Effect of Laser Therapy on Chronic Osteoarthritis of the Knee in Older Subjects

Introduction: Osteoarthritis (OA) is a common degenerative joint disease particularly in older subjects. It is usually associated with pain, restricted range of motion, muscle weakness, difficulties in daily living activities and impaired quality of life. To determine the effects of adding two different intensities of low-level laser therapy (LLLT) to exercise training program on pain severity, joint stiffness, physical function, isometric muscle strength, range of motion of the knee, and quality of life in older subjects with knee OA.Methods: Patients were randomly assigned into three groups. They received 16 sessions, 2 sessions/week for 8 weeks. Group-I: 18 patients were treated with a laser dose of 6 J/cm2 with a total dose of 48 J. Group-II: 18 patients were treated with a laser dose of 3 J/cm2 with a total dose of 27 J. Group-III: 15 patients were treated with laser without emission as a placebo. All patients received same exercise training program including stretching and strengthening exercises. Patients were evaluated before and after intervention by visual analogue scale (VAS), the Western Ontario and McMaster Universities Osteoarthritis (WOMAC) index for quality of life, handheld dynamometer and universal goniometer.Results: T test revealed that there was a significant reduction in VAS and pain intensity, an increase in isometric muscle strength and range of motion of the knee as well as increase in physical functional ability in three treatment groups. Also analysis of variance (ANOVA) proved significant differences among them and the post hoc tests (LSD) test showed the best improvements for patients of the first group.Conclusion: It can be concluded that addition of LLLT to exercise training program is more effective than exercise training alone in the treatment of older patients with chronic knee OA and the rate of improvement may be dose dependent, as with 6 J/cm2 or 3 J/cm2

Influence of using Straight and Twisted Elliptical Section Heater Tubes on Stirling Engine Performance

The heat transfer area of the heater tubes is a significant factor that deeply affects net output power and thermal efficiency in Stirling engines. It is greatly affected by the input heat transfer rate, heater tube geometries, and heat transfer removal rate. The alpha Stirling engine heater is our concern in this study. An ordinary circular and straight elliptical section heater tubes having different twisting ratios with a rectangular section-connecting duct are used to study the different heater tube configurations and twisting effect on the heat transfer characteristics and working fluid motion inside the engine. Three twisting ratios of two, three, and four with each section of the heater tube are used in this study. The 3D simulation model using the SST K-ω model using ANSYS FLUENT-16 is used for simulating airflow through the hot cylinder, heater tubes, regenerator, cooler, and cold cylinder of the Stirling engine, during a complete engine cycle. The results showed that increasing the twisting value increases the net output power and the thermal efficiency. The maximum net power output occurs at the elliptical section heater tube with a two-twist ratio with a value of 1249.26 W by an increase of 86.90 W with respect to the ordinary circular heater. In addition, the maximum thermal efficiency occurs at the elliptical section heater tube with a two-twist ratio with a value of 29.55% by an increase of 1.07% with respect to the ordinary circular heater

Optimized superpixel and AdaBoost classifier for human thermal face recognition

Infrared spectrum-based human recognition systems offer straightforward and robust solutions for achieving an excellent performance in uncontrolled illumination. In this paper, a human thermal face recognition model is proposed. The model consists of four main steps. Firstly, the grey wolf optimization algorithm is used to find optimal superpixel parameters of the quick-shift segmentation method. Then, segmentation-based fractal texture analysis algorithm is used for extracting features and the rough set-based methods are used to select the most discriminative features. Finally, the AdaBoost classifier is employed for the classification process. For evaluating our proposed approach, thermal images from the Terravic Facial infrared dataset were used. The experimental results showed that the proposed approach achieved (1) reasonable segmentation results for the indoor and outdoor thermal images, (2) accuracy of the segmented images better than the non-segmented ones, and (3) the entropy-based feature selection method obtained the best classification accuracy. Generally, the classification accuracy of the proposed model reached to 99% which is better than some of the related work with around 5%

HPLC method with monolithic column for simultaneous determination of irbesartan and hydrochlorothiazide in tablets

A simple, sensitive and accurate HPLC method with high throughput has been developed and validated for the simultaneous determination of irbesartan (IRB) and hydrochlorothiazide (HCT) in combined pharmaceutical dosage forms. The proposed method employed, for the first time, a monolithic column in the analysis. Optimal chromatographic separation of the analytes was achieved on Chromolith® Performance RP-18e column using a mobile phase consisting of phosphate buffer (pH 4):acetonitrile (50:50, V/V) pumped isocratically at a flow rate of 1.0 mL min–1. The eluted analytes were monitored with a UV detector set at 270 nm. Under the optimum chromatographic conditions, linear relationship with a good correlation coefficient (R ≥ 0.9997) was found between the peak area and the corresponding concentrations of both IRB and HCT in the ranges of 10–200 and 1–20 ng mL–1. The limits of detection were 2.34 and 0.03 ng mL-1 for IRB and HCT, respectively. The intra- and inter-assay precisions were satisfactory as the RSD values did not exceed 3 %. The accuracy of the proposed method was > 97 %. The proposed method had high throughput as the analysis involved a simple procedure and a very short run-time of 3 min. The results demonstrated that the method is applicable in the quality control of combined pharmaceutical tablets containing IRB and HCT

Complimentary protein extraction methods increase the identification of the Park Grass Experiment metaproteome

Although the Park Grass Experiment is an important international reference soil for temperate grasslands, it still lacks the direct extraction of its metaproteome. The identification of these proteins can be crucial to our understanding of soil ecology and major biogeochemical processes. However, the extraction of protein from soil is a technically fraught process due to difficulties with co-extraction of humic material and lack of compatible databases to identify proteins. To address these issues, we combined two protein extraction techniques on Park Grass experiment soil, one based on humic acid removal, namely a modified freeze-dry, heat/thaw/phenol/chloroform (HTPC) method and another which co-extracts humic material, namely an established surfactant method. A broad range of proteins were identified by matching the mass spectra of extracted soil proteins against a tailored Park Grass proteome database. These were mainly in the categories of “protein metabolism”, “membrane transport”, “carbohydrate metabolism”, “respiration” “ribosomal and nitrogen cycle” proteins, enabling reconstitution of specific processes in grassland soil. Protein annotation using NCBI and EBI databases inferred that the Park Grass soil is dominated by Proteobacteria, Actinobacteria, Acidobacteria and Firmicutes at phylum level and Bradyrhizobium, Rhizobium, Acidobacteria, Streptomyces and Pseudolabrys at genus level. Further functional enrichment analysis enabled us to connect protein identities to regulatory and signalling networks of key biogeochemical cycles, notably the nitrogen cycle. The newly identified Park Grass metaproteome thus provides a baseline on which future targeted studies of important soil processes and their control can be built

Electrical power output prediction of combined cycle power plants using a recurrent neural network optimized by waterwheel plant algorithm

It is difficult to analyze and anticipate the power output of Combined Cycle Power Plants (CCPPs) when considering operational thermal variables such as ambient pressure, vacuum, relative humidity, and temperature. Our data visualization study shows strong non-linearity in the experimental data. We observe that CCPP energy production increases linearly with temperature but not pressure. We offer the Waterwheel Plant Algorithm (WWPA), a unique metaheuristic optimization method, to fine-tune Recurrent Neural Network hyperparameters to improve prediction accuracy. A robust mathematical model for energy production prediction is built and validated using anticipated and experimental data residuals. The residuals’ uniformity above and below the regression line suggests acceptable prediction errors. Our mathematical model has an R-squared value of 0.935 and 0.999 during training and testing, demonstrating its outstanding predictive accuracy. This research provides an accurate way to forecast CCPP energy output, which could improve operational efficiency and resource utilization in these power plants

Forecasting wind power based on an improved al-Biruni Earth radius metaheuristic optimization algorithm

Wind power forecasting is pivotal in optimizing renewable energy generation and grid stability. This paper presents a groundbreaking optimization algorithm to enhance wind power forecasting through an improved al-Biruni Earth radius (BER) metaheuristic optimization algorithm. The BER algorithm, based on stochastic fractal search (SFS) principles, has been refined and optimized to achieve superior accuracy in wind power prediction. The proposed algorithm is denoted by BERSFS and is used in an ensemble model’s feature selection and optimization to boost prediction accuracy. In the experiments, the first scenario covers the proposed binary BERSFS algorithm’s feature selection capabilities for the dataset under test, while the second scenario demonstrates the algorithm’s regression capabilities. The BERSFS algorithm is investigated and compared to state-of-the-art algorithms of BER, SFS, particle swarm optimization, gray wolf optimizer, and whale optimization algorithm. The proposed optimizing ensemble BERSFS-based model is also compared to the basic models of long short-term memory, bidirectional long short-term memory, gated recurrent unit, and the k-nearest neighbor ensemble model. The statistical investigation utilized Wilcoxon’s rank-sum and analysis of variance tests to investigate the robustness of the created BERSFS-based model. The achieved results and analysis confirm the effectiveness and superiority of the proposed approach in wind power forecasting