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Oral hygiene, an integral part of the body’s general well-being, it is the practice of keeping the mouth clean and disease free by using tooth paste, mouth wash etc. Plants have been a great source of medical agents for many years and even be considered the origin of modern medicine. Phytochemicals the secondary metabolites of the plants are active ingredients that possess therapeutic properties that are considered as a medicine or drug for therapeutic purposes for the curing of diseases and improving human health. The main objective of this study is to formulate and develop an herbal toothpaste from herbal source and to evaluate its antibacterial activity. The herbal toothpaste containing leaves and flowers of Acmella ciliate, clove, honey and leaves of guava as the herbal ingredients. Different types of formulations (F1-F3) were formulated using Calcium carbonate as cleaning and polishing agent, Glycerine as humectant, Sodium lauryl sulphate, Sodium chloride as a stain remover from the teeth, Honey. The three formulations were evaluated by various parameters like colour, taste, fragrance, consistency, pH, shape maintenance, threading property, moisture content and foaming character. The herbal toothpaste having aqueous extract of herbal source are tested for antibacterial activity against K. pneumoniae, S. aureus, P. aeruginosa and S. pyogenes. The result shows that the bacteria were found to be more sensitive to the formulated toothpaste as seen by zone of inhibition. Further studies would benefit from a uniform method of assessment for clinical effectiveness of plaque and gingivitis and safety of the formulated herbal toothpaste. Keywords: Phytochemicals,Acmella ciliate,herbal toothpast, antibacterial activity

Polyamines are the natural compound, act as anti-senescent agents, reduce rate of respiration, delay ethylene production, retard colour change, maintain fruit firmness. Show that plays a pivotal role in postharvest life of fruits and vegetables. The objective of this study was to investigate the effect of post-harvest application of polyamines in different concentration on physico-physiological quality parameters of papaya fruits during ambient storage (33-36oC). Papaya fruits are treated with different concentration of polyamines i.e., T2-0.50mM Spermine, T3- 1.0mM Spermine,T4- 1.5mM Spermine,T5- 0.50 mMSpermidine, T6- 1 mM Spermidine,T7- 1.5mM Spermidine,T8- 2 mM Putrescine,T9- 3 mM Putrescine,T10- 4 mM Putrescine and T1-Control. Among the treatments, papaya fruits treated with 4 mM of Putrescine (T10) recorded significantly minimum physiological loss in weight, respiration rate and colourvalues (L*, a*, b*) of peel and pulp, fruit disease index and maximum firmness, shelf life compared to control (T1). Hence it is confirmed from the study that Putrescine at 4mM (T10) was found to be effective in delaying the physico-chemical and physiological process of papaya fruit cv. Red Lady.

An experiment was conducted in the Department of Agricultural Microbiology and Post-Harvest Technology, COH, Bagalkot, Karnataka during the year 2017-2018 with intention of minimizing the post harvest diseases during storage by using ecofriendly approach that is use of biocontrol agents and their effects on fruit colour development and sensory parameters. A wide range of fungi, bacteria, yeast and actinomycetes can be used as antagonistic microorganisms against post-harvest pathogens in many horticulture produce. In the present experiment, five effective antagonistic microorganisms were assessed for their antifungal activity against Colletotrichum gleosporiodies, the causal agent of post-harvest Anthracnose disease of papaya fruits and fortheir effects on fruit development and sensory properties of papaya var. Red Lady. In the present experiment the papaya fruits treated with biocontrol agents and observed that, the papaya fruits treated with BCA5-Isolate No.10(T5) exhibited higher L*(56.53), a*(12.33), b*(43.60) colour values, higher carotene value (1.85 mg/100 g), total sugar content (11.6%) and good sensory scores for colour (8073), taste (8.6) and textue (9.03) at 9 days after storage under ambient storage condition followed by BCA-Isolate No.09(T4) and carbendazium at 0.1% for 5 min (T7). Treated on inoculated papaya fruits with C. gloeosporioides, T5 (BCA5 -Isolate No. 10) recorded higher L*(55.67), a*(11.61), b*(41.78) colour values, higher carotene value (1.42 mg/100 g), total sugar content (10.4%) and good sensory scores for colour (8.3), taste (8.13) and textue (8.73) at 9 days after storage under ambient storage condition followed by BCA4 -Isolate No.9 (T4) and carbendazium at 0.1% (T7). These two isolates identified in this experiment can be effectively used alternative to chemicals against anthracnose which were effective in fruit colour development and sensory parameters.

Recent news reveal a powerful attacker which breaks data confidentiality by acquiring cryptographic keys, by means of coercion or backdoors in cryptographic software. Once the encryption key is exposed, the only viable measure to preserve data confidentiality is to limit the attacker’s access to the ciphertext. This may be achieved, for example, by spreading ciphertext blocks across servers in multiple administrative domains—thus assuming that the adversary cannot compromise all of them. Nevertheless, if data is encrypted with existing schemes, an adversary equipped with the encryption key, can still compromise a single server and decrypt the ciphertext blocks stored therein. In this paper, we study data confidentiality against an adversary which knows the encryption key and has access to a large fraction of the ciphertext blocks[3]. To this end, we propose Bastion, a novel and efficient scheme that guarantees data confidentiality even if the encryption key is leaked and the adversary has access to almost all ciphertext blocks. We analyze the security of Bastion, and we evaluate its performance by means of a prototype implementation. We also discuss practical insights with respect to the integration of Bastion in commercial dispersed storage systems.

Alzheimer's disease (AD) is one of the most common neurodegenerative diseases and is considered to be the main cause of cognitive impairment in elderly people. It is a progressive disease that destroys memory and other important mental functions and causes problems with memory, thinking and behavior. Symptoms usually develop slowly and worsen over time Symptoms may become severe enough to interfere with daily life, and lead to death. In 2022, 55 million people worldwide suffered from this disease. AD is predicted to affect 1 in 85 people globally by 2050, and at least 43% of prevalent cases need a high level of care. Alzheimer's Disease Neuroimaging Initiative (ADNI) give datasets that can be utilized for different Alzheimer's Disease related examinations. The dataset consists of a longitudinal MRI data of 150 subjects aged 60 to 96.72 of the subjects were grouped as 'Nondemented' throughout the study.64 of the subjects were grouped as 'Demented' at the time of their initial visits and remained so throughout the study.14 subjects were grouped as 'Nondemented' at the time of their initial visit and were subsequently characterized as 'Demented' at a later visit. These fall under the 'Converted' category. In our project, we propose some machine learning models to detect the Alzheimer's disease in earlier stage by finding the accuracy levels and determining the attributes that helps us to find the maximum accuracy rate.

In households, especially old ones, a lot of objects would have not been moved for a long time. This may have allowed insects, some dangerous, to accumulate under those objects. Placing a camera module integrated with an embedded and alarm system in those spots with a minimal lighting will alert the inmates of the building of the presence of such insects. The embedded system is loaded with CNN image processing module trained by a dataset of images of dangerous insects in that light setting. The collected images and video footages can be employed for future data analytics.

Zirconia is a bioinert ceramic biomaterial. Zirconia having composition of 97% Zirconia oxide and 3% Yttria oxide finds its vital application in the field of dental ceramics as an implant material ...

Globally, climate change impacts are manifold, severely affecting the agriculture sector. Climate-resilient agriculture (CRA) is viewed as a panacea for overcoming the adverse effects of climate change on agriculture. This article critically reviews the literature on CRA to delineate the emerging patterns of climate-resilient agriculture. It explores multiple dimensions of CRA as related to practices, technologies, policies, innovations and interventions across different parts of the world. In the end, a schematic approach towards undertaking empirical research on CRA in the Indian context is presented. The review finds that, globally, CRA practices mainly include systematic strategies for management of critical inputs, namely, land and water, cropping systems and livelihood management. It emerges that innovations and institutions have a crucial role in accelerating the rate of adoption of CRA practices. The article highlights the need for undertaking more empirical research to get a deeper understanding of the emerging patterns of CRA in the Indian context.

Badiadka Narayana, a professor in Chemistry from Mangalore University, is a prolific writer in Chemistry and a well-known expert in Crystallography. 691 articles in international journals, 49 in national journals and 165 papers in conference proceedings are the vivid record of his prolific writings. A significant number of articles (392) appeared between the years 2007 and 2011. 325(59.2%) papers of Narayana have appeared in the international journal 'Acta crystallographica section e-structure'. Most of his collaborative works, 242 articles in total, are with H. S. Yathirajan and Sarojini Balladka K.

Recommender systems are used now-a-days as information filtering tools for predicting user’s preferences, predominantly from big data and recommends things to their taste/choice. Recommender System for health domain provides a mean to assist users in categorizing users with similar symptoms of diseases. Users often feel uncertain when investigating on their own about the medical information that is available on the various online sources. The primary objective is to suggest the preferred cardiologist to the users based on the symptom parameters they have given as input. This information is analyzed and users with similar medical information are identified. Different users may have different expectations in the selection of doctors. So users are asked to prioritize their expectations. The proposed system is based on both collaborative and content filtering approach that makes use of the information provided by users, analyzes them and identifies the similarity between users through the clusters created using K-Means algorithm. In K-Means clustering, the two types of clusters will be formed based on the health parameters given by the users and the other one will be formed based on the users priority. The similar users that are available in both the clusters are identified and ratings of those similar users are taken into matrix. The doctor with the maximum average is recommended. Based on the ratings for the doctors given by the similar users and priority of their expectations, doctors are recommended. We strongly believe that the idea of recommending the doctors based on the users similarity and priority can be adapted to cope with the special requirements of the health domain.

In today’s world, healthcare has become one of the prominent domains of the real-world because it is associated with individuals looking after or retainment of health through disease prevention by early diagnosis, proper treatment, on-time recovery, relief from disease, a period of sickness, and injury. The diagnosis is treated as the major aspect of healthcare because the right diagnosis of disease at the right time avoids the individual from accidental death or helps to perform early-stage chronic disease detection and avoids further damage. The disease prediction helps to determine the existence of disease at earlier stages using the disease symptoms and patient personal details. The disease prediction is mainly achieved by a category of data analytics called predictive analytics, which makes use of patient disease symptoms and personal details to perform disease prediction. The patient privacy breach is treated as one of the critical issues related to disease prediction because of the usage of patient personal details. To preserve the patient privacy, the data used to perform prediction is altered using privacy-preserving techniques but it leads to another issue called data utility; i.e., what extent modified data become useful for predictive analytics after applying privacy-preserving operations. Hence, there is a tradeoff between prediction data privacy and utility. The existing privacy-preserving approaches mainly focus on preserving privacy and result in loss of data usefulness. Our proposed approach called sensitivity context aware privacy preserving disease prediction mainly deals with how to preserve the privacy of data and meantime retain its usefulness while performing disease prediction.

Wireless networks exploiting massive MIMO technologies come in handy to meet the demands of increasing data traffic and enhance network capacity and coverage for imminent communications. A large number of RF chains and energy-efficient antenna arrays are used on a broad scale to intensify the data rate and system throughput. This results in computational complexity and high energy requirements. To overcome this concern, a transceiver with Hybrid Beamforming is designed at 3.5 GHz (IMT range 3.5-3.8 GHz) using SystemVue© software. 3.5 GHz is preferred for its technical characteristics that make it effective in delivering high-speed services. It has the capacity to afford a considerable amount of contiguous spectrum that will support channels with wide bandwidth, ideal for 5G deployment. Transceiver designs with Hybrid Beamforming for sub 6 GHz MIMO systems with OFDM source are viable to reduce the volume of RF chains, disintegrating the signal processing into the analog and digital domains. Simulation results are analyzed based on various beamforming parameters like antenna array spacing, sidelobe suppression, directivity, beamforming gain and the use of a minimum number of RF chains is ensured. The proposed design provides a near-optimal performance close to the digital Beamforming with sharp beams.

The effect of corrosion inhibiting properties on concrete and mortar is investigated experimentally. The mortar was prepared by using Ordinary Portland Cement (OPC) and Pozzolanic Portland Cement (PPC). Based on the efficiency obtained, OPC was used to prepare concrete. Based on the literatures, Azadirachta indica (Neem) is in two forms, one as powder and another as solution and acting as a Green Corrosion Inhibitor (GCI). The efficiency of two forms of GCI was compared. The physical properties such as consistency and initial setting time have been evaluated by mortar paste, and the mechanical properties such as compressive strength and split tensile strength have been tested by casting mortar cubes, concrete cubes, and cylinders. The addition of GCI in the form of powder decreases the compressive strength due to drought resistance of the neem powder, whereas solution shows the best result due to hydroscopic property of ethanol. Therefore, extracts of plants and natural products are used to achieve cheap, environmentally acceptable, abundant source, readily available and effective molecules having very high inhibition effectiveness and low or zero ecological impact.