bluuebunny/crossref_metadata_2025 huggingface dataset:
Grassland Ecological Compensation, Income Level and Policy Satisfaction: An Empirical Analysis Based on a Survey of Herders in Ecological Protection Redline Areas
Authors: Yang Li , Qiao Guanghua
Published: January 2023

In the past 10 years, the implementation of grassland ecological compensation policy has played an important role in the sustainable development of the pastoral economy. How much impact on herders will delineating the ecological conservation redline have? Such delineation is significant for the smooth implementation of the ecological conservation redline. Based on this, taking the three banner counties with a large area under the control area of the ecological redline of the Xilin Gol as examples, OLS and quantile regression were used to analyze the impact of a grassland ecological compensation policy on herdsmen’s income level, and the ordered Probit model was used to analyze the influencing factors of herdsmen’s satisfaction with the policy. The results show that: (1) grassland ecological compensation has a significant positive impact on low-income herders in ecological protection redline areas; (2) grassland ecological compensation, income and the implementation of current policy have a positive impact on the satisfaction of herders in the redline area; (3) herdsmen are highly satisfied with the grassland ecological compensation policy, but there is still a lot of room for improvement in the compensation policy after the redline is delineated. In this regard, we should increase compensation for areas with a high proportion of ecological conservation redlines, and explore ways to increase income from animal husbandry products. At the same time, we should strengthen the publicity of ecological protection redline policies and promote the timely disbursement of funds, reconstruct the grassland ecological compensation mechanism by strengthening policy incentives and hardening regulatory constraints, and effectively improve the policy efficiency of ecological protection redlines.
Unmasking the Action-Oriented ESD Approach to Acting Environmentally Friendly
Authors: Saleem Atif , Dare Philip Saagyum
Published: January 2023

Since the United Nations’ declaration on education for sustainable development (ESD), countries and universities across the globe aim to enhance sustainability consciousness, knowingness, attitude, and behavior among learners. However, there is no consensus on pragmatic approaches to achieving environmental sustainability. The ESD action-oriented approach unmasks the complexities of sustainable development and develops learners’ action competence to act environmentally friendly. The present research measures the action competence outcomes of learners’ sustainability consciousness in Malaysian universities through the ESD action-oriented framework, which comprised five components: action-taking, student leadership, peer interaction, community involvement, and interdisciplinarity. A survey was carried out to examine the perception of 975 students and 458 faculty members. The findings revealed that the ESD action-based pedagogy is implemented and practiced as a key strategy in teaching methods to prepare students to understand and respond to the changing world and drive sustainable development in Malaysia. Moreover, action-based ESD demonstrated the potential to be successful. Although the classroom teaching and learning practices in Malaysian universities benefit greatly from the adoption of the ESD action approach, the action-taking and interdisciplinarity components of the action-oriented framework are shown to contribute to sustainability consciousness. The present study offered a new insight for sustainability practitioners and stakeholders on the action-oriented ESD approach to sustainability consciousness and how it can promote the action competence of learners.
Biomass Estimation of Subtropical Arboreal Forest at Single Tree Scale Based on Feature Fusion of Airborne LiDAR Data and Aerial Images
Authors: Yan Min , Xia Yonghua , Yang Xiangying , Wu Xuequn , Yang Minglong , Wang Chong , Hou Yunhua , Wang Dandan
Published: January 2023

Low-cost UAV aerial photogrammetry and airborne lidar scanning have been widely used in forest biomass survey and mapping. However, the feature dimension after multisource remote sensing fusion is too high and screening key features to achieve feature dimension reduction is of great significance for improving the accuracy and efficiency of biomass estimation. In this study, UAV image and point cloud data were combined to estimate and map the biomass of subtropical forests. Firstly, a total of 173 dimensions of visible light vegetation index, texture, point cloud height, intensity, density, canopy, and topographic features were extracted as variables. Secondly, the Kendall Rank correlation coefficient and permutation importance (PI) index were used to identify the key features of biomass estimation among different tree species. The random forest (RF) model and XGBoost model finally were used to compare the accuracy of biomass estimation with different variable sets. The experimental results showed that the point cloud height, canopy features, and topographic factors were identified as the key parameters of the biomass estimate, which had a significant influence on the biomass estimation of the three dominant tree species in the study area. In addition, the differences in the importance of characteristics among the tree species were discussed. The fusion features combined with the PI index screening and RF model achieved the best estimation accuracy, the R2 of 0.7356, 0.8578, and 0.6823 were obtained for the three tree species, respectively.
“First Among Equals”: Unpacking Patterns of EFL Teachers’ Sustainable Feedback Strategies in Learner-Centered Language Learning Classrooms in the Chinese Context
Authors: Liu Hao , Zhang Wenzhong
Published: January 2023

This narrative case study investigated English as a Foreign Language (EFL) teachers’ sustainable strategies in giving feedback and the factors influencing feedback decision-making in learner-centered language learning classroom in the Chinese context. Data from transcripts of videotaped classroom interaction, audio-taped stimulated recalls, and semi-structured interviews were collected and analyzed to report the findings. Results indicated that there were six types of naturally occurring teacher feedback in the learner-centered language learning classroom, including negotiated feedback, content feedback, task feedback, informational feedback, affective feedback, and learning strategy feedback. Among them, informational feedback was most preferred, but learning strategy feedback was least used across the course. Furthermore, there were different patterns of distribution between different feedback types as well as between oral and written teacher feedback. More importantly, results revealed that the teacher’s feedback decision-making was influenced by four factors: teacher-related factors, learner-related factors, task-related factors, and learning context-related factors. The paper provides possible explanations for interpretations of EFL teacher feedback strategies from qualitative data and offers some implications for sustaining teacher feedback to promote students’ learning and development.
Machine Learning-Based GPR with LBFGS Kernel Parameters Selection for Optimal Throughput Mining in 5G Wireless Networks
Authors: Isabona Joseph , Imoize Agbotiname Lucky , Ojo Stephen , Do Dinh-Thuan , Lee Cheng-Chi
Published: January 2023

Considering the ever-growing demand for an efficient method of deductive mining and extrapolative analysis of large-scale dimensional datasets, it is very critical to explore advanced machine learning models and algorithms that can reliably meet the demands of modern cellular networks, satisfying computational efficiency and high precision requirements. One non-parametric supervised machine learning model that finds useful applications in cellular networks is the Gaussian process regression (GPR). The GPR model holds a key controlling kernel function whose hyperparameters can be tuned to enhance its supervised predictive learning and adaptive modeling capabilities. In this paper, the limited-memory Broyden–Fletcher–Goldfarb–Shanno (LBFGS) with kernel parameters selection (KPS) algorithm is employed to tune the GPR model kernel hyperparameters rather than using the standard Bayesian optimization (BOP), which is computationally expensive and does not guarantee substantive precision accuracy in the extrapolative analysis of a large-scale dimensional dataset. In particular, the hybrid GPR–LBFGS is exploited for adaptive optimal extrapolative learning and estimation of throughput data obtained from an operational 5G new radio network. The extrapolative learning accuracy of the proposed GPR–LBFGS with the KPS algorithm was analyzed and compared using standard performance metrics such as the mean absolute error, mean percentage error, root mean square error and correlation coefficient. Generally, results revealed that the GPR model combined with the LBFGS kernel hyperparameter selection is superior to the Bayesian hyperparameter selection method. Specifically, at a 25 m distance, the proposed GPR–LBFGS with the KPS method attained 0.16 MAE accuracy in throughput data prediction. In contrast, the other methods attained 46.06 and 53.68 MAE accuracies. Similarly, at 50 m, 75 m, 100 m, and 160 m measurement distances, the proposed method attained 0.24, 0.18, 0.25, and 0.11 MAE accuracies, respectively, in throughput data prediction, while the two standard methods attained 47.46, 49.93, 29.80, 53.92 and 47.61, 52.54, 53.43, 54.97, respectively. Overall, the GPR–LBFGS with the KPS method would find valuable applications in 5G and beyond 5 G wireless communication systems.
Coupling Effects of Terracing and Vegetation on Soil Ecosystem Multifunctionality in the Loess Plateau, China
Authors: Wu Dan , Wei Wei , Li Zongshan , Zhang Qindi
Published: January 2023

In semi-arid and arid terrestrial ecosystems, terracing and vegetation may improve soil conditions and enhance habitats. Considerable recent works have focused on evaluating how terracing and vegetation affect individual ecosystem function, but none of these evaluations included soil ecosystem multifunctionality (SEMF), which has a positive significance for optimizing soil ecosystem management. Based on the survey data of six different combinations of terracing and vegetation in the Chinese Loess Plateau, 15 functional indicators related to soil fertility, nutrient transformation/cycling, and water conservation were selected. The maximum conversion of the mean value method was employed to quantify SEMF. Concerning individual ecosystem services, the capacities of half-moon terraces-Pinus tabulaeformis (Ht-P. tabulaeformis) and level benches-Caragana korshinskii (Lb-C. korshinskii) to maintain soil fertility were 43.25% and 42.01% higher than those of counter-slope terraces-Platycladus orientalis (Ct-P. orientalis). On the contrary, Ct-P. orientalis showed better nutrient transformation and cycling services, which was 9.23% higher than those of Ht-P. tabulaeformis, therefore, we observed the highest SEMF in the Ht-P. tabulaeformis. Terracing, with a 29.2% explained variation, had a greater influence than that of vegetation (12.6%), while the coupling effect of terracing and vegetation (37.9%) was the most important factor that determined the SEMF. Thus, Ht-P. tabulaeformis and Lb-C. korshinskii should be promoted in the Loess Plateau area. The results of this study have significance in terms of understanding the interactions between terracing, vegetation, and soil ecosystems.
Examining the Relationship between Brand Symbolism and Brand Evangelism through Consumer Brand Identification: Evidence from Starbucks Coffee Brand
Authors: Osmanova Irada , Ozerden Seden , Dalal Bassam , Ibrahim Blend
Published: January 2023

In today’s competitive environment, it is important to understand that consumers’ evangelical behavior depends on symbolic brand attachment. This study, drawing on social identity theory (SIT), investigates the direct relationship between brand symbolism and three characteristics of brand evangelism, namely purchase intention, positive brand referrals, and oppositional brand referrals, while also considering the mediating role of consumer brand identification (CBI) and the moderating role of generational cohorts. A total of 323 Starbucks coffee shop consumers were analyzed, and the structural equation modeling (SEM) approach was employed using Smart PLS 3.2. The findings revealed that brand symbolism is the strongest predictor of positive brand referrals, followed by purchase intention and oppositional brand referrals. CBI was a significant mediator between brand symbolism and the three aspects of brand evangelism. The results of the moderation effect of the generational cohort showed a significant relationship between brand symbolism and purchase intention. Interestingly, the generational cohort was not a significant moderator between brand symbolism and positive brand referrals and oppositional brand referrals. The study concludes with theoretical and managerial implications, as well as some suggestions for future studies.
Thermomechanical Analyses of Alkali-Treated Coconut Husk-Bagasse Fiber-Calcium Carbonate Hybrid Composites
Authors: Verma Deepak , Okhawilai Manunya , Goh Kheng Lim , Sharma Mohit
Published: January 2023

Natural fiber-reinforced composites can contribute to reducing carbon footprint goals due to their ability to reduce overall product weight, bio-diverse feedstocks, and recyclability potential. In this work, natural fiber-based composites containing the reinforcement of coconut husk and bagasse fiber with calcium carbonate (CaCO3) ingredients were prepared and analyzed. The composites were analyzed for mechanical, thermomechanical, and morphological properties. The reinforcements were chemically functionalized using 5% w/v NaOH to enhance their interactions with the epoxy resins. The chemical functionalization created perforation on the fiber surface, improving the interlocking of fibres with the resin material and strengthening the mechanical performance of the composite. The composites developed using modified reinforcement treatment resulted in increased tensile strength (64.8%) and flexural strength (70%). The reinforcement treatment influenced the hydrophilicity, and the water absorption of treated composites was reduced more than five times compared to the unmodified composites. Scanning electron microscopy revealed morphological changes due to fiber modification, the underlaying mechanism of fiber contraction, and enhanced fiber matrix interface interlocking and adhesion strengthening. Thermal analysis confirmed that alkali treatment improves the crystallinity of the fiber and thereto the degradation temperature of treated fiber composites (both bagasse and coconut husk), which is 375.27 °C, the highest amongst the developed hybrid composites.
The Impact of the Mining Equipment, Technological Trends, and Natural Resource Demand on Climate Change in Congo
Authors: Massonini Ngoma Railh Gugus Tresor , Abraham Mahanga Tsoni Cety Gessica , Meng Xiangrui , Bashiru Danwana Sumaiya
Published: January 2023

Mining exploitation and its impact on global warming have become significant problems. The main objective of this study is to investigate the impact of mining industry equipment and natural resource demand on global warming in Congo. A cross-sectional survey was conducted from October 2021 to July 2022, using a self-administered questionnaire. The survey was carried out with 1360 respondents from Congo who worked in the mining sector for 18 years and above. For our research, we used the Partial least squares structural equation modeling approach (PLS-SEM) with SmartPLS software to analyze data. The results show that technological trends are the strongest predictor of global warming in Congo. Equipment was also the most significant predictor of climate change in Congo. Faced with this danger, we are sounding the alarm and asking for urgent intervention from the government. Moreover, all the scientific evidence on this subject is now irrefutable. Even if the climate of Congo may prove less worrying for the moment, the probability that it survives seems very low. However, the Congolese government must take immediate possible precautions to prevent Congo from finding itself in the same challenge currently faced by developed countries.
Flight Training Risk Identification and Assessment Based on the HHM-RFRM Model
Authors: Sun Hong , Yang Fangquan , Zhang Peiwen , Zhao Yunxiang
Published: January 2023

Due to the unavoidable operational risks and insufficient risk management capabilities of beginner pilots in flight training, the challenge of risk control in aviation schools has become increasingly prominent. To ensure the safety of flight training in aviation schools and to reduce costs and increase revenue, the essential prerequisite for improving efficiency is risk management. Therefore, it is necessary to explore risk identification and assessment methods. This paper adopts the holographic modeling (HHM) method and risk filtering, rating and management (RFRM) theory. First, the HHM idea is used to construct a risk identification framework (HHM-PAVE) for flight training. Second, based on the dual criteria, multiple criteria and cloud model (CM) in the RFRM approach, an improved risk assessment matrix-cloud model (IPC-CM) is proposed and combined with the N-K model and Bayes’ theorem to propose a coupled risk scenario hazard measurement model (CR-HM) based on the HHM-RFRM approach in risk assessment. In the assessment process, the impact of risk factors on system stability as well as the uncertainty problem and coupling–risk quantification problem in expert assessment are considered to obtain scientific and objective quantitative assessment results. Finally, the risk identification and assessment experiments were conducted using HHM-RFRM on the flight training. The results show that the method can more accurately identify critical risk factors in a flight training system and provide a new perspective for risk prevention and control.
The Impact of “Dual-Control” Regulations on the Green Total Factor Efficiency of Shaoxing’s Industrial Sector
Authors: Pei Zhigang , Chen Jiaming , Fang Jun , Fan Jiangpeng , Gong Zhilan , Zheng Qingying
Published: January 2023

Promoting the decoupling between economic growth and carbon emissions through government intervention is very crucial for China to achieve carbon neutrality. This paper evaluates the green development performance with the help of the green total factor efficiency index and explores the impact of “Dual-Control” regulations on the green development of the industrial sector in Shaoxing using the differences-in-differences model. The results show that there are great diversities in the green development of different industries and that the energy-intensive industries have relatively poorer performance. The “Dual-Control” regulations significantly narrow the differences and promote the green development of Shaoxing’s industry but result in profit erosion for industrial enterprises owing to direct energy-saving expenditure and other indirect policy execution costs. The results of the dynamic analysis reveal that the negative economic impact has hysteresis and persistence. Different from previous studies, this paper considers the cross effects of different policies and examines the comprehensive effect of the policy package under the “Dual-Control” regulations. The conclusion provides a supplement to revealing the relationship between government regulation and energy conservation and emission reduction.
An Artificial Intelligence-Based Stacked Ensemble Approach for Prediction of Protein Subcellular Localization in Confocal Microscopy Images
Authors: Aggarwal Sonam , Gupta Sheifali , Gupta Deepali , Gulzar Yonis , Juneja Sapna , Alwan Ali A. , Nauman Ali
Published: January 2023

Predicting subcellular protein localization has become a popular topic due to its utility in understanding disease mechanisms and developing innovative drugs. With the rapid advancement of automated microscopic imaging technology, approaches using bio-images for protein subcellular localization have gained a lot of interest. The Human Protein Atlas (HPA) project is a macro-initiative that aims to map the human proteome utilizing antibody-based proteomics and related c. Millions of images have been tagged with single or multiple labels in the HPA database. However, fewer techniques for predicting the location of proteins have been devised, with the majority of them relying on automatic single-label classification. As a result, there is a need for an automatic and sustainable system capable of multi-label classification of the HPA database. Deep learning presents a potential option for automatic labeling of protein’s subcellular localization, given the vast image number generated by high-content microscopy and the fact that manual labeling is both time-consuming and error-prone. Hence, this research aims to use an ensemble technique for the improvement in the performance of existing state-of-art convolutional neural networks and pretrained models were applied; finally, a stacked ensemble-based deep learning model was presented, which delivers a more reliable and robust classifier. The F1-score, precision, and recall have been used for the evaluation of the proposed model’s efficiency. In addition, a comparison of existing deep learning approaches has been conducted with respect to the proposed method. The results show the proposed ensemble strategy performed exponentially well on the multi-label classification of Human Protein Atlas images, with recall, precision, and F1-score of 0.70, 0.72, and 0.71, respectively.
Real-Time Tracking of Time-Varying Cable Frequency Based on a Time-Domain Signal Processing Method
Authors: Shi Zhongqi , Zhong Rumian , Jin Nan
Published: January 2023

In this paper, a time-domain signal processing method is proposed to extract the real-time time-varying cable frequency. The proposed conjugate-pair decomposition (CPD) method uses empirical mode decomposition (EMD) to obtain intrinsic mode functions (IMFs), and then the instantaneous frequency can be extracted by post-processing the conjugate-pair of IMFs. Several numerical simulations and a composite cable-stayed bridge experiment are used to validate the accuracy and capability of the proposed method for tracking time-varying cable frequency. Moreover, the proposed method may be further used to assess cable fatigue damage.
Efficacy of Electrocoagulation Treatment for the Abatement of Heavy Metals: An Overview of Critical Processing Factors, Kinetic Models and Cost Analysis
Authors: Khan Saif Ullah , Khalid Mohammad , Hashim Khalid , Jamadi Mehdi Hassanvand , Mousazadeh Milad , Basheer Farrukh , Farooqi Izharul Haq
Published: January 2023

The electrocoagulation (EC) process introduces coagulants by electrochemical means, and is widely adopted for removing heavy metals, besides other contaminants, such as organic pollutants, suspended and dissolved solids, colloidal materials, etc. However, its capability can vary significantly, depending on the operating conditions. Although most of the investigations so far are limited at the laboratory level with artificially prepared solutions or industrial effluent lacking full- and field-scale studies, the success of the process depends a lot on optimizing the process variable. It has been found that the current density (typically 1–20 mA/cm2), type of electrode (generally aluminum or iron) and minimum electrolysis time are the key process parameters that influence performance. Furthermore, key mechanisms involved in the EC process, including charge neutralization, reduction-oxidation and precipitation/co-precipitation, are crucial for pollutant abatement. This review presents a detailed study undertaking all significant parameters that play a crucial role in the EC process, its mechanism, and improving the efficiency of this process by optimization of these parameters, along with suitable kinetic models.
Tourist Movement Patterns and the Effects of Spatial Configuration in a Cultural Heritage and Urban Destination: The Case of Madaba, Jordan
Authors: Jamhawi Monther M. , Zidan Roa’a J. , Sherzad Mohammed Fareed
Published: January 2023

This study investigated the impact of spatial configuration on tourist movement patterns and its role in managing cultural heritage destinations in the city of Madaba. Understanding tourist movement patterns is important for the development of sustainable tourism practices in cultural heritage destinations, as it can help to identify attractions of high tourist impact and inform the development of strategies to mitigate the negative impacts on site preservation and management. Madaba is one of the largest homes to ancient mosaics and is one of Jordan’s most important cultural heritage tourism destinations. This study was carried out using a variety of methods, including observation, site exploration, tourist diaries, questionnaires, and interviews. Space syntax was one of the spatial analytical tools used to assess connectivity and integration. The study found that the arrangement of attraction points and routes, physical and visual connectivity, accessibility, and the location of tourist activities all influenced the spatial behavior of tourists. The findings of this study can be used to guide management and development policies such as redistributing tourist flow, managing and developing tourism resources, and promoting the adaptation of sustainable tourism.
Identification of Urban Jobs–Housing Sites Based on Online Car-Hailing Data
Authors: Bi Shuoben , Wang Luye , Liu Shaoli , Zhang Lili , Yuan Cong
Published: January 2023

With the development of cities, the organization of jobs–housing space is becoming more complex, and the rapid, effective identification of both residences and workplaces is crucial to sustainable urban development. The long time series of online car-hailing data conveys a large amount of activity trajectory information about urban populations, which can represent the social functions of urban areas, including workplaces and residences. This paper constructs a jobs–housing site identification model based on human activity characteristics. This model uses a time series dataset of online car hailing that characterizes the changes in regional passenger flow and implements the similarity measure and semi-supervised learning of time series to determine the classification of urban areas. Then, the jobs–housing factor method is introduced to extract the jobs–housing characteristics of different regions, which achieves the jobs–housing site identification. Finally, the empirical analysis of Chengdu city shows that the proposed model method can effectively mine the distribution of urban jobs–housing sites. The identification results are consistent with the actual situation, and the combination of the time series similarity and the jobs–housing feature variable improves the identification effect, providing a new way of thinking about urban jobs–housing space research.
Influence of Physical Symmetries on the Magnetization Dynamics in Magnetic Fibers
Authors: Blachowicz Tomasz , Steblinski Pawel , Ehrmann Andrea
Published: January 2023

Magnetic nanofibers belong to the geometries which are intensively investigated in simulations and experiments due to their unique magnetic properties, varying in their lengths, cross-sections, and bending radii. Besides basic research of different magnetization reversal processes and magnetization dynamics in bent nanofibers, these structures are of potential interest for data storage applications, data transport, or other tasks in spintronics devices. While previous simulations concentrated on the domain wall transport through coupled bent nanofibers, creating networks with many in- and outputs to establish nanofiber-based domain wall logics, here we show the influence of the constricted area, in which a rotating magnetic field is applied in the middle of bent or straight magnetic nanofibers, on the magnetization dynamics. Our micromagnetic simulations, performed by Magpar, reveal a strong impact not only of this area, but also of the curvature of the nanofiber as well as of an additional Dzyaloshinskii–Moriya interaction (DMI).
K2CdGe3S8: A New Infrared Nonlinear Optical Sulfide
Authors: Wu Zi-Xuan , Chen Wen-Fa , Liu Bin-Wen , Jiang Xiao-Ming , Guo Guo-Cong
Published: January 2023

A quaternary metal chalcogenide, namely K2CdGe3S8 (I), is obtained through a high-temperature solid-state approach. Compound I crystallizes with the non-centrosymmetric space group P212121. It features a 2D layer structure with [CdGe3S8] layers consisting of tetrahedral GeS4 and CdS4 units, and counter K+ embedded between the layers. The compound exhibits a powder second-harmonic generation (SHG) response of ~0.1 times that of KH2PO4 (KDP) with phase-matchable character at the laser wavelength of 1064 nm. Remarkably, it has a wide band gap (3.20 eV), which corresponds to a favorable high laser-induced damage threshold of 6.7 times that of AgGaS2. In addition, the calculated birefringence (Δn) is 0.039 at the wavelength of 1064 nm, which satisfies the Δn criteria for a promising infrared NLO material.
A Study on k-Hyperideals in Ordered Semihyperrings
Authors: Kou Zheng , Gheisari Mehdi , Omidi Saber
Published: January 2023

In this study, we propose the concept of left extension of a hyperideal by generalizing the concept of k-hyperideals in ordered semihyperrings with symmetrical hyper-operation ⊕. By using the notion of extension of a k-hyperideal, we prove some results in ordered semihyperrings. The results of this paper can be viewed as a generalization for k-ideals of semirings.
Output Layer Structure Optimization for Weighted Regularized Extreme Learning Machine Based on Binary Method
Authors: Yang Sibo , Wang Shusheng , Sun Lanyin , Luo Zhongxuan , Bao Yuan
Published: January 2023

In this paper, we focus on the redesign of the output layer for the weighted regularized extreme learning machine (WRELM). For multi-classification problems, the conventional method of the output layer setting, named “one-hot method”, is as follows: Let the class of samples be r; then, the output layer node number is r and the ideal output of s-th class is denoted by the s-th unit vector in Rr (1≤s≤r). Here, in this article, we propose a “binarymethod” to optimize the output layer structure: Let 2p−1<r≤2p, where p≥2, and p output nodes are utilized and, simultaneously, the ideal outputs are encoded in binary numbers. In this paper, the binary method is employed in WRELM. The weights are updated through iterative calculation, which is the most important process in general neural networks. While in the extreme learning machine, the weight matrix is calculated in least square method. That is, the coefficient matrix of the linear equations we solved is symmetric. For WRELM, we continue this idea. And the main part of the weight-solving process is a symmetry matrix. Compared with the one-hot method, the binary method requires fewer output layer nodes, especially when the number of sample categories is high. Thus, some memory space can be saved when storing data. In addition, the number of weights connecting the hidden and the output layer will also be greatly reduced, which will directly reduce the calculation time in the process of training the network. Numerical experiments are conducted to prove that compared with the one-hot method, the binary method can reduce the output nodes and hidden-output weights without damaging the learning precision.
The Impact of Internet Use on Citizens’ Trust in Government: The Mediating Role of Sense of Security
Authors: Wang Zicheng , Liu Huiting , Li Tianfeng , Zhou Lijuan , Zhou Mingxing
Published: January 2023

With the rapid development of communication technologies, the Internet use has become the main channel for citizens to obtain information and knowledge. It has been widely established that Internet use can have a significant impact on citizens’ expectations, perceptions, and behaviors. Government trust is the reasonable expectation of citizens on in the administrative activities of the government and its administrators, which should rightly be influenced by the behavior of citizens’ Internet use. However, limited studies have investigated the relationship between Internet use and citizens’ trust in the government. Therefore, in this study, the effect of Internet use on trust in the government was investigated using data from the 2017 Chinese Social Survey. The baseline regression results revealed that Internet use reduces trust in the government. This phenomenon was persistently observed after several robustness tests. A heterogeneity analysis revealed that Internet use negatively influenced citizens from Eastern and Western China, lower age groups, and agricultural households. Social amplification of the risk and the theory of rational choice revealed that a sense of security partially mediates the relationship between Internet use and citizens’ trust in the government. Internet use reduces citizens’ sense of security and subsequently decreases trust in the government. Our findings revealed establishing a network information supervision and public opinion guidance mechanism. At the same time, consider the role of social security services in resolving social risks. These initiatives are essential to ensure citizens’ trust in their government.
Optimization Model for the Energy Supply Chain Management Problem of Supplier Selection in Emergency Procurement
Authors: Noh Jiseong , Hwang Seung-June
Published: January 2023

In energy supply chain management (ESCM), the supply chain members try to make long-term contracts for supplying energy stably and reducing the cost. Currently, optimizing ESCM is a complex problem with two social issues: environmental regulations and uncertainties. First, environmental regulations have been tightened in countries around the world, leading to eco-friendly management. As a result, it has become imperative for the energy buyer to consider not only the total operating cost but also carbon emissions. Second, the uncertainties, such as pandemics and wars, have had a serious impact on handling ESCM. Since the COVID-19 pandemic disrupted the supply chain, the supply chain members adopted emergency procurement for sustainable operations. In this study, we developed an optimization model using mixed-integer linear programming to solve ESCM with supplier selection problems in emergency procurement. The model considers a single thermal power plant and multiple fossil fuel suppliers. Because of uncertainties, energy demand may suddenly change or may not be supplied on time. To better manage these uncertainties, we developed a rolling horizon method (RHM), which is a well-known method for solving deterministic problems in mathematical programming models. To test the model and the RHM, we conducted three types of numerical experiments. First, we examined replenishment strategies and schedules under uncertain demands. Second, we conducted a supplier selection experiment within a limited budget and carbon emission regulations. Finally, we conducted a sensitivity analysis of carbon emission limits. The results show that our RHM can handle ESCM under uncertain situations effectively.
Acknowledgment to the Reviewers of Taxonomy in 2022
Authors:   Taxonomy Editorial Office
Published: January 2023

High-quality academic publishing is built on rigorous peer review [...]
Acknowledgment to the Reviewers of Toxics in 2022
Authors:   Toxics Editorial Office
Published: January 2023

High-quality academic publishing is built on rigorous peer review [...]
Gas Particle Partitioning of PAHs Emissions from Typical Solid Fuel Combustions as Well as Their Health Risk Assessment in Rural Guanzhong Plain, China
Authors: Zhang Bin , Peng Zezhi , Lv Jing , Peng Qin , He Kun , Xu Hongmei , Sun Jian , Shen Zhenxing
Published: January 2023

Air pollutants from the incomplete combustion of rural solid fuels are seriously harmful to both air quality and human health. To quantify the health effects of different fuel–stove combinations, gas and particle partitioning of twenty-nine species of polycyclic aromatic hydrocarbons (PAHs) emitted from seven fuel–stove combinations were examined in this study, and the benzo (a) pyrene toxicity equivalent (BaPeq) and cancer risks were estimated accordingly. The results showed that the gas phase PAHs (accounting for 68–78% of the total PAHs) had higher emission factors (EFs) than particulate ones. For all combustion combinations, pPAHs accounted for the highest proportion (84.5% to 99.3%) in both the gas and particulate phases, followed by aPAHs (0.63–14.7%), while the proportions of nPAHs and oPAHs were much lower (2–4 orders of magnitude) than pPAHs. For BaPeq, particulate phase PAHs dominated the BaPeq rather than gas ones, which may be due to the greater abundance of 5-ring particle PAHs. Gas and particle pPAHs were both predominant in the BaPeq, with proportions of 95.2–98.6% for all combustion combinations. Cancer risk results showed a descending order of bituminous coal combustion (0.003–0.05), biomass burning (0.002–0.01), and clean briquette coal combustion (10−5–0.001), indicating that local residents caused a severe health threat by solid fuel combustion (the threshold: 10−4). The results also highlighted that clean briquette coal could reduce cancer risks by 1–2 orders of magnitude compared to bulk coal and biomass. For oPAH, BcdPQ (6H-benzo(c,d)pyrene-6-one) had the highest cancer risk, ranging from 4.83 × 10−5 to 2.45 × 10−4, which were even higher than the total of aPAHs and nPAHs. The dramatically high toxicity and cancer risk of PAHs from solid fuel combustion strengthened the necessity and urgency of clean heating innovation in Guanzhong Plain and in similar places.
Efficient Wastewater Treatment and Removal of Bisphenol A and Diclofenac in Mesocosm Flow Constructed Wetlands Using Granulated Cork as Emerged Substrate
Authors: Bessadok Salma , Kraiem Khadija , Arous Fatma , Al Souki Karim Suhail , Tabassi Dorra , El Toumi Safa , Jaouani Atef
Published: January 2023

Constructed wetlands (CWs) are considered as low-cost and energy-efficient wastewater treatment systems. Media selection is one of the essential technical keys for their implementation. The purpose of this work was essentially to evaluate the removal efficiency of organic pollution and nitrogen from municipal wastewater (MWW) using different selected media (gravel/gravel amended with granulated cork) in mesocosm horizontal flow constructed wetlands (HFCWs). The results showed that the highest chemical oxygen demand (COD) and ammonium nitrogen removal of 80.53% and 42%, respectively, were recorded in the units filled with gravel amended with cork. The influence of macrophytes (Phragmites australis and Typha angustifolia) was studied and both species showed steeper efficiencies. The system was operated under different hydraulic retention times (HRTs) i.e., 6 h, 24 h, 30 h, and 48 h. The obtained results revealed that the COD removal efficiency was significantly enhanced by up to 38% counter to the ammonium rates when HRT was increased from 6 h to 48 h. Moreover, the removal efficiency of two endocrine-disrupting compounds (EDCs) namely, bisphenol A (BPA) and diclofenac (DCF) was investigated in two selected HFCWs, at 48 h HRT. The achieved results proved the high capacity of cork for BPA and DCF removal with the removal rates of 90.95% and 89.66%, respectively. The results confirmed the role of these engineered systems, especially for EDC removal, which should be further explored.
Prenatal Lipopolysaccharide Exposure Alters Hepatic Drug-Metabolizing Enzyme Expression in Mouse Offspring via Histone Modifications
Authors: Zhu Hanhan , Liu Guangming , Chang Qi , Yan Mengyao , Yang Kun , Li Yanxin , Nie Yali , Li Xiaotian , Han Shengna , Wang Pei , Zhang Lirong
Published: January 2023

Inflammation is a major regulator of drug-metabolizing enzymes (DMEs), therefore contributing to the interindividual variability of drug effects. However, whether prenatal inflammation affects DMEs expression in offspring remains obscure. This study investigated the effects of prenatal lipopolysaccharide (LPS) exposure on hepatic expression of inflammatory-related genes, nuclear receptors, and DMEs in offspring mice. Prenatal LPS exposure on gestational day (GD) 10 led to higher expression of NF-κB, Pxr, and Cyp2b10, while lower expression of Car, Ahr, Cyp3a11, and Ugt1a1 in postnatal day (PD) 30 offspring. However, multiple doses of LPS exposure on GD10-14 resulted in higher levels of inflammatory-related genes, Cyp1a2, and Cyp2b10, and lower levels of Pxr and Cyp3a11 in PD30 offspring liver. For PD60 offspring, decreased hepatic expression of NF-κB and IL-6, and increased expression of Pxr and Cyp3a11 were seen in single-dose LPS groups, whereas opposite results were observed in the multiple-dose LPS groups. Notably, enhanced H3K4me3 levels in the PXR response elements of the Cyp3a11 promoter were observed in the liver of PD60 offspring mice from dams treated with multiple doses of LPS during pregnancy. Overall, this study suggests that parental LPS exposure could persistently alter the hepatic expression of DMEs, and histone modifications may contribute to the long-term effects.
Methyl Parathion Exposure Induces Development Toxicity and Cardiotoxicity in Zebrafish Embryos
Authors: Chen Tianyi , Chen Haoze , Wang Anli , Yao Weixuan , Xu Zhongshi , Wang Binjie , Wang Jiye , Wu Yuanzhao
Published: January 2023

Methyl parathion (MP) has been widely used as an organophosphorus pesticide for food preservation and pest management, resulting in its accumulation in the aquatic environment. However, the early developmental toxicity of MP to non-target species, especially aquatic vertebrates, has not been thoroughly investigated. In this study, zebrafish embryos were treated with 2.5, 5, or 10 mg/L of MP solution until 72 h post-fertilization (hpf). The results showed that MP exposure reduced spontaneous movement, hatching, and survival rates of zebrafish embryos and induced developmental abnormalities such as shortened body length, yolk edema, and spinal curvature. Notably, MP was found to induce cardiac abnormalities, including pericardial edema and decreased heart rate. Exposure to MP resulted in the accumulation of reactive oxygen species (ROS), decreased superoxide dismutase (SOD) activity, increased catalase (CAT) activity, elevated malondialdehyde (MDA) levels, and caused cardiac apoptosis in zebrafish embryos. Moreover, MP affected the transcription of cardiac development-related genes (vmhc, sox9b, nppa, tnnt2, bmp2b, bmp4) and apoptosis-related genes (p53, bax, bcl2). Astaxanthin could rescue MP-induced heart development defects by down-regulating oxidative stress. These findings suggest that MP induces cardiac developmental toxicity and provides additional evidence of MP toxicity to aquatic organisms.
Impact of the COVID-19 Pandemic on Malaria Control in Africa: A Preliminary Analysis
Authors: Gao Liping , Shi Qi , Liu Zhiguo , Li Zhenjun , Dong Xiaoping
Published: January 2023

Malaria remains a significant public health concern in Africa, and the emerging coronavirus disease 2019 (COVID-19) pandemic may have negatively impacted malaria control. Here, we conducted a descriptive epidemiological analysis of malaria globally, and preliminarily explored the impact of COVID-19 on the malaria elimination program in regions of Africa (AFR). The present analysis found that there was a vast heterogeneity of incidence of deaths caused by malaria globally in different continents, and the highest malaria burden was observed in AFR. In 2020, there was an obviously increasing trend in the malaria epidemic in AFR, while the other four continents exhibited stable and declining patterns. Historically, malaria has been largely concentrated in high-malaria-burden regions, such as West Africa, and there has been an obvious increasing trend in Nigeria. These data suggest that dynamic changes in the malaria epidemic situation worldwide have primarily originated from AFR, and West Africa has played an important role in the global malaria increase in recent years. Under the coercion of COVID-19, multiple factors have co-driven the increase in malaria in AFR, including insufficient financial investments, a high native malaria burden, weak surveillance systems, limited medical resources, and low socioeconomic development levels. In addition, the shift of medical resources (e.g., health workers and personal protective equipment (PPE), the manufacturing of diagnostic reagents, and drugs) from malaria control to emergency COVID-19 response in the pandemic’s early stage caused disruptions, reductions, and delays in pillar malaria control measures, leading to a significant negative impact on malaria control. In particular, a funding shortfall at both the international and domestic levels led to a “significant threat,” resulting in vast gaps in access to proven malaria control tools. Although there has been a declining trend in malaria control over time due to COVID-19, the effect still cannot be ignored. Hence, we recommend the implementation of medical and technical resource assistance as a priority strategy to support Africa (West Africa) in order to curb further transmission.
Acknowledgment to the Reviewers of Uro in 2022
Authors:   Uro Editorial Office
Published: January 2023

High-quality academic publishing is built on rigorous peer review [...]
A Comprehensive Overview of Epidemiology, Pathogenesis and the Management of Herpes Labialis
Authors: Gopinath Divya , Koe Kim Hoe , Maharajan Mari Kannan , Panda Swagatika
Published: January 2023

Herpes labialis remains exceedingly prevalent and is one of the most common human viral infections throughout the world. Recurrent herpes labialis evolves from the initial viral infection by herpes simplex virus type 1 (HSV-1) which subsequently presents with or without symptoms. Reactivation of this virus is triggered by psychosocial factors such as stress, febrile environment, ultraviolet light susceptibility, or specific dietary inadequacy. This virus infection is also characterized by uninterrupted transitions between chronic-latent and acute-recurrent phases, allowing the virus to opportunistically avoid immunity and warrant the transmission to other vulnerable hosts simultaneously. This review comprehensively evaluates the current evidence on epidemiology, pathogenesis, transmission modes, clinical manifestations, and current management options of herpes labialis infections.
HSV Replication: Triggering and Repressing STING Functionality
Authors: Krawczyk Eric , Kangas Chase , He Bin
Published: January 2023

Herpes simplex virus (HSV) has persisted within human populations due to its ability to establish both lytic and latent infection. Given this, human hosts have evolved numerous immune responses to protect against HSV infection. Critical in this defense against HSV, the host protein stimulator of interferon genes (STING) functions as a mediator of the antiviral response by inducing interferon (IFN) as well as IFN-stimulated genes. Emerging evidence suggests that during HSV infection, dsDNA derived from either the virus or the host itself ultimately activates STING signaling. While a complex regulatory circuit is in operation, HSV has evolved several mechanisms to neutralize the STING-mediated antiviral response. Within this review, we highlight recent progress involving HSV interactions with the STING pathway, with a focus on how STING influences HSV replication and pathogenesis.