Electrical bicycle sharing scheme for medium-scale and large-scale organizations

This paper deals with the development of an Electrical Bicycle sharing scheme, in which we are discussing the availability of different types of Electrical Bicycles also about different components available for the development of Electrical bicycles. The proposed scheme uses a simple RFID (Radio Frequency Identification) card or Id-password based locking and unlocking as well as a prepaid charging system, in which time-based charges are fed from the user. Also, this paper discusses the Solar charging system for Electrical bicycles, calculation of the payback period for Electrical bicycles. Lastly, the different benefits of sharing scheme have been included

HBONext: An Efficient Dnn for Light Edge Embedded Devices

Indiana University-Purdue University Indianapolis (IUPUI)Every year the most effective Deep learning models, CNN architectures are showcased based on their compatibility and performance on the embedded edge hardware, especially for applications like image classification. These deep learning models necessitate a significant amount of computation and memory, so they can only be used on high-performance computing systems like CPUs or GPUs. However, they often struggle to fulfill portable specifications due to resource, energy, and real-time constraints. Hardware accelerators have recently been designed to provide the computational resources that AI and machine learning tools need. These edge accelerators have high-performance hardware which helps maintain the precision needed to accomplish this mission. Furthermore, this classification dilemma that investigates channel interdependencies using either depth-wise or group-wise convolutional features, has benefited from the inclusion of Bottleneck modules. Because of its increasing use in portable applications, the classic inverted residual block, a well-known architecture technique, has gotten more recognition. This work takes it a step forward by introducing a design method for porting CNNs to lowresource embedded systems, essentially bridging the difference between deep learning models and embedded edge systems. To achieve these goals, we use closer computing strategies to reduce the computer’s computational load and memory usage while retaining excellent deployment efficiency. This thesis work introduces HBONext, a mutated version of Harmonious Bottlenecks (DHbneck) combined with a Flipped version of Inverted Residual (FIR), which outperforms the current HBONet architecture in terms of accuracy and model size miniaturization. Unlike the current definition of inverted residual, this FIR block performs identity mapping and spatial transformation at its higher dimensions. The HBO solution, on the other hand, focuses on two orthogonal dimensions: spatial (H/W) contraction-expansion and later channel (C) expansion-contraction, which are both organized in a bilaterally symmetric manner. HBONext is one of those versions that was designed specifically for embedded and mobile applications. In this research work, we also show how to use NXP Bluebox 2.0 to build a real-time HBONext image classifier. The integration of the model into this hardware has been a big hit owing to the limited model size of 3 MB. The model was trained and validated using CIFAR10 dataset, which performed exceptionally well due to its smaller size and higher accuracy. The validation accuracy of the baseline HBONet architecture is 80.97%, and the model is 22 MB in size. The proposed architecture HBONext variants, on the other hand, gave a higher validation accuracy of 89.70% and a model size of 3.00 MB measured using the number of parameters. The performance metrics of HBONext architecture and its various variants are compared in the following chapters

Isolation and Characterization of Nodule-Associated Exiguobacterium sp. from the Root Nodules of Fenugreek (Trigonella foenum-graecum) and Their Possible Role in Plant Growth Promotion

One of the ways to increase the competitive survivability of rhizobial biofertilizers and thus achieve better plant growth under such conditions is by modifying the rhizospheric environment or community by addition of nonrhizobial nodule-associated bacteria (NAB) that cause better nodulation and plant growth when coinoculated with rhizobia. A study was performed to investigate the most commonly associated nodule-associated bacteria and the rhizospheric microorganisms associated with the Fenugreek (Trigonella foenum-graecum) plant. Isolation of nonrhizobial isolates from root nodules of Fenugreek was carried out along with the rhizospheric isolates. About 64.7% isolates obtained from Fenugreek nodules were gram-negative coccobacilli, 29.41% were gram-positive bacilli, and all rhizospheric isolates except one were gram-positive bacilli. All the isolates were characterized for their plant growth promoting (PGP) activities. Two of the NAB isolates M2N2c and B1N2b (Exiguobacterium sp.) showed maximum positive PGP features. Those NAB isolates when coinoculated with rhizobial strain—S. meliloti, showed plant growth promotion with respect to increase in plant's root and shoot length, chlorophyll content, nodulation efficiency, and nodule dry weight

Machine Vision Using Cellphone Camera: A Comparison of deep networks for classifying three challenging denominations of Indian Coins

Indian currency coins come in a variety of denominations. Off all the varieties Rs.1, RS.2, and Rs.5 have similar diameters. Majority of the coin styles in market circulation for denominations of Rs.1 and Rs.2 coins are nearly the same except for numerals on its reverse side. If a coin is resting on its obverse side, the correct denomination is not distinguishable by humans. Therefore, it was hypothesized that a digital image of a coin resting on its either size could be classified into its correct denomination by training a deep neural network model. The digital images were generated by using cheap cell phone cameras. To find the most suitable deep neural network architecture, four were selected based on the preliminary analysis carried out for comparison. The results confirm that two of the four deep neural network models can classify the correct denomination from either side of a coin with an accuracy of 97%.Comment: 6 Pages, 4 Figures, 6 Tables, Conference pape

Proceedings of the 4th International Conference on Innovations in Automation and Mechatronics Engineering (ICIAME2018)

The Mechatronics Department (Accredited by National Board of Accreditation, New Delhi, India) of the G H Patel College of Engineering and Technology, Gujarat, India arranged the 4th International Conference on Innovations in Automation and Mechatronics Engineering 2018, (ICIAME 2018) on 2-3 February 2018. The papers presented during the conference were based on Automation, Optimization, Computer Aided Design and Manufacturing, Nanotechnology, Solar Energy etc and are featured in this book

Dynamin inhibitors induce caspase-mediated apoptosis following cytokinesis failure in human cancer cells and this is blocked by Bcl-2 overexpression

<p>Abstract</p> <p>Background</p> <p>The aim of both classical (e.g. taxol) and targeted anti-mitotic agents (e.g. Aurora kinase inhibitors) is to disrupt the mitotic spindle. Such compounds are currently used in the clinic and/or are being tested in clinical trials for cancer treatment. We recently reported a new class of targeted anti-mitotic compounds that do not disrupt the mitotic spindle, but exclusively block completion of cytokinesis. This new class includes MiTMAB and OcTMAB (MiTMABs), which are potent inhibitors of the endocytic protein, dynamin. Like other anti-mitotics, MiTMABs are highly cytotoxic and possess anti-proliferative properties, which appear to be selective for cancer cells. The cellular response following cytokinesis failure and the mechanistic pathway involved is unknown.</p> <p>Results</p> <p>We show that MiTMABs induce cell death specifically following cytokinesis failure via the intrinsic apoptotic pathway. This involves cleavage of caspase-8, -9, -3 and PARP, DNA fragmentation and membrane blebbing. Apoptosis was blocked by the pan-caspase inhibitor, ZVAD, and in HeLa cells stably expressing the anti-apoptotic protein, Bcl-2. This resulted in an accumulation of polyploid cells. Caspases were not cleaved in MiTMAB-treated cells that did not enter mitosis. This is consistent with the model that apoptosis induced by MiTMABs occurs exclusively following cytokinesis failure. Cytokinesis failure induced by cytochalasin B also resulted in apoptosis, suggesting that disruption of this process is generally toxic to cells.</p> <p>Conclusion</p> <p>Collectively, these data indicate that MiTMAB-induced apoptosis is dependent on both polyploidization and specific intracellular signalling components. This suggests that dynamin and potentially other cytokinesis factors are novel targets for development of cancer therapeutics.</p

HBONext: HBONet with Flipped Inverted Residual

The top-performing deep CNN (DCNN) architectures are presented every year based on their compatibility and performance ability on the embedded edge applications, significantly for image classification. There are many obstacles in making these neural network architectures hardware friendly due to the limited memory, lesser computational resources, and the energy requirements of these devices. The addition of Bottleneck modules has further helped this classification problem, which explores the channel interdependencies, using either depthwise or groupwise convolutional features. The classical inverted residual block, a well-known design methodology, has now gained more attention due to its growing popularity in portable applications. This paper presents a mutated version of Harmonious Bottlenecks (DHbneck) with a Flipped version of Inverted Residual (FIR), which outperforms the existing HBONet architecture by giving the best accuracy value and the miniaturized model size. This FIR block performs identity mapping and spatial transformation at its higher dimensions, unlike the existing concept of inverted residual. The devised architecture is tested and validated using CIFAR-10 public dataset. The baseline HBONet architecture has an accuracy of 80.97% when tested on CIFAR-10 dataset and the model's size is 22 MB. In contrast, the proposed architecture HBONext has an improved validation accuracy of 88.30% with a model reduction to a size of 7.66 MB

A pharmacovigilance study in patients of chronic non-infective respiratory diseases attending outpatient department of pulmonary medicine in a tertiary care teaching hospital

Background: Adverse drug reactions (ADR) are the known dangers of any medicinal therapy. They are not only responsible for increasing the mortality and morbidity but also for multiplying the health care expenditure. It is important to monitor the adverse effects of the drugs in the patients on treatment for chronic non-infective respiratory diseases attending OPD of pulmonary medicine in a tertiary care teaching hospitalMethods: The study was single-centric, non-randomized and observational hospital-based study which was carried out for a period of 1 and a half years in JJ Hospital. The patients who were included in the study suffered from either of the 4 diseases-Chronic obstructive pulmonary disease (COPD), asthma, bronchiectasis or interstitial lung diseases (ILD). Data were analyzed by using Microsoft excel sheet. Based on the outcome of modified Hartwig and Siegel severity assessment scale, ADRs were grouped into various severity categories.Results: One hundred and thirty-two number of ADRs were seen in 69 out of 352 patients (19.6 %) of the study population. The occurrence of ADR was found slightly higher in males i.e., 53.62% as compared to females i.e., 46.38%. The patients who were on treatment for ILD showed highest percentage of ADRs i.e., 57.89% which is followed by bronchiectasis (17.39%), COPD (16.17%) and lastly asthma (10.26%). The ADRs belonging to GIT system were highest in number i.e., 80. The most frequently occurring ADR in the study was palpitation which occurred in 14 cases i.e., 20.29%. Out of 132 ADRs observed, 96 i.e., 72.73% belonged to the mild category and 36 ADRs i.e., 27.27% belonged to the moderate category. Not a single severe ADR was found in the study.Conclusions: It was found that 19.6% of the patient population suffered from ADRs, which is a considerable number. It is essential that health care professionals should support ADR monitoring process for the safety of the medicinal product. Proper implementation of ADR monitoring will help to reduce the harmful effects by early detection of drug safety problems in patients, assessing the risk-benefit in an individual and the population, improving the selection, rational use of drugs through the provision of timely warning to healthcare professionals