bluuebunny/crossref_metadata_2025 huggingface dataset:
Robust Dual Spatial Weighted Sparse Unmixing for Remotely Sensed Hyperspectral Imagery
Authors: Deng Chengzhi , Chen Yonggang , Zhang Shaoquan , Li Fan , Lai Pengfei , Su Dingli , Hu Min , Wang Shengqian
Published: August 2023

Sparse unmixing plays a crucial role in the field of hyperspectral image unmixing technology, leveraging the availability of pre-existing endmember spectral libraries. In recent years, there has been a growing trend in incorporating spatial information from hyperspectral images into sparse unmixing models. There is a strong spatial correlation between pixels in hyperspectral images (that is, the spatial information is very rich), and many sparse unmixing algorithms take advantage of this to improve the sparse unmixing effect. Since hyperspectral images are susceptible to noise, the feature separability of ground objects is reduced, which makes most sparse unmixing methods and models face the risk of degradation or even failure. To address this challenge, a novel robust dual spatial weighted sparse unmixing algorithm (RDSWSU) has been proposed for hyperspectral image unmixing. This algorithm effectively utilizes the spatial information present in the hyperspectral images to mitigate the impact of noise during the unmixing process. For the proposed RDSWSU algorithm, which is based on ℓ1 sparse unmixing framework, a pre-calculated superpixel spatial weighting factor is used to smooth the noise, so as to maintain the original spatial structure of hyperspectral images. The RDSWSU algorithm, which builds upon the ℓ1 sparse unmixing framework, employs a pre-calculated spatial weighting factor at the superpixel level. This factor aids in noise smoothing and helps preserve the inherent spatial structure of hyperspectral images throughout the unmixing process. Additionally, another spatial weighting factor is utilized in the RDSWSU algorithm to capture the local smoothness of abundance maps at the sub-region level. This factor helps enhance the representation of piecewise smooth variations within different regions of the hyperspectral image. Specifically, the combination of these two spatial weighting factors in the RDSWSU algorithm results in an enhanced sparsity of the abundance matrix. The RDSWSU algorithm, which is a sparse unmixing model, offers an effective solution using the alternating direction method of multiplier (ADMM) with reduced requirements for tuning the regularization parameter. The proposed RDSWSU method outperforms other advanced sparse unmixing algorithms in terms of unmixing performance, as demonstrated by the experimental results on synthetic and real hyperspectral datasets.
Application of Medical Image Navigation Technology in Minimally Invasive Puncture Robot
Authors: Hu Shuai , Lu Rongjian , Zhu Yinlong , Zhu Wenhan , Jiang Hongzhe , Bi Suzhao
Published: August 2023

Microneedle puncture is a standard minimally invasive treatment and surgical method, which is widely used in extracting blood, tissues, and their secretions for pathological examination, needle-puncture-directed drug therapy, local anaesthesia, microwave ablation needle therapy, radiotherapy, and other procedures. The use of robots for microneedle puncture has become a worldwide research hotspot, and medical imaging navigation technology plays an essential role in preoperative robotic puncture path planning, intraoperative assisted puncture, and surgical efficacy detection. This paper introduces medical imaging technology and minimally invasive puncture robots, reviews the current status of research on the application of medical imaging navigation technology in minimally invasive puncture robots, and points out its future development trends and challenges.
Wavelet Analysis of Respiratory Muscle sEMG Signals during the Physiological Breakpoint of Static Dry End-Expiratory Breath-Holding in Naive Apneists: A Pilot Study
Authors: Mišić Nataša Ž. , Ostojić Mirko , Cvetković Saša , Miodragović Petar , Aničić Zdravko , Kovačić Popović Anita , Stefanović Đorđe
Published: August 2023

The wavelet spectral characteristics of three respiratory muscle signals (scalenus (SC), parasternal intercostal (IC), and rectus abdominis (RA)) and one locomotor muscle (brachioradialis (BR)) were analyzed in the time–frequency (T-F) domain during voluntary breath-holding (BH), with a focus on the physiological breakpoint that is commonly considered the first involuntary breathing movement (IBM) that signals the end of the easy-going phase of BH. The study was performed for an end-expiratory BH physiological breaking point maneuver on twelve healthy, physically active, naive breath-holders/apneists (six professional athletes; six recreational athletes, and two individuals in the post-COVID-19 period) using surface electromyography (sEMG). We observed individual effects that were dependent on muscle oxygenation and each person’s fitness, which were consistent with the mechanism of motor unit (MU) recruitment and the transition of slow-twitch oxidative (type 1) to fast-twitch glycolytic (type 2) muscle fibers. Professional athletes had longer BH durations (BHDs) and strong hypercapnic responses regarding the expiratory RA muscle, which is activated abruptly at higher BHDs in a person-specific range below 250 Hz and is dependent on the BHD. This is in contrast with recreational athletes, who had strong hypoxic responses regarding inspiratory IC muscle, which is activated faster and gradually in the frequency range of 250–450 Hz (independent of the person and BHD). This pilot study preliminarily indicates that it is possible to noninvasively assess the physiological characteristics of skeletal muscles, especially oxygenation, and improve physical fitness tests by determining the T-F features of elevated myoelectric IC and RA activity during BH.
A Residual-Dense-Based Convolutional Neural Network Architecture for Recognition of Cardiac Health Based on ECG Signals
Authors: Ahmed Alaa E. S. , Abbas Qaisar , Daadaa Yassine , Qureshi Imran , Perumal Ganeshkumar , Ibrahim Mostafa E. A.
Published: August 2023

Cardiovascular disorders are often diagnosed using an electrocardiogram (ECG). It is a painless method that mimics the cyclical contraction and relaxation of the heart’s muscles. By monitoring the heart’s electrical activity, an ECG can be used to identify irregular heartbeats, heart attacks, cardiac illnesses, or enlarged hearts. Numerous studies and analyses of ECG signals to identify cardiac problems have been conducted during the past few years. Although ECG heartbeat classification methods have been presented in the literature, especially for unbalanced datasets, they have not proven to be successful in recognizing some heartbeat categories with high performance. This study uses a convolutional neural network (CNN) model to combine the benefits of dense and residual blocks. The objective is to leverage the benefits of residual and dense connections to enhance information flow, gradient propagation, and feature reuse, ultimately improving the model’s performance. This proposed model consists of a series of residual-dense blocks interleaved with optional pooling layers for downsampling. A linear support vector machine (LSVM) classified heartbeats into five classes. This makes it easier to learn and represent features from ECG signals. We first denoised the gathered ECG data to correct issues such as baseline drift, power line interference, and motion noise. The impacts of the class imbalance are then offset by resampling techniques that denoise ECG signals. An RD-CNN algorithm is then used to categorize the ECG data for the various cardiac illnesses using the retrieved characteristics. On two benchmarked datasets, we conducted extensive simulations and assessed several performance measures. On average, we have achieved an accuracy of 98.5%, a sensitivity of 97.6%, a specificity of 96.8%, and an area under the receiver operating curve (AUC) of 0.99. The effectiveness of our suggested method for detecting heart disease from ECG data was compared with several recently presented algorithms. The results demonstrate that our method is lightweight and practical, qualifying it for continuous monitoring applications in clinical settings for automated ECG interpretation to support cardiologists.
Binary Hunter–Prey Optimization with Machine Learning—Based Cybersecurity Solution on Internet of Things Environment
Authors: Khadidos Adil O. , AlKubaisy Zenah Mahmoud , Khadidos Alaa O. , Alyoubi Khaled H. , Alshareef Abdulrhman M. , Ragab Mahmoud
Published: August 2023

Internet of Things (IoT) enables day-to-day objects to connect with the Internet and transmit and receive data for meaningful purposes. Recently, IoT has resulted in many revolutions in all sectors. Nonetheless, security risks to IoT networks and devices are persistently disruptive due to the growth of Internet technology. Phishing becomes a common threat to Internet users, where the attacker aims to fraudulently extract confidential data of the system or user by using websites, fictitious emails, etc. Due to the dramatic growth in IoT devices, hackers target IoT gadgets, including smart cars, security cameras, and so on, and perpetrate phishing attacks to gain control over the vulnerable device for malicious purposes. These scams have been increasing and advancing over the last few years. To resolve these problems, this paper presents a binary Hunter–prey optimization with a machine learning-based phishing attack detection (BHPO-MLPAD) method in the IoT environment. The BHPO-MLPAD technique can find phishing attacks through feature selection and classification. In the presented BHPO-MLPAD technique, the BHPO algorithm primarily chooses an optimal subset of features. The cascaded forward neural network (CFNN) model is employed for phishing attack detection. To adjust the parameter values of the CFNN model, the variable step fruit fly optimization (VFFO) algorithm is utilized. The performance assessment of the BHPO-MLPAD method takes place on the benchmark dataset. The results inferred the betterment of the BHPO-MLPAD technique over compared approaches in different evaluation measures.
Methods and Applications of 3D Ground Crop Analysis Using LiDAR Technology: A Survey
Authors: Micheletto Matias J. , Chesñevar Carlos I. , Santos Rodrigo
Published: August 2023

Light Detection and Ranging (LiDAR) technology is positioning itself as one of the most effective non-destructive methods to collect accurate information on ground crop fields, as the analysis of the three-dimensional models that can be generated with it allows for quickly measuring several key parameters (such as yield estimations, aboveground biomass, vegetation indexes estimation, perform plant phenotyping, and automatic control of agriculture robots or machinery, among others). In this survey, we systematically analyze 53 research papers published between 2005 and 2022 that involve significant use of the LiDAR technology applied to the three-dimensional analysis of ground crops. Different dimensions are identified for classifying the surveyed papers (including application areas, crop species under study, LiDAR scanner technologies, mounting platform technologies, and the use of additional instrumentation and software tools). From our survey, we draw relevant conclusions about the use of LiDAR technologies, such as identifying a hierarchy of different scanning platforms and their frequency of use as well as establishing the trade-off between the economic costs of deploying LiDAR and the agronomically relevant information that effectively can be acquired. We also conclude that none of the approaches under analysis tackles the problem associated with working with multiple species with the same setup and configuration, which shows the need for instrument calibration and algorithmic fine tuning for an effective application of this technology.
The Global Institutionalization of Multicultural Education as an Academic Discourse
Authors: Lee Saerom , Cha Yun-Kyung , Ham Seung-Hwan
Published: August 2023

Multicultural education has been widely recognized as an educational approach to deal with social and cultural diversity towards a more inclusive and just society. Conventional perspectives tend to assume that multicultural education would be of greater interest as a research topic in countries with growing levels of diversity. However, based on a macro-phenomenological perspective, this study accounts for influences from the wider institutional environment that gives collective meaning and value to legitimize multiculturalism as an academic discourse topic. Using a cross-national research design, this study examined the national-level characteristics associated with the formation of academic discourse on multicultural education. Scholarly articles on multicultural education published in the field of education by 2020 were collected using the research platform Web of Science. A total of 105 countries with 14,220 articles were analyzed using multiple regression analysis. Our results showed that countries with stronger ties to global civil society were more likely to have articles on multicultural education, indicating a higher institutionalization level of relevant academic discourse within the country. These findings suggest that the popularity of multicultural education as an academic discourse may not solely be in response to national-level societal demands but rather may be an institutional embodiment of universalistic norms and values.
A Sustainable Way to Determine the Water Content in Torreya grandis Kernels Based on Near-Infrared Spectroscopy
Authors: Xiang Jiankai , Huang Yu , Guan Shihao , Shang Yuqian , Bao Liwei , Yan Xiaojie , Hassan Muhammad , Xu Lijun , Zhao Chao
Published: August 2023

Water content is an important parameter of Torreya grandis (T. grandis) kernels that affects their quality, processing and storage. The traditional drying method for water content determination is time-consuming and laborious. Water content detection based on modern analytical techniques such as spectroscopy is accomplished in a fast, accurate, nondestructive, and sustainable way. The aim of this study was to realize the rapid detection of the water content in T. grandis kernels using near-infrared spectroscopy. The water content of T. grandis kernels was measured by the traditional drying method. Meanwhile, the corresponding near-infrared spectra of these samples were collected. A quantitative water content model of T. grandis kernels was established using the full spectrum after 10 outlier samples were removed by the Mahalanobis distance method and concentration residual analysis. The results showed that the prediction model developed from the partial least squares regression (PLS) method after the spectra were pretreated by the standard normal variate transform (SNV) achieved optimal performance. The correlation coefficient of the calibration set (R2c) and the cross-validation set (R2cv) were 0.9879 and 0.9782, respectively, and the root mean square error of the calibration set (RMSEC) and the root mean square error of the cross-validation set (RMSECV) were 0.0029 and 0.0039, respectively. Thus, near-infrared spectroscopy is feasible for the rapid nondestructive detection of the water content in T. grandis seeds. Detecting the water content of agricultural and forestry products in such an environmentally friendly manner is conducive to the sustainable development of agriculture.
Spatial Pattern Evolution of the Manufacturing Industry in the Yangtze River Economic Belt and Its Impact on PM2.5
Authors: Liu Yan , Cheng Yu , Wang Dan , Zhao Hongxiao , Wang Yaping
Published: August 2023

Instead of being merely an important embodiment of regional productivity, the manufacturing industry also serves as a significant sector of economic operation and the supply chain system that is highly dependent on resources and the environment. Studying the spatial pattern of the manufacturing industry and its environmental effect is extremely significant for optimizing the spatial layout of urban industry, allocating production factors in a rational manner, and promoting the green transformation of industry. In this regard, this study aimed to further reveal the spatial pattern characteristics of the regional manufacturing industry and its impact on PM2.5. Using data from micro-enterprises in the manufacturing industry in the Yangtze River Economic Belt, its spatial pattern characteristics are explored and an econometric model is constructed to analyze the impact of the manufacturing industry on PM2.5 by comprehensively applying approaches including kernel density estimation, nearest proximity index, and Dagum Gini coefficient decomposition. Three research conclusions were drawn: (1) an obvious “core-edge” feature is present in the spatial distribution of the manufacturing industry in the studied area showing an apparent pattern of “high in the east and low in the west”. The core density of the manufacturing industry in the central cities is significantly higher than that in the surrounding cities. (2) In the manufacturing industry and its subdivisions, the characteristics of spatial agglomeration are unveiled, while the agglomeration and spatial differences are diminished during the study period for the spatial equilibrium of the manufacturing industry. (3) A significantly positive impact is exerted on PM2.5 pollution that is not limited to local cities by the manufacturing industry, which, due to the development differences within the study region, is also heterogeneous. In view of this, policy proposals for aspects such as forging a green manufacturing cluster area, establishing an industrial integration development platform, giving play to regional advantages and technological potential, etc., are put forward in this study, so as to provide a useful reference for optimizing the industrial pattern and promoting the green transformation of industries.
Spatiotemporal Patterns and Driving Factors of the Ecological Environmental Quality along the Jakarta–Bandung High-Speed Railway in Indonesia
Authors: Dai Xin , Chen Jianping , Xue Chenli
Published: August 2023

Economic globalization and rapid urbanization have affected ecological environments in global regions to different degrees. Timely, objective and quantitative evaluations of the spatiotemporal variations in regional ecological environmental quality are the key to environmental protection and decision making. The spatial distributions of ecological environment quality levels along the Jakarta–Bandung high-speed railway from 2000 to 2020 were obtained based on Google Earth Engine and the Remote Sensing Ecological Index (RSEI). Then, the Theil–Sen median and Mann-Kendall methods were used to assess the temporal RSEI trend; the spatial autocorrelation evaluation index was used to evaluate RSEI clustering on a spatial scale. The results show that the overall ecological environmental quality from 2000 to 2020 was moderate, with temporally fluctuating changes and very significant spatial clustering. Approximately 20% of the area showed very strong changes (improvement or degradation). Areas with relatively better ecological quality were located mainly in relatively high-elevation and high-vegetation-coverage regions, while areas with poorer ecological quality were located mainly in the four major cities, including Jakarta, with concentrated populations and developed economies. The research results provide local governments with scientific suggestions regarding the synergistic development of high-speed railway construction and ecological environmental protection.
Circulating the E-Waste Recovery from the Construction and Demolition Industries: A Review
Authors: Farjana Shahjadi Hisan , Mungombe Tatenda Miriam , Gamage Hasith Madhumahda Kahanda , Rajwani Anmol Sarfraj , Tokede Olubukola , Ashraf Mahmud
Published: August 2023

E-waste or electronic waste uses electrical power from a power cord/plug/battery. Construction and demolition (C&D) industries use various electronic components such as cables, switches, sockets, electrical heat pumps, air conditioning systems, and solar panels, which become e-waste at the end-of-life-cycle stages. E-waste contains valuable metals/non-metals/plastics that are recoverable and recyclable. E-waste disposal is banned from landfills in Victoria (Australia), because of their toxic components that require an additional waste separation process to avoid considerable environmental emissions and costs of separation and safe disposal. This paper aims to review the alternative circularity scenarios for recoverable materials from e-waste the C&D industries. Alternative scenarios for e-waste handling and management originating from the C&D industries are assessed in the current study. We identify and assess the important circularity indicators and waste management steps that would drive towards the identification of future initiatives or policy development to increase the resource recovery from e-waste. The policies would help to advocate for policy development for the C&D industries’ e-wastes.
Is There a Conflict between Automation and Environment? Implications of Artificial Intelligence for Carbon Emissions in China
Authors: Xu Xianpu , Song Yuchen
Published: August 2023

While artificial intelligence (AI) has had a great impact on the global economy, it has also brought new hope and opportunities for environmental protection. In this context, the authors of this paper collected balanced panel data for 30 Chinese provinces during 2006–2019 and studied the impact of AI development on local carbon emissions by using a two-way fixed-effect model. The results show that AI has significantly lowered carbon emissions. Using a series of robustness tests and instrumental variable (IV) analysis, it was found that the results are still reliable. Furthermore, mechanism analysis revealed that AI mainly reduces carbon emissions by improving energy structure and technological innovation. The lower the dependence on fossil energy, the higher technological innovation becomes, and the better the carbon reduction effect of AI. In addition, the regional heterogeneity test detected that the emission reduction effect of AI is best in the East, followed by the West, and not significant in the Central region. Therefore, to fully exploit the positive effects of AI on carbon emissions, this paper suggests accelerating intelligent transformation, formulating differentiated AI development strategies, promoting the green transformation of energy usage, and strengthening local human capital accumulation.
Influence of Customer Perception Factors on AI-Enabled Customer Experience in the Ecuadorian Banking Environment
Authors: Tulcanaza-Prieto Ana Belen , Cortez-Ordoñez Alexandra , Lee Chang Won
Published: August 2023

This study reviews the relationship between customer perception factors and AI-enabled customer experience in the Ecuadorian banking industry. The study employs a self-designed online questionnaire with five factors for customer perception (convenience in use, personalization, trust, customer loyalty, and customer satisfaction) and two categories for AI-enabled customer experience (AI-hedonic customer experience and AI-recognition customer service). The final valid dataset consisted of 226 questionnaires. The data analysis and the hypotheses tests were conducted using SPSS 26 and structural equation modeling, respectively. The main findings displayed that all five customer perception factors (individual and joint effect) have a positive and significant effect (at least at the 5% level) on AI-enabled customer experience, AI-hedonic customer experience, and AI-recognition customer service in the Ecuadorian banking industry. Study results are aligned with previous findings from other countries, particularly the banking environment in the United Kingdom, Canada, Nigeria, and Vietnam. The AI techniques involved in the financial sector increase the valuation of customer experience due to AI algorithms recollecting, processing, and analyzing customer behavior. This study contributes a complete statistical and econometric model for determinants of AI-enabled customer experience. The main limitations of the study are that, in the analysis of the most demanded AI financial services, not all services and products are included and the inexistence of a customer perception index. For upcoming research, the authors recommend performing a longitudinal study using quantitative data to measure the effect of AI-enabled customer experience on the Ecuadorian banks’ performance.
A Review of the Improvements in the Performance and Stability of Ternary Semi-Transparent Organic Solar Cells: Material and Architectural Approaches
Authors: Amin Peshawa O. , Muhammadsharif Fahmi F. , Saeed Salah Raza , Ketuly Kamal Aziz
Published: August 2023

In the past decade, considerable efforts have been made to develop semi-transparent organic solar cells (ST-OSCs). Different materials and architectures were examined with the aim of commercializing these devices. Among these, the use of ternary active layers demonstrated great promise for the development of efficient semi-transparent organic solar cells with the potential for future applications, including but not limited to self-powered greenhouses and powered windows. Researchers seek alternative solutions to trade-off between the power conversion efficiency (PCE) and average visible transmittance (AVT) of ST-OSCs, with photoactive materials being the key parameters that govern both (PCE) and (AVT), as well as device stability. Several new organic materials, including polymers and small molecules, were synthesized and used in conjunction with a variety of techniques to achieve semi-transparent conditions. In this review paper, we look at the working principle and key parameters of semi-transparent organic solar cells, as well as the methods that have been used to improve the performance and stability of ternary-based semi-transparent organic solar cells. The main approaches were concluded to be spectral enhancement and increments in the transparency of the active layer through band gap tuning, utilizing novel organic semi-conductors, optical engineering, and the design architecture of the active layers.
Development and Characterization of Coal-Based Thermoplastic Composite Material for Sustainable Construction
Authors: Zhang Haibin , Liao Wenyu , Chen Genda , Ma Hongyan
Published: August 2023

The exploitation of coal and the disposal of waste plastic present significant environmental and economic challenges that require sustainable and profitable solutions. In response, we propose a renewable construction composite material of coal-based thermoplastic composite (CTC) that can be made from low-grade coal and plastic waste. We developed and tested the hot-press fabrication method for this CTC, using coal with a maximum particle size of 4.75 mm and recycled high-density polyethylene (HDPE). The effects of the coal fraction (50–80 wt%) on compressive properties, thermal properties, microstructure, and ecological and economic efficiencies of the CTC were investigated. Test results revealed that the compressive strength and modulus decrease as the coal fraction increases. However, the thermal properties, including thermal conductivity and specific heat, increase with higher coal contents. Compared to concrete, the CTC has about half the thermal conductivity and twice the specific heat, making it a more energy-efficient construction material. Microstructure testing helped to reveal the mechanisms behind the above behaviors of CTC from the observation of binder volume, bonding quality between coal and HDPE, and porosity variation. The life cycle analysis indicated that the CTC production reduced embodied energy, carbon footprint, and cost by up to 84%, 73%, and 14%, respectively. Therefore, we recommend the CTC with 50–70% coal fraction as an innovative construction material with satisfied mechanical and thermal properties, better cost efficiency, and a reduced ecological impact.
Revisiting Traditional Fishing Rights: Sustainable Fishing in the Historic and Legal Context
Authors: Wang Weikang , Xue Guifang
Published: August 2023

Poor fishing practices and overfishing are now imperiling livelihoods on small-scale fishing. Traditional fishing rights as one of the legal guarantees for small-scale artisanal fishers under SDG 14 may be abused in various maritime zones, which is precisely because such rights are not well-reflected in the United Nations Convention on the Law of the Sea (UNCLOS), leading to uncertainty between theories and practice. In order to better implement SDG 14 and its targets for sustainable fishing, this paper examines the practical meaning of traditional fishing rights through tracing back the origins, nature and legal elements of such fishing rights by jurisprudence and state practice, and it differentiates its distinctions between ‘historic rights’. Based on this, the paper analyzes the application of these fishing rights in different maritime zones and suggests sustainable ways of making a balance between the jurisprudence and practice for a healthy ocean.
The Effect of Company Ownership on the Environmental Practices in the Supply Chain: An Empirical Approach
Authors: Rivera Liliana , Ortiz Norma , Moreno Gabriel , Páez-Gabriunas Iliana
Published: August 2023

Investors are increasingly drawn to ESG-based investing because they seek well-run businesses, believing that companies integrating ESG factors are better managed. However, the impact of company ownership on environmental performance remains unclear. This study aims to address this gap by examining the relationship between company ownership and company interest in measuring the environmental impact of its supply chains, as the environmental aspect is directly linked to supply chain activities and has quantifiable measures. Using random effects ordered logistic regression on panel data from 2017 to 2022 for 2811 companies, we show that companies with long-term investment sources demonstrate a greater interest in measuring environmental variables in their supply chain compared to those financed with short-term investments. Sovereign wealth funds and other long-term investment sources exhibit a positive and significant correlation with higher utilization of ESG indicators in the supply chain. These findings suggest that policymakers and private companies aiming to enhance sustainability should prioritize longer-term investment sources, which display stronger commitments to sustainability and ESG practices and are more likely to use environmental initiatives in their supply chains.
A Study on the Influence of Double Tunnel Excavations on the Settlement Deformation of Flood Control Dikes
Authors: Zhao Qingxiong , Li Kaihui , Cao Ping , Liu Yinzhu , Pang Yongkang , Liu Jingshuo
Published: August 2023

This article combines numerical simulation and field monitoring methods to study the stability of the overlying Liuyang River embankment in the tunnel crossing between Huaqiao Station and Rice Museum Station of Changsha Metro’s Line 6. Using AutoCAD, 3Dmine, and COMSOL Multiphysics, a calculation model of the entire subway tunnel section crossing the flood control embankment under the coupling of fluids and solids was established. The process of tunnel-crossing the embankment and the variation in spatial displacement and plastic strain in different geological layers were analyzed from the perspective of time evolution and spatial distribution. The research results show that during the process of crossing the embankment, the deformation of the east bank is greater than that of the west bank, and crossing the west bank is the relatively riskier stage of the entire project. Moreover, during the process of crossing the embankment, the overlying soil layer will produce a plastic strain zone, and only a small amount of plastic strain is generated in the surrounding sandstone layer of the tunnel walls. In terms of the magnitude of plastic strain, the plastic strain area produced by the leading tunnel’s surrounding rocks is larger than that of the following tunnel. As the excavation progresses, a funnel-shaped settlement displacement gradually forms during the passage of the leading tunnel, and this settlement funnel gradually expands during the passage of the following tunnel, with the maximum settlement point transitioning from directly above the leading tunnel to the middle position between the two tunnels. Using the jitter filter algorithm and the adjacent average method to process the field monitoring data, the results show that the monitored deformation results well match the simulated settlement results.
Benefits and Barriers of Implementing Building Information Modeling Techniques for Sustainable Practices in the Construction Industry—A Comprehensive Review
Authors: Datta Shuvo Dip , Tayeh Bassam A. , Hakeem Ibrahim Y. , Abu Aisheh Yazan I.
Published: August 2023

The benefits and barriers of implementing building information modeling (BIM) and sustainability have all been the subject of numerous studies that have been performed both separately and in pairs. Despite this, there are presently no studies that include both of these ideas. This paper aims to integrate various technologies, methodologies, and concepts to close this gap specific to the architecture, engineering, and construction (AEC) sectors by outlining how concepts could coexist and support one another. To that goal, a thorough literature study was conducted to determine how recently academics had investigated the synergies between these fields. Results point to synergies, mostly the benefits and barriers of BIM in the sustainable construction sector. After the literature review, 46 identified factors associated with benefits and 21 factors associated with barriers were obtained. Among the factors, “Promoting carbon emission reduction” and “Enhancing material wastage reduction” are the top environmental benefits of implementing BIM in sustainable construction projects. The popular economic benefits were “Improving design efficiency”, “Reducing the overall project costs”, and “Promoting productivity” and the most important social benefit was “Enhancing project safety and health performance”. On the other hand, the lack of experts was the major barrier to BIM implementation in sustainable construction projects. Thus, the findings assist the BIM and sustainability integration’s benefits and barriers for a better and sustainable construction industry.
Competitiveness Evaluation of Express Delivery Enterprises Based on the Information Entropy and Gray Correlation Analysis
Authors: Deng Hongxing , Wen Wen , Zhou Jie
Published: August 2023

With the rise of e-commerce in China, the express delivery industry has developed rapidly; however, in this stage of rapid growth, the development of express delivery enterprises is uneven. There are problems such as high loss, damage rates, and poor service attitudes. An evaluation of the competitiveness of express delivery enterprises can help these companies understand their shortcomings and learn from each other’s strengths, which can promote the long-term development of express delivery enterprises. In this study, the information entropy method and the gray prediction method were used to establish an index system and analyze the core competitiveness of four listed express delivery companies in China, using indicators such as service quality, price level, market position, guarantee ability, and comprehensive strength; the purpose was to study the competitive advantages of these express companies. By using the gray correlation analysis to calculate the degree of correlation and comparing the size of the degree of correlation, it can be concluded that among the four express delivery companies, ZTO Express shows the strongest competitiveness and that Shentong Express is the weakest. Through the evaluation and analysis of the competitiveness of express delivery enterprises, this study provides a reference basis for operation development and competitiveness improvement of express delivery enterprises in China.
Life Cycle Assessment of PLA Products: A Systematic Literature Review
Authors: Fonseca Ana , Ramalho Edgar , Gouveia Ana , Figueiredo Filipa , Nunes João
Published: August 2023

The rising concerns about environmental harm and pollution create a setting for the search for better materials to produce more sustainable products. Plastic plays a crucial role in modern life and most of the commonly used are of fossil origin. Polylactic Acid (PLA) has been appointed as a more sustainable alternative, due to its origins in biodegradable raw materials. This paper aims to review scientific research, where Life Cycle Assessment (LCA) is performed on this material, in order to further understand the environmental impacts and to assess whether it is a more viable option when compared to the most commonly used plastics. A systematic literature review of 81 LCA studies focused on the LCA of PLA products was conducted. An assessment of key aspects, including the system boundaries, raw materials origin, and quantitative analysis of five environmental impact categories was performed. In this comparative analysis, in addition to presenting the results for PLA products, they are also compared with other fossil-based plastics. This leads to the conclusion that PLA has higher environmental impacts on Marine Eutrophication, Freshwater Eutrophication, and Human Toxicity, which are mainly related to the agricultural phase of growing the raw materials for PLA production. For Climate Change, Polystyrene (PS) presents the higher Greenhouse Gas (GHG) emissions, and for the Ozone Layer Depletion category, Polyethylene terephthalate (PET) presents the higher impact. PLA is a solution to replace fossil plastics. However, the use of alternative biomass sources without competition with the feed and food sector could be a key option for biobased materials production, with lower environmental and socioeconomic impacts. This will be a pathway to reduce environmental impacts in categories such as climate change, marine eutrophication, and freshwater eutrophication.
Drifting from the Sustainable Development Goal: Style Drift in ESG Funds
Authors: He Zehua , Hu Kexin , Li Zhongfei
Published: August 2023

In this paper, we investigate the impact of fund style drift away from sustainability on future fund flows and performance within the Chinese market. Utilizing data from four mainstream ESG rating systems, we develop a novel measure of ESG fund style drift, which enables us to quantify the deviation of a fund’s actual style from its declared sustainable objectives. Our analysis, based on panel and logistic regression techniques, reveals a significant positive relationship between ESG-drift and ESG fund’s flow-performance sensitivity, with a dominant effect on fund flow from individual investors. Interestingly, compared with ESG funds that stick with their sustainability-oriented objectives, funds experiencing ESG-drift exhibit poorer returns, underperformance, smaller fund size, lower subscription rates, lower industry concentration, and lower fund flows, highlighting the crucial role of funds following objectives that prioritize sustainability. However, the ESG-drift does not significantly influence the fund’s future performance. This paper provides pivotal insights into the complex dynamics between a fund’s ESG commitment and its actual style, with important implications for enhancing ESG policies within the regulatory framework of the Chinese mutual fund market.
Conditional Uncertainty Distribution of Two Uncertain Variables and Conditional Inverse Uncertainty Distribution
Authors: Wang Lihui , Ning Yufu , Chen Xiumei , Chen Shukun , Huang Hong
Published: August 2023

It is noted that some uncertain variables are independent while others are not. In general, there is a symmetrical relationship between independence and dependence among uncertain variables. The utilization of conditional uncertain measures as well as conditional uncertainty distributions proves highly efficacious in resolving uncertainties pertaining to an event subsequent to the acquisition of knowledge about other events. In this paper, the theorem about the conditional uncertainty distribution of two uncertain variables is proposed. It is demonstrated that the theorem holds regardless of whether the two variables are independent or not. In addition, it is also found that uncertainty distribution possesses an inherent inverse function when it is a regular uncertainty distribution within the framework of Uncertainty Theory; therefore, this paper delves into investigating the conditional inverse uncertainty distribution, including specific cases of the conditional inverse uncertainty distributions. Meanwhile, illustrative examples are applied to clarify the findings.
Rippled Graphene as an Ideal Spin Inverter
Authors: Buša Ján , Pudlák Michal , Nazmitdinov Rashid
Published: August 2023

We analyze a ballistic electron transport through a corrugated (rippled) graphene system with a curvature-induced spin–orbit interaction. The corrugated system is connected from both sides to two flat graphene sheets. The rippled structure unit is modeled by upward and downward curved surfaces. The cooperative effect of N units connected together (the superlattice) on the transmission of electrons that incident at the arbitrary angles on the superlattice is considered. The set of optimal angles and corresponding numbers of N units that yield the robust spin inverter phenomenon are found.
Physics-Informed Neural Networks Integrating Compartmental Model for Analyzing COVID-19 Transmission Dynamics
Authors: Ning Xiao , Guan Jinxing , Li Xi-An , Wei Yongyue , Chen Feng
Published: August 2023

Modelling and predicting the behaviour of infectious diseases is essential for early warning and evaluating the most effective interventions to prevent significant harm. Compartmental models produce a system of ordinary differential equations (ODEs) that are renowned for simulating the transmission dynamics of infectious diseases. However, the parameters in compartmental models are often unknown, and they can even change over time in the real world, making them difficult to determine. This study proposes an advanced artificial intelligence approach based on physics-informed neural networks (PINNs) to estimate time-varying parameters from given data for the compartmental model. Our proposed PINNs method captures the complex dynamics of COVID-19 by integrating a modified Susceptible-Exposed-Infectious-Recovered-Death (SEIRD) compartmental model with deep neural networks. Specifically, we modelled the system of ODEs as one network and the time-varying parameters as another network to address significant unknown parameters and limited data. Such structure of the PINNs method is in line with the prior epidemiological correlations and comprises the mismatch between available data and network output and the residual of ODEs. The experimental findings on real-world reported data data have demonstrated that our method robustly and accurately learns the dynamics and forecasts future states. Moreover, as more data becomes available, our proposed PINNs method can be successfully extended to other regions and infectious diseases.
Diagnostic Imaging of Diseases Affecting the Guttural Pouch
Authors: Thomas-Cancian Aurélie , Ségard-Weisse Emilie , Drumond Bianca , Cadoré Jean-Luc
Published: August 2023

The most common diseases of the guttural pouch are empyema, tympany, mycosis and temporohyoid osteoarthropathy. The challenge in diagnosis of guttural pouch diseases lies in the complex anatomy of the guttural pouch and adjacent associated structures. Diagnostic imaging is a good complement to endoscopy for the diagnosis of some guttural pouch diseases, especially to make a full assessment of the lesions involving the pouch and surrounding structures. This review article describes the value of each diagnostic imaging technique in the diagnosis of guttural pouch disease and the corresponding imaging findings. Radiography is generally used as the first line to complement endoscopic findings, and can give useful additional information although it is limited by superimposition. Ultrasonographic examination of the guttural pouch is of limited value due to the presence of gas in the guttural pouch but can eventually be used to detect fluid within the pouch or can help to evaluate the soft tissues located lateral and ventral to the guttural pouch. Cross-sectional imaging, especially CT, is increasingly available and appears to be the best technique to fully assess the surrounding soft tissues and to precisely identify lesions of the temporohyoid apparatus, temporal bone and skull base that are associated with guttural pouch disease.
The Evaluation of a High-Fidelity Simulation Model and Video Instruction Used to Teach Canine Dental Skills to Pre-Clinical Veterinary Students
Authors: Fairs James , Conan Anne , Yvorchuk-St. Jean Kathleen , Gingerich Wade , Abramo Nicole , Stahl Diane , Walters Carly , Artemiou Elpida
Published: August 2023

In recent years, there has been an increased focus on the teaching of small-animal dentistry to veterinary students in order to address the recognized gap between dental skill training and the expectations of employers regarding the competencies of new graduates in this field. In this study, third-year veterinary students were trained in three canine dental core skills using either a high-fidelity model (Group A) or video instruction (Group B). An objective structured clinical examination was used to assess skill acquisition and questionnaires were distributed in order to assess student confidence and perceptions related to small-animal dentistry practice and related skills before and after the training. All results were compared between the two groups. Group A outperformed Group B in skill acquisition (p < 0.001) and there was greater improvement in skill confidence for Group A than Group B (p < 0.001). There was no statistical difference in perceptions related to small-animal dentistry between the two groups after the training (p ≥ 0.1). Group A rated their training experience more highly than Group B (p < 0.001). Although dental skill acquisition shows greater improvement when training is provided by models rather than video instruction, a blended approach to teaching dental skills is likely to be the best approach to optimizing dental skill acquisition.