smart autonomous machinery, have been introduced in the meat industry in recent decades; however, the natural diversity of animals, potential anatomical disorders and soft, slippery animal tissues require the use of a wide range of sensors, software and intelligent tools. This paper presents the development of a smart robotic gripper for deployment in the meat industry. A comprehensive review of the available robotic grippers employed in the sector is presented along with the relevant recent research projects. Based on the identified needs, a new mechatronic design and early development process of the smart gripper is described. The integrated force sensing method based on strain measurement and magnetic encoders is described, including the adjacent laboratory and on-site tests. Furthermore, a combined slip detection system is presented, which relies on an optical flow-based image processing algorithm using the video feed of a built-in endoscopic camera. Basic user tests and application assessments are presented.
Gearbox Fault Diagnosis Method in Noisy Environments Based on Deep Residual Shrinkage Networks
Authors: Cao Jianhui , Zhang Jianjie , Jiao Xinze , Yu Peibo , Zhang Baobao
Published: July 2024

Gearbox fault diagnosis is essential in the maintenance and preventive repair of industrial systems. However, in actual working environments, noise frequently interferes with fault signals, consequently reducing the accuracy of fault diagnosis. To effectively address this issue, this paper incorporates the noise attenuation of the DRSN-CW model. A compound fault detection method for gearboxes, integrated with a cross-attention module, is proposed to enhance fault diagnosis performance in noisy environments. First, frequency domain features are extracted from the public dataset by using the fast Fourier transform (FFT). Furthermore, the cross-attention mechanism model is inserted in the optimal position to improve the extraction and recognition rate of global and local fault features. Finally, noise-related features are filtered through soft thresholds within the network structure to efficiently mitigate noise interference. The experimental results show that, compared to existing network models, the proposed model exhibits superior noise immunity and high-precision fault diagnosis performance.
How Do We Calibrate a Battery Electric Vehicle Model Based on Controller Area Network Bus Data?
Authors: Tollner Dávid , Nyerges Ádám , Jneid Mahmoud Said , Geleta Attila , Zöldy Máté
Published: July 2024

Transforming an up-to-date vehicle into a measurement system is a rewarding task due to the large number of different sensors in the onboard control and diagnostic systems. These procedures are not performed by a single control unit; it is necessary to share the signal values over a communication network, to which an external device can be connected to record the real traffic. The paper aims to use these recorded data for 1 DOF longitudinal vehicle and powertrain model validation. For repeatability, three city routes are selected: plain road, smaller road grade, and higher road grade in both directions. Therefore, the drivetrain system is tested in a high load range, even with long-term recuperation. The altitude changes are recorded with a DGPS system. By the recorded measurements, the vehicle and the drivetrain model can be calibrated, such as the air drag parameters, the rolling resistances, and the efficiencies of the drivetrain. The validation criteria are defined for speed tracking, and the relative tolerance of the cumulated energy should be below 10%. At the end of the day, a developed model is ready for energetic analysis or control strategy design. The energy balance of the applied cycles is also presented to prove that.
Extracting Vehicle Trajectories from Partially Overlapping Roadside Radar
Authors: Schrader Maxwell , Hainen Alexander , Bittle Joshua
Published: July 2024

This work presents a methodology for extracting vehicle trajectories from six partially-overlapping roadside radars through a signalized corridor. The methodology incorporates radar calibration, transformation to the Frenet space, Kalman filtering, short-term prediction, lane-classification, trajectory association, and a covariance intersection-based approach to track fusion. The resulting dataset contains 79,000 fused radar trajectories over a 26-h period, capturing diverse driving scenarios including signalized intersections, merging behavior, and a wide range of speeds. Compared to popular trajectory datasets such as NGSIM and highD, this dataset offers extended temporal coverage, a large number of vehicles, and varied driving conditions. The filtered leader–follower pairs from the dataset provide a substantial number of trajectories suitable for car-following model calibration. The framework and dataset presented in this work has the potential to be leveraged broadly in the study of advanced traffic management systems, autonomous vehicle decision-making, and traffic research.
Delineation of 12-Lead ECG Representative Beats Using Convolutional Encoder–Decoders with Residual and Recurrent Connections
Authors: Krasteva Vessela , Stoyanov Todor , Schmid Ramun , Jekova Irena
Published: July 2024

The aim of this study is to address the challenge of 12-lead ECG delineation by different encoder–decoder architectures of deep neural networks (DNNs). This study compares four concepts for encoder–decoders based on a fully convolutional architecture (CED-Net) and its modifications with a recurrent layer (CED-LSTM-Net), residual connections between symmetrical encoder and decoder feature maps (CED-U-Net), and sequential residual blocks (CED-Res-Net). All DNNs transform 12-lead representative beats to three diagnostic ECG intervals (P-wave, QRS-complex, QT-interval) used for the global delineation of the representative beat (P-onset, P-offset, QRS-onset, QRS-offset, T-offset). All DNNs were trained and optimized using the large PhysioNet ECG database (PTB-XL) under identical conditions, applying an advanced approach for machine-based supervised learning with a reference algorithm for ECG delineation (ETM, Schiller AG, Baar, Switzerland). The test results indicate that all DNN architectures are equally capable of reproducing the reference delineation algorithm’s measurements in the diagnostic PTB database with an average P-wave detection accuracy (96.6%) and time and duration errors: mean values (−2.6 to 2.4 ms) and standard deviations (2.9 to 11.4 ms). The validation according to the standard-based evaluation practices of diagnostic electrocardiographs with the CSE database outlines a CED-Net model, which measures P-duration (2.6 ± 11.0 ms), PQ-interval (0.9 ± 5.8 ms), QRS-duration (−2.4 ± 5.4 ms), and QT-interval (−0.7 ± 10.3 ms), which meet all standard tolerances. Noise tests with high-frequency, low-frequency, and power-line frequency noise (50/60 Hz) confirm that CED-Net, CED-Res-Net, and CED-LSTM-Net are robust to all types of noise, mostly presenting a mean duration error < 2.5 ms when compared to measurements without noise. Reduced noise immunity is observed for the U-net architecture. Comparative analysis with other published studies scores this research within the lower range of time errors, highlighting its competitive performance.
Fall Detection Method for Infrared Videos Based on Spatial-Temporal Graph Convolutional Network
Authors: Yang Junkai , He Yuqing , Zhu Jingxuan , Lv Zitao , Jin Weiqi
Published: July 2024

The timely detection of falls and alerting medical aid is critical for health monitoring in elderly individuals living alone. This paper mainly focuses on issues such as poor adaptability, privacy infringement, and low recognition accuracy associated with traditional visual sensor-based fall detection. We propose an infrared video-based fall detection method utilizing spatial-temporal graph convolutional networks (ST-GCNs) to address these challenges. Our method used fine-tuned AlphaPose to extract 2D human skeleton sequences from infrared videos. Subsequently, the skeleton data was represented in Cartesian and polar coordinates and processed through a two-stream ST-GCN to recognize fall behaviors promptly. To enhance the network’s recognition capability for fall actions, we improved the adjacency matrix of graph convolutional units and introduced multi-scale temporal graph convolution units. To facilitate practical deployment, we optimized time window and network depth of the ST-GCN, striking a balance between model accuracy and speed. The experimental results on a proprietary infrared human action recognition dataset demonstrated that our proposed algorithm accurately identifies fall behaviors with the highest accuracy of 96%. Moreover, our algorithm performed robustly, identifying falls in both near-infrared and thermal-infrared videos.
A Parallel Compression Pipeline for Improving GPU Virtualization Data Transfers
Authors: Peñaranda Cristian , Reaño Carlos , Silla Federico
Published: July 2024

GPUs are commonly used to accelerate the execution of applications in domains such as deep learning. Deep learning applications are applied to an increasing variety of scenarios, with edge computing being one of them. However, edge devices present severe computing power and energy limitations. In this context, the use of remote GPU virtualization solutions is an efficient way to address these concerns. Nevertheless, the limited network bandwidth might be an issue. This limitation can be alleviated by leveraging on-the-fly compression within the communication layer of remote GPU virtualization solutions. In this way, data exchanged with the remote GPU is transparently compressed before being transmitted, thus increasing network bandwidth in practice. In this paper, we present the implementation of a parallel compression pipeline designed to be used within remote GPU virtualization solutions. A thorough performance analysis shows that network bandwidth can be increased by a factor of up to 2×.
Silenced Voices in Portuguese Public TV News: An Intersectional Analysis of the Representation of Women with Disabilities in RTP’s Telejornal
Authors: Cruz Carla , Cunha Maria João , Belim Célia
Published: July 2024

(1) Background: Studies on women with disabilities (WwD) are rare and tend to reveal certain representation patterns. This study aims to understand how and to what extent WwD and chronic diseases are deemed newsworthy in the Portuguese public news TV station RTP1 primetime news program. Feminist disability, standpoint, agenda-setting, and framing theories are used alongside the concept of intersectionality. (2) Methods: A mixed-method approach is adopted, combining quantitative content analysis of all broadcasted news in January 2020 (n = 704), and qualitative discourse analysis of news items on PwD (n = 5). (3) Results: The results reveal that disability is a reduced issue in Telejornal’s agenda. PwD, in general, are often portrayed in secondary roles and without a voice. The protagonists of news stories about disability or persons with disabilities are predominantly women without disabilities, occupying traditional roles as caregivers (mothers, nurses), while men are more often portrayed as public agents. Discourse analysis deepens understanding by uncovering the prevalence of negative news values and a problem-centred framing, often associated with negativity, rather than presenting solutions. (4) Conclusions: Consequently, WwD were found to be deprived of news representation with a more positive or ‘normal’ focus and an intersectional approach reveals a lack of inclusion, with the few existing news tending to focus on exclusion issues, portraying only white Portuguese women. This study underscores the urgent need for a more equitable approach in media representation, recognising the diversity and positive contributions of WwD to promote an inclusive narrative.
Perceptions of Ukrainian and Other Refugees among Eighth-Graders in Slovenia: Characteristics of Students towards Inclusion of Refugee Students in Mainstream Schools
Authors: Klemenčič Mirazchiyski Eva
Published: July 2024

Background: There is a lack of empirical data on the school-age population’s attitudes toward refugees. Despite this, the attention being paid to the integration of refugee students in schools worldwide is increasing. Objectives: First, the present study aims to explore the attitudes of eighth-grade students in Slovenia regarding refugees and to investigate whether there are differences in their attitudes based on the geographical areas from where refugees originate. Second, the study also explores the connections between attitudes and some characteristics of students. Methods: The present study is quantitative; sample: 3466 respondents (representative sample; year 2022). Descriptive statistics and binary logistic regression are used. Results: Most of the students would be willing to help refugees coming from any country. The students are least willing to help refugees from Syria and Afghanistan or Africa. A high proportion of students express fears about what refugees bring, though these fears are varied. Logistic regression shows that civic knowledge and positive attitudes towards immigrants are significantly and negatively related to students’ opinion that refugee children should study in separate schools, while student socio-economic status (SES) and own immigration status are not. Conclusions: The results call for different approaches to be maintained/introduced in schools for tolerance and reducing prejudices for (certain) groups of immigrants/refugees.
Low-Carbon Transformation in Megacities: Benefits for Climate Change Mitigation and Socioeconomic Development—A Case Study of Shenzhen, China
Authors: Ji Junping , Cao Lei , Bi Yuanmeng , Zeng Yuan , Wang Dong
Published: July 2024

Megacities are the main sources of carbon emissions and are pillars of socioeconomic development due to economic prosperity, industrial development, and population agglomeration. Taking the megacity of Shenzhen, China, as an example, this research explored the advantages of low-carbon transformation in both climate change mitigation and socioeconomic progress. Soft linking of the Low Emissions Analysis Platform (LEAP) model with input–output analysis demonstrates that adopting feasible low-carbon transformation strategies has the potential to reverse the ongoing trend of carbon emission growth in Shenzhen, resulting in a peak before 2025. The peaks in carbon intensity and carbon emissions per capita occurred earlier than those in total carbon emissions. Furthermore, a total low-carbon investment of 462.04 billion CNY could yield approximately 799.49 billion CNY in output, 311.42 billion CNY in value-added, and 156.10 billion CNY in resident remuneration and create 1.79 million job opportunities during policy implementation. Taking into account both the reduction in carbon emissions and the socioeconomic benefits of low-carbon transformation, this research elucidates a potential pathway for Shenzhen to achieve synergy between mitigating climate change and promoting socioeconomic development, thus offering a valuable model for cities facing similar challenges.
Spatial Analyses in the Planning of Quiet Areas: A Case Study of Radom and Lublin (Poland)
Authors: Adamczyk Patrycja , Andrzejewska Martyna , Bernat Sebastian
Published: July 2024

The 2020 Environmental Noise in Europe report shows that a vast majority of European countries have legal definitions of quiet areas and criteria for their designation. As much as 60% of them have designated at least one quiet area in their territory and have taken measures to protect the acoustic climate in these areas. In Poland, although the instrument was introduced in the national environmental legislation, guidelines for delimitation have not been defined yet in the form of a binding legal act. The objective of this article is to propose the method for identifying quiet areas through spatial analysis and present it on the example of two cities—Radom and Lublin. These cities represent different approaches to the designation of quiet areas. This work is a continuation of the survey conducted and published by the authors in 2023. At the outset, the legislation and literature on the subject were analysed. Based on that, the conditions that potential quiet areas must meet were determined. Spatial analyses were then conducted for the quiet areas proposed earlier in the survey and in the environmental noise programme to see if the criteria selected by the authors were actually met in these areas: acoustic, functional, related to land cover, size, as well as temporal, spatial and transport accessibility, and location, including distances from areas with the highest noise emissions and adequate population density. Quiet areas are forest areas, public green areas, and surface water areas with recreational functions. The criteria for the elimination of the selected quiet areas were mainly location related (distance from main roads and population density). The accessibility of the quiet areas is also important. A method devised by the authors enables the delimitation of quiet areas in cities. Furthermore, this method is primarily based on publicly available data, which makes it applicable to other cities as well.
Socialscape Ecology: Integrating Social Features and Processes into Spatially Explicit Marine Conservation Planning
Authors: Baker-Médard Merrill , Concannon Katherine , Gantt Courtney , Moen Sierra , White Easton R.
Published: July 2024

Conservation planning is the process of locating, implementing, and maintaining areas that are managed to promote the persistence of biodiversity, ecosystem function, and human use. In this review, we analyze the ways in which social processes have been integrated into Marxan, a spatially explicit conservation planning tool used as one step in a broader process to select the location and size of protected areas. Drawing on 89 peer-reviewed articles published between 2005 and 2020, we analyzed the ways in which human activity, values, and processes are spatialized in the environment, something we call socialscape ecology. A socialscape ecology approach to conservation planning considers not only the spatial configuration of human activity in a land or seascape but also the underlying drivers of these activities, how resource use rights and access operate in an area, and how resource users contribute to data collection and decision making. Our results show that there has been a small but statistically significant increase in the total number of cost variables into Marxan analysis over time, with uneven performance across seven of the nine categories assessed. One notable area of improvement has been the increase over time in number of studies integrating socio-environmental change (e.g., climate change) in their analysis. Including accurate, context-specific, and detailed accounts of social features and processes within land and seascapes is essential for developing conservation plans that are cost-effective, ecologically sound, socially desirable, and just.
The Relationship between Learning Outcomes and Graduate Competences: The Chain-Mediating Roles of Project-Based Learning and Assessment Strategies
Authors: Li Ming , Rohayati Mohd Isa
Published: July 2024

Addressing the skill gap between labor market requirements and graduate readiness is crucial for the sustainable development of China’s vocational education system. This study investigated how outcome-based education (OBE) influences the attainment of graduate competence in China’s higher vocational education system, using the theory of Constructive Alignment (CA) as its foundation. The OBE model incorporates intended learning outcomes, project-based learning, and assessment strategies to ensure graduate competence aligns with professional sustainability practices. This study assessed the impact of intended learning outcomes, project-based learning, and assessment strategies on graduate competence attainment through surveys administered to 320 Cross-border E-commerce learners in April 2024, resulting in 301 usable responses. Data were analyzed using structural equation modeling (SEM) with SPSS 23.0 and AMOS 24.0. The results indicated that project-based learning and assessment strategies were directly and positively related to graduate competency attainment, while intended learning outcomes were indirectly associated with graduate competence through the mediating roles of project-based learning and assessment strategies. Assessment strategies had the most significant mediating effect, followed by project-based learning and the combined mediating effect. These findings advance the theoretical understanding of OBE and provide methods for promoting sustainability in vocational education. Beneficiaries include educators, policymakers, and accreditation bodies, who can use these insights to implement sustainable educational practices and ensure graduates contribute to sustainable development.
Renewable Wind Energy Implementation in South America: A Comprehensive Review and Sustainable Prospects
Authors: Cacciuttolo Carlos , Navarrete Martin , Atencio Edison
Published: July 2024

South America is a region that stands out worldwide for its biodiversity of ecosystems, cultural heritage, and potential considering natural resources linked to renewable energies. In the global crisis due to climate change, South American countries have implemented actions to carry out a progressive energy transition from fossil energies to renewable energies and contribute to the planet’s sustainability. In this context, South American countries are implementing green strategies and investment projects linked to wind farms to move towards achieving the sustainable development goals for the year 2030 of the UN agenda and achieving low-carbon economies for the year 2050. This article studies the advances in wind energy implementation in South America, highlighting progress and experiences in these issues through a review of the scientific literature considering the year 2023. The methodology applied in this article was carried out through the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines and the generation of scientific maps. As a result, this article presents the main developments, lessons learned/gaps, and future sustainable prospects on the road to 2050. According to the results, renewable wind energy infrastructure was applied in South America during the global climate change crisis era. Different levels of development in on-shore wind farms have been reached in each country. Also, a promising future exists for off-shore wind energy considering the highest potential. Finally, this article concludes that implementing emerging technologies like the production of green hydrogen and synthetic e-fuels looks like a synergetic clean energy solution combined with wind energy, which may transform the region into a world-class sustainable territory.
Sustainability and the Circular Economy Business Development
Authors: Rosário Albérico Travassos , Lopes Paula , Rosário Filipe Sales
Published: July 2024

Currently, there has been a tendency for companies to focus on the development of their businesses following circular economy trends associated with sustainability. European Community guidelines have established deadlines for the implementation of management strategies that allow creating and maintaining an effective and resource-efficient economic system, reducing the amount of energy and materials used in production. This study aims to analyze the existing literature review on the integration of sustainability and circular economy (CE) principles into business development. The research methodology is a systematic literature review using the SCOPUS database, in which 97 academic articles were analyzed after exclusion using keywords. The findings show that, after the analysis carried out in VOSviewer, business development, and circular economy are themes studied by many authors, with links related to the theme of sustainability. The conclusions indicate that CE and sustainability are interlinked, and companies must implement appropriate sustainability and CE strategies to increase their competitiveness and improve resource efficiency. These strategies can integrate innovative technologies, the use of sharing platforms, extending the useful life of products, recovering resources to minimize waste, and integrating sustainability concepts into business.
The Plastic-Reduction Behavior of Chinese Residents: Survey, Model, and Impact Factors
Authors: Wang Boyu , Yang Ronggang , Bai Peiyuan , Fang Qinhua , Jiang Xiaoyan
Published: July 2024

The persistent generation of plastic waste represents a critical environmental challenge. Despite the implementation of various management policies spanning from usage to disposal that are aimed at incentivizing plastic reduction, the accumulation of plastic debris continues to pose a significant threat to both terrestrial and marine ecosystems. Moreover, this environmental issue adversely impacts economic development and human health. This study seeks to analyze the influencing factors of plastic-reduction behavior at the individual level. Through a diverse sample of 869 participants across 29 provincial-level administrative regions, this research utilizes structural equation modeling to elucidate the intricate factors influencing plastic-reduction behaviors. This study examines the factors influencing plastic-reduction behavior through the frameworks of economic savings and spiritual satisfaction, focusing on two specific behavioral intentions: economically driven behavior and spiritually driven persuasive behavior. Results show that the primary motivator for plastic reduction is the decrease in individual expenses. Environmental values and policy regulations have become important influencing factors, which also promote behaviors regarding plastic reduction through economic savings. The study distinguishes between the impacts of environmental knowledge and skills, highlighting the necessity of practical skills for effective behavior change. Moreover, the study reveals the pivotal role of local attachment in encouraging plastic-reduction actions. The urban natural environment, particularly when characterized by accessible and well-maintained features, such as lakes and rivers, can foster residents’ appreciation for the environment, thereby motivating them to engage in plastic-reduction actions to attain personal spiritual satisfaction.
Spatio-Temporal Differentiation of Non-Grain Production of Cropland and Its Influencing Factors: Evidence from the Yangtze River Economic Belt, China
Authors: Zeng Kun , Zhai Youlong , Wang Liangsong , Wang Youhan
Published: July 2024

Food security is important to guarantee national security and people’s livelihoods, but the increasingly serious problem of non-grain production (NGP) on croplands has exacerbated the risk of food security and directly affected the sustainable development of the national economy and society. This study adopted 130 cities (states) in the Yangtze River Economic Belt as the research units and used ArcGIS10.8, GeoDA1.22, and Origin2022 software and spatial autocorrelation, standard deviational ellipse, and GeoDetector methods to conduct analyses. This study explored the spatial evolution patterns and factors influencing cropland NGP in the Yangtze River Economic Belt. The results show, firstly, that the NGP rate of cropland in the Yangtze River Economic Belt increased from 35.85% in 2006 to 38.62% in 2022. The number of cities (states) with mild and moderate NGP decreased, while the number of cities (states) with severe NGP increased significantly. Secondly, the spatial distribution of the rate of cropland NGP in the Yangtze River Economic Belt had a strong positive correlation, with “high–high agglomeration” tending to be dispersed, “low–low agglomeration” tending to be concentrated, and the overall trajectory of the center of gravity migrating from the northeast to the southwest. Thirdly, the single-factor detection found that the per capita food possession, slope, elevation, and average annual precipitation had strong explanatory power regarding the spatial difference in cropland NGP in the Yangtze River Economic Belt, and the interaction of any two influencing factors showed nonlinear enhancement. The results of this study can help to precisely identify the spatial and temporal evolution characteristics of cropland NGP in the Yangtze River Economic Belt, which is of great significance for supporting the country in controlling the risk of NGP cultivation, promoting the sustainable development of the Yangtze River Economic Belt, and guaranteeing food security.
Known and Unknown Environmental Impacts Related to Climate Changes in Pakistan: An Under-Recognized Risk to Local Communities
Authors: Adnan Muhammad , Xiao Baohua , Bibi Shaheen , Xiao Peiwen , Zhao Peng , Wang Haiyan , Ali Muhammad Ubaid , An Xianjin
Published: July 2024

This study prioritized initiatives within the China–Pakistan Economic Corridor (CPEC), foreign funding, and the associated environmental and national issues. Additionally, it analyzed these factors’ effects on improving infrastructure, commerce, and economic cooperation between China and Pakistan. Besides that, it also studies the current climatic, economic, and political challenges, mainly focused on water and agriculture issues. Climate, economic, and political issues affect the environment. These concerns deserve global attention. Pakistan mainly relies on agriculture, and its water scarcity predisposes it to economic losses, urbanization, and many socioeconomic problems. Climate change and the current flood have devastated the agriculture sector. Water scarcity affects agriculture too and significantly impacts the economy and food resources. The nation has not previously experienced such a profoundly distressing epoch. Pakistan has faced several environmental, economic, and political challenges; specifically, the fields of agriculture and water present notable apprehensions. Unfavorable climatic conditions impede the attainment of sustainable agriculture in Pakistan. Considering the strong reliance of agriculture on water resources, it is crucial to acknowledge that industrialization has resulted in substantial water contamination due to the presence of microplastics and heavy metals. Moreover, the South Asian region experiences a significant scarcity of water resources. Besides that, CPEC is the solution for the financial issues, but it is a big challenge for environmental degradation in the current stage, especially since foreign funding is a key challenge for increasing corruption and bringing more burden on the economy. Unfortunately, foreign funding is not good for Pakistan. To ensure safety, security, and sustainability, CPEC projects should follow environmental regulations. This study provides a new list of CPEC initiative priority tasks that more openly disrupt the initiative, serve the whole project, and give appropriate recommendations for future research and policy-making.