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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).

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

The paper presents the study of mechanical recycling of three-layer PP/EVOH/PP packaging containers made by the co-injection molding process. It is in alignment with European Circular Economy guidelines. The main objective was to evaluate the recyclability of these packages and their reuse as a secondary material. Thin wall packaging by special cutting and grinding methods were disintegrated, and recorded cutting force allowed the determination of energy per single cut. During these processes, delamination between PP and EVOH layers was observed. The recyclate r(PP/EVOH) was used to produce samples in the standard injection molding process. The similar samples were prepared from recycled virgin PP using the same technology. The paper presents the results of the mechanical properties of samples manufactured from recyclate r(PP/EVOH) and compares them with the properties of samples obtained from recycled polypropylene (rPP). It was observed, that the content EVOH in the PP matrix does not significantly affect the mechanical properties of r(PP/EVOH) samples. It was also found that r(PP/EVOH) blends are characterized by favorable apparent viscosity and MFI and can be a valuable raw material for reuse.

About 99% of sulphur dioxide present in the atmosphere comes from anthropogenic activities, the main source being industrial activity that processes sulphur materials, including burning fuels (coal, oil or gas), thus releasing sulphur dioxide into the air. The purpose of this paper is to monitor the concentration of sulphur dioxide during two seasons, in the ambient air of Petroșani, with the aim of highlighting the possible atmospheric pollution caused by thermal power plants that use solid fuel during the winter period. The monitoring periods were distributed over a period of 6 months (three in summer and three in winter), namely the summer months (May, June, July of 2022), the winter months of December (2022), January and February of 2023. The main objective of the measurements is to identify if there is an increase in sulphur dioxide concentration in the atmosphere, resulting from the burning of solid fuels from household activities during the winter period. Results indicate that there is a higher degree of SO2 pollution in the winter period, but the recorded values are far below the limits set by national legislation. The paper presents some measures to limit sulphur dioxide pollution in the context of climate change.

When transporting and storing liquid fuels, explosive atmospheres can form which, under certain conditions, can be initiated with different sources, of which static electricity has proven to be the source of ignition in many cases of fires and explosions that have occurred. As a result of the environmental regulatory requirements to reduce the noxes emitted by diesel engines, the oil industry introduced into production fuels with ultra-low sulfur content (ULSD Ultra Low Sulfur Diesel), clean burning diesel, and low sulfur content (LSD Low Sulfur Diesel). The new types of fuels present a greater risk of explosion than fuels with a high sulfur content because they have other explosive characteristics and are electrostatically charged much more easily. The paper presents some aspects regarding the risk of explosions during the transport and storage of liquid fuels with the highlighting of additional risks in the case of ULSD Also, the paper highlights the factors that must be taken into account when establishing the technical and organizational measures that must be taken to prevent ignition and that must be implemented in the work procedures of manufacturers, suppliers, transporters and users of ULSD.

The purpose of this paper is to present the importance of the specific tests for electric motors with type of protection increased safety „e” designed to be used in explosive atmosphere. This paper presents the voltage impulse ignition test applicable to electric motors with increased safety protection type. Due to the fact that electric motors with type of protection Increased Safety, whose supply voltage exceeds 1000 V, presents a high risk of sparks occurring in windings, it is necessary to perform tests to verify that the insulation of the windings is adequate and does not lead to electric discharge (through electric springs or sparks) at winding levels Evaluation of explosion-proof protected electrical equipment in scope of certification is extremely important considering the risk of explosion that has to be minimized in order to ensure life safety and health of workers and to prevent damaging of property and the environment, as well as free movement of goods when they meet the essential safety requirements at European level. The standard SR EN 60079-0 (Explosive atmospheres Part 0: General requirements) and one or more of the standards containing the specific requirements for the type(s) of protection applied to equipment (ex. SR EN 60079-7 for the type of protection increased safety “e”), are used to perform the assessment.

The paper presents the implementation of a feedback loop controller for the suction valves used within the gas compressors skids manufactured by Romanian Research and Development Institute for Gas Turbines COMOTI. These compressors are installed at beneficiaries, in potentially explosive environments (e.g., gas extraction and transportation applications). The proposed solution addresses the problems observed due to the high suction pressures of screw compressors, which may occur at start-up or during operation, leading to failed start-ups or emergency shutdowns. An automatic sequence implemented in the control software and displayed on the operating panel, could assist the operator during high suction pressures (above 1.2 bar). These pressure conditions normally lead to alarm limits and to performing an automatic shutdown sequence for protecting the compressor. The paper herein discusses the implementation of a start-up sequence in the programmable logic controller’s software, which can be applied without critical interventions on the compressor packages, only through minor control software modifications which can safely extend the inlet pressure range and the exploitation of the compressors. Thus, since no hardware modifications are required, we also ensure maintaining the ATEX certifications of equipment and assemblies.

The paper presents the estimation of the uncertainty in the measurement of the maximum surface temperature of voltaic cells exposed to specific tests for the type of intrinsic safety protection. For this purpose, equivalent test conditions using the short-circuit test stand are considered. In the first part, the risk of explosion is presented and it is mentioned that the risk of explosion arises due to the presence of technical equipment in areas where flammable substances may be present. In the second part, the requirements for testing cells are briefly presented. It also introduces the configuration of the stand used for testing. The third part of the paper was devoted to presenting and discussing the results obtained. The analysis of the test results revealed that the measurement uncertainty depends on the test conditions and the application of the temperature sensors. This result would help estimate the surface temperature of cells considering the favorable conditions in terms of minimum uncertainty.

Heat is normally generated as a result of metabolic processes taking place in the body. As a result of muscle activity, when working or exercising, a considerable amount of heat is released. The amount of heat generated is proportional to the intensity of activities. Intervention and rescue activities in special conditions can be carried out exclusively by personnel trained and authorized in this regard, personnel using individual breathing protection equipment. The decisive factor in ensuring the success of an intervention and rescue operation in toxic or chemically aggressive environments resides in the optimal and efficient design of a training process of personnel, in correlation, among others, with their testing in high temperatures and humidity environments. The current paper presents the structuring of rescuers’ physical effort during training, taking into account high temperature and humidity environments, structuring that aims to make the training process of intervention and rescue personnel in toxic / explosive / flammable environments more efficient. The paper analyses a correlation between changes in the physiological parameters of intervention and rescue personnel and different values of temperature and humidity of the training environment.

Lightning strikes pose a significant threat to photovoltaic (PV) systems, which are increasingly utilized for renewable energy generation. This paper presents a comprehensive overview of the potential risks associated with lightning strikes on PV systems and explores various protection measures to enhance their resilience. The study delves into the characteristics of lightning and its interaction with PV installations, identifies vulnerabilities within the system, and discusses the principles and techniques for effective lightning protection. The paper emphasizes the importance of comprehensive risk assessment, surge protection devices, grounding systems, and maintenance practices to mitigate the damaging effects of lightning strikes. By implementing appropriate protection strategies, PV system owners and designers can safeguard their installations and ensure the longevity and reliability of their renewable energy infrastructure.

This paper presents an overview of the standardization activity in the field of explosive atmospheres both at the national and international level, as well as the evolution of this activity in different areas related to these explosive atmospheres. In the first part of the paper, the aspects regarding explosion risk, explosion prevention and protection, classification of explosive atmospheres, as well as factors influencing explosion protection are briefly presented. In the second part of the paper, the context of standardization at the national level is presented together with the reference institutions in the field of standardization for or in relation to explosive atmospheres. On this occasion, the international institutions that administer the standards that have an impact in the field of regulations for explosive atmospheres at the national level are also presented. In the last part of the paper, the analysis carried out for several representative standards in the field of explosion protection is presented, from the point of view of the significant changes made in the current edition compared to the previous edition.

The paper analyzes the technical and safety factors for the storage of different gaseous products (air, carbon dioxide, hydrogen, methan gas, etc.), in the eligible salt caverns from Romania, especially in the compact salt caverns created by dillution process. This new approach to research and project results, will be an important contribution to the evaluation of large-scale storage capacities in Romania and is necessary for decision-makers to consider these technologies as some of the most appropriate in terms of risks. Especially was studied the geological and lithological diagrams and colected data from the conserved salt mines in order to analyze the storage conditions. The paper analized the Slanic river area, a Trotus tributarry, where disollution method was well implemented, near Targu Ocna town, Bacau County, Romania. The mining area is located near a well-known industrial center for chemical processing, Borzesti Chemical Works, with a great interes in the gas storage facilities. The storage of different gases implies different technologies for gas compression and gas transportation, this beeing in the near future a new challenge for research for reducing greenhouse gas emissions, according to the UN 2030 Agenda for Sustainable Development.

In today's context, training in the field of Occupational Safety and Health (OSH) has become essential due to the increase in the number of employees and the diversification of industries. Globalization and the accelerated technologization of jobs have imposed the need to adapt and continuously update knowledge in this field. At the same time, the covid-19 pandemic has led to an increase in interest in online training, making access to quality information and training easier for more people, regardless of location. Thus, occupational health and safety training faces new challenges and opportunities in terms of course delivery and accessibility. In this paper, the advantages and disadvantages of the online training system in the field of OSH are presented, as they emerge both from the specialized literature and from the authors' own experience. This paper also provides a number of recommendations for improving the online OSH training system at the national level.

improving system performance and reliability. Key leaks and inadequate key strength are common issues with numerical global key models and public block cipher algorithms that use pseudo-random numbers. Additionally, because of the spatial correlation between neighboring pixel values, no current crypto scheme is appropriate for picture encryption. An intellectual domain-based image cryptosystem with hierarchical modification structures is proposed to overcome all these obstacles. The suggested framework also aims to maximize confusion metrics and improve security efficiency. In this paper modified DCT transform domain based frequency domain image encryption is proposed along with optimized pixel grouping based cipher key formulation with reduced complexity overhead. The proposed image encryption model can achieve 100 percentage transformations irrespective to the image non-linear characteristics and associated spatial dimensions. To validate the performance metrics of proposed image encryption different biomedical images collected and the superiority of proposed system is verified using quantitative and qualitative analyzes. The proposed implementation helps in offering privacy in crypto system based network security. It also validated that this hybrid combination also extend the applicability of crypto system for EMR applications.

The introduction of Optical Character Recognition (OCR) technology revolutionized text digitization, allowing physical documents to be converted into editable and searchable digital representations. This paper goes into the unique challenges and advancements in OCR designed exclusively for the Devanagari script. It helps to preserve cultural heritage by digitizing historic manuscripts and religious writings and making them more accessible. Furthermore, Devanagari OCR has practical uses in administrative activities, data entry, and educational content digitization.

Cold formed steel (CFS) channel sections are the sections which are widely using from last few decades and are designated as light gauge sections. Such sections are being widely utilized in industrial, commercial and residential purposes as a secondary bearing member. Further developments of thin walled members made it possible to use the CFS Sections as preliminary load carrying structural members. The reason is hot-rolled steel sections posses’ very low strength to weight ratio on CFS sections, and the production process of these sections is cost-effective and simple. This paper is about comparing the analysis results from CUFSM and ABAQUS of different channel sections. The loads will be applied at the edges of the members axially i.e., as a compression member with different boundary conditions.

Artificial Intelligence (AI) is transforming all of the technologies we use every day. More than ever, we are very near to the objective of vehicle autonomy, which has long been desired. Large automakers are also spending billions of dollars on the development of autonomous vehicles (AVs). Among the advantages of this new technology are the possibility for increased passenger safety, less congested roads, reduced traffic, optimized traffic, reduced fuel consumption, less pollution, and improved travel experiences. However, there are also new security and privacy problems associated with this paradigm change. Previously simple mechanical devices, vehicles are today computerized, networked, and intelligent. They gather vast amounts of data, which must be shielded from intrusions. In this paper, we examine privacy issues and security hurdles in AVs. We investigate several attacks using a layer-by-layer methodology. It summarizes the contributions of these research works and categorizes them based on application domains. It also identifies open problems and research challenges that need to be addressed to fully realize the potential of AI in advancing V2X systems. Our intention is to provide insights into the unresolved research issues surrounding AVs and to suggest future lines of inquiry.

In this paper introduces a novel method for controlling the balance of dc capacitor voltage in a STATCOM, along with a comprehensive logical approach for the balance management system. A balancing control approach has been presented to address the abnormality of the dc capacitor voltage, which is generated by the anomaly of dynamic power absorbed and spent by the chain. This method takes into account the superposition of dynamic voltage vectors. In this approach, a voltage component is added to the output voltage of the append operation in order to modify its inherent power. Furthermore, a thorough scientific approach grounded on vector analysis is introduced. This technique facilitates the deconstruction of the equilibrium control mechanism, including both its stability and boundary constraints. The STATCOM uses a fuzzy logic controller to enhance its aesthetic appeal. A vector analysis-based evaluation is administered to ascertain the optimal approach for equilibrium control. This analysis contrasts the proposed methodology with the other two commonly used methodologies. The findings of this comparison indicate that the suggested strategy for equilibrium control has the benefit of notable stability and a well-defined threshold limit. Reenactments are conducted to illustrate this particular component of the system.

Motion or inertial sensors, like the accelerometer and gyroscope that are frequently found in smartphones and smartwatches, can measure the acceleration and angular velocity of bodily movements and be used to teach bots so that they may guess human activities. These models can be selected to a variety of fields, including biometrics and remote patient health monitoring. Because deep learning-based methods employ representing teaching methods, those may automatically identify hidden patterns in data and generate optimal objects from basic information generated from sensors without human intervention, they got popular in earlier in recognizing human activities. Along with recognize human activity, this paper suggests a novel called HDDN-model called CNN-GRU, which combines convolutional units. This model exhibited accuracy that is suggestively better than other state-of-the-art DNN models like Inception Time and Deep Conv LSTM developed using Auto ML, and was successfully verified on the WISDM dataset.

The popularity of anime is increasing exponentially in every part of the world due to its unique storyline, nonstop entertainment, fights, and similar type of content that can hold viewers and keeps them at the edge of their seats. However, with the increase of popularity in anime there has also been an exponential increase in violence and brutality in anime videos. Violent scenes have become much more common in anime videos when compared to generic cinema. This survey paper presents a comprehensive view on the detection of violence in movies and different scenarios using various techniques. Most commonly to automate detection of violence, machine learning is used for training the machine to detect violence. Convolution neural networks (CNN) are used very commonly to understand image pattern recognition with high accuracy. Moreover, use of other different methods such as LSTM and Markov models are also used to detect violence. The main goals kept in mind while working is to detect violence with high accuracy and to use less computation or to perform the action at a high-speed rate.

The execution of initiatives that are focused at employee satisfaction is always required in order to maintain skilled human resources, regardless of whether the firm in question is small, medium, local, or global. The turnover rate of employees has a significant impact on the total performance of an organization, which includes the performance of each individual employee, as well as the profitability of any given company. It is possible that the ineffective administration of human resources is the result of a lack of awareness regarding the significance of working individuals as well as a lack of continual practices regarding talent management. Without regard to the nature and level of the organizations in which the workers are engaged, it is possible to view them as "knowledge banks," and it is also possible that they are not necessarily positioned at the highest levels of the organization. The primary objective of carrying out this research study is to investigate the relationship between the practices of talent management in information technology industries and the retention of employees. Listed below are the two primary goals that the paper aims to accomplish: to learn about the strategies that are used for talent management in the information technology sector. And to investigate and demonstrate the relationship between the practices of talent management and the maintenance of employee retention. There are a few important results in this study about how to keep employees. It has been seen that workers with less than two years of experience are having a hard time adjusting to their new jobs and aren't seeing enough career growth. However, experienced workers have seen growth and are able to adapt to the work culture and freedom in their schedules.

The KGRCET Notice Board Project introduces a contemporary solution to the longstanding challenge of efficiently disseminating information within organizations and communities. This innovative digital platform harnesses web-based technologies to establish a centralized and user-friendly system for the posting, management, and retrieval of announcements, notices, and updates. This abstract provides a succinct overview of the project's goals, functionalities, and advantages. By substituting traditional physical notice boards with a digital counterpart, the project optimizes information sharing, diminishes paper consumption, and improves accessibility. Users can effortlessly submit, view, and search for notices, ensuring the timely delivery of crucial information to its intended audience. Additionally, the platform can be tailored to meet various needs, spanning educational institutions and corporate offices to residential communities and public organizations. As we delve into the intricacies of this project, we underscore its potential to elevate to a more communication, promote sustainable and interconnected future.

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

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

The subject of airfare is examined in this paper. As a result, a collection of factors that characterize a typical flight are selected under the presumption that they have an impact on airline ticket costs. The price of a plane ticket is influenced by the length of the trip, the location, the schedule, and several other factors, like holidays or vacations. Therefore, many people will surely save time and effort by having a basic awareness of airline expenses prior to making trip arrangements. The performance of the seven different machine learning (ML) models used to anticipate the price of airline tickets is compared after three datasets were analysed to acquire insight into airline fares. The objective is to investigate the factors that influence flight prices. The data can then be used to build a system that can predict how much a flight will cost.

The endeavors to explore and map the dynamic terrain of cloud computing, designated as the CloudVerse. Through a meticulous examination of recent advancements and emerging patterns in cloud technologies, this study aims to furnish a comprehensive portrayal of the present state and future trajectories of cloud computing. Utilizing a blend of literature review and industry scrutiny, the paper identifies pivotal domains of innovation within the CloudVerse, encompassing hybrid cloud infrastructures, serverless computing models, edge computing paradigms, and AI-driven cloud solutions. Furthermore, it delves into the ramifications of these advancements across diverse sectors and deliberates on strategic imperatives for enterprises embracing cloud technologies. Ultimately, this inquiry strives to demarcate the contours and potentials inherent in the CloudVerse, proffering nuanced insights for navigating the intricacies of contemporary cloud computing landscapes.

Municipal waste management is crucial for cities as it enhances the urban atmosphere, conserves assets, and safeguards the ecological balance. An adequate and effective waste management strategy leads to significant environmental issues. The absence of dustbins, littering, and improper usage of dustbins create unsanitary conditions in cities and harm the ecosystem. The theft or destruction of the dustbins is a significant issue. This research uses deep learning-based classifiers with the Internet of Things (IoT) and a cloud computing approach to accurately categorize trash at the start of garbage collection. The research categorizes recyclable garbage into six groups: plastics, glass, paper or cardboard, metallic items, textiles, and other recyclable materials to aid future waste disposal. Convolutional Neural Networks (CNN) are used for trash categorization. This study tries to provide a basic answer to this issue via IoT technologies. A function will be added to the user's website to inform them about the present condition of the closest smart waste bins. This will allow users to locate and use the nearest bin if the one in their area is full. This research intends to enhance the safety of smart waste bins by securing the sensors and implementing bins with a concrete body to prevent theft and damage.

The fingerprint pattern stands out as the most authentic and unique characteristic defining human identity. This unique pattern is immutable and persists unaltered until an individual's demise. In various circumstances, particularly in legal proceedings, fingerprint evidence is highly regarded. The distinctive minutiae pattern of each person is unparalleled, with the probability of resemblance being exceedingly low, nearly one in sixty-four thousand million. This distinctiveness holds true even for identical duplet. The individualistic ridge pattern persists unchanged from birth, serving as a constant aspect of personal identity. This paper presents a method involving the comparison of specific feature patterns derived from fingerprints for personal identification systems. Fingerprint data is employed in the investigation of blood group determination as well. In the process of fingerprint matching, ridge frequency is assessed, and spatial features are extracted using a Gabor filter for this specific purpose. Consequently, blood group determination can be performed using fingerprint analysis.

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

Breast carcinoma stands as one of the most perilous afflictions affecting females, lacking an effective treatment to date. Recent advancements in deep learning techniques, coupled with artificial intelligence (AI), have demonstrated promising results in breast cancer identification. This innovation facilitates early detection, consequently enhancing patient survival rates. Deep learning necessitates minimal human intervention for feature extraction, contrasting with traditional machine learning methods. The ML and DL techniques are practised and comparison of all these techniques were shown. Specifically, emphasis is placed on genomic and histopathologic imaging data. Various algorithms, including R, SVM, logistic regression, KNN, Naïve Bayes, CNN, and ANN, are thoroughly researched and valuated to gauge their efficacy. Furthermore, many screening protocols were deployed to identify and examine the datasets. Lastly, the paper explores the challenges encountered and the most possible directions to detect the breast cancer .Hence researchers and clinicians with a thorough understanding and insights into this deep learning domain.

In digital systems, the wave-pipelining concepts are used to achieve maximal rate of operation. A high operating frequency are obtained by adjusting the two factors called clock skew, & clock period in order to latch the combinational logic circuit output as stable. In literature, only trial & error, manual procedures are used for the choice of the optimum clock period and clock skew values between input/outputs register of wave-pipelined circuit.The major contribution of this paper is to propose automated ASIC implementation using wave-pipelined circuits.to verify the proposal a dual-tree complex wavelet transformation algorithm is taken into consideration in which scheme is again carried out into 3 types i.e., Pipelining, wave-pipelining, non-pipeliningWith the implementation result survey, it is observed that wave -pipelined circuits are 30% more faster than that of non-pipelining circuits, pipelined circuits are 49% more faster than that of wave-pipelined circuits but occupies 30% more area and 3% more power dissipating than that of wave-pipelined circuits.

The "Cyber Watch" initiative addresses the escalating need for comprehensive online security in our interconnected society. This paper is dedicated to safeguarding digital assets and ensuring a secure digital environment for users of all technical backgrounds. By employing cutting-edge tools and vigilant monitoring, "Cyber Watch" proactively identifies and thwarts cyber threats, maintaining a step ahead of cybercriminals to provide a worry-free online experience. Beyond technological measures, the project places a strong emphasis on user empowerment through educational initiatives on potential risks and protective measures. Adopting a multifaceted cybersecurity approach, it integrates threat detection, prevention, and mitigation strategies. Through the incorporation of machine learning and threat intelligence, "Cyber Watch" strives to establish resilient digital ecosystems capable of withstanding sophisticated cyberattacks.

It is widely acknowledged that Basalt Fiber (BF) and Steel Fiber (SF) concrete possess a significant characteristic of superior resistance to cracking and crack propagation. This unique ability to arrest cracks results in fiber composites having enhanced extensibility and Ts, both at the initial crack and at the ultimate stage, particularly under flexural loading. Moreover, these fibers are capable of maintaining the integrity of the matrix even after extensive cracking. This research paper delves into the mechanical properties, technologies, and applications of BF and SFs. The study involved experimental investigations using M40 grade concrete mix, and tests were conducted in accordance with recommended procedures outlined in relevant codes. The laboratory experiments included the design of cubes, beams, and cylindrical specimens with varying percentages of BF and SF concrete ranging from 0% to 1.25% (0%, 0.25%, 0.5%, 0.75%, 1% and 1.25%). The obtained data was analyzed and compared with a control specimen (0% Fiber), and ultimately, the test results for BF and SF were compared. Additionally, a graphical representation was provided to illustrate the relationship between Cs, Fs, Ts, and Age i.e. 7 days and 28 days.

Natural fibers are appealing due to its inexpensive price, small weight, and high specific modulus, renewable nature, as well as biodegradability. Bamboo fibers is one of the most promising natural fibers (along with jute, sisal, bamboo, coir, banana, and others) owing to its low cost, light weight, quick growing cycle, and wide availability. The goal of this paper is to look into fibers orientation reinforced hybrid polymer nano composites that combine BAMBOO/E-GLASS and epoxy polymer in the ratios of E-glass fibre80 percent +bamboo 20 percent, E-glass Fibre70 percent +bamboo 30 percent, and E-glass Fibre60 percent +bamboo 40 percent of volume. The mechanical characteristics will be calculated by performing tests on the specimen's tension, hardness, and flexural qualities in line with ASTM standards. Finally, the values are verified when the testing results have been obtained.

Balanced Incomplete Block Designs (BIBD) are the designs obtained from the arrangement of symbols in blocks following established rules and satisfying relevant necessary conditions. BIBDs have broad applications in many fields.The entries in a Hadamard matrix H are all +1’s or -1’s, satisfying the property HHT = n In [2]. Rhotrix is a new concept in mathematics having applications in cryptography. In this paper, using SBIB designs the Hadamard Rhotrices are constructed. AMS Subject classification: 05B05, 05B20, 62K10.

In engineering and transportation, beams are frequently utilized as structural elements for both static and dynamic loads. This paper focuses on a smart structure method that incorporates ANSYS software for modeling and simulation. Piezoelectric transducers are employed for actuation and sensing throughout the structure. The governing equation of a finite element that converts the mechanical energy of the structure into the electric energy of the piezoelectric material is derived using Hamilton's principle. Debonding of piezoelectric sensors/actuators is caused by structural alterations in sandwich beams, and this leads to a notable fluctuation in both static and dynamic responses. It has been shown that Finite Element Modeling with ANSYS software yields accurate numerical solutions over a broad range of related parameters. The results are displayed and vibration modes are animated by utilizing the graphical post processing capabilities. It has been observed that the material and structural alterations are what cause the increase in natural frequencies. With an input of 1 V, the load is adjusted between 0 and 5000 Hz for every material configuration.

The intellect and awareness of humans are thought to set them apart from one another. Studies conducted over the last few decades have demonstrated that women represent a substantial untapped reservoir of human capital. A growing number of competent women are entering fields that were previously thought to be exclusively male, thanks to the development of information technology and improved educational facilities. Because it is so important for businesses to draw in and keep workers in this fiercely competitive market, senior management has been paying enough attention to the creation of a women-friendly workplace. The bulk of research indicates that encouraging gender diversity has a beneficial impact on creativity and innovation. As a result, modern IT companies in India are implementing policies that guarantee a diverse workplace that is friendly to female employees. The aim of this paper is to examine the correlation between workplace assent of gender diversity, IT industry activities, and barriers to their implementation.

Nowadays home automation is based on the automation process or else the Internet of Things. This paper presents combination of artificial intelligence and the Internet of things-based automation system. Here we develop some fundamental algorithms of artificial intelligence and then integrate them with electronic sensors. The main part of the Internet of Things is a Controller, we are using an Arduino based controller module here. All the sensors used in home automation are in turn connect to this Arduino controller. These Sensors will read the data based on the specifications, then that data will send to the microcontroller to analyze and process and later the Artificial Intelligence starts working as per the data received. Our home appliances start acting depending on the sensor's values obtained. For suppose, if the climate is too cool it will automatically switch OFF the FAN (or) AC. Using AI based face recognition system will operate the doors to close and open, also it can give alerts. By using a water level sensor, we will come to know the water level of the tank, with the help of this ta the motor pump will be ON or OFF according to the values of the water level sensor. And using an MQ-08 sensor the smoke value will be measured in the house, if the smoke value will be HIGH, it may give an alert.

The paper examines the use of genetic algorithm (GA) methods to optimize hybrid renewable energy microgrids by merging various renewable sources and energy storage technologies. An examination of meteorological data over many days reveals fluctuations in solar irradiance ranging from 4.8 kW/m² to 5.5 kW/m² and wind speed oscillating between 3.9 m/s and 4.5 m/s, indicating the presence of dynamic weather conditions. An analysis of energy generating capabilities reveals a wide range of potentials, with solar capacities varying from 80 kW to 150 kW and wind capacities ranging from 60 kW to 120 kW across different sources. An analysis of Energy Storage System (ESS) specifications shows a range of values for maximum capacities, charge/discharge efficiencies (ranging from 85% to 96%), and maximum charge/discharge rates (from 60 kW to 100 kW), highlighting the need for flexible energy storage systems. The examination of microgrid load profiles reveals the presence of diverse energy needs, with residential loads oscillating between 48 kW and 55 kW, commercial loads ranging from 40 kW to 47 kW, and industrial loads spanning from 30 kW to 36 kW. A percentage change study reveals the ability to adapt, with solar irradiance and wind speed showing mild fluctuations of roughly 14% and nearly 15% respectively. In contrast, renewable source capacity demonstrate significant percentage changes ranging from around 40% to 50%. These results highlight the ever-changing characteristics of renewable energy sources, underlining the need for strong optimization tactics in microgrid systems. The study emphasizes the potential of GA-based approaches in developing efficient microgrids, promoting sustainable and dependable energy solutions in the face of changing environmental circumstances and varied energy requirements.

Technological progress is enhancing the accessibility and potential of solar energy as a renewable source. This paper introduces a strategy leveraging Ohm's law and the power equation to derive additional energy from solar photovoltaic (PV) panels. The proposed method involves the integration of an automated solar tracking system employing both dualaxis and polar single-axis configurations. This system is comprised of a stationary vertical axis and a flexible horizontal axis, both under motor control. To enhance power output and efficiency, the trackers autonomously follow the sun, constantly adjusting the solar panel positions. The tracking system self-adjusts in the event of a 2 to 3-degree misalignment to minimize power loss due to continuous motor operation. Light intensity sensors analyze illumination levels on each side, guiding the panels toward the light source. The tracking motion continues until equal light exposure on both sides is detected, leading to an enhanced solar irradiance for the panels

Skin cancer is the most lethal because skin cells develop abnormally. Finding skin cancer early is very important and may help stop some kinds of skin cancer, like melanoma and focal cell carcinoma. Early detection and classification of skin cancer are difficult and costly. Recurrent networks and ConvNets can automatically extract complex data. This paper proposes to use a handmade features-based multi-layer perceptron and a cascaded ensembled network to upgrade ConvNet models. This convolutional neural network model detects non-handmade picture qualities and generates features like color moments and material properties. With ensembled DL, accuracy increased from 85.3% with convolutional neural networks to 98.3%.

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

The present paper aims at the results of an experimental examination of hybrid metal matrix composites using stir-casting technique.Aluminum alloy (Al 6062) samples reinforced with Tungsten Carbide (WC) and Molydedneum diSulphide are given. 2wt.% , 4wt.%,6wt.% of MoS2 and 3 wt.%, 6 wt.%, 9 wt.% tungsten carbide were mixed together with the aluminum alloy to create the hybrid composite. The tribological aspects of aluminium composites were studied using Pin on disc Method under wet sliding condition.Testing the hybrid composites' hardness revealed that raising the weight percentage of tungsten carbide might raise the hybrid composites' hardness. To examine the wear behavior of hybrid composites, the Taguchi method was utilized in the design of experiments (DOE) approach. The factors that were found to affect wear rate included load, sliding speed, and reinforcing percentage of WC and MoS2 are the most influencing parameters.

This work focuses on the compact design of a self-standing electric scooter, specifically engineered to require minimal parking space. The paper details the design and modelling of an innovative electric scooter that possesses the unique ability to stand on its own, utilizing a footprint smaller than that of a typical umbrella stand when parked. Employing a minimalist design approach, the scooter achieves an aesthetically appealing, simple, and futuristic appearance. The primary objective of this project is to propose a practical and compact electric scooter design by utilizing CATIA V5 software for modelling and ANSYS for required calculations and SolidWorks for visualization,A design that not only enhances efficiency for short-distance travels but also effectively mitigates issues related to traffic congestion and limited parking space.

This paper presents the development of an innovative pavement maintenance and management system leveraging advanced drone imagery and Convolutional Neural Network (CNN) image classification. Our system is designed to perform 2D modelling of road surfaces using high-resolution images captured by drones. These images are then analysed by a CNN model specifically trained to detect and classify pavement damages in accordance with the IRC:82 'Code of Practice for Maintenance of Bituminous Surfaces of Highways'. The classification process identifies various types of road distresses such as cracks, potholes, and surface wear. Each identified distress is documented in a comprehensive report detailing the nature of the damage and recommending specific remedies as per IRC guidelines. Furthermore, the system categorizes the severity of the damages, facilitating the dispatch of these results to maintenance authorities for immediate action. This ensures that repair efforts are prioritized effectively, contributing to the maintenance of safer and higher quality roadways. By automating the detection and classification of road damages, this system not only accelerates the repair process but also plays a crucial role in reducing road accidents by maintaining better road conditions. This approach showcases the potential of integrating artificial intelligence and drone technology in the field of road maintenance, marking a significant step towards smarter and safer road infrastructure.

The growing need for energy, in addition to the depletion of fossil fuel supply, has underlined the importance of renewable energy for long-term growth. Renewable energy stands out among these, but its broad usage is hampered by the inherent uncertainty of wind power generation. This study uses machine learning to predict wind energy yield. Several regression models were used, including decision tree regression, linear regression, and random forest regression. The results emphasize the random forest regression, which has a high R-squared score, suggesting strong predictive ability. The paper also contains wind power output projections, which provide insights for optimal wind energy planning and usage. Overall, this attempt gives vital insights to improving the effective use of renewable energy, advancing the cause of sustainable development.

Robots are becoming increasingly important for security and surveillance applications. This research paper presents the design and hardware implementation of a low-cost and easy-to-assemble site surveillance robot using an Arduino Mega 2560 microcontroller and a Bluetooth module HC-05. The robot is equipped with various modules and sensors, including a camera and ultrasonic sensors, for capturing images and detecting obstacles, respectively. The Arduino Mega 2560 is used to control the movement of the robot and process sensor data, while the Bluetooth module is used for wireless communication between the robot and an Android device. The robot is capable of patrolling a designated area and sending real-time surveillance footage to an Android device via Wi-Fi. The developed system was tested and evaluated to demonstrate its performance and efficiency. The results showed that the robot is able to patrol a designated area accurately and efficiently and that the real-time surveillance footage is of high quality. The system has the potential to be used in a variety of applications, such as object retrieval, security, and surveillance in residential and commercial areas and spying on certain sites.

Selecting a supplier is a crucial task for any manufacturing company aiming to optimize its supply chain operations and gain a competitive advantage. The complexities of this decision-making process, influenced by various criteria and uncertainties, underscore the need for advanced methodologies such as Multi-Criteria Decision-Making (MCDM) techniques. These methodologies provide a structured framework for evaluating and prioritizing potential suppliers based on numerous criteria, often with conflicting considerations. This paper demonstrates the integration of the Analytical Hierarchy Process (AHP) and Simple Additive Weighting (SAW) through a practical example involving ten different alternatives and four distinct criteria. The alternatives are assessed and ranked for performance, utilizing systematic and objective approaches to address the inherent multi-criteria complexity of supplier selection. The strength of MCDM lies in its capability to handle both quantitative and qualitative factors, supported by decision support tools, making it a valuable asset for improving the supplier selection process.

Addressing the challenges faced by individuals with motor disabilities, which impedes ease in movement, coordination, or sensation, a low-weight hand exoskeleton was developed considering accessibility, low cost, adaptability, and mass manufacturing to overcome the impact of hand functioning disorders on people's physical and social well-being. In this paper, a 12-DOF Hand exoskeleton consisting of a 6-DOF Arm and 6-DOF finger exoskeleton was designed and later its kinematic analysis was performed. By taking into consideration the constraints provided by the exoskeleton to human hand motion, the Forward kinematics, using Denavit- Hartenberg (D-H) parameters were conducted to determine its workspace.

This paper presents a comprehensive study on optimizing the design of a vane-type air swirl burner, along with a computational analysis comparing the combustion of diesel and hydrogen fuel within the same burner. For the combustion simulations, a coupled approach was employed involving a Lagrangian approach with the Discrete Phase Model to track the trajectories of fuel particles providing a comprehensive understanding of the particulate dynamics within the system. This research aimed to analyze hydrogen's potential as a sustainable energy carrier in comparison to its conventional counterparts such as diesel fuel. The results showed that the contours of hydrogen fuel revealed a well-defined and intense flame front whereas it was not the same in the case of diesel fuel which had a less intense flame front.