Nottingham Trent University

About: Nottingham Trent University is a education organization based out in Nottingham, United Kingdom. It is known for research contribution in the topics: Population & Addiction. The organization has 4702 authors who have published 12862 publications receiving 307430 citations. The organization is also known as: NTU & Trent Polytechnic.

Anxiety and Suicidal Thoughts During the COVID-19 Pandemic: Cross-Country Comparative Study Among Indonesian, Taiwanese, and Thai University Students

Abstract: Background: The COVID-19 pandemic has negatively affected the mental health of university students. Objective: This study examined the psychological responses toward COVID-19 among university students from 3 countries—Indonesia, Taiwan, and Thailand. Methods: We used a web-based, cross-sectional survey to recruit 1985 university students from 5 public universities (2 in Indonesia, 1 in Thailand, and 1 in Taiwan) via popular social media platforms such as Facebook, LINE, WhatsApp, and broadcast. All students (n=938 in Indonesia, n=734 in Thailand, and n=313 in Taiwan) answered questions concerning their anxiety, suicidal thoughts (or sadness), confidence in pandemic control, risk perception of susceptibility to infection, perceived support, resources for fighting infection, and sources of information in the context of the COVID-19 pandemic. Results: Among the 3 student groups, Thai students had the highest levels of anxiety but the lowest levels of confidence in pandemic control and available resources for fighting COVID-19. Factors associated with higher anxiety differed across countries. Less perceived satisfactory support was associated with more suicidal thoughts among Indonesian students. On the other hand, Taiwanese students were more negatively affected by information gathered from the internet and from medical staff than were Indonesian or Thai students. Conclusions: Our findings suggest that health care providers in Thailand may need to pay special attention to Thai university students given that high levels of anxiety were observed in this study population. In addition, health care providers should establish a good support system for university students, as the results of this study indicate a negative association between support and suicidal thoughts.

Molecular-imprinted, polymer-coated quartz crystal microbalances for the detection of terpenes

Abstract: A piezoelectric sensor coated with an artificial biomimetic recognition element has been developed for the determination of L-menthol in the liquid phase. A highly specific noncovalently imprinted polymer (MIP) was cast in situ on to the surface of a gold-coated quartz crystal microbalance (QCM) electrode as a thin permeable film. Selective rebinding of the target analyte was observed as a frequency shift quantified by piezoelectric microgravimetry with the QCM. The detectability of L-menthol was 200 ppb with a response range of 0-1.0 ppm. The response of the MIP-QCM to a range of monoterpenes was investigated with the sensor binding menthol in favor of analogous compounds. The sensor was able to distinguish between the D- and L-enantiomers of menthol owing to the enantioselectivity of the imprinted sites. To our knowledge, this is the first report describing enantiomeric resolution within an MIP utilizing a single monomer-functional moiety interaction. It is envisaged that this technique could be employed to determine the concentration of terpenes in the atmosphere.

Development of an advanced noise reduction method for vibration analysis based on singular value decomposition

Abstract: The paper developed a reasonable and practical method for identifying the useful information from the signal that has been contaminated by noise, so that to provide a feasible tool for vibration analysis. A new concept namely the Singular Entropy (SE) was proposed based on the singular value decomposition technique. With the aid of the SE, a series of investigations were done for discovering the distribution characteristics of noise contaminated and pure signals, and consequently an advanced noise reduction method was developed. The experiments showed that the proposed method was not only applied for dealing with the stationary signals but also applied for dealing with the non-stationary signals.

Observation and simulation of root reinforcement on abandoned Mediterranean slopes

Abstract: The mechanics of root reinforcement have been described satisfactorily for a single root or several roots passing a potential slip plane and verified by field experiments. Yet, precious little attempts have been made to apply these models to the hillslope scale pertinent to landsliding at which variations in soil and vegetation become important. On natural slopes positive pore pressures occur often at the weathering depth of the soil profile. At this critical depth root reinforcement is crucial to avert slope instability. This is particularly relevant for the abandoned slopes in the European part of the Mediterranean basin where root development has to balance the increasing infiltration capacity during re-vegetation. Detailed investigations related to root reinforcement were made at two abandoned slopes susceptible to landsliding located in the Alcoy basin (SE Spain). On these slopes semi-natural vegetation, consisting of a patchy herbaceous cover and dispersed Aleppo pine trees, has established itself. Soil and vegetation conditions were mapped in detail and large-scale, in-situ direct shear tests on the topsoil and pull-out tests performed in order to quantify root reinforcement under different vegetation conditions. These tests showed that root reinforcement was present but limited. Under herbaceous cover, the typical reinforcement was in the order of 0.6 kPa while values up to 18 kPa were observed under dense pine cover. The tests indicate that fine root content and vegetation conditions are important factors that explain the root reinforcement of the topsoil. These findings were confirmed by the simulation of the direct shear tests by means of an advanced root reinforcement model developed in FLAC 2D. Inclusion of the root distribution for the observed vegetation cover mimics root failure realistically but returns over-optimistic estimates of the root reinforcement. When the root reinforcement is applied with this information at the hillslope scale under fully saturated and critical hydrological conditions, root pull-out becomes the dominant root failure mechanism and the slip plane is located at the weathering depth of the soil profile where root reinforcement is negligible. The safety factors increase only slightly when roots are present but the changes in the surface velocity at failure are more substantial. Root reinforcement on these natural slopes therefore appears to be limited to a small range of critical hydrological conditions and its mitigating effect occurs mainly after failure.

Adaptive Energy-Aware Algorithms for Minimizing Energy Consumption and SLA Violation in Cloud Computing

Abstract: In cloud computing, high energy consumption and service-level agreements (SLAs) violation are the challenging issues considering that the demand for computational power is growing rapidly, thereby requiring large-scale cloud data centers. Although, there are many existing energy-aware approaches focusing on minimizing energy consumption while ignoring the SLA violation at the time of a virtual machine (VM) selection from overloaded hosts. Also, they do not consider that the current network traffic causes performance degradation and thus may not really reduce SLA violation under a variety of workloads. In this context, this paper proposes three adaptive models, namely, gradient descent-based regression (Gdr), maximize correlation percentage (MCP), and bandwidth-aware selection policy (Bw), that can significantly minimize energy consumption and SLA violation. Energy-aware methods for overloaded host detection and VM selection from an overloaded host are necessary to improve the energy efficiency and SLA violation of a cloud data center after migrating all VM from underloaded host turn to idle host, which switch to energy-saving mode is also beneficial. Gdr and MCP are adaptive energy-aware algorithms based on the robust regression model, for overloaded host detection. A Bw dynamic VM selection policy selects VM according to the network traffic from the overloaded host under SLAs. Experimental results on the real workload traces show that the proposed algorithms reduce energy consumption while maintaining the required performance levels in a cloud data center using a CloudSim simulator to validate the proposed algorithms.