The influence of the coordination of (reversible) cross-links on the mechanical properties of aligned fiber bundles is investigated. Two polymeric systems containing cross-links of different ...coordination (two- and three-fold coordination) but having the same binding energy are investigated. In particular, the response to loading of these systems is compared. Mechanical parameters (strength, stiffness and work-to-fracture) are obtained by computational loading tests. The influence of coordination is studied for simple test systems with pre-defined topologies that maximize strength as well as for more realistic fiber bundles containing nine chains. The results show that a higher coordination of cross-links has a beneficial effect on the strength and the stiffness of the systems, while the work-to-fracture was found larger for the system having a smaller coordination of cross-links. It can be concluded that controlling the coordination of cross-links is a versatile tool to specifically tailor the mechanical properties of polymeric structures.
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We investigate the influence of the coordination of cross-links on the plastic (i.e., permanent) deformation in cross-linked fiber bundles. Yield strain and strength as well as the ...resilience are studied as a function of cross-linker and grafting density. It is found that classical twofold coordinated cross-links lead to a pronounced strain concentration in the system, while cross-links with higher coordination allow for a more homogeneous load transfer through the system. This results in inferior mechanical properties related to plastic behavior in the first system compared to the latter. Particularly, in twofold coordinated systems, the resilience shows a non-monotonic behavior with respect to cross-linker density. This means that inserting always more and stronger cross-links do not necessarily improve the mechanical performance of a material. These findings may help to interpret experimental findings on the fracture energy in hydro-gels cross-linked with zinc ions.
Minimally invasive surgery (MIS) and robotics have revolutionized the field of Otolaryngology. MIS and robotics have reshaped traditional otolaryngological practices, offering patients a multitude of ...benefits. Reduced incision sizes and tissue manipulation minimize postoperative pain and discomfort, while also improving cosmetic outcomes. MIS has facilitated enhanced visualization and access to intricate anatomical structures, enabling the treatment of previously inaccessible lesions. MIS procedures also offer shorter hospital stays, reduced blood loss, and faster healing times whilst enhancing patient satisfaction and overall quality of life The ongoing progress in minimally invasive approaches solidifies their role as a cornerstone in modern Otolaryngology, and surgeons navigating this transformative landscape must embrace the learning curve associated with these advanced techniques, recognizing the potential for improved patient outcomes. This article explores the transformative impact of MIS and robotics on the diverse branches of Otolaryngology, highlighting the technological advancements that have enabled these techniques to flourish.
Solar energy is a major type of renewable energy, and its estimation is important for decision-makers. This study introduces a new prediction model for solar radiation based on support vector ...regression (SVR) and the improved particle swarm optimization (IPSO) algorithm. The new version of algorithm attempts to enhance the global search ability for the PSO. In practice, the SVR method has a few parameters that should be determined through a trial-and-error procedure while developing the prediction model. This procedure usually leads to non-optimal choices for these parameters and, hence, poor prediction accuracy. Therefore, there is a need to integrate the SVR model with an optimization algorithm to achieve optimal choices for these parameters. Thus, the IPSO algorithm, as an optimizer is integrated with SVR to obtain optimal values for the SVR parameters. To examine the proposed model, two solar radiation stations, Adana, Antakya and Konya, in Turkey, are considered for this study. In addition, different models have been tested for this prediction, namely, the M5 tree model (M5T), genetic programming (GP), SVR integrated with four different optimization algorithms SVR-PSO, SVR-IPSO, Genetic Algorithm (SVR-GA), FireFly Algorithm (SVR-FFA) and the multivariate adaptive regression (MARS) model. The sensitivity analysis is performed to achieve the highest accuracy level of the prediction by choosing different input parameters. Several performance measuring indices have been considered to examine the efficiency of all the prediction methods. The results show that SVR-IPSO outperformed M5T and MARS.
One of the most important issues in the field of water resource management is the optimal utilization of dam reservoirs. In the current study, the optimal utilization of the Aydoghmoush Dam Reservoir ...is examined based on a hybrid of the bat algorithm (BA) and particle swarm optimization algorithm (PSOA) by increasing the convergence rate of the new hybrid algorithm (HA) without being trapped in the local optima. The main goal of the study was to reduce irrigation deficiencies downstream of this reservoir. The results showed that the HA reduced the computational time and increased the convergence rate. The average downstream irrigation demand over a 10-year period (1991–2000) was 25.12 × 106 m3, while the amount of water release based on the HA was 24.48 × 106 m3. Therefore, the HA was able to meet the irrigation demands better than some other evolutionary algorithms. Moreover, lower indices of root mean square error (RMSE) and mean absolute error (MAE) were obtained for the HA. In addition, a multicriteria decision-making model based on the vulnerability, reliability, and reversibility indices and the objective function performed better with the new HA than with the BA, PSOA, genetic algorithm (GA), and shark algorithm (SA) in terms of providing for downstream irrigation demands.
The coronavirus disease 2019 (COVID-19) pandemic has deteriorated the healthcare system and economy worldwide. Globally, by making the primary vaccination against the coronavirus necessary, the surge ...in cases waned, but as the effects of this vaccination decreased after some time, to prevent another pandemic, vaccination was still necessary. As a result, receiving a COVID-19 booster shot can boost immunity against the coronavirus. This study aimed to assess knowledge of COVID-19 booster vaccines in Pakistan among the general public and understand the factors affecting the vaccination process in the state. In this cross-sectional study, non-probability convenience sampling was done. Its physical data collection was conducted in September 2022 in a tertiary care hospital in Karachi, Pakistan. Data were collected from 384 individuals who visited the hospital with consent before filling out the questionnaire. The mean age of respondents was 35.81 (standard deviation (SD) = ±13.006), and 98.7% of individuals were primarily vaccinated for COVID-19, but out of these, only 60.1% received the booster jab. The most commonly reported side effects of primary doses of COVID-19 and its booster were pain at the injection site, fatigue, and fever, but these effects did not appear to have as much of an impact on the vaccination process as education did. The results are evident that out of primarily vaccinated individuals against COVID-19, 40.16% are reluctant to receive its booster. Therefore, it is essential to create awareness among the masses about vaccination and its importance.
According to most of physical therapists, physical inactivity (85%), high fat diet (81%) and overeating (80.2%) are the main reasons that cause obesity and the combination of diet and exercise are ...considered as essential treatment. According to the WHO, typically the incidence of obesity has increased thrice worldwide since 1975 together with the number of heavy adults in 2016 hitting 1.9 billion(WHO.,2018 ). Canadian physiotherapy Association claims that physical therapist is often the first to get in touch with the individual having vague pain or restricted movement caused by obesity (CPA.,2007).Increasing physical exercise can reduce the particular complications associated with unhealthy weight and also promote weight reduction (Slentz et al., 2007 and Haskell et al., 2007 and Hill et al., 2005). ...according to several studies physiotherapists have clear understanding that dealing with obesity is the part of their treatment but few of the studies also shows that many physiotherapists have neutral attitude towards obese patients(You et al.,2012 and Rinne et al., 2018).Studies in France on general physician showed that they do not consider obesity as a multi factorial 2 disease and they do not treat their patients with that concern of a chronic disease (Bocquier et al., 2005 and Thuan et al., 2005). Some other studies on health care providers showed that they do not have enough knowledge about obesity, and they do not take obesity as a serious health concern (Poon & Tarrant., 2009 and Martins & Norsett-Carr., 2017 and Bucher et al., 2018). ...the aim of this study is to justify the behavior, practice and knowledge of physiotherapy approaches associated with overweight people since physiotherapists are the part of multidisciplinary team that promote wellness, health, fitness along with treating problems affecting the quality of life of people.
Accurate short-term load forecasting (STLF) is essential for the efficient operation of the power sector. Due to heightened volatility and intrinsic stochasticity, forecasting load at a fine ...resolution, such as weekly load, is difficult. Existing STLF techniques only rely on temporal data and auto-regressive processes to forecast load. However, the power grid has a graphical structure that provides spatial information too. This paper proposes an innovative STLF method fusing both spatial and temporal information. We propose a creative way to convert load data into graphical form, which is fed into graph convolutional networks (GCN) to learn spatial embeddings. The GCN embeddings are used along with temporal features to predict the load. We perform extensive experiments using state-of-the-art machine learning and deep learning techniques to validate our approach. The results demonstrate that by using spatial information, we can sub-stantially improve the forecasting performance.
Stock Price Forecasting using Hidden Markov Models Shabbir, Attia; Ali, Raja Hashim; Zeeshan Shabbir, Muhammad ...
2023 International Conference on IT and Industrial Technologies (ICIT),
2023-Oct.-9
Conference Proceeding
We address the challenging task of predicting stock values in this study, which has long been of interest to shareholders due to the complex, nonlinear, and dynamic nature of the stock market. The ...focus of this study is on forecasting future trends in stock market groups. The effectiveness of the Gaussian Hidden Markov Model (HMM) method for predicting stock prices is demonstrated, with evaluation conducted on four prominent groups: Apple Inc., CMCST corporation, Google LLC, and Qualcomm Inc. Historical stock market data, typically in the form of OHLC (Open-High-Low-Close) prices, is utilized as training data for the model. By leveraging this historical data, accurate predictions for the next day's stock prices are made. The Mean Absolute Percentage Error (MAPE) metric is employed to evaluate the performance of the models, and the results are satisfactory. The proposed approach is applicable to predicting the stock prices of any company by training the HMM on the specific company's stock dataset, making it a generalizable method.
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