bluuebunny/crossref_metadata_2025 huggingface dataset:
Tracking Moisture Dynamics in a Karst Rock Formation Combining Multi-Frequency 3D GPR Data: A Strategy for Protecting the Polychrome Hall Paintings in Altamira Cave
Authors: Bayarri Vicente , Prada Alfredo , García Francisco , De Las Heras Carmen , Fatás Pilar
Published: October 2024

This study addresses the features of the internal structure of the geological layers adjacent to the Polychrome Hall ceiling of the Cave of Altamira (Spain) and their link to the distribution of moisture and geological discontinuities mainly as fractures, joints, bedding planes and detachments, using 3D Ground Penetrating Radar (GPR) mapping. In this research, 3D GPR data were collected with 300 MHz, 800 MHz and 1.6 GHz center frequency antennas. The data recorded with these three frequency antennas were combined to further our understanding of the layout of geological discontinuities and how they link to the moisture or water inputs that infiltrate and reach the ceiling surface where the rock art of the Polychrome Hall is located. The same 1 × 1 m2 area was adopted for 3D data acquisition with the three antennas, obtaining 3D isosurface (isoattribute-surface) images of internal distribution of moisture and structural features of the Polychrome Hall ceiling. The results derived from this study reveal significant insights into the overlying karst strata of Polychrome Hall, particularly the interface between the Polychrome Layer and the underlying Dolomitic Layer. The results show moisture patterns associated with geological features such as fractures, joints, detachments of strata and microcatchments, elucidating the mechanisms driving capillary rise and water infiltration coming from higher altitudes. The study primarily identifies areas of increased moisture content, correlating with earlier observations and enhancing our understanding of water infiltration patterns. This underscores the utility of 3D GPR as an essential tool for informing and putting conservation measures into practice. By delineating subsurface structures and moisture dynamics, this research contributes to a deeper analysis of the deterioration processes directly associated with the infiltration water both in this ceiling and in the rest of the Cave of Altamira, providing information to determine its future geological and hydrogeological evolution.
Anti-Rain Clutter Interference Method for Millimeter-Wave Radar Based on Convolutional Neural Network
Authors: Zhan Chengjin , Zhang Shuning , Sun Chenyu , Chen Si
Published: October 2024

Millimeter-wave radars are widely used in various environments due to their excellent detection capabilities. However, the detection performance in severe weather environments is still an important research challenge. In this paper, the propagation characteristics of millimeter-wave radar in a rainfall environment are thoroughly investigated, and the modeling of the millimeter-wave radar echo signal in a rainfall environment is completed. The effect of rainfall on radar detection performance is verified through experiments, and an anti-rain clutter interference method based on a convolutional neural network is proposed. The method combines image recognition and classification techniques to effectively distinguish target signals from rain clutter in radar echo signals based on feature differences. In addition, this paper compares the recognition results of the proposed method with VGGnet and Resnet. The experimental results show that the proposed convolutional neural network method significantly improves the target detection capability of the radar system in a rainfall environment, verifying the method’s effectiveness and accuracy. This study provides a new solution for the application of millimeter-wave radar in severe weather conditions.
ER-MACG: An Extreme Precipitation Forecasting Model Integrating Self-Attention Based on FY4A Satellite Data
Authors: Lu Mingyue , Zhang Jingke , Yu Manzhu , Liu Hui , He Caifen , Dong Tongtong , Mao Yongwei
Published: October 2024

Extreme precipitation events often present significant risks to human life and property, making their accurate prediction an essential focus of current research. Recent studies have primarily concentrated on exploring the formation mechanisms of extreme precipitation. Existing prediction methods do not adequately account for the combined terrain and atmospheric effects, resulting in shortcomings in extreme precipitation forecasting accuracy. Additionally, the satellite data resolution used in prior studies fails to precisely capture nuanced details of abrupt changes in extreme precipitation. To address these shortcomings, this study introduces an innovative approach for accurately predicting extreme precipitation: the multimodal attention ConvLSTM-GAN for extreme rainfall nowcasting (ER-MACG). This model employs high-resolution Fengyun-4A(FY4A) satellite precipitation products, as well as terrain and atmospheric datasets as inputs. The ER-MACG model enhances the ConvLSTM-GAN framework by optimizing the generator structure with an attention module to improve the focus on critical areas and time steps. This model can alleviate the problem of information loss in the spatial–temporal convolutional long short-term memory network (ConvLSTM) and, compared with the standard ConvLSTM-GAN model, can better handle the detailed changes in time and space in extreme precipitation events to achieve more refined predictions. The main findings include the following: (a) The ER-MACG model demonstrated significantly greater predictive accuracy and overall performance than other existing approaches. (b) The exclusive consideration of DEM and LPW data did not significantly enhance the ability to predict extreme precipitation events in Zhejiang Province. (c) The ER-MACG model significantly improved in identifying and predicting extreme precipitation events of different intensity levels.
Remote Sensing LiDAR and Hyperspectral Classification with Multi-Scale Graph Encoder–Decoder Network
Authors: Wang Fang , Du Xingqian , Zhang Weiguang , Nie Liang , Wang Hu , Zhou Shun , Ma Jun
Published: October 2024

The rapid development of sensor technology has made multi-modal remote sensing data valuable for land cover classification due to its diverse and complementary information. Many feature extraction methods for multi-modal data, combining light detection and ranging (LiDAR) and hyperspectral imaging (HSI), have recognized the importance of incorporating multiple spatial scales. However, effectively capturing both long-range global correlations and short-range local features simultaneously on different scales remains a challenge, particularly in large-scale, complex ground scenes. To address this limitation, we propose a multi-scale graph encoder–decoder network (MGEN) for multi-modal data classification. The MGEN adopts a graph model that maintains global sample correlations to fuse multi-scale features, enabling simultaneous extraction of local and global information. The graph encoder maps multi-modal data from different scales to the graph space and completes feature extraction in the graph space. The graph decoder maps the features of multiple scales back to the original data space and completes multi-scale feature fusion and classification. Experimental results on three HSI-LiDAR datasets demonstrate that the proposed MGEN achieves considerable classification accuracies and outperforms state-of-the-art methods.
Detection of Complex Formations in an Inland Lake from Sentinel-2 Images Using Atmospheric Corrections and a Fully Connected Deep Neural Network
Authors: Mantsis Damianos F. , Moumtzidou Anastasia , Lioumbas Ioannis , Gialampoukidis Ilias , Christodoulou Aikaterini , Mentes Alexandros , Vrochidis Stefanos , Kompatsiaris Ioannis
Published: October 2024

The detection of complex formations, initially suspected to be oil spills, is investigated using atmospherically corrected multispectral satellite images and deep learning techniques. Several formations have been detected in an inland lake in Northern Greece. Four atmospheric corrections (ACOLITE, iCOR, Polymer, and C2RCC) that are specifically designed for water applications are examined and implemented on Sentinel-2 multispectral satellite images to eliminate the influence of the atmosphere. Out of the four algorithms, iCOR and ACOLITE are able to depict the formations sufficiently; however, the latter is chosen for further processing due to fewer uncertainties in the depiction of these formations as anomalies across the multispectral range. Furthermore, a number of formations are annotated at the pixel level for the 10 m bands (red, green, blue, and NIR), and a deep neural network (DNN) is trained and validated. Our results show that the four-band configuration provides the best model for the detection of these complex formations. Despite not being necessarily related to oil spills, studying these formations is crucial for environmental monitoring, pollution detection, and the advancement of remote sensing techniques.
An Energy Approach to the Modal Identification of a Variable Thickness Quartz Crystal Plate
Authors: Wang Zhe , Huang Bin , Guo Yan , Jiang Yanan , Khan Asif
Published: October 2024

The primary objective of modal identification for variable thickness quartz plates is to ascertain their dominant operating mode, which is essential for examining the vibration of beveled quartz resonators. These beveled resonators are plate structures with varying thicknesses. While the beveling process mitigates some spurious modes, it still presents challenges for modal identification. In this work, we introduce a modal identification technique based on the energy method. When a plate with variable thickness is in a resonant state of thickness–shear vibration, the proportions of strain energy and kinetic energy associated with the thickness–shear mode in the total energy reach their peak values. Near this frequency, their proportions are the highest, aiding in identifying the dominant mode. Our research was based on the Mindlin plate theory, and appropriate modal truncation were conducted by retaining three modes for the coupled vibration analysis. The governing equation of the coupled vibration was solved for eigenvalue problem, and the modal energy proportions were calculated based on the determined modal displacement and frequency. Finally, we computed the eigenvalue problems at different beveling time, as well as the modal energies associated with each mode. By calculating the energy proportions, we could clearly identify the dominant mode at each frequency. Our proposed method can effectively assist engineers in identifying vibration modes, facilitating the design and optimization of variable thickness quartz resonators for sensing applications.
Method and Verification of Liquid Cooling Heat Dissipation Based on Internal Heat Source of Airborne Long-Focus Aerial Camera
Authors: Yuwen Ziming , Li Xinyang , Yuan Guoqin , Li Haixing , Zhang Jichao , Zhang Mingqiang , Ding Yalin
Published: October 2024

The traditional passive heat dissipation method has low heat dissipation efficiency, which is not suitable for the heat dissipation of the concentrated heat source inside the long-focal aerial camera, resulting in temperature level changes and temperature gradients in the optical system near the heat source, which seriously affect the imaging performance of the aerial camera. To solve this problem, an active heat dissipation method of liquid cooling cycle is proposed in this paper. To improve the solving efficiency and ensure simulation accuracy, a dynamic boundary information transfer method based on grid area weighting is proposed. The thermal simulation results show that the liquid cooling method reduces the heat source temperature by 70.12%, and the boundary temperature transfer error is 0.015%. The accuracy of thermal simulation is verified by thermal test, and the simulation error is less than 6.44%. In addition, the performance of the optical system is further analyzed, and the results show that the MTF of the optical system is increased from 0.077 to 0.194 under the proposed active liquid cooling cycle heat dissipation method.
Dual-Modal Illumination System for Defect Detection of Aircraft Glass Canopies
Authors: Li Zijian , Yao Yong , Wen Runyuan , Liu Qiyang
Published: October 2024

Defect detection in transparent materials typically relies on specific lighting conditions. However, through our work on defect detection for aircraft glass canopies, we found that using a single lighting condition often led to missed or false detections. This limitation arises from the optical properties of transparent materials, where certain defects only become sufficiently visible under specific lighting angles. To address this issue, we developed a dual-modal illumination system that integrates both forward and backward lighting to capture defect images. Additionally, we introduced the first dual-modal dataset for defect detection in aircraft glass canopies. Furthermore, we proposed an attention-based dual-branch modal fusion network (ADMF-Net) to enhance the detection process. Experimental results show that our system and model significantly improve the detection performance, with the dual-modal approach increasing the mAP by 5.6% over the single-modal baseline, achieving a mAP of 98.4%. Our research also provides valuable insights for defect detection in other transparent materials.
Laser Scan Compression for Rail Inspection
Authors: Hauck Jeremiasz , Gniado Piotr
Published: October 2024

The automation of rail track inspection addresses key issues in railway transportation, notably reducing maintenance costs and improving safety. However, it presents numerous technical challenges, including sensor selection, calibration, data acquisition, defect detection, and storage. This paper introduces a compression method tailored for laser triangulation scanners, which are crucial for scanning the entire rail track, including the rails, rail fasteners, sleepers, and ballast, and capturing rail profiles for geometry measurement. The compression technique capitalizes on the regularity of rail track data and the sensors’ limited measurement range and resolution. By transforming scans, they can be stored using widely available image compression formats, such as PNG. This method achieved a compression ratio of 7.5 for rail scans used in the rail geometry computation and maintained rail gauge reproducibility. For the scans employed in defect detection, a compression ratio of 5.6 was attained without visibly compromising the scan quality. Lossless compression resulted in compression ratios of 5.1 for the rail geometry computation scans and 3.8 for the rail track inspection scans.
A Deep Learning-Based Two-Branch Generative Adversarial Network for Image De-Raining
Authors: Zhao Liquan , Long Jie , Zhong Tie
Published: October 2024

Raindrops can scatter and absorb light, causing images to become blurry or distorted. To improve image quality by reducing the impact of raindrops, this paper proposes a novel generative adversarial network for image de-raining. The network comprises two parts: a generative network and an adversarial network. The generative network performs image de-raining. The adversarial network determines whether the input image is rain-free or de-rained. The generative network comprises two branches: the A branch, which follows a traditional convolutional network structure, and the U branch, which utilizes a U-Net architecture. The A branch includes a multi-scale module for extracting information at different scales and a residual attention module to reduce redundant information interference. The U branch contains an encoder module designed to address the loss of details and local information caused by conventional down-sampling. To improve the performance of the generative network in image de-raining, this paper employs a relative discriminator incorporating a mean squared error loss. This discriminator better measures the differences between rainy and rain-free images while effectively preventing the occurrence of gradient vanishing. Finally, this study performs visual and quantitative comparisons of the proposed method and existing methods on three established rain image datasets. In the quantitative experiments, the proposed method outperforms existing methods regarding PSNR, SSIM, and VIF metrics. Specifically, our method achieves an average PSNR, SSIM, and VIF of approximately 5%, 3%, and 4% higher than the MFAA-GAN method, respectively. These results indicate that the de-rained images generated via the proposed method are closer to rain-free images.
Blood Biomarker Detection Using Integrated Microfluidics with Optical Label-Free Biosensor
Authors: Li Chiung-Hsi , Chang Chen-Yuan , Chen Yan-Ru , Huang Cheng-Sheng
Published: October 2024

In this study, we developed an optofluidic chip consisting of a guided-mode resonance (GMR) sensor incorporated into a microfluidic chip to achieve simultaneous blood plasma separation and label-free albumin detection. A sedimentation chamber is integrated into the microfluidic chip to achieve plasma separation through differences in density. After a blood sample is loaded into the optofluidic chip in two stages with controlled flow rates, the blood cells are kept in the sedimentation chamber, enabling only the plasma to reach the GMR sensor for albumin detection. This GMR sensor, fabricated using plastic replica molding, achieved a bulk sensitivity of 175.66 nm/RIU. With surface-bound antibodies, the GMR sensor exhibited a limit of detection of 0.16 μg/mL for recombinant albumin in buffer solution. Overall, our findings demonstrate the potential of our integrated chip for use in clinical samples for biomarker detection in point-of-care applications.
Deformation Monitoring and Analysis of Baige Landslide (China) Based on the Fusion Monitoring of Multi-Orbit Time-Series InSAR Technology
Authors: Ye Kai , Wang Zhe , Wang Ting , Luo Ying , Chen Yiming , Zhang Jiaqian , Cai Jialun
Published: October 2024

Due to the limitations inherent in SAR satellite imaging modes, utilizing time-series InSAR technology to process single-orbit satellite image data typically only yields one-dimensional deformation information along the LOS direction. This constraint impedes a comprehensive representation of the true surface deformation of landslides. Consequently, in this paper, after the SBAS-InSAR and PS-InSAR processing of the 30-view ascending and 30-view descending orbit images of the Sentinel-1A satellite, based on the imaging geometric relationship of the SAR satellite, we propose a novel computational method of fusing ascending and descending orbital LOS-direction time-series deformation to extract the landslide’s downslope direction deformation of landslides. By applying this method to Baige landslide monitoring and integrating it with an improved tangential angle warning criterion, we classified the landslide’s trailing edge into a high-speed, a uniform-speed, and a low-speed deformation region, with deformation magnitudes of 7~8 cm, 5~7 cm, and 3~4 cm, respectively. A comparative analysis with measured data for landslide deformation monitoring revealed that the average root mean square error between the fused landslide’s downslope direction deformation and the measured data was a mere 3.62 mm. This represents a reduction of 56.9% and 57.5% in the average root mean square error compared to the single ascending and descending orbit LOS-direction time-series deformations, respectively, indicating higher monitoring accuracy. Finally, based on the analysis of landslide deformation and its inducing factors derived from the calculated time-series deformation results, it was determined that the precipitation, lithology of the strata, and ongoing geological activity are significant contributors to the sliding of the Baige land-slide. This method offers more comprehensive and accurate surface deformation information for dynamic landslide monitoring, aiding relevant departments in landslide surveillance and management, and providing technical recommendations for the fusion of multi-orbital satellite LOS-direction deformations to accurately reconstruct the true surface deformation of landslides.
Piezo-VFETs: Vacuum Field Emission Transistors Controlled by Piezoelectric MEMS Sensors as an Artificial Mechanoreceptor with High Sensitivity and Low Power Consumption
Authors: Ge Chang , Chen Yuezhong , Yu Daolong , Liu Zhixia , Xu Ji
Published: October 2024

As one of the most promising electronic devices in the post-Moore era, nanoscale vacuum field emission transistors (VFETs) have garnered significant attention due to their unique electron transport mechanism featuring ballistic transport within vacuum channels. Existing research on these nanoscale vacuum channel devices has primarily focused on structural design for logic circuits. Studies exploring their application potential in other vital fields, such as sensors based on VFET, are more limited. In this study, for the first time, the design of a vacuum field emission transistor (VFET) coupled with a piezoelectric microelectromechanical (MEMS) sensing unit is proposed as the artificial mechanoreceptor for sensing purposes. With a negative threshold voltage similar to an N-channel depletion-mode metal oxide silicon field effect transistor, the proposed VFET has its continuous current tuned by the piezoelectric potential generated by the sensing unit, amplifying the magnitude of signals resulting from electromechanical coupling. Simulations have been conducted to validate the feasibility of such a configuration. As indictable from the simulation results, the proposed piezoelectric VFET exhibits high sensitivity and an electrically adjustable measurement range. Compared to the traditional combination of piezoelectric MEMS sensors and solid-state field effect transistors (FETs), the piezoelectric VFET design has a significantly reduced power consumption thanks to its continuous current that is orders of magnitude smaller. These findings reveal the immense potential of piezoelectric VFET in sensing applications, building up the basis for using VFETs for simple, effective, and low-power pre-amplification of piezoelectric MEMS sensors and broadening the application scope of VFET in general.
Neural Approach to Coordinate Transformation for LiDAR–Camera Data Fusion in Coastal Observation
Authors: Garczyńska-Cyprysiak Ilona , Kazimierski Witold , Włodarczyk-Sielicka Marta
Published: October 2024

The paper presents research related to coastal observation using a camera and LiDAR (Light Detection and Ranging) mounted on an unmanned surface vehicle (USV). Fusion of data from these two sensors can provide wider and more accurate information about shore features, utilizing the synergy effect and combining the advantages of both systems. Fusion is used in autonomous cars and robots, despite many challenges related to spatiotemporal alignment or sensor calibration. Measurements from various sensors with different timestamps have to be aligned, and the measurement systems need to be calibrated to avoid errors related to offsets. When using data from unstable, moving platforms, such as surface vehicles, it is more difficult to match sensors in time and space, and thus, data acquired from different devices will be subject to some misalignment. In this article, we try to overcome these problems by proposing the use of a point matching algorithm for coordinate transformation for data from both systems. The essence of the paper is to verify algorithms based on selected basic neural networks, namely the multilayer perceptron (MLP), the radial basis function network (RBF), and the general regression neural network (GRNN) for the alignment process. They are tested with real recorded data from the USV and verified against numerical methods commonly used for coordinate transformation. The results show that the proposed approach can be an effective solution as an alternative to numerical calculations, due to process improvement. The image data can provide information for identifying characteristic objects, and the obtained accuracies for platform dynamics in the water environment are satisfactory (root mean square error—RMSE—smaller than 1 m in many cases). The networks provided outstanding results for the training set; however, they did not perform as well as expected, in terms of the generalization capability of the model. This leads to the conclusion that processing algorithms cannot overcome the limitations of matching point accuracy. Further research will extend the approach to include information on the position and direction of the vessel.
Effects of Varying Text Message Length and Driving Speed on the Disruptive Effects of Texting on Driving Simulator Performance: Differential Effects on Eye Glance Measures
Authors: Taneja Rimzim , Alali Kawther , Mohammed  , Malone Ki-Jana , Buchanon Brandon , Blanchette Ashley , Ho Dung , Head Doreen , Commissaris Randall
Published: October 2024

Eye glance analysis and driving performance during texting while driving: Differential effects of varying driving speed versus text message length. Background and Objective. Texting while driving continues to be a significant public health concern. Eye glances off the roadway are a measure of the visual distraction associated with texting while driving. In the present study, we examined the effects of two ‘real-world’ factors relating to the adverse effects of texting on driving performance and eye glances off the roadway: (1) text message length and (2) driving speed. Methods. Subjects ‘drove’ a fixed-base simulator and read, typed and sent text messages while driving. In study #1, the driving speed was 60 mph and the effects of short (1 word) versus longer (8–10 words) texts were compared. In study #2, the text messages were short only and driving speed was 60 or 80 mph. Driving performance was assessed using the Standard Deviation of Lane Position (SDLP). Video recordings of the drivers’ faces were used to assess eye glances from the road to the phone—and back—during texting. Results. Texting while driving impaired driving performance as measured by SDLP, and both longer text messages and faster drive speeds made driving performance even worse. Analysis of the eye glance data, however, revealed different effects of these two manipulations. Specifically, longer text messages were associated with an increase in the number of eye glances to the phone during a text message episode, an increase in the total time spent with the eyes off the road, and an increase in the single longest eye glance from the road. Moreover, with longer text messages the longest single eye glance away from the road typically occurred at or near the end of the text message episode. In contrast, increasing driving speed to 80 mph did not affect any of these eye glance measures relative to driving at 60 mph. Conclusion and Application. Both text message length and driving speed while texting adversely affect driving performance, but they do so via different mechanisms. These results have implications for how to tailor “don’t text and drive” messaging to better serve the public health.
No-Till and Crop Rotation Are Promising Practices to Enhance Soil Health in Cotton-Producing Semiarid Regions: Insights from Citizen Science
Authors: Hailu Tirhas A. , Devkota Pawan , Osoko Taiwo O. , Singh Rakesh K. , Zak John C. , van Gestel Natasja
Published: October 2024

This on-farm study was conducted to assess the impact of six prevalent crop management practices adopted by growers in West Texas on various indicators of soil health. This study is a part of a citizen science project, where we collaborated with cotton growers who helped with standardized sample and data collection from 2017 to 2022. This project aimed to identify soil management practices that increase carbon sequestration, enhance biological activities, and improve overall soil health. We monitored soil moisture, soil organic matter (SOM), inorganic nitrogen (NH4+-N and NO3−-N) and other exchangeable nutrients, and soil microbial abundances as obtained via fatty acid methyl ester (FAME) in 85 fields, incorporating different management practices during the cotton growing season. In our study, volumetric moisture content (VWC) was increased by no-till, irrigation, and crop rotation, but the addition of residue decreased VWC. No-till, irrigation, and crop rotation increased SOM, but a cover crop decreased SOM. No-till and residue retention also increased microbial biomass carbon (MBC). Tillage, irrigation, and crop rotation influenced the abundance of the main microbial groups, including bacterial, fungi, and arbuscular mycorrhizal fungi (AMF). Additionally, water content, SOM, and microbial abundances are correlated with clay percentage. Our results indicate that no-till and crop rotation are the two most crucial soil management approaches for sustainable soil health. As such, implementing both no-till and crop rotation in the cropping systems has the most promising potential to increase the soil resilience in dryland cotton production in semiarid regions, thereby helping growers to maintain cotton production.
Process Approach in a Mining Company: LW Bogdanka S.A. Case Study
Authors: Dyczko Artur , Stecuła Kinga , Stańczak Lilianna , Aydın Barış , Kamiński Paweł
Published: October 2024

This article concerns the management of the Polish mining company Lubelski Węgiel (LW) Bogdanka S.A. The main aim of this paper was to describe the whole process of implementing a process approach to management in one of the most important mining companies in Poland. The objective of the research presented was the incorporation of the process approach in LW Bogdanka S.A. to enhance its management and to improve the effectiveness of its mining process. This paper uses a case study method and presents a practical incorporation of the process approach and its results. The first phase of the proposed solution included building a map of business processes. Then, the research consisted of an analysis of existing processes; the introduction of new solutions, mainly in the field of infrastructure and IT systems; and changing the awareness of key process owners. The motivation for applying this approach was to achieve greater efficiency and continuous improvement in LW Bogdanka S.A. The main results and achievements were not merely limited to the development of a map of processes and subprocesses, but also led to an improvement in organizational structure, changes in approach to company management, a diagnosis of problems in the most important company departments, and the proposal of solutions to them.
Choosing Recovery Strategies for Waste Electronics: How Product Modularity Influences Cooperation and Competition
Authors: Lai Xuxin , Wang Nengmin , Jiang Bin , Jia Tao
Published: October 2024

Modular design facilitates easy disassembly and reduces the manufacturer’s remanufacturing costs. However, the simplicity and modular structure of products can intensify competition between manufacturers and third-party recyclers. To improve recovery efficiency, this study examines the impact of modular design on the manufacturer’s selection of recovery strategies, including centralized, cooperation, and competition strategies. We examine the optimal recovery strategy for achieving both economic goals, such as supply chain profit, and environmental goals, such as collection quantity. Our results indicate that the manufacturer should adopt cooperation recovery and invest in higher modularity when faced with strong competition from third-party recyclers. Conversely, when the competitiveness of third-party recovery is relatively low, a competition recovery strategy is more advantageous. Contrary to conventional wisdom, which suggests limiting product disassembly to reduce third-party recovery competitiveness, our results indicate that manufacturers should invest in higher modularity and avoid engaging in price wars to prevent third-party entry. Moreover, competition recovery leads to a higher collection quantity, while cooperation recovery is preferred in terms of supply chain profit. This study provides theoretical guidance for manufacturers in selecting optimal recovery strategies and offers recommendations for governments on regulating product disassembly effectively.
Exploring Public Acceptance of Climate Technologies: A Study on Key Influences in South Korea
Authors: Song Jaeryoung , Yeo Joonho , Son Minsu
Published: October 2024

The accelerating climate crisis is driving a rapid increase in interest in climate technologies aimed at overcoming it. However, even if effective technologies emerge, their impact may be hindered without public acceptance. Therefore, it is necessary to analyze the factors that influence the acceptance of new climate technologies. This exploratory study investigates the key factors influencing public acceptance and the recommendation of climate technologies in South Korea. Utilizing an extended Unified Theory of Acceptance and Use of Technology (UTAUT) model, survey data from 200 participants were analyzed. The study identified that facilitating conditions, innovativeness, social influence, and trust in government are the most significant predictors of public intent to accept or recommend climate technologies. The results highlight the critical role of strategic communication, infrastructure, and public trust in promoting the adoption of climate solutions. Furthermore, qualitative interviews suggest that a shift towards Personally Determined Contributions (PDCs) could empower individuals to become proactive agents in climate action. The study offers valuable insights for policymakers, providing a strategic direction to enhance the public’s engagement with climate technologies and foster a more sustainable future.
Performance of Windcatchers in Improving Indoor Air Quality, Thermal Comfort, and Energy Efficiency: A Review
Authors: Ma Qingsong , Qian Guangwei , Yu Menghui , Li Lingrui , Wei Xindong
Published: October 2024

The growing concerns over climate change and energy scarcity have highlighted the need for building energy conservation. Windcatchers, renowned for their effective ventilation capabilities, have emerged as a pivotal solution for saving energy and improving indoor thermal comfort. Serving as rooftop installations, windcatchers harness high-altitude airflow to provide fresh indoor air while expelling stale air. This comprehensive review encompasses over 96 studies published between 2000 and 2024, examining the performance of various types of windcatchers. The review reviews previous articles and concludes that using different types of wind catchers in hot and arid areas can increase indoor air velocity by about 10–50%, reduce building energy consumption by about 20–50%, and increase thermal comfort duration by about 25–50%. The article combines the perspectives of multiple disciplines such as architecture, environmental engineering, and sustainable design, providing a new perspective for the study of windcatchers. The article not only summarizes the design and performance of existing wind catchers, but also provides 13 suggestions for the design of wind capture towers, while also identifying areas for future research, such as combining wind capture machines with other passive cooling technologies and evaluating their performance under different climate and urban conditions.
A Novel Framework for the Iraqi Manufacturing Industry Towards the Adoption of Industry 4.0
Authors: Mannadhan Prabhu , Szymański Jerzy Ryszard , Zurek-Mortka Marta , Sathiyanarayanan Mithileysh
Published: October 2024

This study investigates the readiness of manufacturing industries in the Iraqi sector to adopt and implement Industry 4.0 (I4.0) technologies. The research focuses on manufacturing industries, including automotive, electronics, textiles, food processing, etc. The study’s main objective is to investigate the relationship between adopting I4.0 technologies and performance benefits in these sectors. A structured survey was conducted across 240 manufacturing companies, including specific I4.0 technologies (IoT, Big Data Analytics, Cloud Computing, Artificial Intelligence, etc.), usage levels, operations, products/services, and sustainability. Data were collected through telephone interviews and personal contacts, where the respondents rated the benefits of I4.0 technology adoption and performance benefit dimensions on a five-point Likert scale. The study utilized Partial Least Squares Structural Equation Modelling (PLS-SEM) using SmartPLS 3.2.9 software for data analysis. Findings show a positive relationship between I4.0 technology adoption and industrial performance benefits, emphasizing productivity and production efficiency improvements more than sustainability improvements and resource benefits. This research contributes to the understanding of I4.0 readiness in emerging economies and provides insight for policymakers and industry leaders in Iraq’s manufacturing sector.
Cultural Perspectives on the Sustainable Use and Added Value of Plant-Based Food Dyes—A Case Study from Bulgaria
Authors: Chervenkov Mihail , Ivanova Teodora , Bosseva Yulia , Dimitrova Dessislava
Published: October 2024

Raised personal health awareness and social environmental responsibility put pressure on the agri-food industry to adopt more sustainable ways of production, including the use of more natural ingredients, reducing waste, conservation and the regeneration of resources and energy. Plant-based colorants are ecologically friendly alternatives to artificial food dyes, especially with regards to the current reports on the adverse effects of some of the latter on human health. Various plants are traditionally used by many cultures to obtain vivid food coloration; however, the knowledge and means to produce them becomes less and less accessible to urbanized societies, and affordable organic alternatives are not always available on the market. An online questionnaire was performed to explore the awareness on plant-based dyes and pro-environmental attitudes of Bulgarian customers through the lens of Orthodox Easter eggs dyeing and the obtaining of plant dyes. From a total of 294 adult participants, only 5% reported a strict preference for natural dyes, while more than half of them (54%) were found to use natural and artificial ones concomitantly or switching between both. Of 45 plant taxa used for egg coloration, 12 were most frequently cited with many new additions of imported plants. Most of the used plant-based dyes were common fruits, spices, herbal infusions and even food waste like onion peels and avocado pits that were readily available from home gardens, markets and food shops. Additionally, we made a review of the scientific literature regarding their antioxidant and antimicrobial activity against food spoilage bacteria and foodborne pathogens isolated from eggs. All frequently used taxa were reported to exhibit antibacterial activities against Gram-positive and Gram-negative bacteria and possess antioxidant activity due to the presence of various polyphenols, essential oils and other compounds. Grape and red wine, roselle and stinging nettle are the species with the most diverse antibacterial activity, effective against 15 out of the 16 bacterial species of spoilage and foodbourn microorganisms included in our focus. The antimicrobial activities, however, were found mostly tested against bacterial strains in vitro, and further studies are needed to confirm their potential antibacterial activity when applied to Easter/boiled eggs or other food products. Our findings suggest that traditional cultural practices, as a multifaceted and engaging phenomenon, have the potential to promote environmental responsibility and a healthy lifestyle using both contemporary and traditional knowledge.
Sulfuric Acid Leaching Recovery of Rare Earth Elements from Wizów’s Phosphogypsum in Poland
Authors: Maina Linda , Kiegiel Katarzyna , Samczyński Zbigniew , Haneklaus Nils , Zakrzewska-Kołtuniewicz Grażyna
Published: October 2024

Rare earth elements (REEs) are considered vital raw materials for the economy and are on the European Union’s list of critical raw materials (CRMs). Europe is mainly dependent on REE imports. This dependence could be reduced if locally available primary or secondary resources would be processed. In Poland, there are, for instance, over 5 million metric tons of phosphogypsum (PG), a fine powdery byproduct from the fertilizer industry, available near the former Wizów Chemical Plant near Bolesławiec. This material that is considered a waste in Poland contains significant amounts of REEs that could theoretically be recovered and contribute to Europe’s economy. This work is the first systematic analysis of REE leaching studies with sulfuric acid and PG from Wizów. Process parameters such as temperature, particle size, concentration of the leaching solution, and the addition of oxidant and reductant agents were tested to determine the most efficient process. Ultimately, a leaching efficiency of 99% was obtained. Lanthanum exhibited the highest leaching efficiency at almost 100%, followed by Yttrium, Neodymium, Terbium, and Dysprosium. The results of the laboratory experiments are promising and suggest that larger pilot or commercial experiments can be performed next.
Identification and Evaluation of Synergy Between Carbon Emissions and Air Pollutants in Inter-Industrial Trade Among Provinces in China
Authors: Niu Le , Wang Jiaoyue , Zhao Hongyan , Ma Mingjing , Xi Fengming
Published: October 2024

With the vigorous promotion in China of efforts to reduce pollution and carbon emissions, examining their synergies becomes increasingly crucial. This study used the multi-regional input–output (MRIO) table to build the consumption-based industrial emissions inventories of CO2 and three major air pollutants (PM2.5, NOx, and SO2) and constructed synergistic emission indices of the intensity and magnitude to identify and evaluate the synergy between carbon emissions and air pollutants in inter-industrial trade among 30 provinces in mainland China. The results show that more than 85% and 40% of inter-provincial and inter-industrial trades have synergistic emissions between CO2 and air pollutants, respectively. We identified 77 inter-provincial trades and 84 inter-industrial trades among provinces with strong synergistic emissions. They are mainly reflected in the demand of the construction industry in Zhejiang and Guangdong for the nonmetal mineral products manufacturing industry in Henan, and the metal smelting and processing industry in Hebei, along with the demand of the service industry in Beijing for the electric power, steam, and hot water production and supply industry in Inner Mongolia. Our study provides new insights into the synergistic reduction of CO2 and air pollutants within the supply chain, thereby enriching the discourse on regional and industrial synergies in achieving sustainable development goals.
Scenario-Based Supply Chain Resilience Analysis of Bearings
Authors: Lyu Feng , Liu Fen , Zhang Shuping , Zhang Zhiwen
Published: October 2024

Bearings, as an indispensable part of modern industry, and the related supply chain resilience in regard to maintaining the overall operational efficiency and competitiveness of the manufacturing industry, are highly significant. Integrating the triangular fuzzy number (TRFN), cross-influence analysis (CIA), and the adversarial interpretive structure model (AISM), this paper proposes a TCIA-AISM scenario model to analyze the resilience of the bearing supply chain. A hierarchical structure diagram is formed to clarify the transmission path of events that affect bearing supply chain resilience, identify the root cause and direct events that affect the results, and realize the visual analysis of such events. The probability of the outcome is predicted and the simulation of the scenario development trajectory provides a scientific basis for decision-makers to formulate reasonable emergency strategies. The validity of the method is verified by using an interruption event involving the G-enterprise case study as an example.
Current Status of Green Hydrogen Production Technology: A Review
Authors: Tuluhong Ayiguzhali , Chang Qingpu , Xie Lirong , Xu Zhisen , Song Tengfei
Published: October 2024

As a clean energy source, hydrogen not only helps to reduce the use of fossil fuels but also promotes the transformation of energy structure and sustainable development. This paper firstly introduces the development status of green hydrogen at home and abroad and then focuses on several advanced green hydrogen production technologies. Then, the advantages and shortcomings of different green hydrogen production technologies are compared. Among them, the future source of hydrogen tends to be electrolysis water hydrogen production. Finally, the challenges and application prospects of the development process of green hydrogen technology are discussed, and green hydrogen is expected to become an important part of realizing sustainable global energy development.
Analysis of Urban Spatial Morphology in Harbin: A Study Based on Building Characteristics and Driving Factors
Authors: Shen Tao , Wu Jia , Yuan Shuai , Kong Fulu , Liu Yongshuai
Published: October 2024

With the advancement of urbanization, the complexity and diversity of urban spatial forms have become increasingly prominent, profoundly and widely affecting aspects such as urban spatial layout and planning, as well as residents’ quality of life. This paper focuses on the buildings in Harbin City, comprehensively reflecting the spatial form of Harbin through multiple dimensions including building height, volume, and area. This research precisely quantifies three key indicators of urban buildings: building coverage, building expandability, and building staggeredness. Subsequently, these indicators are intertwined with the main driving factors of urban development (including economic development and resident population) to conduct a multidimensional spatial form analysis. The results indicate that the diversity of Harbin’s urban spatial form is the result of the interplay of multiple factors, including economic and demographic influences. These analytical outcomes not only reveal the evolution mechanism of Harbin’s current urban spatial form but also provide data support and theoretical basis for future urban planning and management.
Data Modeling and Synchronization Method to Align Power Trading Rules for Integrated Energy Management Systems
Authors: Zhou Yingya , Chong Chin Hao , Ni Weidou , Li Zheng , Zhou Xiaoyong , Ma Linwei
Published: October 2024

Integrated energy systems (IESs) couple multiple energy sources to promote clean energy and reduce emissions. IESs need to participate in business activities, such as power trading, aided by automated data-driven systems to achieve optimal and economical operation. However, challenges arise due to the lack of unified data model standards and the semantic ambiguity of rules, on top of the difficulty of synchronizing data across heterogeneous subsystems of integrated energy management systems (IEMSs). Previous research on power trading data models was limited to certain application scenarios and heterogeneities. This study pivots from proposing model standards to a standard neutral way to align power trading rules across IEMS subsystems. The method features a framework for power trading rules and a software platform called the power trading rule synchronizer. The proposed approach can minimize semantic ambiguity and ensure the automated rule synchronization across subsystems of IEMSs. A case study demonstrated the application of the proposed method, saving an estimated 672 man-days in implementing the rules in 16 subsystems of the IEMS, contributing directly and indirectly to reducing emissions. This study provides a foundation for aligning other rules in IEMSs, such as carbon and gas trading rules.