The comparison of hearing loss among diabetic and non-diabetic patients

Introduction: The prevalence of Diabetes Mellitus is steadily increasing. This is a multi- systemic abnormality, causing side- effects which are mainly irreversible. Hearing loss is one of the common symptoms, and there are many studies, with contradictory results. Aim of this study was to compare the hearing loss among diabetic with non- diabetic patients. Material and Methods: In this study 50 diabetic patients were chosen randomly from those overt diabetic patients referred to the Diabetes clinic in Gorgan hospital northern Iran, 50 other patients who referred to the 5th Azar hospital, with any other ENT complaint were also randomly chosen as control group. The case and control groups were matched. Demographic questionnaires were filled for each subjects in case and control groups, and those with intervening factors were omitted from this study. Data were analyzed using SPSS. Results: In this study 66% and 34% of participants were men and women respectively. The age distribution of the patient were 15-75 years. The hearing loss among diabetic patients and non- diabetic subjects were 16% and 5% respectively, which showed that the diabetic patient has3.2 times more possibility to acquire hearing problem. It was also shown, that there was a direct correlation between increasingage and hearing loss. More women are at risk, of getting hearingloss than men. The hearing loss also has a correlation with the duration of disease onset and the consumption of Glibanclamid. Conclusion The careful periodical assessment of hearing loss, and the application of hearing facilities to improve the qualityof diabetic patients life is recommended due to chronic and irreversible Symptom of the disease

On computing joint invariants of vector fields

A constructive version of the Frobenius integrability theorem -- that can be programmed effectively -- is given. This is used in computing invariants of groups of low ranks and recover examples from a recent paper of Boyko, Patera and Popoyvich \cite{BPP}

Similarities Between Classical Timelike Geodesics in a Naked Reissner-Nordstrom Singularity Background and the Behaviour of Electrons in Quantum Theory

It is generally assumed that naked singularities must be physically excluded, as they could otherwise introduce unpredictable influences in their future null cones. Considering geodesics for a naked Reissner-Nordstrom singularity, it is found that the singularity is effectively clothed by its repulsive nature. Regarding electron as naked singularity, the size of the clothed singularity (electron) turns out to be classical electro-magnetic radius of the electron, to an observer falling freely from infinity, initially at rest. The size shrinks for an observer falling freely from infinity, with a positive initial velocity. For geodetic parameters corresponding to negative energy there are trapped geodesics. The similarity of this picture with that arising in the Quantum Theory is discussed.Comment: 8 pages, 6 figure

Nonintrusive Load Monitoring (NILM) Using a Deep Learning Model with a Transformer-Based Attention Mechanism and Temporal Pooling

Nonintrusive load monitoring (NILM) is an important technique for energy management and conservation. In this paper, a deep learning model based on an attention mechanism, temporal pooling, residual connections, and transformers is proposed. This article presents a novel approach for NILM to accurately discern energy consumption patterns of individual household appliances. The proposed method entails a sequence of layers, including encoders, transformers, attention, temporal pooling, and residual connections, offering a comprehensive solution for NILM while effectively capturing appliance-specific energy usage in a household. The proposed model was evaluated using UK-DALE, REDD, and REFIT datasets in both seen and unseen cases. It shows that the proposed model in this paper performs better than other methods stated in other papers in terms of F1-score and total error of the results (in terms of SAE). This model achieved an F1-score equal to 92.96 as well as a total SAE equal to −0.036, which shows its effectiveness in accurately diagnosing and estimating the energy consumption of individual home appliances. The findings of this research show that the proposed model can be a tool for energy management in residential and commercial buildings

Sequence-to-Sequence Model with Transformer-based Attention Mechanism and Temporal Pooling for Non-Intrusive Load Monitoring

This paper presents a novel Sequence-to-Sequence (Seq2Seq) model based on a transformer-based attention mechanism and temporal pooling for Non-Intrusive Load Monitoring (NILM) of smart buildings. The paper aims to improve the accuracy of NILM by using a deep learning-based method. The proposed method uses a Seq2Seq model with a transformer-based attention mechanism to capture the long-term dependencies of NILM data. Additionally, temporal pooling is used to improve the model's accuracy by capturing both the steady-state and transient behavior of appliances. The paper evaluates the proposed method on a publicly available dataset and compares the results with other state-of-the-art NILM techniques. The results demonstrate that the proposed method outperforms the existing methods in terms of both accuracy and computational efficiency

Non-Intrusive Load Monitoring (NILM) using Deep Neural Networks: A Review

Demand-side management now encompasses more residential loads. To efficiently apply demand response strategies, it's essential to periodically observe the contribution of various domestic appliances to total energy consumption. Non-intrusive load monitoring (NILM), also known as load disaggregation, is a method for decomposing the total energy consumption profile into individual appliance load profiles within the household. It has multiple applications in demand-side management, energy consumption monitoring, and analysis. Various methods, including machine learning and deep learning, have been used to implement and improve NILM algorithms. This paper reviews some recent NILM methods based on deep learning and introduces the most accurate methods for residential loads. It summarizes public databases for NILM evaluation and compares methods using standard performance metrics

Concurrent Probabilistic Control Co-Design and Layout Optimization of Wave Energy Converter Farms using Surrogate Modeling

Wave energy converters (WECs) are a promising candidate for meeting the increasing energy demands of today's society. It is known that the sizing and power take-off (PTO) control of WEC devices have a major impact on their performance. In addition, to improve power generation, WECs must be optimally deployed within a farm. While such individual aspects have been investigated for various WECs, potential improvements may be attained by leveraging an integrated, system-level design approach that considers all of these aspects. However, the computational complexity of estimating the hydrodynamic interaction effects significantly increases for large numbers of WECs. In this article, we undertake this challenge by developing data-driven surrogate models using artificial neural networks and the principles of many-body expansion. The effectiveness of this approach is demonstrated by solving a concurrent plant (i.e., sizing), control (i.e., PTO parameters), and layout optimization of heaving cylinder WEC devices. WEC dynamics were modeled in the frequency domain, subject to probabilistic incident waves with farms of $3$, $5$, $7$, and $10$ WECs. The results indicate promising directions toward a practical framework for array design investigations with more tractable computational demands.Comment: 14 pages, 7 figure

Experiments on the Structure of Turbulence in Fully Developed Pipe Flow

This paper describes a series of two-point measurements in fully developed pipe flow. Measurements of the phase shifts between the Fourier components of the axial velocity fluctuations at two points were made with three different orientations of the two points. In all cases the two points were close enough together that the turbulent structure remained essentially frozen while passing between the sensors. The phase velocities, C1(f), and inclinations, α(f) (defined previously by Heidrick et al. (8)), of each frequency component, f, were determined from these measurements. In general, the Fourier components are inclined to the wall - the lower frequencies making smaller angles with the wall than the higher frequencies. The higher frequency disturbances became more nearly perpendicular to the wall in the central region of the pipe. For points very near the wall the disturbances appear to be very obliquely inclined. In the core region, the phase velocity increases with increasing frequency and there is little discernable trend in the phase velocities with respect to position when they are normalized by the local mean velocity. The group velocity of small scale disturbances (large wavenumbers) in the core region appears to be approximately constant and of the order of the local mean velocity. This means that a frozen pattern hypothesis should be valid for these scales. All measurements became more scattered at values of y+ ~\u3c26. This may be due to the intermit- tent nature of the flow near the wall since recent studies in turbulent shear flow suggest that energy transfer from the mean flow to fluctuations near the wall may be due to local instabilities ( bursts ). Thus, the time averaged model is not entirely adequate, and it was necessary to separate out the burst intervals for further study. By suitably processing the two-point velocity signals it was possible to determine when the flow was bursting. The behavior of the velocity and vorticity within measured bursts was determined. This behavior and short-term energy spectra within bursts indicate a weakly periodic behavior. The overall behavior of the flow was shown to be similar to the last stage of the laminar-to-turbulent flow transition