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The outbreak of COVID-19 has caused a global health crisis and has severely impacted the economy and daily life of people. Predicting the spread of COVID-19 is of utmost importance to effectively control the spread of the disease. In this study, we propose a COVID-19 prediction model using Support Vector Machine (SVM) and Linear Regression algorithms. We collected data on the number of confirmed cases, recovered cases, and deaths caused by COVID-19 from January 2020 to March 2023. We divided the data into training and testing datasets and applied feature engineering techniques to extract relevant features. We then trained our model using SVM and Linear Regression algorithms on the training dataset. The results of our experiments show that the SVM model achieved little bit less accuracy than the Linear Regression model in predicting the number of confirmed cases, recovered cases, and deaths. Our model can provide accurate predictions and insights into the future trends of COVID-19 cases.

Network Functions Virtualization (NFV) and Software-Defined Networking (SDN) are two innovative technologies that have emerged in recent years to address the limitations of traditional network architectures. NFV enables network functions to be implemented on standard hardware, rather than on dedicated proprietary devices, while SDN separates the control plane from the data plane, enabling centralized control and management of network traffic. Together, NFV and SDN provide a new era of network agility and flexibility, enabling network operators to dynamically provision and scale network services to meet changing demands, improve network efficiency and reduce costs.

The project involves using Block chain technology and non-fungible tokens (NFTs) to manage assets such as residential, commercial, and agricultural lands. Traditional methods of tracking ownership of these assets require long and complex processes, including the need to obtain title deeds from government offices. The proposed system aims to simplify this process by providing a secure and verifiable way to represent ownership of assets through the use of NFTs and smart contracts on the Ethereum network. This system not only makes it easier to track ownership of assets, but also helps to ensure that assets are properly insured and that regular payments are made for insurance, taxes, and subscriptions like electricity, water, and natural gas. By using NFTs, the system can also represent rare and unique assets that cannot be replicated or replaced, which increases their value and collectability.

Introduction: Uncomplicated urinary tract infection is among the more common infections in females. The S3 guideline contains recommendations based on current evidence for the rational use of anti - microbial agents and for the prevention of inappropriate use of certain classes of antibiotics and thus of the resulting drug resistance. Methodology: A hospital based, prospective, observational type of study to be conducted in Inpatient department, Government general hospital, Kadapa, Andhra Pradesh. Study was conducted over a period of 6 months (September 2019 to Feb 2020). Results: According to our study, patients with illiterate (26) were mostly affected when compared to literate (24), housewives were mostly affected with uUTI (44) with age group of 18-33 were mostly affected (23), 49-63 age group people were less affected with uUTI(8) . Cystitis was the mostly commonly seen in patients (43) when compare to pyelonephritis. Ceftriaxone was mostly commonly prescribed drug when compare to other antibiotics (27). Conclusion: We found that majority of patients were treated with third- generation cephalosporine class of drug (ceftriaxone) followed by ciprofloxacin, nitrofurantoin.

Adhesives can be used as sealing agents, to eliminate the effect of self-loosening caused by dynamic loads, sealing of areas to prevent oxidation and corrosion, waterproofing, and so on. The aim of the present study is to prepare an adhesive from fresh cassava roots and to evaluate its physicochemical properties such as density, viscosity, and pH. Adhesives are prepared by mixing starch, hydrochloric acid, and borax. Three samples of adhesives were prepared by mixing various proportions (S1: 5 g starch, 100 mL hydrochloric acid and 5 g borax; S2: 8 g starch, 100 mL hydrochloric acid and 5 g borax; S3: 5 g starch, 120 mL hydrochloric acid and 8 g borax). The physicochemical properties of prepared adhesives were compared with commercial adhesive and S2 was found to have properties closer to the commercial adhesive. Hence, it could be concluded that an increase in starch content by 60% could improve the properties of adhesives.

Greenhouse energy management is one of the most significant factors of consideration in greenhouse agriculture. Besides implementing energy supply systems to the facility, energy-saving measures must also be taken into consideration. To address the issue of energy demand by greenhouses in a tropical environment, three greenhouse models were developed to simulate their thermal environments utilizing the Transient Systems Simulation Program (TRNSYS 18) as a building energy simulation (BES) platform. The proposed models were used to examine the impact of greenhouse design parameters; roof shape, orientation, covering (polyethene), and ventilation, on their temperature, relative humidity (RH), vapour pressure deficit (VPD), and cooling load. It was found that the most suitable roof design and orientation was the split-gable roof design with the ventilation switched on and 0o (E-W) orientation that had the lowest mean temperature of 24.12 oC and the least cooling demand of 454.59W. While the tunnel greenhouse had the highest cooling load of 21.30 kW. The split-gable greenhouse had. Also, the RH and VPD in the split-gable greenhouse with ventilation were within the acceptable ranges of 50-75% and 0.8 and 1.1 kPa, respectively, for successful greenhouse crop production. The developed models can aid greenhouse farmers in knowing the cost-benefit of a greenhouse before venturing into greenhouse agriculture in the tropical regions.

Agricultural sector faces a major loss that is caused by animal assaults and is one of the greatest threats in diminishing crop production. Pests, natural disasters, and animal damage pose severe risks to Indian farmers, lowering productivity. Crop raiding is one of the most acrimonious human-animal conflicts as a result of the extension of farmed land into former animal habitat. Traditional methods followed by farmers are not that effective and it is not feasible to hire huge manpower to keep an eye on crops and prevent animals from destroying them. Since safety of both human and animals is equally vital, it is important to protect the crop from damage caused by animal as well as divert the animal without any harm. The goal is to develop a model that can accurately detect animals in agricultural lands. The system will use deep learning to detect animals entering into the farm by using convolutional neural network concept. This system will monitor the entire farm at regular intervals through a camera which will be recording the surrounding throughout the day. With the help of a deep learning model, the system detect the entry of animals and also use sound based scare tactics to deter animals from the agricultural land

Due to the widespread use of direct current (DC) power sources, including fuel cells, solar photovoltaic (PV), and other DC loads, high-level integration of various energy storage systems, including batteries, supercapacitors, and DC microgrids, has become more significant in recent years. Additionally, DC microgrids do not experience problems with synchronization, harmonics, reactive power regulation, or frequency control like traditional AC systems do. The control of DC bus voltage as well as power sharing is complicated by the inclusion of various distributed generators, such as PV, wind, fuel cells, loads, and energy storage devices, in the same DC bus. Several control strategies, including centralized, decentralized, distributed, multilevel, and hierarchical control, are described to assure the secure and safe functioning of DC microgrids.

Concrete is the foremost building material broadly used in building construction, but cracks formed in concrete are inescapable and one of the major reasons for the weakness of concrete. The major downside of concrete is its low tensile strength due to which micro crack occurs when the load applied is more than its limit and this paves way for the seepage of water and other salts. This initiates corrosion and makes the whole structure vulnerable and leads to the failure of structure. To remediate this type of failure due to cracks and fissures, an approach of using bio mineralisation in concrete has evolved in recent years. In this method, of enhancing the performance of concrete, the calcite precipitating spore forming bacteria is introduced into concrete. When water enters through the cracks, it reacts with bacteria and forms precipitates of calcium carbonate, as a by-product, which fills the cracks and makes crack free concrete. This type of concrete prepared with bacteria is called as bacterial concrete. Various researches have shown positive results by adding calcite precipitating bacteria in concrete. Here in this project, we are going to study the mechanical properties of self-healing bacterial concrete with varying quantities of Bacteria and Calcium lactate. Many researches shown that optimum result of Self-Healing bacterial concrete was obtained at 0.5% and 2.5% Calcium lactate along with 10 and 15ml bacteria. Here we have decided to conduct an experimental study on Self-Healing Bacterial concrete with addition of 10ml bacteria to 0.5% and 2.5% calcium lactate and 15ml bacteria to 0.5% and 2.5% calcium lactate. Here in this study, mechanical properties of SHBC were observed and compared with conventional concrete and also observed that healing of cracks are done

This study was designed to investigate the influence of pre-estrus (PE) synchronization and post-mating (PM) vitamins AD3E treatment on the reproductive performance of Awassi ewes raised at four Iraqi provinces. Seventy six Awassi ewes were randomly divided within each province into two groups. The first group (n=15, except for Karbala, n= 11) were im injected with five AD3E injections (150 IU / dose), biweekly interval. The 1st injection was 1.5 months PM, while the 2nd and 3rd injections were at the time of progestagen-impregnated vaginal sponges insertion. The 4th injection was at the time of the vaginal sponge’s removal, whereas the 5th injection was 14 days PM. The control group (n=5) was intramuscularly injected with 5 ml normal saline at similar periods. The AD3E groups exhibited higher (P≤0.01) fertility and conception rates as compared to the control groups in Al-Najaf and Karbala provinces. Higher (P≤0.01) lambing rate and liter size were noticed in AD3E groups than those of the control group for all provinces. In conclusion, pre-estrus synchronization and post-mating vitamin AD3E treatment improved the reproductive performance of Awassi ewes.

Giant cell tumors (GCT) are benign tumors with potential for aggressive behaviour and capacity to metastasize. It is a locally destructive tumor that occurs predominantly in long bones of adolescents and young adults in the epiphysis. Although rarely lethal, benign bone tumors may be associated with a substantial disturbance of the local bony architecture that can be particularly troublesome in peri-articular locations. It is characterized by a proliferation of mononuclear stromal cells and the presence of many multi-nucleated giant cells with homogenous distribution. There are varying surgical techniques ranging from intra-lesional curettage to wide resection. As most giant cell tumors are benign and are located near a joint in young adults, several authors favour an intralesional approach that preserves anatomy of bone. Although GCT is classified as a benign lesion, few patients develop progressive lung metastases with poor outcomes. Malignant transformation without radiotherapy exposure, is an uncommon event, occurring in less than 1% of giant cell tumors of bone. Here we reported a case of recurrent GCT of tibia that at the time of final recurrence was found to have undergone malignant transformation over a period of 6 years following several limb salvaging procedures. Concurrent metastases were found in the lung, but these were non-transformed GCT following which the patient has undergone above knee amputation.

The medicine recommendation system is intended to suggest alternative medicines based on the cosine similarity between a patient's symptoms and the effects of various medications. The system makes use of a database of medications and their indications, as well as a list of symptoms that a patient may exhibit. It vectorizes the data, applies filters, and makes suggestions. Medicines with a higher cosine similarity are considered more relevant and recommended to patients. In the scenario of a medical emergency, when physicians or prescribed medications are unavailable, this recommender serves as a valuable resource. The proposed medicine recommendation system has the potential to help healthcare professionals and patients make educated decisions about alternative medications. The system can reduce the risk of adverse drug reactions and improve patient outcomes by suggesting alternative medicines that are more effective and have fewer side effects. Overall, the proposed medicine recommendation system has the potential to significantly improve patient care by making effective recommendations for alternative medications. It can also reduce healthcare professionals’ workload by automating the process of identifying.

Result Processing System forms the key activities in the life span of a student. The demand for effective and efficient result computation and output presents the need to automate existing manual result processing systems. The digitized process provides capabilities such as a centralized repository for storage, management and dissemination of result information to those concerned. This project will enhance the college's existing system of publishing results by providing students with easy and quick access to their results. This paper presents a case study on analyzing academic performance of students at the end of a university degree at an early stage of the degree program, in order to help universities not only to focus more on bright students but also to initially identify students with low academic achievement and find ways to support them. The results show that it is possible to analyze the graduation performance in 4th year at university using only pre-university marks and marks of 1st and2nd year courses, no socio-economic or demographic features, with reasonable accuracy. The system is unique in that it can be adopted and adapted to suit the result processing.

This proposes the development of a price negotiating chatbot on e-commerce websites using Natural Language Processing (NLP) technology. The chatbot aims to assist customers in negotiating prices with the sellers, enhancing their shopping experience. The proposed chatbot will be built using the Python programming language and the TensorFlow library for Natural Language Processing. The chatbot's architecture consists of several components, including a user interface, an NLP module, a price prediction module, and a negotiation module. The NLP module will enable the chatbot to understand and interpret the customer's messages, while the price prediction module will predict the minimum price that the seller is willing to accept. The negotiation module will use reinforcement learning algorithms to negotiate with the seller and come up with the best possible price for the customer. The proposed chatbot will provide customers with a convenient way to negotiate prices with sellers, enhancing their shopping experience. The chatbot's architecture, which includes a user interface, an NLP module, a price prediction module, and a negotiation module, will enable the chatbot to understand and interpret customer messages and negotiate prices with sellers on behalf of the customer. The chatbot will be trained and assessed on a substantial dataset of negotiation conversations, and its performance will be compared to other contemporary negotiation models. The results will demonstrate that the proposed chatbot significantly improves the shopping experience of customers, attaining higher success rates, shorter negotiation times, and increased customer.

Firmware attacks on IoT devices have become a growing concern in recent years. These attacks exploit vulnerabilities in the firmware, which is the low-level software that controls the hardware of the device, to gain access to sensitive data or control the device remotely. Firmware attacks are particularly dangerous because they can be difficult to detect and can allow attackers to maintain access to a device for an extended period of time. In this paper, we explore the silent threat of firmware attacks on connected devices and the potential consequences of a successful attack. We discuss the methods that attackers use to exploit firmware vulnerabilities and the impact these attacks can have on the security and privacy of users. We also provide recommendations for protecting against firmware attacks, including keeping firmware up to date, using strong passwords, and monitoring for suspicious activity on the network. By understanding the risks and taking proactive steps to protect against firmware attacks, users can help to ensure the security and integrity of their connected devices.

Data structures are the foundation of computing, providing efficient ways to store and manipulate data. They are essential for designing and implementing algorithms that can handle large amounts of information quickly and accurately. In this paper, we explore the world of data structures, examining their basic principles, properties, and uses. We start by introducing the most common data structures, such as arrays, linked lists, stacks, queues, trees, and graphs, and discuss their advantages and limitations.

As the need for machine learning models continues to grow, concerns about data privacy and security become increasingly important. Federated learning, a decentralized machine learning approach, has emerged as a promising solution that allows multiple parties to collaborate and build models without sharing sensitive data. In this comprehensive survey, we explore the principles, techniques, and applications of federated learning, with a focus on its privacy-preserving aspects

The Internet of Things (IoT) is rapidly growing and becoming an integral part of our daily lives. However, the increasing use of IoT devices also raises significant security concerns. One of the most pressing threats to IoT security is the blackhole attack, where an attacker can selectively drop or discard packets to disrupt communication between IoT devices. In this paper, we conduct a comprehensive survey on blackhole attacks in IoT networks. We explore the types of blackhole attacks, the methods attackers use to exploit vulnerabilities in IoT devices, and the potential impact of these attacks. We also review existing solutions and strategies for mitigating the effects of blackhole attacks in IoT networks. Through our survey, we provide a deeper understanding of the blackhole attack's nature and the potential implications for the security and reliability of IoT networks. Ultimately, our findings highlight the need for increased awareness of this type of attack and the implementation of robust security measures to protect IoT devices and networks.

The mixing of dyes is a major technique in several industries such as textiles, paint, and leather. It can be done in improper proportions the result should be in vivid shades of color. So, the proposed method is used for the dyeing companion that works automatically with different dye ratios. The system will execute the color taking from the vessel using a syringe using a PIC microcontroller (PIC16F874A/877A). The syringe can be controlled by the keyboard and the values are displaced in LCD. The controller can give the choice of the user to choose parameters with a specific amount (green, red, and blue). The intensity of the color between 0 to 255 gives the values for each parameter. The specific amount of selected color is poured with empty vessels and mixed with a mixer at a suitable time. This method of design gives several periods of working involved in the color system mechanism toward a fully automated color-making system. This method is used to increase the productivity level and reduce the health problem faced by the workers.

With the increasing volume of data being generated every day, the need for data security has become more crucial than ever. Encryption is one of the most effective techniques for protecting sensitive data from unauthorized access or theft. This comparative survey aims to provide a comprehensive analysis of different data encryption methods in the context of big data. The survey covers both traditional encryption techniques and newer, more advanced methods such as homomorphic encryption and quantum cryptography. The effectiveness, advantages, and limitations of each technique are examined, and a comparison of their performance in terms of speed, scalability, and security is presented. The study also explores the challenges and issues associated with implementing data encryption in big data environments. The findings of this survey will be useful for organizations seeking to secure their big data assets and for researchers interested in the latest developments in data encryption techniques.

Osteoarthritis is one of the oldest known chronic degenerative joint disorders characterized by joint pain, tenderness, limitation of movements, crepitus, occasional effusion and variable degrees of local inflammation. In Ayurveda, this disease clinically simulates with Sandhigata vata which occurs due to the localization of the vitiated Vata dosha in the Asthi sandhis of the body. It is characterized by the symptoms pertaining to the Asthi sandhis like Sandhi soola, Sandhi sopha etc. Acharya Susrutha has mentioned the treatment for Sandhigata vata clearly i.e., Snehana, Upanaha, Agnikarma, Bandhana and Unmardana. In Ashtanga Hridaya, Snehana is mentioned as one of the main treatment of Sandhigata vata. Aim: To evaluate the effect of Samana snehapana with Sahacharadi mezhukupaka for a duration of 14 days in patients of OA knee joint in the age group of 40–60 years. Materials and Method: A case series of 5 patients aged between 40-60 years diagnosed as osteoarthritis of knee joint with pain, stiffness, difficulty in physical function and mobility by using WOMAC Osteoarthritis index and TUG test. They are advised to take Sahacharadi mezhukupaka for duration of 14 days after doing Rookshana with Gandharvahastadi kashyam and Vaiswanara choorna for 5 days. Assessment was done on 20th day and 35th day after treatment. Results: After completion of Snehapana for 14 days followed by assessment, there was significant relief in pain, stiffness and improvement in physical function and mobility. WOMAC Index and TUG test scores shows significant improvement in patients. Conclusion: Osteoarthritis is a muculoskeletal degenerative disease affecting the bones, cartilage, joint and connective tissue leading to the impairment of function. In Ayurveda, by using Samana snehapana patients got satisfactory relief in pain, stiffness and improvement in physical function and mobility.

Parkinson’s disease is a chronic progressive neurodegenerative disorder of CNS affecting the motor system, with increasing frequency in nearly 1% of the population older than 60 years of age. Affects men more frequently than women. The symptoms start insidiously and tend to be unilateral or asymmetrical at the onset. The initial manifestations may be tremor, slowness, clumsiness of an arm or leg. Tremors, rigidity, akinesia and postural disturbances are the major clinical abnormalities. Clinical features may include mask face, slurred & indistinct speech, festinant gait, stooped posture, resting tremors, rigidity (cogwheel or lead pipe). Based on the symptoms like Kampa, Stambha, Chestasanga, Vakvikriti it can be correlated to Kampavata which is mentioned under Vataja Nanathmaja Vyadhi. Application of Ayurvedic principles on the pathology involved, reveals a distinct vitiation of Vata dosha along with vitiation of Udana and Vyana vayu. The current paper is the case report of a 45 year old female patient presenting with complaints of tremors of both hands aggravate at rest, slow body movements and activities, decreased handwriting size, swaying while walking since 1 year. The case was diagnosed as Parkinson’s disease based on clinical manifestations and neurological examination. A multimodality treatment in the form of Panchakarma therapies including Basti, Shirovasti, Nasya along with Samana oushadhis for a period of 33 days yields a better result in this condition. The condition of the patient improved with gradual course of treatment. The assessment was done before and after treatment and during follow up using Hoehn and Yahr scale and Unified Parkinson’s Disease Rating Scale. Follow up with Samana oushadhis are given for 1 month. Hoehn and Yahr scale changes to stage 3 after 15 days from stage 4 and changed to 2.5 after treatment. Unified Parkinson’s Disease Rating Scale was reduced from 39 to 19. During follow up period also the values remained the same.

The main objective of this project is to build an Arduino nano based All in one multimeter, a device that can be used to measure the voltage, current and the power consumed by a load. There are number of ways that you can implement the Arduino nano based all in one multimeter Project. One of the easy ways is to interface a Voltage Sensor and a Current Sensor with Arduino, measure the voltage and current values and finally with some mathematics, you can calculate the Power in Watts. The Sensor Part of the circuit is responsible for measuring the Voltage across the load and Current through the load. Both these values, which are analog in nature, are given to the Arduino to its ADC. Arduino converts these values to digital values and makes a few calculations as displays the results on the LCD.

The public sector audit is a cornerstone for an effective corporate governance. However, its effectiveness largely depends on the scope of audit conducted in public sector agencies. Therefore, this paper examines scope of audit and its effects on the effectiveness of public sector audit in Malaysia. The respondents include the public sector auditors. By employing non-probability purposive sampling using 203 respondents and analysed using Partial Least Square Structural Equation Modeling (PLS-SEM), it was discovered that the scope of audit have a significant positive effect on the effectiveness of public sector audit. The finding reveals that performance and compliance auditing is crucial in public sector. A sound regulatory mechanism in terms of rewards and punishment should be fair and clear. This is important to ensure that the internal auditors strictly address the non-performance behaviours of the government. The research topic is relatively new in Malaysian public sector. This study is therefore likely to significantly contribute to the knowledge about how scope of audit operates in a public sector agencies context that differs significantly from the contexts of private sector agencies.

A common biometric approach is another way of face recognition techniques. The method is convenient, easy access to the user and direct comparison with other methods of face recognition due to its rapid development in recent years. Resisting spoofing attacks made on face recognition systems requires Face Anti-Spoofing Systems (FAS) techniques. FAS based on deep learning perform exceptionally well and dominates this area with the emergence of large-scale academic datasets in recent years. The data requirements for training effective anti-spoofing models in this field, however, are large, and there is no way to perform live spoofing. Our paper proposes a combined method of face liveliness detection using Haar-Cascade algorithms and mobile-net classifiers. As a contribution to stimulating future research, we present an overview of technique like deep learning-based FASs. It covers numerous novel and insightful Anti-spoofing approach structured using the modules,1) Eye opening action evaluates with the blinking eye systems and 2) mobile-net classifier module which makes use of a pre-trained version. We wrap up our study with gradually merged these two modules and added them to a basic facial recognition system. Software Python-based results comparisons between classifiers are used to explain efficiency of the suggested approach.

Abstract This study takesup” Dimensions of Gender Equality in India.”While examining the dimensions the researchers compares the gender development index reports of states in India Gender equality-a driver for economic growth, matters in its own right,and as as pre requisite for the health and development of families and economies. The term gender equality has been on the corpet for the past two decades. The millennium development goals set gender equality and women empowerment at 3 rd place. Earlier women empowerment had been assessed interns of access to resources and later on moved to control over resources. The resources include physical resources. Fiscal resopurces, financial resources and intellectual resources. The term empowerment has been pronounced with the term empowerment after the millennium year. The post 2015 framework present a unique opportunity to build on the achievement of the millennium development goal, while also addressing that lag behind. The sustainable development goal also set Gender equality at fifth place. The reason is that Gender inequality persists everywhere and stagnates the progress of a country. The un equal treatment or perceptions of the individuals on the basis of gender have been witnessed not only in developing countries like India, but also in developed countries aswell. Gender related inequality in human well being is one of the most crucial issues in many states in India.The female dis advantage in survival is the important dimension that has drawn the attention of policy makers. InIndia the situation is not even among the states; Kearela has lesser discrimination while Odissa has higher discrimination. The sex ratio is also high in kerela which shows positive symptoms of women empowerment. Maharastra introduced property rights for women. Its percapita income is high compared to kerela but GDI is low. Female Education growth in kerela promotes gender equality.Has education alone dis appear inequalities is the million dollar question? Per capita income is not so high in kerela as compared to other southern states. The study examines gender equality on the basis of Gender Development Index, which measures gender gap in human development by accounting disparities between men and women in three dimensions of human develoipme3nt, a long and healthy life, knowledge and a decent standard of living. The global gender gap report places India at 135 out of 146 countries. India has moved from 140 to 135 from last year. Though India is moving towards one of the developed nations in the world, Gender inequality pulling its growth. Which factor is hindering the SGDs 5 the goal gender equality in India? The issue of gender inequality had been in the lime light in all academic conferences, seminars and symposiums. Why gender inequality prevails in higher degree in some states of India? What are the policy measures have to be pinned up? What are the measures to be added in those states? Any other indicators to be included along with HDI indicators? These are the research questions to be addressed. This paper is an attempt made by the researchers to address these issues

The global economy is no stranger to recessions, which can have severe impacts on businesses, individuals, and entire nations. As a result, it is essential to analyze economic data to identify and understand recession patterns, allowing for effective policy-making and planning. In this paper, we explore the use of Python as a tool for recession analysis. We discuss various techniques for data collection, processing, and analysis using popular Python libraries such as Pandas, NumPy, and Matplotlib

The Internet of Things (IoT) has brought about significant changes to various domains such as healthcare, transportation, and manufacturing. However, security remains a critical challenge in IoT due to the large number of connected devices with varying processing capabilities and memory constraints. Traditional cryptographic algorithms are not well-suited for IoT devices due to their high computational and memory requirements. Lightweight cryptography algorithms have emerged as a promising solution for securing IoT devices with limited resources. In this abstract, we provide an overview of lightweight cryptography algorithms for IoT, including their design principles, security properties, and performance evaluation.

Datafication has emerged as a key driver of the digital economy, enabling businesses, governments, and individuals to extract value from the growing flood of data. In this comprehensive survey, we explore the various dimensions of datafication, including the technologies, practices, and challenges involved in turning information into structured data for analysis and decision-making. We begin by providing an overview of the historical context and the rise of big data, and then delve into the latest developments in artificial intelligence and machine learning. We examine the key drivers of datafication across industries and sectors, and explore the ethical, legal, and social implications of the data revolution. Finally, we consider the challenges and opportunities presented by datafication, including issues of data privacy and security, the need for new skills and competencies, and the potential for data to drive innovation and social change. Overall, this survey provides a comprehensive and up-to-date overview of the datafication landscape, helping readers to better understand and navigate the rapidly-evolving world of data.

Matlab has become a popular choice for researchers across various fields due to its versatility, ease of use, and powerful analytical capabilities. In this paper, we explore the role of Matlab as the ultimate solution for research challenges. We first discuss the benefits of using Matlab in research, including its ability to handle complex mathematical computations, data visualization, and simulation of complex systems.

The convergence of the Internet of Things (IoT) and blockchain technology is revolutionizing the field of security. IoT devices are becoming increasingly prevalent in our daily lives, from smart homes to wearable technology, but they also pose a significant risk to cybersecurity. Blockchain technology offers a decentralized, tamper-proof network that can enhance the security of IoT devices. By creating secure identities, communication channels, and transactions, blockchain can protect IoT devices from potential cyber-attacks. This paper explores the advantages of using blockchain to enhance IoT security and highlights the potential of this technology to create a safer and more secure IoT ecosystem

As cities around the world are growing at an unprecedented rate, there is a pressing need for smarter, more sustainable, and efficient urban infrastructure. In recent years, blockchain technology has emerged as a potential solution for various challenges faced by smart cities. Blockchain's unique features such as decentralization, immutability, and transparency offer new opportunities for secure and efficient management of city systems. This paper explores the potential of blockchain technology in smart city development, highlighting its applications in areas such as energy management, transportation, governance, and public services. By analyzing various use cases and examples, the paper demonstrates how blockchain can enhance the reliability, efficiency, and transparency of smart city infrastructure. Additionally, the paper discusses the challenges and limitations of blockchain adoption in smart cities and suggests potential solutions for overcoming these obstacles. Overall, this paper argues that blockchain technology has the potential to transform the future of smart city development, enabling cities to become more resilient, sustainable, and citizen-centric

Study is on the performance of earthworm species in vermicomposting salvinia was reported earlier. In this study on the performance of reactors inoculated with single species and combination of two or three species in vermicomposting salvinia is reported. The experiment was carried out on seven set of vermireactors, i.e. mono species - Eudrilus eugeniae, Eisenia fetida, Perionyx excavatus., di species - Eudrilus eugeniae +Eisenia fetida., Eisenia fetida + Perionyx excavatus., Perionyx excavatus + Eudrilus eugeniae and multi species - Eudrilus eugeniae, Eisenia fetida, Perionyx excavatus. The performance of multi–species reactor achieved conversion of about 67% of the feed mass (100 g dry wt,) per fortnight. The vermireactors were sustainable as the animals have remained consistently healthy and reproductive over a period of ten months.

"THE ALGORITHM FOR CALCULATING THE FATIGUE RESISTANCE OF THE GONDOLA WAGON BODY"

The study entitled the financial performance analysis and Company. The objective of this study is to compare the current financial performance with last five years and to study the existing financial position of Company. The data used in this study is secondary data through annual report. The data that used in this study, comparative balance sheet, common size balance sheet, comparative balance sheet analysis ,that the current liabilities is higher than the current asset in every year and it is to be suggest that the company can concentrate on their increasing the level of the current asset. So the company improves this financial position. The study of financial performance on The Company has revealed the great deal of their various financial aspects for five years. Findings of the study, Table shows, current assets and current liabilities over a period of 10 years from 2012-2013 to 2021- 2022. The Interest Incidence % (times) mean 25.65 and its Standard Deviation is 24.25 Coefficient of Variation is 23.40 and CAGR is 23.28 Interest Incidence % (times) is high during period 2013– 2014. It indicates the firm is fluctuation trend in this ratio during the whole study period 2013-14 - 2021- 2022.Conclude this study. It helps to understand the working of the bank. From the study of financial performance of HDFC BANK it can be concluded that the bank has satisfactory position with regard to profitability and the bank needs to improve its liquidity and solvency.

Hypertension (High blood pressure) is ranked as the third most important risk factor for attributable burden of disease in south Asia. Hypertension (HTN) exerts a substantial public health burden on cardiovascular health status and healthcare systems in India. Overall prevalence for hypertension in India is 29.8% (95% confidence interval: 26.7–33.0). Significant differences in hypertension prevalence were noted between rural and urban parts of the population. [27.6% (23.2–32.0) and 33.8% (29.7–37.8); P = 0.05) It is estimated that at least one in four adults in India has hypertension, but, only about 12% of them have their blood pressure under control. India has set a target of 25% relative reduction in the prevalence of hypertension (raised blood pressure) by 2025. HT Kot tablet is used in the treatment and management of hypertension. HT Kot contain 3 ingredients namely Jadamamsi (Nardostachys jatamansi), Sarpagandha (Raufolia serpentine) and Ruraksha (Elaeocarpus sphaericus). It is observed that HT kot helps in improving the cardiovascular functions. It is also observed that HT Kot helps to reduce nervousness and anxiety. A descriptive cross sectional survey was done among Ayurvedic physicians in different parts of India. Out of 128 physicians, 86 (67.18%) physicians having more than 10 years of experiences. Survey reveals that majority of the physicians (more than 75%) opined that HT kot is effective in stage 1&2 hypertension. The added effect of HT Kot is that it helps to improve the quality of sleep, reduce hyperactivity in children and anxiety. Majority of the respondents opined that the ideal time of administration of HT kot is after breakfast and dinner. The combination therapy with HT Kot is good in many conditions like hyperactivity, nervousness, hyperacidity and fatigue. The present article is based on the survey conducted among the Ayurvedic practioners in different parts of India, to assess the effectiveness and additional benefits observed with HT kot.

The main methodological innovations today are the use of interactive learning technologies, involving each student in the learning process, i.e. in the process of cognition. The introduction of modern interactive learning technologies creates conditions for improving the quality of the training session, as well as methods of teaching chemistry. Each teacher uses new learning technologies to ensure that classes are systematic, high-quality, interesting, and effective. It is known that students have different ways of thinking and when conducting lessons, performing tasks with the help of illustrations, video materials, animation, and games increases interest in the subject. In this paper, interactive apps identified and described that contribute to increasing the activity of students and the effectiveness of the learning process in lower secondary school, as well as interactive chemistry lessons on the topic "Mineral fertilizers" are developed. In implementing the developed interactive lessons, such teaching methods as interactive lectures, interactive exercises, and didactic games were used. A relatively small sample size limited this study, but the results obtained have essential data for the professional development of teachers and the development of modern interactive lessons using ICT. The results obtained after a survey of students, in which 100 students participated, indicate that interactive lessons support learning and increase students' motivation, and this has a positive effect on their attitude to the subject

Introduction and Aim: Depressive disorder is a prevalent psychiatric disorder worldwide. Thyroid disorders have symptoms like major depressive disorder (MDD). Anxiety disorders are the most prevalent psychiatric disorders. Thyroid dysfunction is associated with increasing anxiety in generalized anxiety disorder (GAD). This study aimed to evaluate thyroid status in major depressive disorder and generalized anxiety disorder. Materials and Methods: This cross-sectional study was conducted in a 17-months period. Diagnosis was made using DSM 5 (The Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition Criteria). Estimation of serum free thyroxine (FT4), free triiodothyronine (FT3) and ultrasensitive thyroid stimulating hormone (U-TSH) was done by ELISA. The study included 126 patients, 66 GAD and 60 MDD patients. Results: In both GAD and MDD, females outnumbered males. Age-group and disorder-type had significant association. In GAD and MDD, the mean values of FT3, FT4 and U-TSH were all within normal range. Among GAD patients, maximum were euthyroids followed by hyperthyroids and hypothyroids. In MDD patients, maximum were euthyroids followed by hypothyroids and hyperthyroids. The percentage of abnormal thyroid status in GAD and MDD were 21.2% and 23.3 % respectively. Conclusion: Abnormal thyroid status is prevalent in both GAD and MDD patients with differences in thyroid response. Thyroid profile may play and important role in proper management of the diseases.

nvestigations on indigenous and non-indigenous invasive flora and fauna of a certain geographical area are always important to assess their impact on native biodiversity and control their spread by making conservation strategies to protect biodiversity. In this study, we have encountered overwhelming growth of six seaweed species, Caulerpa racemosa, C. serrulata, C. sertularioides, C. taxifolia, C. peltata, and Halimeda opuntia on the reef flat. These algal covers on reef area appear to compete with coral polyps and restrict their settlement and resilience. Our concern on invasive species distribution in Gulf of Mannar revealed these overgrowing invasive species, which can become potential threats to coral reefs, benthic diversity, and reef assemblage. These observations will help us to develop multidisciplinary management approaches related to the expansion, mitigation, and control of these opportunistic invasive species. Further seasonal monitoring on these algal species is under process to investigate their spatial shift patterns on various coral forms.

Reinforcement learning is a subfield of machine learning that deals with developing algorithms that enable an agent to learn from experience through trial-and-error interactions with its environment. It is a paradigm of learning by receiving rewards or punishments based on its actions and adjusting its behavior to maximize its cumulative reward over time. Reinforcement learning has been successfully applied in a wide range of fields, including robotics, game playing, recommendation systems, and finance. It has also shown promising results in solving complex problems that are difficult to solve using traditional methods. Despite these challenges, reinforcement learning has already proven to be a powerful tool for developing intelligent systems that can learn and adapt to changing environments, and it is likely to play an increasingly important role in the development of future AI technologies.

Highway construction is a crucial component of any region's infrastructure, and it is carried out in several different ways nowadays. Adaptive Highways Network (AHN) uses sensors and microprocessors for automatic design. Innovative highway design employs roadside controls and intelligent vehicles for traffic management and control. The Automated Highway System enhances highway safety, operational efficiency, and other vehicle and user characteristics. This innovation has enhanced the architecture of highways and reduced vehicle emissions. AHS, or Smart Road, is a projected intelligent transportation system technology for driverless automobiles on specific roadways. It is commonly employed to alleviate traffic congestion since it reduces following distances and permits more vehicles on the road. Together, the car and the roadway strive to avoid barriers, boost traffic flow, and decrease congestion. The AHS concept integrates vehicle intelligence, intelligent highway infrastructure, and vehicle-to-infrastructure communication technology.

In Wireless Sensor Network (WSN) communication, every node transmits data packets to further nodes and spend its resources like battery power, CPU time and memory. In ideal situation all the nodes forward packets to other nodes according to their requirements .Presence of selfish nodes is a very big issue in WSNs. A selfish node doesn't forward packets and utilize to its own profit but it is hesitating using personal resources for others. If such activities occurs within most of the nodes in the network, the network is disrupted. Selfish behavior detection and punishment is an essential requirement in wireless sensor networks. In our paper we have described an efficient algorithm for detection and punishment of a selfish node and the necessary action to be taken if the node is a critical node.

A good voice is the basic need of humans. For professional voice users, voice governs their livelihood and social attraction. A good singing voice is attained by the regular vocal exercises and vocal training. Singing involves the skills like respiration, phonation, resonance and articulation. Bhramari Pranayama (BP) involves both humming and respiration. Humming is the resultant of the activity vocal structures which may have effect on the quality of the voice. Aim: To evaluate the effect of Bhramari Pranayama (BP) on voice quality Settings and Design: The study is an open clinical trial involving 30 healthy prospective singers fulfilling the inclusion and exclusion criteria, consenting for the study, who were enrolled through a survey in music schools in the region of Belagavi, Karnataka. Methods and Material: The voice of the 30 healthy prospective singers was recorded using PRAAT software with standard vocal tasks on day 0 of the study. Participants practiced 21 cycles of Bhramari Pranayama in the morning for 30 days. On Day 30, after the practice the voice was re-recorded with same vocal tasks and standard operative procedures being maintained. The recorded voice samples were saved and were subjected for voice analysis using softwares PRAAT, Vaghmi, CSL at AIISH, Mysuru. Statistical analysis used: Statistical analysis was done using SPSS 21 and Paired ‘t’ test was applied to derive the results. Statistical Significance was set up at p

Rheumatoid arthritis is a chronic, progressive autoimmune arthropathy and characterized by bilateral symmetrical involvement of joints with some systemic clinical features. Treatment in allopathic medicine involves NSAIDS and steroids, which carry their own collateral burden of side effects in long term use. According to clinical features, described in Ayurveda, Vatarakta closely resembles with Rheumatoid arthritis. The study is done to find the effect of Sodhananga Achasnehapana with Guggutiktaka ghritha followed by Virechana with Nimbamrita eranda taila in Rheumatoid Arthritis. Methods: It is a simple random single case study done at Government Ayurveda College Panchakarma Hospital, Poojappura, Thiruvananthapuram. Here a case of Vatarakta (Vatakapahadikam) was treated with Sodhananga Achasnehapana with Guggutiktaka ghritha followed by Virechana with Nimbamrita eranda taila after Rukshana karma. Results: The treatment was effective in reducing the ESR, RA factor and CRP levels of the patient diagnosed with Rheumatoid arthritis. Conclusion: The case report shows that Ayurvedic treatment is potent and effective in the management of Rheumatoid arthritis. There was no adverse effect found in the treatment. Hence it can be concluded that RA can be effectively and safely treated by using the Chikitsa siddhanta mentioned in Ayurveda

When an unsuspecting victim visits a malicious website, it infects her machine to steal valuable information, redirects her to malicious targets, or compromises her system to launch future attacks. While current approaches have. There are still open issues in effectively and efficiently addressing: filtering of web pages from the wild, coverage of a wide range of malicious characteristics to capture the big picture, continuous evolution of web page features, systematic combination of features, semantic implications of feature values on characterizing web pages, ease and cost of flexibility and scalability of analysis and detection technology. In this position paper, we highlight our ongoing efforts towards effective and efficient analysis and detection of malicious websites, with a particular emphasis on broader feature space and attack-payloads, technique flexibility with changes in malicious characteristics and web pages, and, most importantly, technique usability in defending users against malicious websites.

The project is primarily concerned with detecting credit card fraud in the real world. The phenomenal increase in the number of credit card transactions has recently resulted in a significant increase in fraudulent activities. The goal is to obtain goods without paying for them or to withdraw unauthorized funds from an account. In order to minimize losses, all credit card issuing banks must implement effective fraud detection systems. One of the most difficult challenges in starting the business is that neither the card nor the cardholder must be present at the time of purchase. This makes it impossible for the merchant to determine whether or not the customer making a purchase is the legitimate cardholder. Using the proposed scheme and the random forest algorithm, the accuracy of detecting fraud can be improved. Random forest algorithm classification process to analyze data set and user current dataset. Finally, improve the accuracy of the output data. The techniques' performance is measured using accuracy, sensitivity, specificity, and precision. Then, by processing some of the provided attributes, the fraud detection is identified and the graphical model visualization is provided. The techniques' performance is measured using accuracy, sensitivity, specificity, and precision.

The present paper study on the study of pollen diversity of 31 plants from various gardens of Sri Ram Narayana Khedia Government Degree College Banswada, Kamareddy district, Telangana state, India. These plants some are medicinal, ornamental and tree members. The pollens of these plants have diversity of morphological characters viz., symmetry, shape, polarity, aperture patterns and sculpture. In the present study, a total of 31 plant species belongs to 19 families. The plants with Spheroidal shape pollen Catharanthus roseus, Lantana camara, Bougainvillea spectabilis, Tridax procumbens, Pseuderanthamum, Ixora coccinea, Jasminum sambac, Hibiscus rosa-sinensis, Pulmeria alba, Euphorbia heterophylla, Cynodon dactylon, Ghompherna celosioides, Hibiscus syriacus, Tecomaria capensis, Turnera ulmifolia, Punica grantum , Cleome gynandra, Oxalis dilenii, Nerium oleander, Ghompherna globosa, Datura stramonium, Jatropa integrerrima, Cyanthillium cinereum, Delonix regia, Tecoma stans . The plants with Symmetrical pollen is Parthenium hysterophorus. The plant with Irregular pollen shape is Callaindra. The plants with Cylindric pollen shape Bauhinia racemosa, Crossandra infundibuliformis. The plant with Boat shaped pollen is Hymencallis littoralis, are repotted. This plant species diversity record which is helps to the identification of Indian pollen flora.

Complex partial seizure are often associated with impaired or loss of consciousness clinically proved to have an impact on masial temporal sclerosis (Hippocampus) region of brain. Seizures admits several etiopathophysiological events leading to several neuro functional changes in the reticulothalamocortical circuitry zones of the CNS .This paves the episodes of complex partial seizures events. A clinical case report of clinical partial seizures in a 47 years age adult patient came to the hospital with impaired consciousness. The brainmagnetic resonance imaging scanning of the patient detected a spectrum of findings more in favour of idiopathic intracranial hypertension and T1 ,T2 isointense lesion in the posterial mid line falx-2.falcine meningioma, focal area of glosis in the left high parietal lobe and age approximate bilateral cerebral atrophy it grade-1 fazellas white matter changes. The patient was conformed with Complex partial seizures, and she was treated with benzodiazepines twice a day. The patient was recovered from loss of impaired consciousness and she was called for follow up examination once in 1-month period.

Herbal remedies are an integral part of ancient folk medicinal systems. The aim of the present study was to investigate the phytochemical constituents, adjacent composition and antimicrobial of (Garlic) Allium sativum L. extract. The qualitative phytochemical screening of Allium sativum aqueous and ethanol extracts indicated the presence of Alkaloid, terpenoids, flavonoids, steroid, phenol, Anthraquinones, saponin, tannin and glycoside. Quantitatively phytochemical was analysed alkaloid, tannin and saponin and adjacent composition of nutrients carbohydrates, protein, fats, fibre, moisture and ash. Garlic extract has antioxidant, antimicrobial activity and antidiabetic activity. Garlic contains high levels of sulfur compounds that are responsible for its medicinal effects. The available reports on phytochemicals, antioxidant, and antimicrobial, antidiabetic activity of Garlic are discussed in this review.