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Cognitive impairment and treatment outcomes among people attending an alcohol intervention service for those aged 50+
Get PDFPurpose: No studies have evaluated the relationship between cognitive impairment and alcohol treatment outcomes among older drinkers. This study sought to explore the extent of cognitive impairment among older adults seeking alcohol treatment, and examine the relationship between cognitive impairment, treatment retention and alcohol use following treatment. Design/ methodology/ approach: The study used data from the Drink Wise Age Well programme; an alcohol intervention service for older adults (aged 50+). The Montreal Cognitive Assessment (MoCA) was used to screen for cognitive impairment; alcohol use was assessed using the Alcohol Use Disorders Identification Test (AUDIT). Findings: 531 participants completed assessment at treatment entry. Over half the sample were male (57%), with a mean age of 60 years (SD: 7.09). Almost half (48.4%) had cognitive impairment at entry to treatment: 51.6% had normal cognitive function, 41.4% had mild cognitive impairment, 5.8% had moderate cognitive impairment and 1.1% had severe cognitive impairment. Cognitive impairment was not associated with increased treatment drop-out and was not predictive of alcohol use following treatment. Alcohol treatment was associated with a significant improvement in cognitive functioning. Originality/ value: This study suggests there may be a significant amount of unidentified cognitive impairment among older adults attending alcohol treatment. Assessment and routine screening for cognitive impairment in drug and alcohol services may help in care planning and setting treatment goals; in the absence of routine screening opportunities for treatment planning and intervention may be missed
Review of low aspect ratio blade dynamics for electrical axial fans and compressors
Get PDFThe quest for lighter and shorter propulsion systems has led to the reduction of axial compressor and fan blade chord-lengths. Theoretical and experimental results show that the geometrical aspect-ratio criterion significantly affects the overall performance of axial flow compressors and fans. In conducting this review, it was found that the experimental results differ from one literature source to another. Highlighting that the way in which blade aspect ratio affects the performance of axial flow compressors and fans is still not fully understood. Nonetheless, the reviewed literature has still proven valuable for compressor and fan design. This paper focuses on creating a review of the available literature relating to how low aspect ratio blades may affect the performance of electrical axial compressors and fans
Design of Zero Clearance SIW Endfire Antenna Array Using Machine Learning-Assisted Optimization
No full textIn this communication, a substrate integrated waveguide (SIW) end-fire antenna array with zero clearance is proposed for fifth-generation (5G) mobile applications using machine learning-assisted optimization. In particular, a novel impedance matching architecture that involves three arbitrary pad-loading metallic vias is investigated and adopted for the antenna element. Due to the stringent design requirements, the locations and sizes of the vias and pads are obtained via a state-of-the-art machine learning assisted antenna design exploration method, parallel surrogate model-assisted hybrid differential evolution for antenna synthesis (PSADEA). Keeping a very low profile, the array optimized by PSADEA covers an operating frequency bandwidth from 36 to 40 GHz. The in-band total efficiency is generally better than 60% and the peak gain is above 5 dBi. The beam scanning range at 39 GHz covers from −20° to 35°
Aerodynamic space tether system as a system with distributed parameters
Get PDFThe motion of the aerodynamic tether system includes two stages – the deployment and the motion of the deployed system. The initial condition for the deployment model is that of a spacecraft with a rigidly connected second body in a circular orbit about the planet. The deployment process is based on the aerodynamic forces acting on bodies. After the separation of the rigid connection, further motions of the bodies are then controlled by the tether release mechanism which unreels the tether and controls the rate at which the tether is unreeled. The
motion of the system is described by the multi-point model, splitting the tether into a number of
parts and using the series of separations of material points from the spacecraft, thus forming a mathematical model of the system with distributed parameters. The aerodynamic forces acting on the tether are taken into consideration. This model makes it possible to calculate numerically elastic deformations and curvature of the tether and provides more accurate modelling if
compared with the two-point model
Determining specific power loss in joint area of laminated magnetic core
Get PDFThis paper discusses the method of analysis of specific power loss in the joint area of steel laminated core. The method is based on comparison of power loss data obtained from the tests of a circular shape magnetic core. Initially, the core build using uncut (solid) lamination sheets was tested to obtain the loss reference values. Then the laminations were cut onto four sections, and the core was tested again to obtain the loss increase associated with the implemented joint area. The test readings were processed to separate losses due to tangential and normal fluxes in the laminates. The processed data represented as relative p.u. values of increase in specific power loss have been extended to conduct an analysis of a rectangular magnetic core made using anisotropic, grain-oriented silicon electrical steel. The results demonstrated that the power loss produced by the normal magnetic flux dominates in the structure of total power loss in the rectangular core (approx. 85%). The proposed approach utilises the septation of losses produced by tangential and normal fluxes; it can be applied for the analysis of loss in the joint areas of magnetic cores of various power transformers
Self-tuning speed controller with load parameters observer for servo drives
Get PDFServo drives are widely employed in a large range of industrial applications where the load inertia and torque are varying. This paper proposes a control approach utilizing the load parameters adaptive observers to provide a stable servo drive operation under mechanical parameters variation. In contrast to other solutions, the observers are processing real speed and acceleration values instead of their command. It was shown that the observers are capable of estimating the load torque and total inertia of the drive system under different types of commands. The simulation of the proposed control approach demonstrated that the proportional gain of the speed controller tunes automatically according to the estimated inertia ensuring the required quality of the transient regardless variation of the mechanical subsystem parameters
The ventilation of buildings and other mitigating measures for COVID-19: a focus on wintertime
Get PDFThe year 2020 has seen the emergence of a global pandemic as a result of the disease COVID-19. This report reviews knowledge of the transmission of COVID-19 indoors, examines the evidence for mitigating measures, and considers the implications for wintertime with a focus on ventilation
Simulation of carbon fibre composites in an industrial microwave
Get PDFThe ability of microwave radiation to penetrate and interact directly with materials has led to its extensive use in food and drug industries, and more recently in composites manufacturing. Microwave heating of composites allows rapid heat transfer throughout the material thickness with reduced thermal gradients and processing times as well as energy efficiency. Design of microwave systems to process composite parts with various geometries and sizes demands improved understanding of electromagnetic energy distribution and factors influencing it. Finite-element (FE) models can be efficient design tools in such cases, as physical experimentation can be impractical.
In this study, a fully-coupled FE model of a carbon fibre composite in an industrial microwave environment is developed using COMSOL Multiphysics®. The effects of the heating process parameters including the number of active magnetrons, specimen thickness and the variation in the frequency of radiation on the electromagnetic field distribution are studied. The FE model showed that a substantial difference in the electromagnetic field distribution exists for the frequencies above 1 GHz compared to the lower frequencies in the microwave regime, resulting in non-uniform heating
Detection and Classification of DDoS Flooding Attacks on Software-Defined Networks: A Case Study for the Application of Machine Learning
Get PDFSoftware-defined networks (SDNs) offer robust network architectures for current and future Internet of Things (IoT) applications. At the same time, SDNs constitute an attractive target for cyber attackers due to their global network view and programmability. One of the major vulnerabilities of typical SDN architectures is their susceptibility to Distributed Denial of Service (DDoS) flooding attacks. DDoS flooding attacks can render SDN controllers unavailable to their underlying infrastructure, causing service disruption or a complete outage in many cases. In this paper, machine learning-based detection and classification of DDoS flooding attacks on SDNs is investigated using popular machine learning (ML) algorithms. The ML algorithms, classifiers and methods investigated are quadratic discriminant analysis (QDA), Gaussian Naïve Bayes (GNB), k -nearest neighbor (k-NN), and classification and regression tree (CART). The general principle is illustrated through a case study, in which, experimental data (i.e. jitter, throughput, and response time metrics) from a representative SDN architecture suitable for typical mid-sized enterprise-wide networks is used to build classification models that accurately identify and classify DDoS flooding attacks. The SDN model used was emulated in Mininet and the DDoS flooding attacks (i.e. hypertext transfer protocol (HTTP), transmission control protocol (TCP), and user datagram protocol (UDP) attacks) have been launched on the SDN model using low orbit ion cannon (LOIC). Although all the ML methods investigated show very good efficacy in detecting and classifying DDoS flooding attacks, CART demonstrated the best performance on average in terms of prediction accuracy (98%), prediction speed ( 5.3×105 observations per second), training time (12.4 ms), and robustness
Supervised machine learning for audio emotion recognition
Get PDFThe field of Music Emotion Recognition has become and established research sub-domain of Music Information Retrieval. Less attention has been directed towards the counterpart domain of Audio Emotion Recognition, which focuses upon detection of emotional stimuli resulting from non-musical sound. By better understanding how sounds provoke emotional responses in an audience, it may be possible to enhance the work of sound designers. The work in this paper uses the International Affective Digital Sounds set. A total of 76 features are extracted from the sounds, spanning the time and frequency domains. The features are then subjected to an initial analysis to determine what level of similarity exists between pairs of features measured using Pearson’s r correlation coefficient before being used as inputs to a multiple regression model to determine their weighting and relative importance. The features are then used as the input to two machine learning approaches: regression modelling and artificial neural networks in order to determine their ability to predict the emotional dimensions of arousal and valence. It was found that a small number of strong correlations exist between the features and that a greater number of features contribute significantly to the predictive power of emotional valence, rather than arousal. Shallow neural networks perform significantly better than a range of regression models and the best performing networks were able to account for 64.4% of the variance in prediction of arousal and 65.4% in the case of valence. These findings are a major improvement over those encountered in the literature. Several extensions of this research are discussed, including work related to improving data sets as well as the modelling processes