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The modern lifestyle and work culture are impelling the human beings into various health issues, both physically and mentally. Schizophrenia is one such prevalent mental illness that most adults are facing these days. The most common symptoms that Schizophrenic patients exhibit are classified into positive and negative symptoms. The positive symptoms include hallucinations, abnormal movement, delusions and disorganized thinking. The negative symptoms involve Anhedonia, Alogia, Asociality, Avolition, etc. The patients’ exhibit assorted such symptoms and involves several such features namely cognition, language, emotion, interest levels and motor inhibitions. It is a psychiatric disorder of brain, and the traditional machine learning models are unable to handle the high dimensional non-linear nature of the brain and understand Schizophrenia to the full extent. The existing methods like Hopfield Networks, CNN, ANN and RNN are implemented to identify Schizophrenia by considering only one of the aforementioned symptoms. Multi-view learning model is employed in this article for early prognosis of Schizophrenia taking into consideration multiple aspects like Electroencephalogram (EEG) pursuing the electrical activity records of brain, Functional Magnetic Resonance Imaging (fMRI) measuring the change in blood flow in response to the activity of brain, Electronic Health Record (EHR) that captures the digitized patient’s health record, Actigraph data that records the motor inhibitions from wearable sensor devices. It is identified that the multi-view learning model outperformed the existing methods when compared against Accuracy, Recall Precision and F1 Score. The proposed method obtained these metrics with values 93.6%, 93.9%, 88.7% and 90.8% respectively.

This paper presents a study on the integration of fuel cell and electrolyzer systems for efficient renewable energy storage and conversion. The increasing reliance on renewable energy sources such as solar and wind power necessitates the development of robust energy storage solutions to manage the intermittent nature of these resources. An integrated system combining electrolyzers and fuel cells offers a promising solution by converting excess renewable energy into hydrogen via electrolysis, which can be stored and later converted back into electricity using fuel cells during periods of low renewable energy availability. This study models the operation of such a system, analyzing key performance metrics including hydrogen production, storage efficiency, and electricity generation. The results demonstrate the potential of integrated fuel cell and electrolyzer systems to enhance the reliability and flexibility of renewable energy systems, providing a sustainable approach to energy management and contributing to the advancement of energy storage technologies.

The increasing adoption of electric vehicles (EVs) is driving the need for efficient and sustainable energy sources, such as fuel cells, to enhance vehicle range and performance. This paper explores the application of AI-driven optimization techniques to improve the performance of fuel cells in electric vehicles. By leveraging machine learning algorithms, particularly reinforcement learning and predictive modeling, the system can optimize key parameters such as temperature, pressure, and hydrogen consumption in real-time, thereby maximizing efficiency and extending the operational lifetime of the fuel cells. The study demonstrates that AI-based approaches can significantly enhance energy output and fuel utilization while adapting to dynamic driving conditions. This research provides a promising pathway for improving fuel cell performance, thus promoting the broader adoption of hydrogen-based electric vehicles as a viable alternative to traditional internal combustion engines.

The development of products in a variety of industries is now being increasingly influenced by material advancements. Many experts are looking for basic materials that are strong, light, and inexpensive. Solids are often rather thick, whereas light materials are weaker. To attain significant strength while lowering weight, we use composite materials. This work deals with the mixed effects of composites made by hand using an epoxy tar and a hardener along with different fiberes of chopped mate (KC), Kenaf (KA), and Kevlar (KB). Mechanical studies such as tensile (UTS), flexural (FL), impact (IM), and hardness (BHN) were conducted after creating specimens according to standard measurements.

This review article explores the transformative influence of Internet of Things (IoT), Unmanned Aerial Vehicles (UAVs), and Deep Learning (DL) in modern agriculture, outlining their applications and impact on Smart Agriculture Systems (SAS). Examining various wireless communication technologies within IoT, including LoRa, Zigbee, and cellular networks like 5G, the study delineates their roles in enabling real-time monitoring and data transmission across expansive agricultural landscapes. Moving to UAVs, the review highlights their pivotal role in precision agriculture, elucidating how these aerial platforms equipped with diverse sensing technologies and cameras facilitate crop monitoring, disease detection, and targeted pesticide spraying. The integration of Deep Learning techniques, particularly Convolutional Neural Networks (CNNs), is discussed to emphasise their significance in disease detection, pest management, soil parameter estimation, and weed identification. The synthesis of these technologies reshapes traditional agricultural methodologies, empowering farmers with data-driven decision-making tools for optimized yield, sustainable practices, and efficient resource utilization. This comprehensive exploration aims to provide insights into the synergy of IoT, UAVs, and DL, laying the groundwork for the evolution of agricultural practices worldwide towards increased productivity and sustainability.

The evolving landscape of agriculture has undergone a significant shift towards a technology-driven and data-centric approach, mainly propelled by the advent of Artificial Intelligence (AI) and the Internet of Things (IoT). This short review talks about numerous studies exploring the transformative impact of these cutting-edge technologies in sustainable agriculture practices. It outlines how the traditional statistical methods have given way to a more data-driven and quantitative approach, reshaping every facet of farming. The article emphasizes the immense potential of IoT in revolutionizing agriculture, shedding light on the challenges encountered when integrating these technologies into conventional farming systems. It explores the diverse applications of sensors and IoT devices across various agricultural stages, from land preparation and irrigation systems to pest control and harvesting techniques, while also highlighting the integration of advanced tools like unmanned aerial vehicles (UAVs) for crop monitoring and yield optimization. Overall, it emphasizes the urgency to embrace these innovative technologies, addressing key challenges for their seamless integration into traditional farming and aquaculture practices to ensure a sustainable and efficient future for agriculture.

The global population is rapidly aging, creating a demand for innovative solutions to support elderly individuals in maintaining their independence and well-being. This shift has led to the integration of Internet of Things (IoT) technology into smart homes as a transformative approach to elderly care. By utilizing continuous monitoring and real-time data collection, IoT-based solutions offer personalized support tailored to the unique needs of seniors. A wearable device, incorporating Arduino Uno microcontroller, ESP01, DHT11, ADXL335, and pulse sensor, provides crucial real-time information on vital signs, particularly beneficial for those with conditions like Alzheimer’s. These technologies not only detect falls but also facilitate immediate alerts to caregivers, promoting safety and rapid response. Moreover, IoT-integrated smart homes promote sustainability by optimizing energy usage and reducing waste, contributing to a more ecofriendly future. Unlike traditional methods, these IoT solutions offer proactive interventions and remote connectivity, enhancing communication between seniors, caregivers, and healthcare professionals while minimizing healthcare costs. This holistic approach prioritizes the autonomy and privacy of the elderly while harnessing technological advancements to improve their quality of life.

The behavior of composite materials under tensile and bending loading differs, and material designs optimized for tensile loading may not be suitable for applications subjected to bending loading. In this study, the bending behavior of two different composite materials, namely glass and carbon fiber-reinforced composites, was analyzed under a three-point bending load. The output from the analysis of these two materials was compared, revealing that, in addition to longitudinal direction strength, transverse and through-thickness directional strength, as well as shear strength in all planes of the composite, are essential to minimize deformations, normal stress, and shear stress. In the study, under the same loading and boundary conditions, the carbon epoxy composite exhibited 84% more deformation than the FR-4 composite at the maximum load considered for the study. Furthermore, the strength of the FR-4 composite in the loading direction was 2.4 times greater than that of the carbon epoxy composite. The present work is used for the effective design of the composite material under bending loading.

Efficiency in technology, distributive effectiveness, and environmental effectiveness all have the potential to be included in the general concept of "resource use efficiency in agriculture." An effective farmer distributes his resources, including labourers, water, and land, appropriately in the best possible way to enhance his revenue while minimising expenses over time. Multiple research studies, however, demonstrate that farmers frequently make inadequate use of their resources. This study presents a comprehensive analysis of various farming practices and technologies, highlighting their impact on agricultural efficiency and resource use. Also, this review investigates the incorporation of advanced technologies such as the Internet of Things (IoT) into agriculture, emphasising the significance of environmental and economic factors in achieving sustainable agricultural productivity. With a focus on the ASEAN region and specific case studies, it assesses how carbon emissions, deforestation, renewable energy, and biodiversity affect farming efficiencies. The paper underscores the value of biodiversity, including underutilized and neglected species, and the potential of IoT and associated technologies to enhance agricultural operations. Moreover, it discusses about how resource optimization models can be used to increase agricultural yields and address environmental issues. A fundamental necessity for strategic management of water resources and joint utilization of water resources to support sustainable agriculture is also emphasized in the evaluation.

Now-a-days in dynamic industrial landscape, the need for real-time monitoring, data-driven decision making, and operational optimization has become paramount. The Industry Monitoring System using IoT emerges as a transformative solution that leverages the capabilities of interconnected devices and sensors to provide comprehensive visibility into industrial processes and assets. This innovative system deploys a network of sensors and IoT devices strategically with in industrial facilities to capture critical data parameters, ranging from environmental conditions to equipment performance metrics. These sensors communicate seamlessly through wired or wireless connections, transmitting data to a centralized hub or cloud-based platform. This proactive approach not only enhances equipment reliability but also reduces downtime and maintenance costs. Furthermore, the integration of IoT in industry monitoring facilitates data-driven decision-making, fostering a more agile and responsive industrial ecosystem.

A constant supply of fresh air is vital for eliminating harmful bacteria and supporting the body's immune system against viruses and infections. Poor indoor air quality poses significant health risks, affecting human well-being and productivity. To address this issue, indoor air quality analysis is essential for enclosed spaces like homes, offices, and schools. According to the United States Environmental Protection Agency, indoor air can be up to 1000 times more polluted than outdoor air, with modern lifestyles leading to people spending 80-90% of their time indoors. Air pollution, as reported by the World Health Organization, causes 7 million deaths annually, primarily affecting the lungs and increasing the risk of cardiovascular diseases. Monitoring indoor air quality parameters such as CO2 and humidity levels is crucial for a healthy lifestyle. By utilizing sensors like MQ135 and DHT11, we can measure indoor pollutants and evaluate ventilation systems' effectiveness. Automation, such as using servo motors for ventilation, helps maintain safe indoor air quality levels. This approach can be implemented across various sectors, including schools, malls, and offices, to create healthier environments. Ultimately, indoor air quality analysis plays a vital role in enhancing indoor air quality and promoting occupant well-being.

Massive and unpredicted rainfall-induced landslides are becoming more common in India. 12.6% of the area of India is prone to landslides. This problem presents a comprehensive approach to addressing this critical need by leveraging the Internet of Things (IoT). The existing works mainly focus on vibration sensors and slope movement sensors which may lead to inadequate real-time data. Our proposed work uses rainfall measurement and soil moisture sensors with vibration and slope movement sensors, which increases the data precision and integration. The main ideology is to evacuate the landslide-prone area before the landslide occurs hence saving the lives of people. After detecting the landslide it sends warning alerts to the disaster management authorities in that particular area. As a result, the IoT-based landslide detection system presents a significant advancement in addressing the challenges posed by landslides by focusing on early detection and warning capabilities but also minimizes environmental impacts by facilitating proactive evacuation measures.

The urban transport system is a heterogeneous system and is in serious trouble. Traffic congestion has become a major socio-economic problem in both urban and rural areas. Traffic congestion is managed through signage, interchanges, and road widening. Traffic analysis remains a difficult task, especially in countries like India where the number of vehicles is constantly increasing and the roads are increasingly busy, because it is difficult to generate alternatives. Traffic simulation systems such as PTV VISSIM are tools dedicated to the analysis of traffic problems. VISSIM is a simulation software that is widely used to create simulations in dynamic traffic situations before evaluating traffic conditions and creating a realistic plan. Dynamic simulation of traffic flow using VISSIM and appropriate techniques to reduce traffic congestion. The main objective is to examine the study area and prepare a sample for it to find out the root causes of the traffic problem and give the necessary suggestions to solve the problem.

This study explores innovative methods and technologies that promote efficiency and environmental stewardship in the handling of industrial waste, providing sustainable alternatives to disposal solutions. Waste from various manufacturing techniques, both commercial and municipal, creates very demanding conditions due to its volume and impact on the environment. This study looks at recycling techniques, bio electrochemical systems, smart waste monitoring systems helped by IoT and advanced computing, and sustainable waste management practices in addition to the 3R concept (reduce, reuse, recycle). The paper also identifies the detrimental effects of conventional disposal methods. The studies demonstrate how sustainable-practice integration can lead to significant waste reduction, resource conservation, and environmental safety in many global contexts and industries. This review promotes the implementation of sustainable waste control techniques across all sectors to lessen the negative effects of commercial waste by analyzing different techniques and their results. It does this by highlighting the scalability and value of these strategies.

The construction industry's success hinges on effective materials management, a complex process vital for timely and cost-effective material availability. Despite its significance, a notable gap exists in academic research on this critical aspect. This study addresses the gap by introducing a delay prediction model aimed at preempting project delays due to material-related challenges, offering a proactive project management strategy. Materials account for 60-70% of direct costs, and delays resulting from inefficiencies incur significant financial implications. The proposed algorithm, rooted in rigorous academic exploration, utilizes correlation matrices to predict time and cost overruns. The research concludes with a comprehensive analysis, validating the algorithm's utility by revealing average delays and cost overruns for diverse construction components. This study underscores the paramount importance of materials management in construction project success and offers a practical algorithm with potential real-time applications, suggesting avenues for future research expansion.

The necessity of sustainable energy sources and storage technologies is emerging due to growing energy demands. Thus, it encourages the need to perform sustainability analysis in terms of energy efficiency. For battery technologies, energy production and recycling holds a significance. In this study, the direct and indirect requirements of various battery technologies including production to transportation . The five battery technologies taken into account for the analysis are Lithium ion , Nickel Metal Hydride , Lead acid , Valve Regulated lead Acid, and Nickel Cadmium . The characteristics analyzed here are cycle life, energy density and energy efficiency . The study also covers the life cycle assessment in an structured way from raw to evaluation of materials , energy flow, installation , usage to end of life.

Unsustainable energy utilization is negatively affecting the planet’s atmosphere, eco-capacities, and present (just as future) prosperity around the globe. With such a great amount of work to be done, overseas environmental associations having some expertise in sustainable energy source every now and again need international volunteers to join their group. International energy conservation in sustainable energy source offers you the chance to gain up hands-on experience working with environmental people and have an immediate effect in the field of sustainability for Smart cities development. Regardless of whether it’s chipping away at hydropower frameworks in landlocked nations, helping spread the reception of Solar powered energy generators in nations that do not have the foundation for power or helping an organization on the cutting edge of another innovation, the open doors are astonishing and about perpetual. This research is much focused on the overseas Dimensional array based heuristic Fuzzy classification systems (DA-HFCS) science and technology guidance system which aims for sustainable energy source volunteer projects will offer one of kind social viewpoints that will upgrade and illuminate present growing perspective. The case studies have been evaluated and statistical analysis shows prominent outcomes.

The efficiency of a GaAs solar cell was 24.3 percent, which at the time was the best of any single-junction solar cell. This paper shows the variation of output power when subjected to a variety of irradiation at different locations in India When compared to the previous record efficiency of 24.3%, set by direct deposition of the III-V semiconductor layer on silicon, the efficiency of wafer-bonded silicon-on-gas-silicon (S-S) cells has increased to 34.1%. The maximum power per unit area (Pm) and open circuit voltage (Voc) of GaAs/Ge solar cells are also temperature dependent. Thin film cells are now more adaptable and less voluminous as a result. This semi-transparent solar glazing material is laminated onto windows in building-integrated photovoltaic systems. Because solar panels cannot work in these conditions, realistic solar arrays will produce less power per unit area. To make matters worse, we don’t account for losses caused by shadowing from electrical connections or the close proximity of solar cells on panels.

This research examines the application of Particle Swarm Optimization (PSO) to optimize energy storage optimizations with the objectives of improving energy generation, cost-efficiency, system dependability, and environmental sustainability. The optimisation of solar panel and energy storage capacities was conducted using empirical data from various microgrid locations: Site 1, which had a capacity of 90 kW solar and 40 kW wind, Site 2, which had a capacity of 50 kW wind and 80 kW solar, Site 3, which had a capacity of 60 kW wind and 110 kW solar, and Site 4, which had a capacity of 45 kW wind and 85 kW solar. The findings suggest that energy generation increased significantly by 15% to 25% across all sites following optimization. Furthermore, significant decreases in the levelized cost of energy (LCOE) between 10% and 14% were noted, providing confirmation of the economic feasibility. Increased grid stability of 17% to 24% during periods when microgrids were operating under stable conditions demonstrates that PSO-optimized configurations are dependable. The positive environmental effects of solutions derived from PSO were apparent, as evidenced by the conservation of carbon emissions and ecological footprints, which decreased by 7% to 15%. The sensitivity analysis validated the optimized configurations' robustness, establishing their ability to withstand changes in parameters. In summary, the utilization of PSO to optimize energy storage optimizations showcases its capacity to enhance the efficiency, dependability, cost-effectiveness, and environmental impact of these systems. This advances the possibility of constructing microgrids that exclusively utilize sustainable renewable energy sources.

Biofuels are a viable alternative to fossil fuels, providing potential advantages in mitigating greenhouse gas emissions and improving energy security. Nonetheless, the environmental sustainability of biofuels requires thorough evaluation across their whole life cycle. This report provides a thorough Life Cycle Assessment (LCA) of biofuels, emphasizing greenhouse gas emissions, energy use, and resource depletion. Monte Carlo simulation is used to address variability and uncertainty in critical parameters, yielding a reliable assessment of environmental consequences. Findings demonstrate that biofuels may decrease greenhouse gas emissions by 45-60% relative to fossil fuels; however, considerable heterogeneity is seen based on feedstock type, agricultural methods, and conversion technology.

Accurate estimation of interfacial tension (IFT) between nitrogen and crude oil during nitrogen-based gas injection into oil reservoirs is imperative. The previous research works dealing with prediction of IFT of oil and nitrogen systems consider synthetic oil samples such n-alkanes. In this work, we aim to utilize eight machine learning methods of Decision Tree (DT), AdaBoost (AB), Random Forest (RF), K-nearest Neighbors (KNN), Ensemble Learning (EL), Support Vector Machine (SVM), Convolutional Neural Network (CNN) and Multilayer Perceptron Artificial Neural Network (MLP-ANN) to construct data-driven intelligent models to predict crude oil – nitrogen IFT based upon experimental data of real crude oils samples encountered in underground oil reservoirs. Several statistical indices and graphical approaches are used as accuracy performance indicators. The results show that virtually all the gathered datapoints are suitable for the purpose of model development. The sensitivity analysis indicated that pressure, temperature and crude oil API all negatively affect the IFT, with pressure being the most effective factor. The evaluation study proved that Random Forest is the most accurate developed intelligent model as it was characterized with acceptable R-squared (0.959), mean square error (1.65), average absolute relative error (6.85%) of unseen test datapoints as well as with correct trend prediction of IFT with regard to all input parameters of pressure, temperature and crude oil API. The developed model can be considered an accurate an easy-to-use tool for the prediction of crude oil/N2 IFT values for enhance oil recovery study optimization and upstream reservoir investigations. [ABSTRACT FROM AUTHOR]

The density functional theory calculations were conducted to explore the impacts of the incorporation of the Au metals on the adsorption capabilities of MoTe2 toward thiol based S containing molecules. The most favorable positioning of Au atom is on the hollow site of MoTe2 above three Te atoms, which gives rise to the coordination between Au and Se atoms. Most of the adsorption events focus on the substantial interaction between the S atom of considered organic molecules and the surface Au atom, which are fully described and interpreted based on the charge density and density of states plots. Thus, the favorite adsorption model for all the organic molecules is the adsorption based on strong Au-S mutual interactions. The primary aim in this work is to develop a theoretical basis to expose the potential talents of Au-MoTe2 based nanosheets for use in smart sensors of thiol based organic molecules. [ABSTRACT FROM AUTHOR]

In this study, we enhance the cooling efficiency of electronic systems, addressing the increased heat dissipation due to high-performance microprocessors. The trend of miniaturization in electronics necessitates innovative thermal management solutions. Our research focuses on a convective-radiative porous heat sink embedded with functionally graded material (FGM), designed to improve cooling for consumer electronics. Using numerical simulations and the Galerkin Method to solve thermal models, we examine the thermal properties of FGMs following linear and power-law functions. We examine the effects of the FGM's inhomogeneity index, and convective and radiative parameters, on the thermal execution of the porous heat sink. Results indicate that increasing the inhomogeneity index and enhancing convective and radiative parameters significantly improve thermal efficiency. Additionally, the temperature gradient along the FGM fin remains minimal compared to conventional fins, regardless of the parameters. To validate our findings, we compare our thermal predictions from the Galerkin Method with results from the Runge-Kutta method combined with shooting and homotopy analytical methods, showing strong agreement. This study highlights the potential of convective-radiative porous heat sinks with FGM to significantly improve thermal management in electronic systems. [ABSTRACT FROM AUTHOR]