This research explored the life cycle analysis and environmental cost-benefit assessment of converting ash waste from hospital medical waste incineration into environmentally safe paving block raw materials. The growing concerns about medical waste disposal and its environmental impact necessitate innovative solutions for sustainable waste management. This research aimed to evaluate the feasibility and environmental implications of reusing hospital waste into raw materials for paving block mixtures. This research, a comprehensive life cycle analysis, examined the environmental impacts of medical waste collection for the production and use of paving blocks. Additionally, we conducted an environmental cost-benefit assessment to ascertain the economic feasibility and potential environmental impact forecasts of this recycling approach. The research results show that converting hospital medical waste ash into mixed raw materials for paving blocks not only immobilizes heavy metals but also provides a sustainable alternative for non-building materials. These findings highlight the potential for significant environmental and economic benefits, making this approach a promising strategy for waste management and sustainable construction practices. The cost of preventing environmental damage (eco-cost) in the process of converting ash from the incineration of medical waste into a mixture of raw materials for paving blocks is IDR 600,180.9 per cycle.

In recent decades, abnormal rainfall and temperature patterns have significantly impacted the environment and human life, particularly in East Nusa Tenggara. The region is known for its low rainfall and high temperatures, making it vulnerable to drought events, which have their own complexities due to being random and changing over time. This study aimed to analyze the trend of short-term meteorological drought intensity in Timor Island, East Nusa Tenggara. The analysis was carried out by utilizing the standardized precipitation evapotranspiration index (SPEI) for a 1-month period to characterize drought in intensity, duration, and severity. A power law process approach was used to model the intensity of the event, which is inversely proportional to the magnitude of the drought event. Intensity parameters of the power law process were estimated using the maximum likelihood estimation (MLE) method to predict an increase in the intensity of drought events in the future. The probability of drought was calculated using the non-homogeneous Poisson process. The analysis showed that “extremely dry” events in Timor Island are less frequent than “very dry” and “dry” events. The power law process model’s estimated intensity parameter showed a beta value greater than 1, indicating an increase in future drought events. In the next 12 months, two months of drought are expected in each region of Timor Island, East Nusa Tenggara, with the following probabilities for each region: 0.264 for Kupang City, 0.25 for Kupang, 0.265 for South Central Timor, 0.269 for North Central Timor, 0.265 for Malaka, and 0.266 for Belu. This research provides important insights into drought dynamics in vulnerable regions such as East Nusa Tenggara and its potential impact on future mitigation and adaptation planning.

Health performance and well-being are crucial elements of Saudi Arabia's Vision 2030, aiming to improve the overall quality of life and promote a prosperous community. Within this context, this study intended to examine the impact of recent innovations, logistical measures, Information and Communication Technology (ICT) diffusion, environmental quality improvements, economic growth, and green (renewable) energy exploitation on health performance and well-being, in Saudi Arabia from 1990 to 2022, by implementing machine learning models (random forest and gradient boosting) and regression algorithms (ridge and lasso). Overall, the findings of machine learning models indicate a strong impact of digital connectivity on health spending by internet users, with scores of 0.673 and 0.86. Further, economic growth also influences health costs but to a lesser extent, with scores of 0.145 and 0.082. Mobile user penetration and CO₂ emissions have moderate to low importance, suggesting nuanced interactions with health expenditure. Patent applications and logistics performance show minimal impact, indicating a limited direct influence on health costs within this study. Similarly, the share of renewable energy is negligible, reflecting its minimal impact on the analyzed data. Finally, regression analyses using ridge and lasso models confirmed similar trends, further validating these findings. Limitations and several policy implications are also debated.

Nowadays, piano key (PK) weir with an expanded crest length are often used to deal with surplus discharge in dams due to unexpected climate change effects, increasing safety. The present study deals with the numerical modelling of a group of PK weirs with auxiliary geometrical parameters to predict the flow over a PK weir using different FLOW-3D turbulence models. The numerical outcomes were compared with the experimental results to check the accuracy of the underlying FLOW-3D models. It was found that the k-𝜀𝜀 turbulence model of FLOW-3D estimated the flow over a piano key weir more closely to the experimental results than the RNG (renormalized group) and LES (large eddy simulation) models. Statistical parameters were used to evaluate the simulated results. It was observed that the coefficient of correlation (CC) was close to one and the root mean square error (RMSE) close to zero when numerical outcomes were compared with experimental datasets. The results show that the FLOW-3D software is quite effective in estimating the flow. Therefore, the present study will help to understand the best combination of mesh, models, adaption and convergence processes in simulation and provide an insight into the numerical analysis of flow configuration over PKW by considering one of the best numerical models.

Accurate flood forecasting is a crucial process for predicting the timing, occurrence, duration, and magnitude of floods in specific zones. This prediction often involves analyzing various hydrological, meteorological, and environmental parameters. In recent years, several soft computing techniques have been widely used for flood forecasting. In this study, flood forecasting for the Narmada River at the Hoshangabad gauging site in Madhya Pradesh, India, was conducted using an Artificial Neural Network (ANN) model, a Fuzzy Logic (FL) model, and an Adaptive Neuro-Fuzzy Inference System (ANFIS) model. To assess their capacity to handle different levels of information, three separate input data sets were used. Our objective was to compare the performance and evaluate the suitability of soft computing data-driven models for flood forecasting. For the development of these models, monthly discharge data spanning 33 years from six gauging sites were selected. Various performance measures, such as regression, root mean square error (RMSE), and percentage deviation, were used to compare and evaluate the performances of the different models. The results indicated that the ANN and ANFIS models performed similarly in some cases. However, the ANFIS model generally predicted much better than the ANN model in most cases. The ANFIS model, developed using the hybrid method, delivered the best performance with an RMSE of 211.97 and a coefficient of regression of 0.96, demonstrating the potential of using these models for flood forecasting. This research highlighted the effectiveness of soft computing techniques in flood forecasting and established useful suitability criteria that can be employed by flood control departments in various countries, regions, and states for accurate flood prognosis.

High levels of poverty in rural areas constitute one of the main challenges for developing countries. Since agricultural employment is the main source of income in these areas, the design of tools that simulate and help public policymakers will be remarkably useful. This work proposes the development of a model for agricultural employment in Colombia, considering input variables such as education, contract, and income, and the output is the amount of agricultural employment. Real data measured in Colombia are used for the design and adjustment of the model. To design the fuzzy system for an agricultural employment model, the methods employed are fuzzy C-means clustering and neuro- fuzzy systems. The systems were tested with different cluster configurations, and a fuzzy system was obtained with an adequate distribution of the fuzzy sets and the respective rules that relate the sets. It was observed that as the clusters increase, the adjustment function decreases. The implementation of neuro-fuzzy systems to model agricultural employment will allow public policymakers to generate guidelines that adjust to their political agendas with a lower degree of uncertainty.

We utilized density functional theory (DFT) to investigate the electronic structure and Raman spectrum of Azinphos-methyl (AzM) (C₁₀ H₁₂ N₃ O₃ PS₂) both in isolation and in combination with gold nanoclusters (Au_n, n=2, 4, and 6). The research highlights a significant enhancement in Raman activity with increasing gold atom count from AzM-Au₂ to AzM-Au₄. The DFT calculations provide a comprehensive analysis of various electronic properties, including HOMO and LUMO energies, gap energy (Eg), ionization potential (IP), and electron affinity (EA), comparing these with experimental results from Liu et al. (2012). We also examined reactivity parameters, electrostatic properties, molecular electrostatic potential (MEP), Natural bond orbital (NBO) analysis, and atoms-in-molecules theory (AIM). The binding energy trends among the (AzM)-Aun complexes revealed a hierarchy: (AzM)-Au₂ > (AzM)-Au₆ > (AzM)-Au₄. Monte Carlo simulations were used to explore AzM interactions with gold nanoparticles (AuNPs) of various shapes and sizes, indicating that increased Raman intensity correlates with higher global electrophilicity and total polarizability. The results suggested that the stability of the complexes improves with more gold atoms, as evidenced by greater charge transfer, interaction energies, and second-order stabilization energies (E²). Among the complexes studied, AzM-Au₂ showed the highest stability. Monte Carlo simulations revealed that the right circular cone-shaped structure, especially at 7 nm, demonstrated the most negative adsorption energy, indicating stronger adsorption interactions. This research fills a gap in previous studies on AzM, providing valuable insights and serving as a reference for future work.

Industrialization, financial activities, and intensive human activities have reduced continuous habitats to smaller patches, threatening the safety of the ecosystem. However, as technological innovation and digitization increase, this negative impact will be somewhat neutralized. To address this issue, the current study examined the role of economic, socioeconomic, and green indicators on the ecological footprint in the Gulf Cooperation Council (GCC). By using data from 1990–2019, we have applied multiple panel tests to determine the long-run and short-run relationships among the variables. The findings show that economic growth increases the long-term ecological footprint in the GCC. The human development index and financial inclusion coefficients are also positively and significantly linked with the ecological footprint. The socioeconomic index, however, reveals a negative relationship between ecological footprint and GCC. Similarly, digitalization and environmental technologies have a negative and major impact on the ecological footprint. It indicates that green growth factors contribute to long-term improvements in environmental quality. So, GCC nations should emphasize investing in green growth factors and enact strict environmental regulations to safeguard their country from environmental problems.

We explored the aging effects on insulating materials used in power transformers through dielectric spectroscopy. Frequency domain spectroscopy (FDS) was conducted for both aged and new Kraft paper and pressboard samples to determine their dielectric properties across a temperature range of 40–100 °C for pressboard and 40–70 °C for Kraft paper. Principal component analysis (PCA) was employed to classify the data, identify key factors contributing to variance, and elucidate the relationships between different parameters. The analysis revealed positive correlations between conductivity and frequency, as well as between permittivity and tan delta, with distinct differences in behavior observed between aged and new samples at lower frequencies. Additionally, the impact of temperature was evident, with increased temperature leading to an upward shift in dielectric behavior. At lower temperatures, aging had a reduced effect on the materials’ properties. These findings provide significant insights into the dielectric behavior and aging mechanisms of insulation materials in power transformers.

Environmental concern is a determinant in acquiring new green innovation. We aimed to investigate the relationship between environmental concern, consumer attitude, and behavioral intention to switch in the context of drone delivery. The motivation that comes from a green perspective is believed to create a behavior that is also keen on innovative green products. One of the examples is the implementation of drones in delivering parcels, which is believed to cut the carbon footprint. Our purpose was to analyze the direct impact of environmental concern on consumers’ behavioral intentions regarding e-commerce drone delivery. Additionally, we aimed to examine the mediating role of consumers’ attitudes toward innovation in the relationship between environmental concern and behavioral intention. We sought to provide insights into how environmental awareness and the adoption of innovative delivery technologies like drones can influence consumer behavior, contributing to more sustainable and eco-friendly e-commerce practices. Structured questionnaires were provided to e-commerce users, reliability and validity tests were confirmed, and structural equation modeling (SEM) was used to analyze the relationships among variables. The results of the SEM analysis proved that environmental concern and consumer attitudes have a positive impact on behavioral intention. Customer attitude mediates the relationship between environmental concern and behavioral intention. This research provides a deeper understanding of how environmental concerns influence consumer behavior towards drone delivery innovation within the e-commerce sector. The implications integrate environmental concerns with consumer behavior and innovation adoption, providing a comprehensive view that goes beyond traditional marketing and consumer research.