Performance Perfectionism and its Relation to Academic Procrastination and Depression among Early Childhood Student Teachers

Academic procrastination is a complex, psychologically heterogenous phenomenon in academic settings. It involves postponing or evading the accomplishment of academic assignments and is associated with self-regulation, forgetfulness and intellectual dogmatism. The present study aimed at identifying the correlation between performance perfectionism and academic procrastination and depression among early childhood student teachers. Three tools were administered online to 600 randomly selected early childhood student teachers: The Performance Perfectionism Scale, the Active Academic Procrastination Scale, and the Depression Scale. Statistical analyses were conducted and hypotheses were tested using SPSS 20.0 and AMOS 20.0 (both by IBM). The results of the study indicated that there is a statistically significant positive correlation between performance perfectionism and academic procrastination (0.283), while there is a statistically significant negative correlation between performance perfectionism and depression (-0.223). The results of the study also indicated that there is a statistically significant negative correlation between depression and academic procrastination (-0.425). The results of the present study increase our insight into the academic procrastination problem and shed light on the variables that are considered to be a reason for its increased prevalence among school and university students. They also focus on the negative outcomes that affect learners’ psychology, performance, personality traits and quality of life. The study recommends educating school and university students, school teachers, faculty members and families on the causes of the spread of academic procrastination among students, mainly external factors. The study maintains that schools and universities should provide an encouraging and reinforcing environment for both young and adult learners (e.g., balanced division of workload, and not pressuring students with courses that challenge their ability, rely on memorization, are not connected with reality, and are not needed by the labor market), and train teachers to vary the teaching strategies used in the classrooms, taking into consideration individual differences, whether in abilities and competences or in personality traits. The study also recommends that schools and universities should prepare educational counselors and psychological specialists in kindergartens, schools and universities so that they can raise children and students’ awareness, and train them to employ and develop adaptive emotional regulation strategies

Heterogeneous wireless networks for smart grid distribution systems: Advantages and limitations

Supporting a conventional power grid with advanced communication capabilities is a cornerstone to transferring it to a smart grid. A reliable communication infrastructure with a high throughput can lay the foundation towards the ultimate objective of a fully automated power grid with self-healing capabilities. In order to realize this objective, the communication infrastructure of a power distribution network needs to be extended to cover all substations including medium/low voltage ones. This shall enable information exchange among substations for a variety of system automation purposes with a low latency that suits time critical applications. This paper proposes the integration of two heterogeneous wireless technologies (such as WiFi and cellular 3G/4G) to provide reliable and fast communication among primary and secondary distribution substations. This integration allows the transmission of different data packets (not packet replicas) over two radio interfaces, making these interfaces act like a one data pipe. Thus, the paper investigates the applicability and effectiveness of employing heterogeneous wireless networks (HWNs) in achieving the desired reliability and timeliness requirements of future smart grids. We study the performance of HWNs in a realistic scenario under different data transfer loads and packet loss ratios. Our findings reveal that HWNs can be a viable data transfer option for smart grids. 2018 by the authors. Licensee MDPI, Basel, Switzerland.Acknowledgments: This work was made possible by the United Arab Emirates University UPAR Grant No. 31N226.Scopu

Clinical and molecular characterization of both methicillin-resistant andsensitive staphylococcus aureus mastitis

NO ABSTRACT AVAILABLEThis study targeted bovine mastitis as a possible source of livestock-associated methicillin-resistant Staphylococcus aureus (MRSA), to identify clinical signs associated with MRSA- and non-MRSA-associated mastitis. Thirty-eight mastitis cases (68 infected quarters) were investigated. Gram-positive cocci-shaped isolates were selected based on Baird Parker agar growth as well as Gram-stained bacterial smears. Molecular screening for Staphylococcus aureus (S. aureus) yielded 17 isolates, of which five (29.41%) were methicillin resistant. The five isolates were mecA positive, but mecC negative. Multilocus sequence typing (MLST) indicated that sequence type 1 (ST1) was the identified type of all isolates of MRSA. S. aureus-associated cases showed different clinical forms of mastitis, including subclinical, acute, chronic, and gangrenous. Additionally, subclinical mastitis was the only detected condition associated with MRSA, which may represent a potential hidden risk for humans. Phenotypically, isolates of MRSA showed resistance to all of the tested β-lactam antimicrobials, with marked resistance to tetracycline and gentamycin. Based on our knowledge, this is the first report to identify MRSA ST1 in Egypt. Bovine mastitis could be a source for the dissemination of MRSA to humans and other animals. Additionally, while methicillin-resistance may have no effect on the clinical outcome of mastitis, it does affect therapeutic success, particularly when β-lactam antimicrobials are used

Association of anthropometric qualities with vertical jump performance in elite male volleyball players

Aim: The objective of this study was to examine the association between physical and anthropometric profiles and vertical jump performance in elite volleyball players. Methods: Thirty-three elite male volleyball players (21±1 y, 76.9±5.2 kg, 186.5±5 cm) were studied. Several anthropometric measurements (body mass, stature, body mass index, lower limb length and sitting height) together with jumping height anaerobic power of counter movement jump with arm swing (CMJ arm)) were obtained from all subjects. Forward stepwise multiple linear regression analysis was performed to determine if any of the anthropometric parameters were predictive of CMJ arm. Results: Anaerobic power was significantly higher (P≤0.05) in the tallest players relative to their shorter counterparts. A significant relationship was observed between CMJ arm and lower limb length (r 2=0.69; P<0.001) and between the lower limb length and anaerobic power obtained with CM-J arm(r 2=0.57; P<0.01). While significantly correlated (P≤0.05) with CMJ arm performance, stature, lower limb length/stature and sitting height/stature ratios were not significant (P>0.05) predictors of CMJ arm performance. Conclusion. This study demonstrates that lower limb length is correlated with CMJ arm in elite male volleyball players. The players with longer lower limbs have the better vertical jump performances and their anaerobic power is higher. These results could be of importance for trained athletes in sports relying on jumping performance, such as basketball, handball or volleyball. Thus, the measurement of anthropometric characteristics, such as stature and lower limb length may assist coaches in the early phases of talent identification in volleyball

Towards 3D Process Simulation for In-Situ Hybridization of Fiber-Metal-Laminates (FML)

Fiber-metal-laminates (FML) provide excellent fatigue behavior, damage tolerant properties, and inherent corrosion resistance.To speed up manufacturing and simultaneously increase the geometrical complexity of the produced FML parts, Mennecart et al. proposed a new single-step process combining deep-drawing with infiltration (HY-LCM). Although the first experimental results are promising, the process involves several challenges, mainly originating from the Fluid-Structure-Interaction (FSI) between deep-drawing and infiltration. This work aims to investigate those challenges to comprehend the underlying mechanisms. A new close-to-process test setup is proposed on the experimental side, combining deep-drawing of a hybrid stack with a linear infiltration. A process simulation model for FMLs is presented on the numerical side, enabling a prediction of the dry molding forces, local Fiber Volume Content (FVC) within the three glass fiber (GF) interlayers, and simultaneous fluid progression. The numerical results show that the local deformation of the hybrid stack and required forces are predictable. Furthermore, lateral sealing of the hybrid stacks leads to deviations from the intended initially one-dimensional fluid progression. Eventually, the numerical results demonstrate that most flow resistance originates from geometrically critical locations. Future experimental and numerical work will combine these insights to focus on the flow evaluation during deformation and a successful part-level application

FORWARD MASKING THRESHOLD ESTIMATION USING NEURAL NETWORKS AND ITS APPLICATION TO PARALLEL SPEECH ENHANCEMENT

Forward masking models have been used successfully in speech enhancement and audio coding. Presently, forward masking thresholds are estimated using simplified masking models which have been used for audio coding and speech enhancement applications. In this paper, an accurate approximation of forward masking threshold estimation using neural networks is proposed. A performance comparison to the other existing masking models in speech enhancement application is presented. Objective measures using PESQ demonstrates that our proposed forward masking model, provides significant improvements (5-15 %) over four existing models, when tested with speech signals corrupted by various noises at very low signal to noise ratios. Moreover, a parallel implementation of the speech enhancement algorithm was developed using Matlab parallel computing toolbox

Effect of intensive melt shearing on the formation of Fe-containing intermetallics in LM24 Al-alloy

Fe is one of the inevitable and detrimental impurities in aluminium alloys that degrade the mechanical performance of castings. In the present work, intensive melt shearing has been demonstrated to modify the morphology of Fe-containing intermetallic compounds by promoting the formation of compact α-Al(Fe,Mn)Si at the expense of needle-shaped β-AlFeSi, leading to an improved mechanical properties of LM24 alloy processed by MC-HPDC process. The promotion of the formation of α -Al(Fe, Mn)Si phase is resulted from the enhanced nucleation on the well dispersed MgAl 2O 4 particles in the melt. The Fe tolerance of LM24 alloy can be effectively improved by combining Mn alloying and intensive melt shearing