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3C Customized Cascaded Cryptography for Mobile Application" is an authoritative and comprehensive guide that explores the innovative approach of 3C customized cascaded cryptography for securing mobile applications. Authored by experts in the field of cryptography and mobile security, this book serves as a valuable resource for researchers, mobile app developers, and security professionals interested in enhancing the security of mobile applications using advanced cryptographic techniques.In this book, the authors introduce the concept of 3C customized cascaded cryptography, which combines three fundamental cryptographic components-encryption, hashing, and digital signatures-to provide a robust and tailored security solution for mobile applications. They present a detailed analysis of the architecture, algorithms, and implementation strategies for 3C cryptography, offering practical insights into its benefits and applications.Key topics covered in this book include:Introduction to mobile application security: The authors provide an overview of the security challenges faced by mobile applications, including data breaches, unauthorized access, and malicious activities. They discuss the importance of robust cryptographic techniques in ensuring the confidentiality, integrity, and authenticity of mobile app data.Fundamentals of cryptography: The book explores the fundamental cryptographic components of encryption, hashing, and digital signatures. It explains their purpose, algorithms, and their roles in securing mobile application data.Customized cascaded cryptography: The authors introduce the concept of customized cascaded cryptography, which involves combining the three cryptographic components in a tailored manner to suit the specific security requirements of mobile applications. They outline the advantages of this approach and its ability to provide strong and adaptable security measures.Architecture and implementation strategies: The book presents the architectural framework for integrating 3C cryptography into mobile applications. It discusses the design considerations, key management techniques, and implementation strategies for ensuring seamless integration and efficient performance.Security analysis and evaluation: The authors provide a comprehensive security analysis of 3C cryptography, examining its resistance against various attacks and vulnerabilities. They discuss the strengths and limitations of the approach, offering insights into its effectiveness in protecting mobile application data.Real-world applications and case studies: The book includes practical examples and case studies that demonstrate the application of 3C customized cascaded cryptography in different mobile application scenarios. It covers areas such as mobile banking, secure messaging, e-commerce, and data sharing, showcasing the versatility and effectiveness of the approach.Future directions and emerging trends: The authors discuss the future directions and emerging trends in mobile application security and customized cryptography. They explore areas such as post-quantum cryptography, blockchain integration, and secure decentralized applications, providing insights into the evolving landscape of mobile security.Throughout the book, the authors provide theoretical explanations, algorithmic details, and practical implementation guidelines to facilitate the understanding and adoption of 3C customized cascaded cryptography for mobile applications. By leveraging this innovative approach, "3C Customized Cascaded Cryptography for Mobile Application" enables readers to enhance the security posture of their mobile applications and protect sensitive data in an increasingly connected and vulnerable digital environment.

"Scalable Framework for Cyber Threat Situational Awareness" is a comprehensive and practical guide that explores the development and implementation of a scalable framework for achieving effective cyber threat situational awareness. Authored by cybersecurity experts and researchers, this book serves as a valuable resource for security professionals, analysts, and decision-makers seeking to enhance their understanding of cyber threats and improve their response capabilities.In this book, the authors address the critical need for organizations to establish robust situational awareness capabilities to detect, analyze, and respond to cyber threats in real-time. They present a scalable framework that integrates various data sources, analysis techniques, and visualization tools to provide a holistic view of the evolving threat landscape.Key topics covered in this book include:Introduction to cyber threat situational awareness: The authors provide an overview of the concept of cyber threat situational awareness, its importance in modern cybersecurity, and the challenges faced in achieving comprehensive awareness in dynamic and complex environments.Scalable framework architecture: The book presents the architecture of a scalable framework for cyber threat situational awareness. It covers the integration of diverse data sources, including network logs, intrusion detection systems, threat intelligence feeds, and user behavior data. The authors discuss the design principles and components necessary for building a scalable and adaptable framework.Data collection and aggregation: The authors delve into the process of collecting and aggregating data from various sources within the organization and external feeds. They explore techniques for data normalization, filtering, and enrichment to ensure the availability of high-quality data for analysis.Threat detection and analysis: The book covers advanced analytics techniques and algorithms for detecting and analyzing cyber threats. It explores anomaly detection, machine learning, and behavioral analysis approaches to identify patterns, indicators, and potential threats within the data.Visualization and reporting: The authors discuss visualization tools and techniques for presenting cyber threat information in a meaningful and intuitive manner. They highlight the importance of visualizing complex data to aid in decision-making, incident response, and collaboration among security teams.Incident response and mitigation: The book explores strategies for incident response and mitigation based on the insights gained from the cyber threat situational awareness framework. It covers incident triage, prioritization, and response coordination to ensure timely and effective actions against identified threats.Scalability and adaptability: The authors address the scalability and adaptability considerations of the framework, enabling organizations to handle large volumes of data, accommodate evolving threats, and integrate new data sources and analysis techniques.Integration with existing security systems: The book provides guidance on integrating the cyber threat situational awareness framework with existing security systems, such as security information and event management (SIEM) platforms, intrusion detection systems (IDS), and security orchestration, automation, and response (SOAR) tools.Emerging trends and future directions: The authors discuss emerging trends and technologies in cyber threat situational awareness, including threat intelligence sharing, collaborative defense, and leveraging artificial intelligence (AI) and machine learning (ML) for automated threat analysis.

Genuis is a 12-year-old girl who is surprised to find out she is a sorceress. She attends a finishing school called The Grand Miikay Finishing School where she meets Ava and Khan. One day, their curiosity gets the better of them when they read a note they find on the ground. What a huge mistake! Come with them on their adventures and find out how they escape the grasp of a huge evil bear. Just as they escape, they get sucked into another mess!

"Crime in Cyberspace: An Analytical Study" is a comprehensive and insightful book that delves into the world of cybercrime, providing an in-depth analysis of its various dimensions and implications. Authored by experts in the field of cybersecurity and criminology, this book serves as an essential resource for researchers, law enforcement professionals, policymakers, and anyone interested in understanding and combating cybercrime.In this book, the authors explore the evolving landscape of crime in cyberspace, examining the motivations, methods, and impact of cybercriminal activities. They employ an analytical approach to dissect the complex nature of cybercrime, shedding light on its underlying factors, patterns, and trends. The book covers a wide range of cybercrimes, including hacking, identity theft, fraud, cyberbullying, online harassment, data breaches, and more.Key topics covered in this book include:Introduction to cybercrime: The authors provide an overview of cybercrime, its definitions, and its significance in the digital age. They discuss the unique challenges posed by cybercrime, such as anonymity, global reach, and the rapidly evolving nature of technology.Types of cybercrime: The book explores different categories of cybercrime, offering detailed insights into the various techniques employed by cybercriminals. It covers computer intrusion, phishing, ransomware attacks, social engineering, intellectual property theft, and other prevalent forms of cybercrime.Cybercriminal motivations and profiles: The authors delve into the motivations and characteristics of cybercriminals. They examine factors such as financial gain, ideology, revenge, and thrill-seeking, providing a nuanced understanding of the individuals or groups behind cybercriminal activities.The impact of cybercrime: The book addresses the far-reaching impact of cybercrime on individuals, organizations, and society as a whole. It discusses financial losses, reputational damage, privacy breaches, psychological effects, and the erosion of trust in online environments.Cybercrime investigation and prevention: The authors explore investigative techniques, tools, and strategies employed in cybercrime detection and prevention. They discuss digital forensics, incident response, threat intelligence, and the role of collaboration between law enforcement agencies, private organizations, and international bodies.Legal and policy considerations: The book examines the legal and policy frameworks surrounding cybercrime. It analyzes existing laws, regulations, and international treaties aimed at combating cybercrime, highlighting the challenges and opportunities for effective law enforcement and international cooperation.Emerging trends and future challenges: The authors discuss emerging trends in cybercrime, such as the rise of cryptocurrencies, the Internet of Things (IoT), and artificial intelligence (AI)-based attacks. They also explore the future challenges that law enforcement and cybersecurity professionals may face as technology continues to evolve.Throughout the book, the authors provide real-world examples, case studies, and practical insights to illustrate the complexities of cybercrime and the strategies employed to combat it. By presenting a comprehensive analytical study of crime in cyberspace, this book equips readers with the knowledge and tools necessary to understand, investigate, and mitigate the ever-growing threat of cybercrime in our interconnected digital world.

An artificial intelligence (AI) platform for web security solutions refers to a comprehensive system that utilizes AI techniques to enhance the security of websites and web applications. In today's digital landscape, where online threats and attacks are prevalent, it is essential to have robust security measures in place to protect sensitive data, prevent unauthorized access, and ensure the integrity of web platforms. An AI-powered web security platform leverages advanced algorithms and machine learning models to analyze and detect potential security risks in real-time. It combines various security mechanisms, such as intrusion detection systems (IDS), web application firewalls (WAF), and behavioral analytics, to provide a layered defense against cyber threats. The platform utilizes AI algorithms to continuously monitor web traffic, user behavior, and system logs, identifying patterns and anomalies that could indicate malicious activities. It can detect and block common attack vectors, including SQL injections, cross-site scripting (XSS) attacks, and distributed denial-of-service (DDoS) attacks, among others. Machine learning algorithms are trained on large datasets of known attack patterns and security incidents, enabling the AI platform to recognize and classify new and emerging threats effectively. As the system encounters new types of attacks or suspicious activities, it learns from them and improves its detection capabilities over time. Furthermore, an AI platform for web security can also provide adaptive and proactive defenses by analyzing user behavior and profiling normal activity patterns. It can detect anomalous behavior, such as unauthorized access attempts or unusual data transfers, and raise alerts or take preventive actions to mitigate potential security breaches.

Software Estimation Artificial Neural Networks (SEANN) is a cutting-edge approach to estimating software development effort using artificial neural networks (ANNs). It leverages the power of machine learning and neural networks to predict the time, resources, and effort required to complete software development projects. SEANN is designed to address the challenges and complexities associated with software estimation, which traditionally relies on subjective judgment and expert opinion. By utilizing ANNs, SEANN can learn from historical data and identify patterns, relationships, and dependencies that impact software development effort. This enables more accurate and reliable estimations compared to traditional methods. The core of SEANN lies in its neural network architecture, which is inspired by the structure and functioning of the human brain. The network consists of interconnected nodes, or artificial neurons, organized into multiple layers. Each neuron receives input signals, performs computations, and produces an output signal that is passed on to the next layer. Through a process of training, the network adjusts the connections between neurons to optimize its performance and improve estimation accuracy.

The Certain Investigation Artificial Intelligence (AI) for Power Transformer Protection is an advanced system designed to monitor, analyze, and protect power transformers in electrical substations. This AI-driven solution leverages the power of machine learning algorithms and data analytics to provide real-time insights and proactive measures for ensuring the reliable operation of power transformers. At its core, this AI system continuously gathers and processes data from various sensors and monitoring devices installed in power transformers. These sensors measure parameters such as temperature, oil levels, winding currents, voltage fluctuations, and gas composition. The AI analyzes this data to identify patterns, anomalies, and potential risks associated with the transformer's performance. By employing machine learning techniques, the AI model is trained to recognize normal operating conditions and detect any deviations that may indicate a fault or impending failure. It can identify issues such as overheating, insulation breakdown, abnormal vibration, or excessive load conditions. The AI system can also differentiate between transient disturbances and critical events, minimizing false alarms and ensuring accurate and timely notifications.

Artificial intelligence evaluation quality in fruits ripening refers to the use of AI techniques to assess and determine the quality of fruits based on their ripeness. Ripeness is an essential factor in the fruit industry, as it directly affects the taste, texture, and nutritional value of fruits. Accurately evaluating fruit quality is crucial for growers, distributors, and consumers to ensure optimal harvesting, storage, transportation, and consumption decisions. AI techniques employed in fruit ripening evaluation leverage various data sources and advanced algorithms to make precise assessments. These techniques often involve image processing and computer vision algorithms to analyze visual features of fruits, such as color, shape, size, and texture. By capturing images or using specialized sensors, AI models can extract relevant information and compare it with known patterns to determine the ripeness stage of each fruit. Machine learning algorithms play a vital role in training and developing accurate models for fruit ripening evaluation. These algorithms learn from large datasets of labeled fruits, where ripeness stage annotations are provided by human experts or through scientific measurements. By processing and analyzing this data, the AI models can identify correlations and patterns that indicate specific ripeness characteristics and develop predictive models.

Supply chain digitalization refers to the process of integrating digital technologies and systems into various aspects of the supply chain management process. This transformative approach aims to streamline operations, enhance efficiency, and optimize decision-making by leveraging advanced technologies and data-driven insights. In the context of studying issues related to supply chain digitalization, researchers and industry professionals delve into the challenges, opportunities, and potential risks associated with adopting digital solutions in supply chain management. The purpose of such studies is to gain a deeper understanding of the implications, best practices, and strategies for successful implementation of digital technologies in the supply chain. One of the key areas of focus in studying supply chain digitalization issues is the technological infrastructure required to support digital transformation. This includes evaluating the compatibility of existing systems, identifying gaps in capabilities, and exploring the integration of emerging technologies such as blockchain, Internet of Things (IoT), artificial intelligence (AI), and machine learning (ML) into supply chain operations. Researchers also examine the potential benefits and drawbacks of adopting these technologies, considering factors such as cost, scalability, security, and interoperability. Another significant aspect of studying supply chain digitalization issues involves analyzing the impact on various stakeholders, including suppliers, manufacturers, distributors, and customers. Researchers investigate how digitalization affects the roles, responsibilities, and relationships within the supply chain ecosystem. They explore the implications for workforce skills and training, organizational structures, and the overall business models of companies operating in the supply chain.

The widespread of using bar codes for most commercial products increased the capabilities of both generating and collecting data. The computerization of business management, government administration, scientific and engineering data management has led to the growth of powerful databases in which analysis is important. For this purpose, data mining emerged aiming to transform the processed data into useful information and knowledge. Data mining is defined as the process of extracting hidden, previously unknown, and potentially useful information from data. The idea is to build machine learning algorithms that sift through databases automatically in order to discover regularities or patterns.

Electric load forecasting is an application of artificial intelligence techniques that aims to predict the future electricity consumption of a particular region or system. It utilizes advanced algorithms and data analysis methods to estimate the amount of electricity that will be required over a specific period, such as hours, days, weeks, or even months ahead. The primary goal of electric load forecasting is to enable efficient and reliable operation of the electrical grid. By accurately predicting future electricity demand, power grid operators can make informed decisions regarding power generation, distribution, and resource allocation. This helps in avoiding power shortages or excess capacity, optimizing energy production, and ensuring a stable and cost-effective electricity supply. Artificial intelligence techniques play a crucial role in electric load forecasting due to the complexity and volatility of electricity consumption patterns. These techniques leverage historical data, such as previous load profiles, weather conditions, economic indicators, and demographic information, to develop mathematical models and algorithms that capture the underlying patterns and relationships between various factors influencing electricity demand.

Intelligent System Exploration of Web User Behavior is a field that combines artificial intelligence, data analytics, and user behavior analysis to understand and explore how individuals interact with websites and online platforms. It involves the use of computational intelligence techniques to extract valuable insights from user actions, preferences, and patterns in order to enhance user experience, optimize website design, and improve business outcomes. By employing advanced algorithms and machine learning models, Intelligent System Exploration can collect and analyze vast amounts of data related to user behavior on websites. This data includes user clicks, navigation paths, time spent on pages, interactions with forms or buttons, and other relevant metrics. These patterns and behaviors are then processed and interpreted to gain a deeper understanding of user preferences, intentions, and needs.

A comprehensive analysis of high-efficient maximum power point tracking (MPPT) techniques for solar photovoltaic (PV) grid-connected systems involves a detailed examination and evaluation of various MPPT algorithms and strategies to maximize the power output from PV panels and optimize their performance in grid-connected applications.The analysis begins by reviewing the fundamentals of MPPT and its significance in solar PV systems. It explores the challenges faced in grid-connected systems, such as varying environmental conditions, partial shading, and module mismatch, which affect the PV system's efficiency and power generation. The goal is to identify the most effective MPPT techniques that can address these challenges and enhance the overall system performance.Various MPPT techniques are analyzed, including Perturb and Observe (P&O), Incremental Conductance, Fractional Open Circuit Voltage, and many more. Each technique is assessed based on its ability to accurately track the maximum power point (MPP) under dynamic conditions and its suitability for grid-connected systems. Factors such as tracking speed, accuracy, convergence behavior, and stability are considered to determine the most efficient and reliable MPPT strategy.Additionally, the analysis includes a thorough investigation of the impact of different system parameters and configurations on MPPT performance. Parameters such as PV panel characteristics, converter topologies, control algorithms, and communication interfaces are examined to understand their influence on the effectiveness of MPPT techniques in grid-connected PV systems. The analysis also explores the interactions between MPPT and grid requirements, including voltage and frequency regulations, to ensure seamless integration and compliance with grid standards.Furthermore, performance evaluation and comparison of different MPPT techniques are conducted using simulation models and experimental setups. Various scenarios, including different solar irradiance levels, temperature variations, shading conditions, and module configurations, are considered to assess the techniques' performance under real-world operating conditions. The analysis aims to quantify the efficiency improvements, power extraction gains, and overall system performance achieved by each MPPT technique.The findings of the comprehensive analysis provide valuable insights into the selection and implementation of high-efficient MPPT techniques for solar PV grid-connected systems. They guide system designers and engineers in choosing the most suitable MPPT algorithm based on specific system requirements, thereby enhancing the energy generation efficiency, maximizing the power output, and ensuring optimal utilization of solar energy in grid-connected applications. Ultimately, this analysis contributes to the advancement and optimization of solar PV systems in meeting the growing demand for clean and renewable energy.

Performance improvement of maximum power point tracking (MPPT) controllers focuses on enhancing the effectiveness, accuracy, and efficiency of MPPT algorithms and techniques used in photovoltaic (PV) systems. The primary goal is to optimize power extraction from PV panels by continuously tracking and maintaining the system's operation at the maximum power point (MPP) under varying environmental conditions.The performance improvement of MPPT controllers involves several aspects, including algorithm design, control strategies, and system optimization. Researchers and engineers work on developing advanced MPPT algorithms that offer better tracking accuracy, faster response time, and improved stability compared to traditional techniques.One area of focus is algorithm design, where novel MPPT algorithms are developed or existing algorithms are enhanced to overcome limitations and improve performance. These algorithms utilize sensor measurements, such as PV panel voltage, current, and irradiance, to dynamically adjust the operating point of the PV system for maximum power extraction. Advanced techniques, such as adaptive algorithms, fuzzy logic, neural networks, and machine learning, may be employed to optimize the tracking efficiency and adaptability of the MPPT controllers.Another aspect is control strategy improvement, where the control parameters and feedback loops of the MPPT controllers are optimized. This involves carefully tuning the control parameters to ensure stable and efficient operation of the MPPT system. The feedback loops are designed to accurately measure and adjust the system's operating conditions, compensating for variations in solar irradiance, temperature, and load changes. Robust control techniques are implemented to handle uncertainties and disturbances, improving the overall performance of the MPPT controller.System optimization is also crucial for performance improvement. This includes considering factors such as PV panel characteristics, converter topologies, sensor placement, and communication interfaces. Optimizing these system parameters ensures compatibility and efficient operation of the MPPT controllers, leading to improved power extraction and higher energy yield from the PV system.To evaluate the performance improvement of MPPT controllers, simulations and experimental tests are conducted. Various operating conditions, such as different solar irradiance levels, temperature variations, shading effects, and load profiles, are considered to assess the controllers' effectiveness under real-world scenarios. Performance metrics, such as tracking accuracy, convergence speed, energy yield, and power losses, are analyzed to quantify the improvement achieved by the enhanced MPPT controllers.Overall, the performance improvement of MPPT controllers aims to enhance the energy conversion efficiency, increase power output, and maximize the utilization of solar energy in PV systems. It contributes to the advancement of renewable energy technologies, making PV systems more efficient, reliable, and economically viable for a wide range of applications.

The design of maximum power point tracking (MPPT) techniques for standalone photovoltaic (PV) systems involves the development and implementation of strategies to optimize the power extraction from PV panels in off-grid or standalone applications. Standalone PV systems are commonly used in remote areas or places without access to the electrical grid, where they provide independent and sustainable power generation.The MPPT design techniques for standalone PV systems aim to address the challenges faced in maximizing the power output in varying environmental conditions, such as changing solar irradiance levels, temperature variations, and shading effects. These techniques focus on accurately tracking and maintaining the PV system's operation at the maximum power point (MPP) under dynamic conditions to ensure efficient energy conversion.There are various MPPT design techniques employed in standalone PV systems. These techniques utilize different control algorithms and strategies to continuously monitor the PV panel's operating conditions and adjust the system's operating parameters for optimal power extraction. Examples of MPPT design techniques include Perturb and Observe (P&O), Incremental Conductance, Fractional Open Circuit Voltage, and Model Predictive Control, among others.The design of MPPT techniques for standalone PV systems involves selecting the most appropriate algorithm based on the specific system requirements and characteristics. Factors such as algorithm complexity, tracking accuracy, convergence speed, stability, and robustness are considered in the selection process. The chosen MPPT technique should be capable of adapting to changing environmental conditions, compensating for PV panel degradation, and providing reliable and efficient operation in standalone applications.The design also encompasses considerations related to hardware implementation, including sensor selection, converter topologies, and control circuitry. Sensors, such as current and voltage sensors, are used to measure the PV panel's electrical parameters, while converters, such as buck, boost, or buck-boost converters, are employed to optimize the power transfer between the PV panel and the load or energy storage system.Furthermore, the design of MPPT techniques for standalone PV systems involves performance evaluation and optimization. Simulation models and experimental setups are used to assess the MPPT technique's performance in various operating conditions and load profiles. The objective is to maximize the energy yield, minimize power losses, and ensure reliable and efficient operation of the standalone PV system.In summary, the design of MPPT techniques for standalone PV systems involves selecting the appropriate control algorithm, optimizing hardware implementation, and evaluating the performance under different operating conditions. These design techniques aim to enhance the energy conversion efficiency, increase the power output, and ensure the reliable and sustainable operation of standalone PV systems in areas without access to the electrical grid.

Performance analysis of maximum power point tracking (MPPT) techniques involves a thorough evaluation and assessment of the effectiveness, accuracy, and efficiency of MPPT algorithms and controllers used in photovoltaic (PV) systems. The primary objective is to analyze the performance of the MPPT techniques in maximizing the power output from PV panels under various operating conditions.The performance analysis of MPPT techniques entails several key aspects. One of the primary focuses is the tracking accuracy of the MPPT algorithms. The analysis examines how well the algorithms track the maximum power point (MPP) of the PV system, considering factors such as solar irradiance variations, temperature fluctuations, and shading effects. The accuracy of the MPPT technique is crucial in ensuring that the PV system operates consistently at its MPP, thereby maximizing power generation.Efficiency analysis is another vital component of the performance assessment. It involves evaluating the energy conversion efficiency of the MPPT techniques and their impact on the overall system efficiency. The analysis considers losses in the conversion process, such as conduction losses, switching losses, and control losses, to quantify the efficiency improvement achieved by the MPPT algorithms. Comparisons are made between different MPPT techniques to identify the most efficient approach.Stability analysis is also an important part of the performance evaluation. The analysis examines the stability of the MPPT controllers under various operating conditions and system parameters. It ensures that the MPPT techniques are robust and capable of maintaining stable operation, even in the presence of disturbances or uncertainties. Stability analysis helps identify any oscillations, instability, or performance degradation that may occur and provides insights for system optimization.Furthermore, the performance analysis of MPPT techniques may involve studying the response time and dynamic behavior of the algorithms. This includes evaluating the speed at which the MPPT technique adapts to changes in solar irradiance or other system parameters. The analysis focuses on the response time and the ability of the MPPT algorithms to quickly and accurately track the MPP, particularly during rapid changes in environmental conditions.To perform the performance analysis, various methodologies are utilized, such as simulations, mathematical modeling, and experimental tests. Simulations allow for the evaluation of different scenarios and operating conditions, providing insights into the performance of the MPPT techniques. Mathematical modeling helps in analyzing the theoretical performance limits and predicting the behavior of the MPPT algorithms. Experimental tests are conducted in real-world PV systems to validate the performance of the MPPT techniques under practical conditions.The findings from the performance analysis of MPPT techniques provide valuable insights into the strengths, weaknesses, and limitations of different algorithms. This analysis guides system designers and engineers in selecting the most suitable MPPT technique for a given PV system and optimizing its performance. By improving the performance of MPPT techniques, PV systems can achieve higher energy yields, maximize power generation, and contribute to more efficient and sustainable use of solar energy.

Investigations on automatic maximum power point tracking (MPPT) for single-phase standalone photovoltaic (PV) systems involve in-depth studies and analysis of MPPT algorithms and their application in optimizing the power output of PV systems operating independently from the grid. Standalone PV systems are commonly used in remote areas or off-grid applications to generate electricity from solar energy.The investigations begin by examining the principles of MPPT and the importance of accurate tracking in maximizing the power generation from PV panels. Various MPPT algorithms, such as Perturb and Observe (P&O), Incremental Conductance, and Fractional Open Circuit Voltage, are evaluated and compared in terms of their effectiveness, accuracy, and response time. The focus is on identifying the most suitable algorithm for automatic MPPT control in single-phase standalone PV systems.The investigations also consider the design and implementation aspects of automatic MPPT control in standalone PV systems. Factors such as system architecture, sensor selection, control loop design, and communication interfaces are taken into account to ensure efficient and reliable MPPT operation. Additionally, considerations related to system stability, noise immunity, and anti-islanding protection are addressed to ensure safe and robust operation of the standalone PV system.The performance evaluation of the automatic MPPT algorithms is a crucial part of the investigations. Real-world experiments are conducted under different environmental conditions, including varying solar irradiance levels, temperature changes, and shading effects. The investigations analyze the algorithms' ability to accurately track the maximum power point (MPP) and assess their performance in terms of power extraction efficiency, response time, and tracking accuracy.Furthermore, the investigations explore the impact of system parameters, such as PV panel characteristics, converter topologies, and energy storage systems, on the performance of the automatic MPPT control. The aim is to understand the interactions and dynamics between different components and optimize the system design for improved MPPT performance and overall energy generation.The findings of the investigations provide valuable insights into the automatic MPPT control for single-phase standalone PV systems. They contribute to the development of more efficient and reliable PV systems that can autonomously track and extract the maximum power from solar energy. These investigations also pave the way for advancements in standalone PV system technologies, enabling reliable and sustainable electricity generation in areas without grid access or in off-grid applications.

A thorough and updated knowledge of existing and new drugs is essential for the physician. Various promotional tools are used in the pharmaceutical industry to inform, educate and influence the prescribing behavior of physicians. The promotion aimed at physicians thus performs a two-pronged role. On the one hand it informs and educates physicians because they are primarily interested in patient care and would like o keep abreast of scientific advances in the field.

Hydrodynamic stability is a fascinating field in the fluid mechanics, that deals with the transition of laminar flow to turbulence. In fact, this field discloses the originofinstabilityandleadstothephysicalmechanism. Italsounveilsavari- ety of complex phenomena on the fluid surface including the emergence of pri- mary and secondary waves and their behaviors in various physical conditions when the controlling flow parameters alter. More specifically, hydrodynamic instability enables us to decipher the rationale of complex waves appeared in different regimes of flow parameter. The study of hydrodynamic instability was initiated in the beginning of 19th century.

This study makes several important contributions to the existing empirical literature in terms of both data and methodology. First, our measure of TFP is adjusted for heterogeneous quality of labour and different vintages of capital assets which helps us in improving the reliability of our elasticity estimates.4 Second, as discussed above, the estimated elasticity of infrastructure is quite sensitive towards the estimator applied, hence, we use a bootstrap panel cointegration test that helps us to control for both sectoral dependencies and serial correlation.

With the emotion recognition system, AI can detect the emotions of a person through their facial expressions. Detected emotions can fall into any of the six main data of emotions: happiness, sadness, fear, surprise, disgust, and anger. For example, a smile on a person can be easily identified by the AI as happiness.

The criteria for the product quality used in crystallisation are typically the crystal size distribution and the crystal shape. The product specifications are important as they affect the downstream processing unit operations as also are specific depending upon the area ofapplications. For example, different polymorphic forms have different solubility, different sizes/shapes of crystals have different flowability and filterability.

Cloud computing is a modern framework to allows to user to access on demand network access for sharing resources such as storage, networks, services, servers and applications) which can be released and reserved with minimum effort of management and interaction of service provider. Cloud computing always implements the service delivery process at minimum cost as per requirement which made it advance for using the online virtual system, different platforms and storage effectively with minimum cost.

The objective of the study is to evaluate the thermal environmental conditions and quantify thermal adaptation for occupants of these buildings. The study ascertains thermal neutrality and thermal acceptability and compares adaptation with field studies referred by a pool of researchers and scientists. The methodology of the study was through questionnaire administered to building occupants to record sensations and preferences for thermal environment variables. Simultaneously, physical measurements of environment variables were recorded

In the developing countries provision of financial literacy could be treated as first step towards alleviation of poverty and development. In India it is although more important because of large section of the population especially in the rural areas which are deprived of formal financial set up. India has world's best efficient financial markets in terms of technology, system and it's regulation.

In general the digital image will be contaminated with noise. Retrieval of actual image by removing of noise is referred to as imagedenoising. The development of algorithms for image denoising is one of the most challenging task because removal of noise from themedical image should be done without destroying particular textures of image that are important for medical diagnosis and treatment.Recently, several denoising algorithms are proposed by many researchers in literature in order to preserve the important textures ofmedical images.

Marketers do not direct food advertisements at children only. Rather, they try to capture attention of general-audience (child-audience and adult-audience both) as sometimes older children and adults also watch programs appeared on children'stelevision networks . Therefore, on comparing the nature of themes/appeals used in food advertisements directed at audience of different age groups, it is found that the most frequently used appeals in commercials aimed at children areplay, action-adventure, fun, courage, affection for animals and collecting . Whereas, commercials aimed at teenagers are found to use appeals of being modern, being cool, seizing opportunities, having the best, belonging to a group and energy. Similarly, appeals of convenience, financial security, health, tidiness, love, physical attractiveness, having natural, self-esteem and career were found to be usedfrequently in television food advertisements targeted at general-audience.

Cereals, the major group of grains, are grown on nearly 60% of the cultivated land in the world. This category mainly includes rye, oats, barley, wheat, rice, maize, millets, and sorghum. Cereal-based foods are the most important staple foods for mankind. They contain the major macronutrients (carbohydrate, fat and protein) required by humans for growth and maintenance. Cereals also supply importantvitamins, minerals as well as other micronutrients essential for good health.

The specific application of target classification especially, in the context of the vehicle classification in the Harsh Amphibious Terrain (HAT), the combined use of the acoustic sensor to measure sound signals and the unattained ground sensors such as a seismic sensor to measurethe vibration created by the particular types of vehicle may provide better results of classification.

The solar cells are made of semiconductor material with a p-n junction fabricated in a thin wafer layer. These cells, when exposed to light, aphoto current proportional to the solar radiation is generated. In the dark, the I-V characteristics of a solar cell have an exponential characteristic similar to that of a diode . In order to maximize the output power from a PV cell with the help of MPPT control, the modeling of PV cell is necessary. The ideal equivalent circuit of a PV cell is a current source in parallel with a single-diode.