bluuebunny/crossref_metadata_2025 huggingface dataset:
Highway Visibility Estimation in Foggy Weather via Multi-Scale Fusion Network
Authors: Xiao Pengfei , Zhang Zhendong , Luo Xiaochun , Sun Jiaqing , Zhou Xuecheng , Yang Xixi , Huang Liang
Published: December 2023

Poor visibility has a significant impact on road safety and can even lead to traffic accidents. The traditional means of visibility monitoring no longer meet the current needs in terms of temporal and spatial accuracy. In this work, we propose a novel deep network architecture for estimating the visibility directly from highway surveillance images. Specifically, we employ several image feature extraction methods to extract detailed structural, spectral, and scene depth features from the images. Next, we design a multi-scale fusion network to adaptively extract and fuse vital features for the purpose of estimating visibility. Furthermore, we create a real-scene dataset for model learning and performance evaluation. Our experiments demonstrate the superiority of our proposed method to the existing methods.
A Density-Based Multilevel Terrain-Adaptive Noise Removal Method for ICESat-2 Photon-Counting Data
Authors: Wang Longyu , Zhang Xuqing , Zhang Ying , Chen Feng , Dang Songya , Sun Tao
Published: December 2023

The photon point clouds collected by the high-sensitivity single-photon detector on the Ice, Cloud, and Land Elevation Satellite-2 (ICESat-2) are utilized in various applications. However, the discretely distributed noise among the signal photons greatly increases the difficulty of signal extraction, especially the edge noise adjacent to signals. To detect signal photons from vegetation coverage areas at different slopes, this paper proposes a density-based multilevel terrain-adaptive noise removal method (MTANR) that identifies noise in a coarse-to-fine strategy based on the distribution of noise photons and is evaluated with high-precision airborne LiDAR data. First, the histogram-based successive denoising method was used as a coarse denoising process to remove distant noise and part of the sparse noise, thereby increasing the fault tolerance of the subsequent steps. Second, a rotatable ellipse that adaptively corrects the direction and shape based on the slope was utilized to search for the optimal filtering direction (OFD). Based on the direction, sparse noise removal was accomplished robustly using the Otsu’s method in conjunction with the ordering points to identify the clustering structure (OPTICS) and provide a nearly noise-free environment for edge searching. Finally, the edge noise was removed by near-ground edge searching, and the signal photons were better preserved by the surface lines. The proposed MTANR was validated in four typical experimental areas: two in Baishan, China, and two in Taranaki, New Zealand. A comparison was made with three other representative methods, namely differential, regressive, and Gaussian adaptive nearest neighbor (DRAGANN), used in ATL08 products, local distance statistics (LDS), and horizontal ellipse-based OPTICS. The results demonstrated that the values of the F1 score for the signal photon identification achieved by the proposed MTANR were 0.9762, 0.9857, 0.9839, and 0.9534, respectively, which were higher than those of the other methods mentioned above. In addition, the qualitative and quantitative results demonstrated that MTANR outperformed in scenes with steep slopes, abrupt terrain changes, and uneven vegetation coverage.
A Thermal Anemometry Method for Studying the Unsteady Gas Dynamics of Pipe Flows: Development, Modernisation, and Application
Authors: Plotnikov Leonid
Published: December 2023

A detailed study of the gas-dynamic behaviour of both liquid and gas flows is urgently required for a variety of technical and process design applications. This article provides an overview of the application and an improvement to thermal anemometry methods and tools. The principle and advantages of a hot-wire anemometer operating according to the constant-temperature method are described. An original electronic circuit for a constant-temperature hot-wire anemometer with a filament protection unit is proposed for measuring the instantaneous velocity values of both stationary and pulsating gas flows in pipelines. The filament protection unit increases the measuring system’s reliability. The designs of the hot-wire anemometer and filament sensor are described. Based on development tests, the correct functioning of the measuring system was confirmed, and the main technical specifications (the time constant and calibration curve) were determined. A measuring system for determining instantaneous gas flow velocity values with a time constant from 0.5 to 3.0 ms and a relative uncertainty of 5.1% is proposed. Based on pilot studies of stationary and pulsating gas flows in different gas-dynamic systems (a straight pipeline, a curved channel, a system with a poppet valve or a damper, and the external influence on the flow), the applications of the hot-wire anemometer and sensor are identified.
Optimizing Temperature Setting for Decomposition Furnace Based on Attention Mechanism and Neural Networks
Authors: Liu Shangkun , Shen Wei , Wu Chase Q. , Lyu Xukang
Published: December 2023

The temperature setting for a decomposition furnace is of great importance for maintaining the normal operation of the furnace and other equipment in a cement plant and ensuring the output of high-quality cement products. Based on the principles of deep convolutional neural networks (CNNs), long short-term memory networks (LSTMs), and attention mechanisms, we propose a CNN-LSTM-A model to optimize the temperature settings for a decomposition furnace. The proposed model combines the features selected by Least Absolute Shrinkage and Selection Operator (Lasso) with others suggested by domain experts as inputs, and uses CNN to mine spatial features, LSTM to extract time series information, and an attention mechanism to optimize weights. We deploy sensors to collect production measurements at a real-life cement factory for experimentation and investigate the impact of hyperparameter changes on the performance of the proposed model. Experimental results show that CNN-LSTM-A achieves a superior performance in terms of prediction accuracy over existing models such as the basic LSTM model, deep-convolution-based LSTM model, and attention-mechanism-based LSTM model. The proposed model has potentials for wide deployment in cement plants to automate and optimize the operation of decomposition furnaces.
Integrated Controller Design and Application for CNC Machine Tool Servo Systems Based on Model Reference Adaptive Control and Adaptive Sliding Mode Control
Authors: Zhang Taihao , Li Xuewei , Gai Hongdong , Zhu Yuheng , Cheng Xiang
Published: December 2023

In order to reduce the effect of nonlinear friction and time-varying factors on the servo system of a computer numerical control (CNC) machine tool and improve its motion control accuracy, this paper uses an adaptive sliding mode control (ASMC) method based on model reference adaptive control (MRAC). The method adopts ASMC in the control outer loop and obtains the optimal control parameters by making the sliding mode control (SMC) law continuous and adaptively estimating the control parameters. At the same time, MRAC is used in the control inner loop to enhance the “invariance” of the controlled object so that the switching gain of SMC can satisfy the disturbance matching condition even under lesser conditions. Simulation and experimental results show that compared with the traditional SMC, the ASMC based on MRAC proposed in this paper effectively reduces the influence of nonlinear friction on the system performance, and the reduction in following error reaches 71.2%, which significantly improves the motion control accuracy of the control system. The spectral analysis of the following errors shows that the maximum magnitude reduction rate of the high-frequency chattering is 89.02%, which significantly reduces the effect of the high-frequency chattering and effectively improves the stability performance of the control system.
Automated Service Height Fault Detection Using Computer Vision and Machine Learning for Badminton Matches
Authors: Goh Guo Liang , Goh Guo Dong , Pan Jing Wen , Teng Phillis Soek Po , Kong Pui Wah
Published: December 2023

In badminton, accurate service height detection is critical for ensuring fairness. We developed an automated service fault detection system that employed computer vision and machine learning, specifically utilizing the YOLOv5 object detection model. Comprising two cameras and a workstation, our system identifies elements, such as shuttlecocks, rackets, players, and players’ shoes. We developed an algorithm that can pinpoint the shuttlecock hitting event to capture its height information. To assess the accuracy of the new system, we benchmarked the results against a high sample-rate motion capture system and conducted a comparative analysis with eight human judges that used a fixed height service tool in a backhand low service situation. Our findings revealed a substantial enhancement in accuracy compared with human judgement; the system outperformed human judges by 3.5 times, achieving a 58% accuracy rate for detecting service heights between 1.150 and 1.155 m, as opposed to a 16% accuracy rate for humans. The system we have developed offers a highly reliable solution, substantially enhancing the consistency and accuracy of service judgement calls in badminton matches and ensuring fairness in the sport. The system’s development signifies a meaningful step towards leveraging technology for precision and integrity in sports officiation.
Provably Secure Lightweight Mutual Authentication and Key Agreement Scheme for Cloud-Based IoT Environments
Authors: Ju Sieun , Park Yohan
Published: December 2023

A paradigm that combines cloud computing and the Internet of Things (IoT) allows for more impressive services to be provided to users while addressing storage and computational resource issues in the IoT environments. This cloud-based IoT environment has been used in various industries, including public services, for quite some time, and has been researched in academia. However, various security issues can arise during the communication between IoT devices and cloud servers, because communication between devices occurs in open channels. Moreover, issues such as theft of a user’s IoT device or extraction of key parameters from the user’s device in a remote location can arise. Researchers interested in these issues have proposed lightweight mutual authentication key agreement protocols that are safe and suitable for IoT environments. Recently, a lightweight authentication scheme between IoT devices and cloud servers has been presented. However, we found out their scheme had various security vulnerabilities, vulnerable to insider, impersonation, verification table leakage, and privileged insider attacks, and did not provide users with untraceability. To address these flaws, we propose a provably secure lightweight authentication scheme. The proposed scheme uses the user’s biometric information and the cloud server’s secret key to prevent the exposure of key parameters. Additionally, it ensures low computational costs for providing users with real-time and fast services using only exclusive OR operations and hash functions in the IoT environments. To analyze the safety of the proposed scheme, we use informal security analysis, Burrows–Abadi–Needham (BAN) logic and a Real-or-Random (RoR) model. The analysis results confirm that our scheme is secure against insider attacks, impersonation attacks, stolen verifier attacks, and so on; furthermore, it provides additional security elements. Simultaneously, it has been verified to possess enhanced communication costs, and total bit size has been shortened to 3776 bits, which is improved by almost 6% compared to Wu et al.’s scheme. Therefore, we demonstrate that the proposed scheme is suitable for cloud-based IoT environments.
Conceptual and Methodological Arguments against the Use of Location Quotient as an Area-Based Measure of Residential Segregation: A Measurement Perspective
Authors: Oka Masayoshi
Published: December 2023

Among a wide range of practical applications, the location quotient (LQ) has been used as an area-based measure of residential segregation by race/ethnicity in some studies. However, it does not correspond to any of the five dimensions of residential segregation. Rather, an application of LQ in demographic data analyses brings about an atypical way to quantify the population composition of areal units by race/ethnicity. To clarify misconceptions, the purpose of this study was to demonstrate the relationships between proportions, percentages, and LQs of six racial/ethnic groups in the conterminous United States (US). Since populations change over time, demographic data on race and ethnicity were obtained from the 2000, 2010, and 2020 Census through the US Census Bureau’s website. Using census tracts and counties as the units of analysis, a sequence of scatterplots and associated Pearson’s correlation coefficients (r) was used to display the analytical results of census-tract- and county-based measures at three different time periods. Despite the different levels of aggregation, the relationships between proportions, percentages, and LQs of six racial/ethnic groups consistently showed perfect positive correlations at three different time periods (r = 1.00). These suggest that census-tract- and county-based measures expressed as the proportion, percentage, and LQ of a racial/ethnic group capture the same distributional pattern, but the units of measurement simply differ from one another. Hence, the study of residential segregation and its societal consequences needs to be specific to the dimension under study and to build upon the conceptual and methodological foundations established by sociologists-demographers and geographers.
Benchmarking Economic Sustainability: What Factors Explain Heterogeneity Between Wine Businesses?
Authors: Bennett Anthony , Loose Simone
Published: December 2023

To assess a wine producer’s economic sustainability, it is useful to benchmark its economic indicators against a suitable reference group. Existing research mainly compares wine businesses either by region or by size alone. There is a research gap concerning which of the two benchmarking factors can be more suitable or whether both factors are required. Using a framework of economic sustainability benchmarking figures, the effects of region and size, as well as the effect of their interactions, on 10 economic indicators were estimated through an ANOVA and the estimation of effect sizes. The analysis is based on a unique data set of business data averages of 382 German wine estates across six agricultural years (2014–2019). Region and size both had a significant influence on 7 out of 10 benchmark indicators. Wine estates from distinct regions more strongly differed in their primary indicators of production factors, price and yield as well as secondary indicators of cost and productivity. Contrarily, wine estates of diverse size groups more strongly differed in their tertiary indicators of profitability and return, which are key indicators of economic sustainability. Both size and region should be utilized for suitable economic indicators when benchmarking wine businesses for future assessments of economic sustainability. Hereby, this paper provides a first step in making economic sustainability less subjective for the German wine industry and how to move forward in regards to benchmarking within empirical frameworks and tools of economic sustainability.
Performance Factors in Sport Climbing: A Systematic Review
Authors: Diez-Fernández Pelayo , Ruibal-Lista Brais , Rico-Díaz Javier , Rodríguez-Fernández José Eugenio , López-García Sergio
Published: December 2023

Background: Our aim was understanding and identifying the main performance factors involved in sport climbing. Methods: A systematic review was conducted using the Google Scholar, Dialnet, Scielo, and Redalyc databases. Results: After establishing the selection criteria, a total of 27 documents related to the subject of study were examined. A limited number of publications with scientific evidence related to performance factors in sport climbing were found, despite the rise of sport climbing following its inclusion in the Olympic Games in Tokyo 2020. The results have been organized based on different performance factors analyzed, such as strength, muscular endurance, psychological factors, etc. Key determinants in climbing performance, and thus those present in elite athletes, include improved climbing efficiency, greater ability to apply maximum force or finger and palm pressure resistance, and increased arm locking strength. Additionally, it has been observed that those who can apply higher and more consistent loads experience better muscle oxygenation and have greater flexibility and lateral foot reach. Conclusions: Climbing performance is the result of factors that can be enhanced through training. Therefore, further research is needed to understand the performance factors involved in this sports discipline and how to improve them.
Digital Finance and County Ecological Performance—New Evidence from China Counties
Authors: Yuan Hui , Cen Wei , Du Tao
Published: December 2023

As a new product that combines finance and digital technology, digital finance is of great significance to the governance of the ecological environment. Based on the panel data of 2128 counties in China from 2014 to 2020, the fixed effect model and a panel threshold model are established, and the direct impact, heterogeneity, and transmission paths of digital finance development on ecological environment quality are empirically analyzed. The results demonstrate that the progress of digital finance has a significant negative effect on ecological environmental performance, and robustness tests support this conclusion. Additionally, industrial agglomeration and structural transformation are crucial mechanisms through which digital finance hinders ecological environmental performance. Moreover, the adverse influence of digital finance development on the ecological environment is particularly pronounced in densely populated areas, county-level cities, and non-poverty-stricken counties. Fourthly, based on the development level of digital finance itself, digital finance has a double threshold effect on the performance of county ecological environment. When digital finance is at a low level and a high level, its negative impact on eco-environmental performance is the greatest. Between the low level and the high level of digital finance, digital finance has the greatest negative impact on the performance of the ecological environment. Finally, suggestions are put forward to promote the green development of digital finance, foster balanced regional development, and expedite industrial transformation in underdeveloped regions.
Anthropogenic Drivers of Hourly Air Pollutant Change in an Urban Environment during 2019–2021—A Case Study in Wuhan
Authors: Zhang Yi , Song Jie , Zhu Bo , Chen Jiangping , Duan Mingjie
Published: December 2023

Wuhan experienced a noticeable enhancement in air quality from January to April 2020 due to the epidemic lockdown. The improvement was a combined result of anthropogenic emission reduction and meteorological variability. Environmental policymakers are often concerned about the impact of industrial production and human activities on improvements in environmental sustainability. This study split and quantified the impact of anthropogenic emissions on the pollution level changes of six major air pollutants (CO, SO2, NO2, O3, PM10, and PM2.5) for the first half year of 2019 to 2021 in Wuhan with an improved meteorological normalization algorithm. The results show sharp decreases in anthropogenic pollutant loads during 2020, except for O3, with the ranking of NO2 > PM10 > SO2 > CO > PM2.5. The decrease in NO2 emissions caused by humans was more than 50% compared to 2019. The low NO2 led to a decrease in O3 consumption, resulting in high O3 concentrations from February to April 2020 during the city lockdown. Moreover, except O3, the impact of anthropogenic and weather influences on air pollution exhibited opposing effects; that is, meteorology tended to aggravate pollution, while human intervention was conducive to improving air quality, and human factors played the dominant role. Of all six pollutants, O3 is the one that is relatively least subject to anthropogenic emissions. Although concentrations of SO2, NO2, PM10, and PM2.5 rebounded in 2021, none of them were able to return to their pre-lockdown levels, suggesting the epidemic’s continuous inhibition of people’s activities. Compared with 2019 and 2021, the atmospheric oxidation capacity and secondary aerosol formation showed an overall decreasing trend during 2020. This study provides a reference for assessing the effectiveness of anthropogenic emission reduction policies.
Micro- and Nano-Bubbles Enhanced the Treatment of an Urban Black-Odor River
Authors: Xu Qinqin , Zhou Zheng , Chai Xiaoli
Published: December 2023

Black-odor water is prevalent in southeastern coastal regions of China, compromising both the aquatic ecosystem and urban aesthetics. Micro- and nano-bubbles (MNBs) aeration, identified as an innovative approach, offers potential improvements in water ecological function. This study introduces and implements an MNBs technique to rehabilitate an urban black-odor river. Results indicate that MNBs aeration achieved a significantly higher increment rate of dissolved oxygen (89.4%) and higher removal efficiencies of biological oxygen demand (54.4%), chemical oxygen demand (39.0%), ammonia nitrogen (63.2%), total phosphorus (28.0%) and dimethyl trisulfide (100%) in the water compared to conventional blast aeration. Concurrently, a 25.0% increase in the ratio of iron/aluminum-bonded phosphorus (Fe/Al-P) to total sediment phosphorus effectively curtailed endogenous phosphorus release. Additionally, MNBs aeration markedly reduced plankton biomass, suggesting direct removal by MNBs. This enhanced performance is attributable to the improved oxygen mass transfer coefficient and oxygenation capacity, fostering more efficient pollutants. Furthermore, MNBs significantly encouraged the growth of aerobic microorganisms (e.g., Actinobacteria, Firmicutes and Myxococcota) in the sediment, bolstering the water’s self-purification ability. Consequently, this study validates MNBs as a highly promising solution for treating black odorous water bodies.
From Start-Up Capital to Diversification and Sustainability of Personal Branding Activities
Authors: Walczak-Skałecka Agnieszka
Published: December 2023

Personal branding has become a common practice in the business world. In a knowledge-based economy, this seems to be a natural direction for the development of individuals’ professional activities. However, the diversity in the activities undertaken is significant. Therefore, it is important to examine not only the final results of the activities undertaken but also their internal variation and sources, which can influence the long-term development of the individual and the ability to maintain these activities in the long term. The purpose of this article is to present the results of research on the impact of an individual’s start-up capital and occupation on the diversification of activities undertaken while building a personal brand. Literature research was conducted to identify the components of start-up capital and to establish the relationship between the personal branding process and the start-up capital possessed. The empirical research used a diagnostic survey method, a questionnaire technique. It was conducted among a representative sample of Polish internet users in May 2021. Analyses revealed relationships between individual components of start-up capital and the variation in personal branding activities undertaken, as well as among those in occupations requiring creativity, broad decision-making and dependence on individual performance. The study provides relevant information for the discussion on how the concept of personal branding can be practically applied among people from different social and professional groups.
Bentonite Clays from Southeastern Spain as Sustainable Natural Materials for the Improvement of Cements, Mortars and Concretes
Authors: Costafreda Jorge L. , Martín Domingo A. , Sanjuán Miguel Ángel , Costafreda-Velázquez Jorge L.
Published: December 2023

The effects of global climate change are becoming more evident and accelerating at an unprecedented pace. For this reason, human activities urgently need a paradigm shift to stop this entropic process before the consequences become irreversible. In this sense, the use of highly eco-efficient materials aimed at conveniently neutralizing CO2 greenhouse gas emissions entering into the atmosphere can contribute significantly to mitigating and reversing this process. This work aims to demonstrate the positive effects obtained when Portland cement is partially replaced by bentonite clays of volcano-sedimentary origin. The samples were initially characterized by various methods, such as Thin-Section Petrographic Study (TSP) and the analysis of mineral phases with XRD, chemical composition was determined via XRF, and morphological analysis was determined via scanning electron microscopy (SEM). To determine the technical properties of the samples, a qualitative chemical analysis (QCA) was performed, as well as a chemical analysis of pozzolanicity (CAP) at 8 and 15 days, respectively, and a study of the mechanical compressive strengths at 2, 7, 28 and 90 days. Characterization studies using TSP, DRX, FRX and SEM established that these bentonite clays have a complex mineralogical variety, composed mainly of smectite, mordenite, plagioclase and biotite, as well as altered volcanic glass and sericite. The results of the qualitative chemical analysis establish that more than 93% of the SiO2 present in the samples is reactive. Chemical analysis of pozzolanicity (CAP) showed significant pozzolanic behavior in all samples analyzed at both 8 and 15 days, while mechanical tests highlighted significant increases in mechanical strengths, with maximum values varying between 52.2 and 70.6 MPa at 90 days. These results show that the materials can be used as quality pozzolans for the manufacture of cements, mortars and concretes, which could be considered as a favorable factor and, therefore, relevant in the management and control of greenhouse gas emissions responsible for the deterioration of the environment.
Functional Measurements, Pattern Evolution, and Coupling Characteristics of “Production-Living-Ecological Space” in the Yangtze Delta Region
Authors: Zhu Jiaheng , Shang Zhengyong , Long Cheng , Lu Song
Published: December 2023

Based on the “Production-Living-Ecological Space” evaluation system, we hereby form its functional index and take the results of corresponding functional measurement to analyze the spatial pattern, functional evolution, and coupling characteristics of the “Production-Living-Ecological Space” of the Yangtze Delta Region. The results show that: (1) From the spatial pattern, the production space is mainly distributed in the plain areas. The living space is concentrated in the dense urban areas along the Yangtze River, the East Sea, and the East Jiangsu–Gansu Line. The ecological space is concentrated in mountainous hill areas. (2) From the spatial transformation, production space expands slightly, living space continues to expand, and ecological space shrinks significantly. (3) The functionality of “Production-Living-Ecological Space” exhibits a high level in hilly mountainous areas and a low level in plains, with an overall downward trend. Notably, the core cities within the Yangtze Delta Region have experienced the most significant decline in functionality. (4) The natural environment is the decisive factor for the overall pattern of “Production-Living-Ecological Space”, while economic and social development is the core driving force of the evolution of the spatial pattern, and regional integration is the catalyst of the evolution of the spatial pattern. (5) The coupling coordination of “production-ecology” is exceptionally strong, with the living function holding an overwhelmingly dominant position.
Water Purification Using Active Charcoal with Microbes and Chelated Iron Soaked into Its Micropores
Authors: Xu Hui-lian , Cai Ruitao , Kong Mengmeng , Ye Tao , Gu Jinsong , Liu Xiaoyong
Published: December 2023

Urbanization in China has led to a significant increase in surface water pollution, posing a threat to the health and safety of residents and hindering sustainable economic development. Individual traditional methods have been used to purify polluted water, including the use of bamboo-derived activated charcoal, microbial material, and zero-valent iron. However, these methods have been found to have certain limitations. This study investigates the effects of an activated charcoal material combined with beneficial microbes and chelated nano-iron in removing nitrates. The experiments were conducted at various scales, including a bench-scale study, and studies of a small river, sewage plant tailwater, and artificially constructed wetlands. The microbes used included Bacillus spp., Lactobacillus spp., and yeasts. During the fermentation process, nano-scale iron powder was added, resulting in the formation of bivalent iron ions under anaerobic conditions. These ions were subsequently chelated by organic acids. Bamboo-derived activated charcoal was then soaked in the fermented liquid, allowing the microbes, chelated iron ions, and organic acids to infiltrate the pores of the activated charcoal. This activated charcoal material, containing microbes and chelated iron ions, demonstrated effective nitrate removal in laboratory experiments and sewage plant tailwater treatment, and water purification in wetlands and rivers. It is important to note that this research solely focused on the removal of nitrates, and further studies are required to confirm its effectiveness in other aspects of water purification.
Effect of the Strategic Emerging Industry Support Program on Corporate Innovation among Listed Companies in China
Authors: Ma Ruiguang , Yin Jiangbin , Huang Xiaoyan
Published: December 2023

In this paper, we analyze the relationship between the Chinese government’s Strategic Emerging Industry Support Program and the innovation level of strategic emerging industry firms. We use panel data of A-share-listed firms from 2007 to 2017 and apply a difference-in-difference model. We obtain evidence that firms exhibit strategic innovation behavior in response to the Strategic Emerging Industry Support Program, as evidenced by firms focusing solely on increasing their research and development (R&D) input and number of patent outputs, neglecting innovation efficiency and quality, and failing to convert patent results into firm profitability. Moreover, we observe that the aforementioned support program, through the provision of subsidies, tax incentives, and easy access to credit, as well as the reduction in stock liquidity, prompts increases in firm R&D input and, consequently, the number of patents firms are granted. Our results also indicate that for state-owned enterprises in strategic emerging industries, government support in the form of subsidies and tax incentives has the strongest effect on innovation; by contrast, for private firms, innovation is more strongly stimulated through convenient credit support, tax incentives, and reduced stock liquidity. This paper provides empirical evidence on whether industrial policy promotes firm innovation and argues more generally whether government industrial policy is effective.
The Impact of E-Commerce Transformation of Cities on Green Total Factor Productivity
Authors: Ding Mengqi , Gao Qijie
Published: December 2023

The e-commerce-oriented transformation of cities is an important measure to enhance the vitality of economic development, improve the efficiency of resource allocation, and provide a new boost to the green and high-quality development of regions. Taking the quasi-natural experiment of national e-commerce demonstration city construction as the starting point, using the panel data of 281 prefecture-level cities in China from 2005 to 2021, we measure the green total factor productivity of cities by using the super-efficient SBM model with non-expected outputs and the global reference GML index method, and use the multi-period propensity score matching double-difference method to examine the impact of urban e-commerce-oriented transformation on the green total factor productivity of the city and the intrinsic mechanism of the effect. The results show that the urban e-commerce transformation policy can significantly promote regional green total factor productivity, and this result still holds after a series of robustness tests, such as changing the time point of the policy, randomly selecting the placebo proposal for the treatment group, and changing the matching method; the effect is regionally heterogeneous, and is more pronounced in large cities, non-provincial capitals, eastern cities, central cities, and non-resource-based cities; the urban e-commerce transformation mainly promotes the improvement of urban green total factor productivity through three channels: the industrial structure upgrading effect, the economic agglomeration effect, and the green technology innovation effect.
Harnessing Online Knowledge Transfer for Enhanced Search and Rescue Decisions via Multi-Agent Reinforcement Learning
Authors: Song Luona , Wen Zhigang , Teng Junjie , Zhang Jian , Nicolas Merveille
Published: December 2023

In the rapidly evolving domain of the Internet of Things (IoT), devices play an instrumental role in high-stakes scenarios like search and rescue (SAR) operations. Traditional decision-making processes within SAR missions often struggle to cope with the dynamic and unpredictable nature of such environments, leading to inefficiencies and delayed responses. This paper aims to explore the potential of multi-agent reinforcement learning (MARL) to improve the decision-making process within SAR operations underpinned by IoT. Functional, current methods are limited by their static decision frameworks and inability to adapt in real time to the chaotic variables present in SAR situations. We introduced a novel MARL framework and compared its performance against benchmark strategies, specifically the multi-agent deep deterministic policy gradient (MADDPG) approach. Uniquely enhanced by online knowledge transfer, the framework leverages the capabilities of the deep deterministic policy gradient (DDPG) method. The preliminary findings underscore the proposed framework’s superior efficiency and speed in SAR contexts. Our research highlights MARL’s transformative potential, positing it as a groundbreaking strategy for IoT-based decision making in high-pressure SAR environments with suggestions for further studies in varied real-world scenarios.
Some Simpson-like Inequalities Involving the (s,m)-Preinvexity
Authors: Chiheb Tarek , Meftah Badreddine , Moumen Abdelkader , Mesmouli Mouataz Billah , Bouye Mohamed
Published: December 2023

In this article, closed Newton–Cotes-type symmetrical inequalities involving four-point functions whose second derivatives are (s,m)-preinvex in the second sense are established. Some applications to quadrature formulas as well as inequalities involving special means are provided.
Towards Reliable Federated Learning Using Blockchain-Based Reverse Auctions and Reputation Incentives
Authors: Ouyang Kai , Yu Jianping , Cao Xiaojun , Liao Zhuopeng
Published: December 2023

In recent years, the explosion of big data has presented unparalleled opportunities for the advancement of machine learning (ML). However, the vast size and sensitive nature of these datasets present significant challenges in terms of privacy and security. Federated Learning has emerged as a promising solution that enables a group of participants to train ML models without compromising the confidentiality of their raw data. Despite its potential, traditional federated learning faces challenges such as the absence of participant incentives and audit mechanisms. Furthermore, these challenges become more significant when dealing with the scale and diversity of big data, making efficient and reliable federated learning a complex task. These limitations may compromise model quality due to potential malicious nodes. To address the above issues, this paper proposes a BlockChain-based Decentralized Federated Learning (BCD-FL) model. In BCD-FL, we design a smart contract approach based on the reverse auction-based incentive mechanism and a reputation mechanism to promote the participation of reliable and high-quality data owners. Theoretical analysis shows that the BCD-FL model satisfies several desirable properties, such as individual rationality, computational efficiency, budget balance, and truthfulness. In addition, experimental results also show that the proposed model enables more efficient federated learning and provides some level of protection against malicious nodes. Therefore, the BCD-FL model presents a potential solution to the challenges in federated learning and opens up new possibilities for achieving efficient large-scale machine learning.
Numerical Modeling of the Interaction of Dark Atoms with Nuclei to Solve the Problem of Direct Dark Matter Search
Authors: Bikbaev Timur , Khlopov Maxim , Mayorov Andrey
Published: December 2023

The puzzle of the direct dark matter search can be resolved by examining the concept of «dark atoms», which consist of hypothetical stable lepton-like particles with a charge of −2n, where n is any natural number, bound to n nuclei of primordial helium. These «dark atoms», known as «XHe» (X-helium) atoms, remain undiscovered in experiments due to their neutral atom-like states. In this model, the positive results of the DAMA/NaI and DAMA/LIBRA experiments could be explained by the annual modulation of radiative capture of XHe atoms engaging in low-energy bound states with sodium nuclei. This specific phenomenon does not occur under the conditions of other underground experiments. The proposed solution to this puzzle involves establishing the existence of a low-energy bound state of «dark atoms» and nuclei while also considering the self-consistent influence of nuclear attraction and Coulomb repulsion. Resolving this complex issue, which has remained unsolved for the past 17 years, necessitates a systematic approach. To tackle this problem, numerical modeling is employed to uncover the fundamental processes behind the interaction of «dark atoms» with nuclei. To comprehend the essence of XHe’s interaction with baryonic matter nuclei, a classical model is employed wherein quantum physics and nuclear size effects are progressively incorporated. A numerical model describing the interaction between XHe «dark atoms» and nuclei is developed through the continuous inclusion of realistic features of quantum mechanics in the initial classical three-body problem involving the X-particle, the helium nucleus, and the target nucleus. This approach yields a comprehensive numerical model that encompasses nuclear attraction and electromagnetic interaction between the «dark atom» and nuclei. Finally, this model aids in supporting the interpretation of the results obtained from direct underground dark matter experiments through the lens of the «dark atom» hypothesis.
Geometric Stochastic Resonance in an Asymmetric T-Shaped Chamber
Authors: Duan Shouhui , Fan Bixuan , Duan Zhenglu
Published: December 2023

The investigation of a Brownian particle subjected to an AC force that diffuses within a T-shaped chamber was conducted. This T-shaped chamber is composed of a strip cavity and a trapezoidal cavity positioned below it. The interplay between the AC force and asymmetric geometry creates a spatially bistable potential perpendicular to the AC force. With the assistance of noise, the particles can transition between two stable states and oscillate along the AC force at corresponding amplitudes at every spatially stable state. The asymmetric geometry facilitates the trapezoid cavity’s ability to more easily trap the Brownian particle than the upper strip cavity in the weak noise limit. Our observations reveal that proper noise can ensure the particle’s efficient trapping within the upper strip cavity and synchronization with the AC force, indicating the occurrence of geometric stochastic resonance. The T-shaped chamber serves as a simplified model, aiding in the further understanding of geometric stochastic resonance induced by irregular geometries and enabling the manipulation of microscopic particles in various small-scale systems.
Binomial Series-Confluent Hypergeometric Distribution and Its Applications on Subclasses of Multivalent Functions
Authors: Aldawish Ibtisam , El-Deeb Sheza M. , Murugusundaramoorthy Gangadharan
Published: December 2023

Over the past ten years, analytical functions’ reputation in the literature and their application have grown. We study some practical issues pertaining to multivalent functions with bounded boundary rotation that associate with the combination of confluent hypergeometric functions and binomial series in this research. A novel subset of multivalent functions is established through the use of convolution products and specific inclusion properties are examined through the application of second order differential inequalities in the complex plane. Furthermore, for multivalent functions, we examined inclusion findings using Bernardi integral operators. Moreover, we will demonstrate how the class proposed in this study, in conjunction with the acquired results, generalizes other well-known (or recently discovered) works that are called out as exceptions in the literature.
A Literature Review of BIM Definitions: Narrow and Broad Views
Authors: Borkowski Andrzej Szymon
Published: December 2023

In the scientific community, it is difficult to find a consensus on defining BIM. Just as the acronym BIM has developed in different ways, it is also understood in different ways. Depending on its understanding, different definitions emerge. It is defined differently by organizations and standards, differently still even by academics. Many years of academic discourse on the subject have failed to produce a solution. Despite the fact that the acronym BIM has already done its work for the construction industry, it still stirs up excitement. There is still no clear definition, as the view of BIM varies from one perspective to another. This article attempts to sort out the definitions cited so far by important organizations and key academics. This review was based on a deep literature study that has attempted to be inclusive and consistent. The question still remains open: do we need a single, correct definition of BIM? The aim of this article is to try to answer this question, open up a renewed discussion and come to a satisfactory consensus. BIM can be identified with an activity, product or system. This article breaks down the definitions of BIM, identifies six key attributes of BIM, presents the evolution of the understanding of BIM and proposes new definitions in a narrow and broad approach.
Radiation-Induced Intestinal Injury: Injury Mechanism and Potential Treatment Strategies
Authors: Lu Qianying , Liang Yangfan , Tian Sijia , Jin Jie , Zhao Yanmei , Fan Haojun
Published: December 2023

Radiation-induced intestinal injury (RIII) is one of the most common intestinal complications caused by radiotherapy for pelvic and abdominal tumors and it seriously affects the quality of life of patients. However, the treatment of acute RIII is essentially symptomatic and nutritional support treatment and an ideal means of prevention and treatment is lacking. Researchers have conducted studies at the cellular and animal levels and found that some chemical or biological agents have good therapeutic effects on RIII and may be used as potential candidates for clinical treatment. This article reviews the injury mechanism and potential treatment strategies based on cellular and animal experiments to provide new ideas for the diagnosis and treatment of RIII in clinical settings.
Bile Acids Promote Hepatic Biotransformation and Excretion of Aflatoxin B1 in Broiler Chickens
Authors: Chen Liang , Wen Tian , Cao Aizhi , Wang Jianmin , Pan Hua , Zhao Ruqian
Published: December 2023

Aflatoxin B1 (AFB1) is a hazardous mycotoxin that often contaminates animal feed and may potentially induce severe liver damage if ingested. The liver is the primary organ responsible for AFB1 detoxification through enzyme-catalyzed xenobiotic metabolism and bile acid (BA)-associated excretion. In this study, we sought to investigate whether exogenous BA improves hepatic AFB1 detoxification to alleviate AFB1-induced liver injury in broiler chickens. Five-day-old broiler chicks were randomly assigned to three groups. CON and AFB1 received a basal diet; AFB1 + BA received a basal diet with 250 mg/kg BA for 20 days. After a 3-day pre-feed, AFB1 and AFB1 + BA were daily gavaged with 250 μg/kg BW AFB1, while CON received gavage solvent for AFB1 treatment. Dietary BA supplementation protected chickens from AFB1-induced hepatic inflammation and oxidative stress. The hepatic biotransformation of AFB1 to its metabolite AFBO was improved, with accelerated excretion to the gallbladder and cecum. Accordantly, AFB1-induced down-regulation of detoxification genes, including cytochrome P450 enzymes, glutathione S-transferases, and the bile salt export pump, was rescued by BA supplementation. Moreover, liver X receptor α, suppressed by AFB1, was enhanced in BA-treated broiler chickens. These results indicate that dietary BA supplementation improves hepatic AFB1 detoxification and excretion through LXRα-involved regulation of xenobiotic enzymes.
Cholesterol 25-Hydroxylase Suppresses Swine Acute Diarrhea Syndrome Coronavirus Infection by Blocking Spike Protein-Mediated Membrane Fusion
Authors: Liu Dakai , Shi Da , Shi Hongyan , Zhang Liaoyuan , Zhang Jiyu , Zeng Miaomiao , Feng Tingshuai , Yang Xiaoman , Zhang Xin , Chen Jianfei , Jing Zhaoyang , Ji Zhaoyang , Zhang Jialin , Feng Li
Published: December 2023

Swine acute diarrhea syndrome coronavirus (SADS-CoV) is an emerging porcine intestinal coronavirus that can cause acute diarrhea, vomiting, rapid weight loss, and high mortality in newborn piglets. Cholesterol 25-hydroxylase (CH25H) is a molecular mediator of innate antiviral immunity and converts cholesterol to 25-hydroxycholesterol (25HC). Previous studies have reported that CH25H and 25HC have an antiviral effect against multiple viruses. However, the interplay between SADS-CoV infection and CH25H or 25HC is still uncertain. Here, we found that CH25H and its enzymatic product 25HC restrained SADS-CoV replication by blocking membrane fusion. Our results show that CH25H was upregulated by SADS-CoV infection in vitro and in vivo, and that it was an IFN-stimulated gene in porcine ileum epithelial cells. Moreover, CH25H and CH25H mutants lacking catalytic activity can inhibit SADS-CoV replication. Furthermore, 25HC significantly suppressed SADS-CoV infection by inhibiting virus entry. Notably, we confirmed that CH25H and 25HC blocked SADS-CoV spike protein-mediated membrane fusion. Our data provide a possible antiviral therapy against SADS-CoV and other conceivable emerging coronaviruses in the future.