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High-strength composite materials have been created as a result of the rising need for materials to improve the performance of civil, aeronautical and marine components. Among these, Al Metal Matrix Composites are commonly used to meet engineering requirements. The present research works focuses on manufacturing and exploring Al7075 alloy micro-structure, mechanical and wear characteristics of Silicon Carbide (SiC) particulates with weight percentages of 3, 6 and 9 respectively. Al7075-SiC composites have been produced by using metallurgical route. The microstructural study revealed a uniform distribution of SiCp. The existence of SiC has been affirmed by Energy Dispersive Spectroscopy (EDS) analysis performed on the developed composite (Al7075-9 wt. % SiCp). To study the loss of wear, experiments were performed on a tribometer attached to a hard steel disk. The wear parameters were optimized using the central composite design in response to the surface technique. The obtained results indicated that the mechanical properties of composites improved when SiCp was added. The developed model effectively predicted the wear rate with 95% reliability and results of all models were confirmed by ANOVA (Analysis of Variance).

Indeed, high yielding and high-grade crop production are crucial in modern agriculture, and smart farming technologies like vertical hydroponic farming can play a significant role in achieving these goals. The proposed vertical hydroponic system appears to be quite advanced and promising. Maintaining a cool and controlled environment is essential for optimal plant growth. Grow lights provide the necessary spectrum and intensity for photosynthesis, enabling plants to grow efficiently. Proper monitoring of water levels and moisture content ensures that plants receive an adequate supply of nutrients, preventing over or under-watering. The use of an Arduino controller with a keypad allows for user inputs and customization of the hydroponic system. Farmers can set parameters for water changes, flow rates, and indoor temperature to tailor the system to specific plant requirements. Monitoring and controlling various environmental parameters like light intensity, pH levels, electrical conductivity, water temperature, and relative humidity are essential for achieving the best possible crop yields and quality. Sensors and actuators help achieve this precision. The Internet of Things (IoT) integration allows for real-time data transfer and retrieval. Hydroponic Hardware prototype is proposed by combining these advanced technologies, the system can create an optimal environment for plant growth while minimizing resource wastage and maximizing yields. It’s worth noting that as technology continues to evolve, there will likely be further advancements in smart farming systems.

Technological Pedagogical Content Knowledge (TPACK) studies have surged over the past few years, however, there is a lack of studies that have comprehensively reviewed and synthesized data on teachers’ TPACK self-efficacy. The present review aimed to provide data on research methods, study samples, subject domains, and evaluation approaches used in the TPACK studies to date. The review also aimed to analyze teachers’ TPACK self-efficacy, self-efficacy beliefs, computer self-efficacy, and technology support concerning professional development. Five best bets (most searched databases) were selected on the Electronic Business Source Complete (EBSCO) host platform. An abstract level screening was conducted for 136 peer-reviewed articles, and 75 articles were selected for the detailed screening. The analyses were focused on year-wide appearance of TPACK studies, research methods, study samples, subject domains, and evaluation approaches used. The growth and development of TPACK self-efficacy was examined using the narrative approach. Results indicated that professional development interventions were effective in improving teachers’ TPACK self-efficacy. Also, TPACK-based argumentation practices helped participants strengthen their perceptions toward the integration of technology in classrooms. The implications of the findings for teacher preparation programs and other professional development activities were presented.

Accurate modelling of the wastewater discharge parameters is necessary to lower the operational costs of such a wastewater treatment facility. The article presents a unique method for predicting the effluent prominence features for such an industrialized wastewater treatment facility. Artificial intelligence modelling is one such technique that enables analysis of the pollutant characteristics in treatment facilities and more effective regulation of them during processing. The study suggests using a fuzzy logic approach to regulate biological wastewater treatment. These computer programs are designed to forecast treatment issues in the future intervene promptly and efficiently in the facility, lessen or completely eradicate environmental pollution, enhance the ecosystem, and assess the wastewater treatment plant's treatment efficiency. The study intends to employ fuzzy logic models of artificial intelligence to accomplish the best treatment process, including O&G, BOD, COD, TSS, and TDS, while also assuring that the treated wastewater complies with regulations. Additionally, statistical analysis of the data was performed. The best value and the highest R2 value were discovered; these numbers demonstrate that the expected and investigational performances are comparable, and that the plant's presentation could be correctly predicted utilizing a fuzzy logic model, allowing fuzzy logic to be employed to describe the method.

The automotive industry has undergone a transformation with the growing popularity of electrical vehicles, which provide an eco-friendlier and cleaner substitute for conventional fuel-powered cars. However, the scarcity of EV charging stations is impeding the widespread use of electric vehicles. This research offers a novel wireless EV charging technology that runs on solar energy as a solution to this problem, providing EV owners worldwide with affordable and environmentally friendly options. The wireless power transmission technology at the centre of the suggested system allows EVs to charge without the need for physical hookups. This technology improves user accessibility and safety while doing away with the need for cords. By using solar energy, one can lessen their reliance on traditional power sources increasing the cost- and environmentally-effectiveness of the charging. Comparing this strategy to the conventional plug-in EV charging techniques reveals a number of benefits. The system's ability to charge electric vehicles (EVs) while they are in motion further improves user convenience and reduces discharge time. Solar power offers a sustainable and renewable energy source, minimizing reliance on the power grid and environmental impact. Wireless charging also eliminates the need for physical connections, improving safety and convenience. The purpose of this study is to solve the shortcomings of the conventional electric vehicle systems and offer an innovative solution that will further the development of sustainable transportation. The combination of solar energy and wireless charging for electric vehicles not only promotes the use of sustainable energy. It also encourages the growth of an energy ecosystem that is more flexible and interconnected, paving the path for more environmentally friendly and effective city transport in the future.

Direct Metal Deposition (DMD) is a metal Additive Manufacturing (AM) process. It is used for producing sustainability Functionally Graded Materials (FGM) and repairing of sophisticated parts. In this present research, a commercially available DMD machine deposited three partial FGM blocks of size 26 mm wide × 34 mm thick × 32 mm heights. The commonly influence parameters on Ultimate Tensile Strength (UTS) are scan velocity and laser power. The powders used for deposition were Stainless Steel 316L (SS316L), Inconel 625 (IN625), and their three different compositions. ASTM E8 tensile samples were cut from those blocks by wire cut-EDM. Micro-tensile tests were carried out on ASTM E8 samples using a SHIMADZU micro-tensile machine. The results revealed that partial FGM sample-2 had high sustainability UTS of 532 MPa as compared to remaining two samples. It is illustrated that for joining two dissimilar materials to obtain high UTS thick layered (i.e., thickness more than 1 mm) gradient path method should be selected at the medium laser power available on the DMD machine. However, the sample-3 has higher hardness at high laser power.

The current work focuses on using rectangular inserts with water as the working fluid to investigate heat transfer experimentally and numerically in a horizontal circular tube [10]. The study considers variations in the number of rectangular slots within the same strip. The tube has dimensions of 0.0266 m diameter. Twisted tapes, featuring rectangular cuts, are constructed from a 3 mm thick Stainless-steel strip with a length of 1035 mm. For experimentation, two different types of rectangular inserts were considered: one with nine cuts and the other with eighteen cuts. The dimensions of the cuts were as follows: length of 1000 mm, width of 13 mm, depth of cut 8 mm, and thickness 3 mm. The heat flux applied to the horizontal tube was steady and uniform. The range of the Reynolds number was 9,000–19,000. A plain tube without an insert was used to compare the results. The horizontal tube along with rectangular insert was modelled in Ansys Fluent software with fine meshing and analysed. Initially, CFD analysis was done for plain tube with and without insert, and results have been verified by comparison with experimental values. A comprehensive comparison is established, evaluating different heat transfer parameters, including convective heat transfer coefficient (h), heat transfer rate (q), and Nusselt number (Nu).

Mobiles are becoming a part of our daily life and the usages of mobile phones are increasing day by day to simplify our lifestyle. The charging in the battery is very important to operate the mobile. However, the availability of charging points is very difficult to find in public places including bus stands, railway stations, most populated areas etc. Hence installing a charging device to charge various mobiles effectively is needed at many places in the society. Moreover, unlimited power supply is required to such a device for an effective operation. Further the utilizing of renewable energy sources to provide sufficient power supply to the charging device will be more effective. Renewable energy sources such as Photovoltaic (PV) and wind based hybrid power supply systemsare incorporated into the charging device to supply required reliable power. However, different voltage levels are required to charge batteries of various mobiles. Hence, a multi-output dc to dc converter along with a master control unit is implemented in this paper. OPAL RT modules are utilized to create a Hardware-in-the-Loop (HIL) system, which showcases outcomes across different operational modes.

The long transmission system is required to transfer generated power to distribution system. Hence, maximum power transfer devices with flexible operation are essential in transmission system. Thus, it is necessary to add Flexible AC Transmission System (FACTS) devices at the proper locations. Unified Power Flow Controller (UPFC) is the best FACTS transmission system device for ensuring adequate power flow between generating and distribution via transmission lines. The UPFC consists of two converters which likely to work as STATCOM and SSSC to make a proper power flow. In this paper modeling and analysis of UPFC is presented under platform of MATLAB/Simulink. Extensive responses are incorporated in results section with proper analysis under various operating conditions.

The inverter system is an important piece of equipment for efficiently absorbing the electricity generated from renewable sources and ensuring reliable grid connections. As power electronics advance, inverter conversion efficiency is improving and photovoltaic (PV) energy is becoming a major contributor to the global supply of renewable energy. However, a key consideration is how reliable the PV inverters are. The dependability performance of PV inverters can be influenced by various environmental parameters, such as solar radiation and temperature (collectively referred to as Mission Profile). Given that these conditions vary by location, it is necessary to take them into account when evaluating the dependability performance of a PV inverter. The Mission Profile, including the local solar radiation and surrounding temperature, are important factors determining the lifespan of a PV inverter. This research examines how environmental variables and geographic position affect the dependability performance of PV converters and finds that these elements have a significant bearing on that performance.

The comprehensive study explores the vital role of the batteries in sustainable development. It evaluates the performance of batteries in both internal combustion engine (ICE) and Electric Vehicles (EVs) while emphasizing the environmental benefits of Green Batteries. The study also explores the battery maintenance and recycling practices for everyday automotive use. Based on lithium-ion battery data, it underscores the urgent need for effective recycling solutions and the rising battery recycling sector. The environmental impacts of battery production, especially for EVs, are recognised, emphasising the importance of transition to renewable energy sources. A comparison between fossil and nonfossil fuels demonstrates the potential benefits of cleaner energy sources. The study also compares the performance of conventional batteries with green batteries, and ultimately recommends the adoption of green batteries powered by renewable energy as a sustainable solution.

The transition to sustainable urban mobility solutions is imperative in the face of escalating environmental and health challenges. This paper reviews the work in the areas of decarbonizing power systems, urban mobility and transportation modes, and the integration of renewable energy in smart cities. The integration of renewable energy sources, such as wind and solar power, into power systems is pivotal for decarbonization. However, their intermittent nature poses challenges for high-level integration. Energy storage emerges as a crucial solution, with technologies like fuel cell vehicles, water electrolysis, and flow batteries playing significant roles. Urban mobility, dominated by internal combustion engines, has led to environmental degradation and health issues. Transitioning to cleaner transportation modes, integrating electric vehicle stations with renewable energy, and ensuring uninterrupted power supply are essential steps forward. Furthermore, the potential of smart cities harnessing non-dispatchable photovoltaic and wind power is vast. Yet, the challenge of large-scale energy storage remains. The concept of using renewable energy to produce hydrogen, which can then stabilize the grid, offers a promising avenue. This review underscores the importance of integrating hydrogen energy storage to revolutionize urban mobility solutions.

The CO2 emissions from cement have had significant negative consequences on the environment, necessitating the substitution of cement in the production of concrete. This research tends to cover the mechanical characteristics and microstructural studies of concrete formed by replacing some of the cement with Alccofine (AL) by percentages of 5%, 10%, 15%, 20%, 25% and 30% and some of fine aggregate with Glass Powder (GP) by percentages of 10%, 20%, 30%, 40% and 50% in the concrete of the M40 grade. A pozzolanic substance called alccofine and high silica content material called glass powder was extensively utilized in the building industry. When the mechanical properties of concrete were tested at various time periods, the optimal partial cement and fine aggregate substitution was determined to be 20% Alccofine and 50% of Glass Powder. When compared to conventional concrete of the same ratio, the compressive strength of the concrete has been enhanced at 20 percent replacement of AL and 50 percent replacement of GP. Moreover, concrete was subjected to a microstructural examination to observe the hydration process at various time intervals, and the results clearly demonstrated how pores and C-S-H (calcium-silicate hydrate) gel are created in both early ages and later ages. According to the study's findings, cement may be replaced in part without impairing its properties and can help lessen the impact of CO2 on the environment.

The cockle shell is categorized as industrial waste and often disposed of in open dumping zones, especially in coastal areas. Therefore, an eco-friendly solution to this disposal issue is crucial, as it can transform waste into a high-value product, such as a partial cement replacement. Also, as there is a growing interest in using alternatives to sand as a replacement for fine aggregate in concrete, in this study we used plastic coated sea sand as a partial replacement for fine aggregate. The design mix is formulated for a non-traffic application, with varying percentages of cockle shell ash and plastic-coated sea sand replacing cement and fine aggregate, respectively. Two curing methods, normal water curing and accelerated curing, are applied. The samples are then tested for compression strength and water absorption after 28 days of curing. The findings indicate that compressive strength value increased with increase in both cockle shell ash content as well as plastic coated sea sand content. The water absorption value increased with increase in cockle shell ash content and decreased with increase in plastic coated sea sand content. Hence, this composite material is suitable for use in paver blocks.

Greenhouse cultivation plays a pivotal role in modern agriculture by providing a controlled environment for plant growth. The significance of greenhouse insulation lies in its ability to create optimal conditions for plant development, ensuring increased crop productivity and quality. This paper emphasizes the criticality of greenhouse insulation and the necessity for effective predictive models to anticipate plant growth and yield accurately. This research proposes the utilization of Machine Learning (ML)and Deep Learning (DL) techniques to forecast plant growth and yield within controlled greenhouse settings. To achieve this, a novel deep recurrent neural network (RNN) architecture employing the Long Short-Term Memory (LSTM) neuron model is deployed in the prediction process.The study offers a comparative analysis involving various ML methodologies such as support vector regression and random forest regression. The performance evaluation of these diverse methods is conducted using the mean square error criterion to assess their effectivenessin predicting plant growth and yield. The model's sophisticated architecture enables it to produce accurate and timely predictions regarding specific growth parameters by leveraging an advanced neural network. This holistic approach introduces a novel perspective in greenhouse tomato cultivation, providing growers with valuable insights to facilitate informed decision-making, streamline resource distribution, and promote heightened agricultural sustainability.

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.

Nowadays, there is an increase in vehicle traffic everywhere, particularly in cities. In the future, intelligent traffic control will become a critical issue as the number of road users continues to rise. Traffic congestion is starting to become a major problem. Many cars are stuck at traffic signals for extended periods of time, which increases human time consumption and causes numerous issues for those who commute to work and for business purposes. The current system does not regulate traffic in accordance with density and does not lessen the impact of traffic in urban areas. Nowadays, there is an increase in vehicle traffic everywhere, particularly in cities. So by using image processing based on the density the signal will display along with the timer and also has the priority for the emergency vehicles.

This paper presents a method for measuring a wind turbine's MPP (Maximum Power Point) with a three-bladed VSWT that is driven by a DFIG (Double-Fed Induction Generator) (Variable Speed Wind Turbine). The rotor incorporates a DFIG with successive PWM voltage-source converters within its integrated rotor integrated circuit. Grid-side converters generate and establish sinusoidal currents while independently managing the active and reactive power drawn from the grid supply using vector control techniques. The rotor-side converter needs to be vector controlled in order for the speed range to function. The vector approach manages the permit circuit optimally to maximize the amount of wind energy gathered. The control needs of the permit circuit take this into account. Such a system is analyzed using MATLAB software

Brain tumor identification, along with an investigation, is harmful to the patient. Segmentation, therefore, of paying attention to near-neighborhood growth remains accurate, effective, and healthy. Fully Convolution Neural Network (FCNN) is a reliable picture model to capitulate the hide quality. The form of the multifaceted with the incessant pixels taught with the crest state and the symbolic picture taught. In this research, the making of a totally convoluted method to obtain the participation of a random element and the production of correspondingly large-scale output with a resourceful assumption and information.. The approach has had several difficulties, as measurements are accurate for a variety of images. The improvement in the mortality rate of the programmed order is a critical condition. The scheduling of the mind tumor is an exceedingly troublesome task in the exceptional spatial and basic fluctuation that accompanies the local brain tumor. In this research, a programmed detection of Brain tumors proposed using the characterization of CNN. The most critical method of construction is the completion of the use of small holes. CNN's has less predictability and 97.5 accuracies.

Cloud computing has evolved as an effective technology in the past few years. It is playing an important role in information technology. The main characteristics and challenges of cloud computing are 100% availability and Scalability. To achieve these two things we have concepts like Load balancing and auto-scaling in the cloud. In this paper, we will discuss auto-scaling and the two different ways to achieve auto-scaling. The commonly used auto-scaling technique is fixed threshold-based auto-scaling and it is further modified as adaptive threshold-based auto-scaling. Auto-scaling can scale up or down the cloud resources based on demand, which means the virtual machines are automatically launched or deleted based on the requirement. As Auto-scaling provides better fault tolerance, better availability, and cost management, it improves an organization’s service level agreements.

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.

In the realm of financial markets, understanding the interplay between mutual fund investment growth and equity market performance is paramount for investors, fund managers, and policymakers alike. This empirical study embarks on a comprehensive exploration of this intricate relationship, employing advanced econometric techniques to dissect historical data and uncover underlying patterns and dynamics. Building upon hypotheses positing significant associations between mutual fund investment growth and equity market performance, the study delves into regression analysis and statistical modelling to discern causality and quantify the impact of mutual fund investments on equity market behavior. In addition, the study investigates the influence of mutual fund performance metrics, such as annualized return, on equity market returns, shedding light on the part of mutual funds in shaping market outcomes. Despite inherent limitations, including data constraints and potential endogeneity issues, the findings contribute valuable empirical evidence and actionable insights for stakeholders navigating financial markets. Through its rigorous empirical approach and robust analytical framework, this study advances financial research and informs decision-making processes, empowering stakeholders to navigate the complex landscape of investments and market participation effectively.The findings contribute to a deeper understanding of how mutual fund investments impact equity market performance, offering valuable implications for the various players in navigating financial markets.

A worldwide issue affecting the world is waste disposal; hence, the need to construct a waste detection system that is based on deep learning. Ten wastes are evaluated in this article with a view of providing an integrated framework for measurement and evaluation. It also offers a comprehensive account of the wastes studied further by looking at its detail finding and the challenges they pose as well as views associated with waste detection. Recycling both has economical and ecological benefits which help to eliminate pollution. Technology can now be used to turn waste into resource materials thus recycling more than before especially through the use of deep learning among other technologies.

An instable stimulated vibration resulting from the combination of aerodynamic, inertia, and aeroelastic forces is known as "active flutter" in the context of aircraft wing structures. The primary destabilizing element that the airplane structures display and which causes active flutter is limit cycle oscillation. Ultimately, it results in fatiguerelated harm and catastrophic aircraft mishaps. Two modern techniques, namely enhanced aero-elastic analysis and flutter control for wings and panel structures, are established by means of these developing technologies. comparable stiffness values and comparable stresses are assessed, together with theoretical and practical approaches, to enhance the features of aero-elastic analysis of aircraft structures. Additionally, it makes reducing flutter mandatory in order to enhance aero-elastic stability. Using Ansys software, a thorough FE analysis is carried out to improve and analyze the benefits of Al7075+ SiC alloy aircraft wing structure. With the aid of Ansys software, a thorough FE study is carried out to enhance their aeroelastic stability and to offer recommendations for the design of revised wings and panel structures. For the chosen NACA4412 aircraft wing, modal analysis is carried out, and frequencies are assessed. The advantages of AL7075+ SiC alloy material will be examined with equal stress, equivalent strain, and equivalent stiffness.

Translational CT (TCT), in developing nations, a low-end computed tomography (CT) technology are relatively common. The limited-angle TCT scanning mode is often used with large-angle scanning to scan items within a narrow angular range, reduce X-ray radiation, scan long objects, and prevent detector discrepancies.. However, this scanning mode greatly reduces the picture quality and diagnostic accuracy due to the added noise and limited-angle distortions. A U-net convolutional neural network-based approach for limited-angle TCT image reconstruction has been created to reconstruct a high-quality image for the limited-angle TCT scanning mode (CNN). The limited-angle TCT projection data are first examined using the SART method, and the resulting picture is then fed into a trained CNN that can reduce artifacts and maintain structures to provide a better reconstructed image. Simulated studies are used to demonstrate the effectiveness of the algorithm designed for the limitedangle TCT scanning mode. In contrast to certain modern techniques, the developed algorithm considerably lowers noise and limited-angle artifacts while maintaining image structures.

The desire for a carbon-free planet has prompted a few experts to advocate for the most abundant energy known to us i.e Solar Energy. There are lots of solar powered energy systems in which the solar chimney power plant has been significant among passive methods in generating carbon free power. There are many attempts taken both experimentally as well as theoretically in developing efficient solar chimneys. This article reviews the major activities carried out in the various topographical regions covering significant research articles. In addition, the survey focuses on experimental research, understanding and recreational works, applications, and analogous work references, all of which are based on a broad range reference. Further, discussion on the research gap in each of the section is included with recommendations for future enhancement of this technology.

The foundation of a country’s sustainable economic development rests on the availability of an ample supply of electrical energy. To remain competitive on the global stage, India must ensure the provision of sufficient electricity infrastructure to fuel its economic progress. Achieving developed nation status necessitates extending electricity access to every corner of the country. India, being primarily an agrarian nation, possesses abundant agricultural waste resources. This wealth of agricultural waste presents a valuable opportunity for electricity generation. Notably, agricultural husk emerges as a promising raw material for electricity generation, provided it undergoes proper and systematic processing. This endeavor aims to introduce the concept of electricity generation from agricultural husk, with a particular focus on rural areas in India. Agricultural husk-based small and medium power plants offer a pragmatic solution for generating and distributing electricity to underserved rural communities. Within this paper, we delve into the availability of agricultural husk resources in India, explore various feasibility studies, and elucidate the principal processes for generating electricity from rice husk. The overarching goal is to support India’s journey towards future development. This paper serves as a valuable resource for both governmental and private sector entities interested in establishing agricultural husk-based power plants in rural India. Ultimately, we aspire for this document to serve as a practical guideline for addressing the substantial electricity demand prevalent in India’s rural areas.

This paper explores the field of data analytics for dynamic urban operations and provides a systematic analysis of the importance and possible implications of this field. Our investigation indicates significant data volumes in an urban setting that is data-rich: 500 GB are generated by traffic sensors, 300 GB by environmental monitors, 150 GB by mobile apps, and 75 GB by emergency calls. A variety of analytics techniques, each with a different processing time, are built upon these data sources. These techniques include descriptive, predictive, prescriptive, and diagnostic analytics. The outcomes, which include 90% accuracy, an average processing time of 40 minutes, 80% resource utilization, and 4.2 user satisfaction ratings, highlight the benefits of data analytics. According to the comparison study, diagnostic analytics has a score of 7.8, indicating room for development, while prescriptive analytics leads with an efficiency score of 8.4. As urban stakeholders and academics work to improve urban systems and solve urban issues, the results give a thorough understanding of the effectiveness and application of data analytics in the context of dynamic urban operations.

Peste des petits ruminants (PPR) or goat plague is considered a leading, highly contagious, and most lethal infectious viral disease of small ruminants affecting the worldwide livestock economy and international animal trade. Although sheep and goats are the primarily affected, the PPR Virus (PPRV) host range has expanded to other livestock (large ruminants) and wildlife animals over the last few decades, resulting in serious concern to the ongoing PPR global eradication program, which is primarily optimized, designed, and targeted towards accessible sheep and goat population. A systematic review and meta-analysis study was conducted to estimate the prevalence and spill-over infection of PPRV in large ruminants (bovine and camel) and wildlife. Published articles from 2001 to October 2021 on the "PPR" were searched in four electronic databases of PubMed, Scopus, Science direct, and Google Scholars. The articles were then selected using inclusion criteria (detection/prevalence of PPRV in bovine, camel, and wildlife population), exclusion criteria (only sheep or goats, lack of prevalence data, experimental trial, test evaluation, and reviews written in other languages or published before 2001), and the prevalence was estimated by random effect meta-analysis model. In the current study, all published articles belonged to Africa and Asia. The overall pooled prevalence of PPR estimates was 24% (95% CI: 15-33), with 30% in Asia (95% CI: 14-49) and 20% in Africa (95% CI: 11-30). The overall estimated pooled prevalence at an Africa-Asia level in bovine and camel was 13% (95% CI: 8-19), and in wildlife, it was 52% (95% CI: 30-74) with significant heterogeneity (I2 = 97%) in most pooled estimates with a high prevalence in atypical hosts and wildlife across Asia and Africa. Over the last two decades, the host range has increased drastically in the wildlife population, even for prevalent PPR in the unnatural hosts only for a short time, contributing to virus persistence in multi-host systems with an impact on PPR control and eradication program. This observation on the epidemiology of the PPRV in unnatural hosts demands appropriate intervention strategies, particularly at the livestock-wildlife interface.

The primary motive of this research was to check for the presence of microbial species which has the ability to produce biosurfactant. The soil samples were collected from petrol bunk which was contaminated with petroleum oil and enrichment technique was carried out with minimal salt medium (MSM) for isolation of microbial strain. Serial dilutions of the enriched culture were performed. Two bacterial isolates and one fungal isolate showed activity for biosurfactant production. The methods employed for screening were oil displacement, oil drop collapse, blood hemolysis, blue agar plate method and confirmatory tests like phenol-sulphuric acid method. Both, bacterial isolates (MB1 and MB2) and fungal isolate (JAD1) showed emulsification activity of 41.66 %, 64.70 % and 90.90 % respectively. Biosurfactant was extracted and screened for antimicrobial activity against Gram positive and Gram-negative clinical pathogens. These biosurfactant producing isolates can be used to enhance in situ bioremediation of soils contaminated with polycyclic aromatic hydrocarbons (PAHs).

Pyoderma gangrenosum (PG) is a rare neutrophilic disorder with an incidence rate of 3–10 cases per million per year, characterized by classically painful and aseptic ulcers, which may be associated with underlying systemic diseases. The pathergy reaction is seen in one-fourth of patients with PG. Accurate and timely diagnosis is crucial, as PG is known for its rapid progression. The management of PG is challenging and depends on its severity and rate of progression. An underlying systemic involvement should be sought even in spite of no symptoms. Herein, we report a case of giant pyoderma gangrenosum involving almost the entire left buttock with exceptionally raised c-ANCA levels, but no underlying systemic abnormality. The patient reported intense pain, rapid progression of the ulcers, an inability to perform daily activities, was significantly morbid and pathergy-positive. Aggressive and early management is required in cases such as this. A dramatic response was achieved with a combination of cyclosporine, dapsone, and methylprednisolone pulses.

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 state of Tamilnadu is located along the south west of Indian peninsula. The state has nearly 40 river basin and most of them are seasonal. The river Thamirabarani is one of the perennial rivers covering 3 districts namely Tenkasi, Tirunelveli and Tuticorin. The river originates from the eastern side of the west coast and confluences with Bay of Bengal at a village called Punnakayal in Tuticorin district. The Thamirabarani river is known for its irrigation potential in the upper reaches. By the time it reaches the lower end, the flow is very low. The east coast is well known for its littoral drift which is dominantly towards the north direction. The village is famous for its fishing potential. The study area of Punnakayal is influenced by two monsoons namely south west and north east. Due to absence of high flow in the non-monsoon period, the estuaries are highly blocked by sand bar formation. Because of this sand bar formation, the fisherman find it difficult to go in to deep sea during low tide time. Hence studies were made and remedial measures were suggested and completed. The details of hydrologic analyses, hydraulic modelling, hydrodynamic studies, assessment of littoral drift remedial measures and post project performance are detailed in the paper.

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.

Diabetes during pregnancy is a major source of health problems in unborn infants and their moms. Because gestational diabetes can develop to permanent diabetes, ML is an important method for predicting the likelihood of such progression based on the given features. Although the current study may predict lifelong diabetes in pregnant women, it cannot predict the likelihood of neonatal diabetes. As a result, new characteristics are required to improve the forecasting of neonatal mellitus and provide the most accurate and feasible diabetes persistence results in pregnant women. Python scripting and the application of Machine Learning methods such as SVM, KNN, and LR can assist in achieving this aim. The preprocessing ML dataset focusing on Diabetes from the Pima Indian diabetes database collected through Kaggle. In addition, two new attributes were added to the paper’s dataset. According to research, machine learning models using characteristics like SVM and decision trees may successfully predict the risk of diabetes in pregnant women. Various factors have been used to predict the beginning of this condition during pregnancy.

Every area of engineering is advancing, which is driving up demand for more compact and efficient heat transfer devices. Pulsating heat pipe development results from this (PHP). For managing moderate to high heat fluxes, PHP is a passive two-phase heat transfer device best suited for power electronics and related applications. A tiny diameter tube that is closed end to end to form a loop, is typically used. After being evacuated, the tube is partially filled with a working fluid. The applied heat flux determines the internal flow patterns in a PHP. The thermo-hydrodynamic properties of these devices are highlighted in this work. According to the state of the art, the device has to satisfy at least three thermo-mechanical boundary criteria in order to work as a pulsing heat pipe. Internal tube diameter, applied heat flux, and filling ratio are examples of this. The number of turns and thermo-physical characteristics of the working fluid also have a significant impact on the thermal behaviour. In addition, the report offers a review of previous studies on the use of pulsing heat pipes. Lastly, open questions regarding the process by which PHP operates with various working fluids, validation methods, and applications are explored.

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.

The EcoSense project intends to create a sustainable IoT system to detect and track whether living circumstances are suitable for individuals. The system gathers sensor data and measures important aspects including temperature, air quality, and noise level. The degree of sustainable air pollution is determined by air pollution sensors, which monitor several dangerous chemicals including CO and CO2 in the atmosphere. The noise sensor measures the level of noise and pinpoints the source of excessive noise. Environmental temperature and humidity are measured using sensors. These sensors sustainably and continuously track all of these parameters, sending out notifications when specified thresholds are reached or exceeded. In order to assess the habitat’s general compatibility, the sensor data is processed, saved in the cloud, and then examined. Following that, this information is used to give locals immediate feedback so they may decide for themselves how best to enhance their living arrangements as well as the environment and public health. their general wellbeing. This initiative helps to spread the word about living sustainably. The EcoSense initiative has the potential to have a big impact on the environmental sustainability industry. The initiative can assist people and communities in making educated decisions about their environment and taking constructive action to enhance it by offering real-time data on quality of life. The project has the potential to increase public awareness of environmental problems and inspire individuals to take action to safeguard the environment.

Credit card fraud is a major problem that has caused several challenges for practitioners in the accounting and finance industry due to a large number of daily transactions as well as the difficulties encountered in identifying fraudulent transactions. The purpose of this study is to investigate the application of artificial intelligence techniques as a fraud detection mechanism that can effectively and efficiently detect credit card fraud and identify fraudulent financial transactions. The data was acquired from 100 respondents across the accounting and finance industry and analysed using SPSS. Researcher analysed the data using regression analysis, Pearson correlation coefficient, and reliability analysis. Findings revealed that the three artificial intelligence techniques machine learning, data mining, and fuzzy logic have a significant positive relationship with credit card fraud detection. However, fuzzy logic was discovered to be the least utilized by experts due to its low accuracy/precision in comparison with machine learning and data mining. Based on these findings, our study concludes that the application of artificial intelligence techniques provides experts with better accuracy and efficiency in detecting fraudulent transactions. Therefore, it is recommended that fraud examiners, auditors, accountants, bankers, and organizations should implement and apply artificial intelligence techniques in order to spot anomalies faster and identify fraudulent financial transactions effectively and efficiently.

Banks and other financial institutions are required by law to comply with AML requirements to guarantee they do not facilitate money laundering. However, financial institutions are exceptionally susceptible to money laundering activities in Malaysia. With this concern, this project aimed to identify the determinants which contribute to money laundering activities among commercial banks in Malaysia. Since there are only a few studies have analysed various factors that influence money laundering, this study intends to achieve the aim of the study which is to figure out the level of influence that the factors identified as independent variables on the dependent variable, money laundering. In this study, technology, legal framework, income, and ethical behaviour are acknowledged as the independent variables. A total of 102 survey questionnaires were delivered to commercial bank employees via a convenience sample strategy. Descriptive and inferential statistics were compiled by entering the replies of a total of 102 respondents one by one into SPSS. The results revealed that there is a positive relationship and correlation between all four independent variables; technology, legal framework, income and ethical behaviour towards the dependent variable, money laundering. Consequently, the null hypothesis formulated for this research has been disproved, however, the alternative hypotheses have all been accepted and validated by the data analysis conducted. In addition, this research is claimed to be capable of resolving money laundering difficulties and educating commercial banks on how to identify and prevent money laundering in their organisations.

In recent times, many of industrial applications require smooth control over a wide range of speeds or smooth torque control without effecting motor performance and efficiency. So, cascaded H-Bridge multilevel inverters convert the low or medium level voltage to medium or high level of output voltages. In this paper, a cascaded H-Bridge multilevel seven level inverter is operated by using sinusoidal pulse width modulation technique. This PWM technique will improves the smoothness of waveform by reducing the lower order harmonics. And analysed its performances like speed, torque, THD etc.

Because of the rising worry about global warming and the depletion of fossil fuel supplies, many individuals are looking into sustainable energy alternatives to protect the environment for future generations. Because of the situation of our deteriorating globe, eco-friendly items are more in demand than ever. The outcomes of using PI and PID controllers in solar and wind integration are compared in this research. This investigation contains a boost converter, an inverter, a converter, and three induction motors. Wind and solar energy are transformed to DC voltage, then to AC voltage via an inverter, and ultimately to a three-phase induction motor. The boost converter receives output from the wind energy conversion system (WECS). The solar output is routed to the three-phase complete converter. Sinusoidal pulse width modulation used in generating trigger pulses to the inverter. The output of the boost converter is fed into the common dc link by the three phase Sinusoidal pulse width modulation (SPWM) inverter, which converts its dc input to three phase AC output. This system's behaviour is examined using MATLAB Simulink.

The soils of Indian Black Cotton are well-known examples of Expansive soils, which display significant swelling and shrinkage in response to changes in moisture content. The distressing cyclical swelling and shrinking of soils under moisture fluctuations lead to serious failures. Stabilization of soil can be used to improve it in order to get around this. An agricultural industrial waste called rice husk ash is created when rice husk, a byproduct of paddy milling, is burned. In delta regions, it is widely distributed. It is a pozzolanic material that is widely distributed in India and has potential use in a number of geotechnical areas. In this stabilisation process, black cotton soil is mixed with RHA-lime and various percentages of cementitious material, particularly lime. Tests such as plasticity, compaction, swell, and CBR were also conducted with different water contents of OMCs (such as OMC + 2% water content and OMC + 5% water content). From the test findings, it can be concluded that adding lime reduces flexibility and increases strength. With the addition of RHA and lime, expansive soil gets higher CBR values at varied water contents more than 50% for dosages of 10% lime + 20% RHA and 10% lime + 30% RHA. The expansive soil becomes non-plastic, non-swelling, and has increased strengths, making it suitable for use as a building material for roads.

This paper presents the simulation of a 15-level cross-H bridge multilevel inverter using control techniques like sinusoidal pulse width modulation and third harmonic injection pulse width modulation. This paper aims to enhance output voltage level using fewer switches and to decrease total harmonic distortion by improving overall efficiency. Compared to other inverter topologies cross H bridge topology uses a lesser number of switches and dc voltages. Analysis and simulation of 15-level cross H bridge MLI are performed and presented.

In recent years, the power system has been evolved into micro grids, which are little pockets of self-contained entities. Different distributed, interconnected generation units, loads, and energy storage units make up a typical microgrid system. The increased energy efficiency of these units on micro grids is gaining popularity Day-by-Day. Because of their stochastic behavior, renewable generation causes an imbalance in the power system, which needs microgrid energy management. An efficient energy management system for a small-scale Hybrid Wind-Solar- Battery based microgrid is proposed in this paper. The wind and solar energy conversion systems and battery storage system have been developed along with power electronic converters, control algorithms and controllers to test the operation of hybrid microgrid. The power balance is maintained by an energy management system for the variations of renewable energy power generation and also for the load demand variations. In this system, for reliable and stable operation of a microgrid with multiple DGs, coordinated control of energy management system is proposed. The control algorithms of microgrid system are verified by Matlab Simulation.

Now-a-days, non-conventional energy sources are playing a vital role in the generation of electrical energy. This is due to the disadvantages in the conventional sources like pollution, non-availability of the resources, greenhouse effect. Hence more concentration is made by the researcher’s on these non-conventional sources so called as renewable energy sources. These renewable sources are used with the converters along with filters to give the best output. In this work, we are focusing on the renewable energy called solar energy and hence the converter is inverter in this case. Basically we have conventional inverters as well as multilevel inverters. Multilevel inverters are basically classified into three types and they are cascaded H-bridge multilevel inverter, diode clamped multilevel inverter, flying capacitor multilevel inverter. Among these cascaded multilevel inverter as many advantages like reduced number of components. This advantage is being used in this work and is simulated by using MATLAB.

Martensitic stainless steels find less application in commercial products as they have high hardness, strength, and wear resistance. It lacks in ductility and exhibits moderate corrosion resistance compared to other stainless steels. As a result, annealing process were been used to strengthen the ductility and maintain stability in hardness of martensitic stainless steel material. AISI 410 was chosen for this research work and the samples were made to cylindrical shape for the following dimensions: 50 mm length and 08 mm diameter. The specimens were annealed at temperatures of 730°C, 830°C, and 930°C. The untreated material is kept aside for results comparison. All specimens were subjected to wear test using a pin on disc wear test apparatus. All the specimens were examined using a scanning electron microscope for the surface morphological changes. The outcomes were compared and the best specimen for the required application was chosen. It was discovered that there was a phase change from the martensite stage to the residual austenite stage.**

Stainless steel is a popular material used in various industries due to its excellent corrosion resistance, high strength, and durability. Gas nitriding has been found significantly to improve wear resistance. Chosen for this research work AISI 304 were examined and the impact of gas nitriding to 12 hrs, 24 hrs and 36 hrs were analyzed for better wear behavior. To evaluate the tribological behavior, wear tests were carried out using pin on disc tribometer. The samples were undergone with hardness tests and the microstructures were examined through scanning electron microscope The difference between the compound layer and the diffusion layer were analyzed on the microstructure. The results of untreated samples and nitride samples were compared.

Present days renewable energy sources based grids are developing which needs inverters to convert power from DC to AC. 2-level inverters were used for this purpose to overcome the drawbacks of 2-level inverters multilevel inverters are developed. Because of the combination of numerous devices in a series structure, multilevel inverters provide outstanding solutions to high voltage, high power applications. With the use of suitable simulations and mathematical analysis, this paper compares the performance of 19 and 21-level cascaded H-Bridges with cross H-Bridge MLI. Total Harmonic Distortion levels, switching device count, and inverter output voltage and current are all compared. The primary issues brought up in this study are limitations of a number of switching devices that can support high voltage in the inverter. The benefit of this research is that it identifies the right inverter that can be utilised for real-time applications by taking the variables into account. The count of switching devices, output voltage, current, and harmonic distortion, among other factors. The Matlab/Simulink Platform is used to validate the analysis.