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Language death is a phenomenon with symptoms related to demeaning vocabulary count and depletion of domains of language use along with the simplicity of linguistic structures. It commences by exhibiting traits of a declining number of fluent speakers, dwindling attitude of the speakers with regard to their heritage language, language shift, lack of inter-generational language transfer accompanied by a feeling that heritage language is inferior to outside languages, and Gulgulia exhibits every trait of such a dying language. It has become a waning language that is very close to its permanent extinction. The present study elucidates the ethnolinguistic vitality of Gulgulia tested through chosen sociolinguistic parameters which were found suitable to Gulgulia’s scenario. It also explores the linguistic situation of the Gulgulian community in Dhanbad, the community members’ language use in their homes and in their miscellaneous interethnic interactions and examines what relation prevails between the community’s language preference and their vitality. It was found that speaker variables, such as age, gender, and language competence governed the speaker’s attitude toward the heritage language. The location of the speech community is also a regulating factor in determining the inclination for preservation or attrition of the native language. The analysis of the speech behavior in the Gulgulian community confirms the loss of major genres such as the art of narration. Out of all the genres of language use, only two are surviving, which is alarming.

This paper focuses on the issue of translations in recent years from Spanish to Hindi, which is spoken in large part of India and also in countries where there is a majority of Indians. Indian readers have known the works of the Spanish Golden Age for a long time since one of the greatest Spanish work, Don Quixote, was translated into Hindi and also into other Indian languages from the second half of the eighteenth century. This work attempts to present the reception of translations from the Spanish Golden Age into Hindi and also analyzes the boom in popularity of translations of Spanish masterpieces of this era. This article has also analyzed the different problems of translation when working with texts from the same period.

This research elucidates the pursuit of operational excellence in greenhouse gas emission, employing a multifaceted approach to analyze key performance indicators (KPIs), supply chain dynamics, electric vehicle, and technology integration. The investigation reveals a consistent improvement in production efficiency, with an average increase of 0.53%, reduced downtime by an average of 5 hours per month, and a marginal decrease in cycle time. Supply chain metrics exhibit commendable performance, with an average on-time delivery rate of 93.0%, order accuracy of 97.9%, and lead time of 5.5 days. Electric vehicle showcases a strategic investment with an average of 19 hours per month, leading to certifications such as Level 3 and Level 4 for advanced machine operations and programming CNC machines. The integration of advanced technologies demonstrates substantial efficiency improvements, with IoT sensors contributing to a 20% increase, automated robotics showcasing a remarkable 35% enhancement, and advanced data analytics and augmented reality (AR) each exhibiting 25% and 30% improvements, respectively. The findings collectively underscore the interconnected strategies driving operational excellence in greenhouse gas emission, providing valuable insights for organizational decision-making.

Metal based additive manufacturing techniques such as laser powder bed fusion (LPBF) can produce parts with complex designs as compared to traditional manufacturing. The quality is affected by defects such as porosity or lack of fusion that can be reduced by online control of manufacturing parameters. The conventional way of testing is time consuming and does not allow the process parameters to be linked to the mechanical properties. In this paper, ultrasound data along with supervised learning is used to estimate the manufacturing parameters of 316L steel cubes. Nine cubes with varying manufacturing parameters (speed, hatch distance and power) are examined with ultrasound using focused transducers. The volumetric energy density (VED) is calculated from the process parameters for each cube. The ultrasound scans are performed in a dense grid in the built and transverse direction. The ultrasound data is used in partial least square regression algorithm by labelling the data with speed, hatch distance and power and then by labelling the same data with the VED. These models are computed for both measurement directions and as the samples are anisotropic, we see different behaviours of estimation in each direction. The model is then validated with an unknown set from the same 9 cubes. The manufacturing parameters are estimated and validated with a good accuracy making way for online process control.

Colonialism's deleterious impact on Indigenous epistemologies has engendered an exigent concern in the project of decoloniality, calling for a re-existence of marginalized cosmovisions. To accomplish this, an epistemic delinking from the paradigm of Eurocentric discourses is imperative in the interest of a comprehensive appreciation and recognition of Indigenous knowledge systems. In this vein, the present study employs the literary trope of animist realism to analyze two short stories from the anthology Boats on Land by the Khasi author, Janice Pariat. Her creative writing explores the animist philosophy of the Khasi community who dwell in the humid tropical State of Meghalaya, India. Through an attentive reading of the animist belief in water spirits and shamanic mantra rituals, this paper critiques colonial narratives of Khasi animist worldviews as "satanic", "supernatural", or psycho-pathological aberrations. The paper presents Khasi animist wisdom as a sophisticated and equitable principle of mutual coexistence and respectful relationality between human and more-than-human realms, replete with spiritual, ecological, and cosmological overtones. Indigenous animist epistemologies are indispensable as sustainable alternatives to the knowledge structures of colonial modernity. The present study contributes to the envisioning of a coexistence of Indigenous and Western knowledge systems in the spirit of mutual recognition and constructive engagement within an evolving epistemological landscape in the ongoing decolonial enterprise.

Maximizing the efficiency of solar panels is crucial for enhancing the viability of solar energy in both residential and commercial sectors. In this study, we employ Genetic Algorithms (GAs) to optimize various parameters affecting solar panel performance, such as tilt angle, azimuth angle, and environmental conditions like temperature and solar irradiance. We develop a model that simulates the efficiency of solar panels under varying conditions and apply GAs to find the optimal configuration. The results demonstrate a significant improvement in energy output, with optimized parameters yielding up to a 15% increase in solar panel efficiency. This research shows the potential of GAs in solving complex optimization problems in renewable energy systems.

Vehicles that run on petrol face competition from electric vehicles (EVs), which are more environmentally friendly and consume less energy than gasoline-powered automobiles. If we can predict the states that have an effect on charging, we might be able to estimate how much charging electric vehicle owners will require in the future. It is also capable of operating and managing charging infrastructure, in addition to providing users with individualised charge capacity statistics based on where they are precisely at the moment. As a result of this, developing a reliable model that can accurately predict the charging state of an electric vehicle has become an important issue. Based on the findings of this study, it is recommended to employ a combination of machine learning and deep learning in order to guarantee that the charging process is both secure and dependable, and that the battery does not become overcharged or over-drained. It has been suggested that a process of feature extraction using Recursive Neural Networks (RNNs) be utilised in order to obtain sufficient feature information regarding the battery. The bidirectional gated recurrent unit framework (GRU) was then established in the research project in order to make an educated guess as to the state of the electric vehicle. It is because of the information that the GRU obtains from the output of the RNNs that the model is significantly more useful. As a result of its more straightforward structure, the RNN-GRU is less effective when it comes to computing. In light of the findings of the tests, it is clear that the GRU method is capable of accurately monitoring the mileage of an electric vehicle. Based on the results of numerous tests conducted in the real world, it has been demonstrated that a mixed deep learning-based prediction method has the potential to provide a faster convergence speed and a lower error rate than the conventional method of obtaining an estimate of the state of charge.

The pursuit of sustainable development has prompted increased utilization of alternative materials in landfill cover systems. Despite evaluating these materials for hydraulic and strength properties, their performance with geosynthetics remains inadequately explored. This study focuses on investigating the use of recycled asphalt pavement (RAP) as an alternative drainage material and determining the interface friction angle between the drainage layer and the geomembrane through large-scale direct shear tests. Further, a numerical model of a municipal solid waste landfill in Italy was developed using Finite Element Method (FEM) software. Model validation was achieved by applying analytical two-wedge theory of veneer shear failure in layered strata. The study demonstrates a superior performance of RAP aggregates as drainage layer materials as compared to natural aggregates. It also emphasizes the importance of site-specific parameter determination and precise interface property characterization. Limitations of use of commercial software in identification of weak interfaces has also been highlighted.

To incorporate leadership in the governance of the Employees, one must have a thorough awareness of the various advantages of EI in HEI. Following the development of artificial intelligence came the emergence of emotional artificial intelligence, being aware that increasing the presence of emotions in AI would raise the likelihood of parallels between humans and machines. It will also be able to comprehend humans and be more likely to identify the root cause and consequences of an issue. Many of the gadgets in our bedrooms and kitchens are artificially intelligent to assist us with everyday activities, but they lack the emotional intelligence to adjust to our needs. An artificial intelligence that satisfies a person's needs needs to be capable of adjusting to their mental state. At the MIT laboratory, several technologies are being created. A total of 309 publications on the relevance of emotional intelligence in leadership were found in the scientific databases Scopus and Emotional Intelligence Important in Leadership, out of which 105 were chosen for further study. The Bibliometric tool was used to process the data; it included details on yearly production, journal analysis, author analysis, document analysis, keyword analysis, etc. Managers and policymakers in organizations in general and Higher Educational Institutions in specific can get some valuable inputs from the study's findings that will help promote artificial intelligence with emotional intelligence in their respective organizations that will ensure their growth, stability, and prosperity.

In response to the enhancing utilization of technologies and the present state of the field, this study aims to explore the E-learning revolution in Higher Educational institutes in India. Through a systematic review using bibliometric methods, data mining, and analytics, a total of 293 publications were examined. The study reveals a consistent growth in research on E-learning, particularly in recent years, with the USA, China, and India in this field. The primary contributors to this research area are Education and Information Technology, followed by an International review of research in open and distance. This study employs the Scopus database to identify trends in publications, influential papers, key themes, and keywords. It also explores futuristic themes and target concepts. The average citation value over three years stands at 6.768. Issues In Information Systems, T and D and Developments In Marketing Science: Proceedings Of The Academy Of Marketing Science. Research has shown that it is effective in increasing the effectiveness of e-learning in educational settings, especially in the areas of organizational behavior, corporate professionals, training and development, and finance, among others. It makes significant contributions to topics like financial crises, sustainability, COVID-19 issues, team challenges, organizational conflicts, and digital transformation. The study recognizes its limitations and explores possible directions for future investigation.The findings also highlight crucial research gaps, underscoring the need for further investigations. This knowledge can inform the development of strategic approaches to tackle challenges and leverage opportunities related to E-learning in higher educational institutes. Ultimately, the study aims to provide insights for strategies that support the advancement of E-Learning

The primary objective of the current research is to optimize machining performance in Al 7010 alloyreinforced with silicon nitride nanoparticles. This has been accomplished through a combination ofexperimental analysis and predictive modeling methodologies. Initially, composite materials were createdusing stir casting, and varied percentages of silicon nitride were incorporated into the material to supplementits mechanical properties. Wire Electrical Discharge Machining was performed using different parameters suchas Pulse On Time , Pulse Off Time , and Current , and a range of these parameters was defined according tolevels . Material Removal Rate and Surface Roughness were chosen as the machining responses and indicatedhigh sensitivity to variations in chosen parameters. Each response was thoroughly investigated and detectedusing these responses before establishing the optimized levels. Taguchi design of experiments and signal-tonoiseratio were two common techniques used to investigate parameter interactions, and they were also used todetermine the optimum combinations for both the parameters for optimizing MRR and minimizing SR.Moreover, an Artificial Neural Network (ANN) model was also established to foresee the response readingswith great precision and predict the parameter effect to enhance further predictive modeling capabilities inmachining. The present research optimization results indicated that the maximum MRR is obtained at Pulse OnTime , Pulse Off Time , and Current levels, while the minimum SR is obtained at Pulse On Time , Pulse OffTime , and Current levels. These findings provide promising avenues of research in the field of aerospace,indicating the possibility of machining components with superior machinability and mechanical strength.Furthermore, the predicting ability of an ANN model helps in obtaining the insights to engineers to optimizetheir process by gaining information about performance and material response.

The university setting significantly impacts the student’s learning and academic achievement. A vital aspect shaping their productivity and well-being within this environment is the indoor soundscape. Different organizations, nationally and internationally, have emphasized the importance of addressing noise-related issues and have established guidelines. This study investigates how indoor soundscapes affect student learning and achievement in university classrooms. The present study analyses noise levels at nine locations during morning, afternoon, and evening study hours using a class 1 sound level meter. Results show noise levels ranging from 49.8 to 77.6 dBA, averaging 66.1 dBA. Over 96% exceeded the ANSI, CPCB noise limit of 50 dBA, with 62.96% surpassing WHO’s 55 dBA standards. Seven of nine locations maintained acceptable noise levels 95% of the time, considering a ±10 dBA fluctuation allowed by CPCB. Libraries and computer labs generally had lower noise, while engineering labs had higher levels due to noisy equipment. This research holds significance for educational institutions, policymakers, and stakeholders in creating conducive learning environments. Understanding prevailing soundscapes informs the design and management of study areas, and helps optimize learning spaces for improved productivity.

Biodiesel manufacturing from waste cooking oil has emerged as a potential alternative in the search of sustainable energy. This process helps mitigate environmental pollution and reduces reliance on fossil fuels. This research examines the catalytic efficiency of environmentally friendly catalysts in this process, with a specific emphasis on catalysts based on enzymes. It assesses their effectiveness in terms of the production of biodiesel, the rate of the chemical reactions, cost efficiency, and their influence on the environment. Experimental evidence demonstrates that enzyme-based catalysts have enhanced catalytic activity, leading to an average biodiesel production of 90%, outperforming traditional catalysts such as solid acids, bases, and heterogeneous metal catalysts. Moreover, enzyme catalysts exhibit enhanced reaction rates due to their unique enzymatic activity and gentle reaction conditions. The cost study shows that the manufacturing costs for enzyme catalysts are competitive, with an average total cost of $800, which is equivalent to traditional catalysts. Environmental impact evaluation emphasizes the sustainability of enzyme catalysts by demonstrating their lower energy consumption, waste production, and greenhouse gas emissions compared to traditional alternatives. The results highlight the capacity of green catalysts, namely enzyme- based catalysts, to enhance sustainable biodiesel production methods, hence promoting a more eco-friendly and robust energy framework.

This research article explores the viability of biodegradable nanomaterials for sustainable food packaging applications. The study involves a thorough analysis of the materials, including material characterisation, mechanical testing, barrier testing, and biodegradation tests. The material characterisation demonstrates a consistent distribution of nanoparticles throughout the polymer matrix, as validated by SEM and TEM examination. XRD analysis also indicates the presence of crystalline phases in the nanomaterials. FTIR spectroscopy is a technique that can identify specific functional groups and chemical bonds in a substance, allowing us to get knowledge about its composition and compatibility. Ongoing research and development are crucial to enhance the efficiency and economic feasibility of packaging materials made from biodegradable nanomaterials. This will help advance the sustainability of the food packaging sector.

In order to tackle the worldwide problems of water pollution and shortage, this work explores the green synthesis of nanocomposite membranes for sustainable water filtering. Graphene oxide, cellulose nanocrystals, and silver nanoparticles were used as nanofillers in the fabrication of nanocomposite membranes, which were made using renewable polymer matrix (PES, PVDF, PAN). By including different polymer matrices and nanofillers, the composition analysis demonstrated the adaptability of nanocomposite membrane manufacturing, enabling the customization of membrane characteristics. Improved membrane shape and structural integrity were shown to result from the homogeneous dispersion of nanofillers inside the polymer matrix, according to characterization tests. Nanocomposite membranes demonstrated high flux rates and rejection rates for different pollutants, confirming their excellent performance in filtration experiments. The membranes' improved fouling resistance also increased their service life and decreased the frequency of maintenance needs. Supporting the eco-friendliness of nanocomposite membrane production, an environmental impact evaluation found that it used less energy and generated less trash than traditional techniques. All things considered, nanocomposite membranes have shown great promise as long- term water treatment solutions due to their superior performance, durability, and environmental friendliness, as well as their effective production and characterisation. More study is needed to perfect membrane characteristics and solve the remaining problems that prevent their broad use in water treatment systems.

The packing industry makes extensive use of terephthalate polyesters because of their chemical durability and optical qualities. Examples of these materials are polyethylene terephthalate (PET) and glycol-modified PET (PETG). They also supply building materials, medical technology, technical polymers, and the textile sector. PET is made of terephthalic acid as well as ethylene glycol, whereas 30% of the diol moles in PETG are replaced with CHDM during synthesis. Detailed structural analyses of polyethylene terephthalate glycol-modified (PETG) are presented in this study. In two directions, PETG square blocks were tested with a load of 12,200 N to determine their durability and mechanical response. This block experienced a total deformation of 0.2318 mm under vertical loading, with the outer layer experiencing 33.93 MPa, and the middle layer experiencing 23.148 MPa. According to its performance under vertical stress, PETG had a maximum fatigue life of approximately 572,540 cycles and a minimal safety factor of 0.035116. A deformation of 0.23192 mm was recorded under horizontal loading. The bottom layer had a stress of 46.317 MPa and the top layer had a stress of 20.174 MPa, with a better fatigue life of 616,880 cycles and a safety factor of 0.35979.

The paper gives a comprehensive review of how human activities influence normal hydrological forms by clarifying the diverse impacts on the accessibility, quality, and conveyance of water. The paper revealed the complex exchange between data and natural morals that are reconfiguring the worldwide water scene through conflicting facts of major causes like urbanization, deforestation, industrialization, and climate change. The natural areas are all disturbed by human activities and pollution and by these changes the impact is on run-off patterns, infiltration rates, and groundwater recharge processes. In metropolitan cities, there are more impermeable spaces which leads to more runoffs. In these areas, the water filtration rate and groundwater recharge are very low. Dams and other hydropower and water system processes that disturb the natural stream of waterways in addition to their aquatic habitat division have too changed stream morphology. Urban stormwater, industrial and farming runoff, are among numerous other sources contaminate freshwater systems with pathogens heavy metals, chemicals supplements, etc hence risking both human life and natural maintainability. Changes in precipitation and temperature impact the timing of snow melt, patterns of river flow, and restoration of groundwater; changes in disaster patterns together with sea level rise worsen coastal flooding and fresh-water salinization. These adjustments to common hydrologic processes have significant suggestions for biological systems, communities, and economies consisting of water accessibility for farming, industry, and domestic purposes, energy generation, and human well being. Among procedures aimed at reacting to these challenges is the Integrated Water Resource Management (IWRM) which contends for comprehensive approaches that accommodate competing requests over water resources while guaranteeing their supportability as well as adaptability to hydrological unpredictability of availability. Feasible water administration requires an extend of activities such as water preservation and proficiency measures, contamination control, watershed management, and climate alteration adjustment. As it may, social orders can explore these exceedingly complicated interconnected issues on their water assets by adopting administrative systems, innovative advancements, and shifts in social conduct that will be pointed at economical water utilization for the eras to come.

In recent years, there has been a marked shift in focus towards sustainable and renewable energy solutions, particularly in the context of developing nations. This article explores the immense potential of hybrid renewable energy (RE) systems for ushering in a transformative change in electrifying remote and rural areas within these countries. The importance of such endeavours becomes paramount as a multitude of nations globally seek strategies to diminish carbon footprints. Renewable energy systems, combining sources such as solar, wind, hydro, and biomass, emerge as crucial assets in this drive, especially when considering regions that remain largely isolated from major electrical grids. The growing emphasis on research in this domain, coupled with favourable policies, signifies a promising trajectory for RE in developing nations. Such hybrid models, by capitalising on the diverse spectrum of renewable resources at hand, are on the brink of initiating a revolution in rural electrification strategies. This review accentuates the urgency and significance of rapidly integrating these avant-garde systems, setting the stage for a future that is not only environmentally conscious but also ensures broader and more equitable access to energy.

This paper reviews the work in the areas of machine learning’s role in bolstering renewable energy within smart grids. As the global shift towards eco-friendly energy sources such as wind and solar gains momentum, the challenge lies in managing these unpredictable energy sources efficiently. Innovative learning techniques are emerging as potential solutions to these challenges, optimising the use and benefits of renewable energies. Furthermore, the landscape of energy distribution is evolving, with a growing emphasis on automated decision-making software. Central to this evolution is machine learning, with its applications spanning a range of sectors. These include enhancing energy efficiency, seamlessly integrating green energy sources, making sense of vast data sets within smart grids, forecasting energy consumption patterns, and fortifying the security of power systems. Through a comprehensive review of these areas, this paper highlights the potential of machine learning in paving the way for a greener, more efficient energy future.

The problem of interference in radio communication is well studied. There exist number of approaches to handle the issue, which consider angle of radio device and frequency as the key in handling the interference problem. However, they suffer to achieve higher performance in QoS requirements. To handle this issue, an efficient LAQ (Latency Availability Quality) based interference minimization model (LAQ-IM) is presented in this paper. The method focused on improving the transmission performance by choosing a optimal radio device with reduced interference. The method considers the set of radio devices available for transmission and considers the latency, availability and quality of the radio in the selection of transmission. Using all these features, the method computes Latency minimization support (LMS), Availability Support (AS) and Quality Support (QS) for the radio devices identified. Based on the value of LMS, AS and QS, the method computes the value of Interference Minimization Weight (IMW) based on which an optimal radio has been selected to perform data transmission. The proposed method improves the performance of radio transmission.

This review explores Peer-to-Peer (P2P) Energy Trading Platforms in Local Energy Markets, drawing insights from three key studies. These markets, using P2P trading, efficiently distribute electricity within communities. Research assesses P2P trading’s impact in a Norwegian neighbourhood, comparing it to scenarios without local markets. It also examines integrating PV systems, batteries, and electric vehicles on grid operations. Findings reveal minimal grid impacts with PVs alone, but adding batteries increases voltage fluctuations and losses. However, P2P trading benefits end-users with cost savings and supports Distribution System Operator operations. The paper surveys global P2P energy trading projects, emphasising communication and control networks within local Microgrids. It discusses the transition from passive consumers to prosumers in power networks and introduces the concept of a federated power plant, combining virtual power plants and P2P transactions among prosumers to address challenges and unlock additional value. This review fills research gaps, shedding light on P2P energy trading’s multifaceted aspects in local markets and its transformative potential for the energy sector.

Artificial Intelligence is a popular topic these days. It is believed that artificial intelligence will be the solution to all existing societal problems, reducing human effort while also improving human interpretation of the world around them. Artificial intelligence, within the realm of computer science, is focused on developing machines capable of emulating human thought processes, operations, and adaptability. At present, artificial intelligence finds utility across diverse domains, including but not limited to medicine, engineering, agriculture, self-driving vehicles, and aviation. Another emerging arena for AI deployment is in energy platforms. This paper delves into the applications of artificial intelligence and their significance in fostering sustainability across various trends. Consequently, it provides insights and analysis regarding the longterm viability of AI, encompassing environmental and economic development. Furthermore, it highlights the positive impact of AI applications on sustainable development, encompassing aspects such as fulfilling energy requirements, generating employment opportunities, and enhancing environmental preservation.

The problem of voltage regulation in smart agriculture unit is well studied. There exists number of approaches to support voltage regulation in agriculture sector. However, the methods consider only the number of motors share the voltage as the key in regulating the artificial voltage to the agriculture unit. The methods suffer to achieve higher performance in smart agriculture. To solve this issue, an efficient Machine Learning Based Voltage Regulation Model (MLVRM) is presented in this paper. The method maintains the agriculture trace and uses them to compute mean voltage utilization (MVU) at various duty cycles. With the information like no of smart motors connected, average voltage utilization of motors, and other features, the method computes MVU value. The method trains the neural network with the features extracted. The network is designed with number of intermediate layers where each layer neuron computes the value of MVU according to the features available. The output layer neurons produces number of MVU value. Based on the MVU values obtained, the method computes Optimal Regulation Voltage (ORV) for the current input voltage according to the required voltage for the smart motor connected. The proposed model improves the performance of voltage regulation and smart agriculture.

The problem of power regulation has been well studied and there exist number of approaches in regulating the power supply to multi storage buildings. Some of the method uses number of supply units present in each floor, and number of control units present in the floors of the building. However, they suffer to achieve higher performance in power regulation. To handle this issue, a Genetic Algorithm Based power regulation model (GAPRM) is presented in this paper. The model considers various factors like number of electric stoves, no of heater, no of iron, no of vacuum cleaner, no of television, no of hair dryer, no of refrigerator, no of electric kettle, no of microwave oven, no of washing machines and Voltage requirement of each towards power regulation. The model fetches the details of each storage level and identifies the set of all electric devices present in the floor. Using these details, the method converts them into feature vector. With the incoming voltage, the method applies genetic algorithm to identify maximum units can be triggered with the supply at each floor. The genetic algorithm generates number of mutations and for each mutation; the method computes the fitness value in the name of Smart Voltage Fitness (SVF) measure. Based on the value of SVF, the method populates a subset of population. For the selected population, the method computes Power Regulation Value (PRV). According to the value of PRV, specific voltage has been regulated to the specific storage. This will be iterated for each storage level of the building. The proposed method improves the performance of power regulation in smart multi storage building.

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.

Traditional synthesis techniques that use perilous solvents and poisonous precursors give rise to environmental and health apprehensions. This research explores sustainable methods of synthesizing materials utilizing environmentally friendly substances to tackle these difficulties. The synthesis of perovskite films was carried out utilizing environmentally friendly solvents, namely ethanol and water, and lead-free or recycled precursors, under optimum circumstances. An assessment was conducted on the efficiency of solar cell devices made from environmentally friendly perovskite materials. The results showed that these devices achieved efficiencies ranging from 11.8% to 13.1%, open-circuit voltages between 0.72 V and 0.78 V, short-circuit current densities from 17.8 mA/cm² to 18.5 mA/cm², and fill factors ranging from 76% to 80%. Material characterization demonstrated that the green perovskite films exhibited similar properties to traditionally produced films, including comparable bandgaps, crystallinity percentages, surface areas, and absorption peaks. The use of life cycle assessment (LCA) measures revealed significant decreases in energy consumption, waste production, and CO2 emissions linked to green synthesis techniques, hence highlighting the positive environmental effects. By using sustainable synthesis procedures, there was a significant improvement in environmental performance. Specifically, energy consumption decreased by 25%, trash production reduced by 20%, and CO2 emissions decreased by 30% compared to traditional methods. The results emphasize the capacity of green synthesis techniques to increase the sustainability of perovskite solar cells, without compromising or even enhancing device performance. In summary, this study makes a significant contribution to the progress of perovskite technology that is ecologically sustainable and offers vital insights for the advancement of renewable energy solutions.

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.

This study used a combination of cutting-edge technology and tools, including remote sensing and geographic information systems (GIS), to ascertain the geomorphological features of the Rapti River basin. The watershed area, perimeter, stream order, basin length, stream length, mean stream length, bifurcation ratio, stream length ratio, form factor, drainage density, stream frequency, circularity ratio, elongation ratio, compactness coefficient, constant of channel maintenance, length of overland flow, etc. where among the various geomorphological characteristics found in the represent study. Studying geomorphology is crucial to comprehending how watersheds behave hydrologically. Measuring the drainage network's linear and shaped features as well as the contributing ground slopes can help with the morphometric study of a drainage basin and its stream system. Sub watersheds could be prioritized using morphometric analysis by calculating linear and shape characteristics. The morphometric parameters of a watershed are useful in describing a watershed when there is a lack of extensive hydrological data. A basin's morphological feature reflects its characteristics, which can be used to combine hydrological response. When quantified and expressed in morphometric parameters, basin characteristics can be examined to see how they affect runoff. Surface runoff, infiltration, and erosion susceptibility within the basin can all be qualitatively assessed through the interpretation and quantitative analysis of five drainage parameters. We learned about the wide range of uses for geographic information systems, global positioning systems, remote sensing, and other instruments. GIS and remote sensing methods are widely used in NRM due to this application diversity. These approaches aren't used in case of Rapti basin which will help in better planning and management of resources of the basin.

The study aimed to compare ARIMA and Holt's models for predicting coconut metrics in Kerala. The coconut data series was collected from the period 1957 to 2019. Of this, 80% of the data (from 1957 to 2007) is treated as training data, and the rest (20% from 2008 to 2019) is treated as testing data. Ideal models were selected based on lower AIC and BIC values. Their accuracy was evaluated through error estimation on testing data, revealing Holt's exponential, linear, and ARIMA (0,1,0) models as the bestfit choices for predicting coconut area, production, and productivity respectively. After using the testing data, we tried for the forecasting for 2020-2024 using these models, and the DM test confirmed their significant forecasting accuracy. This comprehensive analysis provides valuable insights into effective prediction models for coconut-related metrics, offering a foundation for informed decision-making and future projections.

Agriculture is the backbone of Indian Economy. Proper forecast of food crops and cash crops are necessary for the government in policy making decisions. The present paper aims to forecast Wheat and Sugarcane yield using Random Forest Regression. For the development of Random Forest models, Yield has been taken as dependent variable and variables like Gross Cropped Area, Maximum Temperature, Minimum Temperature, Rainfall, Nitrogen, Phosphorous Oxide, Potassium Oxide, Minimum Support Price and Area under Irrigation are taken as independent variables for both Wheat and Sugarcane crop. Values of R2 for Wheat and Sugarcane is 0.995 and 0.981 which indicates that the model is a good fit and other performance measures are calculated and results are satisfactory.

The present study aims to develop yield forecast models for the Sugarcane crop of the Coimbatore district in Tamilnadu using two different techniques namely Variables and Months in Discriminant function analysis. For this, the Sugarcane yield data for 57 years along with the monthly data on seven weather variables have been taken. For applying discriminant analysis, the yield data of sugarcane has been divided into two categories namely two groups and three groups. The discriminant scores from the two and three-group discriminant functions were employed as independent variables in the development of yield forecast models. The yield forecast models for both strategies were created utilizing scores and trend values as independent variables. The first 52 years of yield data (1960-2012) were used to create the model, and the last five years of data (2012-2016) were used for validation. The comparison has been made between two and three groups for both techniques. The results indicate the technique using the variable-wise method gives better results based on goodness of fit. Among the two categories in the variable-wise method, three groups performed better.

This study offers a detailed comparative analysis of maize crop cultivation in the kharif and rabi seasons within the agricultural landscape of Gonda District. 50 respondents were carefully selected from various villages in the block, with proportional representation for Marginal, Small, and Medium-sized farmers. The research delves into the economics of maize cultivation, emphasizing factors such as the cost of cultivation, input expenses, income generation, and input-output ratios. In the kharif season, it was distinguished that the cost of cultivation of maize with the farm's size. Marginal farms spent an average of ₹ 48125.93 per hectare, small farms incurred ₹ 51002.89, and large farms invested ₹ 54295.17. Similarly, during the rabi season, the cost of cultivation increased with farm size, with marginal farms investing an average of ₹ 52397.57, small farms spending ₹ 55444.93, and large farms allocating ₹ 58604.68 per hectare. Crucially, the study found that input-output ratios remained consistent across farm sizes in both seasons, reflecting uniform agricultural practices. The findings underscore the importance of efficient management, the adoption of advanced agricultural techniques, the use of high-quality seeds, and the timely application of irrigation and plant protection practices in enhancing net income, particularly on marginal farms.

Sugarcane is the primary agricultural industry that sustains and promotes economic growth in India. In 2018, the majority of India's sugarcane production, specifically 79.9%, was allocated for the manufacturing of white sugar. A smaller portion, 11.29%, was used to produce jaggery, while 8.80% was utilized as seed and feed components. A total of 840.16 million metric tonnes of cane sugar was shipped in the year 2019. The primary objective of this research is to determine the most suitable forecasting model for predicting the monthly export price of sugarcane in India. The input consists of a time series with 240 monthly observations of the export price of sugarcane in India, spanning from January 1993 to December 2013. The SARIMA approach was employed to predict the monthly export price of sugarcane and it is concluded that the SARIMA (0, 1, 1), (0, 0, 0)12 model is the best-fitted one by the expert modeler method. As a result, the fitted model appears to be adequate. The RMSE and MAPE statistics are used to analyze the precision of the model.

To model and forecast complex time series data, machine learning has become a major field. This machine learning study examined Moscow rainfall data's future performance. The dataset is split into 65% training and 35% test sets to build and validate the model. We compared these deep learning models using the Root Mean Square Error (RMSE) statistic. The LSTM model outperforms the BILSTM and GRU models in this data series. These three models forecast similarly. This information could aid the creation of a complete Moscow weather forecast book. This material would benefit policymakers and scholars. We also believe this study can be used to apply machine learning to complex time series data, transcending statistical approaches.

Online shopping can be done from our convenient places like home, office, etc., and the product will be delivered to the respective places. There are many factors influencing online shopping. The purpose of this study is to develop a statistical model that is used to determine the factors that influence online shopping. In this study, using factor analysis five main factors have been obtained from 15 variables that influence online shopping. These five factors have significant effects on satisfaction of customers and accounted up to 56% of total variation. Using the factor scores as independent variables, multiple regression model has been developed for predicting customers satisfaction in online shopping. Customer satisfaction has been used as dependent variable in the regression model. The five main factors that contribute online shopping are: preference of consumers towards online shopping, the risk involved in purchasing products through online, time effectiveness in online shopping, difficulties faced during online shopping and getting products from trustworthy websites.

Weather has a profound influence on crop growth, development and yield. The present study deals with the use of weather parameters for sugarcane yield forecasting. Machine learning techniques like K- Nearest Neighbors (KNN) and Random Forest model have been used for sugarcane yield forecasting. Weather parameters namely maximum temperature and minimum temperature, rainfall, relative humidity in the morning and evening, sunshine hours, evaporation along with sugarcane yield have been used as inputs variables. The performance metrics like R2, Mean Square Error (MSE), Mean Absolute Error (MAE), Root Mean Square Error (RMSE), Mean Absolute Percentage Error (MAPE) have been used to select the best model for predicting the yield of the crop. Among the models, Random Forest algorithm is selected as the best fit based on the high R2 and minimum error values. The results indicate that among the weather variables, rainfall and relative humidity in the evening have significant influence on sugarcane yield.

Sugarcane is the largest crop in the world in terms of production. We use sugarcane and its byproducts more and more frequently in our daily lives, which elevates it to the status of a unique crop. As a result, the assessment of sugarcane production is critical since it has a direct impact on a wide range of lives. The yield of sugarcane is predicted using ARIMA and ANN models in this study. The models are based on sugarcane yield data collected over a period of 56 years (1951-2017). Root Mean Square Error (RMSE) and Mean Absolute Percentage Error (MAPE) have been used to analyze and compare the performance of different models to obtain the best-fit model. The results show that the RMSE and MAPE values of the ANN model are lower than those of the ARIMA model and that the ANN model matches best to this data set.

Building thermography is a non-destructive and effective method for diagnosing the thermal insulation of buildings with various purposes. Regardless of the specifics of conducting thermographic surveys, the obtained thermal images (thermograms) can be analyzed both qualitatively and quantitatively. The first method of analyzing thermal images is implemented using specialized software typically provided with the thermal imaging camera. The second method is carried out based on the results of thermographic surveys following the methodologies regulated by the country’s normative documents. Using the example of thermographic inspection of enclosing structures in a multi-story residential building constructed from reinforced concrete panels, a qualitative assessment of thermal images was conducted to identify potential thermal defects. Thermal bridges were detected in the area of vertical joints of panel plates, and excessive heat losses were identified in the region where the heating device niche is located.Quantitative assessment of thermal images revealed that the thermal resistance in the temperature anomaly area is nearly twice as low as the thermal resistance in the baseline section of the thermogram. One of the variations for evaluating the error of indirect measurements of relative thermal resistance is presented, taking into account the range of measuring instruments used. Verification of the thermal resistance of the enclosing structure of the external wall of the residential building was conducted to ensure compliance with sanitary and hygienic requirements. According to the results of in-situ measurements, it was determined that the temperature on the inner surface of the enclosure was lower than the specified value.

This study introduces a sophisticated anomaly detection system based on machine learning. The system is specifically developed to enhance the dependability and safeguard the security of electric transportation networks, with a particular emphasis on the charging infrastructure for electric vehicles (EVs). Utilizing extensive datasets, the research examines several facets of charging stations, charging records, identified abnormalities, and following maintenance measures. The examination of the charging station demonstrates the system’s versatility in accommodating many charging circumstances, as seen by the range of power ratings, consumption patterns, and energy provided. Further examination of charging records provides comprehensive understanding of individual charging sessions, enabling the detection of irregularities such as atypical energy surges and extended charging durations. The machine learning system, having been trained and verified using this data, has a commendable degree of precision in identifying anomalies, as shown by the congruence between anticipated abnormalities and real results. The maintenance and repair measures carried out in reaction to identified abnormalities highlight the practical ramifications of the system, with proactive tactics utilized to reduce downtime and enhance charging station operations. The performance measures, including accuracy, recall, and F1 score, unequivocally validate the resilience of the anomaly detection system, guaranteeing precise identification while mitigating the occurrence of false positives and negatives. The seamless incorporation of machine learning into electric transportation networks, as shown by the results, not only amplifies the dependability and safeguarding of EV charging infrastructure but also establishes the system as an invaluable instrument for practical implementations. The research, in addition to offering a thorough examination of the system’s performance, elucidates forthcoming avenues for scalability, real-time monitoring, and interpretability, thereby making a valuable contribution to the wider discussion on the revolutionary capabilities of machine learning in the ever-changing realm of electric transportation.

Ensuring the required microclimate parameters of a cultivation structure, including indoor air temperature, relative humidity, and soil surface temperature, plays a crucial role in obtaining a rich harvest of vegetables and fruits in the winter season. Creating favourable conditions for growing plants in the protected ground is possible only by using modern, high-tech heating systems that will compensate for heat energy losses and maintain a set temperature regime in the room. Gas-fired radiant heating is one such system. This heating method, using ceiling gas infrared emitters as heat sources, directs the required heat flux directly to the soil surface. At the same time, direct or indirect emissions of harmful substances into the environment are minimal, and due to the absence of heat losses during heat production and heat carrier transportation, this type of heating is effective from both energy-saving and economic points of view. The article investigates the influence of meteorological, aerodynamic, heat engineering, and other factors on the air temperature in a cultivation structure under gas-fired radiant heating conditions using a developed software calculation method. An analysis is performed, and explanations are given for the nature of the change in indoor air temperature depending on the changing environmental conditions—the example of the industrial greenhouse “Farmer 7.5” (Russian Federation).

Smart materials have been revolutionizing the field of biomedical engineering due to their unique properties and capabilities. They are able to respond to various external stimuli such as temperature, pH, light, and magnetic fields, among others. In this review, we will discuss the current trends and future challenges in the use of smart materials in biomedical applications. We will focus on the different types of smart materials and their properties, as well as their potential applications in drug delivery, tissue engineering, biosensors, and medical devices. We will also discuss the challenges and limitations associated with the use of smart materials, such as biocompatibility, stability, and scalability. Finally, we will provide an outlook on the future of smart materials in biomedical applications and the potential impact on healthcare.

The integration of solarcellsinto construction materials, such roof tiles, isgaining traction. Nevertheless, solarcells' conversion efficiencyistemperature-dependent, and a high temperaturewouldlowerit. This study examines the functionality of mortar roof tilesthat have builtinsolarcells and safety glass. To control the temperature of the solar cells, phase modifying materials (P CM) are added to the first roof tiles at a concentration of 3% by weight.For solar roof tiles, the impact ofPhase change materials on the generation of solar-to-electrical power isassessed, and life cycle costanalysisiscarried out to determine the viabilityfrom an economicstandpoint.During six summerdays, the electricalenergy production of the solartileswith PCM appliedwas 4.1% higherthanthat of the tileswithout the component modification material. In contrast, during six winterdays, the improvementisbetween 2.2 and 4.3%. With the cost of the inverterincluded, the solar roof tileswith Phase change materials have an economicalpaybackperiod of 5.7 years. The application of Phase change materialsraises the initial cost of roof tiles by 1.2% ;nevertheless, the paybackperiodispredicted to bethreemonths shorter thanthat of the equivalentwithout PCM, given an overallimprovement in energy production of 4.1%.Whilemany countries are takingsteps to reducetheiremissions, the IEA’slatest report on global and carbonemissionsshowed an increase of 1.7 percent in emissionsyear-onyear2018 to 33.1 billion tons (IEA, 2019). Australia's 2018 increase in carbondioxideequivalentemissions of 0.7% raisesseriousconcerns about the countrysability to meetitsemissionreductioncommitmentunder the Paris Agreement (Cox, 2019).

In the pursuit of sustainable transportation solutions, Electric Vehicles (EVs) have emerged as a promising alternative. This research paper provides an in-depth exploration of the crucial role played by Battery Management Systems (BMS) and conducts a comprehensive comparative analysis of various energy storage technologies for Electric Vehicles. The paper begins by elucidating the intricate components and functions of BMS through an illustrative block diagram, emphasizing its significance in ensuring safe and optimal battery operation. The study then conducts a meticulous analysis of key battery attributes, including energy density, power density, capacity, charge/discharge rates, life cycles, and cost per kWh. Supported by simulated data, the comparative analysis sheds light on the distinct performance characteristics of different battery types - Lithium-ion (Li-Ion), Lithium Iron Phosphate (LiFePO4), Nickel-Metal Hydride (NiMH), and Ultracapacitor. The outcomes offer a comprehensive perspective on their strengths and trade-offs, providing valuable insights for selecting suitable energy storage solutions. By translating intricate technical information into meaningful insights, this research empowers EV industry stakeholders to make informed decisions for enhanced EV performance, economic viability, and sustainable electric mobility.

The globalized and fiercely competitive nature of the international market has expanded the range of demands across all sectors of the agri-food business. The dairy business needs to adjust to the prevailing market conditions by enhancing resource efficiency, adopting environmentally sustainable practices, promoting transparency, and ensuring security. The Internet of Things (IoT), Edge Computing (EC), and deep learning play pivotal roles in facilitating these advancements as they enable the digitization of various components within the value chain. Solutions that depend on human observation via visual inspections are susceptible to delayed detection and potential human mistakes and need more scalability. The growing herd numbers raise a significant worry due to the potential negative impact on cow health and welfare, particularly about extended or undiscovered lameness. This condition has severe consequences for cows, eventually leading to a decline in milk output on the farm. To address this issue, an Integrated Approach to Dairy Farming (IA-DF) has been developed, which utilizes sophisticated Artificial Intelligence (AI) and data analytics methodologies using mobile applications to continuously monitor livestock and promptly detect instances of lameness in cattle. Initially, the VGG16 model, pre-trained on the ImageNet dataset, was used as the underlying architecture to extract the sequence of feature vectors associated with each video. This approach was adopted to circumvent the limitations of conventional feature engineering methods, which tend to be both time-consuming and labor-intensive with deep learning-based classification algorithms. IA-DF can extract semantic details from historical data in both forward and backward directions, hence enabling precise identification of fundamental behaviors shown by dairy cows.

The coexistence of human populations with wildlife often leads to conflicts in which harmful animals cause damage to crops and property and threaten human welfare. Certain limitations influence the effectiveness and environmental impacts of traditional methods used to repel animals. The present research outlines a growth of solutions that utilize the Internet of Things and machine learning techniques to address this issue. This study centers on a Smart Animal Repelling Device (SARD) that seeks to safeguard crops from ungulate assaults, substantially reducing production expenditures. This is achieved by developing virtual fences that use Artificial Intelligence (AI) and ultrasonic emission. This study introduces a comprehensive distributed system for resource management in Edge or Fog settings. The SARD framework leverages the principle of containerization and utilizes Docker containers to execute Internet of Things (IoT) applications in microservices. The software system inside the suggested structure can include various IoT applications and resources and power management strategies for Edge and fog computing systems. The experimental findings demonstrate that the intelligent animal-repellent system effectively uses animal detection on power-efficient computational methods. This implementation ensures the system maintains high mean average accuracy (93.25%) while simultaneously meeting real-time demands for anti-adaptive harmful animal deterrence.

The agricultural sector serves as a fundamental cornerstone of the economies of numerous countries, necessitating technological advancements despite limited financial resources. The Internet of Things (IoT) presents a novel aspect within the field of soil health monitoring, which has significant implications for advancing smart agriculture and farming practices. Integrating conventional agricultural practices with cutting-edge technologies, such as the IoT and Wireless Sensor Networks (WSN), can foster Smart Agriculture (SA). This paper presents IoT Integration for Enhanced Turmeric Cultivation (IoT-ETmC) in the context of SA. The TurmFox IoT and Edge-to-Cloud (ETC) technology can analyze gathered data and send it to the user through internet connectivity. The work involves the implementation of TurmFox in experiments focused on turmeric cultivation. The results demonstrate a notable improvement in the quality of turmeric as a direct outcome of this intervention. The curcumin levels in the given product are notably higher, ranging from 4450 to 5450 mg per 120g. This paper also aims to demonstrate the intuitive configuration of sensor-to-actuator connections for implementing desired SA. The real-time data obtained from Turmfox provides information on the pH values, moisture levels, and temperature, allowing for observing dynamic variations in environmental conditions within the specified period. The pH level was 6.5 at 09:00, with a moisture content of 51 g/m3 and a temperature of 293 K.

Diabetes insipidus (DI) is an acquired or hereditary water imbalance disorder characterized by polydipsia and polyuria. It is a condition that involves the excretion of dilute urine in large volumes. The illness can strike at any age, with males and females have identical rates of occurrence of the disease. The two main mechanisms responsible for diabetes insipidus are either insufficient release or production of ADH (antidiuretic hormone) from the hypothalamus (central diabetes insipidus) or ADH resistance in which the kidneys are unable to respond to ADH (nephrogenic diabetes insipidus). Another form of DI is transient diabetes insipidus commonly known as gestational diabetes insipidus that occurs in the second/third trimester of pregnancy due to increased levels of placental vasopressinase that occurs progressively during pregnancy and increases metabolic clearance of vasopressin. The fourth type of DI is primary polydipsia, characterized by elevated levels of water intake that physiologically lower vasopressin and may be psychogenic, iatrogenic or dipsogenic. Signs and symptoms of DI often include water electrolyte-imbalance, excessive or severe thirst, frequent and excessive urination, fatigue, dehydration, and weight loss. Diabetes insipidus (DI) should be distinguished from primary polydipsia, and whether it is caused by a central, nephrogenic, or gestational cause. This distinction is critical since incorrect treatment can result in serious repercussions. Diagnosis of DI includes measurement of plasma sodium and osmolality, baseline copeptin, hypertonic saline stimulation and arginine stimulation test. The treatment for DI includes the use of drugs such as desmopressin, thiazide diuretics, indomethacin and amiloride.

Important practical consequences are shown in this experimental study of AI and autonomous systems integration in the context of Industry 5.0. AI optimization of the product manufacturing process resulted in a 35% decrease in the real faulty rate and a significant 20% rise in production rates, reaching an actual rate of 1440 units per hour. The actual defective rate was just 1.3%. Since autonomous robots were introduced, work completion rates have increased by 18%, totaling 2,520 tasks completed, and maintenance expenses have decreased by 9%, amounting to a $450 real cost savings. Furthermore, with an actual faulty rate of 2.6%, the AI-driven quality control method showed an astounding 35% decrease in defective goods. Ultimately, significant 15% energy consumption decrease was accomplished using AI-based energy optimization solutions, translating into real energy savings of 1,500 kWh. These results highlight the real advantages of combining AI and Autonomous Systems in Industry 5.0, such as increased productivity, lower costs, better product quality, and sustainability.

In order to carefully evaluate the susceptibility of common IoT devices found in smart homes, this research made use of the IoT Security Test framework. The findings showed a significant average drop in vulnerability ratings of 45% after evaluation, clearly indicating that improving IoT device security is feasible. The research classifies vulnerabilities found, highlighting the prevalence of Firmware Problems, Weak Passwords, and Network Vulnerabilities. Moreover, it examines the efficacy of remedial initiatives. These discoveries play a crucial role in enhancing the security of Internet of Things devices, providing a strong barrier for the protection of homeowners and the privacy of their data, especially in the constantly linked world of smart homes.