Robotic path planning using rapidly-exploring random trees

This study concerns the implementation of Rapidly-Exploring Random Trees (RRTs) algorithm for an autonomous robot path planning. RRTs possesses a number of advantages such as relatively simple, suitable for finding a path for a robot with dynamic and physical constraints, the expansion of RRT is heavily biased toward unexplored areas of search space and the number of edges is minimal. However, the planned path by using basic RRT structure might not always be optimal in terms of path length. Therefore, a path pruning method has been proposed to address this issue and improve the overall performance of the RRTs. Through simulations, the path pruning method has been proven to reduce paths lengths while preserving the aforementioned advantages of RRTs. A Graphical User Interface (GUI) has also been developed to demonstrate the RRTs technique in planning a path for an autonomous robot. The GUI package is designed to be interactive and user�friendly even for the users with minimal or no guidance and practice

Comparison of management and treatment options for recurrent breast fibroadenomas in adolescent females

Breast fibroadenomas account for approximately 25% of all lesions in asymptomatic women, resulting in large health care costs every year. There are 3 different variations of the disease: simple, juvenile giant and multiple. Patients may have different management and treatment options available to them depending on which variation they have. Of particular interest are female adolescents, who are at most risk for developing these lesions. With this age group not only is it important to pursue options that are minimally invasive and effective, but there are psychosocial implications to consider regarding the cosmetic changes that may occur with the disease, as well as generalized anxiety over having a breast lump. These issues are important to consider for physicians when recommending a treatment or management option. After a systematic review of all options available, it appears the best management method is the conservative treatment as it minimizes invasive intervention and operates on the principle that 10-40% of lesions regress on their own; however, there may be times that adolescents are uncomfortable with this treatment due to anxiety and other uneasiness about having a lesion remain in their breasts, despite the low chance of malignancy associated with breast fibroadenomas. Minimally invasive procedures are being developed in order to minimize possible iatrogenic injury to the developing breasts as well as maintain efficiency and good cosmesis post-procedure. Cryoablation is a minimally invasive technique utilizing extreme cold temperatures for lesion excision that is not currently widely used, however it has great potential to replace traditional open surgical excision

Libra: An Economy driven Job Scheduling System for Clusters

Clusters of computers have emerged as mainstream parallel and distributed platforms for high-performance, high-throughput and high-availability computing. To enable effective resource management on clusters, numerous cluster managements systems and schedulers have been designed. However, their focus has essentially been on maximizing CPU performance, but not on improving the value of utility delivered to the user and quality of services. This paper presents a new computational economy driven scheduling system called Libra, which has been designed to support allocation of resources based on the users? quality of service (QoS) requirements. It is intended to work as an add-on to the existing queuing and resource management system. The first version has been implemented as a plugin scheduler to the PBS (Portable Batch System) system. The scheduler offers market-based economy driven service for managing batch jobs on clusters by scheduling CPU time according to user utility as determined by their budget and deadline rather than system performance considerations. The Libra scheduler ensures that both these constraints are met within an O(n) run-time. The Libra scheduler has been simulated using the GridSim toolkit to carry out a detailed performance analysis. Results show that the deadline and budget based proportional resource allocation strategy improves the utility of the system and user satisfaction as compared to system-centric scheduling strategies.Comment: 13 page

Electricity Generation of Solar Photovoltaic System

The SPV electricity generation employs various cell technologies namely polycrystalline, mono crystalline, amorphous, CIGS cells. To evaluate the performance of SPV plant a mathematical model was generated base on empirical relations and performance was calculated under various conditions. The electrical output was calculated based on various parameters such as tilt angle, cell technology, radiation levels. Sensitivity analysis was also carried out at five different geographical locations namely New Delhi, Ladhak, Jodhpur Mumbai, and Bangalore. The result shows that electrical output is maximum for polycrystalline cell at a give location and at tilt angle of plus minus 50 of location. Jodhpur was found to be the best geographical local followed by New Delhi

Toward helping users in assessing the trustworthiness of user-generated reviews

With the growing number of systems that provide user-generated reviews come new forms of trust relationships. User trust in vendors can be mediated by trust in either reviews or reviewers, or possibly both. These new forms of trust relationships might be affected by barriers to trust such as uncertainty about offered services and anonymity of users, as well as biased reviews. While current work is dedicated towards investigating the influential factors on trust, this study undertakes a different approach. First, it investigates the influence of interface design on users' assessment of trustworthiness. Second, it explores the way that interface design can affect users' perception of trustworthy and untrustworthy reviews and reviewers, by signalling the influential factors on trust. Third, this study investigates the effect of users' prior beliefs in the form of dispositional trust in the assessment of trustworthiness. To do so, an exploratory study gathering quantitative and qualitative data was conducted with 16 participants who interacted with a high-fidelity prototype. Our results show that users' assessment of trustworthiness is influenced by interface elements that relate not only to the review, but also to the reviewer, implying that trust in reviews is mediated by trust in reviewers. Furthermore, users' dispositional trust appears to affect the perceived trustworthiness of reviewers, especially because of elements relate to the reviewers' background, which transfers onto the reviews. Our results have implications for researchers and designers to help users' assessment of the trustworthiness of reviews and reviewers

An Analysis of the Relationship among EFL Learners’ Autonomy, Self-esteem, and Choice of Vocabulary Learning Strategies

This study seeks to determine whether any significant relationship exists among EFL learners’ autonomy (LA), self-esteem (SE), and choice of vocabulary learning strategies (VLS) as well as whether LA and SE are predictors of these strategies. To achieve these aims, this study employed a descriptive research design. Participants included 157 male and female undergraduate EFL learners, all within the age range of 17 to 25 years. They were studying English within the sub-disciplines of English Literature, Linguistics, and General English. Participants were administered the following three types of questionnaires adapted by the researcher: a) a 30-item VLS questionnaire based on that of Schmitt taxonomy (1997); b) a 30-item LA questionnaire developed by Sakai, et al. (2008); c) and a 30-item SE questionnaire based on Coopersmith’s SE inventory (1967). Upon conducting preliminary analyses of this study’s assumptions, the characteristics of the data were proven legitimate via correlation and regression analyses. Correlation analysis demonstrated that a statistically significant relationship existed between EFL learners’ autonomy and VLS, with (r = .555, p < .05), and SE and VLS, with (r = .678 p < .05). Furthermore, regression analysis revealed LA and SE to be significant predictors of VLS. LA predicted 30.7% of scores in the choice of VLS (R = .555, R2 = .307), and SE predicted 45.9% of scores in the choice of VLS (R = .678, R2 = .456). These findings demonstrate that both LA and SE make strong contributions to VLS

Catabolism of Wax Esters in Acinetobacter calcoaceticus

1. The rate of wax ester degradation was determined in A. calcoaceticus NCIB 8250 and the following results were obtained: (a) The initial and final rates of wax degradation were 22.5 and 3.3 umol (g dry wt bacteria)-1 h-1 respectively. The initial rate of wax degradation was 85% higher than that of final rate (3-6 h). (b) The rate of endogenous respiration was measured during wax ester degradation. The endogenous consumption of oxygen, 0.13 mmol (g dry wt bacteria)-1 h-1, was reduced by 10% during the fast phase of wax degradation and by 20% after 3 h. (c) The viability of the bacteria having a low or high wax content was determined during carbon and energy starvation condition. 50% of the bacteria with a low content of wax esters were not viable after 84 h but 50% of the bacteria with a high wax content were still viable after 154 h. (d) From literature values for the maintenance energy requirements as oxygen consumption, the initial fast rate of wax esters degradation provides approximately 40% of the energy required for the maintenance of viability, and in the final slower rate would only provide about 7% of the maintenance energy required for the maintenance of viability

Development of A Work-based Vestibule Training E-module For Accident Prevention at Malaysian Oil and Gas Drilling Industries: A Proposed Framework

This article discusses the conceptual action plan and detailed methodology for the identification of potential hazard controls and the development of work integrated E-vestibule training module for safe onshore and offshore oil and gas drilling operation at Malaysian industries. According to the previous studies, there is a sheer industrial need of an effective work integrated vestibule training module for accident prevention at oil and gas drilling sites at Malaysian drilling domains. In this proposed study, 80 drilling crew will be randomly selected for quantitative research phase. Similarly, 03 safety experts will be purposively selected for qualitative research from each drilling domain. Whereas, for the identification of hazard controlling measures What-If analysis and thematic analysis approaches will be adopted. Furthermore, the open source vestibule training module will be developed by using ADDIE based on identified hazard controlling measures. However, the visual studio and MySQL software will use to develop the E-Module for drilling crew safety training. The proposed E-vestibule training module development framework will be used as an effective source for the elimination of life-threatening drilling hazards associated with its activities at oil and gas industries. Similarly, the proposed framework can also be implemented on other work-based learning training designs. Moreover, this proposed safety and health vestibule training module will be the first E- drilling safety module which covers all onshore and offshore drilling operation in Malaysian oil and gas extraction settings

Life after COVID-19 outbreak: Expectations and thoughts

The occurrence of the novel coronavirus (COVID-19) pandemic presents an unparalleled health enlightenment challenge. It’s extremely contagious and erratically lethal, and the pervasiveness of asymptomatic prevalence makes it difficult to contain. All infectious disease epidemics rear ethical concerns, from the restraint of individual independence to triaging and resource provision. It seems that we did not take lessons from the preceding epidemics and were poorly prepared to pledge with the threat that COVID-19 epidemic has put forward. The COVID-19 epidemic highlighted the significance of this query to both pandemic preparation strategies and healthcare policies. As the outbreak turned out to be a global pandemic, there is an improved emphasis on finding answers for vaccine preparation, focusing on neglected diseases, more virome study, and research collaboration across the globe in the future, being key tools to resist infection spread in future. Decelerating the COVID-19 spread necessitates people to enthusiastically transform their lives and monitor the finest practices for social isolation and sanitation. This review provides an overview of future research perceptions and offers suggestions on how we can help people to believe in normal life and how this pandemic will strengthen the trade, affect the individual habits and values, revolution in primary health care after these uncertain situations.Keywords: COVID-19; Pandemic; Neglected diseases; Primary health; Virom

An improved data classification framework based on fractional particle swarm optimization

Particle Swarm Optimization (PSO) is a population based stochastic optimization technique which consist of particles that move collectively in iterations to search for the most optimum solutions. However, conventional PSO is prone to lack of convergence and even stagnation in complex high dimensional-search problems with multiple local optima. Therefore, this research proposed an improved Mutually-Optimized Fractional PSO (MOFPSO) algorithm based on fractional derivatives and small step lengths to ensure convergence to global optima by supplying a fine balance between exploration and exploitation. The proposed algorithm is tested and verified for optimization performance comparison on ten benchmark functions against six existing established algorithms in terms of Mean of Error and Standard Deviation values. The proposed MOFPSO algorithm demonstrated lowest Mean of Error values during the optimization on all benchmark functions through all 30 runs (Ackley = 0.2, Rosenbrock = 0.2, Bohachevsky = 9.36E-06, Easom = -0.95, Griewank = 0.01, Rastrigin = 2.5E-03, Schaffer = 1.31E-06, Schwefel 1.2 = 3.2E-05, Sphere = 8.36E-03, Step = 0). Furthermore, the proposed MOFPSO algorithm is hybridized with Back-Propagation (BP), Elman Recurrent Neural Networks (RNN) and Levenberg-Marquardt (LM) Artificial Neural Networks (ANNs) to propose an enhanced data classification framework, especially for data classification applications. The proposed classification framework is then evaluated for classification accuracy, computational time and Mean Squared Error on five benchmark datasets against seven existing techniques. It can be concluded from the simulation results that the proposed MOFPSO-ERNN classification algorithm demonstrated good classification performance in terms of classification accuracy (Breast Cancer = 99.01%, EEG = 99.99%, PIMA Indian Diabetes = 99.37%, Iris = 99.6%, Thyroid = 99.88%) as compared to the existing hybrid classification techniques. Hence, the proposed technique can be employed to improve the overall classification accuracy and reduce the computational time in data classification applications