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Abstract In recent years, the healthcare data system has expanded rapidly, allowing for the identification of important health trends and facilitating targeted preventative care. Heart disease remains a leading cause of death in developed countries, often leading to consequential outcomes such as dementia, which can be mitigated through early detection and treatment of cardiovascular issues. Continued research into preventing strokes and heart attacks is crucial. Utilizing the wealth of healthcare data related to cardiac ailments, a two-stage medical data classification and prediction model is proposed in this study. Initially, Binary Grey Wolf Optimization (BGWO) is used to cluster features, with the grouped information then utilized as input for the prediction model. An innovative 6-layered deep convolutional neural network (6LDCNNet) is designed for the classification of cardiac conditions. Hyper-parameter tuning for 6LDCNNet is achieved through an improved optimization method. The resulting model demonstrates promising performance on both the Cleveland dataset, achieving a convergence of 96% for assessing severity, and the echocardiography imaging dataset, with an impressive 98% convergence. This approach has the potential to aid physicians in diagnosing the severity of cardiac diseases, facilitating early interventions that can significantly reduce mortality associated with cardiovascular conditions.

Abstract The global interest in market prediction has driven the adoption of advanced technologies beyond traditional statistical models. This paper explores the use of machine learning and deep learning techniques for stock market forecasting. We propose a comprehensive approach that includes efficient feature selection, data preprocessing, and classification methodologies. The wavelet transform method is employed for data cleaning and noise reduction. Feature selection is optimized using the Dandelion Optimization Algorithm (DOA), identifying the most relevant input features. A novel hybrid model, 3D-CNN-GRU, integrating a 3D convolutional neural network with a gated recurrent unit, is developed for stock market data analysis. Hyperparameter tuning is facilitated by the Blood Coagulation Algorithm (BCA), enhancing model performance. Our methodology achieves a remarkable prediction accuracy of 99.14%, demonstrating robustness and efficacy in stock market forecasting applications. While our model shows significant promise, it is limited by the scope of the dataset, which includes only the Nifty 50 index. Broader implications of this work suggest that incorporating additional datasets and exploring different market scenarios could further validate and enhance the model's applicability. Future research could focus on implementing this approach in varied financial contexts to ensure robustness and generalizability.

In recent decades, significant interest in natural bioactive compounds has led many researchers to study plants as primary natural sources of bioactive compounds, mainly when considering a few biological properties, including antioxidant, antimicrobial, and anti-inflammatory activities, and as nutritional supplements with health benefits. We designed this study to explore the phytochemicals profiling of Citrallus lanatus and evaluate their in vitro bioactive properties. The methanol extraction of the watermelon's stem, rind, and leaf parts through Soxhlet extraction was analyzed using Gas Chromatography-Mass Spectrometry. The GC-MS profiling of Citrullus lanatus examines the presence of numerous bioactive phytoconstituents with potential biological activities such as antioxidant, anti-inflammatory, antimicrobial, anticancer, and other properties. We have evidenced that the stem, rind, and leaf extracts with significant antioxidant and antimicrobial activities against clinically relevant pathogens like Salmonella typhi, Pseudomonas aeruginosa, Bacillus subtilis, and Staphylococcus aureus. Overall, our study sheds light on exploring novel bioactive phytoconstituents from Citrullus lanatus and explore, harness these compounds' therapeutic potential for human health.

This research introduces a novel approach to developing geopolymer-based artificial coarse aggregate using a cut-blade mechanism, providing an innovative approach to traditional aggregate. A modified concrete drum mixer equipped with additional cutting blades was employed to produce angular aggregates with consistent particle sizes ranging from 10 mm to 20 mm with rough surface texture. Compared to conventional techniques, this method ensures a more uniform aggregate shape, enhancing its mechanical interlocking properties in construction applications. The experimental results demonstrated abrasion values between (13.2–31.5 %), impact value test (9.8–21.6 %), water absorption rates of (2.62–4.2 %), and specific gravity values ranging from (1.18–2.2). To further optimize the alkali activator dosage, a mathematical model was developed using the ANOVA test and Response surface methodology (RSM), achieving high regression predictive accuracy with R2 values > 0.93. The model demonstrated optimal results between experimental results and predicted results for specific gravity, impact value, abrasion value, and water absorption. Microstructural analysis via Scanning electron microscopy (SEM) reveals optimal pore distribution inside aggregates, while X-ray Fluorescence identifies significant intensities of Quartz, Ettringite, and calcium silicate hydrate gel. Overall, this research not only demonstrates the technical feasibility of producing high-performance artificial aggregate from waste materials but also underscores its potential to mitigate environmental impacts and promote sustainable construction practices.

High energy resolution fluorescence detected X-ray absorption spectroscopy is a powerful method for probing the electronic structure of functional materials. The X-ray penetration depth and photon-in/photon-out nature of the method allow operando experiments to be performed, in particular in electrochemical cells. Here, operando high-resolution X-ray absorption measurements of a BiVO4 photoanode are reported, simultaneously probing the local electronic states of both cations. Small but significant variations of the spectral lineshapes induced by the applied potential were observed and an explanation in terms of the occupation of electronic states at or near the band edges is proposed.

Over the last few decades, researchers have devoted significant consideration to the use of nanoscale elements in concrete. Silicon dioxide nanoparticles (SDNs) have been a popular subject of study among the several types of nanoparticles. This article describes the findings of a laboratory investigation that examined the properties of ultra-lightweight foamed concrete (ULFC) including different proportions of SDNs. Wide range of the properties was evaluated specifically the slump flow, density, consistency, flexural strength, modulus of elasticity, compressive strength, split tensile strength, thermal properties, porosity, water absorption, sorptivity, intrinsic air permeability, and chloride diffusion. Additionally, the scanning electron microscopy (SEM) and pore distributions analyses of different mixes were done. Results confirmed a noticeable increase in the mechanical properties of ULFC, with respective improvements in the 28-day compressive, split tensile, and flexural strengths of up to 70.49%, 76.19%, and 51.51%, respectively, at 1.5% of the SDNs inclusion. However, further increases in the SDNs percentage did not result in remarkable enhancements. As the SDN percentage increased from 1.5% to 2.5%, the ULFC's sorptivity, porosity, water absorption, intrinsic air permeability, and chloride diffusion showed substantial improvements. When compared to the control sample, ULFC with SDNs demonstrated higher thermal conductivity values. The reason for this occurrence was determined to be the smaller pore size observed in the ULFC specimens containing SDNs. A great adjustment in the distribution of pore diameters was witnessed in the ULFC mixes when the percentages of SDNs were adjusted. The ULFC specimens, which included SDNs at the percentages of 0.5%, 1.0%, and 1.5%, indicated a reduction in the total number of large voids measuring 500 nm or more, compared to the control ULFC specimen. The findings of this study highlight the potential benefits of incorporating SDNs into ULFC, which may improve its overall properties.

The construction industry recognizes the need for green, lightweight, and self-compacting materials that are also ecologically benign. Recent studies suggest that the novel lightweight foamed concrete (LFC) could potentially reduce the self-weight of structures. Adding natural fibers to FC improves its mechanical properties and contributes significantly to sustainability. One of the greatest difficulties in constructing reinforced LFC is the reinforcing steel bars’ corrosion, which impacts the behavior and lifetime structural integrity of concrete buildings. Therefore, this research aims to investigate the potential use of sugarcane bagasse fiber (SBF) in low-density LFC, after altering it with sodium hydroxide-based alkali treatment (NaOH) to enhance its properties. Low-density FCs are prone to serious durability and performance degradation; hence, in this experiment, FCs with a low density of 800 kg/m 3 were fabricated and evaluated. Quantification and evaluation were conducted on several different characteristics, including the slump, density, thermal conductivity, and compressive, flexural, and splitting tensile strengths. The findings suggest that employing SBF with an optimal reinforcing range of 3% to 4% can improve the mechanical characteristics and thermal conductivity of LFC-SBF composites. The slump flow gradually decreased from 1% to 5% of the SBF’s weight fraction. The lowest slump flow was achieved by adding SBF to the LFC mixture at a weight fraction of 5%. The addition of SBF to LFC resulted in a significant boost in the material’s splitting tensile, compressive, and flexural strengths. By adding 4% SBF to LFC, the optimal strength properties were concluded in the material. In addition to this, the weight percent of SBF contributed to an increase in the thermal conductivity of LFC. This was because the porous structure of LFC, which contained SBF, enabled it to absorb heat.

Accurate prediction of fatigue life is vital in the design of aerospace components subjected to varying stress levels and loading frequencies. In the current research, machine learning (ML) models were developed to predict the fatigue life of AA2024-T6, a popular aerospace grade alloy, under different stress levels and loading frequencies. The aim was to reduce the reliance on expensive and time-consuming experimental fatigue testing. Stress-controlled fatigue tests were conducted, followed by fractographic analysis using a scanning electron microscope to assess failure mechanisms. It was observed that fatigue life decreases with increasing loading frequency, with failure modes transitioning from ductile at higher stress levels to a combination of brittle and ductile at lower stress levels. Three ML models namely Elastic Net, k-NN, and Random Forest were evaluated using the experimental fatigue results as input. The Random Forest model, optimized with an 85%-15% training-testing data split and nine decision trees, outperformed other models with a Root Mean Square Error (RMSE) of 101.62, Mean Absolute Percentage Error (MAPE) of 5.23% and an R-squared value of 1.0. Confirmation experiments validated the model, showing an average deviation of 7.57% between predicted and actual fatigue lives. These results highlight the potential of ML models to accurately predict fatigue life, offering a reliable alternative to tedious and costly experimental methods.

Three different densities (500 kg/m3, 1000 kg/m3, and 1500 kg/m3) of foamed concrete (FC) were tested alongside mortar with a density of 1980 kg/m3 to investigate how high temperatures affect the qualities of FC. A flow table test was used to examine the fresh qualities of the mixtures. The modulus of elasticity, ultrasonic pulse velocity (UPV), bending strength, split tensile strength, compressive strength, thermal conductivity, porosity, and appearance and colour changes at ambient temperature and after exposure to various high temperatures (100 °C, 150 °C, 200 °C, 400 °C, 600 °C, and 800 °C) were evaluated. To study the effects of varying densities, microstructure analysis was performed utilizing scanning electron microscopy and mercury intrusion porosimetry. According to the findings, the four varied densities appeared dissimilar. FC with lower densities (500 kg/m3 and 1000 kg/m3) showed signs of cracking, while FC with a higher density (1500 kg/m3) enabled for precise detection of the pore connectivity and surface spalling occurrences. High temperatures had less effect on the mortar than FC mixtures. As the temperature increased, the modulus of elasticity, split tensile strength, bending strength, compressive strength, thermal conductivity, and mass loss decreased for all the mortar and FC samples. The UPV values increased marginally up to 100 °C before decreasing. This investigation highlighted the need for additional research and code provisions that consider different innovative construction materials and FC constituent classes.

Currently, foamed concrete (FC) is widely employed in building construction and civil engineering works, and by using abundant natural fibers in FC, significant environmental benefits can be obtained. The durability properties of the essential materials acting independently could well be enhanced if the appropriate proportion of natural fiber-strengthened FC were used in the correct volume. This study aimed to develop new composite materials composed of FC and agave fiber (AF). The objective was to ascertain the optimal proportion of AF to be added to FC to increase its durability performance. A low-density FC of 950 kg/m 3 was fabricated utilizing varying percentages of AF, namely 0% (control), 1.5%, 3.0%, 4.5%, 6.0%, and 7.5%. The evaluated properties were the shrinkage, workability, density, water absorption, ultrasonic pulse velocity, porosity, compressive strength, and elastic modulus. Using AFs in FC, the optimal shrinkage and ultrasonic pulse velocity were observed. When the weight fractions of AF were increased from 1% to 4.5% in the mixtures, the porosity, workability, and water absorption of FC were significantly reduced. In addition, FC showed a slight increase in the dry density with the rise in the AF’s weight fraction and the increase in the curing age from day-7 to day-56. This research delivers noteworthy data on the durability properties of FC-AF composites, allowing future researchers to study other properties like the structural performance, strength properties, and thermal conductivity of FC-AF composites.

Climate change poses significant challenges to Indian agriculture, impacting crop yields, water availability, and overall food security. To address these challenges, a combination of adaptation and mitigation strategies is crucial. Adaptation measures involve adjusting agricultural practices to changing climate conditions, such as altering planting schedules, implementing water-saving techniques, and promoting resilient crop varieties. Mitigation strategies focus on reducing greenhouse gas emissions from agricultural activities, like adopting sustainable farming practices and enhancing carbon sequestration in soils. In India, the integration of adaptation and mitigation efforts is essential to enhance the resilience of farmers and agricultural systems to climate change impacts while contributing to global climate goals. By combining traditional knowledge with modern scientific approaches, Indian agriculture can navigate the complexities of climate change, ensuring sustainable food production and livelihood security for millions of farmers across the country. A concerted effort involving policymakers, researchers, extension workers, and farming communities is vital to bolster the resilience of Indian agriculture while contributing to global climate change mitigation efforts. Effective extension services are paramount for educating farmers and ensuring widespread adoption of these strategies. By prioritizing both adaptation and mitigation, Indian agriculture can navigate the challenges of climate change and ensure long-term food security.

Background: Botulinum toxin type A (BTX-A) is widely used to treat spasticity in children. The optimal strategy for the combined treatment of casting and BTX-A injections is not known. This prospective study is conducted to know the functional outcome of immediate versus delayed casting post-BTX-A injection in children with cerebral palsy (CP). Aims and Objectives: The aim of this study is to compare delayed versus immediate casting as an adjunct to botulinum toxin therapy for spasticity of lower limb muscles in CP. The objectives of the study are to test the hypothesis that delayed casting is superior to immediate casting post-botulinum toxin injection and to know the feasibility of using the Edinburgh visual gait score (EVGS) as a single qualitative and quantitative outcome measure. Materials and Methods: A prospective study is conducted to compare immediate casting with delayed casting post-botulinum toxin injection to spastic lower limb muscles in patients with CP from July 2018 to February 2019. Inclusion criteria: A diagnosis of CP with associated spastic monoplegia, diplegia, or hemiplegia with aided or unaided ambulation. Exclusion criteria: History of orthopedic surgery in the preceding 12 months; selective dorsal rhizotomy; mixed CP; ataxia; athetosis; non-ambulatory subjects. Results: The botulinum toxin injection + delayed POP casting group fared better in terms of clinical and functional outcome (as shown by improved EVGS scores) in our study. Conclusion: There is a clear benefit in delaying casting after the injection of Botulinum toxin in the recurrence of spasticity.