G Pulla Reddy Dental College & Hospital

This paper explores theories of motivation, including instinct theory, arousal theory, incentive theory, intrinsic theory, extrinsic theory, the ARCS model, self-determination theory, expectancy-value theory, and goal-orientation theory. Each theory is described in detail, along with its key concepts, assumptions, and implications for behavior. Intrinsic theory suggests that individuals are motivated by internal factors like enjoyment and satisfaction, while extrinsic theory suggests that external factors like rewards and social pressure drive behavior. Arousal theory says that to feel motivated, people try to keep an optimal level of activation or excitement. Incentive theory suggests that behavior is driven by the promise of rewards or the threat of punishment. The ARCS model, designed to motivate learners, incorporates elements of attention, relevance, confidence, and satisfaction. Self-determination theory proposes that individuals are motivated by their needs for autonomy, competence, and relatedness. The expectation-value theory suggests that behavior is influenced by individuals' beliefs about their ability to succeed and the value they place on the task. The goal-orientation theory suggests that individuals have different goals for engaging in a behavior. By understanding these different theories of motivation, educators, coaches, managers, and individuals may analyze what drives behavior and how to harness it to achieve their goals. In essence, a nuanced comprehension of these diverse motivation theories equips individuals across varied domains with a strategic toolkit to navigate the complex landscape of human behavior, fostering a more profound understanding of what propels actions and how to channel these insights toward the attainment of overarching goals.

In this study we analyzed the flow, heat and mass transfer behavior of Casson nanofluid past an exponentially stretching surface under the impact of activation energy, Hall current, thermal radiation, heat source/sink, Brownian motion and thermophoresis. Transverse magnetic field with the assumption of small Reynolds number is implemented vertically. The governing partial nonlinear differential equations of the flow, heat and mass transfer are transformed into ordinary differential equations by using similarity transformation and solved numerically by using Matlab bvp4c package. The impact of each of the Hall current parameter, thermal radiation parameter, heat source/sink parameter, Brownian motion parameter, Prandtl number, thermophoresis parameter and magnetic parameter on velocity, concentration and temperature, is discussed through graphs. The skin friction coefficient along the x-and z-directions, the local Nusselt number and the Sherwood number are calculated numerically to look into the inside behavior of the emerging parameters. It is witnessed that the flow velocity is a diminishing function of the thermal radiation parameter and the behavior has observed in the case of Hall parameter. Moreover, mounting values of Brownian motion parameter reduce the nanoparticle concentration profile.

<p>ABSTRACT<br />To provide an outlook of the various available methods of antioxidant activity. Various available in vitro and in vivo methods are listed and the<br />procedure to perform the method, its mechanism is also explained in brief. 1,1-diphenyl-2-picrylhydrazyl method was found to be used mostly for the<br />in vitro antioxidant activity evaluation purpose while lipid peroxidation was found as mostly used in vivo antioxidant assay. An ethanol was with the<br />highest frequency as a solvent for extraction purpose. Summarized information on the various methods available provides with reliable information<br />to confirm the benefits of antioxidant effects.<br />Keywords: Antioxidant activity, Reactive oxygen species, Free radical, 1,1-diphenyl-2-picrylhydrazyl, Flavonoid.</p>

Vaccines are the preparations given to patients to evoke immune responses leading to the production of antibodies (humoral) or cell-mediated responses that will combat infectious agents or noninfectious conditions such as malignancies. Alarming safety profile of live vaccines, weak immunogenicity of sub-unit vaccines and immunization, failure due to poor patient compliance to booster doses which should potentiate prime doses are few strong reasons, which necessitated the development of new generation of prophylactic and therapeutic vaccines to promote effective immunization. Attempts are being made to deliver vaccines through carriers as they control the spatial and temporal presentation of antigens to immune system thus leading to their sustained release and targeting. Hence, lower doses of weak immunogens can be effectively directed to stimulate immune responses and eliminate the need for the administration of prime and booster doses as a part of conventional vaccination regimen. This paper reviews carrier systems such as liposomes, microspheres, nanoparticles, dendrimers, micellar systems, ISCOMs, plant-derived viruses which are now being investigated and developed as vaccine delivery systems. This paper also describes various aspects of "needle-free technologies" used to administer the vaccine delivery systems through different routes into the human body.

Saliva is an exocrine secretion produced from the salivary glands and has numerous functions, such as cleansing and protection of the oral cavity, antimicrobial effects and aids in digestion. Due to the speedy development in the field of salivaomics, saliva is now well accepted as a pool of biological markers that vary from changes in biochemicals, nucleic acids and proteins to the microflora. Saliva has an immense potential as a diagnostic fluid and offers an edge over other biological fluids as its collection method does not require invasive procedure, economical and is useful for monitoring systemic health. Development of sensitive and precise salivary diagnostic tools and the formulation of defined guidelines following meticulous testing will allow salivary diagnostics to be utilised as chair side tests for various oral and systemic diseases in the near future. The coronavirus disease (Covid-19) pandemic is the biggest challenge and global health crisis for the world since World War Two. Rapid and accurate diagnosis of Covid-19 is crucial in controlling the outbreak in the community and in hospitals. Nasopharyngeal and oropharyngeal swabs are the recommended specimen types for Covid-19 diagnostic testing. The collection of these specimen types requires close contact between healthcare workers and patients and poses a risk of transmission of the virus, causes discomfort and may cause bleeding, especially in patients with condition such as thrombocytopenia. Hence, nasopharyngeal or oropharyngeal swabs are not desirable for sequential monitoring of viral load. Saliva specimens can be obtained easily as the patient is asked to spit into a sterile bottle. The collection of saliva is non-invasive and greatly minimizes the exposure of healthcare workers to Covid-19. Saliva has a high consistency rate of greater than 90% with nasopharyngeal specimens in the detection of respiratory viruses, including coronaviruses. Saliva has also been used in screening respiratory viruses among hospitalized patients without pyrexia or respiratory symptoms. SARS-CoV can be detected in saliva at high titers. Salivary diagnostics is a dynamic field that is being incorporated as part of disease diagnosis, clinical monitoring of systemic health and to make significant clinical decisions for patient care. More research is required to analyze the potential diagnostic of Covid-19 in saliva to develop rapid chair side tests for the detection of Covid-19 and it is also pivotal to improve and develop successful strategies for prevention, especially for dentists and healthcare professionals who are involved in performing aerosol-generating procedures.

A number of disadvantages of traditional networks may be attributed to the close relationship that exists between the control plane and the data plane inside proprietary hardware designs, as described above. The problem of security is one of the most difficult to deal with. There are a plethora of network hazards and attacks that might be encountered these days. DDoS attacks are one of the most popular and disruptive attacks on the internet today, and they affect a wide range of organisations. Despite a large number of traditional mitigation solutions now available, the frequency, volume, and intensity of distributed denial‐of‐service (DDoS) attacks continue to rise. According to the findings of this paper, a new network paradigm is necessary to satisfy the requirements of today’s complex security concerns. It was necessary to develop a software‐defined network (SDN) in order to meet the real‐time needs of the massive network that was expanding at an exponential rate. Many advantages of SDN exist, including simplicity of administration, scalability, and agility, but one of the most critical is security, which is one of the most important considerations when implementing SDN. SDS may be seen as a paradigm in which the implementation of new security regulations in the computer environment is performed via the use of protected software, which is described further below. The goal is to provide a flexible and extensible architecture for DDoS detection and prevention that is both flexible and extendable; the suggested clustering approach, which is based on the Open Day Light (ODL) Controller, is employed to carry out the experimental findings. In this section, we emphasise DDoS penetration techniques from a range of tools, and we evaluate the vulnerability against various tactics. It is necessary to use a Mininet emulation tool to construct a detection and prevention system against distributed denial of service (DDoS) attacks in order to achieve success. There is a range of other simulation tools that are utilised in conjunction with this research in order to bring it to a conclusion. Integration of industry standards such as SNORT and Flow has been accomplished in a variety of situations and parameter settings. During the creation of a framework capable of detecting and mitigating DDoS attacks at an early stage in both the control and application levels, the implementation of this framework has been shown to be crucial in the development of a framework.

Internet of Things (IoT) is an emerging technology that is taking the current technology to the next phase of connectivity by connecting any device to the internet. It may be a mobile phone device, a speaker or even a connected vehicle. The number of IoT-based devices people use is increasing day by day. The quantity of smart gadgets people purchase on both offline and online platforms has increased to a significant number in the past few years. This research article proposes social networking between IoT devices, by the interconnection of IoT devices based on a Cloud computing method. Through cloud computing, number of Edge platform-based, IoT devices can be connected to a common cloud called Fog computing, which in turn connects to the Cloud network. The social networking of IoT devices enables users to switch and share data between their IoT devices. Various computational programs and block chain techniques develop applications that allow the host network to communicate with IoT devices with secure access to the data shared by all IoT devices connected to the Cloud without any data loss or security breach.

BACKGROUND: Antimicrobial resistance (AMR) has posed a serious threat to global public health and it requires immediate action, preferably long term. Current drug therapies have failed to curb this menace due to the ability of microbes to circumvent the mechanisms through which the drugs act. From the drug discovery point of view, the majority of drugs currently employed for antimicrobial therapy are small molecules. Recent trends reveal a surge in the use of peptides as drug candidates as they offer remarkable advantages over small molecules. METHODS: Newer synthetic strategies like organometalic complexes, Peptide-polymer conjugates, solid phase, liquid phase and recombinant DNA technology encouraging the use of peptides as therapeutic agents with a host of chemical functions, and tailored for specific applications. In the last decade, many peptide based drugs have been successfully approved by the Food and Drug Administration (FDA). This success can be attributed to their high specificity, selectivity and efficacy, high penetrability into the tissues, less immunogenicity and less tissue accumulation. Considering the enormity of AMR, the use of Antimicrobial Peptides (AMPs) can be a viable alternative to current therapeutics strategies. AMPs are naturally abundant allowing synthetic chemists to develop semi-synthetics peptide molecules. AMPs have a broad spectrum of activity towards microbes and they possess the ability to bypass the resistance induction mechanisms of microbes. RESULT: The present review focuses on the potential applications of AMPs against various microbial disorders and their future prospects. Several resistance mechanisms and their strategies have also been discussed to highlight the importance in the current scenario. CONCLUSION: Breakthroughs in AMP designing, peptide synthesis and biotechnology have shown promise in tackling this challenge and has revived the interest of using AMPs as an important weapon in fighting AMR.

Nowadays, many organizations and individual users are employing cloud services extensively due to their efficiency, reliability and low cost. A key aspect for cloud data centers is to achieve management methods to reduce energy consumption, increasing the profit and reducing the environmental impact, which is critical in the deployment of leading-edge technologies today such as blockchain and digital finances, IoT, online gaming and video streaming. In this review, various clustering, optimization, and machine learning methods used in cloud resource allocation to increase the energy efficiency and performance are analyzed, compared and classified. Specifically, on the one hand, we discuss how clustering methods and optimization techniques are widely applied in energy management due to their capacity to provide solutions for energy consumption reduction. On the other hand, we study how multi-objective optimization methods focus on reducing energy consumption as well as service level agreement (SLA) violation, and improving quality of services (QoS) simultaneously. Also, we discuss how optimization methods such as the firefly algorithm, whale optimization algorithm (WOA), particle swarm optimization (PSO) and genetic algorithm (GA) provide the highest performance in the field. Moreover, we analyze how machine learning methods such as deep neural network (DNN), random forest, and support vector machine (SVM) are applied to the prediction of energy consumption in the cloud, showing an accurate performance in this prediction. Nevertheless, we study how the existing methods still have limitations of low convergence, trap into local optima and overfitting.

Traffic problems continue to deteriorate because of increasing population in urban areas that rely on many modes of transportation, the transportation infrastructure has achieved considerable strides in the last several decades. This has led to an increase in congestion control difficulties, which directly affect citizens through air pollution, fuel consumption, traffic law breaches, noise pollution, accidents, and loss of time. Traffic prediction is an essential aspect of an intelligent transportation system in smart cities because it helps reduce overall traffic congestion. This article aims to design and enforce a traffic prediction scheme that is efficient and accurate in forecasting traffic flow. Available traffic flow prediction methods are still unsuitable for real-world applications. This fact motivated us to work on a traffic flow forecasting issue using Vision Transformers (VTs). In this work, VTs were used in conjunction with Convolutional neural networks (CNN) to predict traffic congestion in urban spaces on a city-wide scale. In our proposed architecture, a traffic image is fed to a CNN, which generates feature maps. These feature maps are then fed to the VT, which employs the dual techniques of tokenization and projection. Tokenization is used to convert features into tokens containing Vision information, which are then sent to projection, where they are transformed into feature maps and ultimately delivered to LSTM. The experimental results demonstrate that the vision transformer prediction method based on Spatio-temporal characteristics is an excellent way of predicting traffic flow, particularly during anomalous traffic situations. The proposed technology surpasses traditional methods in terms of precision, accuracy and recall and aids in energy conservation. Through rerouting, the proposed work will benefit travellers and reduce fuel use.

Massive infiltration of photovoltaic (PV) systems into electric supply networks creates numerous challenges in the present era, as the PV systems become an alternative to non-renewable energy resources. Partial shading, nevertheless, is an essential problem which affects the productivity and life of PV plants. PV reconfiguration is known as a powerful technique to resolve this effect. It is achieved by rearranging the PV modules according to their temperature and levels of shade. Therefore, in this paper, we have utilized three simple population-based optimization algorithms that are known as the flow regime algorithm (FRA), the social mimic optimization algorithm (SMO), and the Rao optimization algorithm to dynamically restructure the PV array. The effectiveness of the proposed algorithms is evaluated using several metrics such as fill factor, mismatch losses, percentage of power loss, and percentage of power enhancement. Besides, the results obtained are compared with a regular total-cross-tied (TCT) connection and recently published techniques such as the competence square (CS) and genetic algorithm (GA). Furthermore, to demonstrate the suitability of proposed approaches in real-time implementation, real-time irradiation data of a particular location are considered and fed into the proposed algorithms for effective shade dispersion. After successful shade dispersion, the total energy generated using the three proposed algorithms is calculated and compared with the TCT reconfigured system for one year. The presented energy calculations and revenue generation confirm that the power produced by the proposed FRA technique is 13% higher than that generated by the TCT configuration. Furthermore, the presented PV characteristics show a reduced number of multiple peaks in the system. Thus, the proposed FRA technique can be endorsed as a technique that is superior to other existing methods.

This paper presents a comprehensive review of advanced technologies with various control approaches in terms of their respective merits and outcomes for power grids. Distributed energy storage control is classified into automatic voltage regulator and load frequency control according to corresponding functionalities. These control strategies maintain a power balance between generation and demand. Besides, three basic electric vehicle charging technologies can be distinguished, i.e. stationary, quasi-dynamic and dynamic control. For realizing charge-sustaining operation at minimum cost quasi-dynamic and dynamic strategies are adopted for in-route charging, while stationary control can only be utilized when the electric vehicle is in stationary mode. Moreover, power system frequency stability and stabilization techniques in non-synchronous generator systems are reviewed in the paper. Specifically, a synchronverter can damp power system oscillations and ensure stability by providing virtual inertia. Furthermore, it is crucial to manage the massive information and ensure its security in the smart grid. Therefore, several attack detection and mitigation schemes against cyber-attacks are further presented to achieve reliable, resilient, and stable operation of the cyber-physical power system. Thus, bidirectional electrical power flows with two-way digital control and communication capabilities have poised the energy producers and utilities to restructure the conventional power system into a robust smart distribution grid. These new functionalities and applications provide a pathway for clean energy technology. Finally, future research trends on smart grids such as IoT-based communication infrastructure, distributed demand-response with artificial intelligence and machine learning solutions, and synchrophasor-based wide-area monitoring protection and control (WAMPC) are examined in the present study.

The magnetic particles of nickel-zinc ferrite with chemical composition Ni1-xZnxFe2O4 were synthesized successfully by citrate precursor auto-combustion method using high purity nitrates and citric acid as chelating agent. The prepared powder of nickel-zinc ferrites was sintered at 1000℃ for 1 hr to obtain good crystalline phase and was used for further study. The X-ray diffraction technique was employed to confirm the single phase formation of nickel ferrite. The X-ray diffraction pattern shows the Bragg’s peak which belongs to cubic spinel structure. The values of lattice constant, X-ray density, bulk density, and porosity were calculated. The temperature dependence of the electrical conductivity plot shows the kink, which can be attributed to ferromagnetic-paramagnetic transition. The activation energy obtained from resistivity plots in paramagnetic region is found to be more than that in ferrimagnetic region. The conduction mechanism in nickel-zinc ferrite particles has been discussed on the basis of hopping of electrons.

Napier grass is a high-productivity perennial grass that is a very important forage for animals in the tropics. In this research work, fiber strands from Napier grass were extracted and the effect of acetic acid treatment on their chemical composition, morphological and structural changes, and tensile and thermal properties was studied. The acid treatment was carried out using glacial acetic acid solution at three different concentrations (5, 10, and 15%) for 2 h. Chemical analysis indicated lowering of amorphous hemicellulose content on acid treatment. FT-IR spectroscopic studies revealed variation of functional groups on acid treatment. Scanning electron micrographs indicated roughening of the surface of the fiber strands due to the removal of the hemicellulose layer on acid treatment. X-ray diffraction analysis indicated an increase in crystallinity of the fiber strands on acid treatment. The thermal stability and tensile properties of the fiber strands increased on acid treatment. This fiber has competitive advantages when evaluated with other natural fibers and can be developed further as a potential reinforcement in polymer matrix composites.

Short bamboo fiber reinforced epoxy composites have been developed with varying fiber length. The chemical resistance tests indicate that the composite materials are resistant to acetic acid, hydrochloric acid, nitric acid, sodium hydroxide, sodium carbonate, ammonia, benzene, carbon tetrachloride and toluene. The variation of tensile load at break with fiber length has been studied and the tensile load is found to be maximum for the fiber length of 30 mm.

Diabetes Mellitus is one of the growing fatal diseases all over the world. It leads to complications that include heart disease, stroke, and nerve disease, kidney damage. So, Medical Professionals want a reliable prediction system to diagnose Diabetes. To predict the diabetes at earlier stage, different machine learning techniques are useful for examining the data from different sources and valuable knowledge is synopsized. So, mining the diabetes data in an efficient way is a crucial concern. In this project, a medical dataset has been accomplished to predict the diabetes. The R-Studio software was employed as a statistical computing tool for diagnosing diabetes. The PIMA Indian database was acquired from UCI repository will be used for analysis. The dataset was studied and analyzed to build an effective model that predicts and diagnoses the diabetes disease earlier.

Hydroponics is the soil less agriculture farming, which consumes less water and other resources as compared to the traditional soil‐based agriculture systems. However, monitoring of hydroponics farming is a challenging task due to the simultaneous supervising of numerous parameters, nutrition suggestion, and plant diagnosis system. But the recent technological developments are quite useful to solve these problems by adopting the artificial intelligence‐based controlling algorithms in agriculture sector. Therefore, this article focuses on implementation of mobile application integrated artificial intelligence based smart hydroponics expert system, hereafter referred as AI‐SHES with Internet of Things (IoT) environment. The proposed AI‐SHES with IoT consists of three phases, where the first phase implements hardware environment equipped with real‐time sensors such as NPK soil, sunlight, turbidity, pH, temperature, water level, and camera module which are controlled by Raspberry Pi processor. The second phase implements deep learning convolutional neural network (DLCNN) model for best nutrient level prediction and plant disease detection and classification. In third phase, farmers can monitor the sensor data and plant leaf disease status using an Android‐based mobile application, which is connected over IoT environment. In this manner, the farmer can continuously track the status of his field using the mobile app. In addition, the proposed AI‐SHES also develops the automated mode, which makes the complete environment in automatic control manner and takes the necessary actions in hydroponics field to increase the productivity. The obtained simulation results on disease detection and classification using proposed AI‐SHES with IoT disclose superior performance in terms of accuracy, F‐measure with 99.29%, and 99.23%, respectively.

Manual tumor diagnosis from magnetic resonance images (MRIs) is a time-consuming procedure that may lead to human errors and may lead to false detection and classification of the tumor type. Therefore, to automatize the complex medical processes, a deep learning framework is proposed for brain tumor classification to ease the task of doctors for medical diagnosis. Publicly available datasets such as Kaggle and Brats are used for the analysis of brain images. The proposed model is implemented on three pre-trained Deep Convolution Neural Network architectures (DCNN) such as AlexNet, VGG16, and ResNet50. These architectures are the transfer learning methods used to extract the features from the pre-trained DCNN architecture, and the extracted features are classified by using the Support Vector Machine (SVM) classifier. Data augmentation methods are applied on Magnetic Resonance images (MRI) to avoid the network from overfitting. The proposed methodology achieves an overall accuracy of 98.28% and 97.87% without data augmentation and 99.0% and 98.86% with data augmentation for Kaggle and Brat's datasets, respectively. The Area Under Curve (AUC) for Receiver Operator Characteristic (ROC) is 0.9978 and 0.9850 for the same datasets. The result shows that ResNet50 performs best in the classification of brain tumors when compared with the other two networks.

The aim of this study was to analyze renewable energy installed capacity of developing countries by focusing on China as the leading country in renewable energy development to determine the reasons for growth or suspension of developments. Then, recent laws and policies of renewable energy systems were discussed in developing countries, to analyze their policies toward the use of renewable electricity. According to the findings, the most important barrier to further renewable energy developments in such countries is the unwillingness of the private sector to make investments due to considerable expenditures and the late return of capital. It was suggested in this study that governmental supports and the guaranteed purchase of generated electricity could resolve the problems to some extent.

This study was undertaken to evaluate cardio protective effect of rutin against sodium fluoride (NaF)-induced oxidative stress-mediated cardiotoxicity and blood toxicity. Cardiac injury was induced by daily administration of NaF 600 ppm in distilled water for four weeks. The animals exposed to NaF exhibited a significant increase in levels of cardiac serum markers, lipid peroxidative markers, serum total cholesterol, LDL, triglycerides and decrease in HDL levels. Decrease in hematological parameters, namely hemoglobin, red blood cells, mean corpuscular volume, mean corpuscular hemoglobin (MCH), MCH count and increase in white blood cells and erythrocyte sedimentation levels were also observed. Marked histopathological lesions and increased DNA fragmentation in cardiac tissues were observed. Activity of antioxidants-catalase, superoxide dismutase and reduced glutathione contents were decreased (p < 0.01), whereas lipid peroxidation product (malondialdehyde) was increased. A significant decrease in body and heart weight was also observed. Treatment with rutin effectively ameliorated the alterations in the studied parameters of rat through its antioxidant nature. There was also significant improvement in hematological parameters. Thus, results of this study clearly demonstrated that treatment with rutin against NaF intoxication has a significant role in protecting F-induced cardiotoxicity, blood toxicity and dyslipidemia in rats.