bluuebunny/crossref_metadata_2025 huggingface dataset:
Optimal Multiple Wind Power Transmission Schemes Based on a Life Cycle Cost Analysis Model
Authors: Ji Xiaotong , Liu Dan , Luo Heng , Xiong Ping , Tan Daojun , Hu Pan , Ma Hengrui , Wang Bo
Published: July 2024

Due to the high cost and complex challenges faced by offshore wind power transmission, economic research into offshore wind power transmission can provide a scientific basis for optimal decision-making on offshore wind power projects. Based on the analysis of the topology structure and characteristics of typical wind power transmission schemes, this paper compares the economic benefits of five different transmission schemes with a 3.6 GW sizeable onshore wind farm as the primary case. Research includes traditional high voltage alternating current (HVAC), voltage source converter high voltage direct current transmission (VSC-HVDC), a fractional frequency transmission system (FFTS), and two hybrid DC (MMC-LCC and DR-MMC) transmission scenarios. The entire life cycle cost analysis model (LCCA) is employed to thoroughly assess the cumulative impact of initial investment costs, operational expenses, and eventual scrap costs on top of the overall transmission scheme’s total cost. This comprehensive evaluation ensures a nuanced understanding of the financial implications across the project’s entire lifespan. In this example, HVAC has an economic advantage over VSC-HVDC in the transmission distance range of 78 km, and the financial range of a FFTS is 78–117 km. DR-MMC is better than the flexible DC delivery scheme in terms of transmission capacity, scalability, and offshore working platform construction costs in the DC delivery scheme. Therefore, the hybrid DC delivery scheme of offshore wind power composed of multi-type converters has excellent application prospects.
Predictions of Aboveground Herbaceous Production from Satellite-Derived APAR Are More Sensitive to Ecosite than Grazing Management Strategy in Shortgrass Steppe
Authors: Peirce Erika S. , Kearney Sean P. , Santamaria Nikolas , Augustine David J. , Porensky Lauren M.
Published: July 2024

The accurate estimation of aboveground net herbaceous production (ANHP) is crucial in rangeland management and monitoring. Remote and rural rangelands typically lack direct observation infrastructure, making satellite-derived methods essential. When ground data are available, a simple and effective way to estimate ANHP from satellites is to derive the empirical relationship between ANHP and plant-absorbed photosynthetically active radiation (APAR), which can be estimated from the normalized difference vegetation index (NDVI). While there is some evidence that this relationship will differ across rangeland vegetation types, it is unclear whether this relationship will change across grazing management regimes. This study aimed to assess the impact of grazing management on the relationship between ground-observed ANHP and satellite-derived APAR, considering variations in plant communities across ecological sites in the shortgrass steppe of northeastern Colorado. Additionally, we compared satellite-predicted biomass production from the process-based Rangeland Analysis Platform (RAP) model to our empirical APAR-based model. We found that APAR could be used to predict ANHP in the shortgrass steppe, with the relationship being influenced by ecosite characteristics rather than grazing management practices. For each unit of added APAR (MJ m−2 day−1), ANHP increased by 9.39 kg ha−1, and ecosites with taller structured herbaceous vegetation produced, on average, 3.92–5.71 kg ha−1 more ANHP per unit APAR than an ecosite dominated by shorter vegetation. This was likely due to the increased allocation of plant resources aboveground for C3 mid-grasses in taller structured ecosites compared to the C4 short-grasses that dominate the shorter structured ecosites. Moreover, we found that our locally calibrated empirical model generally performed better than the continentally calibrated process-based RAP model, though RAP performed reasonably well for the dominant ecosite. For our empirical models, R2 values varied by ecosite ranging from 0.49 to 0.67, while RAP R2 values ranged from 0.07 to 0.4. Managers in the shortgrass steppe can use satellites to estimate herbaceous production even without detailed information on short-term grazing management practices. The results from our study underscore the importance of understanding plant community composition for enhancing the accuracy of remotely sensed predictions of ANHP.
Best Scanline Determination of Pushbroom Images for a Direct Object to Image Space Transformation Using Multilayer Perceptron
Authors: Ahooei Nezhad Seyede Shahrzad , Valadan Zoej Mohammad Javad , Khoshelham Kourosh , Ghorbanian Arsalan , Farnaghi Mahdi , Jamali Sadegh , Youssefi Fahimeh , Gheisari Mehdi
Published: July 2024

Working with pushbroom imagery in photogrammetry and remote sensing presents a fundamental challenge in object-to-image space transformation. For this transformation, accurate estimation of Exterior Orientation Parameters (EOPs) for each scanline is required. To tackle this challenge, Best Scanline Search or Determination (BSS/BSD) methods have been developed. However, the current BSS/BSD methods are not efficient for real-time applications due to their complex procedures and interpolations. This paper introduces a new non-iterative BSD method specifically designed for line-type pushbroom images. The method involves simulating a pair of sets of points, Simulated Control Points (SCOPs), and Simulated Check Points (SCPs), to train and test a Multilayer Perceptron (MLP) model. The model establishes a strong relationship between object and image spaces, enabling a direct transformation and determination of best scanlines. This proposed method does not rely on the Collinearity Equation (CE) or iterative search. After training, the MLP model is applied to the SCPs for accuracy assessment. The proposed method is tested on ten images with diverse landscapes captured by eight sensors, exploiting five million SCPs per image for statistical assessments. The Root Mean Square Error (RMSE) values range between 0.001 and 0.015 pixels across ten images, demonstrating the capability of achieving the desired sub-pixel accuracy within a few seconds. The proposed method is compared with conventional and state-of-the-art BSS/BSD methods, indicating its higher applicability regarding accuracy and computational efficiency. These results position the proposed BSD method as a practical solution for transforming object-to-image space, especially for real-time applications.
Efficient and Robust Adaptive Beamforming Based on Coprime Array Interpolation
Authors: Chen Siming , Wu Xiaochuan , Li Shujie , Deng Weibo , Zhang Xin
Published: July 2024

Unlike uniform linear arrays (ULAs), coprime arrays require fewer physical sensors yet provide higher degrees of freedom (DOF) and larger array apertures. However, due to the existence of “holes” in the differential co-array, the target detection performance deteriorates, especially in adaptive beamforming. To address these challenges, this paper proposes an efficient and robust adaptive beamforming algorithm leveraging coprime array interpolation. The algorithm eliminates unwanted signals and uses the Gauss–Legendre quadrature method to reconstruct an Interference-plus-Noise Covariance Matrix (INCM), thereby obtaining the beamforming coefficients. Unlike previous techniques, we utilize a virtual interpolated ULA to expand the aperture, enabling the acquisition of a high-dimensional covariance matrix. Additionally, a projection matrix is constructed to eliminate unwanted signals from the received data, greatly enhancing the accuracy of INCM reconstruction. To address the high computational complexity of integral operations used in most INCM reconstruction algorithms, we propose an approximation based on the Gauss–Legendre quadrature, which reduces the computational load while maintaining accuracy. This algorithm avoids the array aperture loss caused by using only the ULA segment in the difference co-array and improves the accuracy of INCM reconstruction. Simulation and experimental results show that the performance of the proposed algorithm is superior to the compared beamformers and is closer to the optimal beamformer in various scenarios.
RGB Color Model: Effect of Color Change on a User in a VR Art Gallery Using Polygraph
Authors: Drofova Irena , Richard Paul , Fajkus Martin , Valasek Pavel , Sehnalek Stanislav , Adamek Milan
Published: July 2024

This paper presents computer and color vision research focusing on human color perception in VR environments. A VR art gallery with digital twins of original artworks is created for this experiment. In this research, the field of colorimetry and the application of the L*a*b* and RGB color models are applied. The inter-relationships of the two color models are applied to create a color modification of the VR art gallery environment using C# Script procedures. This color-edited VR environment works with a smooth change in color tone in a given time interval. At the same time, a sudden change in the color of the RGB environment is defined in this interval. This experiment aims to record a user’s reaction embedded in a VR environment and the effect of color changes on human perception in a VR environment. This research uses lie detector sensors that record the physiological changes of the user embedded in VR. Five sensors are used to record the signal. An experiment on the influence of the user’s color perception in a VR environment using lie detector sensors has never been conducted. This research defines the basic methodology for analyzing and evaluating the recorded signals from the lie detector. The presented text thus provides a basis for further research in the field of colors and human color vision in a VR environment and lays an objective basis for use in many scientific and commercial areas.
Spatial Localization of Transformer Inspection Robot Based on Adaptive Denoising and SCOT-β Generalized Cross-Correlation
Authors: Ji Hongxin , Zheng Chao , Tang Zijian , Liu Xinghua , Liu Liqing
Published: July 2024

In the detection process of the internal defects of large oil-immersed transformers, due to the huge size of large transformers and metal-enclosed structures, the positional localization of miniature inspection robots inside the transformer faces great difficulties. To address this problem, this paper proposes a three-dimensional positional localization method based on adaptive denoising and the SCOT weighting function with the addition of the exponent β (SCOT-β) generalized cross-correlation for L-type ultrasonic arrays of transformer internal inspection robots. Aiming at the strong noise interference in the field, the original signal is decomposed by an improved Empirical Mode Decomposition (EMD) method, and the optimal center frequency and bandwidth of each mode are adaptively searched. By extracting the modes in the frequency band of the positional localization signal, suppressing the modes in the noise frequency band, and reconstructing the Intrinsic Mode Function (IMF) of the independently selected superior modal components, a signal with a high signal-to-noise ratio is obtained. In addition, for the traditional mutual correlation algorithm with a large delay estimation error at a low signal-to-noise ratio, this paper adopts an improved generalized joint weighting function, SCOT-β, which improves the anti-jamming ability of the generalized mutual correlation method at a low signal-to-noise ratio by adding an exponential function to the denominator term of the SCOT weighting function’s generalized cross-correlation. Finally, the accurate positional localization of the transformer internal inspection robot is realized based on the quadratic L-array and search-based maximum likelihood estimation method. Simulation and experimental results show the following: the improved EMD denoising method better improves the signal-to-noise ratio of the positional localization signal with a lower distortion rate; in the transformer test tank, which is 120 cm in length, 100 cm in width, and 100 cm in height, based on the positional localization method in this paper, the average relative positional localization error of the transformer internal inspection robot in three-dimensional space is 2.27%, and the maximum positional localization error is less than 2 cm, which meets the requirements of engineering positional localization.
To Reconstruct or Discard: A Comparison of Additive and Subtractive Charge Sharing Correction Algorithms at High and Low X-ray Fluxes
Authors: Pickford Scienti Oliver L. P. Pickford , Darambara Dimitra G.
Published: July 2024

Effective X-ray photon-counting spectral imaging (x-CSI) detector design involves the optimisation of a wide range of parameters both regarding the sensor (e.g., material, thickness and pixel pitch) and electronics (e.g., signal-processing chain and count-triggering scheme). Our previous publications have looked at the role of pixel pitch, sensor thickness and a range of additive charge sharing correction algorithms (CSCAs), and in this work, we compare additive and subtractive CSCAs to identify the advantages and disadvantages. These CSCAs differ in their approach to dealing with charge sharing: additive approaches attempt to reconstruct the original event, whilst subtractive approaches discard the shared events. Each approach was simulated on data from a wide range of x-CSI detector designs (pixel pitches 100–600 µm, sensor thickness 1.5 mm) and X-ray fluxes (106–109 photons mm−2 s−1), and their performance was characterised in terms of absolute detection efficiency (ADE), absolute photopeak efficiency (APE), relative coincidence counts (RCC) and binned spectral efficiency (BSE). Differences between the two approaches were explained mechanistically in terms of the CSCA’s effect on both charge sharing and pule pileup. At low X-ray fluxes, the two approaches perform similarly, but at higher fluxes, they differ in complex ways. Generally, additive CSCAs perform better on absolute metrics (ADE and APE), and subtractive CSCAs perform better on relative metrics (RCC and BSE). Which approach to use will, thus, depend on the expected operating flux and whether dose efficiency or spectral efficiency is more important for the application in mind.
ARM4CH: A Methodology for Autonomous Reality Modelling for Cultural Heritage
Authors: Giakoumidis Nikolaos , Anagnostopoulos Christos-Nikolaos
Published: July 2024

Nowadays, the use of advanced sensors, such as terrestrial, mobile 3D scanners and photogrammetric imaging, has become the prevalent practice for 3D Reality Modeling (RM) and the digitization of large-scale monuments of Cultural Heritage (CH). In practice, this process is heavily related to the expertise of the surveying team handling the laborious planning and time-consuming execution of the 3D scanning process tailored to each site’s specific requirements and constraints. To minimize human intervention, this paper proposes a novel methodology for autonomous 3D Reality Modeling of CH monuments by employing autonomous robotic agents equipped with the appropriate sensors. These autonomous robotic agents are able to carry out the 3D RM process in a systematic, repeatable, and accurate approach. The outcomes of this automated process may also find applications in digital twin platforms, facilitating secure monitoring and the management of cultural heritage sites and spaces, in both indoor and outdoor environments. The main purpose of this paper is the initial release of an Industry 4.0-based methodology for reality modeling and the survey of cultural spaces in the scientific community, which will be evaluated in real-life scenarios in future research.
Cyberbullying and Resilience: Lessons Learned from a Survey
Authors: Stegmair Juergen , Prybutok Victor
Published: July 2024

Cyberbullying (CB) is a recurring, long-standing phenomenon that negatively affects people’s lives. Resilience is a personality trait that helps people to react to, cope with, and persist in the presence of obstacles such as cyberbullying. To determine variables and factors affecting coping with cyberbullying incidents, an original research survey was administered to 1317 undergraduate students who attended a large public university in Texas (n = 1086). Exploratory factor analysis revealed four relevant factors: Self-Efficacy, Religiosity, Friend and Social Engagement, and Culture. These findings show that factors supporting resiliency are helpful in achieving a positive behavioral response. The discriminant analysis provided a model verification for the theoretical model for these factors and led to a 79.9% predictability of the function.
Our Recipe for Good Inclusive Research
Authors: Crowther Felicity , Beinke Hannah , High Rachel , Ru Chloe , Rillotta Fiona
Published: July 2024

Co-researchers with intellectual disabilities and a Ph.D. student formed an inclusive research team. The co-researchers joined the inclusive research team so they could learn more about research and have control over the research. Our team have held regular research skills meetings. The focus of these meetings was getting to know each other and learning what each person brought to the team, as well as learning what inclusive research is and how to conduct research together. Learning these things has helped to design and undertake an inclusive research project about pets for people with intellectual disabilities. In this article, our team shares our experience of conducting inclusive research. We have learnt that each team member brings their own strengths to the team and we have connected and formed friendships. We completed an inclusive research project and found there were some challenges our team had to overcome. We conclude that people with intellectual disabilities are capable of leading research. Being part of an inclusive research team enables co-researchers to show others what they can do. Sharing what our team has found out can help others to conduct inclusive research.
A Shift Amid the Transition: Towards Smarter, More Resilient Digital Journalism in the Age of AI and Disinformation
Authors: Forja-Pena Tania , García-Orosa Berta , López-García Xosé
Published: July 2024

Digital journalism is facing a growing number of challenges as the wave of digital transformation enters a new phase. The changes, both slow and sudden, are making it increasingly necessary for there to be a shift amid the transition. The surge of artificial intelligence (AI) in the journalistic world has ushered in what appears to be a new phase for digitalisation and journalism’s role as a producer of factual information and knowledge in society. Current debates suggest that we are at a critical time to reinvent journalism for the new wave of digital transformation. In this stage, which recently began and which some are calling the Fourth Industrial Revolution, both professionals and academics point to the need to make journalism more resilient, so it can adapt to the changes without losing its basic essence, and more people-centred, helping to support individuals in the age of AI. This article analyses the challenges from the perspectives of both academics and professional journalists, based on their codes of ethics. The methodology used is a content analysis of 45 European ethical codes and the Delphi method, involving experts from the journalistic sector and academia. The results highlight that codes of ethics have not kept pace with these professionally and expert-led debates, as few mention new technologies such as artificial intelligence and they tend to focus more on disinformation and social engagement.
Predicting the Invasion Range of the Common Myna, Acridotheres tristis Linnaeus, 1766 in Egypt under Climate Change
Authors: Orabi Gamal M. , Semida Fayez M. , Medany Doaa M. , Issa Mohamed A. , Ragab Sanad H. , Kamel Mohamed
Published: July 2024

The common myna bird (Acridotheres tristis Linnaeus, 1766) is widely recognized as one of the most formidable invasive avian species globally. The bird poses significant challenges due to its ability to outcompete a variety of native cavity-nesting birds. Additionally, the common myna is a notable agricultural pest and a substantial threat to indigenous biodiversity. The current study is focused on understanding the distribution pattern of the common myna (Acridotheres tristis Linnaeus, 1766) in Egypt and the significant favorable conditions to predict the invasion scale of the bird to the Egyptian fauna. To determine the environmental variables influencing the invasion range of the common myna in Egypt, a Species Distribution Model (SDM) was employed. The current work documented 117 invasion sites of the species from February to December 2023. The predicted habitats are mainly concentrated close to the Nile Delta of Egypt, the Suez Canal region, North and South Sinai, in addition to scattered areas on the Red Sea coast, along the riverbanks of Upper Egypt, in addition to a few northwestern areas of the Western Desert. The most significant environmental factors affecting the establishment were the Minimum Temperature of the Coldest Month, the Mean Temperature of the Coldest Quarter, and Elevation. The current invaded areas comprise about 0.8% of Egypt (8240 km2 out of roughly one million km2). We found that this is significant and of concern due to the expectation of increasingly favourable conditions due to global warming; this will turn this invasive species into a real threat to Egyptian ecosystems due to its aggressive competition with native cavity-nesting birds, its impact as an agricultural pest, and its potential to disrupt local biodiversity.
A Hybrid Deep Learning Model to Estimate the Future Electricity Demand of Sustainable Cities
Authors: Yıldız Doğan Gülay , Aksoy Aslı , Öztürk Nursel
Published: July 2024

Rapid population growth, economic growth, and technological developments in recent years have led to a significant increase in electricity consumption. Therefore, the estimation of electrical energy demand is crucial for the planning of electricity generation and consumption in cities. This study proposes a hybrid deep learning model that combines convolutional neural network (CNN) and long short-term memory (LSTM) techniques, both of which are deep learning techniques, to estimate electrical load demand. A hybrid deep learning model and LSTM model were applied to a dataset containing hourly electricity consumption and meteorological information of a city in Türkiye from 2017 to 2021. The results were evaluated using mean absolute percent error (MAPE), root mean square error (RMSE), mean absolute error (MAE), and coefficient of determination (R2) metrics. The proposed CNN-LSTM hybrid model was compared to the LSTM model, with lower MAPE, MAE, and RMSE values. Furthermore, the CNN-LSTM model exhibited superior prediction performance with an R2 value of 0.8599 compared to the LSTM model with an R2 value of 0.8086. These results demonstrate the effectiveness of the proposed deep learning model in accurately estimating future electrical load demand to plan electricity generation for sustainable cities.
Industry Heterogeneity and the Economic Consequences of Corporate ESG Performance for Good or Bad: A Firm Value Perspective
Authors: Chen Ying , Zhang Zili
Published: July 2024

An investigation into the relationship between ESG performance and firm value is vital for formulating corporate sustainability strategies. This paper begins by providing a comprehensive overview of the ESG performance across all listed companies in the Chinese stock market. It then examines the effect of a firm’s ESG performance on its firm value, with a particular focus on the heterogeneity within various industries. Our results demonstrate that ESG performance standards are positively correlated with the firm value. Enhancements in ESG performance can significantly bolster a firm’s sustainability. Nevertheless, the degree and direction of the impact of corporate ESG performance on firm value are subject to variation across industries. These results have significant implications for the refinement of corporate ESG practice initiatives and ESG-oriented investors, inspiring them to consider the industry classification of firms in their operational and investment strategies related to ESG.
Performance Evaluation of the Electro-Fenton Process for Distillery Wastewater Treatment
Authors: Minnalkodi Senguttuvan Keerthana Rani , Sellappa Kanmani , Kuppusamy Saranya
Published: July 2024

A fair amount of India’s gross domestic product is contributed by distilleries, which are considered the backbone industries of India. Distilleries indeed play key roles in India’s exports. Distillery wastewater is recognized as one of the recalcitrant wastewaters, containing extremely high organic loading and having an adverse impact when released into the environment. The aim of the present study was to optimize the conditions required for attaining improved COD removal efficiency in distillery wastewater through an electro-Fenton (EF) process. The effect of various operating parameters, viz. H2O2 dosage (555–2220 mg L−1), spacing between the iron electrodes (2–6 cm), electrode dipping area (35–65 cm2), initial pH (2–9), and constant voltage supply (5–15 V), were investigated by carrying out the EF process in batch mode. As a result of the EF study, COD removal efficiency of 79.5% for an initial COD of 5500–6000 mg L−1 was achieved for the distillery wastewater under the condition of 1665 mg L−1 H2O2, 2.5 cm of spacing between the electrodes, 55 cm2 of electrode dipping area, pH 3, and constant voltage supply of 5 V. In the same study, the kinetics of the process was also investigated, and it obeyed the pseudo-first-order reaction. The EF process effectively degrades complex organic compounds in distillery wastewater into simpler, potentially less toxic substances, as demonstrated by gas chromatography–mass spectrometry (GC-MS) analysis and pathway elucidation. The central composite design (CCD) of the response surface methodology (RSM) model was used to optimize the COD removal in distillery wastewater through the EF process. In line with the batch experimental results, RSM projections also indicated that the optimum conditions required for attaining a maximum of 70.8% COD removal efficiency in distillery wastewater are found to be 1402 mg L−1 H2O2 dosage, 3 cm electrode spacing, 60 cm2 dipping area, 5 V voltage, and pH 2.18. The research data supported the conclusion that the EF process is feasible for distillery wastewater treatment, which preferably can be applied extensively.
International Development Trends in the Field of Agricultural Resources and the Environment
Authors: Chuan Limin , Qi Shijie , Zhang Hui , Jia Qian , Wang Ailing , Zhao Jingjuan
Published: July 2024

The development trends and research layout of agricultural resources and the environment (ARE) are the focus of global attention. In this study, we compiled a data set of SCI papers published in the ARE field since the 13th Five-Year Plan. Thereafter, the topic extraction model of Latent Dirichlet Allocation (LDA) was used to mine the text content so as to explore the research layout of global ARE. The results show that, between 2016 and the time of this study, 31,559 articles were published in the ARE field, exhibiting an overall upward trend. During this time, China and the United States were the main forces in paper publishing. The Chinese Academy of Sciences (CAS), Northwest Agriculture and Forestry University, and the US Department of Agriculture were the top three publishing institutions. Research institutes in China, the United States, Russia, Sweden, the Netherlands, Brazil, and Australia cooperated closely, and the CAS was at the center of the cooperation network. The clustering results of text topics based on the LDA model show that three topics—namely, the interaction mechanisms of plants, the rhizosphere, and microorganisms; the mechanisms and predictive evaluation of soil landslides or erosion; and the decomposition and interaction response of organic matter in agroforestry ecosystems—have been the hot research areas in this field since 2016. The management and efficient utilization of farmland nutrients, and the technology and mechanisms of agricultural waste resource utilization have become key research directions since 2019. The research layouts of China and the United States in this field were analyzed, and the similarities and differences were compared. In addition, suggestions for the future development of ARE are proposed. This study is of great significance for the overall development trend of ARE, the timely tracking of scientific research hotspots, outlining key research and development directions, and promoting scientific and technological innovation.
Can a Policy Mix Achieve a Collaborative Effect? Exploring the Nested Implementation Process of Urban Carbon Emission Reduction Policies
Authors: Zhao Yihang , Zhang Yuanyuan , Wang Shengyu
Published: July 2024

China’s government has adopted several carbon emission reduction policies to strive to achieve the dual carbon goal of “carbon peaking and carbon neutrality”. In practice, various multi-objective policy instruments are implemented in a staggered manner, which might lead to the overestimation or underestimation of single-policy effect evaluation. This paper evaluates the combined effect of the Low-carbon City Pilot (LCP) and Comprehensive Demonstration City of Energy Saving and Emission Reduction Fiscal Policy (CCEEFP), investigating whether their carbon emission reduction effects are complementary and achieve collaborative outcomes. The empirical results indicate that the LCP, the CCEEFP, and their nested implementation could all promote carbon emission reduction. Their collaborative policy effects are sustained, being more obvious in higher-grade cities. Furthermore, there is a greater adoption of the source treatment technology for carbon emissions. And the implementing order of these policies could affect governance performance because of the “path dependence” of local government’s attention towards multi-objective policy intentions. The empirical evidence demonstrates that policymakers should carefully design policy mix particulars towards a common purpose and carefully co-ordinate their implementation process.
Evolution and Challenges of Environmental, Social, and Governance Practices: An Analysis of the Brazilian Stock Exchange’s Corporate Sustainability Index
Authors: Paranhos Maria Aparecida Hess Loures , Nääs Irenilza de Alencar , Neto Pedro Luiz de Oliveira Costa
Published: July 2024

Environmental, social, and governance (ESG) practices are increasingly recognized as critical for corporate sustainability and market competitiveness, driven by heightened expectations from investors, governments, and consumers. This study examines the evolution of ESG practices among companies listed on the Brazilian Stock Exchange’s Corporate Sustainability Index (ISE B3) from 2005 to 2022. We analyzed the index portfolio’s composition, focusing on changes in ESG dimensions over time. The only six long-lived companies in this index, from retail, banking, and energy sectors, were selected for in-depth analysis. We further evaluated the quality and consistency of ESG disclosures in company reports. Findings indicate relevant fluctuations in the number of companies included in the ISE B3 and variations in ESG scores for the companies. These variations may be attributed to the voluntary nature of ESG practices and the absence of standard disclosure. This study reveals a lack of homogeneity in ESG reporting, which could be addressed by establishing more straightforward guidelines and global standards. This research underscores Brazilian companies’ challenges navigating diverse ESG frameworks to align with evolving societal and environmental expectations.
Evaluating User Engagement in Online News: A Deep Learning Approach Based on Attractiveness and Multiple Features
Authors: Song Guohui , Wang Yongbin , Chen Xiaosen , Hu Hongbin , Liu Fan
Published: July 2024

Online news platforms have become users’ primary information sources. However, they focus on attracting users to click on the news and ignore whether the news triggers a sense of engagement, which could potentially reduce users’ participation in public events. Therefore, this study constructs four indicators by assessing user engagement to build an intelligent system to help platforms optimize their publishing strategies. First, this study defines user engagement evaluation as a classification task that divides user engagement into four indicators and proposes an extended LDA model based on user click–comment behavior (UCCB), using which the attractiveness of words in news headlines and content can be effectively represented. Second, this study proposes a deep user engagement evaluation (DUEE) model that integrates news attractiveness and multiple features in an attention-based deep neural network for user engagement evaluation. The DUEE model considers various elements that collectively determine the ability of the news to attract clicks and engagement. Third, the proposed model is compared with the baseline and state-of-the-art techniques, showing that it outperforms all existing methods. This study provides new research contributions and ideas for improving user engagement in online news evaluation.
Evolutionary Game Analysis of Government–Enterprise Collaboration in Coping with Natech Risks
Authors: Guo Shaojun , Feng Wei , Zhang Guirong , Wen Ying
Published: July 2024

The synergistic interaction between emergency management departments and enterprises constitutes a fundamental mechanism for mitigating the risks of technological accidents caused by natural disasters (Natech). The efficacy of this collaborative approach is impacted by comprehensive risk analyses and the game between government and enterprise. Predicated on these premises, the evolutionary game analysis of government–enterprise collaboration in coping with Natech risk was carried out. Firstly, an evolutionary game model of government–enterprise collaboration in coping with Natech risk was constructed. Secondly, the evolutionary stability strategy (ESS) was developed. Finally, these strategies were substantiated through numerical simulations. The findings revealed that at lower levels of Natech risk, enterprises will choose low investment in coping capacity building, and emergency management departments will choose delayed disposal. Under moderate Natech risk, enterprises will increase their investments if emergency management departments persist with delayed strategies, and emergency management departments will react proactively if enterprises persist with low investment. Under a high Natech risk, a pattern of strategic misalignment emerges between the two entities. This study contributes a theoretical basis for the optimization of government–enterprise collaboration in coping with Natech risks.
Risk Factors of Optic Neuropathy in Ethambutol Users: Interaction with Isoniazid and Other Associated Conditions of Toxic Optic Neuropathy
Authors: Kim Jiyeong , Ahn Seong Joon
Published: July 2024

(1) Background: To investigate the risk factors associated with optic neuropathy (ON) and validate the hypothesis that concomitant isoniazid use and other causes of toxic ON affect the development of ON in ethambutol users. (2) Methods: This cohort study identified ethambutol users who initiated ethambutol therapy between January 2015 and December 2021 and had no ON prior to ethambutol therapy. ON incidence up to 31 December 2022 was evaluated. The users were grouped on the basis of the presence of ON. Demographic and clinical characteristics were investigated for risk factor analyses of ON. Odds ratios (ORs) were calculated using multivariate logistic regression analyses. (3) Results: Among 204,598 ethambutol users, 5277 (2.6%) patients developed ON over the study period. Patients with ON included a higher percentage of women and had a higher mean age than patients without ON. In the multivariate analyses, the risk factors for ON and visual impairment included sex, age, cumulative dose, extrapulmonary indications for ethambutol use, and systemic conditions such as diabetes, hypertension, hyperlipidemia, diabetes, kidney disease, and liver disease. Malnutrition or nutritional disorders significantly increased the risk of ON (OR = 1.27, 95% confidence interval [CI] = 1.19–1.34), whereas concomitant isoniazid use decreased the risk (OR = 0.78, 95% CI = 0.72–0.86). (4) Conclusion: An increased risk of ON in patients with systemic diseases and nutritional deficiency was identified, whereas concomitant isoniazid use was associated with a decreased risk of ON. Patients with these risk factors should be carefully monitored to minimize the vision-threatening ON.
Micro/Nanoplastic Exposure on Placental Health and Adverse Pregnancy Risks: Novel Assessment System Based upon Targeted Risk Assessment Environmental Chemicals Strategy
Authors: Wan Danyang , Liu Yujie , Chang Qianjing , Liu Zhaofeng , Wang Qing , Niu Rui , Gao Beibei , Guan Quanquan , Xia Yankai
Published: July 2024

Micro/nanoplastics (MNPs), as emerging pollutants, have been detected in both the maternal and fetal sides of the placenta in pregnant women, and their reproductive toxicity has been demonstrated in in vivo and in vitro experimental models. The Targeted Risk Assessment of Environmental Chemicals (TRAEC) strategy has been innovatively devised to facilitate valid risk assessment, encompassing a comprehensive evaluation of reliability, correlation, outcome fitness, and integrity across four dimensions based on the included published evidence and our own findings. This study serves as an application case of TRAEC, with 40 items of research evidence on the toxicity of MNPs to the placenta, which were rigorously screened and incorporated into the final scoring system. The final score for this TRAEC case study is 5.63, suggesting a moderate-to-low risk of reproductive toxicity associated with MNPs in the placenta, which may potentially increase with decreasing particle size. It is essential to emphasize that the findings also report original data from assays indicating that exposure to high-dose groups (100 μg/mL, 200 μg/mL) of 50 nm and 200 nm polystyrene nanoplastics (PS-NPs) induces HTR8/SVneo cell cycle arrest and cell apoptosis, which lead to reproductive toxicity in the placenta by disrupting mitochondrial function. Overall, this study employed the TRAEC strategy to provide comprehensive insight into the potential reproductive health effects of ubiquitous MNPs.
Actin Dysregulation Mediates Nephrotoxicity of Cassiae Semen Aqueous Extracts
Authors: Yang Jinlan , Xiao Sheng , Li Ludi , Zhu An , Xiao Wusheng , Wang Qi
Published: July 2024

Cassiae semen, commonly consumed as roasted tea, has been widely used for both medicinal purposes and dietary supplements. In this study, we investigated the nephrotoxic effects and underlying mechanisms of Cassiae semen aqueous extracts (CSAEs) using computational and animal models. Both male and female Sprague Dawley rats were treated with 4.73–47.30 g/kg (body weight) of CSAEs by oral gavage twice a day for 7–28 days. We found that serum and urinary biomarkers of kidney injury and kidney coefficients were increased in a dose-dependent manner, and were accompanied by morphological alterations in the kidneys of CSAEs-treated rats. Computational and molecular docking approaches predicted that the three most abundant components of CSAEs—obtusifolin, aurantio-obtusin, and obtusin—exhibited strong affinity for the binding of F-actin, ROCK1, and Rac1, and the RhoA–ROCK pathway was identified as the most likely regulatory mechanism mediating the nephrotoxicity of CSAEs. Consistently, immunofluorescence staining revealed F-actin and cytoskeleton were frequently disturbed in renal cells and brush borders at high doses of CSAEs. Results from gene expression analyses confirmed that CSAEs suppressed the key proteins in the RhoA–ROCK signaling pathway and consequently the expression of F-actin and its stabilization genes. In summary, our findings suggest that Cassiae semen can depolymerize and destabilize actin cytoskeleton by inhibition of the RhoA–ROCK pathway and/or direct binding to F-actin, leading to nephrotoxicity. The consumption of Cassiae semen as a supplement and medicine warrants attention.
Effects of Environmental Tobacco Smoke on Oxidative Stress in Childhood: A Human Biomonitoring Study
Authors: Antonucci Arianna , Andreoli Roberta , Maccari Chiara , Vitali Matteo , Protano Carmela
Published: July 2024

Household smoking is one of the main sources of environmental tobacco smoke (ETS) exposure for children, a population considered to be at high risk for associated negative health outcomes. Several studies evidenced the occurrence of early effects related to ETS exposure, including the development of the oxidative stress process. The aim of this study was to evaluate the correlation between urinary levels of 8-oxo-7,8-dihydro-2-deoxyguanosine (8oxodGuo), a nucleic acid oxidation biomarker, and socio-demographic features and lifestyle factors in school children (aged 5–11 years). A cross-sectional study was conducted among 154 healthy children, residing in rural zones of central Italy. For each participant, one urine sample was analyzed by the HPLC-MS/MS technique to simultaneously quantify 8oxodGuo and cotinine (a biomarker of ETS exposure), while information on the children was collected using a questionnaire filled out by the parents. Urinary levels of 8oxodGuo was found to be significantly higher in children exposed to ETS compared to those not exposed (5.53 vs. 4.78 μg/L; p = 0.019). This result was confirmed by the significant association observed between urinary levels of cotinine and 8oxodGuo (r = 0.364, p < 0.0001). Additionally, children exposed to ETS with no smoking ban at home showed a further increased difference than those not exposed (6.35 μg/L vs. 4.78 μg/L; p = 0.008). Considering the great number of adverse effects on human health due to exposure to passive smoking, especially if this exposure begins early in life, it is essential to implement health promotion interventions in this area.
Presence of Intestinal Parasites in Patients with Chronic Non-Communicable Diseases in Masaya (Nicaragua)
Authors: Muñoz-Antoli Carla , Pavón Aleyda , Comas Jacklyn , Toledo Rafael , Esteban José Guillermo
Published: July 2024

Aims: A cross-sectional study was conducted in Masaya (Nicaragua) to estimate the prevalence of intestinal parasite (IP) infections in patients with non-communicable diseases (NCDs) and to determine the associations between the types of NCDs and patients’ epidemiological characteristics of infection. Methods: A total of 157 preserved faecal samples were examined (direct wet mount, formalin/ethyl acetate concentration and modified Ziehl–Neelsen technique). Microscopically positive faecal sample identification was completed by conducting a molecular study. Results: The total prevalence of IP was 52% in NCD patients. Diabetic patients presented an IP prevalence of 42%. Blastocystis presented the highest prevalence (42%). A molecular analysis of Giardia intestinalis (prevalence of 1.3%) revealed 100% of sub-assemblage BIII and the Entamoeba complex (5%) was identified as E. dispar. Blastocystis ST1 appeared in 44% of those suffering from diabetes and ST3 in 66% of those suffering from hypertension, while ST2 only appeared in those suffering with several NCDs simultaneously. In diabetic patients, the risk of infection is associated with having pets (p = 0.021) and land-floor houses. The risk of infection appears to be statistically related (p = 0.019) in those with several NCDs having received a previous helminthic deworming treatment. Conclusions: Coordinated public health activities for IP and NCD screening and diagnosis are crucial to their successful control programmes.
SARS-CoV-2 Variants and Clinical Outcomes of Special Populations: A Scoping Review of the Literature
Authors: Livieratos Achilleas , Gogos Charalambos , Akinosoglou Karolina
Published: July 2024

The ongoing COVID-19 pandemic has significantly impacted special populations, including immunocompromised individuals, people living with HIV (PLWHIV), pediatric patients, and those with chronic liver disease (CLD). This scoping review aims to map the clinical outcomes of these vulnerable groups when infected with various SARS-CoV-2 variants. The review identifies trends and patterns, noting that early variants, such as Alpha and Delta, are associated with more severe outcomes, including higher hospitalization and mortality rates. In contrast, the Omicron variant, despite its increased transmissibility, tends to cause milder clinical manifestations. The review highlights the necessity for ongoing surveillance and tailored healthcare interventions due to the heterogeneity of patient populations and the evolving nature of the virus. Continuous monitoring and adaptive healthcare strategies are essential to mitigate the impact of COVID-19 on these high-risk groups.
Integrating Feature Selection with Machine Learning for Accurate Reservoir Landslide Displacement Prediction
Authors: Ge Qi , Wang Jingyong , Liu Cheng , Wang Xiaohong , Deng Yiyan , Li Jin
Published: July 2024

Accurate prediction of reservoir landslide displacements is crucial for early warning and hazard prevention. Current machine learning (ML) paradigms for predicting landslide displacement demonstrate superior performance, while often relying on various feature engineering techniques, such as decomposing into different temporal lags and feature selection. This study investigates the impact of various feature selection techniques on the performance of ML algorithms for landslide displacement prediction. The Shuping and Baishuihe landslides in China’s Three Gorges Reservoir Area are used to comprehensively benchmark four prevalent ML algorithms. Both static ML models, including backpropagation neural network (BPNN), support vector machine (SVM), and dynamic models, such as long short-term memory (LSTM), and gated recurrent unit (GRU), are included. Each ML model is evaluated under three feature engineering techniques: raw multivariate time series, and feature selection under maximal information coefficient-partial autocorrelation function (MIC-PACF), or grey relational analysis-PACF (GRA-PACF). The results demonstrate that appropriate feature selection methods could significantly improve the performance of static ML models. In contrast, dynamic models effectively leverage inherent capabilities in capturing temporal dynamics within raw multivariate time series, seeing marginal gains with extensive feature engineering compared to no feature selection strategy. The optimal feature selection approach varies based on the ML model and specific landslide, highlighting the importance of case-specific assessments. The findings in this study offer guidance on integrating feature selection techniques with different machine learning models to maximize the robustness and generalizability of data-driven landslide displacement prediction frameworks.