ObjectivesThe study objectives were to identify the stress levels and to explore the impact of students' year of study and gender on the perceived sources of stress among Malaysian dental students.MethodsThis was a cross-sectional study involving dental students from year one to year five from private and public universities in Malaysia. The study was formally approved by the Research and Ethics Committee, International Medical University Malaysia. Dental Environment Stress (DES) questionnaire was used for data collection and the gathered data were analyzed using SPSS® version 18. The Kruskal-Wallis and the Mann-Whitney U tests were used to compare stress items across various academic years and universities.ResultsA total of five hundred and twenty nine (529) students participated in this study. Fear of failing the course at the end of year exams (mean stress level=5.57); concerns regarding completion of clinical work (mean=5.30); and examination results and grades (mean=5.27) were found as top stressors among dental students. Female students had higher stress scores than males with respect to personal issues, academic performance, educational environment and learning of clinical skills. Students from public universities had higher stress scores than their counterparts from private universities.ConclusionThe Malaysian dental students reported higher levels of stress. Present study identified stressors affecting dental students' academic life, and highlights the importance of stress management programs and other measures to minimize the impact of stress on both academic and personal lives of the students.

Objectives: To examine the validity and reliability of the Jefferson Scale of Empathy-Health Care Provider Student version (JSE-HPS) in a sample of dental students in Malaysia, with the secondary aim of assessing empathy levels in first to final year dental students in public and private universities in Malaysia. Methods: The JSE-HPS was administered to 582 first to fifth (final) year dental students; 441 were enrolled at two public universities and 141 at a private university in Malaysia. Both descriptive and inferential statistics were performed using SPSS® version 18. Results:The JSE-HPS demonstrated good internal consistency (Cronbach's α = 0.70). A three-factor solution emerged and included 'perspective taking', 'compassionate care' and 'standing in patient's shoes' factors, accounting for 27.7%, 13.9%, and 6.3% of the variance, respectively. The total mean empathy score was 84.11±9.80, where the actual scores ranged from a low of 22.05 to a high of 133.35. Overall, male students (84.97±11.12) were more empathic than female students (83.78±9.24). Fourth-year students were more empathic than students in other undergraduate years, and public university students had significantly higher mean empathy score compared to those enrolled at a private university (84.74 versus 82.13, p=0.001). Conclusions: This study confirms the construct validity and internal consistency of the JSE-HPS for measuring empathy in dental students. Empathy scores among students vary depending on type of university and year of study. Future studies, preferably longitudinal in design should explore changes in empathy among dental students during progression through undergraduate courses.

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In this paper, a hybrid electricity price forecasting method which is composed of two-stage feature selection method and optimized adaptive neuro-fuzzy inference system (ANFIS) technique as a forecasting engine is proposed to accurately forecast electricity price. A multi-objective feature selection approach comprises of multi-objective binary-valued backtracking search algorithm (MOBBSA) as an efficient evolutionary search algorithm and ANFIS method is developed in this paper to extract the most influential subsets of input variables with maximum relevancy and minimum redundancy. Through the combination of backtracking search algorithm (BSA) in learning process of ANFIS approach, a hybrid machine learning algorithm has been developed to forecast the electricity price more accurately. Real-world electricity demand and price dataset from Ontario power market; which is reported as among the most volatile market worldwide, has been used as case study to validate the performance of the proposed approach. From the simulation results, it has been seen that the proposed hybrid forecasting method was effective in accurately forecast the Ontario electricity price. In addition, to prove the superiority of the proposed hybrid forecasting method the simulation results obtained using ANN and ANFIS models optimized by other well-known optimization methods have been compared with that of proposed method.INDEX TERMS Adaptive neuro-fuzzy inference system, backtracking search algorithm, electricity price forecasting, feature selection.

In this paper, a rotated Y-shaped antenna is designed and compared in terms of performance using a conventional and EBG ground planes for future Fifth Generation (5G) cellular communication system. The rotated Y-shaped antenna is designed to transmit at 38 GHz which is one of the most prominent candidate bands for future 5G communication systems. In the design of conventional antenna and metamaterial surfaces (mushroom, slotted), Rogers-5880 substrate having relative permittivity, thickness and loss tangent of 2.2, 0.254 mm, and 0.0009 respectively have been used. The conventional rotated Y-shaped antenna offers a satisfactory wider bandwidth (0.87 GHz) at 38.06 GHz frequency band, which gets further improved using the EBG surfaces (mushroom, slotted) as a ground plane by 1.23 GHz and 0.97 GHz respectively. Similarly, the conventional 5G antenna radiates efficiently with an efficiency of 88% and is increased by using the EBG surfaces (slotted, mushroom-like) to 90% and 94% respectively at the desired resonant frequency band. The conventional antenna yields a bore side gain of 6.59 dB which is further enhanced up to 8.91dB and 7.50 dB by using mushroom-like and slotted EBG surfaces respectively as a ground plane. The proposed rotated Y-shaped antenna and EBG surfaces (mushroom, slotted) are analyzed using the Finite Integration Technique (FIT) employed in Computer Simulation Technology (CST) software. The designed antenna is applicable for future 5G applications.