When is a Function Securely Computable?

A subset of a set of terminals that observe correlated signals seek to compute a given function of the signals using public communication. It is required that the value of the function be kept secret from an eavesdropper with access to the communication. We show that the function is securely computable if and only if its entropy is less than the "aided secret key" capacity of an associated secrecy generation model, for which a single-letter characterization is provided

Recent Advances in Phytoremediation of Hazardous Substances using Plants: A Tool for Soil Reclamation and Sustainability

Phytoremediation techniques have emerged as a promising approach for soil reclamation and remediation of contaminated sites. This review article provides a comprehensive analysis of the different phytoremediation techniques used for soil reclamation and their effectiveness in removing contaminants from soil. The aim is to evaluate the current state of knowledge and to highlight potential avenues for future research in this field. The review begins with a discussion of the principles underlying phytoremediation, emphasizing the ability of plants to accumulate, tolerate, and detoxify contaminants through various mechanisms such as phytoaccumulation, rhizo-degradation, and rhizo-filtration. Different plant species and their suitability for phytoremediation are reviewed, considering factors such as metal tolerance, biomass production, and pollutant uptake efficiency. In addition, the role of soil amendments and their impact on improving phytoremediation efficiency is critically evaluated. Commonly used amendments, including chelating agents, organic matter, and pH adjusters, are reviewed with emphasis on their ability to increase metal bioavailability and plant uptake. The review also addresses challenges associated with phytoremediation, such as plant growth limitations, long-term sustainability, and potential risks associated with the release of pollutants into the atmosphere during biomass disposal. Strategies to mitigate these challenges, including plant breeding and genetic engineering, are discussed

In vitro evaluation of Transdermal Patch of Palonosetron for Antiemetic Therapy

Skin is one of the routes for systemic delivery of drugs through various drug delivery system. A transdermal Drug Delivery System (TDDS) is one of the most reliable and useful system to deliver drug systemically through skin. Generally medicated patch is placed on skin for delivery of medication through it into the blood stream. The aim of present study was to formulate and evaluate Palonosetron transdermal patch in vitro that could be used for antiemetic therapy. The incorporation ofPalonosetron a serotonin 5-HT3 antagonist drug was envisaged. The TDDS was prepared by solvent evaporation technique and was evaluated for organoleptic characteristics and other physicochemical properties Thickness, Weight variation, Drug content uniformity, Tensile strength, % Elongation, Folding endurance & Moisture content. The in vitro permeation study of the patch was carried out through KesaryChein diffusion cell as barrier membrane. Phosphate buffer pH 7.4 was used as dissolution medium and the temperature was maintained at 37 ± 10C. The in vitro permeation study of the prepared patch indicated a time dependent increase in drug release throughout the study. The percentage of cumulative drug release was found to be 76.25% in 24 hours.The study shows a new approach to work in with Palonosetron

Bibliometric Review of FPGA Based Implementation of CNN

Nowadays Convolution Neural Network (CNN) has become the state of the art for machine learning algorithms due to their high accuracy. However, implementation of CNN algorithms on hardware platforms becomes challenging due to high computation complexity, memory bandwidth and power consumption. Hardware accelerators such as Graphics Processing Unit (GPU), Field Programmable Gate Array (FPGA), Application Specific Integrated Circuit (ASIC) are suitable platforms to model CNN algorithms. Recently FPGAs have been considered as an attractive platform for CNN implementation. Modern FPGAs have various embedded hardware and software blocks such as a soft processor, DSP slice and memory blocks. These embedded resources along with customized logic blocks, makes FPGA a perfect candidate for CNN model. Also, the major advantage of FPGA in the case of CNN is its parallelism and pipelining architecture which helps to accelerate CNN operations. The primary goal of this bibliometric review is to determine the scope of current literature in the field of implementing CNN algorithms on various hardware platforms, with a particular emphasis on the FPGA platform for CNN-based applications. Data from Scopus is mostly used in this bibliometric analysis. It reveals that researchers from China, India, and the United Kingdom make the most significant contributions in the form of conferences, journals, and book proceedings. All the documents are from subject areas of Engineering, Computer Science, Mathematics, Physics and Astronomy, Decision Sciences, and Material Science make significant contributions

Machine learning model for classification of predominantly allergic and non-allergic asthma among preschool children with asthma hospitalization

OBJECTIVE: Asthma is the most frequent chronic airway illness in preschool children and is difficult to diagnose due to the disease's heterogeneity. This study aimed to investigate different machine learning models and suggested the most effective one to classify two forms of asthma in preschool children (predominantly allergic asthma and non-allergic asthma) using a minimum number of features.METHODS: After pre-processing, 127 patients (70 with non-allergic asthma and 57 with predominantly allergic asthma) were chosen for final analysis from the Frankfurt dataset, which had asthma-related information on 205 patients. The Random Forest algorithm and Chi-square were used to select the key features from a total of 63 features. Six machine learning models: random forest, extreme gradient boosting, support vector machines, adaptive boosting, extra tree classifier, and logistic regression were then trained and tested using 10-fold stratified cross-validation.RESULTS: Among all features, age, weight, C-reactive protein, eosinophilic granulocytes, oxygen saturation, pre-medication inhaled corticosteroid + long-acting beta2-agonist (PM-ICS + LABA), PM-other (other pre-medication), H-Pulmicort/celestamine (Pulmicort/celestamine during hospitalization), and H-azithromycin (azithromycin during hospitalization) were found to be highly important. The support vector machine approach with a linear kernel was able to diffrentiate between predominantly allergic asthma and non-allergic asthma with higher accuracy (77.8%), precision (0.81), with a true positive rate of 0.73 and a true negative rate of 0.81, a F1 score of 0.81, and a ROC-AUC score of 0.79. Logistic regression was found to be the second-best classifier with an overall accuracy of 76.2%.CONCLUSION: Predominantly allergic and non-allergic asthma can be classified using machine learning approaches based on nine features.</p

PRESCRIPTION PATTERN, COST AND APPROPRIATENESS OF ANTIMICROBIAL USE BY RURAL PRIVATE PRACTITIONERS- A PROSPECTIVE OBSERVATIONAL STUDY

OBJECTIVES: Antimicrobial consumption in India is on a continuous increase and so are the chances of the emergence of antimicrobial resistance. This present study intends to identify, and assess the appropriateness of antimicrobials prescribed by rural private practitioners and the average cost of antimicrobials per prescription. MATERIALS &amp;METHODS: This prospective observational study was conducted by the Department of Pharmacology, Government Medical College, Jaulan (Orai). All the prescriptions coming to the chemist shops in the rural town area, having one or more antimicrobials written by private practitioners were included in the study and were assessed for prescription pattern, appropriateness, and cost. &nbsp; RESULTS: The majority of antimicrobials were prescribed for Gastrointestinal infection (33.50%) followed by fever (27.70%). According to the assessment tool used for evaluating the appropriateness of antimicrobials only in 12.60% of prescriptions need for use of antimicrobials in treatment was established (Category I), and in 56.20% of prescriptions rationale for the use of antimicrobials was not established (Category IV &amp; V) &nbsp; CONCLUSIONS: In our study, it was found that maximum antimicrobials prescribed in private settings were by doctors having only MBBS degrees and more than half of the prescriptions were Inappropriate. More efforts are required to train our medical graduates in AMS and ASPIC programs along with nursing staff to make these programs successful at the ground level