bluuebunny/crossref_metadata_2025 huggingface dataset:
Highway Ecological Environmental Assessment Based on Modified Remote Sensing Index—Taking the Lhasa–Nyingchi Motorway as an Example
Authors: Wang Xinghan , Liu Qi , Jia Pengfei , Huang Xifeng , Yang Jianhua , Mao Zhengjun , Shen Shengyu
Published: January 2024

The Lhasa to Nyingchi Expressway in Xizang made efforts to protect the ecological environment during its construction, but it still caused varying degrees of damage to the fragile ecosystems along the route. Accurately assessing the process of change in the ecological environment quality in this region holds significant research value. This study selected the Linzhi-to-Gongbo’gyamda section of the Lhasa-to-Nyingchi Expressway as the research area. Firstly, based on the remote sensing ecological index (RSEI), this study constructed an ecological environmental quality evaluation system for the Xizang region. Subsequently, using the Google Earth Engine (GEE) platform, sub-indicators were extracted, and the combination weighting method of game theory was employed to determine indicator weights. This process resulted in the calculation of the MRSEI for the study area from 2012 to 2020. Finally, by utilizing the spatial distribution of the MRSEI, monitoring the level of MRSEI changes, and employing the transition matrix, this study analyzed the changing trend of the ecological environmental quality from 2012 to 2020. The results indicate that the MRSEI are 0.5885, 0.5951, 0.5296, 0.6202, 0.59, 0.5777, 0.5898, 0.5703, and 0.5987, showing a gradual increasing trend with an initial decrease followed by an ascent. This trend is mainly attributed to concentrated road construction and subsequent ecological restoration, leading to an improvement in the restoration effect. Simultaneously, the ecological environmental quality remains relatively stable, with 69.5% of the region showing no change, and the remaining 30.5% experiencing improvement exceeding degradation. Specifically, there were significant improvements in the land with ecological quality levels categorized as poor, fair, moderate, and good. The types of degradation primarily involved lands originally classified as excellent and good degrading to good and moderate levels, respectively. The above results serve as a theoretical reference for the ecological restoration project of the Lhasa-to-Nyingchi Expressway.
Joint Analysis and Morphological Characterization of HFSWR Echo Properties during Severe Typhoon Muifa
Authors: Wang Rong , Lyu Zhe , Yu Changjun , Liu Aijun , Quan Taifan
Published: January 2024

Investigating the dynamic evolution process of the ocean and ionosphere in sudden sea conditions poses a challenging problem. To address this objective, this study utilizes actual data from high-frequency surface wave radar (HFSWR) to analyze, validate, summarize, and characterize the echo properties of the ocean and ionosphere during the severe Typhoon Muifa. By employing the short-time Fourier transform (STFT) method, the HFSWR ocean and ionosphere echoes stimulated by typhoon-induced gravity waves are observed, and the joint gravity wave features of the ocean and ionosphere echoes at different time scales are extracted. Additionally, the phase-space reconstruction method is employed to characterize the dynamical evolution of the joint gravity wave features in higher-dimensional space. Furthermore, the chaotic dynamics behavior of the joint gravity wave features is analyzed using the largest Lyapunov exponents. By combining the gravity wave features with chaotic dynamics, this study introduces a method to characterize the joint gravity wave features. The extraction of joint gravity wave features in HFSWR echoes stimulated by typhoons, along with the construction of a chaotic characterization scheme for the gravity wave features, provides an innovative approach and a solid technical foundation for studying the ocean and ionosphere using HFSWR under sudden sea conditions.
The MS-RadarFormer: A Transformer-Based Multi-Scale Deep Learning Model for Radar Echo Extrapolation
Authors: Geng Huantong , Wu Fangli , Zhuang Xiaoran , Geng Liangchao , Xie Boyang , Shi Zhanpeng
Published: January 2024

As a spatial–temporal sequence prediction task, radar echo extrapolation aims to predict radar echoes’ future movement and intensity changes based on historical radar observations. Two urgent issues still need to be addressed in deep learning radar echo extrapolation models. First, the predicted radar echo sequences often exhibit echo-blurring phenomena. Second, over time, the output echo intensities from the model gradually weaken. In this paper, we propose a novel model called the MS-RadarFormer, a Transformer-based multi-scale deep learning model for radar echo extrapolation, to mitigate the two above issues. We introduce a multi-scale design in the encoder–decoder structure and a Spatial–Temporal Attention block to improve the precision of radar echoes and establish long-term dependencies of radar echo features. The model uses a non-autoregressive approach for echo prediction, avoiding accumulation errors during the recursive generation of future echoes. Compared to the baseline, our model shows an average improvement of 15.8% in the critical success index (CSI), an average decrease of 8.3% in the false alarm rate (FAR), and an average improvement of 16.2% in the Heidke skill score (HSS).
Intelligent Reconstruction of Radar Composite Reflectivity Based on Satellite Observations and Deep Learning
Authors: Zhao Jianyu , Tan Jinkai , Chen Sheng , Huang Qiqiao , Gao Liang , Li Yanping , Wei Chunxia
Published: January 2024

Weather radar is a useful tool for monitoring and forecasting severe weather but has limited coverage due to beam blockage from mountainous terrain or other factors. To overcome this issue, an intelligent technology called “Echo Reconstruction UNet (ER-UNet)” is proposed in this study. It reconstructs radar composite reflectivity (CREF) using observations from Fengyun-4A geostationary satellites with broad coverage. In general, ER-UNet outperforms UNet in terms of root mean square error (RMSE), mean absolute error (MAE), structural similarity index (SSIM), probability of detection (POD), false alarm rate (FAR), critical success index (CSI), and Heidke skill score (HSS). Additionally, ER-UNet provides the better reconstruction of CREF compared to the UNet model in terms of the intensity, location, and details of radar echoes (particularly, strong echoes). ER-UNet can effectively reconstruct strong echoes and provide crucial decision-making information for early warning of severe weather.
Hyperspectral Image Denoising Based on Principal-Third-Order-Moment Analysis
Authors: Li Shouzhi , Geng Xiurui , Zhu Liangliang , Ji Luyan , Zhao Yongchao
Published: January 2024

Denoising serves as a critical preprocessing step for the subsequent analysis of the hyperspectral image (HSI). Due to their high computational efficiency, low-rank-based denoising methods that project the noisy HSI into a low-dimensional subspace identified by certain criteria have gained widespread use. However, methods employing second-order statistics as criteria often struggle to retain the signal of the small targets in the denoising results. Other methods utilizing high-order statistics encounter difficulties in effectively suppressing noise. To tackle these challenges, we delve into a novel criterion to determine the projection subspace, and propose an innovative low-rank-based method that successfully preserves the spectral characteristic of small targets while significantly reducing noise. The experimental results on the synthetic and real datasets demonstrate the effectiveness of the proposed method, in terms of both small-target preservation and noise reduction.
Multi-Scale Image- and Feature-Level Alignment for Cross-Resolution Person Re-Identification
Authors: Zhang Guoqing , Wang Zhun , Zhang Jiangmei , Luo Zhiyuan , Zhao Zhihao
Published: January 2024

Cross-Resolution Person Re-Identification (re-ID) aims to match images with disparate resolutions arising from variations in camera hardware and shooting distances. Most conventional works utilize Super-Resolution (SR) models to recover Low Resolution (LR) images to High Resolution (HR) images. However, because the SR models cannot completely compensate for the missing information in the LR images, there is still a large gap between the HR image recovered from the LR images and the real HR images. To tackle this challenge, we propose a novel Multi-Scale Image- and Feature-Level Alignment (MSIFLA) framework to align the images on multiple resolution scales at both the image and feature level. Specifically, (i) we design a Cascaded Multi-Scale Resolution Reconstruction (CMSR2) module, which is composed of three cascaded Image Reconstruction (IR) networks, and can continuously reconstruct multiple variables of different resolution scales from low to high for each image, regardless of image resolution. The reconstructed images with specific resolution scales are of similar distribution; therefore, the images are aligned on multiple resolution scales at the image level. (ii) We propose a Multi-Resolution Representation Learning (MR2L) module which consists of three-person re-ID networks to encourage the IR models to preserve the ID-discriminative information during training separately. Each re-ID network focuses on mining discriminative information from a specific scale without the disturbance from various resolutions. By matching the extracted features on three resolution scales, the images with different resolutions are also aligned at the feature-level. We conduct extensive experiments on multiple public cross-resolution person re-ID datasets to demonstrate the superiority of the proposed method. In addition, the generalization of MSIFLA in handling cross-resolution retrieval tasks is verified on the UAV vehicle dataset.
Inversion of Near-Surface Aerosol Equivalent Complex Refractive Index Based on Aethalometer, Micro-Pulse Lidar and Portable Optical Particle Profiler
Authors: Ma Xuebin , Luo Tao , Li Xuebin , Liu Changyu , Liu Nana , Liu Qiang , Zhang Kun , Chen Jie , Zhu Liming
Published: January 2024

In order to investigate the equivalent complex refractive index of atmospheric aerosols near the Earth’s surface, we conducted measurements in the Hefei region from March to April 2022. These measurements utilized a micro-pulse lidar, an Aethalometer, and a Portable Optical Particle Profiler. These measurements encompassed aerosol particle size distribution as well as standard meteorological parameters including temperature, humidity, atmospheric pressure, and wind speed. Subsequently, this dataset was employed to develop an optimization algorithm for retrieving the equivalent complex refractive indices of near-surface aerosols. The methodology relies on lookup tables containing data for extinction efficiency and absorption efficiency factors. It operates on the premise of aerosol property stability within a defined time frame, utilizing measured extinction and absorption coefficients as simultaneous constraints during this period to inversely derive both the real and imaginary parts of the aerosol complex refractive index. Results from the simulation analysis reveal that the newly optimized retrieval algorithm, which relies on lookup tables, exhibits reduced sensitivity to instrument errors when compared to single-point constraint algorithms. This enhancement results in a more efficient and dependable approach for retrieving the aerosol complex refractive index. Empirical inversion and simulation studies were carried out to determine the aerosol equivalent complex refractive index in the Hefei region, utilizing measured data. This inversion process yielded an average complex refractive index of 1.48-i0.017 for aerosols in the Hefei region throughout the experimental period. Correlation analysis unveiled a positive association between the real part of the aerosol complex refractive index and the single-scattering albedo (SSA), whereas the imaginary part displayed a linear negative correlation with the SSA. The mathematical relationship between the real part and the SSA is y=0.19x+0.62, and the corresponding relationship between the imaginary part and the SSA is y=−5.3x+0.99. This research offers a novel method for the retrieval of the aerosol equivalent complex refractive index.
Evaluating and Correcting Temperature and Precipitation Grid Products in the Arid Region of Altay, China
Authors: Zhang Liancheng , Jiapaer Guli , Yu Tao , Umuhoza Jeanine , Tu Haiyang , Chen Bojian , Liang Hongwu , Lin Kaixiong , Ju Tongwei , De Maeyer Philippe , Van de Voorde Tim
Published: January 2024

Temperature and precipitation are crucial indicators for investigating climate changes, necessitating precise measurements for rigorous scientific inquiry. While the Fifth Generation of European Centre for Medium-Range Weather Forecasts Atmospheric Reanalysis (ERA5), ERA5 of the Land Surface (ERA5-Land), and China Meteorological Forcing Dataset (CMFD) temperature and precipitation products are widely used worldwide, their suitability for the Altay region of arid and semi-arid areas has received limited attention. Here, we used the Altay region as the study area, utilizing meteorological station data and implementing the residual revision method for temperature and the coefficient revision method for precipitation to rectify inaccuracies in monthly temperature and precipitation records from ERA5-Land, ERA5, and CMFD. We evaluate the accuracy of these datasets before and after correction using bias, Taylor diagrams, and root-mean-square error (RMSE) metrics. Additionally, we employ Tropical Rainfall Measuring Mission satellite precipitation data (TRMM) as a benchmark to assess the performance of ERA5-Land, ERA5, and CMFD monthly precipitation before and after correction. The results revealed significant differences in the temperature and precipitation capture capabilities of ERA5-Land, ERA5, and CMFD in the Altay region. Overall, these data exhibit substantial errors and are not directly suitable for scientific research. However, we applied residual and coefficient revision methods. After this revision, ERA5-Land, ERA5, and CMFD showed significantly improved temperature and precipitation capture capabilities, especially for ERA5-Land. In terms of temperature, post-revision-CMFD (CMFDPR) demonstrated better temperature capture capabilities. All three datasets showed weaker performance in mountainous regions compared to plains. Notably, post-revision-ERA5 (ERA5PR) seemed unsuitable for capturing temperature in the Altay region. Concerning rain, CMFDPR, post-revision-ERA5-Land (ERA5-LandPR) and ERA5PR outperformed TRMM in capturing precipitation. CMFDPR and ERA5-LandPR both outperform ERA5PR. In summary, the revision datasets effectively compensated for the sparse distribution of meteorological stations in the Altay region, providing reliable data support for studying climate change in arid and semi-arid areas.
Offshore Oil Spill Detection Based on CNN, DBSCAN, and Hyperspectral Imaging
Authors: Zhan Ce , Bai Kai , Tu Binrui , Zhang Wanxing
Published: January 2024

Offshore oil spills have the potential to inflict substantial ecological damage, underscoring the critical importance of timely offshore oil spill detection and remediation. At present, offshore oil spill detection typically combines hyperspectral imaging with deep learning techniques. While these methodologies have made significant advancements, they prove inadequate in scenarios requiring real-time detection due to limited model detection speeds. To address this challenge, a method for detecting oil spill areas is introduced, combining convolutional neural networks (CNNs) with the DBSCAN clustering algorithm. This method aims to enhance the efficiency of oil spill area detection in real-time scenarios, providing a potential solution to the limitations posed by the intricate structures of existing models. The proposed method includes a pre-feature selection process applied to the spectral data, followed by pixel classification using a convolutional neural network (CNN) model. Subsequently, the DBSCAN algorithm is employed to segment oil spill areas from the classification results. To validate our proposed method, we simulate an offshore oil spill environment in the laboratory, utilizing a hyperspectral sensing device to collect data and create a dataset. We then compare our method with three other models—DRSNet, CNN-Visual Transformer, and GCN—conducting a comprehensive analysis to evaluate the advantages and limitations of each model.
Application of the Hydrodeoxygenation of Black Soldier Fly Larvae Lipids in Green Diesel Production
Authors: Lee Ji Eun , Jang Hyun Sung , Yun Yeo Jin , Han Gi Bo , Park Young Kyu , Yang Young Cheol , Jang Jung Hee
Published: January 2024

To produce green diesel from black soldier fly larvae (BSFL; Hermetia illucens), the maximization of lipids in production and hydrodeoxygenation (HDO) reactions was investigated. In this study, BSFL were fed 12 diets based on three different substrates (ground corn, food waste, and meat by-product). The proximate compositions of larvae were analyzed, and rearing time, production rate, and feeding mixture prices were also recorded. To maximize the lipid yield, the effects of growing temperature, drying method, and extraction temperature were investigated. The HDO reaction of BSFL oil with 1 wt % Pt/Al2O3 catalyst was carried out in a trickle bed reactor. The components of the lipids produced under optimal conditions and the components of lipids produced through the HDO reaction were compositionally analyzed. As a result of being fed ground corn, food waste, and meat by-products, it was confirmed that the diet with 30% ground corn and 70% meat by-product led to the highest lipid content in the BSFL. After considering the prices of the diets, we found that the most ideal feeding conditions that could be applied to actual insect farming were 70% food waste and 30% meat by-products. From the perspective of the rearing period, the most appropriate BSFL-rearing temperature was a medium temperature of 38 °C. After harvesting the BSFL, it was confirmed that the lipid yield improved when extracted at a temperature of 65–75 °C after drying using a microwave. The analysis results showed that the carbon distribution in hydrodeoxygenated BSFL oil offered an advantage when used as drop-in fuel, and this represents a promising future step for the HDO of BSFL lipids.
Effect of Slope on Stormwater Infiltration into Porous Asphalt Pavements
Authors: Yang Qiuxia , Hu Jiaxuan , Beecham Simon
Published: January 2024

Porous asphalt pavements and water-retentive asphalt pavements are widely used because of their ability to infiltrate both rainfall and stormwater runoff. There is very limited information available to assist designers of porous asphalt pavement systems to be installed on sloping sub-catchments. This is because the infiltration performance of these systems has only been investigated experimentally for horizontal pavements, and their performance on sloping terrains has only been investigated theoretically. This experimental study investigates the relationship between rainfall intensity, pavement slope, runoff and infiltration rates for dense-graded asphalt, porous asphalt and water-retentive asphalt concrete pavements. Three rates of simulated rainfall were applied to porous, water-retentive and dense-graded asphalt specimens set at three different pavement slopes, namely 0°, 3° and 5°. The relationship between the porosity and permeability of the porous asphalt pavements was also determined. A porosity of 20% resulted in a permeability of greater than 1 mm/s. It was found that the porous asphalt specimens had excellent runoff retention and infiltration rates at all slopes. The water-retentive asphalt specimens also produced good infiltration rates at horizontal slopes, but these decreased at higher pavement slopes.
Does Green Overseas Investment Improve Public Perception in Host Countries? Evidence from Chinese Energy Engagement in 32 African Countries
Authors: Nedopil Christoph , Yue Mengdi
Published: January 2024

This study examines whether and how green overseas economic engagement impacts public perception in host countries as a form of soft power. We build an extensive country-level dataset on Chinese bilateral engagement in 32 African countries from 2015 to 2019 and use a fixed-effect model. We find that increased investment in green energy improves the average public perception of China. In contrast, for non-green energy investment (like coal, gas, and oil), which might also be considered as contributing to economic and employment growth, we did not find such effects. The results indicate that green economic engagement has positive non-monetary returns on the macro-scale and that by taking environmental considerations into investment decision-making, long-term bilateral relationships can be positively impacted.
Sustainable Development of STEAM and Mathematics Education with Active and Innovative Methodology
Authors: Jeong Jin Su , González-Gómez David
Published: January 2024

There is a broad consensus in recognizing the importance of having a citizenry competent in mathematics which, in this way, contributes to ensuring both their socioeconomic progress and their employability, as well as their personal fulfillment, social inclusion, and active participation as citizens [...]
Computational Approach towards Repetitive Design Tasks: The Case Study of Parking Lot Automated Design
Authors: Cudzik Jan , Nessel Michał
Published: January 2024

The study aims to develop and assess an algorithm for efficiently generating parking spot layouts within predefined area outlines. The algorithm is an attempt to streamline the decision-making process by producing different design variants and optimizing the utilization of available space. The algorithm’s primary objective is to streamline decision-making by generating diverse design variants while optimizing the use of available space, with a distinct focus on mitigating environmental impact and fostering ecological well-being. Researchers conduct thorough tests on the algorithm across various outlines, resulting in multiple layout options for each scenario. They analyzed five representative parking locations and compare the algorithm’s results with the existing parking spot layouts. Throughout the evaluation process, they consider quantitative and qualitative data, considering the complexities of communication solutions within each context. The study findings indicate that the algorithm demonstrates comparable or superior performance to existing solutions. Overall, the study highlights the promising potential of algorithmic design approaches in the context of parking lot automated design. Achieving a balance between innovative designs and user-friendly layouts is crucial, and this is achievable by conducting comprehensive analyses that consider various factors. The consistent findings underscore the algorithm’s potential to significantly contribute to sustainable design practices in parking lot layouts, highlighting decreased environmental strain, efficient land use, and creating urban spaces that prioritize ecological benefits. Furthermore, seamlessly integrating algorithmic solutions with existing communication systems is paramount to ensure practical applicability in real-world scenarios. This integration will enable more effective and practical implementation of the algorithm’s outputs in actual parking lot design projects.
Survey of Antifungal in Surface- and Groundwater: A Portuguese Environmental Case Study
Authors: De Mello-Sampayo Cristina , Viana Paula , Lopes Ana , Carvalho da Silva Rita , de Jesus Rosário , Sarmento Georgina , Almeida Anabela , Meisel Leonor
Published: January 2024

Antifungals support modern medical and agricultural practices, and extensive screening environmental data are missing. This work aims to survey antifungals for medical and agricultural purposes in surface- and groundwater in Portugal. A passive sampling technique and a high-resolution chromatographic system were used. Antifungals applied in medical practice were not detected as only antifungals for agricultural purposes (fungicides). Thirty-nine different fungicide molecules were found; its detection frequency in surface- and groundwater was dominated by rabenzazole (61%, 92%) and ethoxyquin (54%, 77%); among the five fungicides with similar surface- and groundwater catching rates, ferimzone was the most polluting (54%, 54%); oxadixyl (61%), kresoxim-methyl (61%) and fenamidone (46%) were primarily designated surface water contaminants; for azoles, the occurrence in surface water ranged from a residual (10%) to a moderate detection rate for two compounds (31%—propiconazole and tebuconazole). Surprisingly, only 51% of detected fungicides are authorised. The frequently detected fungicides that are either not authorised (ethoxyquin and its impurity, furmecyclox, oxadixyl), without data in the EU (rabenzazole, ferimzone), or authorised at the national level (fenamidone), should be included in environmental monitoring programmes and followed as emerging (micro)contaminants. Policy makers should gather their efforts to allow the implementation of proper risk management and effective contamination control strategies to achieve Sustainable Development Goals.
Factors Influencing the Double-Up Adoption of Climate Change Adaptation Strategies among Smallholder Maize Farmers in Malawi
Authors: Tikita Blessings Youngster , Lee Sang-Ho
Published: January 2024

Maize remains the staple grain in Malawi; hence, the cropping system of most smallholder farmers is dominated by the crop, often mono-cropped for food security. Consequently, Malawi’s agriculture sector is made vulnerable to the adverse impacts of climate change. For instance, crop failure results in food insecurity and the low income of farm households. In response, there are coping mechanisms, which can be adopted by farmers to mitigate these negative climate change effects, namely maize–legume diversification, organic manure use, and the practice of agroforestry. Therefore, the underlying objective of this study was to assess the factors influencing smallholder maize farmers’ decision to engage in the double-up adoption of climate change adaptation strategies in Malawi. Both descriptive statistics and the logistic regression model were employed to statistically analyze these factors, and the results of the analysis revealed that landholding size, inorganic fertilizer use, access to credit, seed access, adherence to extension services, and input coupon access were significant in influencing dual adoption. Furthermore, this study recommends policies, which underscore land access and safeguard the land rights of smallholder maize farmers, and also private sector engagement in complementing government efforts in ensuring increased access to seeds. Additionally, improving farmers’ adherence to agricultural extension services is recommended. Thus, addressing the constraints of small-scale farmers observed in this study will act as an incentive for farmers to consider dual adoption, which is perceived to be a feasible method to combat climate change effects.
Optimization of a Low-Cost Corona Dielectric-Barrier Discharge Plasma Wastewater Treatment System through Central Composite Design/Response Surface Methodology with Mechanistic and Efficiency Analysis
Authors: Xiao Yiting , Tian Yang , Zhan Yuanhang , Zhu Jun
Published: January 2024

Water pollution, which is intensified by the release of hard-to-degrade pollutants, poses severe threats to ecosystems, human health, and economic development. The existing advanced oxidation processes often involve high operational costs and can potentially result in secondary pollution, highlighting the necessity for innovative and more sustainable solutions. To address these challenges, our study introduces a cost-effective and eco-friendly corona dielectric-barrier discharge for wastewater treatment. Using the central composite design/response surface methodology, a high decolorization rate of 98% of methylene blue (MB) was achieved within 10 min by optimizing parameters such as pH and voltage. Furthermore, the mechanisms underlying the generation of reactive oxygen species through this device were discussed in detail and the degradation pathways of MB were elucidated. Moreover, this device is very energy-efficient, exhibiting a low energy density and electrical energy per order of 0.15 watt/mL and 5.79 kWh/m3/order, respectively. In conclusion, the plasma discharger developed in this study provides a cost-effective and environmentally sustainable solution for dye wastewater treatment. This research contributes significantly to the advancement of sustainable dye wastewater management practices, offering an innovative method that meets both environmental and economic objectives.
An Urban Metro Section Flow Forecasting Method Combining Time Series Decomposition and a Generative Adversarial Network
Authors: Li Maosheng , Zhang Chen
Published: January 2024

Urban metro cross-section flow is the passenger flow that travels through a metro section. Its volume is a critical parameter for planning operation diagrams and improving the service quality of urban subway systems. This makes it possible to better plan the drive for the sustainable development of a city. This paper proposes an improved model for predicting urban metro section flow, combining time series decomposition and a generative adversarial network. First, an urban metro section flow sequence is decomposed using EMD (Empirical Mode Decomposition) into several IMFs (Intrinsic Mode Functions) and a trend function. The sum of all the IMF components is treated as the periodic component, and the trend function is considered the trend component, which are fitted by Fourier series function and spline interpolation, respectively. By subtracting the sum of the periodic and trend components from the urban metro section flow sequence, the error is regarded as the residual component. Finally, a GAN (generative adversarial network) based on the fusion graph convolutional neural network is used to predict the new residual component, which considers the spatial correlation between different sites of urban metro sections. The Chengdu urban metro system data in China show that the proposed model, through incorporating EMD and a generative adversarial network, achieves a 15–20% improvement in prediction accuracy at the cost of a 10% increase in the calculation time, meaning it demonstrates good prediction accuracy and reliability.
Understanding the Relevance of Sustainability in Mergers and Acquisitions—A Systematic Literature Review on Sustainability and Its Implications throughout Deal Stages
Authors: Kayser Christoph , Zülch Henning
Published: January 2024

The importance of transforming business models and activities toward a sustainable economy is more urgent than ever and manifests in the adoption of international agreements and regulatory initiatives. Company transactions, including mergers and acquisitions (M&A), need to pay attention to sustainability concepts and their implications. Consequently, the current and traditional literature on M&A processes acknowledges the role of sustainability as a prerequisite for success in M&A operations. However, reviews of the relationship between sustainability and M&A from an integrative perspective that highlight the pre- and post-deal stages are limited. To bring further transparency to this context, we perform a systematic review of the academic literature on the relevance and implications of sustainability in M&A, focusing on archival studies. We present an overview of major sustainability influences at different stages of the M&A process, using the perspective of the acquirer as well as the target of sustainability. We observe that in all analyzed pre- and post-deal stages, sustainability is identified as having an impact or being impacted by M&A activities. Accordingly, practitioners’ strategic consideration of sustainability for deal origination and performance is required. Furthermore, we highlight several understudied factors and create a research agenda, as research findings are, to some extent, heterogeneous and limited.
Investigating the Interconnection between Environmental, Social, and Governance (ESG), and Corporate Social Responsibility (CSR) Strategies: An Examination of the Influence on Consumer Behavior
Authors: Nugroho Deinera P. D. , Hsu Yi , Hartauer Christian , Hartauer Andreas
Published: January 2024

The objective of this research is to develop a structural relational model that examines the relationship between environmental, social, and governance (ESG) variables and corporate social responsibility (CSR). Additionally, the study seeks to determine the impact of these factors on consumer behavior. This research endeavor included the gathering of data from Taiwan and Indonesia, with the aim of investigating the influence of ESG practices and CSR initiatives on customer attitudes and purchase intentions. The study used STATISTA 10 and LISREL to examine the impact of environmental, social, and governance practices on CSR activities. The findings indicated that the integration of environmental practices had a positive effect on CSR initiatives, but the influence of social and governance practices exhibited variability. The research emphasizes the importance of proficiently communicating environmental policies and proposes that organizations should prioritize environmental actions in order to bolster their brand and gain customer confidence. The results also underscore the need for consumer education about the ESG framework. The research closes by emphasizing the management implications, recommending that organizations should embrace a comprehensive strategy towards ESG and CSR policies in order to enhance company performance and ensure long-term sustainability.
EcoPrintAnalyzer: Assessing Sustainability in Material Extrusion Additive Manufacturing for Informed Decision-Making
Authors: Aruanno Beatrice
Published: January 2024

Sustainability is fundamental in the field of additive manufacturing (AM) for improving eco-consciousness and driving evolution toward environmentally responsible production methods. Compared to traditional manufacturing processes, AM technologies can be more resource-efficient and offer innovative solutions for creating eco-friendly processes and products. Nevertheless, there is significant potential for improvement in additive manufacturing sustainability. The key factors driving this improvement include design optimization and increased awareness. Designers and engineers can create designs that optimize material efficiency and reduce support structures. Raising awareness and educating stakeholders about the environmental benefits of AM can promote responsible choices throughout the industrial process. The development of a tool to assess the environmental impact of AM processes could be a significant contribution to advancing sustainability in the AM field. The EcoPrintAnalyzer, introduced as a complementary plugin for UltiMaker Cura, offers data on the equivalent carbon dioxide footprint and energy consumption in material extrusion additive manufacturing. This tool facilitates informed decision-making regarding materials, designs, and settings, enabling users to optimize their AM processes for reduced waste and enhanced energy efficiency. Beyond aiding decision-making, the EcoPrintAnalyzer fosters environmental consciousness and encourages the adoption of sustainable practices within the AM ecosystem. The efficacy of the tool is demonstrated through the 3DBenchy model case study, showcasing its intuitive interface and seamless integration within the AM process workflow for immediate and comparative environmental impact assessments across different process configurations.
Degradation of Butylated Hydroxyanisole by the Combined Use of Peroxymonosulfate and Ferrate(VI): Reaction Kinetics, Mechanism and Toxicity Evaluation
Authors: Shi Peiduan , Yue Xin , Teng Xiaolei , Qu Ruijuan , Rady Ahmed , Maodaa Saleh , Allam Ahmed A. , Wang Zunyao , Huo Zongli
Published: January 2024

Butylated hydroxyanisole (BHA), a synthetic phenolic antioxidant (SPA), is now widely present in natural waters. To improve the degradation efficiency of BHA and reduce product toxicity, a combination of peroxymonosulfate (PMS) and Ferrate(VI) (Fe(VI)) was used in this study. We systematically investigated the reaction kinetics, mechanism and product toxicity in the degradation of BHA through the combined use of PMS and Fe(VI). The results showed that PMS and Fe(VI) have synergistic effects on the degradation of BHA. The effects of operational factors, including PMS dosage, pH and coexisting ions (Cl−, SO42−, HCO3−, K+, NH4+ and Mg2+), and different water matrices were investigated through a series of kinetic experiments. When T = 25 °C, the initial pH was 8.0, the initial BHA concentration was 100 μM, the initial concentration ratio of [PMS]0:[Fe(VI)]0:[BHA]0 was 100:1:1 and the degradation rate could reach 92.4% within 30 min. Through liquid chromatography time-of-flight mass spectrometry (LC-TOF-MS) identification, it was determined that the oxidation pathway of BHA caused by PMS/Fe(VI) mainly includes hydroxylation, ring-opening and coupling reactions. Density functional theory (DFT) calculations indicated that •OH was most likely to attack BHA and generate hydroxylated products. The comprehensive comparison of product toxicity results showed that the PMS/Fe(VI) system can effectively reduce the environmental risk of a reaction. This study contributes to the development of PMS/Fe(VI) for water treatment applications.
Inhibition of CYP1A1 Alleviates Colchicine-Induced Hepatotoxicity
Authors: Huang Ruoyue , Duan Jingyi , Huang Wen , Cheng Yan , Zhu Beiwei , Li Fei
Published: January 2024

Colchicine, a natural compound extracted from Colchicum autumnale, is a phytotoxin, but interestingly, it also has multiple pharmacological activities. Clinically, colchicine is widely used for the treatment of gouty arthritis, familial Mediterranean fever, cardiovascular dysfunction and new coronary pneumonia. However, overdose intake of colchicine could cause lethal liver damage, which is a limitation of its application. Therefore, exploring the potential mechanism of colchicine-induced hepatotoxicity is meaningful. Interestingly, it was found that CYP1A1 played an important role in the hepatotoxicity of colchicine, while it might also participate in its metabolism. Inhibition of CYP1A1 could alleviate oxidative stress and pyroptosis in the liver upon colchicine treatment. By regulating CYP1A1 through the CASPASE-1-GSDMD pathway, colchicine-induced liver injury was effectively relieved in a mouse model. In summary, we concluded that CYP1A1 may be a potential target, and the inhibition of CYP1A1 alleviates colchicine-induced liver injury through pyroptosis regulated by the CASPASE-1-GSDMD pathway.
New Patterns for Highly Pathogenic Avian Influenza and Adjustment of Prevention, Control and Surveillance Strategies: The Example of France
Authors: Scoizec Axelle , Niqueux Eric , Schmitz Audrey , Grasland Béatrice , Palumbo Loïc , Huneau-Salaün Adeline , Le Bouquin Sophie
Published: January 2024

From 2020 up to summer 2023, there was a substantial change in the situation concerning the high pathogenic avian influenza (HPAI) virus in Europe. This change concerned mainly virus circulation within wildlife, both in wild birds and wild mammals. It involved the seasonality of HPAI detections, the species affected, excess mortality events, and the apparent increased level of contamination in wild birds. The knock-on effect concerned new impacts and challenges for the poultry sector, which is affected by repeated annual waves of HPAI arriving with wild migratory birds and by risks due to viral circulation within resident wild birds across the year. Indeed, exceeding expectations, new poultry sectors and production areas have been affected during the recent HPAI seasons in France. The HPAI virus strains involved also generate considerable concern about human health because of enhanced risks of species barrier crossing. In this article, we present these changes in detail, along with the required adjustment of prevention, control, and surveillance strategies, focusing specifically on the situation in France.
Highly Discriminative Driver Distraction Detection Method Based on Swin Transformer
Authors: Zhang Ziyang , Yang Lie , Lv Chen
Published: January 2024

Driver distraction detection not only helps to improve road safety and prevent traffic accidents, but also promotes the development of intelligent transportation systems, which is of great significance for creating a safer and more efficient transportation environment. Since deep learning algorithms have very strong feature learning abilities, more and more deep learning-based driver distraction detection methods have emerged in recent years. However, the majority of existing deep learning-based methods are optimized only through the constraint of classification loss, making it difficult to obtain features with high discrimination, so the performance of these methods is very limited. In this paper, to improve the discrimination between features of different classes of samples, we propose a high-discrimination feature learning strategy and design a driver distraction detection model based on Swin Transformer and the highly discriminative feature learning strategy (ST-HDFL). Firstly, the features of input samples are extracted through the powerful feature learning ability of Swin Transformer. Then, the intra-class distance of samples of the same class in the feature space is reduced through the constraint of sample center distance loss (SC loss), and the inter-class distance of samples of different classes is increased through the center vector shift strategy, which can greatly improve the discrimination of different class samples in the feature space. Finally, we have conducted extensive experiments on two publicly available datasets, AUC-DD and State-Farm, to demonstrate the effectiveness of the proposed method. The experimental results show that our method can achieve better performance than many state-of-the-art methods, such as Drive-Net, MobileVGG, Vanilla CNN, and so on.
Quantitative Detection of VBNC State Pseudomonas aeruginosa Contributing to Accurate Assessment of Microbial Inactivation in Drinking Water Disinfection
Authors: Fan Zhiheng , Zhu Huichao , Tao Chen , Deng Ning , Huang Xin
Published: January 2024

In harsh environments, bacteria often enter a viable but nonculturable (VBNC) state, which cannot be detected using heterotrophic plate counting (HPC). Importantly, VBNC bacteria can potentially resuscitate under favorable conditions, posing a risk to drinking water safety. This study introduces an innovative approach, combining improved quantitative polymerase chain reaction (qPCR) with propidium monoazide (PMA) dye and HPC to accurately quantify VBNC Pseudomonas aeruginosa (P. aeruginosa). The method was applied to assess the ability of various disinfection techniques to induce P. aeruginosa into the VBNC state. Different disinfection methods, including ultraviolet radiation (UV), sodium hypochlorite (NaClO), and peracetic acid (PAA), significantly reduced bacterial culturability (>99.9%), with the majority entering the VBNC state. Notably, under favorable conditions, UV-induced VBNC cells were resuscitated faster than those induced by NaClO. VBNC P. aeruginosa exhibited relatively high intracellular adenosine triphosphate (ATP) levels, indicating ongoing metabolic activity. Scanning electron microscopy (SEM) reveals that some bacteria maintained cellular integrity for UV and PAA treatment, while evident membrane disruption was observed after NaClO disinfection. This study represents a significant advancement in quantitatively detecting VBNC state P. aeruginosa, contributing to an accurate assessment of microbial inactivation during drinking water disinfection.
Integrated Use of GIS and USLE Models for LULC Change Analysis and Soil Erosion Risk Assessment in the Hulan River Basin, Northeastern China
Authors: Cheng Junhui , Zhang Xiaohong , Jia Minghui , Su Quanchong , Kong Da , Zhang Yixin
Published: January 2024

The Hulan River Basin is located in the black soil region of northeast China. This region is an important food-producing area and the susceptibility of black soil to erosion increases the risk of soil erosion, which is a serious environmental problem that affects agricultural productivity, water supply, and other important aspects of the region. In this paper, the changes in LULC (land use and land cover) in the basin between 2001 and 2020 were thoroughly analysed using GIS (geographic information system) and USLE (universal soil loss equation) models. The soil erosion risk in the Hulan River Basin between 2001 and 2020 was also studied and soil erosion hot spots were identified to target those that remained significant even under the implementation of soil conservation measures. Precipitation data were used to obtain the R factor distribution, LULC classification was adopted to assess the C factor distribution, soil data were employed to estimate the K factor distribution, DEM (Digital Elevation Model) data were used to generate an LS factor map, and slope and LULC data were considered to produce a P factor distribution map. These factors were based on the model parameters of the USLE. The findings of LULC change analysis over the last 20 years indicated that, while there have been nonobvious changes, agricultural land has continued to occupy the bulk of the area in the Hulan River Basin. The increase in areas used for human activities was the most notable trend. In 2001, the model-predicted soil erosion rate varied between 0 and 120 t/ha/yr, with an average of 4.63 t/ha/yr. By 2020, the estimated soil erosion rate varied between 0 and 193 t/ha/yr, with an average of 7.34 t/ha/yr. The Hulan River Basin was classified into five soil erosion risk categories. Most categories encompassed extremely low-risk levels and, over the past 20 years, the northeastern hilly regions of the basin have experienced the highest concentration of risk change areas. The northeastern hilly and mountainous regions comprised the risk change area and the regions that are most susceptible to erosion exhibited a high concentration of human production activities. In fact, the combined use of GIS and USLE modelling yielded erosion risk areas for mapping risk classes; these results could further assist local governments in improving soil conservation efforts.
Water Governance in the Cambodian Mekong Delta: The Nexus of Farmer Water User Communities (FWUCs), Community Fisheries (CFis), and Community Fish Refuges (CFRs) in the Context of Climate Change
Authors: Sithirith Mak , Sao Sok , de Silva Sanjiv , Kong Heng , Kongkroy Chay , Thavrin Tim , Sarun Hy
Published: January 2024

Cambodia faces the challenge of managing excess water during the wet season and insufficient water during the dry season. This harms human life and endangers aquatic and natural resources, agricultural practices, and food security. Water governance is crucial to ensure the well-being of both people and their food security. However, Cambodia’s water governance is hindered by various obstacles, including sectoral and centralized influences, top-down and large-scale strategies, weak coordination among relevant agencies, and limited involvement of local communities. This study examines water governance across different sectors, from centralized to community-based natural resources management, and explores the opportunities that can be done to improve water governance. This study undertakes the literature and case studies of farmer water user communities (FWUCs), community fisheries (CFis), and community fish refuges (CFRs) in three Mekong Delta provinces in Cambodia. This study concludes that water governance has been challenged by FWUCs competing for water resources to intensify rice production at the expense of increased pesticides and fertilizer uses, which undermine the fishery productivity, degrade the natural resources in rivers and water bodies, and increase water conflicts among farmers and sectors in the face of climate change. To enhance water governance in Cambodia, it is critical to integrate it at the district level. This will promote sustainable water use and management across the country and pave the way for a brighter future.