Processing and Characterization of Ball Milled Magnesium for Biomedical Implant

Damage and disease in the bone tissue arises the need for developing biomedical implants. Magnesium and its alloys have been intensively studied as biodegradable implant materials, where their mechanical properties make them a potential candidate for orthopedic applications. As a biocompatible and biodegradable metal, it has several gains over the metallic implants presently in use, including eliminating the effects of stress shielding, improving biocompatibility concerns in vivo and eliminating the requirement of a second surgery. In this present research work magnesium powder of two different particle size as received (AR) and ball milled (BM) were prepared using powder metallurgy technique. The AR and BM powders were compacted, sintered and characterized using x-ray powder diffraction (XRD) and scanning electron microscopy technique (SEM). The density measurement of AR and BM samples were measured using Archimedes’ principle. Bioactivity and degradation studies were evaluated by immersing the samples in simulated body fluid (SBF) for up to 3 weeks. The degradation studies were assessed by determining the weight loss method. The bioactivity was assessed observing the apatite formation when immersed in SBF using SEM and XRD. From this study it was observed that the corrosion resistance of pure magnesium increases with decrease in particle size (i.e. BM samples showed better corrosion resistance)

Computing with Membranes and Picture Arrays

Splicing systems were introduced by Tom Head [3] on biological considerations to model certain recombinant behaviour of DNA molecules. An effective extension of this operation to images was introduced by Helen Chandra et al. [5] and H array splicing systems were considered. A new method of applying the splicing operation on images of hexagonal arrays was introduced by Thomas et al. [12] and generated a new class of hexagonal array languages HASSL. On the other hand, P systems, introduced by Paun [6] generating rectangular arrays and hexagonal arrays have been studied in the literature, bringing together the two areas of theoretical computer science namely membrane computing and picture languages. P system with array objects and parallel splicing operation on arrays is introduced as a simple and effective extension of P system with operation of splicing on strings and this new class of array languages is compared with the existing families of array languages. Also we propose another P system with hexagonal array objects and parallel splicing operation on hexagonal arrays is introduced and this new class of hexagonal array languages is compared with the existing families of hexagonal array languages

Comparison of pattern of self-medication among urban and rural population of Telangana state, India

Background: Self-medication is one of the components of self-care, which may treat the disease or result in worsening of the condition due to irrational use of drug.1 In developing countries like India, self-medication is a common practice as it provides a low-cost alternative for people who cannot afford the high cost of clinical service, and is time efficient.Methods: A total of 110 participants completed the study. A printed questionnaire was given to those who were willing to participate in the study and came to buy medicines without consulting a doctor to various pharmacy outlets.Results: Among the group of drugs used antibiotics were the common drugs used in rural area (74%) and cough suppressants (50%) in urban area. Symptoms for opting self-medication were fever and common cold in both the groups. Individuals in both areas took self-medication based on their previous prescriptions (rural 42% vs urban41.6%) and advertisements. Rural individuals preferred self-medication with the opinion of saving time and urban people felt that it was less expensive.Conclusions: There is a difference in the pattern self-medication among rural and urban individuals. It is also to be noted that use of antibiotics may result in problems related to drug resistance. So, it would be advisable to restrict the sale of antibiotics as over the counter drugs

COMPARISON OF BLOCK MATCHING ALGORITHMS FOR MOTION ESTIMATION

This paper is a review of the block matching algorithms used for motion estimation in video compression. It implements and compares 8 different types of block matching algorithms that range from the very basic Exhaustive Search to the recent fast adaptive algorithms like Adaptive Rood Pattern Search and hybrid search. The algorithms that are evaluated in this paper are widely accepted by the video compressing community and have been used in implementing various standards, ranging from MPEG1 / H.261 to MPEG4 / H.263. The paper also presents a very brief introduction to the entire flow of video compression

First donor-acceptor interaction promoted gelation of organic fluids

Bile acid derivatives functionalized at the 3-position with an aromatic group formed gels in organic solvents in the presence of trinitrofluorenone

Corrigendum to “Discovery of novel interacting partners of PSMD9, a proteasomal chaperone: Role of an Atypical and versatile PDZ-domain motif interaction and identification of putative functional modules” [FEBS Open Bio 4 (2014) 571–583]

10.1016/j.fob.2015.06.010FEBS Open Bio5731-73

Adiabatic Quantum Support Vector Machines

Adiabatic quantum computers can solve difficult optimization problems (e.g., the quadratic unconstrained binary optimization problem), and they seem well suited to train machine learning models. In this paper, we describe an adiabatic quantum approach for training support vector machines. We show that the time complexity of our quantum approach is an order of magnitude better than the classical approach. Next, we compare the test accuracy of our quantum approach against a classical approach that uses the Scikit-learn library in Python across five benchmark datasets (Iris, Wisconsin Breast Cancer (WBC), Wine, Digits, and Lambeq). We show that our quantum approach obtains accuracies on par with the classical approach. Finally, we perform a scalability study in which we compute the total training times of the quantum approach and the classical approach with increasing number of features and number of data points in the training dataset. Our scalability results show that the quantum approach obtains a 3.5--4.5 times speedup over the classical approach on datasets with many (millions of) features

Bidirectional Power Flow between Solar-Integrated Grid to Vehicle, Vehicle to Grid, and Vehicle to Home

The increasing adoption of renewable energy sources, such as solar power, coupled with the growing popularity of electric vehicles (EVs), has opened up new opportunities for bidirectional power flow between various energy systems. This research paper explores the bidirectional power flow between a solar-integrated grid, electric vehicles, and residential homes. Specifically, it focuses on the benefits, challenges, and potential applications of power exchange between these entities. The paper discusses the technical aspects, economic implications, and environmental considerations of bidirectional power flow, highlighting the potential for enhanced grid stability, energy efficiency, and carbon footprint reduction. Additionally, the study addresses the impact of bidirectional power flow on grid infrastructure, smart grid technologies, and policy frameworks. By shedding light on the interplay between the solar-integrated grid, electric vehicles, and residential homes, this research paper aims to contribute to the advancement of sustainable and intelligent energy systems