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To determine the wind energy potential, the probability density function is typically used. For data distribution with modest wind speeds, this paper developed a three-parameter Weibull model. The distribution factors were determined using the maximal likelihood technique. The world renowned, user-friendly programming language Matrix Laboratory (MATLAB) processes all data that needs analysis. A comparison was made between the 3-factor Weibull, the 2-factor Weibull, and Rayleigh through R2 and root mean square error (RMSE). The ECMWF Reanalysis v5 (ERA 5) reanalysis's hourly wind speeds are statistically represented by these three distributions. Due to its placement between the optimal R2 and RMSE, the three-parameter Weibull model achieves good results. Weibull with three parameters has a R2 of 0.9898, Weibull with two parameters has a R2 of 0.9886, and Rayleigh has a R2 of 0.5162. The root-mean-squared errors (RMSEs) for Rayleigh, 2-factor and 3-factor Weibull, are 0.0082 and 0.0070, respectively.

Background: Human papillomavirus (HPV) is the causative agent of verruca and is contagious, recurrent, and recalcitrant due to defects in cell-mediated immunity. No single mode of treatment is completely effective. The most commonly used treatment options are destructive and lead to scarring. The emerging new modality of treatment is immunotherapy, which acts by enhancing cell-mediated immunity (CMI) against HPV to clear treated as well as remote warts. Objectives: The objective was to compare the efficacy of intralesional Measles, Mumps, and Rubella (MMR) vaccine and intralesional vitamin D3 in cutaneous warts. Materials and Methods: A randomized, hospital-based, single-blind study on forty patients divided into two groups (groups A and B), comprising twenty patients each, was conducted. In group A, intralesional MMR vaccine 0.5 mL was injected into the base of warts. In group B, intralesional vitamin D3 0.5 mL was injected into the base of warts after achieving local anesthesia. Sessions were performed in two-week intervals for a maximum number of five sessions. The patients were followed for sixteen weeks. Results: In group A (MMR vaccine), a complete response was seen in 75% (15/20) of the patients, a moderate response in 15% (3/20), a partial response in 5% (1/20), and no response in 5% (1/20). In group B (vitamin D3), a complete response was seen in 65% (13/20) of the patients, a moderate response in 15% (3/20), a partial response in 10% (2/20), and no response in 10% (2/20). No recurrence was noticed after follow-up in either of the two groups. Conclusion: Immunotherapy is an option that is easy, safe, cost-effective, and well-tolerated with minimal side effects.

The rapid progression of electric vehicles (EVs) in the transportation sector poses a potential challenge of increased peak energy demand on existing grid structures. Consequently, the significance of V2G power conversion technology becomes paramount in managing sudden surges in energy requirements. To address this issue, the authors propose a “Bidirectional Converter Based on V2G and G2V Operation” which focuses on facilitating energy transfer between vehicles and the grid through bidirectional converters, catering to both V2G and G2V operations. In this work, the authors analyzed a bidirectional buck-boost converter interface with an H bridge AC/DC converter together with the related control method in a reduced topology. The efficiency of the developed system is evaluated with the help of a developed simulation model in MATLAB/SIMULINK. The developed model was tested under various conditions and was able to perform effectively.

Efficiency of photovoltaic (PV) systems is decreased by mismatch losses resulting from uneven panel irradiation. Current compensation techniques that inject compensatory current and measure currents by periodic row short-circuiting are unavoidably blackout-causing. In this work, a novel current compensation technique is presented that dynamically measures and reacts to variations in irradiance using light sensors on each panel. The program compiles these data to determine the overall irradiance of each row and to precisely modify the injected current to sustain output without any interruptions to operation. The proposed approach lowers mismatch losses more effectively than traditional techniques, as demonstrated by MATLAB/Simulink simulations of seven shading patterns. Under center shading, the proposed approach raised system efficiency by 36%. These results indicate a move towards more flexible solar energy solutions by implying that sensor-driven data analytics can increase PV system operational efficiency and reliability.

For grid-tied photovoltaic (PV) systems, Maximum Power Point Tracking (MPPT) algorithms based on artificial neural networks (ANNs) are prone to initialization issues, which could cause them to converge at local maxima rather than the global maximum power point (MPP). This means that, a regular retraining on big datasets is required. This paper presents a novel hybrid MPPT algorithm that combines Extreme Gradient Boosting (XGBoost) and Vascular Invasive Growth Optimization (VIGO) to address this challenge. The exploration-exploitation conundrum that traditional optimization algorithms have is addressed by VIGO, and the convergence speed and accuracy of MPPT are improved by XGBoost. To assess its performance, the suggested approach is compared with well-known methods such as Grasshopper Optimization Algorithm (GOA), Sparrow Search Algorithm (SSA), and Particle Swarm Optimization (PSO). This comparison study shows that the hybrid VIGO-XGBoost method produces improved maximum power.

This study explores the potential of recyclable polymer matrix nanocomposites for sustainable packaging solutions. Nanocomposites were created by combining different polymer matrices (PET, PLA, HDPE, PP) with various types of nanofillers (Clay, Graphene, Cellulose, Nanoclay), and the nanofiller content varied from 2% to 5%. Graphene-based nanocomposites demonstrated exceptional tensile strength (55 MPa) and Young's modulus (4.0 GPa) in comparison to alternative formulations, as indicated by mechanical properties analysis. Graphene nanocomposites demonstrated the most effective barrier properties, with the lowest oxygen permeability (1.0 cm^3/m^2·day·atm) and water vapor transmission rate (2.3 g/m^2·day). The analysis revealed that cellulose nanocomposites exhibited the highest glass transition temperature (72°C) and melting temperature (185°C) in terms of their thermal properties. The findings highlight the wide range of benefits that recyclable polymer matrix nanocomposites can bring to sustainable packaging applications. These include improved mechanical strength, enhanced barrier performance, and increased thermal stability. This underscores their versatility and potential in this field. Additional research is necessary to enhance synthesis methods, investigate new nanofiller materials, and assess long-term performance in real-world packaging scenarios.

Operations for sheet metal shaping are essential to the production of many different kinds of goods. But there is still a problem with plastic fragility in this industry, which frequently results in faulty goods. To solve this problem during production, it’s critical to take into account a number of factors, including the Forming Limit Diagram (FLD). In this work, the formability of the Aluminum Alloy (AA8011) at strain rates of 0.01 mm/s at ambient temperature, 100 °C, and 150 °C has been investigated. The Nakajima test was used to execute stretch forming in order to achieve the study’s results. The material’s restricting stresses increased as temperatures increased, according to the outcomes, which were examined utilizing fractography investigations carried out under a scanning electron microscope and simulations carried out with LS-dyna software. This work will help create more productive and successful sheet metal-forming techniques by offering insightful information on the formability of AA 8011 sheet at extreme temperatures.

In Modern Era, the demand for wireless technology has surged exponentially, promising enhanced living comfort and safety. Simultaneous Wireless Power and Data Transfer (SWPDT) has emerged as a critical area of study, finding applications in electric vehicles, underwater autonomous vehicles, and biomedical implants. This technology facilitates real-time monitoring while supplying power, eliminating the need for bulky cables and conventional power sources. Hence this paper addresses the state-of-art of SWPDT in different applications This comprehensive review covers various SWPDT implementation methods for applications like electric vehicles, biomedical implants, and autonomous underwater vehicles. Different techniques for implementation of SWPDT using inductive links and its design recommendations.

Current production trends necessitate extensive use of cutting fluids, resulting in adverse climatic changes and increasing industry expenses. Modern manufacturing businesses are looking for new ways to meet the demands for faster machining rates, less waste, and improved product quality while also lowering manufacturing costs. Material removal rate (MRR) and surface roughness (SR) of the work piece machined with coated tools are all evaluated. Cutting speed (CS), pressure flow (PF), and depth of cut(DOC), are the input parameters. An aerosol mixture of air and oil was used to give the least amount of lubrication. To assess the relative performance tuning with the MQL setup, an investigation was conducted. When compared to dry turning, the MQL system for turning super duplex stainless steel UNS 32750 has shown to be more effective. The MQL technique significantly improves cutting performance in terms of increasing tool life and improving machined component quality.

Aluminium alloys are popular in many advanced applications and are considered to be an essential class of engineering materials mainly due to their desirable combination of low density, high strength, and cost-effectiveness compared to competing materials. Aluminium metal matrix composites and hybrid metal matrix composites will further enhance these properties by incorporating hardened particles as reinforcements. Hybrid metal matrix composites are second-generation composites that exhibit superior mechanical properties due to the synergistic combination of different reinforcement combinations. Various combinations of reinforcing particulates are being explored. The fabrication of the hybrid composites is done using a stir casting route, which will significantly influence the mechanical properties, microstructure and reinforcement distribution. Aluminium HMMCs offer lightweight, high strength, good wear resistance, and other properties that allow them to be extensively used in the structural, aerospace, marine, and automotive industries. Hybridization of metal alloys provides flexibility in designing components and quality control during manufacturing. Thus, this review paper is focused on the fabrication and characterization of hybrid Aluminium metal matrix composites, advancements in their mechanical properties, recent developments and applications.

This paper considers and compares the hysteresis stress and strain and the penetration property of spur gear assemblies based on three unique designs. Spur gear plays an important part in mechanical structures, and any mechanical setup should consider the execution of such a mechanical component under distinct designs to improve its mechanical productivity and sustainability. To explore the ways in which the mechanical behaviour of the designs varies with the design configurations, we integrate simulation analysis with an experimental study. The outcomes of this paper indicate considerable differences in both hysteresis stress, strain distribution, and penetration behavior measurements between three designs. The paper explains the stated disparities by the unique geometric layouts and material characteristics of each design. Furthermore, it emphasizes that some of the examined designs have lower hysteresis losses and favourable stress and strain distributions, which positively affects the long-term performance of gear systems. Other designs, however, exhibit severe penetration and stress concentrations leading to rapid gear wear and likely premature failure. In distinguishing these events, the present study offers a valuable approach to the parameters that influence the performance of gear systems and aids in the improvement of the design methodology.

Nitrocarburizing is one of the prominent surface treatment methods for steels. The effects of ferritic nitro carburizing on En-19, En-24 and En-37 steels are compared. Microstructural investigations of FNC treated samples are carried out. Micro hardness profiles of FNC treated steels are drawn. Further wear investigations are carried out. It is observed that FNC treated En-19 steel has more wear resistance in comparison to En-24 and En-37 steels.

In recent years solid-state batteries, have emerged leading research focus, due to their distinct advantages over conventional batteries. Solid-state batteries, having solid electrolytes, offer higher energy and power density, enhanced safety features and longer lifespan. This makes them ideal in fulfilling demand for energy storage in electric vehicle, and smart grid applications. This study aims to evaluate various types of solid-state batteries, analysing their properties, advantages, and disadvantages, assessing their viability in EV applications. The objective is to identify and recommend the most effective solid-state battery that aligns with the specific demands and operational conditions of electric vehicles and conduct a comprehensive analysis of anode and cathode elements of one of the solid-state batteries in their fresh and damaged state using scanning electron microscopy (SEM).

This project focuses on automating the credit card application assessment process using advanced machine learning techniques, including Random Forest, Gradient Boosting, SVMs, Logistic Regression, Regularization Methods, and Hyperparameter Tuning. The objective is to improve the efficiency, accuracy, and fairness of credit card approval decisions. Historical credit card application data, comprising applicant demographics, financial history, and employment details, is collected and pre-processed. Feature engineering and exploratory data analysis (EDA) enhance the dataset’s predictive power. Three machine learning algorithms, Random Forest, Logistic Regression, and Gradient Boosting are applied. Regularization techniques (L1 and L2) and hyperparameter tuning are used to prevent overfitting and optimize model performance. The project assesses model performance by employing metrics such as accuracy, precision, recall, F1-score, and ROC-AUC metrics, and conducts feature importance analysis to identify key factors influencing approval decisions. The project aims to deliver robust, accurate, and fair credit card approval models, benefitting both financial institutions and applicants

This research examines the characteristics and ecological viability of polymer matrix nanocomposites used in sustainable packaging. Nanocomposites were produced by combining varied proportions of polymer and nanofiller material. Through mechanical testing, it was determined that nanocomposite formulation 3 had the maximum tensile strength of 55 MPa, as well as a Young's modulus of 3.5 GPa, showing greater stiffness in comparison to the other formulations. The evaluation of barrier qualities revealed that nanocomposite formulation 2 exhibited the most minimal oxygen permeability at a rate of 8 cc/m2/day and the lowest water vapor transmission rate at 4.5 g/m2/day, showing very efficient performance in preventing the passage of gases and moisture. The environmental impact study showed that nanocomposite formulation 3 had the most efficient energy consumption during manufacture, with a rate of 1.8 kWh/kg. It also had the lowest waste creation, with just 0.08 kg/kg, and the lowest CO2 emissions, with only 0.4 kg/kg. Nanocomposite formulation 3 demonstrated substantial improvements in mechanical characteristics, barrier properties, and environmental impact indicators when compared to the reference formulations, as shown by the percentage change analysis. In summary, this study showcases the capabilities of polymer matrix nanocomposites, specifically formulation 3, as environmentally friendly packaging materials that offer improved mechanical properties, effective barrier performance, and reduced ecological footprint. These findings contribute to the development of sustainable packaging solutions across different industries.

An investigation has been conducted to determine whether or not novel nanocomposite electrolytes have the potential to improve the efficiency and longevity of fuel cell technology. In this investigation, nanocomposite electrolytes were manufactured by using a methodical methodology. The nanoparticles were included into polymer matrices in order to enhance the ion conductivity and mechanical strength of the electrolytes developed. The composition of nanocomposite electrolytes was improved by altering the amount of nanoparticles and polymers that were present. When compared to traditional electrolytes, the introduction of nanoparticles resulted in considerable improvements in the characteristics of the material. These enhancements included a 15% increase in ion conductivity and a 10% increase in mechanical strength. Fuel cell devices that make use of nanocomposite electrolytes have showed improved performance, with a 10% increase in power density and a 15% gain in efficiency when compared to fuel cell devices that make use of traditional electrolytes. In addition, the environmental impact assessment found that the use of nanocomposite electrolytes resulted in a two percent drop in the amount of energy used, a fifteen percent reduction in the amount of waste generated, and a twenty-five percent reduction in the amount of carbon dioxide emissions. As a result of these discoveries, the potential ofnanocomposite electrolytes to enhance the sustainability and efficiency of fuel cell technology has been brought to light. This opens the door for the creation of next-generation fuel cells that have increased performance and decreased their effect on the environment.

The paper details the development, manufacturing, and evaluation of a compact Organic Rankine Cycle (ORC) system fueled by Concentrated Solar Power (CSP). The initial selection of the starting point of the cycle is described, considering the operational conditions of the ORC (such as the properties of the ambient temperature and solar field) and operational limitations. The study outlines the construction of a radial turbine generating 3 kW of power and conducts numerical simulations of fluid flow within the turbine components such as the nozzle and wheel. The course includes system engineering, focusing on the computation and selection of critical components including pumps, exchangers, and sensors. The findings of the laboratory tests are presented in the second part. The experiment utilized a thermal oil boiler for heating. The results show an isentropic efficiency of around 42% and a cycle efficiency of 6% under the initial test conditions, indicating promising performance over a wide range of pressure drops.

This study enhances the domain of optimum energy storage system selection by offering a complete decision support framework that incorporates technical, economic, and environmental factors. The technical investigation examines energy and power density measurements, which demonstrate the exceptional volumetric energy storage capacities of lithium-ion batteries at 250 Wh/L. This highlights their appropriateness for applications that prioritize compactness. Lead-acid batteries are a cost-effective option that have a lower energy density of 80 Wh/L. This makes them particularly advantageous in situations where volume limitations are not as important. Flow batteries and supercapacitors are positioned in the middle, with flow batteries offering a well-balanced profile of energy and power density, while supercapacitors excel in applications that need fast charging and discharging. An in-depth evaluation of the startup costs and ongoing expenditures is conducted to thoroughly assess the economic feasibility of adopting an energy storage system. Although lithium-ion batteries have a higher upfront cost of $100,000, they demonstrate a well-balanced economic profile by offering a significant 66% decrease in operating expenditures compared to lead-acid batteries. Lead-acid batteries, which have an initial price tag of $60,000, demonstrate cost-effectiveness but result in higher ongoing costs. Flow batteries and supercapacitors are positioned as premium alternatives, with respective beginning prices of $150,000 and $200,000. Environmental sustainability is a fundamental factor that is assessed via life cycle analysis. Lithiumion batteries, while very efficient, give rise to problems over resource depletion and environmental damage linked to mining activities. Lead-acid batteries, due to their recyclability, provide a more ecologically sound alternative. Flow batteries, which use readily available and environmentally safe components, are very compatible with objectives aimed at promoting sustainability. Supercapacitors, which often use ecologically friendly materials, have potential in reducing negative environmental impacts. MultiCriteria Decision Analysis (MCDA) is used to integrate technical, economic, and environmental assessments. Lithium-ion batteries are considered the most favorable choice due to their exceptional technical performance and economic viability. Lead-acid batteries have a strong market position due to their cost-efficiency and ecofriendliness. Flow batteries and supercapacitors, while they have distinct benefits, are only suitable for certain applications. The stability of the framework is shown by sensitivity analysis, which identifies energy density and beginning costs as crucial factors. Engaging stakeholders helps confirm the framework’s usefulness, ensuring that it can be practically used and remains relevant in guiding well-informed choices in the ever-changing field of energy storage technologies. This study provides vital insights into the discussion on sustainable and efficient energy solutions, presenting a clear plan for decision-makers to navigate the intricacies of selecting energy storage systems.

Water scarcity is becoming an ever-growing problem in society. This is all due to the rapid increase in human population, harsh changes in the weather climate. New approaches for treating and recycling brine water into freshwater instead of discharging are needed, significantly large reduction in energy usage and decreasing harmful impact to the environment must be achieved in brine recovery. This study aims to introduced green technology into the reclamation of brine water by use of covalent organic frameworks (COFs) materials which will be used as part of a novel triazine-based COFs material which can be used as a nanomembrane for desalination of brine water from water treatment plants at Umgeni Water. Current technological approaches used are inefficient and unsuitable in 3rd world, developing countries including the republic of South Africa. This work focuses on a microwave-assisted synthesis involving Friedel Crafts reaction between monomers to yield a product of a triazine-based covalent organic frameworks (COFs) membranes. To confirm the products high resolution transmission electron microscopy (HRTEM), carbon NMR (13C NMR) with peaks at chemical shifts of 131, 143 and 172 ppm respectively. Fourier-transform infrared spectroscopy (FTIR) was employed and showed N-H stretches at a region of 3396 to 3050 cm-1. The COFs that were successfully synthesised are going to be incorporated on a polymeric substrate to fabricate a nanofiltration membrane and applied for nanofiltration or ultrafiltration purposes.

In this paper we have calculated the thermal partition function of the thermo- dynamical system of Quark Gluon Plasma and also we have calculated the entropy of quark gluon plasma for the zero chemical potential at the thermal equilibrium condition.

The gig economy, characterized by its flexible work arrangements and independent contractors, has witnessed rapid growth in recent years. However, it faces sustainability challenges related to worker welfare, income security, and job classification. This paper explores the emerging role of artificial intelligence (AI) in the gig economy and its potential impact on these sustainability concerns. Through an analytical lens, it examines the current applications of AI in the gig economy across various platforms and tasks, highlighting both efficiencies and potential biases.The anticipated effects of AI on gig workers including job displacement, skill requirements, and potential for wage changes.The implications of AI for the sustainability of the gig economy, analyzing how it might exacerbate existing challenges or pave the way for improvements in worker protections and platform governance.Potential policy considerations and recommendations to ensure a more sustainable and equitable gig economy in light of AI integration. The paper concludes by emphasizing the need for a nuanced understanding of AI's potential in the gig economy, advocating for proactive measures to address potential disruptions and harness its capabilities to create a more sustainable and rewarding future for the flexible workforce.

The current research work is mainly concentrated on the mechanical properties concrete blended with marble stone power resulted from waste sludge marble processing it has a high specific area. M25 grade concrete mix design was considered for this research work. The mechanical properties of concrete i.e. compressive strength, unit weight, splitting tensile strength, modulus of elasticity and flexural strength were considered for the study. The compressive strength of these mixes was measured on 150mm ×150mm × 150mm cubes and tension test split tensile test 150 mm dia × 300 mm height cylinders. The concrete unit weight was considered for calculating the elastic modulus of concrete. The investigational values were matched with ACI, CEB-FIP, BIS and AASHTO LRFD empirical equation and regression analysis was done. The empirical equation result was compared with regression analysis of Artificial Neural Network, and conclusion was brough down that regression analysis of artificial neural network had better prediction than that of above-mentioned empirical equations. The study concluded that 15% replacement of marble power attained highest strength and optimum replacement, 25% replacement was concluded as economical replacement to attain designed strength.

A hybrid is used, combining feature-based method transformed-based features with image-based grey level co-occurrence matrix features. When it comes to classifying cerebral hemorrhages CT images, the combined feature-based strategy performs better than the image-feature-based and transformed feature-based techniques. Natural language processing using deep learning techniques, particularly long short-term memory (LSTM), has become the go-to choice in applications like sentiment analysis and text analysis. This work presents a completely automated deep learning system for the purpose of classifying radiological data in order to diagnose intracranial hemorrhage (ICH). Long short-term memory (LSTM) units, a logistic function, and 1D convolution neural networks (CNN) make up the suggested automated deep learning architecture. These components were all trained and evaluated using a large dataset of 12,852 head computed tomography (CT) radiological reports.

In the past ten years, data from time series extraction has attracted a lot of attention. Several methods have concentrated on classification problems, where the objective is to identify the labelling of a test period, given labelled training data. Feature-based and Instance-based methods are the two fundamental groups into which time series categorization methodologies may be divided. To categorize time series data, instance-based techniques use similarity data in a nearest-neighbor context. While methods in this category deliver reliable findings, their efficacy suffers when dealing with lengthy and noisy time series. Feature-based approaches, on the other together, extract characteristics to address the shortcomings of instance-based methods; nevertheless, these approaches use predetermined features and might not be effective in all classification issues. This paper seeks to introduce a novel deep learning-based Optimal Dynamic Time Warping (ODTW) paradigm for multimodal time’s series data categorization. This model covers several phases. At initial stage, the standard data is gathered from standard public source. Secondly, ODTW is proposed, where the parameters are optimized by Random Opposition Billiards-Inspired Optimization (RO-BIO) for extracting the most essential information. Finally, the classification is carried out through “Deep Belief Network (DBN) and Recurrent Neural Networks (RNN) termed as Deep Belief-RNN (DB-RNN)”. Finally, the extracted deep features are given to the optimized RNN for attaining the final classified results. The simulation results have resulted in superior classification performance in terms of standard performance measures.

Image inpainting can be used to fix broken images and get rid of distracting elements. In exemplar based methods, patch priority computation and exemplar patch selection are crucial to the success of image inpainting technique. The dropping effect occurred in the highest patch priority computation and matching error in the best patch selection are the major issues in the exemplar inpaint methods. In this paper, the enhanced priority calculation technique is employed to avoid the dropping effect and introduced the new similarity measuring process, Mean Squared Error (MSD). The efficacy of the proposed techniques is estimated by comparing with the available methods in the literature qualitatively. It shows that proposed methods outperformed existing techniques.

It is within the framework of situated knowledges in the field of biology and technology studies, in its relation to feminism that the coyote figuration is conceptualized by Donna Haraway. In what respect are they conceived by Donna Haraway to be figures of emancipation? What kind of affinity does it establishes with the figure of cyborg, as figure of posthumanism? Certainly, Donna Haraway hypothesizes the privilege of a ‘partial perspective’ having to play a role in expanding the epistemic horizon of feminist thought. This paper probes into the potential role of coyote figures as metaphors illuminating feminist readings of the relation between philosophy and technology. However, coyote figures have a generic character of transgression, and hence, borders on epistemological reduction of its ontico-ontological phenomenality which Catherine Malabou problematizes. Malabou’s eschewal of flexibility counters this binary by developing the role played by the “fantastic” in the espousal of plasticity of being. Being conceived in this originary mutability eliminates the interior-exterior division of beings. What are the comparable features of coyote figurations and the fantastic? And how does it add to the feminist understanding of philosophy of technology?

his article considers the representation of differentiation operators using Daubechies wavelets to solve PDEs numerically. Derivative approximation using this compactly supported wavelets convert the action into a matrix multiplication. The vanishing moments, dilation equation and quadrature formulas play a significant role in the scheme. The computed solution of the PDEs seems to behave better than the results reported in the literature, and we could progress the solution up to a large time-bound. We experimented with Daubechies wavelet filters with six vanishing moments on test problems and summarised the results.

Refrigeration is a process of maintaining the temperature lower than the surroundings. It can be achieved with the help of components of refrigerator and the refrigerant. Refrigerant is a substance maintaining the temperature lower than the atmosphere. VCR is widely applied in daily as well as in large scale industry to produce a refrigeration effect. The diffuser is a passage that will increase the pressure energy by changing the kinetic energy at the inlets. The main objective of this project work is to design a water cooler test rig system with two different angled diffusers of divergence angle of 13°, 16° are planned for the equivalent inlet and outlet dia. In this project we are evaluating the various constraints such as refrigeration effect & coefficient of performance (COP) with and without diffusers at condenser inlet. The 16° divergence angle of diffuser given the better coefficient of performance was 23.15.

Effective wastewater treatment techniques are needed to mitigate the effects of water contamination, which is a severe environmental issue. The high adsorption capacity and sustainability of adsorption techniques using biochar from agricultural waste, such as coir pith, have made them appear promising. This work intends to optimize the adsorption procedure utilizing biochar made from coir pith using Central Composite Design (RSM-CCD). The effects of pH, time, adsorbent dosage, and adsorbate concentration were examined on adsorption efficiency. In order to comprehend its structural and elemental characteristics, the synthesized biochar was characterized using scanning electron microscopy (SEM), energy-dispersive X-ray spectroscopy (EDX), and X-ray diffraction (XRD). This study focuses on determining the optimal conditions for maximum adsorption efficiency and contributes to environmental engineering by utilizing agricultural waste for water remediation. The results showed that the biochar removed Crystal violet with a remarkable 72% efficiency, highlighting its potential as a powerful adsorbent for water purification.

The research investigates Functionally Graded Materials (FGMs) and their transformational potential in modern engineering. FGMs, which exhibit progressive property fluctuations, call into question traditional material consistency. This study analyses the growth of FGMs and their importance in solving complex engineering difficulties through historical analysis and real-world case studies. The research dives into the design concepts, material selection, manufacturing procedures, and sophisticated characterisation methodologies that underpin FGM development from a methodological standpoint. Mechanical, thermal, and electrical characteristics, in combination with microstructural progression, offer a thorough knowledge of FGM behaviour. The implications for future engineering advances are highlighted, with a focus on the ability to rethink material design and multifunctional performance. Among the many attractive possibilities, issues in scalability, characterisation, and multidisciplinary cooperation need additional investigation. FGMs represent a paradigm shift from homogeneity to targeted heterogeneity, echoing wider shifts in engineering philosophy and influencing technological development.

In the field of medical science, classifying brain tumours is vital. In order to get an effective and proper treatment for the disease, accurate and finding type of the brain tumour is much essential in the case of brain tumour treatment. In addition to providing treatment for tumours as early as possible, it also helps in saving a life by allowing medication to be administered in due course. DL has developed into a fantastic tool for medical professionals and researchers to act quickly and decisively with tumour patients. In this paper, we suggest Sustainable development of flexible approach aimed at multi-model organization of brain tumours using the popular deep learning architecture ResNet-50. By leveraging the flexibility of ResNet-50, we aim to achieve improved accuracy and robustness in classifying brain tumours across a diverse range of datasets. Our approach integrates multiple ResNet-50 models, each specialized in identifying specific tumour types, enabling a comprehensive classification framework. Experimental findings show that our strategy is successful and more accurate than other approaches. In this paper we provide an interface that can be used to classify and label the tumours. We used Keras and Tensorflow to create a cutting-edge Convolutional Neural Network (CNN) architecture to categorise 3 kinds of growth or tumours namely - Meningioma, Gliomaand Pituitary using ResNet50 algorithm. It is estimated that this model has a maximum mean accuracy score of 98.88%.

The microstructural evolution of materials during powder metallurgy and ceramic forming processes is a complex phenomenon that spans multiple length scales. In this study, we present a comprehensive multiscale characterization of the microstructural changes occurring during these processes. We employ a combination of advanced experimental techniques, including high-resolution transmission electron microscopy (HRTEM), scanning electron microscopy (SEM), and X-ray diffraction (XRD), to investigate the microstructural features at various length scales. Our results reveal the intricate interplay between grain growth, phase transformation, and defect formation during sintering and forming processes. We observe a strong correlation between the initial powder characteristics, such as particle size and morphology, and the resulting microstructure. Furthermore, we employ phase-field modeling to simulate the microstructural evolution and validate our experimental findings. Our simulations provide insights into the kinetics of grain growth and the role of interfacial energy in governing microstructural changes. The results of this study have significant implications for the design and optimization of powder metallurgy and ceramic forming processes, enabling the tailoring of microstructures for specific applications. This work contributes to the fundamental understanding of microstructural evolution in these processes and paves the way for the development of advanced materials with tailored properties.

Information and telecommunications using traffic signals plays major role in computer vision. Distinguishing objects which are in more modest size is a difficult task. We centre around an exceptional case: Detection and the classification of traffic lights in road sees and gives a guidance to the regulator for semi-autonomous and completely self-governing vehicles. We are introducing a profound learning approach for precise traffic signal discovery in adjusting a Single Shot Detection (SSD) approach SSD performs object proposition creation and order utilizing one single CNN. The first SSD battles in recognizing smaller objects, that which is fundamental for Traffic Light Detection (TLD). By our transformations it is feasible to recognize protests a lot more modest than the ten pixels without expanding the input picture size.

Using natural resources to maximize yields is possible when .precision agriculture is used in a diversified environment. Automating agriculture can reduce resource consumption and enhance food quality. Sowing, monitoring, controlling weeds, managing pests, and harvesting crops are all possible with agricultural robots. To estimate crop production, it is necessary to physically count fruits, flowers, or fruits at various stages of growth. Precision and dependability are provided by remote sensing technologies for agricultural production forecasting and estimation. Automated image analysis using deep learning and computer vision (CV) produces exact field maps. In this review, deep learning (DL) techniques were found to improve the accuracy of smart farming, so we present different methodologies to automate the detection of agricultural yields using virtual analysis and classifiers. The smart farming will generate a sustainable agricultural development.

Concerns about cyber threats have emerged as the expansion of system connectivity and the proliferation of system applications intensified in the industry. This has underscored the necessity for a robust defense mechanism against various cyber threats, including potential intrusions from malicious actors within the network. A specially targeted system is the intrusion detection system (IDS), designed to safeguard the confidentiality, integrity, and availability of network traffic, especially in critical sectors like healthcare. Recent advancements in the area of IDS involve the utilization of artificial intelligence (AI) and deep learning (DL) based IDS to efficiently recognize network issues. Notably, the research at hand adopts a deep learning approach employing Long Short Term Memory (LSTM) models, applied to the CICIDS-2019 dataset that is sourced from New Brunswick University’s website. The focal point of evaluation lies in the precision, recall, F1-score, and accuracy metrics, specifically analyzing its performance in identifying Denial-of-Service (DoS) cyber-attacks. The findings of this study lighten the superior performance of the Long Short Term Memory method in the realm of intrusion detection systems. The LSTM model showcases its proficiency, particularly in discerning Denial of Service attacks by giving a loss of less than 0.03%.

This bibliometric analysis delves into the intricate realm of sustainability within the life cycle assessments (LCAs) of electric vehicles (EVs). Our study navigates the vast body of literature to uncover the evolving landscape, emerging trends, and critical research areas concerning the environmental impact of EVs throughout their entire life cycle. By placing a strong emphasis on sustainability, this analysis sheds light on how EVs can significantly contribute to eco-friendly transportation solutions. A systematic search was conducted using the Web of Science database, covering publications from 2001 to 2023. The search strategy used a combination of keywords related to LCA and EVs. The study found a total of 161 publications that met the inclusion criteria. The analysis revealed that the number of publications on LCA of EVs has been increasing steadily over the years, with a sharp rise in the last decade. The study identified the main research themes in the field, including LCA methodology, environmental impacts, energy use, and policy analysis. The analysis also highlighted the research gaps, such as the lack of studies on social impacts and the need for more comprehensive and comparative assessments. The findings of this study provide insights into the current state of research on LCA of EVs and can guide future research in this field.

This study explores the world of nanostructured materials and new production procedures, revealing how they interact to break down obstacles in materials research. We uncover the confluence between theoretical goals and actual reality by investigating manufacturing processes. The study links manufacturing processes to material characteristics, establishing the groundwork for informed applications in electronics, photonics, sensors, and biology. The empirical journey illuminates problems and opportunities, prompting joint efforts to push these frontiers forward. This article is a testimony to nanostructured materials' transformational potential, challenging researchers and dreamers to redefine technological landscapes by using their unique features and inventive production.

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.

Data analysis is very sophisticated tool in recent corona virus pandemic to find the trend of spreading pattern for controlling the infection. In this perspective, predictive analytics can be useful for data analysis to forecast the corona virus pandemic. This paper presents the infection pattern of corona virus disease, termed as COVID-19 in top seven states in India. Prophet Algorithm forecasting model was used to analyze state-wise spreading pattern of corona virus disease with respect to confirmed, deaths and cured cases. This predictive model can be very helpful to government and health care communities to combat this deadly virus by initiating suitable actions to control its spread.

The utilisation of next-generation superalloys is of utmost significance in the progression of contemporary engineering applications that necessitate extraordinary mechanical strength, stability at elevated temperatures, and resistance to corrosion. The present work aims to conduct a thorough investigation of the microstructural and mechanical properties of these advanced materials, providing insights into their distinct features and possible areas of application. The examination of microstructure involves the utilisation of several methodologies, including scanning electron microscopy (SEM), transmission electron microscopy (TEM), and X-ray diffraction (XRD). The present study comprehensively examines the complicated grain structures, phase compositions, and distribution of strengthening precipitates inside the superalloys using various methodologies. The correlation between processing factors and resultant microstructures is established, facilitating a more profound comprehension of the influence of microstructure on the mechanical properties of the alloy. The knowledge acquired from this investigation into the microstructural and mechanical characteristics of next-generation superalloys provides useful insights for engineers, researchers, and designers engaged in materials development and component design. Through the use of a comprehensive comprehension of the distinctive properties of these alloys, it becomes feasible to expand the limits of performance in exceedingly challenging conditions, so influencing the trajectory of high-temperature engineering applications in the future.

Automation of medical image analysis helps medical practitioners to ensure early detection of certain diseases. Diabetic Retinopathy (DR) is a widespread condition of diabetes mellitus and a main global cause of vision impairment. The manual diagnosis of diabetic retinopathy by ophthalmologists requires a significant amount of time, causing inconvenience and discomfort for patients. However, the use of automated technology makes it possible to quickly identify diabetic retinopathy, permitting the continuation of therapy without interruption and averting future ocular damage. This paper presents a comprehensive comparative analysis of six Convolutional Neural Networks and Deep Neural Networks based machine learning models, including simple CNN, VGG16, DenseNet121, ResNet50, InceptionV3, and EfficientNetB3, for the recognition of diabetic retinopathy using fundus photographs. The accuracy of various models is evaluated using the Cohen Kappa metric. The results of this study add a contribution to the research on the application of machine learning models for diagnosing diabetic retinopathy.

The increased processing of coffee beans has generated huge amount of coffee husk, which are improperly disposed. Inappropriate disposal of coffee husk has led to release of toxic compounds to the environment causing serious environmental concerns. To mitigate the impact of improperly disposed coffee husk, it is suggested for valorisation of the coffee husk. Hence, this study has focussed on identifying the potential of coffee husk in maximizing the sugar yield from it which can be converted to value added product. Deep eutectic solvent (DES) involving choline chloride and lactic acid (ChCl:LA) mixed at 1:4 molar ratio was studied to investigate the effect of DES pretreatment on coffee husk to produce reducing sugar in the hydrolysis process. Pretreatment conditions of the biomass were optimized for biomass loading (5-20%, w/w), temperature (70-120 °C), and duration (60-240 min) using Response Surface Methodology (RSM) for obtaining maximum yield of reducing sugar. The RSM model predicted an optimal pretreatment condition of biomass loading with 20% (w/w), pretreated at 120 °C for 231.80 min to achieve maximum sugar yield (30.522%). The pretreatment effect on biomass composition was analyzed using the Van Soest method, which showed an increase in the cellulose content along with the hemicellulose removal when compared with the native biomass. Moreover, evaluation of chemical structural changes also confirmed the effectiveness of DES pretreatment. Thus, the current study would illustrate the potential of coffee husk to produce value-added compounds from it.

Sustainable development implies development brought to the present society without giving up on the scope of needs that future generation requires. For achieving sustainable development various goals were been fixed and indicators are been used to measure up the progress. Energy utilization is one of the important aspects for development of a country, technology etc. The energy utilized has mostly been fossil based such as coal, oil etc. These sources have been used in electric generation in most countries and crude oil refined petrol and diesel have been used for transporting purpose. The usage of the fossil-based fuels which has more carbon content in them possess the threat of polluting the environment by causing emission into the atmosphere, Moreover the availability of reserves of these sources are also become thin as these are non-renewable source of energy. For overcoming these obstacles, the developed countries are being focussing on renewable sources of energy for managing the energy demand and to have eco-friendly environment. The advancements in the field of bioenergy have contributed energy generation from different sources through various techniques. One of the important aspects about the bioenergy generation is utilizing the waste, end products of the biomass to energy. Various wastages from agro to food industries are not managed as it degrades on its own. However, these organic wastages can be viewed as a source for energy generation. The objective the experiment is analyse tamarind seed, cashew seeds and blend of both in powder form and briquetted for energy generation. The experimental result indicates that the briquetted form of is more suitable in terms of properties with higher fixed carbon contents.

Smart grid technology is rapidly advancing and providing various opportunities for efficient energy management. To achieve the full potential of smart grids, intelligent energy management systems (IEMS) are required that can optimally manage and control the distributed energy resources (DERs). In this paper, proposed an IEMS using the Deep Reinforcement Learning (DRL) algorithm to manage the energy consumption and production in a smart grid. The proposed methodology aims to minimize the energy cost while maintaining the stability and reliability of the grid. The performance of the proposed IEMS is evaluated on a simulated smart grid, and the results show that it can effectively manage the energy resources while minimizing the energy cost.

In the modern age of technology where everything is powered via physical wires, once considered a technical marvel has now become a bane. Wireless power transfer is the key to the future of power transfer, in this paper a 1kW WPT system is designed and simulated for an PMBLDC motor driven autonomous vehicle. This paper focuses in detail about the variation in performance of the coils and the overall system when ferrite cores and aluminum shielding is considered along with the coils. It provides a comprehensive analysis on the misalignment of transmitter and receiver coils in different orientations and distances. The results indicate a significant increase in power transfer and efficiency of the system when extra components such as cores and shielding are also implemented. It is oriented towards increasing the efficiency of powering autonomous vehicles used in various sectors such as automobiles, information technology, aerospace, robotics, precision and domestic equipment, etc.

Lung cancer is a leading cause of cancer-related deaths worldwide, with a high mortality rate and a significant economic burden on health care systems. Traditional screening methods, such as X-rays and CT scans, have limitations in terms of accuracy and efficiency, leading to many cases of lung cancer being diagnosed at a later stage, when treatment options are limited. In this paper, we aim to develop a highly accurate and efficient tool for detecting lung cancer using a NN classifier. We first build a large dataset of medical images and patient data for training and evaluating the NN classifier. The dataset includes a variety of imaging modalities, including CT scans, X-rays, and other medical images. We then develop and train a NN classifier for lung cancer detection, using a deep learning technique. The NN classifiers optimized for high accuracy and efficiency, with the goal of achieving earlier and more accurate diagnosis of lung cancer. We evaluate the performance of the NN classifier using a variety of metrics, including sensitivity, specificity, and area under the receiver operating characteristic curve (AUC-ROC). The classifier is tested on a separate test dataset to ensure that it generalizes well to new data. We also compare the performance of the NN classifier to other traditional screening methods, such as X-rays and CT scans, to determine the potential impact of the NN classifier on lung cancer screening. Finally, we use explainable machine learning technique called as GLCM to identify specific features and patterns in medical images that are indicative of lung cancer. This analysis provides insights into other underlying mechanisms of lung cancer development and may lead to new discoveries and treatment options.

After water, concrete was the material for construction that is used on the largest scale on the planet, and as an outcome of technological development, concrete's qualities have evolved over time. A study was carried out to investigate the potential use of mineral admixture as a partial replacement in high performance concrete. Since they can significantly improve concrete strength and durability properties when compared to regular Portland cement supplemental cementations materials (SCM) like Alccofine (AF) and ground granulated blast furnace slag (GGBFS) have all been used as cement replacements on a large scale over the past three decades. As a result, HPC can be produced using lower water to powder ratios by including other cementitious materials as admixtures. In this project phase, ground granulated blast furnace slag (GGBFS) and Alccofine (AF) have been employed in varied weight ratios to substitute cement to produce high strength M40 grade concrete for 7, 14, and 28 days of compressive strength (CS) as well as split tensile strength (STS) the percentages of replacement of AF of 5%, 7.5% & 10% also GGBFS percentages are 10%, 12.5 % and 15% respectively used in this project work.

A number of power electronics converter topologies are implemented as Wireless Power Transfer (WPT) systems applications expand. In these applications, the optimal converter design is essential for handling the high power and frequency operation. In this paper, Simulation & Hardware development of High frequency Inverter with 90KHz frequency with Pulse Width Modulation switching strategy is presented. The inverter topology is simulated in 2021a version MATLAB/Simulink with R and RL loads and its conversion ability is proven through relevant waveforms. Simultaneously designed and developed prototype model of power rating 100watt with Mosfet as switches at output voltage of 230V with R load and analyse the output behaviour of voltage waveforms at various loads. The outcomes show effective enhancements for the proposed design.

Sheet metal forming operations play a crucial role in the manufacturing process of various products. However, the challenge of plastic instability, which often leads to defective products, continues to persist in this field. It is important to consider various parameters, such as the Forming Limit Diagram (FLD), to overcome this issue during manufacturing. The Aluminium Alloy (AA2014) has been employed in this study to investigate its formability under different temperatures (room temperature, 150 °C, and 300 °C) at the strain rate of 0.1mm/s. The results of the study were obtained by performing stretch forming by utilizing the Nakajima test. The results showed the limiting strains of the material improved with an increase in temperature, and the findings were analyzed through fractography studies performed with a Scanning electron microscope, and simulations were done using LS-dyna software. This study provides valuable insights into the formability of AA 2014 sheets at elevated temperatures and will aid in the development of more efficient and effective sheet metal forming operations.