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The ball and beam system is laboratory equipment with high nonlinearity in its dynamics. The main ideas of the paper are to model the ball and beam system considering nonlinear factors and coupling effect and to design Proportional Integral Derivative (PID) controller to control the ball position. The system consists of an Arduino microcontroller. It receives the ball position from ultrasonic distance sensor and compares it with the desired distance which can be set by the user. PID algorithm has built in Arduino to process the difference in signal between desired and real position into control signal. Arduino sends control signal to the DC servomotor which rotate to change the ball position and meet the desired distance. MATLAB software program has been used to plot instant system response by interfacing Arduino with computer to determine the system characteristics with different values of controller parameters in order to choose parameters values which obtained best performance for the system.

Heavy vehicle commercial drivers possess a vibrant role in the transportation industry. Driver’s fatigue is one of the most important concerns for the transportation business. The extant literature review indicated that many factors are contributing to driver’s fatigue but still, there is a dire need to explore other factors that can influence heavy vehicle driver’s fatigue in the case of Malaysian Oil and Gas Industry. Hence, the study aims to identify the factors contributing to the heavy vehicle driver’s fatigue and develop a quick assessment tool for this Industry. A mix method approach will be used for data collection. We aim to select heavy vehicle drivers as unit of analysis. The findings of the study will contribute to heavy vehicle driver’s fatigue and further enhance the fatigue literature. Identified indicators will be further used to develop a quick assessment tool for the heavy vehicle commercial driver’s fatigue. © 2017 American Scientific Publishers. All rights reserved.

© 2017 AI Access Foundation. All rights reserved. Markov Decision Processes (MDPs) are an effective model to represent decision processes in the presence of transitional uncertainty and reward tradeoffs. However, due to the difficulty in exactly specifying the transition and reward functions in MDPs, researchers have proposed uncertain MDP models and robustness objectives in solving those models. Most approaches for computing robust policies have focused on the computation of maximin policies which maximize the value in the worst case amongst all realisations of uncertainty. Given the overly conservative nature of maximin policies, recent work has proposed minimax regret as an ideal alternative to the maximin objective for robust optimization. However, existing algorithms for handling minimax regret are restricted to models with uncertainty over rewards only and they are also limited in their scalability. Therefore, we provide a general model of uncertain MDPs that considers uncertainty over both transition and reward functions. Furthermore, we also consider dependence of the uncertainty across different states and decision epochs. We also provide a mixed integer linear program formulation for minimizing regret given a set of samples of the transition and reward functions in the uncertain MDP. In addition, we provide two myopic variants of regret, namely Cumulative Expected Myopic Regret (CEMR) and One Step Regret (OSR) that can be optimized in a scalable manner. Specifically, we provide dynamic programming and policy iteration based algorithms to optimize CEMR and OSR respectively. Finally, to demonstrate the effectiveness of our approaches, we provide comparisons on two benchmark problems from literature. We observe that optimizing the myopic variants of regret, OSR and CEMR are better than directly optimizing the regret.

The oil and gas industry has an active role in the development of Malaysian economy. Oil and gas industry outpouring of products all depends on heavy vehicles transportation. The oil and gas transportation is a very sensitive issue for heavy vehicle drivers from centralized plants to the end users. According to the Malaysian Institute of Road Safety Research (MIROS), there are record fatalities and accidents among heavy vehicle drivers. This is a major health care issue for this industry. This study aims at assessing the health care issues such as; obesity, seat interface and back pain problems, on demographic characteristics; age, education and race among oil and gas truck drivers in Malaysia. The study based on in-depth literature review to find out the drivers health care issues that leads to fatal crashes. According to the best knowledge of the researcher, very rare studies have found out the health care's issues in context of Malaysian oil and gas Industry. This study also put emphasis on literature on safety training interventions in abating fatal crashes among truck drivers. Copyright © 2017 American Scientific Publishers All rights reserved.

Introduction: The current study aims to assess the level of patients’ satisfaction and the factors contributing to patients’ satisfaction toward family physicians (FPs) consultation, visiting primary healthcare centers (PHCCs) working under Ministry of Health, Jeddah. Materials and Methods: In this cross-sectional study conducted in Jeddah from November 1, 2016 to March 1, 2017, we used consultation satisfaction questionnaire and its four subscales with standard cutoffs. These subscales include general satisfaction, professional care, depth of relationship, and length of consultation. Mean scores along with standard deviation of these subscales were measured. Independent sample t-test, ANOVA, and multivariate regression analysis were performed to test the association between satisfaction level and predictors. Results: Overall, patients’ satisfaction was 60%. Around 74% of patients were satisfied with the professional care and 58% with the depth of the relationship. Around 60% of patients need more consultation time with the physicians. Knowledge about the presence of FP in the nearest PHCCs was around 70%. Multivariate regression analysis for the overall high satisfaction showed that the most important predictors of this high satisfaction level are regular visits to a particular FP (P < 0.001), distance from the PHCC (P = 0.044) and gender of the patient (P = 0.027). Conclusion: This study concluded that satisfaction with the FP's consultation is acceptable but needs improvement. Lower satisfaction was reported among males, patients living at a distance from PHCC and who had less knowledge about the presence of FP in their nearest PHCC. Such study data are vital for any corrective measures to boost satisfaction in patients attending PHCCs.

Gitelman syndrome is one of the few inherited causes of metabolic alkalosis due to salt losing tubulopathy. It is caused by tubular defects at the level of distal convoluted tubules, mimicking a thiazide-like tumor. It usually presents in late childhood or in teenage as nonspecific weakness, fatigability, polyuria, and polydipsia but very rarely with seizures. It is classically associated with hypokalemia, hypomagnesemia, hypocalciuria, hyperreninemia, and hyperaldosteronism. However, less frequently, it can present with normal magnesium levels. It is even rarer to find normomagnesemic patients of GS who develop seizures as the main complication since hypomagnesemia is considered the principal etiology of abnormal foci of seizure-related brain activity in GS cases. Interestingly, patients with GS are oftentimes diagnosed during pregnancy when the classic electrolyte pattern consistent with GS is noticed. Our case presents GS with normal serum magnesium in a patient, with seizures being the main clinical presentation. We also did a comprehensive literature review of 122 reported cases to show the prevalence of normal magnesium in GS cases and an overview of clinical and biochemical variability in GS. We suggest that further studies and in-depth analysis are required to understand the pathophysiology of seizures in GS patients with both normal and low magnesium levels.

Introduction: Hypertension is a multifactorial disease and is a major risk factor for cardiovascular disease, particularly in patients of diabetes mellitus. Management of hypertension is one of the critical components of comprehensive clinical management of diabetics. Aim: The aim of this study is to evaluate the effect of physical activity on systemic inflammation in patients with hypertension and type 2 diabetes mellitus in patients with hypertension. Methods: A total of 100 participants of either gender aged 40–69 years of Stage 2 essential hypertension were included in the study and assigned to 12 weeks of moderate physical activity (walking 10,000 steps/day). These study participants were further divided into two groups: type 2 diabetic (n =66) and nondiabetic (n = 34) hypertensives. Anthropometric parameters were noted and biochemical variables estimated at baseline and postintervention and evaluated statistically. Results: Twelve weeks of walking resulted in improved levels of inflammatory cytokine tumor necrosis factor-alpha (TNF-α). Systolic and diastolic blood pressure (BP) were reduced from 168.98 ± 3.76 to 154.29 ± 7.48 mmHg and from 103.38 ± 2.46 to 89.08 ± 3.62 mmHg, respectively. Body weight and body mass index were reduced from 69.8 ± 7.09 to 68.3 ± 6.53 kg and 23.21 ± 3.35 to 22.70 ± 3.16 kg/m2, respectively. A significant decrease in glycated hemoglobin (Group 1: 8.36% ± 1.83% vs. 7.74% ± 1.82%; Group 2: 5.56% ± 0.4% vs. 5.09% ± 0.19%) was found. A significant reduction in TNF-α levels in hypertensives with diabetes and a significant (P

Metal based nanoprobes for fluorescence signal amplification have become a vital component of biosensing research due to their profound electrical, optical and sensing properties. Because of high specific surface area and photoactive reactions on the surface, nano-Titania (nTiO2) has attracted much attention in the scientific community. Titania can be modulated to be applicable in sensing designs through doping with nonmetallic elements. Thus, it is of great significant to investigate the doped counterparts of nTiO2 for biosensing applications. In this context, we herein integrated surface complexation feature of nitrogen doped nTiO2 (N-nTiO2) to contribute towards enhanced fluorescence signal amplification and subsequently employed it for ultrasensitive detection of Ochratoxin A (OTA). Results indicate that N-doping greatly influenced the surface chemistry and functionalities of nTiO2. The higher amplification efficacy of nitrogen doped nTiO2 can be attributed to its capabilities of good adsorbability and high electron mobility that substantially catalyzes the surface complexation of OTA onto these particales, consequently intensifying the fluorescence of OTA efficiently. Characterization tools including SEM, XRD, FTIR, UV–Vis, RAMAN spectroscopy, photoluminescence (PL) and flouremetric analysis were used to inspect the surface chemistry, morphology and signal amplification activity of prepared nanoprobe. Different parameters such as concentration of nano particles and incubation time were optimized for ultrasensitive detection of OTA. Under optimal experemental conditions, the limit of detection for OTA was as low as to 0.1 ng mL−1 with linear range from 0.5 to 100 ng mL−1. In conclusion, we proposed a highly sensitive and selective fluorescent nanoprobe for analytical applications based on surface complexation affinity and fluorescence signal amplification ability of N doped nTiO2.

With the widespread usage of Cloud Computing model in business, organizations prefer to outsource their data to the cloud. In order to preserve confidentiality of data, the data is encrypted before being outsourced. In order to use this encrypted data in decision making, symmetric searchable encryption is employed that enables users to fetch data matching the keywords over encrypted data without decrypting the data. Many existing schemes search for a single keyword without an index, leading to search time linear to the size of data. And moreover, support for multiple keyword searches or phrase search is also a trending challenge in the field of searchable encryption. In this paper, we extend the practical techniques for searches on encrypted data proposed by Song, et.al, by improving the search efficiency with help of an encrypted and dynamic index. We use a very efficient structure to enable dynamic updates to the index in event of changes to the encrypted dataset. Our proposed scheme is the first one to use such a simple yet update-efficient structure for index. Multi-phrase search being implemented helps in obtaining most representative documents for searches, especially in mobile devices. We also implemented and analyzed efficiency of Song et al scheme, which is one of the pioneering works in field of searchable encryption. Security analysis and extensive experiments were conducted to test the security guarantees and efficiency of the proposed extensions.

E-markets and public forums are major threads of generating and utilizing the crowd’s wisdom in terms of intents, behaviour and feedbacks, which are crucial for both buyers and sellers. Sentiment analysis is valuable extraction of user’s intents and experience through their online interactions on public forums and feedbacks. Automation of this task is very significant, especially to gauge commercial metrics about any product or service. Twitter being the most utilized social networking platform where the user gets to share his view about certain topic in a short text. Tweets can be utilized as source of data to detect the sentiment of the users over a topic. As capturing Subjectivity and representational ease trough discreet level of sentiment categories is challenging therefore this paper focus on effective classification though visual representation of sentiments. The proposed work uses an ensemble machine learning approach for sentiment classification of data and an extension of WORDCLOUD for sentiment visual representations. Through various experiments on real world data from twitter aptness and clarity of the proposed scheme can be easily observed.

Robust scientific evidence exists for occupational group of heavy vehicle drivers in oil and gas transport who are infected with acute fatigue. Numerous recent studies relate driver’s perceived stress and fatigue with dangerous driving behaviours during performance of delivery duties. This study investigates the association between driver’s perceived stress on aberrant driving behaviour and determines fatigue to be the mediation factor. Sample size of 378 male drivers from 10 oil and gas transport companies was selected that operate in peninsular west and east Malaysia, who answered adapted version of Driver Behaviour Questionnaire (DBQ) in the conducted survey. Structural Equation Model (SEM) was applied that proved aberrant driving behaviours of heavy vehicle drivers can be predicted through driver’s perceived stress and fatigue. Fatigue is a mechanism that mediates the association amongst driver perceived stress and aberrant driving behaviours. In conclusion, the parameter of fatigue mediates between the perceived stress which is an appropriate prediction tool and consequent aberrant driving behaviour of heavy vehicle drivers. This study suggests abatement in individual stress and recommends fatigue management to alter risky and aberrant driving behaviours in order to encourage and give impetus to safe driving culture in oil and gas transportation sector in Malaysia.

Psychosocial risks are considered as a burning issue in the Asia-Pacific region. The aim of this study was to investigate the impact of psychosocial work environment factors on health of petrochemical industry workers of Malaysia. In lieu to job demands-resources theory, significant positive associations were found between quantitative demands, work-family conflict, and job insecurity with stress, while a significant negative association of role clarity as a resource factor with stress was detected. We also found that quantitative demands were significantly associated with the mean arterial pressure (MAP). Multistage sampling procedure was used to collect study sample. Structural Equation Modeling was used to identify relationship between the endogenous and exogenous variables. Finally, the empirically tested psychosocial work environment model will further help in providing a better risk assessment in different industries and enterprises.