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Cardiovascular diseases (CVDs) continue to be the leading cause of more than 17 million mortalities worldwide. The early detection of heart failure with high accuracy is crucial for clinical trials and therapy. Patients will be categorized into various types of heart disease based on characteristics like blood pressure, cholesterol levels, heart rate, and other characteristics. With the use of an automatic system, we can provide early diagnoses for those who are prone to heart failure by analyzing their characteristics. In this work, we deploy a novel self-attention-based transformer model, that combines self-attention mechanisms and transformer networks to predict CVD risk. The self-attention layers capture contextual information and generate representations that effectively model complex patterns in the data. Self-attention mechanisms provide interpretability by giving each component of the input sequence a certain amount of attention weight. This includes adjusting the input and output layers, incorporating more layers, and modifying the attention processes to collect relevant information. This also makes it possible for physicians to comprehend which features of the data contributed to the model's predictions. The proposed model is tested on the Cleveland dataset, a benchmark dataset of the University of California Irvine (UCI) machine learning (ML) repository. Comparing the proposed model to several baseline approaches, we achieved the highest accuracy of 96.51%. Furthermore, the outcomes of our experiments demonstrate that the prediction rate of our model is higher than that of other cutting-edge approaches used for heart disease prediction.

Drought is a silent meteorological disaster that spreads over time, affecting water availability for agriculture and livelihood in any region. The prediction of drought is a complex phenomenon; however, the negative impacts of drought are mitigated by monitoring drought events over a region. The present study provides spatial and temporal drought climatology over Pakistan, using 60-years (1951–2010) observational gridded data (0.5° × 0.5°) of precipitation from Global Precipitation Climatological Center and soil moisture from Climate Prediction Center. Using precipitation and soil moisture datasets, a novel drought hazard index is developed to determine drought vulnerability across different districts of Pakistan. Our findings identified 19 districts that are extremely vulnerable to drought, with northern regions being vulnerable to mild drought, whereas central and southern districts are vulnerable to high drought events. By using standardized precipitation index and soil moisture anomaly, six severe drought years were identified as 1952, 1969, 1971, 2000, 2001, and 2002 in different parts of the country. Deficiency of monsoon rainfall is a major cause of droughts in southern and rain-fed regions. This study is helpful for drought managers, hydrologists, and contingency planners to prepare mitigation and adaptation plans toward sustainable development in Pakistan.

Various drought indices are normally used to monitor drought and its risk management. Precipitation, temperature and other hydro meteorological parameters are the essential parts to the identification of drought. For this purpose, several drought indices have been developed and are being used around the world. This study identifies the applicability and comparison of drought indices in Pakistan by evaluating the performance of 15 drought indices. The indices include standardized precipitation index (SPI), standardized precipitation temperature index, standardized precipitation evapotranspiration index (SPEI), China Z-Index, deciles index, modified CZI, Z-Score, rainfall variability index, standardized soil moisture anomaly index, weighted anomaly standardized precipitation index, percent of normal precipitation index, self-calibrated Palmer drought severity index, composite index, percentage area weighted departure and reconnaissance drought index (RDI). These indices are compared by utilizing long term data of 58 meteorological stations for the period 1951–2014. The performance, efficiency and significance are also tested by applying different statistical tests. The SPI, SPEI and RDI results showed a good capability to monitor drought status in Pakistan. The positive increasing trend (towards wetness) is noted by several of the aforementioned indices at 95% confidence level. In addition, historical drought years and intensity have been explored along with comparison of recent long episode of drought (1999–2002) and all the indices captured this period successfully.

BackgroundCurrently, nanotechnology and nanoparticles have quickly emerged and have gained the attention of scientists due to their massive applications in environmental sectors. However, these environmental applications of silver nanoparticles potentially cause serious effects on terrestrial and aquatic organisms. In the current study, freshwater fish C. carpio were exposed to blood-mediated silver nanoparticles for toxicity, mortality, bioaccumulation, and histological alterations. Silver nanoparticles were fabricated using animal blood serum and their toxic effect was studied against common carp fish at different concentrations levels (0.03, 0.06, and 0.09 mg/L).ResultsThe findings have revealed a little influence of blood-induced silver nanoparticles on fish behavior at the highest concentration (0.09 mg/L). However, bioaccumulation of blood-mediated silver nanoparticles was reported in different organs of fish. Maximum bioaccumulation of silver nanoparticles was reported in the liver, followed by the intestine, gills, and muscles. Furthermore, the findings have shown that the bioaccumulation of silver nanoparticles led to histopathological alterations; including damaged structure of gill tissue and have caused necrosis. It is summarized that histopathological alteration in gill and intestine mostly occurred by the highest concentration of blood-induced silver nanoparticles (0.09 mg/L).ConclusionThis study provides evidence of the silver nanoparticles influence on aquatic life; however, further systematic studies are crucial to access the effects of AgNPs on aquatic life.

MicroRNA (miRNA), a conservatively evolved single-stranded non-coding RNA, exerts pivotal control over the appearance of target genes and several biological processes. This study conducted a comprehensive screening of candidate microRNAs (miRNAs) associated with Lipoprotein Lipase ( LPL ) in the large yellow croaker ( Larimichthys crocea ), utilizing sophisticated bioinformatics techniques across the species’ muscular and hepatic tissues. The bioinformatics analysis facilitated the compilation and examination of miRNA datasets specific to these tissues. The investigation culminated in the identification of miR-84a and miR-1231-5p as key miRNAs that modulate fat hydrolysis, highlighting their potential roles in lipid metabolism. Subsequent in-depth analysis further implicated these miRNAs, along with miR-891a , as prospective targets of LPL , suggesting their integral involvement in the regulation of this critical enzyme. Validation of these bioinformatics predictions was conducted through the construction of double luciferase reporters concealing the LPL 3′ untranslated region (3′UTR), substantiating that miR-84a and miR-1231-5p can modulate LPL expression via the LPL 3′UTR. Conversely, miR-891a was not concerned with this regulatory mechanism. Site-directed mutagenesis experiments elucidated the specificity of the interaction sequences. Quantitative PCR assays suggested that miR-84a and miR-1231-5p might influence LPL expression during the starvation phase, intimating the regulatory role of miRNA in fatty acid metabolism within hepatic and muscular tissue under starvation. These findings offer a nuanced understanding of LPL’s molecular functionality under stress conditions in fish, emphasizing the regulatory dynamics of miRNA during metabolic stress.

In Pakistan, millions of people are influenced annually by flash floods in the monsoon season (June–September). The eastern river basin, situated in the northeastern part of the country, is especially susceptible to devastating floods. This study examines the frequency and persistence of Extreme Precipitation Events (EPEs) and associated high river flows in the eastern basin, focusing on major historical floods between 1986 and 2020. The long‐term observational precipitation data was used to identify and analyze EPEs applying the peak over threshold method. Furthermore, Climate Hazards Group Infrared Precipitation with Station data (CHIRPS) and ERA5 reanalysis data sets were evaluated for comparative assessment of precipitation extremes. The analysis of five selected EPEs shows consistent synoptic‐scale patterns, including the westward movement of low‐pressure areas (LPAs), strong upper‐level divergence, and higher moisture transport both from Bay of Bengal (BoB) and Arabian Sea. Essential atmospheric features include low mean sea level pressure, upper‐level cyclonic and anticyclonic anomalies, and strong moisture convergence at 850 hPa. These dynamics, especially their interaction with westerly troughs, consistently intensify precipitation over the region. The results highlight recurring atmospheric signatures that can enhance flood forecasting and early warning systems. EPEs and high stream flows occur frequently during El Niño‐Southern Oscillation (ENSO) and the Indian Ocean Dipole (IOD) neutral phases, particularly when both phenomena are simultaneously neutral. The analysis shows that ENSO‐ neutral conditions during pre‐monsoon season (March–May) are significantly linked with increased monsoon EPEs. This relationship is strongly associated with SST anomalies, with the highest correlations observed for the pre‐monsoon season: ENSO (r = 0.51, p = 0.03) and the IOD (r = 0.55, p = 0.02). Both correlations are statistically significant at the 95% confidence level. These results suggest that neutral ENSO and IOD phases play a dominant role in driving EPEs in the eastern river basin. Key Points The significant role of LPAs moving from the BoB towards the ER basin caused high‐flow events (HFEs) under the influence of westerly waves The ENSO‐neutral conditions are the prominent feature that can contribute larger frequency of HFEs across all sub‐basins The outcomes can improve flood forecasting techniques, both for pre‐monsoon flood risk estimation and short‐range predictions

Alternaria is an allergenic fungus that releases airborne spores, causing allergy and asthma in humans. The fungal spore contains twelve 11–58 kDa allergens. The allergy threshold level of airborne Alternaria spores is 100/m 3 . This study examined temporal changes in the Alternaria spore season over 20 years and its correlation with air pollutants and meteorological factors. Alternaria spores’ historical dataset and meteorological factors from 2004 to 2023 were obtained from the Pakistan Meteorological Department. A dataset of ten air pollutants was recorded in Islamabad during 2022-23, standardized, and monthly averages were calculated. Airborne spore concentrations, meteorological factors, and air pollutants were statistically analyzed. The onset and end of the Alternaria spore season occurred in March and October. Seasonal spore integrals were highest in April to October and lowest in November to March. The dominant wind direction was southwest. The airborne Alternaria spore concentrations/m 3 positively correlated with temperature and CO 2 , and negatively correlated with relative humidity. The study concludes that the Alternaria spore season shifted between 2004 and 2023, which correlates with changes in meteorological factors and air pollutants. Findings of these investigations can be utilized by researchers, aerobiologists, clinicians, and the public to study airborne fungal spores’ temporal changes and allergies.

In smart grids, a hybrid renewable energy system that combines multiple renewable energy sources (RESs) with storage and backup systems can provide the most cost-effective and stable energy supply. However, one of the most pressing issues addressed by recent research is how best to design the components of hybrid renewable energy systems to meet all load requirements at the lowest possible cost and with the best level of reliability. Due to the difficulty of optimizing hybrid renewable energy systems, it is critical to find an efficient optimization method that provides a reliable solution. Therefore, in this study, power transmission between microgrids is optimized to minimize the cost for the overall system and for each microgrid. For this purpose, artificial bee colony (ABC) is used as an optimization algorithm that aims to minimize the cost and power transmission from outside the microgrid. The ABC algorithm outperforms other population-based algorithms, with the added advantage of requiring fewer control parameters. The ABC algorithm also features good resilience, fast convergence, and great versatility. In this study, several experiments were conducted to show the productivity of the proposed ABC-based approach. The simulation results show that the proposed method is an effective optimization approach because it can achieve the global optimum in a very simple and computationally efficient way.

Distributed energy resources (DERs) and demand side management (DSM) strategy implementation in smart grids (SGs) lead to environmental and economic benefits. In this paper, a new DSM strategy is proposed for the day-ahead scheduling problem in SGs with a high penetration of wind energy to optimize the tri-objective problem in SGs: operating cost and pollution emission minimization, the minimization of the cost associated with load curtailment, and the minimization of the deviation between wind turbine (WT) output power and demand. Due to climatic conditions, the nature of the wind energy source is uncertain, and its prediction for day-ahead scheduling is challenging. Monte Carlo simulation (MCS) was used to predict wind energy before integrating with the SG. The DSM strategy used in this study consists of real-time pricing and incentives, which is a hybrid demand response program (H-DRP). To solve the proposed tri-objective SG scheduling problem, an optimization technique, the multi-objective genetic algorithm (MOGA), is proposed, which results in non-dominated solutions in the feasible search area. Besides, the decision-making mechanism (DMM) was applied to find the optimal solution amongst the non-dominated solutions in the feasible search area. The proposed scheduling model successfully optimizes the objective functions. For the simulation, MATLAB 2021a was used. For the validation of this model, it was tested on the SG using multiple balancing constraints for power balance at the consumer end.

An energy optimization strategy is proposed to minimize operation cost and carbon emission with and without demand response programs (DRPs) in the smart grid (SG) integrated with renewable energy sources (RESs). To achieve optimized results, probability density function (PDF) is proposed to predict the behavior of wind and solar energy sources. To overcome uncertainty in power produced by wind and solar RESs, DRPs are proposed with the involvement of residential, commercial, and industrial consumers. In this model, to execute DRPs, we introduced incentive-based payment as price offered packages. Simulations are divided into three steps for optimization of operation cost and carbon emission: (i) solving optimization problem using multi-objective genetic algorithm (MOGA), (ii) optimization of operating cost and carbon emission without DRPs, and (iii) optimization of operating cost and carbon emission with DRPs. To endorse the applicability of the proposed optimization model based on MOGA, a smart sample grid is employed serving residential, commercial, and industrial consumers. In addition, the proposed optimization model based on MOGA is compared to the existing model based on multi-objective particle swarm optimization (MOPSO) algorithm in terms of operation cost and carbon emission. The proposed optimization model based on MOGA outperforms the existing model based on the MOPSO algorithm in terms of operation cost and carbon emission. Experimental results show that the operation cost and carbon emission are reduced by 24% and 28% through MOGA with and without the participation of DRPs, respectively.