bluuebunny/crossref_metadata_2025 huggingface dataset:
Methods of Measuring Air Pollution in Cities and Correlation of Air Pollutant Concentrations
Authors: Bodić Milan , Rajs Vladimir , Vasiljević Toskić Marko , Bajić Jovan , Batinić Branislav , Arbanas Miloš
Published: October 2023

The monitoring of air quality continues to be one of the most important tasks when ensuring the safety of our environment. This paper aims to look at correlations between different types of pollutants, so that robust air quality measurement systems can be deployed in remote, inaccessible areas, at a reduced cost. The first matter at hand was to design an affordable and portable system capable of measuring different air pollutants. A custom PCB was designed that could support the acquisition of readings of, among others, particulate and CO sensors. Then, correlations between the concentrations of different pollutants were analyzed to identify if measuring the concentration of one type of pollutant can allow the extrapolation of the concentration of another. This particular study focuses on the correlations between the concentrations of particulate matter and CO. Finally, after observing a moderate correlation, it was proposed to measure the concentrations of pollutants that require less expensive sensors, and to extrapolate the concentrations of pollutants that require a more expensive sensor to measure their concentration. The link between particulate pollution and CO concentrations was identified and discussed as the result of this study.
Regenerative Braking of Electric Vehicles Based on Fuzzy Control Strategy
Authors: Yin Zongjun , Ma Xuegang , Su Rong , Huang Zicheng , Zhang Chunying
Published: October 2023

Regenerative braking technology is a viable solution for mitigating the energy consumption of electric vehicles. Constructing a distribution strategy for regenerative braking force will directly affect the energy saving efficiency of electric vehicles, which is a technical bottleneck of battery-powered electric vehicles. The distribution strategy of the front- and rear-axle braking forces of electric vehicles that possess integrated front-wheel-drive arrangements is established based on the Economic Commission of Europe (ECE) regulations, which enables the clarification of the total braking force of the front axle. The regenerative braking torque model of the motor is adjusted to optimize the ratio of motor braking force to the whole front-axle braking force. The regenerative braking process of electric vehicles is influenced by many factors, such as driving speed and braking intensity, so regenerative braking presents characteristics of nonlinearity, time variability, delay, and incomplete models. By considering the impact of fuzzy controllers having better robustness, adaptability, and fault tolerance, a fuzzy control strategy is employed in this paper to accomplish the regenerative braking force distribution on the front axle. A regenerative braking model is created on the Simulink platform using the braking force distribution indicated above, and experiments are run under six specific operating conditions: New European Driving Cycle (NEDC), World Light-Duty Vehicle Test Cycle (WLTC), Federal Test Procedure 72 (FTP-72), Federal Test Procedure 75 (FTP-75), China Light-Duty Vehicle Test Cycle-Passenger (CLTC-P), and New York City Cycle (NYCC). The findings demonstrate that in six typical cycling road conditions, the energy saving efficiency of electric vehicles has greatly increased, reaching over 15%. The energy saving efficiency during the WLTC driving condition reaches 25%, and it rises to 30% under the FTP-72, FTP-75, and CLTC-P driving conditions. Furthermore, under the NYCC road conditions, the energy saving efficiency exceeded 40%. Therefore, our results verify the effectiveness of the regenerative braking control strategy proposed in this paper.
Transport of Steam-Gas Mixture in Hydrodynamic Devices: A Numerical Study of Steam Reforming of Methane
Authors: Mamytbekov Galymzhan , Shayakhmetov Nurlan , Aizhulov Daniar , Kurmanseiit Maksat , Tungatarova Madina
Published: October 2023

The paper introduces a mathematical model that describes the cavitation process occurring during the passage of a water steam flow in various geometric configurations of a hydrodynamic device. The flow experiences a localized constriction (convergent nozzle) followed by expansion (divergent nozzle), exemplified by a Venturi tube or a Laval nozzle. A narrow flow channel connecting the convergent and divergent sections is equipped with a narrow-section nozzle for injecting methane molecules into the high-speed steam flow. As the steam-gas mixture passes through this zone, it is irradiated with an electron beam and sprayed into a cylindrical chamber at atmospheric pressure, where the distribution of methane molecules in water vapor forms an aerosol. Key geometric parameters of the constriction and expansion zones of the hydraulic system (cavitation-jet chamber) are determined to ensure the uniform distribution of dispersed-phase particles (methane) in the dispersion medium (water vapor). Velocity and pressure distributions of the mixed steam-gas flow are calculated using a turbulent mathematical model, specifically the k-ω model, while the motion of methane particles is simulated using a particle tracing method. The uniformity of methane molecule distribution in water vapor is assessed using Ripley’s K-function. The best performance of the hydrogen-producing chamber was observed when the cavitation-inducing nozzle’s convergence angle exceeded 50 degrees. The divergence angle of the nozzle within the range of 30–40 degrees provided the best distribution in terms of uniformity of the methane particles in the chamber.
Enhancing Textile Wastewater Treatment Performance: Optimization and Troubleshooting (Decision Support) via GPS-X Model
Authors: Wondim Tilik Tena , Dzwairo Rimuka Bloodless , Aklog Dagnachew , Janka Eshetu , Samarakoon Gamunu
Published: October 2023

Textile factory water consumption could be optimized to minimize the generation of wastewater, reduce treatment costs, and promote resource recovery. However, downstream plant operation and management is a prime concern in the textile industry, particularly bringing treated wastewater effluent to an acceptable discharge limit. The aim of the study was to optimize key process control parameters to the observed operational challenges of existing processes and suggest an operational guide to the operators and decision makers to enhance the treatment performance in GPS-X. The formulated troubleshooting and decision support strategy, and the optimization results of waste-activated sludge in the primary and secondary clarifiers, was within the range of 15 ± 5 m3/d and 83 ± 7 m3/d, respectively, with a recycle-activated sludge flow of 150 ± 10 m3/d. The sludge retention time was 5 ± 1 d and 6.7 ± 0.5 d in the secondary and primary clarifiers, respectively. The addition of a carbon source in the form of molasses had a flow of 0.5 ± 0.05 m3/d, and the variation in the influent due to wastewater characteristics and rainfall was optimized to 600 ± 50 m3/d. The optimum air flow into the aeration tank was 550 ± 5 m3/hr and saved 91.5% of energy in the optimized process. Thus, the study is indispensable for the effective and efficient operation of the plant and serves as a good guide to the plant operators and decision makers for the best course of action.
Analysis of Winter Anomaly and Annual Anomaly Based on Regression Approach
Authors: Wang Kaixin , Feng Jiandi , Zhao Zhenzhen , Han Baomin
Published: October 2023

Studying the temporal and spatial dependence of ionospheric anomalies using total electron content (TEC) can provide an important reference for developing empirical ionospheric models. In this study, winter anomaly, annual anomaly, and the contributions of winter anomaly to annual anomaly were investigated during solar cycle 24 (2008–2018) by using the global ionosphere maps of the Center for Orbit Determination in Europe during the geomagnetic activity quiet period (Kp ≤ 5) based on a regression approach. Our detailed analysis shows the following: (1) Winter anomaly is more significant at 11:00–13:00 local time (LT), and the region of winter anomaly extends from North America to the Far East with increasing solar activity levels. (2) The minimum level of solar activity corresponding to the occurrence of winter anomaly was calculated at each grid point, which can provide a reference for single-point ionospheric modeling. (3) The annual anomaly reaches its maximum at 12:00 LT when the TEC in December is 34.4% higher than in June. (4) At 12:00 LT, the winter anomaly contributes up to 32% to the annual anomaly (at this time, the winter hemisphere contributes 57% to the annual anomaly).
Remote Sensing-Based Simulation of Snow Grain Size and Spatial–Temporal Variation Characteristics of Northeast China from 2001 to 2019
Authors: Zhang Fan , Zhang Lijuan , Zheng Yanjiao , Wang Shiwen , Huang Yutao
Published: October 2023

The size of snow grains is an important parameter in cryosphere studies. It is the main parameter affecting snow albedo and can have a feedback effect on regional climate change, the water cycle and ecological security. Larger snow grains increase the likelihood of light absorption and are important for passive microwave remote sensing, snow physics and hydrological modelling. Snow models would benefit from more observations of surface grain size. This paper uses an asymptotic radiative transfer model (ART model) based on MOD09GA ground reflectance data. A simulation of snow grain size (SGS) in northeast China from 2001 to 2019 was carried out using a two-channel algorithm. We verified the accuracy of the inversion results by using ground-based observations to obtain stratified snow grain sizes at 48 collection sites in northeastern China. Furthermore, we analysed the spatial and temporal trends of snow grain size in Northeastern China. The results show that the ART model has good accuracy in inverting snow grain size, with an RMSD of 65 μm, which showed a non-significant increasing trend from 2001 to 2019 in northeast China. The annual average SGS distribution ranged from 430.83 to 452.38 μm in northeast China, 2001–2019. The mean value was 441.78 μm, with an annual increase of 0.26 μm/a, showing a non-significant increasing trend and a coefficient of variation of 0.014. The simulations show that there is also intermonth variation in SGS, with December having the largest snow grain size with a mean value of 453.92 μm, followed by January and February with 450.77 μm and 417.78 μm, respectively. The overall spatial distribution of SGS in the northeastern region shows the characteristics of being high in the north and low in the south, with values ranging from 380.248 μm to 497.141 μm. Overall, we clarified the size and distribution of snow grains over a long time series in the northeast. The results are key to an accurate evaluation of their effect on snow–ice albedo and their radiative forcing effect.
Updated Global Navigation Satellite System Observations and Attention-Based Convolutional Neural Network–Long Short-Term Memory Network Deep Learning Algorithms to Predict Landslide Spatiotemporal Displacement
Authors: Yang Beibei , Guo Zizheng , Wang Luqi , He Jun , Xia Bingqi , Vakily Sayedehtahereh
Published: October 2023

Landslide displacement prediction has garnered significant recognition as a pivotal component in realizing successful early warnings and implementing effective control measures. This task remains challenging as landslide deformation involves not only temporal dependency within time series data but also spatial dependence across various regions within landslides. The present study proposes a landslide spatiotemporal displacement forecasting model by introducing attention-based deep learning algorithms based on spatiotemporal analysis. The Maximal Information Coefficient (MIC) approach is employed to quantify the spatial and temporal correlations within the daily data of Global Navigation Satellite System (GNSS) observations. Based on the quantitative spatiotemporal analysis, the proposed prediction model combines a convolutional neural network (CNN) and long short-term memory (LSTM) network to capture spatial and temporal dependencies individually. Spatial–temporal attention mechanisms are implemented to optimize the model. Additionally, we develop a single-point prediction model using LSTM and a multiple-point prediction model using the CNN-LSTM without an attention mechanism to compare the forecasting capabilities of the attention-based CNN-LSTM model. The Outang landslide in the Three Gorges Reservoir Area (TGRA), characterized by a large and active landslide equipped with an advanced monitoring system, is taken as a studied case. The temporal MIC results shed light on the response times of monitored daily displacement to external factors, showing a lagging duration of between 10 and 50 days. The spatial MIC results indicate mutual influence among different locations within the landslide, particularly in the case of nearby sites experiencing significant deformation. The attention-based CNN-LSTM model demonstrates an impressive predictive performance across six monitoring stations within the Outang landslide area. Notably, it achieves a remarkable maximum coefficient of determination (R2) value of 0.9989, accompanied by minimum values for root mean squared error (RMSE), absolute mean error (MAE), and mean absolute percentage error (MAPE), specifically, 1.18 mm, 0.99 mm, and 0.33%, respectively. The proposed model excels in predicting displacements at all six monitoring points, whereas other models demonstrate strong performance at specific individual stations but lack consistent performance across all stations. This study, involving quantitative deformation characteristics analysis and spatiotemporal displacement prediction, holds promising potential for a more profound understanding of landslide evolution and a significant contribution to reducing landslide risk.
Ship Target Detection Method in Synthetic Aperture Radar Images Based on Block Thumbnail Particle Swarm Optimization Clustering
Authors: Huang Shiqi , Zhang Ouya , Chen Qilong
Published: October 2023

Ship target detection is an important application of synthetic aperture radar (SAR) imaging remote sensing in ocean monitoring and management. However, SAR imaging is a form of coherence imaging, meaning that there is a large amount of speckle noise in each SAR image. This seriously affects the detection of an SAR image ship target when the fuzzy C-means (FCM) clustering method is used, resulting in numerous errors and incomplete detection. It is also associated with a slow detection speed, which easily falls into the local minima. To overcome these issues, a new method based on block thumbnail particle swarm optimization clustering (BTPSOC) was proposed for SAR image ship target detection. The BTPSOC algorithm uses block thumbnails to segment the main pixels, which improves the resistance to noise interference and segmentation accuracy, enhances the ability to process different types of SAR images, and reduces the runtime. When particle swarm optimization (PSO) technology is used to optimize the FCM clustering center, global optimization is achieved, the clustering performance is improved, the risk of falling into the local minima is overcome, and the stability is improved. The SAR images from two datasets containing ship targets were used in verification experiments. The experimental results show that the BTPSOC algorithm can effectively detect the ship target in SAR images and that it maintains good integrity with regard to the detailed information from the target region. At the same time, experiments comparing the deep convolution neural network (CNN) and constant false alarm rate (CFAR) were conducted.
Faster and Lightweight: An Improved YOLOv5 Object Detector for Remote Sensing Images
Authors: Zhang Jiarui , Chen Zhihua , Yan Guoxu , Wang Yi , Hu Bo
Published: October 2023

In recent years, the realm of deep learning has witnessed significant advancements, particularly in object detection algorithms. However, the unique challenges posed by remote sensing images, such as complex backgrounds, diverse target sizes, dense target distribution, and overlapping or obscuring targets, demand specialized solutions. Addressing these challenges, we introduce a novel lightweight object detection algorithm based on Yolov5s to enhance detection performance while ensuring rapid processing and broad applicability. Our primary contributions include: firstly, we implemented a new Lightweight Asymmetric Detection Head (LADH-Head), replacing the original detection head in the Yolov5s model. Secondly, we introduce a new C3CA module, incorporating the Coordinate Attention mechanism, strengthening the network’s capability to extract precise location information. Thirdly, we proposed a new backbone network, replacing the C3 module in the Yolov5s backbone with a FasterConv module, enhancing the network’s feature extraction capabilities. Additionally, we introduced a Content-aware Feature Reassembly (content-aware reassembly of features) (CARAFE) module to reassemble semantic similar feature points effectively, enhancing the network’s detection capabilities and reducing the model parameters. Finally, we introduced a novel XIoU loss function, aiming to improve the model’s convergence speed and robustness during training. Experimental results on widely used remote sensing image datasets such as DIOR, DOTA, and SIMD demonstrate the effectiveness of our proposed model. Compared to the original Yolov5s algorithm, we achieved a mean average precision (mAP) increase of 3.3%, 6.7%, and 3.2%, respectively. These findings underscore the superior performance of our proposed model in remote sensing image object detection, offering an efficient, lightweight solution for remote sensing applications.
Self-Supervised Steering and Path Labeling for Autonomous Driving
Authors: Mihalea Andrei , Samoilescu Robert-Florian , Florea Adina Magda
Published: October 2023

Autonomous driving is a complex task that requires high-level hierarchical reasoning. Various solutions based on hand-crafted rules, multi-modal systems, or end-to-end learning have been proposed over time but are not quite ready to deliver the accuracy and safety necessary for real-world urban autonomous driving. Those methods require expensive hardware for data collection or environmental perception and are sensitive to distribution shifts, making large-scale adoption impractical. We present an approach that solely uses monocular camera inputs to generate valuable data without any supervision. Our main contributions involve a mechanism that can provide steering data annotations starting from unlabeled data alongside a different pipeline that generates path labels in a completely self-supervised manner. Thus, our method represents a natural step towards leveraging the large amounts of available online data ensuring the complexity and the diversity required to learn a robust autonomous driving policy.
Investigation on the Integration of Low-Cost NIR Spectrometers in Mill Flour Industries for Protein, Moisture and Ash Content Estimation
Authors: Boglou Vasileios , Verginadis Dimosthenis , Karlis Athanasios
Published: October 2023

The flour milling industry—a vital component of global food production—is undergoing a transformative phase driven by the integration of smart devices and advanced technologies. This transition promises improved efficiency, quality and sustainability in flour production. The accurate estimation of protein, moisture and ash content in wheat grains and flour is of paramount importance due to their direct impact on product quality and compliance with industry standards. This paper explores the application of Near-Infrared (NIR) spectroscopy as a non-destructive, efficient and cost-effective method for measuring the aforementioned essential parameters in wheat and flour by investigating the effectiveness of a low-cost handle NIR spectrometer. Furthermore, a novel approach using Fuzzy Cognitive Maps (FCMs) is proposed to estimate the protein, moisture and ash content in grain seeds and flour, marking the first known application of FCMs in this context. Our study includes an experimental setup that assesses different types of wheat seeds and flour samples and evaluates three NIR pre-processing techniques to enhance the parameter estimation accuracy. The results indicate that low-cost NIR equipment can contribute to the estimation of the studied parameters.
A Multi-Agent Prediction Method for Data Sampling and Transmission Reduction in Internet of Things Sensor Networks
Authors: Płaczek Bartłomiej
Published: October 2023

Sensor networks can provide valuable real-time data for various IoT applications. However, the amount of sensed and transmitted data should be kept at a low level due to the limitations imposed by network bandwidth, data storage, processing capabilities, and finite energy resources. In this paper, a new method is introduced that uses the predicted intervals of possible sensor readings to efficiently suppress unnecessary transmissions and decrease the amount of data samples collected by a sensor node. In the proposed method, the intervals of possible sensor readings are determined with a multi-agent system, where each agent independently explores a historical dataset and evaluates the similarity between past and current sensor readings to make predictions. Based on the predicted intervals, it is determined whether the real sensed data can be useful for a given IoT application and when the next data sample should be transmitted. The prediction algorithm is executed by the IoT gateway or in the cloud. The presented method is applicable to IoT sensor networks that utilize low-end devices with limited processing power, memory, and energy resources. During the experiments, the advantages of the introduced method were demonstrated by considering the criteria of prediction interval width, coverage probability, and transmission reduction. The experimental results confirm that the introduced method improves the accuracy of prediction intervals and achieves a higher rate of transmission reduction compared with state-of-the-art prediction methods.
A Robot-Operation-System-Based Smart Machine Box and Its Application on Predictive Maintenance
Authors: Chang Yeong-Hwa , Chai Yu-Hsiang , Li Bo-Lin , Lin Hung-Wei
Published: October 2023

Predictive maintenance is a proactive approach to maintenance in which equipment and machinery are monitored and analyzed to predict when maintenance is needed. Instead of relying on fixed schedules or reacting to breakdowns, predictive maintenance uses data and analytics to determine the appropriate time to perform maintenance activities. In industrial applications, machine boxes can be used to collect and transmit the feature information of manufacturing machines. The collected data are essential to identify the status of working machines. This paper investigates the design and implementation of a machine box based on the ROS framework. Several types of communication interfaces are included that can be adopted to different sensor modules for data sensing. The collected data are used for the application on predictive maintenance. The key concepts of predictive maintenance include data collection, a feature analysis, and predictive models. A correlation analysis is crucial in a feature analysis, where the dominant features can be determined. In this work, linear regression, a neural network, and a decision tree are adopted for model learning. Experimental results illustrate the feasibility of the proposed smart machine box. Also, the remaining useful life can be effectively predicted according to the trained models.
Fostering Employees’ Voluntary Green Behavior: The Role of Environmentally Specific Servant Leadership, Positive Affectivity, and Workplace Anxiety
Authors: Yang Liyan , Shao Xiaojing , Jiang Yuan
Published: October 2023

The recent introduction of servant leadership into the research on pro-environmental behavior in organizations has stimulated interest and concern among scholars on how an environmentally specific servant leader fosters their subordinates’ green behavior. Drawing from affective event theory, this study focuses on the underlying affective mechanism linking environmentally specific servant leadership and employee voluntary green behavior. Using two-wave data from 190 employees in two organizations, we found that environmentally specific servant leadership was indirectly related to employee voluntary green behavior via positive affectivity. Moreover, workplace anxiety moderated the indirect effect, such that it was only significant and positive under low levels of workplace anxiety. Overall, our study sheds light on the role the effect plays in unpacking the influence of environmentally specific servant leadership on employee voluntary green behavior.
Exploring the Critical Factors of Biomass Pyrolysis for Sustainable Fuel Production by Machine Learning
Authors: İşçen Asya , Öznacar Kerem , Tunç K. M. Murat , Günay M. Erdem
Published: October 2023

The goal of this study is to use machine learning methodologies to identify the most influential variables and optimum conditions that maximize biochar, bio-oil, and biogas yields for slow pyrolysis. First, experimental results reported in 37 articles were compiled into a database. Then, an explainable machine learning approach, Shapley Additive exPlanations (SHAP), was employed to find the effects of descriptors on the targets, and it was found that higher biochar yields can be obtained at lower temperatures using biomass with low volatile matter and high ash content. Following that, decision tree classification was used to discover the variables leading to high levels of the targets, and the most generalizable path for high biogas yield was found to be where the maximum particle diameter was less than or equal to 6.5 mm and the temperature was greater than 912 K. Finally, association rule mining models were created to find associations of descriptors with very high levels of yields, and among many findings, it was discovered that biomass with larger particles cannot be converted into bio-oil efficiently. It was then concluded that machine learning methods can help to determine the best slow pyrolysis conditions for the production of renewable and sustainable biofuels.
The Application of a Process Approach to the National Governance System for Sustainable Development: A Case Study in Romania
Authors: Popescu Maria , Mandru Lidia
Published: October 2023

This paper aims to present a new manner of describing and managing the national governance framework for “sustainable development (SD)”, focused on a process-based approach. This principle was applied in previous work by the authors related to the key processes of the United Nations governance for SD. The present paper continues the process approach of multi-level governance, aiming to contribute to the conceptualization of governance systems for SD at the national level, particularly in the context of the 2030 Agenda, taking into account the harmonization with higher levels of governance. The workpaper presents in distinct sections: a review of publications related to the national framework of governance for SD; the overall picture of the national SD governance system, with the generic definition of key processes; a case study performed in Romania; and a conclusion. The novelty of this paper consists of the new model tool proposed by the authors and its application in a study case that illustrates the benefits of such an approach. The proposed model is useful to SD experts and governing bodies, as well as academics and researchers, creating the framework for developing and improving SD governance at the country level in a systematic manner.
A Bibliometric Analysis of Integrating Tourism Development into Urban Planning
Authors: Krabokoukis Thomas , Polyzos Serafeim
Published: October 2023

This study conducts a literature analysis on integrating tourism development into urban planning to understand the current state of research in the field and identify potential areas for future study. The analysis, based on 11,134 articles published between 2010 and 2023, in the Scopus database, aims to identify emerging research themes, emphasize interdisciplinary collaboration, and reveal global research networks. Utilizing citation analysis, co-authorship analysis, co-occurrence analysis, bibliographic coupling analysis, and co-citation analysis with the use of VOSviewer (version 1.6.19) and Python programming language, the study sheds light on key trends in the integration of tourism development into urban planning. The findings provide a comprehensive understanding of the field’s current landscape, highlighting the importance of interdisciplinary cooperation and global research networks. This study offers valuable insights for researchers, practitioners, and policymakers, facilitating the advancement of sustainable urban tourism practices and guiding future research endeavors in integrating tourism development into urban planning.
Effects of Technology, Energy, Monetary, and Fiscal Policies on the Relationship between Renewable and Fossil Fuel Energies and Environmental Pollution: Novel NBARDL and Causality Analyses
Authors: Bildirici Melike , Çırpıcı Yasemin Asu , Ersin Özgür Ömer
Published: October 2023

There is a body of research that focuses on the examination of long-run relations between energy–environment–economic growth, and there is also a new type of recent research that focuses on the effects of monetary and fiscal economic policies on the environment. There is a research gap that exists due to omitting the effects of technology and energy policies, and this paper addresses this gap, in addition to merging both fields mentioned above, by including the asymmetric effects of fiscal and monetary policies. To explore the relations between fossil fuel and renewable energies, environmental pollution, and economic growth, in addition to including the roles of energy, technology, monetary, and fiscal policies, this paper employs novel NBARDL and NBARDL Granger Causality methods for yearly data assessments in the USA. The empirical findings of the paper point to the asymmetric impacts of monetary and fiscal policies in the short- and long-run. Interestingly, both contractionary and expansionary fiscal policies lead to higher CO2 emissions. Contractionary monetary policies exert a downward pressure on CO2 emissions, and if expansionary, the monetary policy causes environmental degradation. As an important policy, the energy policy emerges as a potent tool for reducing carbon emissions through not only renewable energy, but as a greater impact through energy efficiency and technology. Therefore, this paper highlights the importance of technology policies exhibiting varying relationships with environmental pollution, featuring unidirectional or bidirectional causality patterns. Renewable energy, energy efficiency combined with adequate technology, and energy policies are determined to have pivotal roles in CO2 emissions outcomes. Such policies should focus on cleaner energy sources accompanied by energy efficiency technologies in the USA to curtail environmental impacts; technology policies are vital in fostering innovations and encouraging cleaner technologies. The policy recommendations include an effective combination of monetary, fiscal, technology, and energy policies, backed by a strong commitment to achieving energy efficiency and renewable energy to mitigate environmental pollution and to contribute to sustainable development.
Bus-Passenger-Flow Prediction Model Based on WPD, Attention Mechanism, and Bi-LSTM
Authors: Pei Yulong , Ran Songmin , Wang Wanjiao , Dong Chuntong
Published: October 2023

The prediction of the bus passenger flow is crucial for efficient resource allocation, frequency setting, and route optimization in bus transit systems. However, it remains challenging for a single model to simultaneously capture the time-series data of the bus passenger flow with periodicity, correlation, and nonlinearity. Aiming at the complex volatility possessed by the time-series data of the bus passenger flow, a new hybrid-strategy bus-passenger-flow prediction model based on wavelet packet decomposition, an attention mechanism, and bidirectional long–short-term memory is proposed to improve the accuracy of bus-passenger-flow prediction. The differences between this study and the existing studies are as follows: Firstly, this model combines decomposition strategies and deep learning. Wavelet packet decomposition can decompose the original data into a series of smoother data components, allowing the model to be more adequate in capturing the temporal characteristics of passenger-flow data. And the model can consider the information after the predicted moment via backward computation. In addition, the model is equipped with the ability to focus on important features by incorporating an attention mechanism to minimize the interference of irrelevant information. Bus-passenger-flow prediction experiments are conducted using the Harbin bus-passenger-flow dataset as an example. The experimental results show that the model proposed in this paper can obtain more accurate bus-passenger-flow prediction results than the five baseline models can obtain.
Symmetric Time-Variant IBLF-Based Tracking Control with Prescribed Performance for a Robot
Authors: Zhang Tan , Yan Pianpian
Published: October 2023

In this work, an improved symmetric integral barrier function-based tracking control is addressed for a robotic manipulator in the presence of position error performance requirements and unknown uncertain terms. First of all, we construct an improved symmetric time-variant integral barrier function to solve the robotic system’s constrained requirements. The novel barrier function is constructed by developing an integral upper limit function that can be used in conjunction with existing performance envelope functions. Then, the equivalent conversion error is taken as the upper limit of the integral function to achieve the specified steady-state as well as transient behaviors of the system. Additionally, a disturbance observer based on the velocity tracking error is introduced to compensate for unknown uncertain terms. In the end, a numeral simulation study on a robot with two degrees is performed to indicate that the present scheme is feasible in handling the performance constraint and uncertain terms.
Assessing the Effects of Urban Digital Infrastructure on Corporate Environmental, Social and Governance (ESG) Performance: Evidence from the Broadband China Policy
Authors: Zhai Chenchen , Ding Xinyi , Zhang Xue , Jiang Shaoxiang , Zhang Yue , Li Chengming
Published: October 2023

Urban digital infrastructure is the cornerstone of optimizing resource allocation and promoting sustainable economic development in the era of digital economy, and it will also affect corporate ESG performance. Based on the data of Chinese A-share listed companies from 2011 to 2021, an asymptotic difference-in-difference model is used to investigate the impact of urban digital infrastructure on corporate ESG performance based on the “broadband China” strategy and its underlying mechanism. This paper finds that urban digital infrastructure can promote corporate ESG performance. Further, urban digital infrastructure can contribute to corporate ESG performance by increasing research and development (R&D) investment, improving corporate governance, and increasing information transparency. Through heterogeneity analysis, the results show urban digital infrastructure contributes more significantly to the ESG performance of state-owned, small and medium, growth-stage, and low-profit companies and is more pronounced in non-heavy polluting companies and companies in the central and western regions. This paper has enhanced the theoretical framework of urban digital infrastructure and corporate ESG (environmental, social, and governance) performance, paving the way for a new approach to the collaborative development of cities and enterprises in pursuit of green and sustainable growth.
IoT for Monitoring Fungal Growth and Ochratoxin A Development in Grapes Solar Drying in Tunnel and in Open Air
Authors: Templalexis Charalampos , Giorni Paola , Lentzou Diamanto , Mozzoni Francesco , Battilani Paola , Tsitsigiannis Dimitrios I. , Xanthopoulos Georgios
Published: October 2023

Optimisation of solar drying to reduce fungal growth and Ochratoxin A (OTA) contamination is a crucial concern in raisin and currant production. Stochastic and deterministic analysis has been utilized to investigate environmental indicators and drying characteristics. The analysis was performed using two seedless grape varieties (Crimson—red and Thompson—white) that were artificially inoculated with Aspergillus carbonarius during open-air and tunnel drying. Air temperature (T) and relative humidity (RH) were measured and analysed during the drying experiment, along with grape surface temperature (Ts), and water activity (aw). The grape moisture content, fungal colonization, and OTA contamination were estimated, along with the water diffusivity (Deff) and peel resistance (rpeel) to water transfer. Monitoring the surface temperature of grapes is essential in the early detection of fungal growth and OTA contamination. As surface temperature should be carried out continuously, remote sensing protocols, such as infrared sensors, provide the most efficient means to achieve this. Furthermore, data collection and analysis could be conducted through the Internet of Things (IoT), thereby enabling effortless accessibility. The average Ts of the grapes was 6.5% higher in the tunnel than in the open-air drying. The difference between the RH of air and that in the plastic crates was 16.26–17.22%. In terms of CFU/mL, comparison between white and red grapes in the 2020 and 2021 experiments showed that the red grapes exhibited significantly higher values than the white grapes. Specifically, the values for red grapes were 4.3 in 2021 to 3.4 times in 2020 higher compared to the white grapes. On the basis of the conducted analysis, it was concluded that tunnel drying provided some advantages over open-air drying, provided that hygienic and managerial requirements are met.
Umbilical Cord Mesenchymal-Stem-Cell-Derived Exosomes Exhibit Anti-Oxidant and Antiviral Effects as Cell-Free Therapies
Authors: Meng Yi , Li Chengcheng , Liang Yicong , Jiang Yu , Zhang Haonan , Ouyang Jianhua , Zhang Wen , Deng Rumei , Tan Qiuping , Yu Xiaolan , Luo Zhen
Published: October 2023

The oxidative stress induced by the accumulation of reactive oxygen species (ROS) can lead to cell aging and death. Equally, the skeletal muscle usually hosts enteroviral persistent infection in inflammatory muscle diseases. As excellent bioactive products, the exosomes derived from umbilical cord mesenchymal stem cells (ucMSCs) have been proven to be safe and have low immunogenicity with a potential cell-free therapeutic function. Here, exosomes derived from ucMSCs (ucMSC-EXO) were extracted and characterized. In a model of oxidative damage to skin fibroblasts (HSFs) under exposure to H2O2, ucMSC-EXO had an observable repairing effect for the HSFs suffering from oxidative damage. Furthermore, ucMSC-EXO inhibited mitogen-activated protein kinases (MAPK), c-Jun N-terminal kinase (JNK), and nuclear factor kappa-B (NF-κB) signaling pathways, thereby promoting p21 protein expression while decreasing lamin B1 protein expression, and finally alleviated oxidative stress-induced cell damage and aging. In a model of rhabdomyosarcoma (RD) cells being infected by enterovirus 71 (EV71) and coxsackievirus B3 (CVB3), the ucMSC-EXO enhanced the expression of interferon-stimulated gene 15 (ISG15) and ISG56 to inhibit enteroviral replication, whereafter reducing the virus-induced proinflammatory factor production. This study provides a promising therapeutic strategy for ucMSC-EXO in anti-oxidative stress and antiviral effects, which provides insight into extending the function of ucMSC-EXO in cell-free therapy.
A Single-Dose Intramuscular Immunization of Pigs with Lipid Nanoparticle DNA Vaccines Based on the Hemagglutinin Antigen Confers Complete Protection against Challenge Infection with the Homologous Influenza Virus Strain
Authors: Nguyen The N. , Kumari Sushmita , Sillman Sarah , Chaudhari Jayeshbhai , Lai Danh C. , Vu Hiep L. X.
Published: October 2023

The Influenza A virus of swine (IAV-S) is highly prevalent and causes significant economic losses to swine producers. Due to the highly variable and rapidly evolving nature of the virus, it is critical to develop a safe and versatile vaccine platform that allows for frequent updates of the vaccine immunogens to cope with the emergence of new viral strains. The main objective of this study was to assess the feasibility of using lipid nanoparticles (LNPs) as nanocarriers for delivering DNA plasmid encoding the viral hemagglutinin (HA) gene in pigs. The intramuscular administration of a single dose of the LNP-DNA vaccines resulted in robust systemic and mucosal responses in pigs. Importantly, the vaccinated pigs were fully protected against challenge infection with the homologous IAV-S strain, with only 1 out of 12 vaccinated pigs shedding a low amount of viral genomic RNA in its nasal cavity. No gross or microscopic lesions were observed in the lungs of the vaccinated pigs at necropsy. Thus, the LNP-DNA vaccines are highly effective in protecting pigs against the homologous IAV-S strain and can serve as a promising platform for the rapid development of IAV-S vaccines.
Predicting COVID-19 and Influenza Vaccination Confidence and Uptake in the United States
Authors: Shen Lijiang , Lee Daniel
Published: October 2023

This study investigates and compares the predictors of COVID-19 and influenza vaccination confidence and uptake in the U.S. Vaccine hesitancy is defined as the reluctance or refusal (i.e., less than 100% behavioral intention) to vaccinate despite the availability of effective and safe vaccines. Vaccine hesitancy is a major obstacle in the fight against infectious diseases such as COVID-19 and influenza. Predictors of vaccination intention are identified using the reasoned action approach and the integrated behavioral model. Data from two national samples (N = 1131 for COVID-19 and N = 1126 for influenza) were collected from U.S. Qualtrics panels. Tobit regression models were estimated to predict percentage increases in vaccination intention (i.e., confidence) and the probability of vaccination uptake (i.e., intention reaching 100%). The results provided evidence for the reasoned approach and the IBM model and showed that the predictors followed different patterns for COVID-19 and influenza. The implications for intervention strategies and message designs were discussed.
High-Dose Hydrocortisone Treatment Does Not Affect Serum C-Reactive Protein (CRP) Concentrations in Healthy Dogs
Authors: Heilmann Romy M. , Grützner Niels , Kook Peter H. , Schellenberg Stefan , Suchodolski Jan S. , Steiner Joerg M.
Published: October 2023

Measuring C-reactive protein (CRP) in serum is a useful surrogate marker for assessing disease progression and treatment response in dogs with autoinflammatory diseases. Affected dogs often receive high-dose glucocorticoid treatment, but the effect of such treatment alone on serum CRP concentrations is unknown. We evaluated serum CRP concentrations via immunoassay (sandwich enzyme-linked immunosorbent assay and particle-enhanced turbidimetric immunoassay) in 12 healthy beagle dogs administered high-dose hydrocortisone (8 mg/kg q12 h) per os vs. placebo over 28 days (days 0, 1, 5, and 28) in a randomized parallel study design. Serum CRP concentrations slightly decreased during treatment or placebo but without a significant association with hydrocortisone administration (p = 0.761). Compared to baseline, serum CRP concentrations were decreased by >2.7-fold (minimum critical difference) in three hydrocortisone-treated dogs and two dogs in the placebo group on day 28, whereas an increase to >2.7-fold was seen in one dog receiving placebo. These results suggest a lack of confounding effects of high-dose hydrocortisone administration on serum CRP concentrations in healthy dogs. This might also hold in dogs with autoinflammatory conditions and/or administration of other high-dose corticosteroids, suggesting that CRP presents a suitable biomarker to monitor inflammatory disease processes. However, this needs confirmation by further studies evaluating corticosteroid-induced cellular (e.g., hepatic) transcriptome and proteome changes.
Research on Energy Management Strategy of a Hybrid Commercial Vehicle Based on Deep Reinforcement Learning
Authors: Xi Jianguo , Ma Jingwei , Wang Tianyou , Gao Jianping
Published: October 2023

Given the influence of the randomness of driving conditions on the energy management strategy of vehicles, deep reinforcement learning considering driving conditions prediction was proposed. A working condition prediction model based on the BP neural network was established, and the correction coefficient of vehicle demand torque was determined according to the working condition prediction results. An energy management strategy and deep reinforcement learning were integrated to build an energy management strategy with deep reinforcement learning based on driving condition prediction. Simulation experiments were conducted according to the actual collected working condition data. The experimental results show that the energy management strategy, i.e., deep reinforcement learning considering working condition prediction, has faster convergence speed and more vital self-learning ability, and the equivalent fuel consumption per 100 km under different driving conditions is 6.411 L/100 km, 6.327 L/100 km, and 6.388 L/100 km, respectively. Compared with the unimproved strategy, the fuel economy can be improved by 3.18%, 3.08%, and 2.83%. The research shows that the energy management strategy, the deep reinforcement learning based on driving condition prediction, is effective and adaptive.
Socio-hygienic monitoring of the incidence of cervical erosion in women of the Republic of Dagestan
Authors:   Dagestan State Medical University of the Ministry of Health of Russia, Makhachkala, Russia, Magomedova U. A. , Bagandova D. S. ,   Dagestan State Medical University of the Ministry of Health of Russia, Makhachkala, Russia, Mamasheva G. D. ,   Dagestan State Medical University of the Ministry of Health of Russia, Makhachkala, Russia
Published: October 2023

Cervical erosion is diagnosed in about 15 % of the female population and its causes are not well understood. Among the main etiological factors mention infections, injuries, inflammation, hormonal disorders.Cervical erosion is often accompanied by unpleasant painful symptoms and in many cases turns into precancerous and cancerous diseases, which requires careful monitoring for early detection of pathology and timely initiation of treatment. Common methods for diagnosing diseases of the cervix are colposcopy, cytological examination and, if necessary, histological analysis. In some cases, the impact of environmental factors, including anthropogenic impacts, may lead to an increase in the incidence of cervical erosion in women. The Republic of Dagestan is dominated by agricultural production and there is an excess application of pesticides to the soils of the region. Despite a significant reduction in the pesticide load in recent years, it is still quite high. Some published papers discuss the relationship between the excess of pesticides in the soil and the incidence of cervical erosion in women of Dagestan. Obviously, in addition to the characteristic risk factors described in the literature, the local female population is exposed to additional harmful effects associated with agricultural production. This makes it relevant to conduct a general monitoring of the incidence of this pathology among women in Dagestan. The purpose of this article is to analyze additional risk factors for the development of cervical erosion in women of the Republic of Dagestan and the possibility of monitoring the female population for early detection of the disease.