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Uncertainty of traffic in cities makes it difficult for metropolitan buses to adhere to predetermined schedules, making it strenuous for commuters to plan travel reliably. The proposed LocateMyBus system leverages Internet of Things(IoT) set-ups at bus stops and buses, and Machine Learning(ML) to assuage this uncertainty by allowing commuters to track live-runningstatus of buses, disseminate tentative and live-status to commuters through Public Announcement(PA) systems at bus-stops and a web-application interface. The schedule prediction module provides a tentative schedule of buses with stop-wise arrival times estimated using ML based on historic and real-time route data. Arrival times of two bus-routes in the Massachusetts Bay Area were collected for a period of four months by periodically querying its real-time General Transit Feed Systems(GTFS). This dataset was used to train and validate the proposed ML methods. The IoT system was modeled on Proteus, and validated with a miniature prototype. LocateMyBus is proposed as a step forward toward minimal intervention algorithmic set-ups to ease the uncertainty associated with bus commute in cities. It enables commuters to track live running status and avail ML-predicted tentative schedules. Furthermore, it eradicates the computation requirements of GPS-based systems, whilst ensuring stop-level tracking granularity. LocateMyBus's ability to log bus arrival times at each stop paves the way to building real-time GTFSs.

This data article contains the data related to the research article “Characterization, biorecognitive activity and stability of WGA grafted lipid nanostructures for the controlled delivery of rifampicin” (Pooja et al. 2015) [1]. In the present study, SLN were prepared by a single emulsification-solvent evaporation method and the various steps of SLN preparation are shown in a flow chart. The preparation of SLN was optimized for various formulation variables including type and quantity of lipid, surfactant, amount of co-surfactant and volume of organic phase. Similarly, effect of variables related to homogezation, sonication and stirring processes, on the size and surface potential of SLN was determined and optimized. Keywords: Solid lipid nanoparticles, Single emulsification-solvent evaporation, Optimization, Formulation parameters, Process variables

Video compression is necessary to recreate a video without sacrificing quality. Nowadays, researchers are focusing on global optimization approaches to determine the optical flow of the neighboring pixels in video processing. In this work, a novel improved diamond search-hybrid teaching-learning based optimization (IDS-HTLBO) methodology has been proposed to compress the videos and increase the video quality. This method uses a diamond search pattern with a secure number of search points for per frame of the video. The hybridization of DS algorithm and TLBO algorithm are applied in this methodology to reduce computational complexity. Moreover, this method reduces the computational unpredictability of block matching. The quality of the image was validated with 3D reconstruction by the structured light approaches. The experimental result shows that the proposed IDS-HTLBO algorithm achieves a maximum average value of 53.17 dB, 0.44 and 11.57 in terms of peak-to-signal-noise ratio, mean squared error, and compression ratio respectively. [ABSTRACT FROM AUTHOR]

Byline: B. Bharathi, Jeffrey. Raj, Kevin. Saldanha, P. Suman Rao, D. Devi INTRODUCTION: Medication errors (MEs) can prolong hospital stay and are a cause of morbidity and mortality. Studies evaluating [...]

Aims and method Adverse effects are a common concern when prescribing and reviewing medication, particularly in vulnerable adults such as older people and those with intellectual disability. This paper describes the development of an app giving information on side-effects, called Medichec, and provides a description of the processes involved in its development and how drugs were rated for each side-effect. Medications with central anticholinergic action, dizziness, drowsiness, hyponatraemia, QTc prolongation, bleeding and constipation were identified using the British National Formulary (BNF) and frequency of occurrence of these effects was determined using the BNF, product information and electronic searches, including PubMed. Results Medications were rated using a traffic light system according to how commonly the adverse effect was known to occur or the severity of the effect. Clinical implications Medichec can facilitate access to side-effects information for multiple medications, aid clinical decision-making, optimise treatment and improve patient safety in vulnerable adults.

Fibre-reinforced polymers (FRPs) are composite materials of plastics reinforced with fibres. Cars, sea, aeronautics, and foundation projects progressively utilize fibre-reinforced polymers. This study aims to study the effect of adding multiwalled nanotubes fillers into the hybridized jute-glass FRP composites and their relative properties. This study uses multiwalled nanotubes (MWCNTs), and particles-hybrid jute-glass composites containing jute fibre chopped layer mats, woven glass mats, epoxy resin, and multiwalled nanotubes fillers were created using the hand layup method. After adding multiwalled nanotubes fillers in various weight proportions, the mechanical behaviours of fibre-reinforced polymers were analysed. The mechanical behaviours of laminated composites were tested using the ASTM standard; the following properties are tensile, flexural, and impact strength. The multiwalled nanotubes with 6% wt. attained the maximum mechanical properties compared to the 2 and 4 wt. % of MWCNTs. The E-based specimen contributes the most to the different types of specimens, with a contribution of 24.21% for tensile, 25.03% for flexural, and 24.56% for impact. The microstructures of hybrid composites were studied using a scanning electron microscope. [ABSTRACT FROM AUTHOR]

English Language Learning or Teaching has two broad areas to cover namely language skills and language areas. Developing language skills have become mandatory in the globalized context The prime motivation of the learners to speak in English is thwarted by the difficulties and ignorance prevailing behind developing that particular skill. This is the greatest challenge formany Indian learners inspite of its importance. This leads the EFL learners to a strange conclusion that English is a difficult language to learn. The teachers of English should carefully divert students from such strong conclusion and provide them avenues to learn micro-skills involved in speaking like pronunciation of words, stress and intonation and the usage of expressions to be used in formal and informal contexts. In recent times, this notion of binomial operation of skills development seems outdated as all the four skills are inter-related and interdependent. The study in this research paper concentrates on how the habit of reading can improve the fluency in speaking among the English as second language learners. This paper envisages how reading could be considered a base to improve fluency in communication and how reading could help the speakers maintain their pace in communication. The role of teachers in inculcating the habit of reading to enable students to communicate in English fluently is also discussed in detail. [ABSTRACT FROM AUTHOR]

The objective of this work is to classify the mind mapping decisions "like", "dislike" and "neutral" in Epilepsy patients by applying the concepts of rough sets. An effective rough set-based classification of mental status in epilepsy patients has been computed using the features such as meditation, familiarity, theta, attention, appreciation, beta, mental effort, delta, alpha and gamma. The significance of features is considered as conditional attributes and the expected mood is represented as decision attributes. To analyze the impact of the features, the cardinality and rough set-based approximation are computed. Grey Relational Analysis (GRA) algorithm is applied for classification of patient decision is either like or dislike or neutral. The experimental results on classification of mind mapping of epilepsy patients using rough set-based approximation yields 95% accuracy. [ABSTRACT FROM AUTHOR]

Maria Campbell is an outstanding Metis writer and known for her novel Halfbreed (1973). She recounts her experiences being subjected to discrimination from white dominated people and the government. She belongs to the group called Metis who are the mixed people in the society of Canada. They are the combination between the community and group of culture who are discriminated in everywhere in the society. Campbell shows the sorrows of such community through her words and writings. The article gives an idea about Metis people and the vulgarity they faced in the society and in particular, the life of Maria which devastated her dream. [ABSTRACT FROM AUTHOR]

Introduction: Research evidence and theoretical models point to the need to implement yoga and mindfulness interventions as they impact different physiological and psychological functioning associated with addiction and relapse. To ensure the safe application of yoga, a standardized yoga module that can be practiced across any drug type of substance addiction is required. This study focuses on developing, validating, and testing a yoga module's feasibility for the management of substance use disorder (SUD). Materials and Methods: The yoga module was developed for SUD by including specific yogic practices derived from research evidence from Google Scholar, PubMed, and traditional literature targeting symptoms related to SUD. Yoga experts validated the module using a 3-point Likert scale. Practices which scored a content validity ratio (CVR) score =0.33 were retained in the yoga module for SUD. A pilot assessment was done online to test the module's feasibility. Participants in a 9-week residential rehabilitation center participated in the study. The module was taught as an add-on to their existing treatment. The motivation for seeking treatment was assessed at baseline, and quality of life was assessed at pre- and post-intervention using the questionnaire de motivation au traitement des toxicomanies and the World Health Organization Quality of Life BREF (WHOQOL BREF), respectively. Results: Thirty experts validated the developed module. Based on the CVR scoring, 37 yoga practices were retained. The final list of practices included breathing exercises, sukshmāvyāyāma (loosening exercise), āsana (postures), prā?āyāma (breathing exercise), and dhyāna (meditation). In the feasibility study, all participants (n = 12) reported satisfaction and mentioned no adverse effects of the yoga intervention. The participants' mean ± standard deviation (SD) of identified motivation was 26.6 ± 1.9, introjected amotivation 22.7 ± 5.1, external motivation 16.4 ± 6.2, and a motivation 15.1 ± 5.7. The Global score or Internalization coefficient mean ± SD was 29.1 ± 18.1. The participant's total score on the WHOQOL-BREF was significant post intervention (P = 0.004). With the practice of the module, there was a significant change in the participant's psychological (P = 0.008), social (P = 0.002), and environmental domains (P < 0.001) but not in physical health (P = 0.17). At 1 month follow-up, 11 participants (n = 12) maintained abstinence. Conclusion: The designed yoga module for SUD was validated by yoga experts and was considered safe and feasible by patients with SUD. In addition, patients reported an increase in their quality of life. This can be further studied in randomized controlled trials. [ABSTRACT FROM AUTHOR]

Magnesium alloys are widely used to lightweight the components in the automobile, aerospace, and electronics industries because of their low density and high specific strength. However, factors like low ductility, low mechanical strength, and high corrosion rate restrict the extensive use of the material. To utilise the alloy with improved mechanical and corrosion properties, friction stir processing followed by heat treatment was performed. The study explored the effect of ageing temperature and ageing time on the microstructure, mechanical and corrosion properties of friction stir processed AZ31 alloy. The results indicate that friction stir processing at TRS of 1200 rpm and TTS of 60 mm/min, followed by heat treatment augments the corrosion resistance of AZ31 alloy. [ABSTRACT FROM AUTHOR]

Stir casting was utilized to generate the composites. The AA 2618–10wt% zirconium carbide 10wt% rice husk ash hybrid composite had the highest hardness value. In comparison to pure AA 2618, it had a 72% increase. The rule of mixture was used to compute theoretical density, whereas the Archimedes rule was utilized to evaluate real density. For AA 2618, AA 2618/zirconium carbide, and rice husk ash hybrid composites, simple carbide inserts were used for turning. Surface roughness, metal removal rate, and tool wear were among the numerous responses examined. RSM was utilized to analyze and optimize the test outcomes. Feed rate was the most critical issue for surface roughness and material removal rate. Tool wear was shown to be most strongly controlled by tool speed, whereas metal removal rate was found to be least significantly influenced by the weight percent of reinforcement. [ABSTRACT FROM AUTHOR]

Deep learning plays a major role in detecting the presence of Coronavirus 2019 (COVID-19) and demands huge data. Availability of annotated data is a hurdle in using Deep learning technique. To enhance the accuracy of detection Deep Convolutional Generative Adversarial Network (DCGAN) is used to generate synthetic data. Densenet-201 is identified as the deep learning framework to detect COVID-19 from X-ray images. In this research, to validate the effectiveness of the Densenet-201, we explored conventional machine learning approaches such as SVM, Random Forest and Convolutional Neural Network (CNN). The feature map for training the machine learning approaches are extracted using Densenet-201 as feature extractor. The results show that Densenet-201 as feature representation with SVM is performing well in detecting COVID-19 with high accuracy. Moreover we experimented the proposed methodology without using DCGAN as well. DenseNet-201 based approach is capable of detecting the presence of COVID-19 with high accuracy. Experiments demonstrated that the proposed transfer learning approach based on DenseNet-201 along with DCGAN based augmentation outperforms the State of the art approaches like ResNet50, CNN, and VGG-16. [ABSTRACT FROM AUTHOR]

In this research, citron peel biochar and opuntia‐cladode fibers (OCF) reinforced epoxy composites were fabricated and characterized for mechanical, wear, and electrical properties. The biochar was prepared from the waste peels of citron edible fruit whereas the opuntia fiber was from the cladode of the opuntia plant. The laminates were fabricated by hand layup process and evaluated in accordance with the ASTM standards. The results revealed that the mechanical properties such as tensile strength, flexural strength, impact toughness, hardness and adhesion strength were increased by 36.2%, 30.6%, 91.3%, 1.1%, and 5.3% for composite designation EC containing 30 vol% of OCF. Similarly, the addition of citron biochar of 2 vol% increased the load bearing and dielectric properties. However, the inclusion of 30 vol% of OCF on composite designation EC the sp. wear rate recorded 0.018 mm3/Nm. Similarly, the lowest coefficient of friction and sp. wear rate is observed to be 0.42 and 0.008 mm3/Nm for the composite with 2.0 vol% biochar. The ECO4 composite designation represented a maximum dielectric constant and dielectric loss of about 7.4 and 1.1, respectively. The SEM fractography demonstrates that the silane‐treatment strengthened the fiber‐matrix interface and improved the interlocking mechanism. Such mechanically robust, wear‐resistant improved and electrically conductive composites could be utilized in applications such as industrial sectors, spacecraft, automobile parts, packaging industries, and electrical appliances. [ABSTRACT FROM AUTHOR]

Artificial Intelligence (AI) has led to advancements in multiple fields of research, and music has always been a field of high interest. Music is an important part of life and various studies have shown the link between better living and listening to music. From completing melodies to composing music from scratch, there are many applications of AI in this domain. This paper aims to analyse one such application of using AI for music generation, specifically for instrumental accompaniment. Instrumental accompaniment is essentially the instrumental music that is composed to support or complement a melody. Creating instrumental accompaniment for music generally requires extensive musical knowledge or forming a band together with skilled instrumentalists. Build Your Own Band (BYOB) attempts to simplify this process with the help of AI. In this research work, three transformer models are employed for training various accompanying instruments. Here, the proposed transformer model accepts a melody line as input and produces an accompanying track with instruments like bass instruments, the guitar and string instruments. One of the main challenges was to make sure that these instruments produce a cohesive sound.