bluuebunny/crossref_metadata_2025 huggingface dataset:
Pulsation Analysis of Hose Pumps with Different Roller Counts Based on Two-Way FSI
Authors: Wang Mengfan , Zhang Lixin , Li Wenchun , Hu Xue , Zhao Jiawei , Fang Xingzi
Published: December 2024

Xinjiang, as a major agricultural region, offers extensive application potential for hose pumps, given their excellent performance as fertilization devices. Analyzing the pulsation characteristics of hose pumps during operation is valuable for reducing noise and extending pump service life. To investigate the pulsation characteristics and unsteady flow of hose pumps with different roller numbers, this study adopts a bidirectional fluid-structure interaction (FSI) method and utilizes ANSYS 19.0 commercial finite element software to analyze the outlet and inlet pressure pulsations, outlet flow velocity pulsations, and the distribution of the flow field at the intermediate plane for both two-roller and three-roller pumps transporting high and low Reynolds number fluids under the same working conditions. It was observed that the three-roller pump exhibited higher outlet pressure compared with the inlet and that the pulsation intensity was lower in the three-roller pump than in the two-roller pump under the same conditions, with an analysis provided on the reasons for this phenomenon. This study offers theoretical support for the selection and further optimization of hose pump designs. To further reduce the negative effects of pulsations, it is recommended to increase the number of rollers in the design while also considering shape optimization of the pump casing or using feedback control systems to adjust and reduce pulsation intensity.
Subjective Quality of Life of Portuguese Karateka: A Comparative Study with General Population
Authors: Tomás João , Severino Sandy , Valido Susana , Mestre Ricardo , Santos Maria João , de Sousa Cristina , Sousa Luís , José Helena
Published: December 2024

Sports and quality of life have a well-established connection. Karate-Dō is now a global scale sport, although it preserves traditional values and behaviors. The main aim of this study is to compare the difference in subjective quality of life levels between people engaged in Karate-Dō and people who are not. This is an exploratory study where a quantitative methodology was used. A globally accepted instrument was used to measure the subjective quality of life: Personal Wellbeing Index©. A total of 186 questionnaires were administered at a national level (online). Of these, 108 were administered to a sample of the general population (not engaged in martial arts) and 78 were administered to a sample of Karate-Dō athletes or karateka. Although the dimension and distribution of the samples does not allow data generalization, these data clearly show a higher quality of life perception, in all domains, in Karate-Dō athletes. Nonetheless, globally and in all the different domains, the level of perceived quality of life can be considered quite positive in Karate-Dō athletes. This finding suggests that the practice of Karate-Dō may have a strong relationship with a high level of subjective quality of life.
Impact of Wash Oil Composition on Degradation: A Comparative Analysis of “Light” and “Heavy” Oils
Authors: Miroshnichenko Denis , Bannikov Artem , Bannikov Leonid , Borisenko Olexandr , Shishkin Andrei , Gavrilovs Pavels , Tertychnyi Volodymyr
Published: December 2024

This study aims to address the limited understanding of wash oil degradation in benzene units by analysing changes in the composition and properties of fresh and operating oils from different manufacturers. The findings will provide insights into the degradation pathways and stability of these oils. Gas chromatography/mass spectrometry was used to analyse the provided samples, and the dynamic viscosity of the oils was determined using a Brookfield LV DV2T rotational viscometer. During operation, the “heavy” oil (HO) becomes less volatile, while the ”light” oil (LO) becomes slightly more volatile. The viscosity of the HO increases 1.25 times during operation. The LO is characterised by a higher total concentration of alkyl derivatives (48 wt.% compared to 44 wt.% for the HO). LO is enriched with naphthalene and indene, while HO loses 1- and 2-methylnaphthalenes and shows an increase in the concentrations of dibenzofuran, fluorene, anthracene, and phenanthrene. The oxidation products of LO include oxidised alkyl groups, while HO shows oxidised non-substituted hydrocarbons. The practical value of such studies lies in guiding the selection of fresh oil under current operating conditions. LO is more resistant to degradation as an absorbent than heavier wash oil.
A Three-Branch Pansharpening Network Based on Spatial and Frequency Domain Interaction
Authors: Wen Xincan , Ma Hongbing , Li Liangliang
Published: December 2024

Pansharpening technology plays a crucial role in remote sensing image processing by integrating low-resolution multispectral (LRMS) images and high-resolution panchromatic (PAN) images to generate high-resolution multispectral (HRMS) images. This process addresses the limitations of satellite sensors, which cannot directly capture HRMS images. Despite significant developments achieved by deep learning-based pansharpening methods over traditional approaches, most existing techniques either fail to account for the modal differences between LRMS and PAN images, relying on direct concatenation, or use similar network structures to extract spectral and spatial information. Additionally, many methods neglect the extraction of common features between LRMS and PAN images and lack network architectures specifically designed to extract spectral features. To address these limitations, this study proposed a novel three-branch pansharpening network that leverages both spatial and frequency domain interactions, resulting in improved spectral and spatial fidelity in the fusion outputs. The proposed method was validated on three datasets, including IKONOS, WorldView-3 (WV3), and WorldView-4 (WV4). The results demonstrate that the proposed method surpasses several leading techniques, achieving superior performance in both visual quality and quantitative metrics.
Combining OpenStreetMap with Satellite Imagery to Enhance Cross-View Geo-Localization
Authors: Hu Yuekun , Liu Yingfan , Hui Bin
Published: December 2024

Cross-view geo-localization (CVGL) aims to determine the capture location of street-view images by matching them with corresponding 2D maps, such as satellite imagery. While recent bird’s eye view (BEV)-based methods have advanced this task by addressing viewpoint and appearance differences, the existing approaches typically rely solely on either OpenStreetMap (OSM) data or satellite imagery, limiting localization robustness due to single-modality constraints. This paper presents a novel CVGL method that fuses OSM data with satellite imagery, leveraging their complementary strengths to enhance localization robustness. We integrate the semantic richness and structural information from OSM with the high-resolution visual details of satellite imagery, creating a unified 2D geospatial representation. Additionally, we employ a transformer-based BEV perception module that utilizes attention mechanisms to construct fine-grained BEV features from street-view images for matching with fused map features. Compared to state-of-the-art methods that utilize only OSM data, our approach achieves substantial improvements, with 12.05% and 12.06% recall enhancements on the KITTI benchmark for lateral and longitudinal localization within a 1-m error, respectively.
LKAFFNet: A Novel Large-Kernel Attention Feature Fusion Network for Land Cover Segmentation
Authors: Chen Bochao , Tong An , Wang Yapeng , Zhang Jie , Yang Xu , Im Sio-Kei
Published: December 2024

The accurate segmentation of land cover in high-resolution remote sensing imagery is crucial for applications such as urban planning, environmental monitoring, and disaster management. However, traditional convolutional neural networks (CNNs) struggle to balance fine-grained local detail with large-scale contextual information. To tackle these challenges, we combine large-kernel convolutions, attention mechanisms, and multi-scale feature fusion to form a novel LKAFFNet framework that introduces the following three key modules: LkResNet, which enhances feature extraction through parameterizable large-kernel convolutions; Large-Kernel Attention Aggregation (LKAA), integrating spatial and channel attention; and Channel Difference Features Shift Fusion (CDFSF), which enables efficient multi-scale feature fusion. Experimental comparisons demonstrate that LKAFFNet outperforms previous models on both the LandCover dataset and WHU Building dataset, particularly in cases with diverse scales. Specifically, it achieved a mIoU of 0.8155 on the LandCover dataset and 0.9326 on the WHU Building dataset. These findings suggest that LKAFFNet significantly improves land cover segmentation performance, offering a more effective tool for remote sensing applications.
A Submicrosecond-Response Ultrafast Microwave Ranging Method Based on Optically Generated Frequency-Modulated Pulses
Authors: Sun Yifei , Chen Yongchao , Tang Longhuang , Jia Xing , Ma Heli , Wang Xiang , Chen Long , Liu Shenggang , Tao Tianjiong , Wu Jian , Li Chengjun , Liu Shuanyu , Chen Weilu , Gu Wei , Shi Jia , Weng Jidong
Published: December 2024

An ultrafast microwave ranging method based on optically generated frequency-modulated microwave pulses is proposed in this study. The theoretical analysis demonstrated that nanosecond-scale linear frequency modulation microwave pulse can be obtained by femtosecond laser interference under the condition of unbalanced dispersion, which can be used to achieve a high temporal resolution of the displacement change in the measurement by the principle of frequency modulation continuous wave (FMCW) radar. The proof-of-principle experiment successfully measured the displacement change with an error of 2.5 mm and a range of 0.6 m, with a response time of 468 ns. Compared to existing microwave ranging technologies, the temporal resolution was improved by two orders of magnitude, which greatly improves the temporal resolution of distance measurement in the field of microwave FMCW radar.
Channel-Hopping Sequence and Searching Algorithm for Rendezvous of Spectrum Sensing
Authors: Choi Young-June , Kim Young-Sik , Jang Ji-Woong
Published: December 2024

In this paper, we propose a method for applying the p-ary m-sequence as a channel-searching pattern for rendezvous in the asymmetric channel model of cognitive radio. We mathematically analyzed and calculated the ETTR when the m-sequence is applied to the conventional scheme, and our simulation results demonstrated that the ETTR performance is significantly better than that of the JS algorithm. Furthermore, we introduced a new channel-searching scheme that maximizes the benefits of the m-sequence and proposed a method to adapt the generation of the m-sequence for use in the newly proposed scheme. We also derived the ETTR mathematically for the new scheme with the m-sequence and showed through simulations that the performance of the new scheme with the m-sequence is superior to that of the conventional scheme with the m-sequence. Notably, when there is only one common channel, the new scheme with the m-sequence achieved approximately four times the improvement in the ETTR compared to the conventional scheme.
Cockpit-Llama: Driver Intent Prediction in Intelligent Cockpit via Large Language Model
Authors: Chen Yi , Li Chengzhe , Yuan Qirui , Li Jinyu , Fan Yuze , Ge Xiaojun , Li Yun , Gao Fei , Zhao Rui
Published: December 2024

The cockpit is evolving from passive, reactive interaction toward proactive, cognitive interaction, making precise predictions of driver intent a key factor in enhancing proactive interaction experiences. This paper introduces Cockpit-Llama, a novel language model specifically designed for predicting driver behavior intent. Cockpit-Llama predicts driver intent based on the relationship between current driver actions, historical interactions, and the states of the driver and cockpit environment, thereby supporting further proactive interaction decisions. To improve the accuracy and rationality of Cockpit-Llama’s predictions, we construct a new multi-attribute cockpit dataset that includes extensive historical interactions and multi-attribute states, such as driver emotional states, driving activity scenarios, vehicle motion states, body states and external environment, to support the fine-tuning of Cockpit-Llama. During fine-tuning, we adopt the Low-Rank Adaptation (LoRA) method to efficiently optimize the parameters of the Llama3-8b-Instruct model, significantly reducing training costs. Extensive experiments on the multi-attribute cockpit dataset demonstrate that Cockpit-Llama’s prediction performance surpasses other advanced methods, achieving BLEU-4, ROUGE-1, ROUGE-2, and ROUGE-L scores of 71.32, 80.01, 76.89, and 81.42, respectively, with relative improvements of 92.34%, 183.61%, 95.54%, and 201.27% compared to ChatGPT-4. This significantly enhances the reasoning and interpretative capabilities of intelligent cockpits.
Characterizing Surface Deformation of the Earthquake-Induced Daguangbao Landslide by Combining Satellite- and Ground-Based InSAR
Authors: Wang Xiaomeng , Zhang Wenjun , Cai Jialun , Wang Xiaowen , Wu Zhouhang , Fan Jing , Yao Yitong , Deng Binlin
Published: December 2024

The Daguangbao landslide (DGBL), triggered by the 2008 Wenchuan earthquake, is a rare instance of super-giant landslides globally. The post-earthquake evolution of the DGBL has garnered significant attention in recent years; however, its deformation patterns remain poorly characterized owing to the complex local topography. In this study, we present the first observations of the surface dynamics of DGBL by integrating satellite- and ground-based InSAR data complemented by kinematic interpretation using a LiDAR-derived Digital Surface Model (DSM). The results indicate that the maximum line-of-sight (LOS) displacement velocity obtained from satellite InSAR is approximately 80.9 mm/year between 1 January 2021, and 30 December 2023, with downslope displacement velocities ranging from −60.5 mm/year to 69.5 mm/year. Ground-based SAR (GB-SAR) enhances satellite observations by detecting localized apparent deformation at the rear edge of the landslide, with LOS displacement velocities reaching up to 1.5 mm/h. Our analysis suggests that steep and rugged terrain, combined with fragile and densely jointed lithology, are the primary factors contributing to the ongoing deformation of the landslide. The findings of this study demonstrate the effectiveness of combining satellite- and ground-based InSAR systems, highlighting their complementary role in interpreting complex landslide deformations.
Improved Deep Support Vector Data Description Model Using Feature Patching for Industrial Anomaly Detection
Authors: Huang Wei , Li Yongjie , Xu Zhaonan , Yao Xinwei , Wan Rongchun
Published: December 2024

In industrial contexts, anomaly detection is crucial for ensuring quality control and maintaining operational efficiency in manufacturing processes. Leveraging high-level features extracted from ImageNet-trained networks and the robust capabilities of the Deep Support Vector Data Description (SVDD) model for anomaly detection, this paper proposes an improved Deep SVDD model, termed Feature-Patching SVDD (FPSVDD), designed for unsupervised anomaly detection in industrial applications. This model integrates a feature-patching technique with the Deep SVDD framework. Features are extracted from a pre-trained backbone network on ImageNet, and each extracted feature is split into multiple small patches of appropriate size. This approach effectively captures both macro-structural information and fine-grained local information from the extracted features, enhancing the model’s sensitivity to anomalies. The feature patches are then aggregated and concatenated for further training with the Deep SVDD model. Experimental results on both the MvTec AD and CIFAR-10 datasets demonstrate that our model outperforms current mainstream approaches and provides significant improvements in anomaly detection performance, which is vital for industrial quality assurance and defect detection in real-time manufacturing scenarios.
Residual Vision Transformer and Adaptive Fusion Autoencoders for Monocular Depth Estimation
Authors: Yang Wei-Jong , Wu Chih-Chen , Yang Jar-Ferr
Published: December 2024

Precision depth estimation plays a key role in many applications, including 3D scene reconstruction, virtual reality, autonomous driving and human–computer interaction. Through recent advancements in deep learning technologies, monocular depth estimation, with its simplicity, has surpassed the traditional stereo camera systems, bringing new possibilities in 3D sensing. In this paper, by using a single camera, we propose an end-to-end supervised monocular depth estimation autoencoder, which contains an encoder with a structure with a mixed convolution neural network and vision transformers and an effective adaptive fusion decoder to obtain high-precision depth maps. In the encoder, we construct a multi-scale feature extractor by mixing residual configurations of vision transformers to enhance both local and global information. In the adaptive fusion decoder, we introduce adaptive fusion modules to effectively merge the features of the encoder and the decoder together. Lastly, the model is trained using a loss function that aligns with human perception to enable it to focus on the depth values of foreground objects. The experimental results demonstrate the effective prediction of the depth map from a single-view color image by the proposed autoencoder, which increases the first accuracy rate about 28% and reduces the root mean square error about 27% compared to an existing method in the NYU dataset.
Fully Canonical Triple-Mode Filter with Source-Load Coupling for 5G Systems
Authors: López-Montes Cristóbal , Montejo-Garai José R.
Published: December 2024

This work presents the design of a novel fully canonical triple-mode filter with source–load coupling for 5G applications, exploiting its very compact size for the FR1 band. The design is carried out using circular waveguide technology to attain power handling and low insertion losses. The fully canonical topology allows for increasing the selectivity of the filter since the number of finite transmission zeros is equal to the order of the filter. Given that this topology needs a source–load coupling level that is not possible to achieve with the classical iris ports, coaxial probes are used as input–output ports. A systematic procedure is developed to obtain the initial geometry before the full-wave optimization. The proof of concept is verified by a manufactured prototype at 3.7 GHz with 1.1% relative bandwidth for high coverage of 5G base stations. The results show an excellent agreement between the simulation and the measurement, validating the triple-mode filter and its underlying design process.
Time-Series Forecasting of PM2.5 and PM10 Concentrations Based on the Integration of Surveillance Images
Authors: Wu Yong , Wang Xiaochu , Wang Meizhen , Liu Xuejun , Zhu Sifeng
Published: December 2024

Accurate and timely air quality forecasting is crucial for mitigating pollution-related hazards and protecting public health. Recently, there has been a growing interest in integrating visual data for air quality prediction. However, some limitations remain in existing literature, such as their focus on coarse-grained classification, single-moment estimation, or reliance on indirect and unintuitive information from visual images. Here we present a dual-channel deep learning model, integrating surveillance images and multi-source numerical data for air quality forecasting. Our model, which combines a single-channel hybrid network consisting of VGG16 and LSTM (named VGG16-LSTM) with a single-channel Long Short-Term Memory (LSTM) network, efficiently captures detailed spatiotemporal features from surveillance image sequences and temporal features from atmospheric, meteorological, and temporal data, enabling accurate time-series forecasting of PM2.5 and PM10 concentrations. Experiments conducted on the 2021 Shanghai dataset demonstrate that the proposed model significantly outperforms traditional machine learning methods in terms of accuracy and robustness for time-series forecasting, achieving R2 values of 0.9459 and 0.9045 and RMSE values of 4.79 μg/m3 and 11.51 μg/m3 for PM2.5 and PM10, respectively. Furthermore, validation results on the datasets from two stations in Kaohsiung, Taiwan, with average R2 values of 0.9728 and 0.9365 and average RMSE values of 1.89 μg/m3 and 5.69 μg/m3 for PM2.5 and PM10 using a pretrain–finetune training strategy, confirm the model’s adaptability across diverse geographical contexts. These findings highlight the potential of integrating surveillance images to enhance air quality prediction, offering an effective supplement to ground-level environmental monitoring. Future work will focus on expanding datasets and optimizing network architectures to further improve forecasting accuracy and computational efficiency, enhancing the model’s scalability for broader regional air quality management.
Monitoring a Railway Bridge with Distributed Fiber Optic Sensing Using Specially Installed Fibers
Authors: Kishida Kinzo , Aung Thein Lin , Lin Ruiyuan
Published: December 2024

This article explores the use of distributed fiber optic sensing (DFOS) technology in monitoring civil infrastructure, with a concrete example of an elevated railway bridge in Taiwan. The field test utilized multiple strain-sensing fibers attached to a 1 km span of a bullet train railway bridge, which were combined to calculate the 3-dimensional bridge deformation. The installed sensing system and continuous measurements enabled quick safety confirmation after earthquakes of Richter scale 6.4 and 6.8 magnitudes occurred. Finally, the dynamic monitoring of a bullet train using Distributed Acoustic Sensing (DAS) demonstrated the merits of fiber optic sensing for both static and dynamic measurements. The empirical data gathered through this work aid in the evaluation of DFOS technology for structural-monitoring applications.
Boundary Layer Control with a Plasma Actuator Utilizing a Large GND Mesh Electrode and Two HV Electrode Configurations
Authors: Gnapowski Ernest , Gnapowski Sebastian , Tomiło Paweł
Published: December 2024

This article presents the results of experimental studies on the influence of the geometry of high-voltage plasma actuator electrodes on the change in flow in the boundary layer and their influence on the change in the lift coefficient. The plasma actuator used in the described experimental studies has a completely different structure. The experimental model of the plasma actuator uses a large mesh ground electrode and different geometries of the high-voltage electrodes, namely copper solid electrodes and mesh electrodes (the use of mesh electrodes, large GND and HV is a new solution). The plasma actuator was placed directly on the surface of the wing model with the SD 7003 profile. The wing model with the plasma actuator was placed in the wind tunnel. All experimental tests carried out were carried out for various configurations. The DBD plasma actuator was powered by a high-voltage power supply with a voltage range from Vp = 7.5–15 kV. The use of a high-voltage mesh electrode allowed for an increase in the lift coefficient (CL) for the angle of attack α = 5 degrees and the air flow velocity in the range from V = 5 m/s to 20 m/s, while the use of copper electrodes HV with the plasma actuator turned off and on, were very small (close to zero). The experimental studies were conducted for Reynolds numbers in the range of Re = 87,985–351,939.
Key Parameters for Performance and Resilience Modeling of 3D Time-of-Flight Cameras Under Consideration of Signal-to-Noise Ratio and Phase Noise Wiggling
Authors: Köhler Niklas Alexander , Geis Marcel , Nöh Claudius , Mielke Alexandra , Groß Volker , Lange Robert , Sohrabi Keywan , Frey Jochen
Published: December 2024

Because of their resilience, Time-of-Flight (ToF) cameras are now essential components in scientific and industrial settings. This paper outlines the essential factors for modeling 3D ToF cameras, with specific emphasis on analyzing the phenomenon known as “wiggling”. Through our investigation, we demonstrate that wiggling not only causes systematic errors in distance measurements, but also introduces periodic fluctuations in statistical measurement uncertainty, which compounds the dependence on the signal-to-noise ratio (SNR). Armed with this knowledge, we developed a new 3D camera model, which we then made computationally tractable. To illustrate and evaluate the model, we compared measurement data with simulated data of the same scene. This allowed us to individually demonstrate various effects on the signal-to-noise ratio, reflectivity, and distance.
Mechanism-Driven Intelligent Settlement Prediction for Shield Tunneling Through Areas Without Ground Monitoring
Authors: Hu Min , Zhao Pengpeng , Lu Jing , Wu Bingjian
Published: December 2024

Ground settlement is a crucial indicator for assessing the safety of shield tunneling and its impact on the surrounding environment. However, most existing settlement prediction methods are based on historical data, which can only be applied with effective monitoring conditions. To overcome this limitation, this paper proposes the mechanism-driven intelligent settlement prediction method (MISPM), which considers the mechanisms of settlement and attitude movements during construction to design new features that can indirectly reflect settlement. Simulation experiments were used to compare the impact of different candidate features and algorithms on prediction performance, verifying the validity and accuracy of the model. The efficacy of MISPM in predicting settlement changes in advance was substantiated by practical engineering applications. Results showed that MISPM could accurately predict settlement changes even without ground monitoring, thereby corroborating its reliability and applicability in supporting safe tunneling in complex geological environments. In the construction of urban infrastructure, this method has the potential to enhance the efficiency of tunnel construction and ensure environmental safety, which is of great significance for the development of smart cities.
Impact of Abiotic Stressors on Soil Microbial Communities: A Focus on Antibiotics and Their Interactions with Emerging Pollutants
Authors: Rasheela Abdul Rashid P. , Khalid Muhammad Fasih , Abumaali Dana A. , Alatalo Juha M. , Ahmed Talaat
Published: December 2024

Soil is a complex and dynamic ecosystem containing a diverse array of microorganisms, and plays a crucial and multifaceted role in various functions of the ecosystem. Substantial fluctuations in the environmental conditions arise from diverse global changes. The microbial shifts in the soil in concordance with the changing environmental factors, or a combination of these factors, are of high significance. Exploring the contribution of global change drivers to the microbial community to improve the predictions of the response of the microbial community to the functioning of the ecosystem is of prime importance. Promoting the health of soil microorganisms maintains the overall health and fertility of the soil, which in turn supports the health of terrestrial ecosystems and agricultural systems. The current review aims to assemble different abiotic factors or stressors that exist in the environment that affect the microbial community. More focus will be given to one of the stressors—antibiotics, a recent emerging pollutant. The effects on the soil microbial community and the future of soil health due to the presence of antibiotics will be addressed. The scope of the interaction of antibiotics with other pollutants like plastics and heavy metals (HMs) will be examined.
Comparative Life Cycle Assessment of Traditional and Modern Materials in Heritage Building Restoration: A Case Study from Ushaiger Village
Authors: Mazzetto Silvia
Published: December 2024

This paper presents a comparative life cycle assessment (LCA) of the traditional and modern materials used in heritage building restoration, focusing on mud, limestone, decorative plaster, blended hydraulic cement, and ready-mix concrete. The analysis examines key environmental impact categories, including global warming potential (GWP), ozone depletion potential (ODP), acidification potential (AP), eutrophication potential (EP), and water use across multiple life cycle stages. The results reveal that mud and limestone, while having lower initial environmental impacts in production, contribute significantly to ODP and GWP during transportation due to their heavy weight. Modern materials like blended hydraulic cement and ready-mix concrete exhibit the highest overall environmental impacts, particularly in GWP, AP, and water use, due to their energy-intensive production processes. Decorative plaster, while lower in initial impacts, gains higher environmental burdens over time due to its frequent replacement. This study highlights the need to optimize transportation and improve recycling practices for traditional materials, while also encouraging the exploration of alternative materials for reducing the environmental footprint of heritage restoration.
Green Supply Chain Management, Business Performance, and Future Challenges: Evidence from Emerging Industrial Sector
Authors: Alkandi Ibrahim , Alhajri Nouf , Alnajim Abdulrhman
Published: December 2024

This research explores the relationships between green supply chain management (GSCM), lean management (LM), corporate social responsibility (CSR), and business performance (BP) in the industrial sector of Saudi Arabia. The project is implemented within the Vision 2030 framework, which aspires to green the Saudi economy by transitioning it from reliance on hydrocarbons to a diversified, sustainable economy. A quantitative approach was implemented, which involved the application of the structural equation modeling analysis for the survey data from 345 managers in the industrial sector in Saudi Arabia’s Eastern Region. The results revealed that GSCM directly makes a significant contribution to LM, CSR, and BP. Also, the study found that LM was the positive factor that mediated the relationship between GSCM and BP. Nevertheless, no such associations were found between CSR and BP, nor was there an indirect effect of CSR as the mediator variable. The research underscores the supreme role lean management plays in translating environmentally friendly supply chain practices into better business performance. It reveals that industrial companies wishing to gain the advantages of narrowing their supply chain by using green practices should also simultaneously implement a lean management system to improve efficiency.
Regional Ecological Management Strategy of Nature’s Contribution to People (NCPs) on the Qinghai–Tibet Plateau
Authors: Zhang Wenjie , Kuang Yvwei , Zhang Yifan , Chai Bohan , Qian Kun , Yang Siqi
Published: December 2024

Balancing human needs with nature’s contributions is essential for sustainable development. The nature’s contribution to people (NCPs) framework helps identify ecosystem benefits; however, few studies have applied it to conservation priorities on the Qinghai–Tibet Plateau. Here, we first proposed an assessment method on the supply and demand of the critical NCPs, including habitat maintenance (NCP1), climate regulation (NCP4), and water quantity regulation (NCP6), and assessed NCPs from 2000 to 2020. Then, we evaluated the importance and vulnerability of NCPs based on the NCP scores and multiple stressors, which refer to current and future socioeconomic and climatic factors and inter-annual variability. Finally, we identified the ecological priority conservation areas of NCPs by comprehensively considering the importance and vulnerability. The results revealed that the following: (1) the supply and demand of three NCPs across the QTP showed similar spatial distributions, with higher values in the southeast and lower in the northwest, whereas NCP4 and NCP6 showed increasing trends over time; (2) the importance of NCPs was higher in the eastern and western parts of the QTP, while the vulnerability of NCPs was greater in the central and northwest areas, with NCP1 and NCP4 recording much higher vulnerability scores (0.94 and 0.90, respectively) compared to NCP6 (0.14); and (3) based on the importance and vulnerability of NCPs, the priority conservation areas were primarily in the eastern QTP (13.68%), covering the Yangtze and Lancang basins, with key maintenance areas focused in the southwest, underscoring a significant ecological conservation emphasis in the eastern region from both supply and demand perspectives. These insights into the NCPs’ ecological conservation support regional coordination and ecological management to enhance human well-being.
Realizing Carbon Neutrality in Top-Emitter Countries: Do Green Technology Innovation, Renewable Energy, Financial Development, and Environmental Tax Matters?
Authors: Sakilu Olani Bekele , Chen Haibo
Published: December 2024

As a result of the growing global climate crisis, many countries have pledged to cut carbon dioxide emissions and other greenhouse gas emissions to achieve net-zero emission goals. These goals can be successfully realized with the rollout of environmental regulations, utilization of green technology innovations, and a greater use of renewable energies. This study explores the influence of green technology innovations, renewable energy, financial development, environmental taxes, and economic growth on CO2 emissions in the 19 highest emitting countries from 1994 to 2022. The results reveal that renewable energy use and environmental taxes negatively affect CO2 emissions, reinforcing the essential role of these variables in the journey toward carbon neutrality. Green technological innovations, financial development, and economic growth have positive effects on CO2 emissions, suggesting that appropriate regulations and policies are necessary to attain net zero carbon emissions. The findings also indicate that financial development positively affects environmental quality by promoting green technological innovations. The causality results indicate a bidirectional causal link between green technology innovations, renewable energy, environmental taxes, economic growth, and CO2 emissions. Additionally, a unidirectional causal relationship exists between financial development and CO2 emissions. Based on these results, the study offers policy suggestions.
Spatio-Temporal Distribution of PM2.5 and PM10 Concentrations and Assessment of Public Health Risk in the Three Most Polluted Provinces of Iran
Authors: Abadi Abbas Ranjbar Saadat , Hamzeh Nasim Hossein , Kaskaoutis Dimitris G. , Vuillaume Jean-Francois , Shukurov Karim Abdukhakimovich , Gharibzadeh Maryam
Published: December 2024

This study examines the spatio-temporal variations of ambient air pollution from fine particulates below 2.5 µm (PM2.5) and particulate matter below 10 µm (PM10) in three of the most polluted provinces in Iran, namely Tehran, Isfahan, and Khuzestan, over a 6-year period (2016–2021). The results reveal distinct patterns of PM10 and PM2.5 concentrations since in Tehran, the highest PM10 and PM2.5 levels occur in winter, while PM2.5 is lowest from March to May. Khuzestan experiences the highest pollution levels in summer due to dust storms, while Isfahan exhibits pollution levels and annual patterns similar to Tehran. Strong correlations are observed between PM10 and PM2.5 concentrations at stations in Tehran and Khuzestan Provinces, suggesting common sources and variation in both coarse and fine PM, with average PM2.5/PM10 ratios of 0.39–0.42, suggesting the dominance of dust. Furthermore, the analysis identifies the role of atmospheric stability, wind speed, and dust storms in controlling the PM levels in the three provinces. Lifetime cancer risks have been identified as unacceptably high, exceeding the threshold limit of 10−4, while Hazard Quotient (HQ) values above 1 indicate a high non-carcinogenic potential risk, particularly at stations in Khuzestan Province. The Excess Lifetime Cancer Risk (ELCR) values for PM2.5 exposure in the most populated Tehran Province range from 139.4 × 10−6 to 263.2 × 10−6, underscoring significant cancer risks across various monitoring sites. This study emphasizes the urgent need for targeted pollution control measures in each province to effectively mitigate the adverse health effects associated with high PM concentrations.
Equivalent Cost Minimization Strategy for Plug-In Hybrid Electric Bus with Consideration of an Inhomogeneous Energy Price and Battery Lifespan
Authors: Xue Di , Wang Haisheng , Wang Junnian , Guan Changyang , Xia Yiru
Published: December 2024

The development of energy-saving vehicles is an important measure to deal with environmental pollution and the energy crisis. On this basis, more accurate and efficient energy management strategies can further tap into the energy-saving potential and energy sustainability of vehicles. The equivalent consumption minimization strategy (ECMS) has shown the ability to provide a real-time sub-optimal fuel efficiency performance. However, when taking the different market prices of fuel and electricity cost as well as battery longevity cost into account, this method is not very accurate for total operational economic evaluation. So, as an improved scheme, the instantaneous cost minimization strategy is proposed, where a comprehensive cost function, including the market price of the electricity and fuel as well as the cost of battery aging, is applied as the optimization objective. Simulation results show that the proposed control strategy for series-parallel hybrid electric buses can reduce costs by 41.25% when compared with the conventional engine-driven bus. The approach also impressively improves cost performance over the rule-based strategy and the ECMS. As such, the proposed instantaneous cost minimization strategy is a better choice for hybrid electric vehicle economic evaluation than the other main sub-optimal strategies.
Transforming Waste into Value: Sustainable Recycling of Agricultural Resources Under the ‘Carbon Peak and Carbon Neutrality’ Vision
Authors: Chen Sonia Chien-i , Dang Xin , Xu Qian-qian , Own Chung-Ming
Published: December 2024

The “Carbon Peak, Carbon Neutrality” vision emphasizes the urgency of adopting sustainable practices. This study primarily stresses on how China’s agricultural waste can be managed for long-term environmental sustainability and energy development. This study presents novel techniques for power generation from biogas and utilizes environmental enzyme technology to recycle agricultural waste, therefore fostering low-carbon development and facilitating green energy transitions. A PEST approach examines the social, economic, political, and technical factors that impact the utilization of agricultural waste resources, while a prototype value-added reuse system is built to demonstrate practical applications. Feasibility studies back up the proposed methods with evidence from a SWOT analysis. Furthermore, a systematic questionnaire survey assesses public knowledge and behavior about the reusing of agricultural waste, thereby offering information on society’s preparedness for implementing these technologies. The results highlight the possibilities of waste crop recycling technology to stimulate industrial activities, especially in the manufacturing of bio-based products fit for national carbon neutrality targets. This study emphasizes the need to include agricultural waste recycling into changes towards sustainable energy. Its encouragement of the invention of green energy solutions helps the study to offer an insightful analysis for the progress of carbon neutrality initiatives.
Effects of Redox Condition on Bacteria-Mediated Hydrochemical Processes and Bacterial Community During Managed Aquifer Recharge
Authors: Qin Mengjie , You Haichi , Zhang Weijie , Liu Longyun , Liu Jinhui , Xia Lu
Published: December 2024

During the process of managed aquifer recharge (MAR), when the aerobic surface water is recharged into the reductive aquifer, the redox environment changes along the water pathway. MAR practice can reshape the initial groundwater bacterial community, and further induce variations in the bacteria-mediated hydrochemical reactions. In this study, laboratory-scale column experiments were conducted to simulate the processes of aerobic/anaerobic recharge to aquifer. The results showed that the concentration of DO during the aerobic recharge was higher than that of the anaerobic recharge, and ORP showed a similar trend. Active nitrogen transformation was observed during the simulated MAR processes. In the early stages of both the aerobic and anaerobic recharges, nitrate reduction occurred due to denitrification and DNRA. However, in the late stages, nitrification might happen in the aerobic column, and nitrate reduction remained the major process in the anaerobic column. For the bacterial community, Massilia, Ralstonia, Legionella, and Curvibacter predominated under the aerobic recharge. Comparatively, Cedecea, Cupriavidus, and Ralstonia maintained high relative abundances under the anaerobic recharge. Our study provides essential information about the characteristics of bacterial-mediated hydrochemical reactions during the MAR process. The result would enhance understanding of MAR activities and provide valuable insights into the groundwater resources’ sustainable development and management.
Enhanced Adsorption and Biomineralization of Cadmium and Arsenic in Irrigation Water by Biological Soil Crusts: The Key Roles of Iron/Manganese and Urea
Authors: Li Anbang , Fei Caiyun , Yang Han , Zhu Mengmeng , Wang Chenlu , Hu Hongxiang , Ye Wenling
Published: December 2024

Heavy metal pollution has become increasingly severe, with distinctive physiological characteristics of rice leading to significant accumulation of arsenic (As) and cadmium (Cd) in grains, posing serious health risks. Biological soil crusts (BSC) are common in paddy soils and exhibit a strong capacity to bind trace heavy metals. This study investigated the effects of exogenous iron (Fe)/manganese (Mn) and urea on the effectiveness of BSC (20 mg L−1) in removing As (2 mg L−1) and Cd (100 μg L−1) and analyzed the heavy metal distribution. Fe/Mn addition increased As adsorption by BSC from 51.2% to 83.0% but reduced Cd adsorption from 73.2% to 50.3%, whereas urea inhibited As uptake but enhanced Cd capture. Under co-contamination, the As removal ability of the BSC remained unchanged, but Cd removal improved. As was primarily present in the non-EDTA exchangeable fraction (79.0%), which increased to 96.4% and 85.8% in the presence of Fe/Mn, and urea, respectively. Cd was mainly in the sorbed fraction (51.6%), which increased to 61.0% with urea. These results confirm that BSC exhibits a strong ability to adsorb As and Cd under irrigated water with combined As and Cd contamination, iron/manganese and urea can also enhance this ability. The application of exogenous Fe/Mn providing the raw material for the mineralization process and the presence of urea enhancing the biological activity of the colonies. This study provides an eco-friendly strategy for remediating As and Cd in paddy fields.
On [r, s]-Superporosity
Authors: Kowalczyk Stanisław , Turowska Małgorzata
Published: December 2024

The main goal of the paper is to characterize families of [r,s]-(upper) superporous subsets of R, which generalize well-known notions of superporosity and strong superporosity of subsets of R. Definitions and properties of [r,s]-superporosity are symmetric to definitions and properties of superporosity and strong superporosity. The purpose in all cases is to define small subsets of the line using the notion of porosity. Superporous sets preserve positive porosity, and strongly superporous sets preserve strong porosity; i.e., if E is superporous (correspondingly, E is strongly superporous), then for every x∈E and for every F such that porosity of F at x is greater than 0 (correspondingly, is equal to 1), porosity of E∪F at x is greater than 0 (correspondingly, is equal to 1). Taking arbitrary positive porosity, instead of 0 or 1, we obtain the symmetric definition as follows: [r,s]-superporosity for 0<r≤s<1 transfers s-porosity to r-porosity; i.e., if E is [r,s]-superporous, then for every x∈E and for every F such that porosity of F at x is not less than s, porosity of E∪F at x is not less than r. Even though the definition and properties of [r,s]-superporosity, superporosity and strong superporosity are symmetric and all of them consist of very small sets, the families of these sets are essentially different. In the paper, we focus on relationships between [r,s]-superporous sets for different indices [r,s]. Furthermore, we compare [r,s]-superporosity to superporosity and strong superporosity. We apply the notion of [r,s]-superporosity to find multipliers and adders of porouscontinuous functions.
The Influence of Network Position on Executives’ Preference for Imitation over Innovation: A Scenario-Based Experimental Approach
Authors: Zhang Guojian , Boadu Francis , Xie Yu
Published: December 2024

This study investigates how the different dimensions of a firm’s network position influence executives’ preference for imitation over innovation in new venture development, thus reflecting their preference for uncertainty. Through a scenario-based experimental approach involving 1024 responses from 64 senior executives, we examine how network centrality, structural equivalence, and structural autonomy shape these strategic preferences. The findings reveal that firms with higher network centrality tend to favor innovation over imitation more strongly. At the same time, those in structurally equivalent positions show a greater preference for imitation. Structural autonomy does not significantly affect these preferences. This study contributes to the literature by revealing how different network structures uniquely influence strategic decision-making in uncertain environments, offering valuable insights for scholars and practitioners. The scenario-based experimental method enhances the precision of the findings and provides actionable implications for real-world decision-making in venture development.
Enhancing Security in International Data Spaces: A STRIDE Framework Approach
Authors: Gavric Nikola , Shalaginov Andrii , Andrushevich Aliaksei , Rumsch Andreas , Paice Andrew
Published: December 2024

The proliferation of Internet of Things (IoT) devices and big data has catalyzed the emergence of data markets. Regulatory and technological frameworks such as International Data Spaces (IDS) have been developed to facilitate secure data exchange while integrating security and data sovereignty aspects required by laws and regulations, such as the GDPR and NIS2. Recently, novel attack vectors have taken a toll on many enterprises, causing significant damage despite the deployed security mechanisms. Hence, it is reasonable to assume that the IDS may be just as susceptible. In this paper, we conduct a STRIDE threat analysis on IDS to assess its susceptibility to traditional and emerging cybersecurity threats. Specifically, we evaluate novel threats such as Man-in-the-Middle (MitM) attacks, compromised end-user devices, SIM swapping, and potential backdoors in commonly used open-source software. Our analysis identifies multiple vulnerabilities, particularly at the trust boundary (TB) between users and the IDS system. These include the traditionally troublesome Denial of Service (DoS) attacks, key management weaknesses, and the mentioned novel threats. We discuss the hacking techniques, tools, and associated risks to the IDS framework, followed by targeted mitigation strategies and recommendations. This paper provides a framework for performing a STRIDE-based threat analysis of the IDS. Using the proposed methodology, we identified the most potent threats and suggested solutions, thus contributing to the development of a safer and more resilient data space architecture.