bluuebunny/crossref_metadata_2025 huggingface dataset:
Study on Low-Carbon Technology Investment Strategies for High Energy-Consuming Enterprises under the Health Co-Benefits of Carbon Emission Reduction
Authors: Liu Zheng , Sun Wenzhuo
Published: June 2023

The excessive use of fossil energy has led to a yearly increase in carbon dioxide and atmospheric pollutant emissions, and climate change has become increasingly prominent, seriously affecting people’s daily lives and physical and mental health. According to statistics, rising temperatures and extreme weather phenomena due to climate change have led to a 68% increase in heat-related deaths today compared to the period between 2000 and 2004, and a 61% increase in the number of days humans face high fire risks in the same period. Currently, in order to achieve synergistic economic and environmental development and enhance the health co-benefits of carbon emission reduction, it is urgent for high-energy-consuming enterprises to make sound low-carbon technology investment decisions. Therefore, in this paper, under the carbon quota and trading policy and carbon tax policy, and considering the existence of low-carbon preferences of consumers, the financial constraints of upstream high energy-consuming enterprises and sufficient funds of downstream retailers, a low-carbon technology investment decision model under intra-supply chain financing is constructed using Stackelberg game theory. Moreover, by applying the inverse induction method, we solve the optimal decision of low-carbon technology investment with three different subsidy methods: no subsidy, cost subsidy and product subsidy. Finally, the validity of the model is verified by numerical simulation, and the effects of different influencing factors on low-carbon technology investment are analyzed. The results show that: (1) the reasonable formulation of carbon trading price, carbon tax rate, cost subsidy ratio and product subsidy coefficient are important factors to promote enterprises’ low-carbon technology investment; (2) the improvement of consumers’ low-carbon preference level and the reduction in repayment interest rate can promote enterprises’ investment; (3) compared with no subsidy, cost subsidy and product subsidy can effectively improve enterprises’ low-carbon technology investment enthusiasm, and the effect of product subsidy is better than that of cost subsidy. The effect of product subsidies is better than that of cost subsidies. This paper aims to provide suggestions for the government to refine low-carbon technology investment incentive policies and for enterprises to optimize low-carbon technology investment decisions, so as to enhance the healthy co-benefits of carbon emission reduction and achieve green and sustainable economic development.
Thermal Infrared Precursor Information of Rock Surface during Failure Considering Different Intermediate Principal Stresses
Authors: Liu Sijie , Wang Jianchao , Chen Guoqing , Meng Kai , Zhang Yan
Published: June 2023

Rock failure generally leads to serious consequences, and it is significant to obtain the precursor information prior to failure using associated techniques. Thus, it is essential to acquire and probe the relevant precursor information. In this study, true triaxial tests are performed on red sandstone specimens under varying intermediate principal stress conditions. The thermal infrared image evolution and the temperature-induced change characteristics of rock failure are also analyzed using infrared thermal imaging technology. In addition, with the assistance of a high-speed photography technique, these characteristics during the true triaxial compression and unloading processes are systematically investigated to determine how the intermediate principal impacts on thermal image, temperature, and fracture propagation. Finally, the evolution mechanism of the specimens is summarized, and a non-contact thermal infrared rock failure precursor indicator is proposed, which can give significant advance notice of rock collapse before the abnormal temperature change. The results show that there exist thermal infrared temperature precursors, thermal image precursors, and rapid development of rock macroscopic cracks before rock failure. Abnormal thermal images are prior to the abnormal temperature changes. As the intermediate principal stress increases, thermal abnormalities will change accordingly. Both temperature changes and thermal image anomalous patches can be utilized as precursor information of rock collapse, and the mechanism and specific information of thermal infrared failure precursors can be preliminarily determined in time and space. Our results can function as a significant frame of reference for the analysis and prevention of rock failure due to sudden instability.
Has the Wind Power Price Policy Promoted the High-Quality Development of China’s Wind Power Industry?—Analysis Based on Total Factor Productivity
Authors: Chen Jingxiao , Zhang Lei , Deng Gaodan
Published: June 2023

The wind power price policy has promoted the rapid development of the wind power industry in China. However, China’s wind power industry is facing high-quality development problems such as wind curtailment and blind investment. Exploring the relationship between the change in wind power price policy and China’s high-quality development of wind power is of great significance for the energy system to achieve carbon neutrality. This paper constructs an SBM-GML global covariance model, calculates the total factor productivity of wind power in 30 provinces in China from 2015 to 2019 and conducts index decomposition, and selects provincial panel data from 2015 to 2019 to empirically test the impact of the wind power price policy on the total factor productivity of wind power in China. The results show that the wind power price policy can significantly improve the total factor productivity of wind power, and the results of the endogenous test and robustness test show the effectiveness of the model. The wind power price policy is helpful to promote the technological progress of wind power, affect the reduction of the price difference between wind power benchmark price and coal power benchmark price, and then promote the improvement of wind power total factor productivity so as to meet the policy requirements of wind power full grid parity. In addition, the impact of wind power price policy on the total factor productivity of wind power has obvious regional heterogeneity. Future price policy formulation should clarify industry development expectations, consider regional differences, and establish a sound, market-oriented electricity pricing mechanism.
An Easy Snowpack Depth Evaluation Using Smartphone, Bluetooth Device, and Augmented Reality Marker of Open Computer Vision Package
Authors: Ishiguro Minoru , Yoshii Yotsumi , Chaki Toshimasa , Kasaya Keigo
Published: June 2023

An easy method to evaluate a remote place’s snowpack depth has been discussed for helping later-stage elderly persons’ life. The method of using a smartphone camera and an augmented reality marker (AR marker) has been investigated. The general smartphone with a high image resolution camera was used to observe snowpack depth in remote places and remote control the robot via Bluetooth device. And image processing using artificially integrated technology (AI technology) was adapted for detecting the AR markers and for evaluating the snowpack depth.
Assessing Forest Quality through Forest Growth Potential, an Index Based on Improved CatBoost Machine Learning
Authors: Cao Lianjun , He Xiaobing , Chen Sheng , Fang Luming
Published: June 2023

Human activities have always depended on nature, and forests are an important part of this; the determination and improvement of forest quality is therefore highly significant. Currently, domestic and foreign research on forest quality focuses on the current states of forests. We propose a new research direction based on the future states. By referencing and analyzing the forest quality standards of domestic and foreign experts and institutions, the concept and model for calculating forest growth potential were constructed. Forest growth potential is a new forest quality indicator. Based on the data of 110,000 subcompartments of forest resources from the Lin’an and Landsat8 satellites’ remote sensing data, the unit volume was predicted using three machine-learning algorithms: random gradient descent SGD, the integrated machine learning algorithm CatBoost, and deep learning CNN. The CatBoost algorithm model was improved based on Optuna; then the improved CatBoost algorithm was selected through evaluation indicators for the prediction of forest volume and finally incorporated into the calculation model for forest growth-potential value. The forest growth-potential value was calculated, and an accurate forest quality improvement scheme based on the subcompartments is preliminarily discussed. The successful calculation of forest growth potential values has a certain reference significance, providing guidance for accurately improving forest quality and forest management. The improved CatBoost calculation model is effective in the prediction of forest growth potential, and the determination coefficient R2 reaches 0.89, a value that compares favorably with those in other studies.
The Transition of Land Use and Road Safety Studies: A Systematic Literature Review (2000–2021)
Authors: Iamtrakul Pawinee , Chayphong Sararad , Mateo-Babiano Derlie
Published: June 2023

About 1.3 million deaths occur every year due to road traffic crashes, making road safety a growing concern in many cities. This study considers the extent to which road safety challenges contribute to the built environment. In this paper, we applied the visualization technology of Bibliometrics supported by VOSviewer software and CitesSpace to develop a systematic review to understand the research status and identify gaps in road safety related to built environmental issues. This method has advantages in comprehensive quantitative statistics, visual information display, accurate description, and evaluation. Data was gathered from Scopus databases between 2000 to 2021, and a final number of 437 publications were retrieved. Road safety and land use were the primary keywords to locate relevant publications and identify their relationship. The analysis included the number of publications, research areas, and keywords for an in-depth evaluation. The result was visualized and bibliographically analyzed by demonstrating the existing occurrences between crucial terms, keywords and research areas. The findings revealed that road safety plays a vital role in significant issues, among others, that relate to land use and urban planning in the particular area associated with road safety. Therefore, it is essential to deliberately consider road safety in the very beginning to ensure that proper future solutions can be implemented through appropriate planning and design that is consistent with the surrounding city.
Corporate Environmental Information Disclosure and Earnings Management in China: Ethical Behaviour or Opportunism Motivation?
Authors: Shang Yu , Chi Yanan
Published: June 2023

China has entered a new stage of high-quality economic development, which puts forward in-depth requirements for environmental protection. Companies in heavily polluting industries are required to disclose environmental information. Fulfilling environmental responsibilities and disclosing this information may be either a responsible moral act or an opportunistic act. Taking listed companies in China’s heavily polluting industries from 2009 to 2020 as a sample, this study examines the economic consequences of enterprise environmental information disclosure (EID) from the earnings management (EM) perspective, as an external representation of ‘ethical behaviour’ and ‘opportunism motivations’ of EID and considers the effects of internal management competency and operating environment volatility. Findings include: (1) EID can restrain EM and support the ‘ethical behaviour’ motivation of EID. (2) Compared with the ‘soft disclosure’ of environmental information, the effect of ‘hard disclosure’ on EM is more obvious. (3) Greater management competency can strengthen the EM governance role of EID, while greater environmental uncertainty weakens this mechanism. (4) EID in enterprises in a mature period, state-owned, western regions, or low public environmental concern areas show an inhibitory effect on EM. Furthermore, its moralistic tendencies are more obvious compared with enterprises in growth or recession periods, non-state-owned, eastern regions, and those with high public environmental concerns.
An Efficient Approach to Monocular Depth Estimation for Autonomous Vehicle Perception Systems
Authors: Afshar Mehrnaz Farokhnejad , Shirmohammadi Zahra , Ghahramani Seyyed Amir Ali Ghafourian , Noorparvar Azadeh , Hemmatyar Ali Mohammad Afshin
Published: June 2023

Depth estimation is critical for autonomous vehicles (AVs) to perceive their surrounding environment. However, the majority of current approaches rely on costly sensors, making wide-scale deployment or integration with present-day transportation difficult. This issue highlights the camera as the most affordable and readily available sensor for AVs. To overcome this limitation, this paper uses monocular depth estimation as a low-cost, data-driven strategy for approximating depth from an RGB image. To achieve low complexity, we approximate the distance of vehicles within the frontal view in two stages: firstly, the YOLOv7 algorithm is utilized to detect vehicles and their front and rear lights; secondly, a nonlinear model maps this detection to the corresponding radial depth information. It is also demonstrated how the attention mechanism can be used to enhance detection precision. Our simulation results show an excellent blend of accuracy and speed, with the mean squared error converging to 0.1. The results of defined distance metrics on the KITTI dataset show that our approach is highly competitive with existing models and outperforms current state-of-the-art approaches that only use the detected vehicle’s height to determine depth.
A Parametric-Simulation Method to Study the Interconnections between Urban-Street-Morphology Indicators and Their Effects on Pedestrian Thermal Comfort in Tropical Summer
Authors: Bedra Komi Bernard , Zheng Bohong , Li Jiayu , Luo Xi
Published: June 2023

Numerous studies have explored the impact of urban morphology and geometry on outdoor thermal comfort, intending to provide practical guidelines for urban designers. However, research findings have been inconsistent, in part due to differences in the climatic settings and the investigated heat-stress indicators. This study proposes a parametric-simulation framework to observe the behavior of thermal comfort according to the possible combinations of building density (BD), street aspect ratio (AR), and orientation. Conducted specifically under a hot-and-humid tropical-savanna summer condition, the study found that building density and aspect ratio were negatively correlated to the Universal Thermal Climate Index (UTCI), with R2 coefficients of 0.99 and 0.91, respectively. The UTCI was improved by a 1.0 °C per 10% increase in BD and by a 1.02 °C per unit of AR increase. The performance of street orientation was significantly influenced by wind direction, and strong inter-influences were found between the three morphology factors. These findings are useful guidelines not only for designing urban morphology but also for intuitively identifying the need for complementary vegetation and cooling materials when morphology indicators cannot reach their efficiency targets (e.g., when AR < 3.0 or building density is limited by local regulations and project specifications).
Potential of Biosynthesized Silver and Zinc Oxide Nanoparticles from Carissa opaca Extracts for Antimicrobial Activity and Wastewater Treatment
Authors: Mehta Malvika , Chopra Chitrakshi , Sistla Srinivas , Bhushan Indu
Published: June 2023

The present study focus on biosynthesis of stable silver nanoparticles (AgNPs) and zinc oxide nanoparticles (ZnO NPs) from the leaf and stem extract of a therapeutic plant Carissa opaca. The visual observation, Fourier Transformed Infrared Spectroscopy (FTIR), Inductively Coupled Plasma analysis (ICP), High Resolution Transmission Electron Microscopy (HRTEM), and Scanning Electron Microscopy-Energy Dispersive Spectroscopy (SEM-EDS) were used to characterize and confirm the synthesized AgNPs and ZnO NPs. Afterwards; the synthesized nanoparticles were used to analyze their antimicrobial activity via in-vitro disk diffusion method against Klebsiella pneumoniae, Pseudomonas aeruginosa, Bacillus subtilis, Aspergillus niger, and Candida albican. Both the nanoparticles showed maximum zone of inhibition against Pseudomonas aeruginosa (bacterial strain), whereas in the case of fungi, higher zone of inhibition was observed using ZnONPs against Candida albican and AgNPs against Aspergillus niger. The biosynthesized AgNPs was also used for degradation of methylene blue under visible-light irradiation and found dye removal efficiency of. 97.4% within 1 h.
A Residual Resource Fitness-Based Genetic Algorithm for a Fog-Level Virtual Machine Placement for Green Smart City Services
Authors: Choudhury Sanjoy , Luhach Ashish Kumar , Rodrigues Joel J. P. C. , AL-Numay Mohammed , Ghosh Uttam , Sinha Roy Diptendu
Published: June 2023

Energy efficient information and communication technology (ICT) infrastructure at all levels of a city’s edifice constitutes a core requirement within the sustainable development goals. The ICT infrastructure of smart cities can be considered in three levels, namely the cloud layer infrastructure, devices/sensing layer infrastructure, and fog layer infrastructure at the edge of the network. Efficiency of a data-centre’s energy infrastructure is significantly affected by the placement of virtual machines (VMs) within the data-centre facility. This research establishes the virtual machine (VM) placement problem as an optimisation problem, and due to its adaptability for such complicated search issues, this paper applies the genetic algorithm (GA) towards the VM placement problem solution. When allocating or reallocating a VM, there is a large quantity of unused resources that might be used, however these resources are inefficiently spread over several different active physical machines (PMs). This study aims to increase the data-centre’s efficiency in terms of both energy usage and time spent on maintenance, and introduces a novel fitness function to streamline the process of computing the fitness function in GAs, which is the most computationally intensive component in a GA. A standard GA and first fit decreasing GA (FFD-GA) are applied on benchmark datasets to compare their relative performances. Experimental results obtained using data from Google data-centres demonstrate that the proposed FFD-GA saves around 8% more energy than a standard GA while reducing the computational overhead by approximately 66%.
Policies and Mechanisms of Public Financing for Social Housing in Peru
Authors: Villanueva-Paredes Karen Soledad , Villanueva-Paredes Grace Ximena
Published: June 2023

Social housing in Peru is a significant contemporary urban issue. This study aims to assess the current state of supply and demand for social housing. The methodology employed involved examining indicators, socioeconomic reports, existing regulations, and relevant literature on social housing. Through this approach, we were able to determine the demand profile, identify the characteristics of the available supply, comprehend the dynamics of social housing programs, and establish the correlation between supply and demand. The ultimate goal was to explore the options for acquiring this type of housing. In Peru, the acquisition of social housing is facilitated through the Fondo MIVIVIENDA program, which operates through two primary mechanisms: Techo Propio and Nuevo Crédito MIVIVIENDA. The findings of this study reveal that the social housing offered in the country falls short of meeting the current demand of various socioeconomic sectors. This discrepancy arises from several factors, indicating that family income alone is not the sole limiting factor. The absence of consistent and well-designed public policies further exacerbates the issue, hindering social development, impeding citizens’ access to a better quality of life, and undermining their social inclusion.
Understanding the Impact Mechanism of User Behavior Pattern toward Real Estate APP Platforms in the Post-COVID Era: A Quantitative Analysis
Authors: Yang Maosheng , Li Ping , Zhou Honghong , Li Zeyu , Feng Lei
Published: June 2023

While technology factors are the main driver of the booming real estate APP platforms with important implications for user behavior pattern during and post-pandemic contexts, there is a lack of adequate research. In response, this study explores the user behavior pattern of real estate APP platforms to promote user mental health by taking the real estate APP platforms users as the participants based on theory of technology readiness and acceptance model. Data collected from offline surveys are analyzed using PLS-SEM. The results reveal the technology readiness index positively affects individuals’ perceived usefulness and satisfaction, ultimately positively affects individuals’ continuance intention with real estate APP platforms; satisfaction with real estate APP platforms mediated the relationship between technology readiness index, perceived ease of use, perceived usefulness, and individuals’ continuance intention with real estate APP platforms. However, the group comparison finds no significant difference in user behavior patterns by gender. The contribution of this study is to reveal the influence mechanisms of digital technology on users’ behavioral patterns toward real estate APP platforms, which can help guide the sustainable development of real estate APP platforms and promote user mental health and wellbeing in the post-COVID era.
Predicting Solar Irradiance at Several Time Horizons Using Machine Learning Algorithms
Authors: Obiora Chibuzor N. , Hasan Ali N. , Ali Ahmed
Published: June 2023

Photovoltaic (PV) panels need to be exposed to sufficient solar radiation to produce the desired amount of electrical power. However, due to the stochastic nature of solar irradiance, smooth solar energy harvesting for power generation is challenging. Most of the available literature uses machine learning models trained with data gathered over a single time horizon from a location to forecast solar radiation. This study uses eight machine learning models trained with data gathered at various time horizons over two years in Limpopo, South Africa, to forecast solar irradiance. The goal was to study how the time intervals for forecasting the patterns of solar radiation affect the performance of the models in addition to determining their accuracy. The results of the experiments generally demonstrate that the models’ accuracy decreases as the prediction horizons get longer. Predictions were made at 5, 10, 15, 30, and 60 min intervals. In general, the deep learning models outperformed the conventional machine learning models. The Convolutional Long Short-Term Memory (ConvLSTM) model achieved the best Root Mean Square Error (RMSE) of 7.43 at a 5 min interval. The Multilayer Perceptron (MLP) model, however, outperformed other models in most of the prediction intervals.
A MOOC Course Data Analysis Based on an Improved Metapath2vec Algorithm
Authors: Xu Congcong , Feng Jing , Hu Xiaomin , Xu Xiaobin , Li Yi , Hou Pingzhi
Published: June 2023

Many real-world scenarios can be naturally modeled as heterogeneous graphs, which contain both symmetry and asymmetry information. How to learn useful knowledge from the graph has become one of the hot spots of research in artificial intelligence. Based on Metapath2vec algorithm, an improved Metapath2vec algorithm is presented, which combines Metapath random walk, used to capture semantics and structure information between different nodes of a heterogeneous network, and GloVe model to consider the advantage of global text representation. In order to verify the feasibility and effectiveness of the model, node clustering and link prediction experiments were conducted on the self-generated ideal dataset and the MOOC course data. The analysis of experimental data on these tasks shows that the Metapath–GloVe algorithm learns consistently better embedding of heterogeneous nodes, and the algorithm improves the node embedding performance to better characterize the heterogeneous network structure and learn the characteristics of nodes, which proves the effectiveness and scalability of the proposed method in heterogeneous network mining tasks. It is also shown through extensive experiments that the Metapath–GloVe algorithm is more efficient than the non-negative matrix decomposition algorithm (NMF), and it can obtain better clustering results and more accurate prediction results in the video recommendation task.
CNTs-Supercapacitors: A Review of Electrode Nanocomposites Based on CNTs, Graphene, Metals, and Polymers
Authors: Pour Ghobad Behzadi , Ashourifar Hassan , Aval Leila Fekri , Solaymani Shahram
Published: June 2023

Carbon nanotubes (CNTs), due to mechanical, electrical, and surface area properties and their ability to adapt to different nanocomposite structures, are very substantial in supercapacitor electrodes. In this review, we have summarized high-performance, flexible, and symmetry CNT supercapacitors based on the CNTs/graphene, CNTs/metal, and CNTs/polymer electrodes. To present recent developments in CNT supercapacitors, we discuss the performance of supercapacitors based on electrical properties such as specific capacitance (SC), power and energy densities, and capacitance retention (CR). The comparison of supercapacitor nanocomposite electrodes and their results are reported for future researchers.
Health Communication Strategies via TikTok for the Prevention of Eating Disorders
Authors: Rando-Cueto Dolores , de las Heras-Pedrosa Carlos , Paniagua-Rojano Francisco Javier
Published: May 2023

Patient assistance with severe eating disorders (EDs) is covered in hospital institutions by the specialized service offered. To a lesser extent, these types of pathologies are treated from health prevention, and there are hardly any experiences of health promotion in EDs through social networks. The main objective of this study was to evaluate the effectiveness of the messages about ED spread on TikTok, particularly those disseminated by international hospitals. For this, a systematic review of the scientific literature has been conducted, and the analytic tools Fanpagekarma and analisa.io have been used to analyse TikTok accounts of hospital entities and an intentional sample of different tiktokers with EDs or in recovery and people who show themselves as valid advisers in this matter, as well as their followers, respectively. Among the results obtained (due to volume and lack of transparency), the strategies of those who participate in TikTok to promote unhealthy eating habits are striking, as well as the amount of content presented against the spread of EDs that has the opposite effect on receivers. This study highlights the influence of TikTok on people affected by an eating disorder or are vulnerable to suffer from it and advocates for the spread of communication proposals via this social network that are supervised or led by health specialists who validate the content of the messages from a hospital environment to prevent such disorders. The definition of lines of action in communication by health institutions in this sense is shown to be necessary to prevent the appearance of EDs or to slow down their growth.
Investigating the Impact of Industry 4.0 Technology through a TOE-Based Innovation Model
Authors: Zhong Yongping , Moon Hee Cheol
Published: May 2023

Technological change has drastically shaped developments in the manufacturing and service industries. Integrating Industry 4.0 technologies in business practice is an emerging trend for future-oriented enterprises. By linking the TOE (technology-organization-environment) framework with product innovation, process innovation, and company performance, this research proposes a TOE-based innovation model to investigate Industry 4.0. The test results identified that Industry 4.0 technology adoption can be determined by compatibility, top management support, and competitive pressures, unexpectedly, not cost or employee capability; technology adoption can only indirectly influence company performance through mediation effects of product and process innovation. Results also revealed that industry type and global trade could play moderation roles in the technology adoption process: compared to the manufacturing industry, employee capability seems to be more influential on technology adoption in the service industry; global trade activities cannot significantly impact the technology adoption process, but trade companies are more likely to achieve more process innovation after such adoption. This study can enrich the theoretical bases of Industry 4.0 and confer a better understanding of the ongoing technological revolution in developing countries, which may offer some new insights for practitioners and academics.
Effects of Xenobiotic Compounds on Preeclampsia and Potential Mechanisms
Authors: Wu Miaoliang , Yan Fuhui , Liu Qian , Liao Ganzhong , Shen Yilin , Bai Zhi , Liu Xiaoshan
Published: May 2023

Preeclampsia (PE) refers to a disease with new hypertension and albuminuria or other end-organ damage after 20 weeks of pregnancy. As a major complication of pregnancy, PE can increase the morbidity and mortality of pregnant women and fetuses and cause serious social burden. Recently, it has been found that exposure to xenobiotic compounds, especially endocrine disruptors in the environment, may contribute to the development of PE. However, the underlying mechanism is still unclear. It is generally believed that PE is related to placental dysplasia, spiral artery remodelling failure, oxidative stress, etc. Therefore, in order to better prevent the occurrence of PE and reduce the damage and impact on mother and fetus, this paper reviews the role and potential mechanism of PE induced by exogenous chemicals and provides an outlook on the environmental etiology of PE.
Quantitative Representation of Water Quality Biotoxicity by Algal Photosynthetic Inhibition
Authors: Hu Li , Liang Tianhong , Yin Gaofang , Zhao Nanjing
Published: May 2023

The method based on the photosynthetic inhibition effect of algae offers the advantages of swift response and straightforward measurement. Nonetheless, this effect is influenced by both the environment and the state of the algae themselves. Additionally, a single parameter is vulnerable to uncertainties, rendering the measurement accuracy and stability inadequate. This paper employed currently utilized photosynthetic fluorescence parameters, including Fv/Fm(maximum photochemical quantum yield), Performance Indicator (PIabs), Comprehensive Parameter Index (CPI) and Performance Index of Comprehensive Toxicity Effect (PIcte), as quantitative toxicity characteristic parameters. The paper compared the univariate curve fitting results with the multivariate data-driven model results and investigated the effectiveness of Back Propagation(BP) Neural Network and Support Vector Machine for Regression (SVR) models to enhance the accuracy and stability of toxicity detection. Using Dichlorophenyl Dimethylurea (DCMU) samples as an example, the mean Relative Root Mean Square Error (RRMSE) corresponding to the optimal parameter PIcte for the dose-effect curve fitting was 1.246 in the concentration range of 1.25–200 µg/L. On the other hand, the mean RRMSEs corresponding to the results of the BP neural network and SVR models were 0.506 and 0.474, respectively. Notably, BP neural network exhibited excellent prediction accuracy in the medium-high concentration range of 7.5–200 µg/L, with a mean RRSME of only 0.056. Regarding the stability of the results, the mean Relative Standard Deviation (RSD) of the univariate dose-effect curve results was 15.1% within the concentration range of 50–200 µg/L. In contrast, the mean RSDs for both BP neural network and SVR results were less than 5%. In the concentration range of 1.25–200 µg/L, the mean RSDs were 6.1% and 16.5%, with the BP neural network performing well. The experimental results of Atrazine were analyzed to further validate the effectiveness of the BP neural network in improving the accuracy and stability of results. These findings provided valuable insights for the development of biotoxicity detection by using the algae photosynthetic inhibition method.
Triazine Herbicide and NPK Fertilizer Exposure: Accumulation of Heavy Metals and Rare Earth Elements, Effects on Cuticle Melanization, and Immunocompetence in the Model Species Tenebrio molitor
Authors: Naccarato Attilio , Vommaro Maria Luigia , Amico Domenico , Sprovieri Francesca , Pirrone Nicola , Tagarelli Antonio , Giglio Anita
Published: June 2023

The increasing use of agrochemicals, including fertilizers and herbicides, has led to worrying metal contamination of soils and waters and raises serious questions about the effects of their transfer to different levels of the trophic web. Accumulation and biomagnification of essential (K, Na, Mg, Zn, Ca), nonessential (Sr, Hg, Rb, Ba, Se, Cd, Cr, Pb, As), and rare earth elements (REEs) were investigated in newly emerged adults of Tenebrio molitor exposed to field-admitted concentrations of a metribuzin-based herbicide and an NPK blend fertilizer. Chemical analyses were performed using inductively coupled plasma tandem mass spectrometry (ICP-MS/MS) supported by unsupervised pattern recognition techniques. Physiological parameters such as cuticle melanization, cellular (circulating hemocytes), and humoral (phenoloxidase enzyme activity) immune responses and mass loss were tested as exposure markers in both sexes. The results showed that NPK fertilizer application is the main cause of REE accumulation in beetles over time, besides toxic elements (Sr, Hg, Cr, Rb, Ba, Ni, Al, V, U) also present in the herbicide-treated beetles. The biomagnification of Cu and Zn suggested a high potential for food web transfer in agroecosystems. Gender differences in element concentrations suggested that males and females differ in element uptake and excretion. Differences in phenotypic traits show that exposure affects metabolic pathways involving sequestration and detoxification during the transition phase from immature-to-mature beetles, triggering a redistribution of resources between sexual maturation and immune responses. Our findings highlight the importance of setting limits for metals and REEs in herbicides and fertilizers to avoid adverse effects on species that provide ecosystem services and contribute to soil health in agroecosystems.
Running Vacuum in the Universe: Phenomenological Status in Light of the Latest Observations, and Its Impact on the σ8 and H0 Tensions
Authors: Solà Peracaula Joan , Gómez-Valent Adrià , de Cruz Pérez Javier , Moreno-Pulido Cristian
Published: May 2023

A substantial body of phenomenological and theoretical work over the last few years strengthens the possibility that the vacuum energy density (VED) of the universe is dynamical, and in particular that it adopts the ‘running vacuum model’ (RVM) form, in which the VED evolves mildly as δρvac(H)∼νeffmPl2OH2, where H is the Hubble rate and νeff is a (small) free parameter. This dynamical scenario is grounded on recent studies of quantum field theory (QFT) in curved spacetime and also on string theory. It turns out that what we call the ‘cosmological constant’, Λ, is no longer a rigid parameter but the nearly sustained value of 8πG(H)ρvac(H) around any given epoch H(t), where G(H) is the gravitational coupling, which can also be very mildly running (logarithmically). Of particular interest is the possibility suggested in past works that such a running may help to cure the cosmological tensions afflicting the ΛCDM. In the current study, we reanalyze the RVM in full and we find it becomes further buttressed. Using modern cosmological data, namely a compilation of the latest SNIa+BAO+H(z)+LSS+CMB observations, we probe to what extent the RVM provides a quality fit better than the concordance ΛCDM model, with particular emphasis on its impact on the σ8 and H0 tensions. We utilize the Einstein–Boltzmann system solver CLASS and the Monte Carlo sampler MontePython for the statistical analysis, as well as the statistical DIC criterion to compare the running vacuum against the rigid vacuum (νeff=0). On fundamental grounds, νeff receives contributions from all the quantized matter fields in FLRW spacetime. We show that with a tiny amount of vacuum dynamics (νeff≪1) the global fit can improve significantly with respect to the ΛCDM and the mentioned tensions may subside to inconspicuous levels.
On the Possibility of Observing Negative Shapiro-like Delay Using Michelson–Morley-Type Experiments
Authors: Bhadra Arunava , Izmailov Ramil N. , Nandi Kamal K.
Published: June 2023

The possibility of observing negative Shapiro-like gravitational time delay (or time advancement) due to the Earth’s gravity employing interferometric experiments on the Earth’s surface is discussed. It is suggested that such a measurement may be realized in the near future with the help of modern versions of Michelson–Morley-type experiments.
Even and Odd Self-Similar Solutions of the Diffusion Equation for Infinite Horizon
Authors: Mátyás László , Barna Imre Ferenc
Published: June 2023

In the description of transport phenomena, diffusion represents an important aspect. In certain cases, the diffusion may appear together with convection. In this paper, we study the diffusion equation with the self-similar Ansatz. With an appropriate change of variables, we have found an original new type of solution of the diffusion equation for infinite horizon. We derive novel even solutions of diffusion equation for the boundary conditions presented. For completeness, the odd solutions are also mentioned as well, as part of the previous works. We have found a countable set of even and odd solutions, of which linear combinations also fulfill the diffusion equation. Finally, the diffusion equation with a constant source term is discussed, which also has even and odd solutions.
ERVW-1 Activates ATF6-Mediated Unfolded Protein Response by Decreasing GANAB in Recent-Onset Schizophrenia
Authors: Xue Xing , Wu Xiulin , Liu Lijuan , Liu Lianzhong , Zhu Fan
Published: June 2023

Schizophrenia, a mental disorder, afflicts 1% of the worldwide population. The dysregulation of homeostasis in the endoplasmic reticulum (ER) has been implicated in schizophrenia. Moreover, recent studies indicate that ER stress and the unfolded protein response (UPR) are linked to this mental disorder. Our previous research has verified that endogenous retrovirus group W member 1 envelope (ERVW-1), a risk factor for schizophrenia, is elevated in individuals with schizophrenia. Nevertheless, no literature is available regarding the underlying relationship between ER stress and ERVW-1 in schizophrenia. The aim of our research was to investigate the molecular mechanism connecting ER stress and ERVW-1 in schizophrenia. Here, we employed Gene Differential Expression Analysis to predict differentially expressed genes (DEGs) in the human prefrontal cortex of schizophrenic patients and identified aberrant expression of UPR-related genes. Subsequent research indicated that the UPR gene called XBP1 had a positive correlation with ATF6, BCL-2, and ERVW-1 in individuals with schizophrenia using Spearman correlation analysis. Furthermore, results from the enzyme-linked immunosorbent assay (ELISA) suggested increased serum protein levels of ATF6 and XBP1 in schizophrenic patients compared with healthy controls, exhibiting a strong correlation with ERVW-1 using median analysis and Mann–Whitney U analysis. However, serum GANAB levels were decreased in schizophrenic patients compared with controls and showed a significant negative correlation with ERVW-1, ATF6, and XBP1 in schizophrenic patients. Interestingly, in vitro experiments verified that ERVW-1 indeed increased ATF6 and XBP1 expression while decreasing GANAB expression. Additionally, the confocal microscope experiment suggested that ERVW-1 could impact the shape of the ER, leading to ER stress. GANAB was found to participate in ER stress regulated by ERVW-1. In conclusion, ERVW-1 induced ER stress by suppressing GANAB expression, thereby upregulating the expression of ATF6 and XBP1 and ultimately contributing to the development of schizophrenia.
Significant Broad-Spectrum Antiviral Activity of Bi121 against Different Variants of SARS-CoV-2
Authors: Subhadra Bobban , Agrawal Ragini , Pal Virender Kumar , Chenine Agnes-Laurence , Mattathil Jeffy George , Singh Amit
Published: June 2023

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has so far infected 762 million people with over 6.9 million deaths worldwide. Broad-spectrum viral inhibitors that block the initial stages of infection by reducing virus binding and proliferation, thereby reducing disease severities, are still an unmet global medical need. We studied Bi121, which is a standardized polyphenolic-rich compound isolated from Pelargonium sidoides, against recombinant vesicular stomatitis virus (rVSV)-pseudotyped SARS-CoV-2S (mutations in the spike protein) of six different variants of SARS-CoV-2. Bi121 was effective at neutralizing all six rVSV-ΔG-SARS-CoV-2S variants. The antiviral activity of Bi121 was also assessed against SARS-CoV-2 variants (USA WA1/2020, Hongkong/VM20001061/2020, B.1.167.2 (Delta), and Omicron) in Vero cells and HEK-ACE2 cell lines using RT-qPCR and plaque assays. Bi121 showed significant antiviral activity against all the four SARS-CoV-2 variants tested, suggesting a broad-spectrum activity. Bi121 fractions generated using HPLC showed antiviral activity in three fractions out of eight against SARS-CoV-2. The dominant compound identified in all three fractions using LC/MS/MS analysis was Neoilludin B. In silico structural modeling studies with Neoilludin B showed that it has a novel RNA-intercalating activity toward RNA viruses. In silico findings and the antiviral activity of this compound against several SARS-CoV-2 variants support further evaluation as a potential treatment of COVID-19.
Henipavirus Matrix Protein Employs a Non-Classical Nuclear Localization Signal Binding Mechanism
Authors: Donnelly Camilla M. , Vogel Olivia A. , Edwards Megan R. , Taylor Paige E. , Roby Justin A. , Forwood Jade K. , Basler Christopher F.
Published: June 2023

Nipah virus (NiV) and Hendra virus (HeV) are highly pathogenic species from the Henipavirus genus within the paramyxovirus family and are harbored by Pteropus Flying Fox species. Henipaviruses cause severe respiratory disease, neural symptoms, and encephalitis in various animals and humans, with human mortality rates exceeding 70% in some NiV outbreaks. The henipavirus matrix protein (M), which drives viral assembly and budding of the virion, also performs non-structural functions as a type I interferon antagonist. Interestingly, M also undergoes nuclear trafficking that mediates critical monoubiquitination for downstream cell sorting, membrane association, and budding processes. Based on the NiV and HeV M X-ray crystal structures and cell-based assays, M possesses a putative monopartite nuclear localization signal (NLS) (residues 82KRKKIR87; NLS1 HeV), positioned on an exposed flexible loop and typical of how many NLSs bind importin alpha (IMPα), and a putative bipartite NLS (244RR-10X-KRK258; NLS2 HeV), positioned within an α-helix that is far less typical. Here, we employed X-ray crystallography to determine the binding interface of these M NLSs and IMPα. The interaction of both NLS peptides with IMPα was established, with NLS1 binding the IMPα major binding site, and NLS2 binding as a non-classical NLS to the minor site. Co-immunoprecipitation (co-IP) and immunofluorescence assays (IFA) confirm the critical role of NLS2, and specifically K258. Additionally, localization studies demonstrated a supportive role for NLS1 in M nuclear localization. These studies provide additional insight into the critical mechanisms of M nucleocytoplasmic transport, the study of which can provide a greater understanding of viral pathogenesis and uncover a potential target for novel therapeutics for henipaviral diseases.
High Incidence of Respiratory Syncytial Virus in Children with Community-Acquired Pneumonia from a City in the Brazilian Pre-Amazon Region
Authors: Fontes Valéria , Ferreira Hivylla , Ribeiro Marilene , Pinheiro Aruanã , Maramaldo Carlos , Pereira Eduardo , Batista Luís , Júnior Antonio , Lobato Luis , Silva Fabiano , Sousa Luis , Lima Washington , Lima Claudia , Soczek Suzany , Carvalho Rafael , Santos Mirleide , Fernandes Elizabeth , Sousa Eduardo , Neto Lidio
Published: June 2023

Introduction: Although fewer children have been affected by the severe form of the coronavirus disease 2019 (COVID-19), community-acquired pneumonia (CAP) continues to be the leading global cause of child hospitalizations and deaths. Aim: This study investigated the incidence of respiratory syncytial virus (RSV) as well its subtypes (RSV A and B), adenovirus (ADV), rhinovirus (HRV), metapneumovirus (HMPV), coronavirus (NL63, OC43, 229E and HKU1), parainfluenza virus subtypes (PI1, PI2 and PI3), bocavirus and influenza A and B viruses (FluA and FluB) in children diagnosed with CAP during the COVID-19 pandemic. Methods: A total of 200 children with clinically confirmed CAP were initially recruited, of whom 107 had negative qPCR results for SARS-CoV-2 and were included in this study. Viral subtypes were identified using a real-time polymerase chain reaction in the nasopharyngeal swab samples. Results: Viruses were identified in 69.2% of the patients. RSV infections were the most frequently identified (65.4%), with type RSV B being the most prevalent (63.5%). In addition, HCoV 229E and HRV were detected in 6.5% and 3.7% of the patients, respectively. RSV type B was associated with severe acute respiratory infection (ARI) and a younger age (less than 24 months). Conclusions: New strategies for preventing and treating viral respiratory infections, particularly RSV infections, are necessary.
A Review of Inactivated COVID-19 Vaccine Development in China: Focusing on Safety and Efficacy in Special Populations
Authors: Hu Lidan , Sun Jingmiao , Wang Yan , Tan Danny , Cao Zhongkai , Gao Langping , Guan Yuelin , Jia Xiuwei , Mao Jianhua
Published: June 2023

The coronavirus disease 2019 (COVID-19) pandemic, caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), has been widespread globally, and vaccination is critical for preventing further spread or resurgence of the outbreak. Inactivated vaccines made from whole inactivated SARS-CoV-2 virus particles generated in Vero cells are currently the most widely used COVID-19 vaccines, with China being the largest producer of inactivated vaccines. As a result, the focus of this review is on inactivated vaccines, with a multidimensional analysis of the development process, platforms, safety, and efficacy in special populations. Overall, inactivated vaccines are a safe option, and we hope that the review will serve as a foundation for further development of COVID-19 vaccines, thus strengthening the defense against the pandemic caused by SARS-CoV-2.