Understanding the bearable link between ecology and health outcomes: the criticality of human capital development and energy use

Unlocking the path to environmental sustainability: navigating economic policy uncertainty, ICT, and environmental taxes for a sustainable future

The study aims to gauge the impact of economic policy uncertainty, ICT, and environmental tax on environmental sustainability, which is measured by carbon emission and ecological footprint in a panel of 22 nations from 1997 to 2021. The present study has implemented the advanced panel data estimation techniques, including continuously updated fully modified (CUP-FM) and continuously updated bias-corrected (CUP-BC), dynamic seemingly unrelated regressions (DSUR), and nonlinear autoregressive distributed lagged (NARDL) in documenting the elasticities of target variables. Moreover, the directional causality has been tested through the D-H causality test. Study findings documented a positive and statistically significant linkage between EPU and environmental degradation. That is, EPU amplifies the emission of CO2 and ecological instability. The effects of ET and ICT are positively associated with environmental sustainability; that is, ET and ICT control the emission of CO2 and bring ecological improvement. This study contributes to the existing body of literature by conducting a thorough analysis of the relationship between various factors and their impact on environmental degradation. The study emphasizes the significance of every factor in influencing environmental outcomes. It provides policy suggestions to reduce CO2 emissions and promote ecological sustainability. The findings add valuable insights to the ongoing conversation about how to tackle environmental challenges in our constantly evolving world.

Transformative and sustainable insights of agricultural waste-based adsorbents for water defluoridation: Biosorption dynamics, economic viability, and spent adsorbent management

With the progression of civilization, the harmony within nature has been disrupted, giving rise to various ecocidal activities that are evident in every spheres of the earth. These activities have had a profound and far-reaching impact on global health. One significant example of this is the presence of fluoride in groundwater exceeding acceptable limits, resulting in the widespread occurrence of "Fluorosis" worldwide. It is imperative to mitigate the concentration of fluoride in drinking water to meet safety standards. While various defluoridation techniques exist, they often have drawbacks. Biosorption, being a simple, affordable and eco-friendly method, has gained preference for defluoridation. However, its limited commercialization underscores the pressing need for further research in this domain. This comprehensive review article offers a thorough examination of the defluoridation potential of agro-based adsorbents, encompassing their specific chemical compositions and preparation methods. The review presents an in-depth discussion of the factors influencing fluoride biosorption and conducts a detailed exploration of adsorption isotherm and adsorption kinetic models to gain a comprehensive understanding of the nature of the adsorption process. Furthermore, it evaluates the commercial viability through an assessment of regeneration potential and a cost analysis of these agro-adsorbents, with the aim of facilitating the scalability of the defluoridation process. The elucidation of the adsorption mechanism and recommendations for overcoming challenges in large-scale implementation offer a comprehensive outlook on this eco-friendly and sustainable approach to fluoride removal. In summary, this review article equips readers with a lucid understanding of agro-adsorbents, elucidates their ideal conditions for improved performance, offers a more profound insight into the fluoride biosorption mechanism, and introduces the concept of effective spent adsorbent management.

The Impact of Economic Corridor and Tourism on Local Community's Quality of Life under One Belt One Road Context

Economic corridors unlock new economic opportunities and tourism development in the region to achieve sustainable development goals. Green economic growth is conducive to environmental sustainability. Economic mega-projects of CPEC promote tourism that leads to communities' well-being and better quality of life. Modern infrastructure development contributes significantly to economic growth and tourism activities. This study's objectives emphasize exploring tourism and sustainable development pursuits under OBOR economic projects that open doors to improving residents' quality of life. The growing world is an eyewitness to a continuous rise in emissions and its severe consequences for humankind. It is necessary to show off the leading factors that result in tourism and economic activities causing environmental pollution rather than blame policymakers. Undoubtedly, many studies previously focused on demonstrating the influence of socio-economic factors that lead to better environmental quality. However, the empirical literature on tourism, social well-being, foreign direct investment, and the Environment in Belt and Road developed economies needed improvement. This research applied a series of advanced estimators that help demonstrate the study's probable results. This study explores the role of Social well-being (HDI), tourism development, FDI, renewable energy, information & communication technology (ICT), and urbanization on CO2 emissions in Belt and Road (BRI) developed economies.Estimated results exhibited the significant contribution of ICT and renewable energy to sustainability. Besides, FDI contributes to emissions reduction after its threshold level. Conversely, urbanization and tourism activities contribute to environmental pollution. The study outcomes stated inverted/EKC U-shaped hypotheses related to specified economies. Finally, the analysis based on the D-H panel causality test constructs exciting results.The present study concludes that economic corridor plays a vital role in tourism development, the community's well-being, and SDGs goals (sustainable development) impact on environmental safety. The findings suggest essential and applicable policies to attain the desired sustainability level. Findings contribute to the literature on tourism, well-being, and sustainability. Further studies can use insights using this methodology.

Formulation of precise exercise intervention strategy for adolescent depression

The high incidence of adolescent depression has become the focus of social and academic attention. Exercise is an important method to improve adolescent depression, but its intervention effect is still controversial. This study first compares and analyzes the relevant studies at home and abroad and finds that exercise prescription in adolescent depression intervention is not accurate enough. A meta-analysis was conducted to develop a precise exercise intervention strategy for adolescent depression. Firstly, this thesis identified how to optimize five elements (exercise intensity, exercise frequency, exercise time, exercise cycle, and exercise type) of exercise prescription to improve depression in adolescents. This is the problem. Furthermore, the concept of "precision exercise" was proposed, and a precision exercise intervention strategy (moderate-intensity aerobic exercise for 8-10 weeks, 3 times/week, 45-50 min/time) was constructed to improve adolescent depression. This paper also presents research that strengthens the cross-sectional research and empirical research on adolescent depression and establishes a precision exercise prescription database for adolescent depression in China. In conclusion, this study not only puts forward the concept of "precision exercise" but also constructs a precision exercise intervention strategy for adolescent depression, which has important theoretical and practical significance for improving the high incidence of adolescent depression.

Predicting air pollutant emissions of the foundry industry: Based on the electricity big data

Industrial enterprises are one of the largest sources of air pollution. However, the existing means of monitoring air pollutant emissions are narrow in coverage, high in cost, and low in accuracy. To bridge these gaps, this study explored a predicting model for air pollutant emissions from foundry industries based on high-accuracy electricity consumption data and continuous emission monitoring system (CEMS). The model has then been applied to the calculation of air pollutant emissions from foundries without CEMS and the optimization of air pollutant emission temporal allocation factors. The results reveal that electricity consumption and PM emissions during the 2022 Beijing Winter Olympics have the same ascending and descending relationship. Furthermore, a cubic polynomial model between electricity consumption and flue gas flow is established based on the whole year data of 2021 (R2 = 0.85). The relative errors between the PM emissions calculated by the model and the emission factor method are small (-17.09-24.12 %), and the results from the two methods revealed a strong correlation (r = 0.93, p < 0.01). In addition, the monthly PM emissions from foundries are mainly concentrated in spring and winter, and the daily emissions on weekends are significantly lower than those on workdays. These results can be useful for environmental regulation and optimization of air pollutant emission inventories of foundry industry.

Environmental regulation, land use efficiency and industrial structure upgrading: Test analysis based on spatial durbin model and threshold effect

Environmental regulation promotes industrial structure change and regional economic transformation through land use adjustment, which gets a new way to explore the path of reforming traditional industrialization and urbanization. Based on the panel data of 128 prefecture-level cities in China 's Yangtze River Economic Belt from 2000 to 2020, this paper uses the spatial Dubin model to analyze the impact of environmental regulation and land use efficiency on the upgrading of industrial structure, and sets the panel threshold model to examine the impact of environmental regulation on the upgrading of industrial structure by affecting land use efficiency. The results show that formal environmental regulation has a significant positive spatial effect on the rationalization and upgrading of industrial structure, which are 0.1734 and 0.2854 respectively. Informal environmental regulation has a negative spillover effect on neighboring provinces but not significant. Heterogeneous environmental regulation has obvious "double threshold effect" on industrial upgrading by affecting land use efficiency. When the threshold of environmental regulation intensity is 0.0315-0.0886, environmental regulation still inhibits land use efficiency and industrial structure upgrading. When the threshold value is greater than 0.0886, environmental regulation has a positive impact on land use efficiency but not significant. With the intensity of environmental regulation from weak to strong, it will produce a double threshold effect of "strong inhibition-weak inhibition-interaction promotion" on the upgrading of manufacturing structure through the adjustment of land use efficiency.

Fine simulation of PM2.5 combined with NPP-VIIRS night light remote sensing and mobile monitoring data

Human activity plays a crucial role in influencing PM2.5 concentration and can be assessed through nighttime light remote sensing. Therefore, it is important to investigate whether the nighttime light brightness can enhance the accuracy of PM2.5 simulation in different stages. Utilizing PM2.5 mobile monitoring data, this study introduces nighttime lighting brightness as an additional factor in the PM2.5 simulation model across various time periods. It compares the differences in simulation accuracy, explores the impact of nocturnal human activities on PM2.5 concentrations at different periods of the following day, and analyzes the spatial and temporal pollution pattern of PM2.5 in urban functional areas. The results show that (1) the incorporation of nighttime lighting brightness effectively enhances the model's accuracy (R2), with an improvement ranging from 0.04 to 0.12 for different periods ranges. (2) the model's accuracy improves more prominently during 8:00-12:00 on the following day, and less so during 12:00-18:00, as the PM2.5 from human activities during the night experiences a strong aggregation effect in the morning of the next day, with the effect on PM2.5 concentration declining after diffusion until the afternoon. (3) PM2.5 is primarily concentrated in urban functional areas including construction sites, roads, and industrial areas during each period. But in the period of 8:00-12:00, there is a significant level of PM2.5 pollution observed in commercial and residential areas, due to the human activities that occurred the previous night.

Possibilities and limits of modelling of long-range economic consequences of air pollution - A case study

Air pollution is the biggest environmental problem in modern societies, causing considerable health damage and requiring substantial financial resources for health care. The goal of the study is to demonstrate the adverse economic consequences of air pollution on example of a small, open Central European country, Hungary, and to provide quantified financial arguments for macroeconomic decision-making for the development of a long-term energy strategy. On the basis of the Cobb-Douglas production function and Solow-Swann model of dynamic economic systems a simple and robust model was constructed to estimate and predict economic losses, caused by the pollution. On base of results it is obvious, that on base of macroeconomic theory and combination of various, publicly available, quality-controlled statistical resources quantifiable models can be constructed to characterise the economic consequences of air pollution, but it should be taken into consideration, that the reliability of economic models considerably depends on their initial parameters and practical validity of assumptions, based on which the underlying economic theories were built. The most important economic burden of air pollution is caused by the loss of working-age population, resulting in a decrease of 4.1-9.4 % a year in Gross Domestic Product (GDP) in the next fifty years. The additional burden of health care costs amounts to 0.1 % of GDP. Reducing air pollution is not only a quality of life improvement but also an investment into the economic development. Notwithstanding of statistical biases it could be proven the importance of combination health economic and econometric methods in preparation of more efficient environmental-related socio-economic decisions.

Ecotoxicological effects of cypermethrin on indigenous climbing perch (Anabas testudineus)

Pesticides including cypermethrin (10% EC) are commonly used pesticide in tea gardens of Bangladesh possess distinct harmful effects on an aquatic community. The experiment was carried out to assess the ecotoxicological effects of cypermethrin (10%) concentrate on indigenous Climbing Perch (Anabas testudineus). A total of 120 A. testudineus (mean length 16 ± 2.67 cm and mean weight 31.6 ± 3.56 g) were exposed to the acute toxicity test when the lethal concentration 50 value (LC50) for 96 h was maintained at 1.00 ppm. Three different sub-lethal concentrations of 0.05 ppm (5%), 0.10 ppm (10%), and 0.20 ppm (20%) were used respectively as three treatments and a control of 0 ppm with three replicates each. Restlessness, erratic movement, increased opercular activities, loss of equilibrium, and irregular response to feeding were observed in all the treatments compared to control one. Concerning histopathological alterations, all the analyzed organs showed highest changes in the T3 (cypermethrin conc. 20%) compared to other treatments while T0 (0 ppm) had normal structure. The major changes in the gill were epithelial cell hyperplasia, necrosis, severe lamellar fusion and epithelial lifting; while necrotic proximal tubules, glomerular shrinkage, disrupted renal corpuscle of the kidney and nuclear pyknosis, degenerated hepatic cells and vacuolation were observed in the liver. Severe melanomacrophage centre (MMC), haemosiderosis and vacuolation were found in spleen. The effect of cypermethrin on the hematological parameters of experimental fish was also studied. Red blood cells, hemoglobin and hematocrit were decreased in the experimental groups and lowest value was in T3 while values of white blood cells were increased in the experimental groups compared to control one. Hence, the present observation revealed that pesticides even at low concentrations can cause harmful effects on A. testudineus.

Computer vision based deep learning approach for toxic and harmful substances detection in fruits

Formaldehyde (CH₂O) is one of the significant chemicals mixed with different perishable fruits in Bangladesh. The fruits are artificially preserved for extended periods by dishonest vendors using this dangerous chemical. Such substances are complicated to detect in appearance. Hence, a reliable and robust detection technique is required. To overcome this challenge and address the issue, we introduce comprehensive deep learning-based techniques for detecting toxic substances. Four different types of fruits, both in fresh and chemically mixed conditions, are used in this experiment. We have applied diverse data augmentation techniques to enlarge the dataset. The performance of four different pre-trained deep learning models was then assessed, and a brand-new model named "DurbeenNet," created especially for this task, was presented. The primary objective was to gauge the efficacy of our proposed model compared to well-established deep learning architectures. Our assessment centered on the models' accuracy in detecting toxic substances. According to our research, GoogleNet detected toxic substances with an accuracy rate of 85.53 %, VGG-16 with an accuracy rate of 87.44 %, DenseNet with an impressive accuracy rate of 90.37 %, and ResNet50 with an accuracy rate of 91.66 %. Notably, the proposed model, DurbeenNet, outshone all other models, boasting an impressive accuracy rate of 96.71 % in detecting toxic substances among the sample fruits.

Corporate capital structure effects on corporate performance pursuing a strategy of innovation in manufacturing companies

Within the sphere of finance, the concept of capital structure has long been a subject of intense debate, serving as a quantitative depiction of the balance between debt, preference shares, and common stock within a company. This structure serves a crucial role in optimizing the utilization of a company's existing resources while simultaneously elevating the revenue streams for stakeholders. This particular study delves into the intricate relationship between corporate performance and capital structure, focusing on 78 publicly listed firms within the Dhaka Stock Exchange (DSE). Bangladesh holds the 29th position globally in terms of purchasing power, lending significant weight to this investigation. To comprehensively analyze this correlation, panel data encompassing the span from 2017 to 2021 was collected for these 78 sample companies operating within the DSE. Several key determinants of capital structure were considered in this analysis, namely the debt-to-equity ratio, short-term leverage ratio, long-term leverage ratio, and total debt ratio. Meanwhile, the performance of these firms was gauged using key metrics such as Return on Assets (ROA), Return on Equity (ROE), and Earnings Per Share (EPS). To ensure a robust analysis, factors such as inflation, liquidity, growth rate, tax rate, and firm size were meticulously controlled for. The findings unveiled a compelling narrative: all forms of debt ratios-be it short-term, long-term, or the total debt ratio-exhibited a substantial negative impact on ROA at a significant level of 1 %. Conversely, specific debt ratios, like the short-term total debt and the total debt-to-total asset ratio, displayed a notable positive correlation with ROE at a 1 % significance level. Intriguingly, the long-term total debt ratio yielded a negative and insignificant effect on ROE. Moreover, within the spectrum of predictors influencing a firm's performance, the liquidity ratio emerged as a non-significant factor-a notable discovery that highlights the nuanced nature of the interplay between capital structure and performance within these companies.

PM2.5 exposure exacerbates seizure symptoms and cognitive dysfunction by disrupting iron metabolism and the Nrf2-mediated ferroptosis pathway

In recent years, air pollution has garnered global attention due to its ability to traverse borders and regions, thereby impacting areas far removed from the emission sources. While prior studies predominantly focused on the deleterious effects of PM2.5 on the respiratory and cardiovascular systems, emerging evidence has highlighted the potential risks of PM2.5 exposure to the central nervous system. Nonetheless, research elucidating the potential influences of PM2.5 exposure on seizures, specifically in relation to neuronal ferroptosis, remains limited. In this study, we investigated the potential effects of PM2.5 exposure on seizure symptoms and seizures-induced hippocampal neuronal ferroptosis. Our findings suggest that seizure patients residing in regions with high PM2.5 levels are more likely to disturb iron homeostasis and the Nrf2 dependent ferroptosis pathway compared to those living in areas with lower PM2.5 levels. The Morris Water Maze test, Racine scores, and EEG recordings in epileptic mice suggest that PM2.5 exposure can exacerbate seizure symptoms and cognitive dysfunction. Neurotoxic effects of PM2.5 exposure were demonstrated via Nissl staining and CCK-8 assays. Direct evidence of PM2.5-induced hippocampal neuronal ferroptosis was provided through TEM images. Additionally, increased Fe2+ and lipid ROS levels indirectly supported the notion of PM2.5-induced hippocampal ferroptosis. Therefore, our study underscores the necessity of preventing and controlling PM2.5 levels, particularly for patients with seizures.

Using expertise as an intermediary: Unleashing the power of blockchain technology to drive future sustainable management using hidden champions

An overview of blockchain fundamentals and its potential benefits for sustainability is provided. The role of expertise as an intermediary on the blockchain to drive transparency and accountability is examined. This research examines the potential of blockchain technology in the field of economic management and to drive future sustainable development in emerging companies, which are referred to as hidden champions. This study addresses the need for transparent and responsive practices that promote social stability, economic growth, and environmental sustainability. The goals are to analyze economic functions, investigate the formation of appropriate economic patterns, facilitate equitable distribution, and support environmental protection efforts. The research method includes case studies and theoretical frameworks to collect relevant data. The results emphasize the importance of balancing competing interests, promoting security, and strengthening inclusive decision-making processes. This study emphasizes the intersection between economic development and environmental protection and highlights the role of sustainability criteria in guiding land use practices. The conclusion emphasizes that sustainable economic practices are critical for social, economic and environmental development, especially in emerging economies. Practical recommendations are provided to policymakers and stakeholders to improve economic governance frameworks and help achieve the Sustainable Development Goals.

Exploration on terrorism, ecological footprint and environmental sustainability in countries with the most terrorism antecedent: Accessing evidence from panel fourier analysis

Amidst increased concerns for global security and ecological balance, the intricate interconnectedness between terrorism and environmental sustainability has attracted significant attention in the existing literature. To this end, the present study explores the interaction among environmental degradation, terrorism, and foreign direct investments in 17 countries with the most terrorism antecedents over the 2002-2018 period through the Panel Fourier cointegration test and the Panel Fourier Toda-Yamamoto causality test. The present study also leverages recent and robust panel analysis for evidence-based results and inferences for policy formulation. The panel Fourier cointegration test presents the cointegration relationship between the outline variables under review. Empirical findings highlight that terrorism does not have a significant influence on the ecological footprint. However, foreign direct investment has a positive influence on the ecological footprint. These findings have implications for environmental sustainability and foreign direct investment inflows in the bloc investigated. More insights are discussed in the concluding section with policy caveats.

Long- and short-term health benefits attributable to PM2.5 constituents reductions from 2013 to 2021: A spatiotemporal analysis in China

Long- and short-term exposure to constituents of fine particulate matter (PM2.5) substantially affects human health. However, assessments of the health and economic benefits of reducing PM2.5 constituents are scarce. This study estimates the number of premature deaths from all-cause, cardiovascular (CVD), and respiratory diseases avoided due to reductions in daily and annual average concentrations of PM2.5 constituents. The Environmental Benefits Mapping and Analysis Program was used for two scenarios: we used yearly concentrations of PM2.5 constituents from 2013 to 2020 as the baseline concentration surface (Scenario I), and 2021 as the baseline year (Scenario II). With reductions in daily and annual average concentrations of PM2.5 constituents, 309,099 (95 % confidence interval [CI]: 37,265-571,485) and 195,297 (95 % CI: 178,192-211,914) premature deaths were avoided in Scenario I, respectively; meanwhile, 347,296 (95 % CI: 79,258-604,758) and 201,567 (95 % CI: 185,038-217,530) premature deaths were avoided in Scenario II, respectively. Moreover, economic benefits associated with the prevention of premature deaths were estimated using the willingness to pay (WTP) and modified human capital (AHC) methods. The total estimated economic benefits amounted to 563.32 billion RMB (WTP) and 322.03 billion RMB (AHC) in Scenario I. In Scenario II, the associated economic benefits were 751.48 billion RMB (WTP) and 427.56 billion RMB (AHC), accounting for 0.657 and 0.374 % of China's gross domestic product in 2021, respectively. Additionally, we analyzed the sensitivity of CVD-related premature deaths to the concentrations of PM2.5 constituents, and found that CVD-related premature deaths were more sensitive to black carbon.

An advanced deep learning predictive model for air quality index forecasting with remote satellite-derived hydro-climatological variables

Forecasting the air quality index (AQI) is a critical and pressing challenge for developing nations worldwide. With air pollution emerging as a significant threat to the environment, this study considers seven study sites of the sub-tropical region in Bangladesh and introduces a novel hybrid deep-learning model. The proposed model, expressed as CLSTM-BiGRU, integrates a convolutional neural network (CNN), a long-short term memory (LSTM), and a bi-directional gated recurrent unit (BiGRU) network. Leveraging nineteen remotely sensed predictor variables and harnessing the grey wolf optimization (GWO) algorithm, the CLSTM-BiGRU model showcases its superiority in air quality forecasting. It consistently outperforms the benchmark models, yielding lower forecasting errors and higher efficiency (i.e., correlation coefficient ~1) values. Hence, this study underscores the feasibility and substantial potential of the hybrid deep learning model, which can provide precise forecasts of air quality index, and will be highly useful for relevant stakeholders and decision-makers. Furthermore, the adaptability and potential utility of this innovative model may be ascertained for air quality monitoring and effective public health risk mitigation in urban environments.

Spatio-temporal distribution and dynamics of antibiotic resistance genes in a water-diversion lake, China

The distribution and dynamics of antibiotic resistance genes (ARGs) in water-diversion lakes are poorly understood. In this study, two comparative in situ investigations of ARG profiles targeting water diversion (DP) and non-diversion periods (NDP) were conducted in Luoma Lake, a vital transfer node for the eastern route of the South-to-North Water Diversion Project in China. The results demonstrated significant spatiotemporal variations in ARG contamination and notable differences in the co-occurrence patterns of ARGs and bacterial communities between DP and NDP. Correlations among ARGs with the 16 S rRNA, and mobile genetic elements indicate that horizontal gene transfer (HGT) and vertical gene transfer (VGT) in NDP, but only HGT in DP, were the primary mechanisms of ARG proliferation and spread, implying that water diversion could be an essential control of the transfer pattern of ARGs in a lake environment. The null model analysis indicated that stochastic processes, with predominant driver of ecological drift in the lake mainly drove the assembly of ARGs. Partial least squares structural equation modeling was developed to analyze the causal effects of the factors in shaping ARG dynamics and identify the major driving forces in the DP and NDP.

Impacts of external factors on Ethiopia's economic growth: Insights on foreign direct investment, remittances, exchange rates, and imports

This study conducts a rigorous examination of relationships that are often assumed but rarely tested within the context of Ethiopia's journey towards sustainable development. An autoregressive distributed lag model is employed using annual time series data from 1982 to 2021 to investigate the short- and long-term impacts of foreign direct investment (FDI), remittances, real exchange rates, and imports on Ethiopia's economic growth. By incorporating additional control variables, this study contributes nuanced insights that were previously lacking in the literature. The findings reveal intriguing patterns that both align with and deviate from existing frameworks. Contrary to some prior studies, foreign investment consistently emerges as a significant driver of economic growth over time. However, remittances demonstrate only transient significance, highlighting the need for cautious policy considerations. The influence of exchange rates on economic growth proves to be unexpectedly complex and nonlinear, challenging conventional assumptions. The empirical validation of these multifaceted realities underscores the importance of this analysis. Furthermore, robustness tests conducted in this study confirm the reliability of the findings while shedding light on additional intricacies. For instance, the relationship between imports and growth is context-dependent and exhibits ambiguities that call for careful consideration. The theoretical and practical implications derived from this research offer valuable insights for researchers and policymakers alike. The recommendations put forth emphasize the promotion of sustained prosperity through evidence-based strategies that prioritize community development.

Sewer networks monitoring through a topological backtracking

The interest in wastewater monitoring is always growing, with applications mainly aimed at detection of pollutants and at the environmental epidemiological surveillance. However, it often happens that the strategies proposed to manage these problems are inapplicable due to the lack of information on the hydraulics of the systems. To overcome this problem, the present paper develops and proposes a topological backtracking strategy for the optimal monitoring of sewer networks, which acts by subrogating the hydraulic information with the geometric ones, e.g., diameter and slope, thus not requiring any hydraulic simulation. The topological backtracking approach aims at evaluating an impact coefficient for each node of the network used to face with the problems of sensor location and network coverage for purposes related to the spread of contaminants and pathogens. Finally, the positioning of the sensors for each monitoring scheme is addressed by a priority rank, based on the efficiency of each sensor in terms of network coverage with respect to a specific weight (e.g., length, flow). The main goal is to design a monitoring scheme that provide the required coverage of the network by minimizing the number of sensors with respect to specific measurement threshold value. The results show the effectiveness of the strategy in supporting the optimal design with the topological-based backtracking approach without the necessity of performing hydraulic simulations, with great advantage in terms of required data and computational time.

Development of an automated optimal distance feature-based decision system for diagnosing knee osteoarthritis using segmented X-ray images

Knee Osteoarthritis (KOA) is a leading cause of disability and physical inactivity. It is a degenerative joint disease that affects the cartilage, cushions the bones, and protects them from rubbing against each other during motion. If not treated early, it may lead to knee replacement. In this regard, early diagnosis of KOA is necessary for better treatment. Nevertheless, manual KOA detection is time-consuming and error-prone for large data hubs. In contrast, an automated detection system aids the specialist in diagnosing KOA grades accurately and quickly. So, the main objective of this study is to create an automated decision system that can analyze KOA and classify the severity grades, utilizing the extracted features from segmented X-ray images. In this study, two different datasets were collected from the Mendeley and Kaggle database and combined to generate a large data hub containing five classes: Grade 0 (Healthy), Grade 1 (Doubtful), Grade 2 (Minimal), Grade 3 (Moderate), and Grade 4 (Severe). Several image processing techniques were employed to segment the region of interest (ROI). These included Gradient-weighted Class Activation Mapping (Grad-Cam) to detect the ROI, cropping the ROI portion, applying histogram equalization (HE) to improve contrast, brightness, and image quality, and noise reduction (using Otsu thresholding, inverting the image, and morphological closing). Besides, the focus filtering method was utilized to eliminate unwanted images. Then, six feature sets (morphological, GLCM, statistical, texture, LBP, and proposed features) were generated from segmented ROIs. After evaluating the statistical significance of the features and selection methods, the optimal feature set (prominent six distance features) was selected, and five machine learning (ML) models were employed. Additionally, a decision-making strategy based on the six optimal features is proposed. The XGB model outperformed other models with a 99.46 % accuracy, using six distance features, and the proposed decision-making strategy was validated by testing 30 images.

Does healthy city construction facilitate green growth in China? Evidence from 279 cities

In the face of the challenge of balancing urban economic development and environmental protection, the concept of a healthy city has emerged as a promising model for sustainable urban development. This study empirically investigates the impact of healthy city construction on green growth by utilizing a difference-in-difference model estimation on a panel dataset of 279 Chinese prefecture-level cities from 2007 to 2019. The findings reveal that healthy city construction significantly contributes to green growth, particularly in pilot cities, and this effect is observed across cities of different sizes and economic bases. Additionally, we identify two channels through which healthy city construction promotes green growth: enhancing innovation capacity and enriching human resources. These findings have implications not only for Chinese cities navigating the path towards green growth but also for other developing nations striving for economic transformation and environmentally sustainable development.

Utilizing waste compost to improve the atmospheric CO2 capturing in the rice-wheat cropping system and energy-cum‑carbon credit auditing for a circular economy

The study aimed to manage industrial wastes and create a module for using compost from waste for crops cultivation to conserve energy, reduce fertilizer use and Greenhouse gas (GHG) emissions, and improve the atmospheric CO2 capturing in agriculture for a green economy. In the main-plot, the experiment's results using NS3 found 50.1 and 41.8 % more grain yield and total carbon dioxide (CO2) sequestration in the wheat-rice cropping sequence, respectively, compared to the NS0. Moreover, the treatment CW + TV in the sub-plot observed 24.0 and 20.3 % higher grain yield and total CO2 sequestration than B + PS. Based on interaction, the NS3× CW + TV resulted in a maximum total CO2 sequestration and C credit of 47.5 Mg ha-1 and US$ 1899 ha-1, respectively. Further, it was 27.9 % lower in carbon footprints (CFs) than NS1 × B + PS. Regarding another parameter, the treatment NS3 observed a 42.4 % more total energy output in the main-plot than that of NS0. Further, in the sub-plot, the treatment CW + TV produced 21.3 % more total energy output than B + PS. Energy use efficiency (EUE) and net energy return in the interaction of NS3× CW + TV were 20.5 and 138.8 % greater than the NS0 × B + PS, respectively. In the main-plot, the treatment NS3 obtained a maximum of 585.0 MJ US$-1 and US$ 0.24 MJ-1 for energy intensity in economic terms (EIET) and eco-efficiency index in terms of energy (EEIe), respectively. While in the sub-plot, the CW + TV was observed at a maximum of 571.52 MJ US$-1 and US$ 0.23 MJ-1 EIET and EEIe, respectively. The correlation and regression study showed a perfect positive correlation between grain yield and total C output. Moreover, a high positive correlation (0.75 to 1) was found with all other energy parameters for grain energy use efficiency (GEUE). The variability in the wheat-rice cropping sequence's energy profitability (EPr) was 53.7 % for human energy profitability (HEP). Based on principal component analysis (PCA), the eigenvalues of the first two principal components (PCs) had been greater than two, explaining 78.4 and 13.7 % of the variability. The experiment hypothesis was to develop a reliable technology for safely using industrial waste compost, minimizing energy consumption and CO2 emissions by reducing chemical fertilizer input in agriculture soils.

Are paid tools worth the cost? A prospective cross-over study to find the right tool for plagiarism detection

Background

The increasing pressure to publish research has led to a rise in plagiarism incidents, creating a need for effective plagiarism detection software. The importance of this study lies in the high cost variation amongst the available options for plagiarism detection. By uncovering the advantages of these low-cost or free alternatives, researchers could access the appropriate tools for plagiarism detection. This is the first study to compare four plagiarism detection tools and assess factors impacting their effectiveness in identifying plagiarism in AI-generated articles.

Methodology

A prospective cross-over study was conducted with the primary objective to compare Overall Similarity Index(OSI) of four plagiarism detection software(iThenticate, Grammarly, Small SEO Tools, and DupliChecker) on AI-generated articles. ChatGPT was used to generate 100 articles, ten from each of ten general domains affecting various aspects of life. These were run through four software, recording the OSI. Flesch Reading Ease Score(FRES), Gunning Fog Index(GFI), and Flesch-Kincaid Grade Level(FKGL) were used to assess how factors, such as article length and language complexity, impact plagiarism detection.

Results

The study found significant variation in OSI(p < 0.001) among the four software, with Grammarly having the highest mean rank(3.56) and Small SEO Tools having the lowest(1.67). Pairwise analyses revealed significant differences(p < 0.001) between all pairs except for Small SEO Tools-DupliChecker. Number of words showed a significant correlation with OSI for iThenticate(p < 0.05) but not for the other three. FRES had a positive correlation, and GFI had a negative correlation with OSI by DupliChecker. FKGL negatively correlated with OSI by Small SEO Tools and DupliChecker.

Conclusion

Grammarly is unexpectedly most effective in detecting plagiarism in AI-generated articles compared to the other tools. This could be due to different softwares using diverse data sources. This highlights the potential for lower-cost plagiarism detection tools to be utilized by researchers.

E-learning enhancement through educational data mining with Covid-19 outbreak period in backdrop: A review

E-learning is fast becoming an integral part of the teaching- learning process, particularly after the outbreak of Covid-19 pandemic. Educational institutions across the globe are striving to enhance their e-learning instructional mechanism in accordance with the aspirations of present-day students who are widely using numerous technological tools - computers, tablets, mobiles, and Internet for educational purposes. In the wake of the evident incorporation of e-learning into the educational process, research related to the application of Educational Data Mining (EDM) techniques for enhancing e-learning systems has gained significance in recent times. The various data mining techniques applied by researchers to study hidden trends or patterns in educational data can provide valuable insights for educational institutions in terms of making the learning process adaptive to student needs. The insights can help the institutions achieve their ultimate goal of improving student academic performance in technology-assisted learning systems of the modern world. This review paper aims to comprehend EDM's role in enhancing e-learning environments with reference to commonly-used techniques, along with student performance prediction, the impact of Covid-19 pandemic on e-learning and priority e-learning focus areas in the future.

Exploring the energy-economy-environment paradox through Yin-Yang harmony cognition

Adopting a symbiotic perspective, this study aimed to examine the paradoxical interrelationship of the energy-economy-environment nexus through the novel lens of Yin-Yang cognitive harmony. With a broad sample of countries (6 African lions, 5 Asian tigers, 3 NAFTA countries, and 10 top European Union economies), we applied the cointegration and fully modified ordinary least squares techniques to evaluate the short- and long-term relationships between energy consumption, economic growth and carbon dioxide (CO2) emissions for the period 1980-2012. The results were heterogeneous across countries, but a curvilinear (inverted U-shaped) relationship between total economic growth and CO2 emissions in conformity with the environmental Kuznets curve was confirmed in many cases. However, there was no evidence that economic growth resulting from energy consumption has been responsible for CO2 reduction, which suggests a 'trilemma' - that is, a challenge in balancing energy production, economic growth and environmental degradation. From a behavioural economic perspective, this paper draws on the Kuznets hypothesis and Jevon's paradox by adopting a paradoxical frame to characterise the complex energy-growth-environment interaction as a balanced, symbiotic coexistence. It thus provides novel insights into the energy-growth-environment trilemma through an unconventional perspective based on Yin-Yang cognitive harmony (Fig. 1, see the Appendix).

Secure transmission for IoT wireless energy-carrying communication systems

The wireless energy-carrying communication method for the Internet of Things (IoT) presents several difficulties for information security such as eavesdropping or data loss. To solve these issues, this paper presents a new secure transmission method for IoT wireless energy-carrying communication systems. In this method, first the secret message is turned into a word, delivered to the intended recipient and unlawful listener, respectively, and the received message is characterized as an entropy function. The message is iteratively solved using the block coordinate descent technique, and for each iteration, a digital baseband signal containing the receiver's secret message symbol and the matching beamforming vector is delivered. By concurrently optimizing the transmit beamforming vector, the noise covariance matrix, and the receiver power allocation factor based on a design that complies with the security rate and energy acquisition limitations for each receiver, the overall system transmit power is reduced. The Lagrangian method is used to solve the secure transmission problem of the communication system based on an iterative block coordinate descent algorithm, as well as to change the nonconvex problem into a convex problem and precisely derive the upper and lower bounds of the original transmission problem. In comparison to the conventional policy transmission scheme, the experimental results demonstrate that the DIPS (Digital Image Processing System) scheme can increase the STP (Signaling Transfer Point) by approximately 34.16 percent in the eavesdropper independent eavesdropping and joint eavesdropping scenarios. The usefulness of the secure transmission strategy for wireless energy-carrying communication systems is confirmed by this investigation.

Scientists' deficit perception of the public impedes their behavioral intentions to correct misinformation

This paper investigates the relationship between scientists' communication experience and attitudes towards misinformation and their intention to correct misinformation. Specifically, the study focuses on two correction strategies: source-based correction and relational approaches. Source-based approaches combatting misinformation prioritize sharing accurate information from trustworthy sources to encourage audiences to trust reliable information over false information. On the other hand, relational approaches give priority to developing relationships or promoting dialogue as a means of addressing misinformation. In this study, we surveyed 416 scientists from U.S. land-grant universities using a self-report questionnaire. We find that scientists' engagement in science communication activities is positively related to their intention to correct misinformation using both strategies. Moreover, the scientists' attitude towards misinformation mediates the relationship between engagement in communication activities and intention to correct misinformation. The study also finds that the deficit model perception-that is, the assumption that scientists only need to transmit scientific knowledge to an ignorant public in order to increase understanding and support for science-moderates the indirect effect of engagement in science communication activities on behavioral intention to correct misinformation using relational strategies through attitude towards misinformation. Thus, the deficit model perception is a barrier to engaging in relational strategies to correct misinformation. We suggest that addressing the deficit model perception and providing science communication training that promotes inclusive worldviews and relational approaches would increase scientists' behavioral intentions to address misinformation. The study concludes that scientists should recognize their dual positionality as scientists and members of their community and engage in respectful conversations with community members about science.

Can the development of renewable energy in China compensate for the damage caused by environmental pollution to residents' health?

China's rapid economic growth in recent decades has caused a growing problem of environmental pollution, which negatively impacts the physical and mental health of residents. In recent years, renewable energy has emerged as a promising solution to alleviate environmental pollution and improve residents' well-being. However, it is unknown whether renewable energy development can counterbalance the health impacts of environmental pollution. Therefore, we conducted a study using data from the China Family Panel Studies (CFPS) to examine the impact of environmental pollution and renewable energy on the health of 20,694 residents. Our analysis showed that renewable energy development can partially offset the negative health effects of environmental pollution. Specifically, we found that a 1% increase in environmental pollution is linked to an average decrease of 0.0911% in physical health (PHY) and 0.0566% in mental health (MEN), whereas each 1% rise in renewable energy corresponds to an average increase of 0.2585% in PHY and 0.1847% in MEN. These positive effects apply to male and female residents, urban and rural residents, young and middle-aged adults, and people with low, medium, and high levels of education. These findings are significant for decision-makers striving to improve Chinese residents' physical and mental health by considering the specific impact of renewable energy and comprehensive environmental pollution.

Moderating the role of social progress with greenhouse gases to determine the health vulnerability in developing countries

Human activities have compelled massive environmental degradation, which causes climate vulnerability and that has emerged as a significant health issue. The present study assesses the role of social progress with greenhouse gases to determine the health vulnerability in 77 developing countries from 2011 to 2020. The empirical results are estimated by using the panel ARDL econometric approach. The study found that greenhouse gas emission proposes a U-shaped relationship to determine health vulnerability. In this study, social progress is used as the moderator variable, which shifts the turning point of the U-shaped curve. For this purpose, the interaction term of social progress with greenhouse gases shifts the turning point to the left side of the U-shaped curve and further flattens it. Furthermore, this study explores that urbanization, export openness, and government education expenditure negatively impact health vulnerability while industrialization increases health vulnerability. The study recommends that government should pay special attention to declining greenhouse gases and rising social progress to improve health vulnerability.Graphical abstact.

The impact of various geological factors on the real estate valuation using AHP analysis: case studies from Turkey

Turkey's diverse geology causes natural disasters that kill and damage towns every year. Population growth in unstable areas without geological variables in value studies endangers people and real estate. This study examines how geological factors affect house values, which are often overlooked in applications. The analytical hierarchy process (AHP) was used to analyze conventional parameters, geological factors, and priorities for three provinces in Turkey with unique geological properties in surface water, groundwater, active faults, and karstic collapse. The AHP analysis was performed in the Ankara-Cankaya district (Ilkbahar quarter), Bolu-Gerede district, and Konya-Karapinar district, test locations with unique geological properties in terms of surface water, groundwater, active faults, and karstic collapse in various regions of Turkey. Statistical software analyzed the test location survey data. The results show that surface water, groundwater, active faults, and karstic collapse all affect real estate value. The findings suggest that house valuation requires multidisciplinary building site investigations with appropriate methods. This reduces the risk of making unreliable decisions and eliminates uncertainties, resulting in reliable results. The geological factors that determine a house's value are crucial to reducing disaster-related deaths and property damage.