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This work investigates the development, characterization, and evaluation of nanostructured catalysts intended especially for environmentally benign energy conversion. We developed nanostructured catalysts by modifying the sol-gel method and varying the precursor material and reaction conditions ratios. The morphological differences between the synthesised catalysts were shown. Among catalyst 3's best features were its large surface area and pore volume. Noteworthy activity and selectivity were shown by catalyst 3. Low overpotential was attained along with high current density and faradaic efficiency. The stability studies proved that Catalyst 3 was durable since, over many cycles, its electrochemical performance scarcely altered. The requirement of carefully adjusting the synthesis conditions to tailor nanostructured catalysts for specific energy conversion applications is highlighted by these findings. The main objectives going forward should be to enhance the processes involved in producing anything and to find novel chemical combinations that may accelerate the effective and environmentally benign conversion of energy. By addressing these problems, nanostructured catalysts have the potential to greatly progress renewable energy technology and lessen environmental impact worldwide.

In this study, we present and examine the implementation of a fuzzy logic-driven energy storage management system devised to enhance the efficiency of charging and discharging activities in modern power grids. By using real data derived from the installation of the system, our research aims to assess its effectiveness in bolstering grid resilience and stability. The system integrates linguistic factors and employs rule-based decision-making, enabling it to adapt charging and discharging techniques in real-time to the prevailing grid circumstances. The results unequivocally indicate a noteworthy enhancement in the efficiency of the system, exhibiting a percentage alteration spanning from 8% to 12%, hence confirming the system’s inherent ability to mitigate energy losses throughout its operating cycles. Moreover, the use of the fuzzy logic controller significantly enhances the Grid Reliability Index, exhibiting a percentage variation ranging from 10% to 15%, so demonstrating a higher level of stability and responsiveness to grid demand. The controller’s flexibility is highlighted by doing sensitivity analysis, which demonstrates its strong decision-making skills across many contexts. The charging and discharging techniques are meticulously tuned, demonstrating a percentage fluctuation of 15% to 20% in accordance with diverse grid situations, effectively optimizing the exploitation of renewable energy sources. The practical usefulness of the proposed system is confirmed by the validation of simulation results against historical data. The research incorporates ethical issues, such as clear communication, user permission, and socio-economic implications, to underscore the appropriate application of modern energy management technology. The results of this study significantly enhance the overall comprehension of fuzzy logic-driven systems, presenting a very promising resolution for the enhancement of energy storage operations and the progression of robust and environmentallyfriendly energy infrastructures.

The biosynthesis of nanoparticles is an important research area focused on developing innovative, cost-effective, efficient, and environmentally friendly synthesis techniques with a wide range of applications. This study employs a straightforward and practical method to produce TiO2 nanoparticles (NPs) for use in photocatalysis, antibacterial, antifungal, and antidiabetic applications. It utilizes extract-assisted biosynthesis with Moringa oleifera. To enhance the material and evaluate the impact of bioinspiration on various parameters, the resulting TiO2 NPs undergo calcination at 500 °C. Structural confirmation, bandgap analysis, and functional group studies of MO/TiO2 (Moringa oleifera/TiO2) nanoparticles are conducted using XRD, UV-DRS, FT-IR, and PL analysis. To examine the surface morphological characteristics of the anatase TiO2 nanoparticles, SEM and TEM-EDS electron microscopy analyses were accomplished. The produced TiO2 nanoparticles are also assessed for their photocatalytic and antidiabetic properties. Photocatalytic activity is determined by decolorizing methylene blue dye in the presence of sunlight, revealing that optimal conditions for complete color removal involve using 0.3 g of TiO2 and a concentration of 4 × 10− 5 M at pH 7. A total decolorization efficiency is achieved after 180 min in the presence of MO/TiO2. The biosynthesized Moringa oleifera leaf extracted TiO2 (MO/TiO2) was used for photocatalytic and biomedical applications. MO/TiO2 exhibited the best photocatalytic activity and showed maximum inhibitoin in invitro antidiabetic activity. Invivo antidiabetic activity showed the Moringa oleifera leaf-extracted TiO2 catalyst significantly reduced blood glucose levels. [ABSTRACT FROM AUTHOR]

This article aims to investigate the mechanical properties and substrate adhesion of the pull-off method in polymer mortars modified with styrene-butadiene resin polymer (SBR) containing glass powder and composite fiber-reinforced slag. Different mix designs were investigated with and without SBR, taking into account different amounts of glass powder and slag separately and in combination, along with the effect of glass, polypropylene, and steel fibers alone and in combination. The flexural performance and energy absorption of beams retrieved with these layers were also assessed. The results revealed significant differences and increases in the substrate adhesion of the restored modified polymer layers containing SBR compared to the polymer-free repair overlays. Furthermore, an improvement was observed in the adhesion performance of the repair overlay using a combination of slag and glass powder and the glass and polypropylene fiber composite. The highest adhesion was related to the modified polymer mortar design containing composite fibers of glass, polypropylene, and steel with 25% replacement of SBR polymer for 10% glass powder, 10% slag, and 5% slag with 5% glass powder. The adhesion was increased by about 3.74, 3.72, and 3.78 times compared to the repair overlay of the control design. Modified polymer mortars had a higher T 150 D toughness. Moreover, the energy absorption was significantly improved by the presence of SBR polymer. The highest toughness values were found in the beams restored with modified polymer mortars containing polypropylene, glass, and steel composite fibers with an increase of 48.51%-66.42% compared to the samples without polymer as a result of the pozzolans used in this mix. [ABSTRACT FROM AUTHOR]

This study evaluates the performance of a geosynthetic reinforced soil integrated bridge system (GRS-IBS) in terms of total displacement by varying different design parameters simultaneously and also suggests optimum values of them. These parameters include, i. backfill internal friction angle (çb) and reinforcement spacing (Sv), ii. Backfill internal friction angle (çb) and geogrid axial stiffness (EA) at varying reinforcement spacing (Sv), iii. Backfill internal friction angle (çb) and number of bearing bed layers, and the effect of retained backfill slope (mb). Simulations were conducted using PLAXIS 2D software. Analysis showed that the cumulative effect of these parameters had a significant effect on total displacement but after a certain point increase or decrease in their values showed no effect on the results while some parameters showed negligible effect on the deformation of the wall. Furthermore, due to the notable effect of çb, Sv and EA on the total displacement of the wall, the impact of these parameters was also investigated on the development of tensile force in the topmost layer of geogrid in GRS IBS. It was noted that the shape of the tensile force distribution graph was the same for all the cases and the order of the parameters in terms of their effect on tensile force was Sv > çb > EA. Also, a detailed analysis of tensile force development in all the layers of geogrids showed that if Sv = 0.2 m, the spacing between reinforcement in the lower portion of GRS IBS can be increased as these layers showed approximately zero tensile load. [ABSTRACT FROM AUTHOR]

Lepidagathis cristata (L. cristata) plant produces reducing and capping agents; this study utilized microwave‐assisted biogenic synthesis to manufacture silver nanoparticles (AgNPs) using this plant. The structure, morphology, and crystallinity phases of prepared nanoparticles (NPs) were characterized by ultraviolet–visible spectroscopy (UV‐viz), powder X‐ray diffraction (XRD), Fourier‐transform infrared (FTIR) spectroscopy, and scanning electron microscopy (SEM). Biologically synthesized AgNPs were treated against pathogenic bacteria species including Escherichia coli (E. coli), Bacillus subtilis (B. subtilis), and Staphylococcus aureus (S. aureus) and its highest zone of inhibition 10 ± 1.45 mm, 10 ± 0.74 mm, and 6 ± 0.43 mm, respectively, at the concentration of 100 μg/mL. The cytotoxic activity of AgNPs against MCF‐7 breast cancer cells revealed significant growth inhibition by inhibiting cell viability, inhibitory concentration of 50% (IC50) of NPs observed at 55.76 μg/mL concentration. Finally, our findings concluded that the L. cristata‐mediated biosynthesized AgNPs proved its potential antibacterial and neoplastic properties against MCF cells by endorsing the inhibition of cell proliferation especially with low concentration. [ABSTRACT FROM AUTHOR]

This research investigates a paradigm shift in heat treatment practises that is characterised by transformational changes. Despite their widespread use, traditional technologies are often linked to issues such as energy inefficiency, pollution, and material waste. In order to tackle these aforementioned issues, this study explores novel methodologies like high-pressure gas quenching, laser-assisted heat treatment, additive manufacturing for customised microstructures, and ultra-fast induction heating. These methodologies provide not only enhanced material functionality but also environmentally friendly outcomes by means of energy conservation and waste minimization. This study highlights the crucial significance of these breakthroughs in defining a future where improved material qualities align with environmentally responsible practises. It does this by examining their advantages, environmental consequences, and problems in implementation. The use of heat treatment techniques has been shown to significantly boost the performance of materials. This academic study aims to explore the sustainability aspects of heat treatment methods, particularly in comparison to conventional approaches. The focus will be on evaluating the energy efficiency and reduction of material waste associated with a specific heat treatment technique known as high-pressure gas quenching.

This study investigates composites and intermetallic, examining their microstructure, characteristics, and applications. The research demonstrates the complicated link between these materials’ fundamental features and their possible practical applications by analysing mechanical strength, thermal conductivity, microstructure, and reinforcement percent. The study discovers a positive relationship between mechanical strength and thermal conductivity, implying the possibility of capitalising on increased strength for enhanced heat transfer. Furthermore, the impact of matrix phases on mechanical properties emphasises the strategic significance of matrix selection. The impact of reinforcement fraction in fine-tuning characteristics highlights the importance of microstructure as a silent architect affecting material behaviour. In conclusion, this study provides insights into the intricate interaction of material characteristics, opening the way for informed material design and application engineering.

This study explores the domain of developing material categories for the purpose of sustainable energy storage, with the objective of addressing the constraints inherent in existing technologies and facilitating the development of inventive resolutions. The research examines the potential of nanomaterials, metal-organic frameworks (MOFs), polymers, and two-dimensional (2D) materials as a means to overcome the obstacles presented by current energy storage systems. This study investigates the qualities and potential of various materials, examining them in conjunction with a range of thorough characterization techniques. These approaches include electrochemical analysis, structural methodologies, nanoscale observations, and computer modelling. In the next analysis, this study will examine the future direction of research on energy storage materials, including prospective advancements and the critical obstacles related to scalability, cost-efficiency, and integration within energy systems. In general, this investigation highlights the significant impact of new materials on the development of a more environmentally friendly energy infrastructure. The present study focuses on the investigation of emerging materials for sustainable energy storage. Specifically, the research explores the potential of nanomaterials, metal-organic frameworks, polymers, and two-dimensional materials in this context. By examining the properties and characteristics of these materials, this study aims to contribute to the understanding and development of efficient and environmentally friendly energy storage solutions.

This study investigates the synthesis, analysis, and utility of films comprising deep eutectic solvent (DES) grafted activated charcoal (AC) within a polyvinyl alcohol (PVA) matrix for optoelectronic device applications. The fabrication process involves the dispersion of DES functionalization AC into the PVA solution, followed by casting onto substrates with controlled drying. Comprehensive characterization encompassing X‐ray diffraction (XRD), scanning electron microscopy (SEM), UV–vis spectroscopy, Fourier‐transform infrared spectroscopy (FTIR), and impedance spectroscopy which discerns the films microstructure, morphology, conductance, band‐gap, and optical traits. The DES grafted AC infusion with variable concentration has significantly influenced optical absorbance and reduced the band gap indicating efficient charge mobility. Furthermore, the impedance analysis has revealed the electrical conduction of the film to be 1.8 × 10−6 Ω−1 m−1. In summary, the dispersion of DES modified AC in the PVA matrix have converted the insulating PVA to a semiconducting polymeric film with reduced band‐gap and increased absorption, which present a propitious avenue for wide array of optoelectronic devices, such as thin film transistors, photovoltaics, LEDs, photodetectors, and many such applications. [ABSTRACT FROM AUTHOR]

The development of novel drug candidates for treating microbial infections and cancer turns into a challenging task. The biopolymer-based nanomaterials have gained popularity for their effective roles in the pharmaceutical sector. The present investigation aims to valorize crab shell wastes to extract chitosan. The obtained (chitosan) biomass was utilized to synthesize chitosan-zinc oxide nanoparticles (Ch-ZnO-NPs). The nanomaterial characterization was achieved by a variety of physicochemical techniques including UV, Fourier transform infrared spectroscopy (FT-IR), X-ray diffraction (XRD), elemental analysis, and High-resolution transmission electron microscopic (HR-TEM) examination. The synthesized nanomaterials have a particle size range of 35-80 nm and are more stable with a zeta potential value of (-40.78) mv. Additionally, the designed nanomaterials were utilized to evaluate the biological efficiency. The minimal inhibitory concentration value of Ch-ZnO-NPs was observed as 10 &#181g/mL. The designed nanomaterials significantly reduced the growth of Vibrio alginolyticus and Bacillus cereus and inhibited biofilm formation. The synthesized Ch-ZnO-NPs unveiled cytotoxic effects on A549 cells with an IC50 (half-maximal inhibitory concentration) of 20 µg/mL. Furthermore, substantial increase in cell death was noticed in A549 cells treated with Ch-ZnO-NPs. Consequently, this investigation concludes that Ch-ZnO-NPs may act as a biomedicine. [ABSTRACT FROM AUTHOR]

The compressive strength of concrete is a critical parameter that affects the safety, durability, and performance of structures. This is a key factor in the design, construction quality control, and long-term functionality of concrete elements in various engineering applications. This is a fundamental indicator of the ability of the concrete to withstand axial loads or pressures. Understanding the compressive strength is essential for ensuring the safety of structures. This helps engineers design concrete elements with sufficient load-bearing capacity to withstand the expected loads and maintain structural stability. The objective of this study is to assess the effectiveness of linear and non-linear machine learning algorithms in accurately predicting the compressive strength of concrete based on varying quantities of constituent components. To address outliers in the data, we utilized an iterative implicit technique during the data preprocessing stage. Our implementation included Linear models such as Linear regression and polynomial regression, Non-linear models such as Decision Tree and Random Forest, and Boosting models such as Gradient Boosting and Extreme Gradient Boosting. The accuracy scores and overfitting of the models were compared. The models were evaluated using the R² scores for both the training and test sets. Among the three categories of models tested, boosting models demonstrated higher accuracy than the other models, while also exhibiting limited overfitting. [ABSTRACT FROM AUTHOR]

Before using concrete for a particular purpose, its strength must be determined because its physical properties vary depending on the type of supplementary cementitious material (SCM). Initially, researchers anticipated it with statistical processes; more recently, they started implementing deep learning (DL) and machine learning (ML) models. This research employed ensemble machine learning (EML) approaches to predict the compressive strength (CS) of concrete that was developed for ground-granulated blast furnace slag (GGBS) along with Alccofine 1203 (AF). Boosting regressions of Gradient Boosting Regression (GBR), Extreme Gradient Boosting Regression (XGBR) and Light Gradient Boosting Regression (LGBR) were considered for model prediction using the Jupyter Notebook. The models were created based on the outcome of the compressive strength under the 120 experimental conditions. Moreover, a comparative study was performed among GBR, XGBR and LGBR to benchmark the proposed model against a given combination of features (parameters/factors) and compressive strength. By using CPC-X Neural Power software, the experimental conditions were optimized using the Particle Swam Optimization (PSO) algorithm after the model was created by the Artificial Neural Network (ANN). Of the three boosting algorithms, the smallest errors along with the largest coefficient of determination were observed in the GBR algorithm and the optimized values of CS is 77.4 MPa and its optimized process factors (Alccofine, GGBS, Cement, C.A, F.A, SP dosage and days) were 24.77 kg/m³, 24.63 kg/m³, 534.92 kg/m³, 1100.08 kg/m³, 628.64 kg/m³, 4.20 lit/m³ and 29.03 days respectively. [ABSTRACT FROM AUTHOR]

Before using concrete for a particular purpose, its strength must be determined because its physical properties vary depending on the type of supplementary cementitious material (SCMs). Initially, researchers anticipated it with statistical processes; more recently, they started implementing deep learning (DL) and machine learning (ML) models. This research employed ensemble machine learning (EML) approaches to predict the compressive strength (CS) of concrete developed for ground-granulated blast furnace slag (GGBFS) along with Alccofine 1203 (AF). Ensemble regressions of Random Forest Regression (RFR) and AdaBoost Regression (ABR) were considered for model prediction using the Jupyter Notebook. The models were created based on the outcome of the compressive strength under the 90 experimental conditions. Moreover, a comparative study was performed between the RFR and ABR to benchmark the proposed model against a given combination of features and CS. The experimental conditions were optimized using a genetic algorithm (GA) after the model was created by ANN. With the two EML algorithms, the smallest errors, along with the largest coefficient of determination, were observed in the RFR algorithm. It was recognized that the optimized value of CS is 48.606 MPa, and their optimized process elements such as cement, alccofine, GGBS, water, and days were 261.506 kg/m³, 0.055 kg/m³, 52.468 kg/m³, 141.007 L, and 27.999 days, respectively. [ABSTRACT FROM AUTHOR]

Cardiovascular diseases (CD), or heart diseases (HD), lead to approximately 17.9 million deaths each year, constituting 32% of global fatalities. Early detection and appropriate treatment of HDs can significantly reduce mortality rates, with timely intervention before disease progression enhancing treatment efficacy. Early detection is achievable through routine medical examinations and monitoring key symptoms, such as cholesterol levels, blood pressure variations, diabetes and obesity. This manuscript introduces a heart disease prediction (HDP) model designed to identify the presence of HDs at an initial stage. The study explores three methodologies: (a) traditional convolutional neural network (CNN), (b) CNN augmented with flower pollination optimization (FPO) algorithm and (c) CNN combining Teaching Learning-Based Optimization (TLBO) coupled with genetic algorithm (GA) for refined HDP. The model progresses through stages of data preparation, model construction, training and evaluation. The traditional CNN model resulted an accuracy of 81.97%, precision of 84%, recall of 81% and F1-score of 83%. Incorporating the FPO algorithm, model's performance is enhanced with accuracy, precision, recall, F1-score of 85.25%, 90%, 81% and 85%, respectively. Further, optimized with TLBO and GA hybrid approach lead to superior performance with an accuracy, precision, recall, F1-score of 86.9%, 87.5%, 87.5% and 87.5%, respectively. The area under curve (AUC) for the receiver operating characteristics (ROC) and precision-recall curve (PRC) highlights the performance of the proposed hybrid methodology. These outcomes underscore the effectiveness of merging bio-inspired algorithms with CNN for early stage HD prediction (HDP), offering significant advancements in healthcare diagnostics. [ABSTRACT FROM AUTHOR]

The challenge of swift and reliable earthquake location prediction within earthquake early warning (EEW) systems underscore the need for innovative solutions. Existing methods provide predictions, yet there is a clear demand for enhanced accuracy and expediency in the process of resolving. The article intends in prediction of both magnitude of earthquakes and the affected areas in a specific region, focusing on California, United States, by leveraging historical data using neural networks (NNs). Neural networks outperform other algorithms such as XGBoost, linear regression, random forest, gradient boosting and support vector machine (SVM), in terms of high R² score and low score of MAE and also mean squared error (MSE). The derived results of R² score, MSE and MAE are 0.1607, 0.1615 and 0.2951, respectively. It seamlessly integrates geospatial visualization through Folium and GeoPandas. These tools enhance the predictive model's capabilities by generating dynamic maps enriched with markers, each representing the anticipated impact of earthquakes. The user interface facilitates interactive input, enabling users to input earthquake parameters for real-time predictions. The resultant map not only showcases the predicted impact zone but also provides valuable insights into the severity of seismic events. It stands as a testament to the synergy between machine learning and geospatial visualization, offering a holistic solution for earthquake prediction and geographical representation. [ABSTRACT FROM AUTHOR]