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Total knee arthroplasty (TKA) is one of the most commonly performed orthopedic surgeries, with nearly one million performed in 2020 in the United States alone. Changing patient demographics, predominately indicated by increases in younger, more active, and more obese patients undergoing TKA, poses a challenge to orthopedic surgeons as these factors present a greater risk of long-term complications. Historically, cemented TKA has been the gold standard for fixation, but long-term aseptic loosening continues to be a risk for cemented implants. Cementless TKA, which relies on the surface morphology of a porous coating for biologic fixation of implant to bone, may provide improved long-term survivorship compared with cement. The quality of this bond is dependent on an interference fit and the roughness, or coefficient of friction, between the implant and the bonebone. Stress shielding is a measure of the difference in the stress experienced by implanted bone versus surrounding native bone. A finite element model (FEM) can be used to quantify and better understand stress shielding in order to better evaluate and optimize implant design. In this study, a FEM was constructed to investigate how the surface coating of cementless implants (coefficient of friction) and the location of the coating application affected the stress-shielding response in the tibia. It was determined that the stress distribution in the native tibia surrounding a cementless TKA implant was dependent on the coefficient of friction applied at the tip of the implant's stem. Materials with lower friction coefficients applied to the stem tip resulted in higher compressive stress experienced by implanted bone, and more favorable overall stress-shielding responses. [ABSTRACT FROM AUTHOR]

Detecting damage constitutes the primary and pivotal stage in monitoring a structure's health. Early identification of structural issues, coupled with a precise understanding of the structure's condition, represents a cornerstone in the practices of structural health monitoring (SHM). While many existing methods prove effective when the number of data points in both healthy and damaged states is equal, this article employs algorithms tailored for detecting damage in situations where data are imbalanced. Imbalance, in this context, denotes a significant difference in the number of data points between the healthy and damaged states, essentially introducing an imbalance within the dataset. Four imbalanced classification algorithms are applied to two benchmark structures: the first, a numerical model of a four-story steel building, and the second, a bridge constructed in China. This research thoroughly assesses the performance of these four algorithms for each structure, both individually and collectively. [ABSTRACT FROM AUTHOR]

Over the last two decades, several experimental and numerical studies have been performed in order to investigate the acoustic behavior of different muffler materials. However, there is a problem in which it is necessary to perform large, important, time-consuming calculations particularly if the muffler was made from advanced materials such as composite materials. Therefore, this work focused on developing the concept of the indirect dual-chamber muffler made from a basalt fiber reinforced polymer (BFRP) laminated composite, which is a monitoring system that uses a deep learning algorithm to predict the acoustic behavior of the muffler material in order to save effort and time on muffler design optimization. Two types of deep neural networks (DNNs) architectures are developed in Python. The first DNN is called a recurrent neural network with long short-term memory blocks (RNN-LSTM), where the other is called a convolutional neural network (CNN). First, a dual-chamber laminated composite muffler (DCLCM) model is developed in MATLAB to provide the acoustic behavior datasets of mufflers such as acoustic transmission loss (TL) and the power transmission coefficient (PTC). The model training parameters are optimized by using Bayesian genetic algorithms (BGA) optimization. The acoustic results from the proposed method are compared with available experimental results in literature, thus validating the accuracy and reliability of the proposed technique. The results indicate that the present approach is efficient and significantly reduced the time and effort to select the muffler material and optimal design, where both models CNN and RNN-LSTM achieved accuracy above 90% on the test and validation dataset. This work will reinforce the mufflers' industrials, and its design may one day be equipped with deep learning based algorithms. [ABSTRACT FROM AUTHOR]

Earthquakes threaten humanity globally in complex ways that mainly include various socioeconomic consequences of life and property losses. Resilience against seismic risks is of high importance in the modern world and needs to be sustainable. Sustainable earthquake resilience (SER) from the perspective of structural engineering means equipping the built environment with appropriate aseismic systems. Shape memory alloys (SMAs) are a class of advanced materials well suited for fulfilling the SER demand of the built environment. This article explores how this capability can be realized by the innovative SMA-based superelasticity-assisted slider (SSS), recently proposed for next-generation seismic protection of structures. The versatility of SSS is first discussed as a critical advantage for an effective SER. Alternative configurations and implementation styles of the system are presented, and other advantageous features of this high-tech isolation system (IS) are studied. Results of shaking table experiments, focused on investigating the expected usefulness of SSS for seismic protection in hospitals and conducted at the structural earthquake engineering laboratory of the University of Bonab, are then reported. SSS is compared with currently used ISs, and it is shown that SSS provides the required SER for the built environments and outperforms other ISs by benefitting from the pioneered utilization of SMAs in a novel approach. [ABSTRACT FROM AUTHOR]

Objective: To understand the consequences of an invalidating environment, it is essential to have a measurement tool with appropriate statistical properties. Thus, the primary aim of this study was to render the ICES (Invalidating Childhood Environment Scale) into Persian and subsequently evaluate the psychometric attributes of this translated version. Method: Data were collected from 1221 nonclinical participants, including 1053 females and 168 males, who were students at medical universities in Tehran, Iran. Several questionnaires, such as the ICES, CTQ (Childhood Trauma Questionnaire), DTS (Distress Tolerance Scale), BIS-11 (Barratt Impulsiveness Scale), Self-Compassion Questionnaire, Dutch Eating Behavior Questionnaire, and EAT-26 (Eating Attitudes Test) were used in the study. The data sets were investigated through SPSS and R language to evaluate the ICES' reliability and construct validity. Additionally, Item Response Theory (IRT) was employed with the Graded Response Model (GRM) to measure the psychometric properties of each item in terms of difficulty and discrimination parameters. Results: Confirmatory factor analysis indicated that both single-factor and two-factor models fit well for both maternal and paternal versions of the ICES. The internal consistency, as assessed by Cronbach's alpha, was high and satisfactory for both maternal (0.87) and paternal (0.87) versions. Notably, the IRT analysis revealed that item 9 performed poorly in both maternal and paternal versions. Compared to the one-factor model, the two-factor model demonstrated a superior fit. Additionally, the test-retest reliability of the ICES over two months demonstrated good reliability for both maternal and paternal versions (0.98). Divergent and convergent validity analysis revealed a significant negative relationship between childhood invalidation environment and distress tolerance (r = 0.175, P < 0.01) as well as self-compassion (r = 0.142, P < 0.01), which were inversely related to the ICES. Furthermore, there was a considerably positive correlation between the invalidating environment experienced during childhood and impulsivity, as evidenced by r = 0.196 and P < 0.01. Conclusion: This study established the favorable psychometric properties of the Persian version of the ICES, indicating that this version is reliable and valid to assess the Invalidating Childhood Environment in the Iranian population. However, further investigations are warranted to reevaluate its validity and reliability. [ABSTRACT FROM AUTHOR]

Monitoring the mass of liquid absorption in laminated composite structures, that have a direct contact surface with working liquid-like pipes, is very important in order to prevent the sudden collapse of the structures because of the degradations in strength and mechanical properties over time. An electrical capacitance sensor technique has been applied for monitoring the mass of liquid absorption, over the time, in laminated composite pipelines by measuring the change of dielectric characteristics of composite pipelines subjected to an internal hydrostatic pressure load of water and thermal effect. Results show the technique is very effective. However, a major difficulty in utilizing this technique is that it is highly time consuming to be used for monitoring, in addition to the detection efforts that it requires to calculate the mass of liquid absorption that exerts high cost and loss of additional time in monitoring. In this paper, a deep neural network model is used to estimate the mass of liquid absorption in glass fiber reinforced epoxy laminated composite pipelines by extracting the features from datasets from experimental and numerical measurements of the electrical capacitance sensor. The experimental and numerical data used in this paper to train and test the new deep neural network model are collected from the literature and the finite element model of the electrical capacitance sensor system respectively. The results show an excellent agreement between the finite element model data, available experimental data, and those predicted by a deep neural network with an average error of 0.067%, and show that the proposed method achieves satisfactory performance with 86.34% accuracy, 82.83% regression rate and 83.74% F-score. The proposed approach overcomes the difficult problem of saving time and effort to accurately detect the mass of liquid absorption over the time, and provides a promising approach for a wider application of this intelligent model. • A FEM is established for monitoring the mass of water absorption over time (M%) of the composite pipeline. • The M% of the composite pipeline is analyzed using the electrical capacitance sensor (ECS) technique. • The ECS results are verified using a dataset of experimental work adapted from literature. • Trained and tested the Convolutional Neural Network (CNN) by the electrical potential difference between ECS electrodes. • The M% over time of composite pipeline is predicted and compared with the FEM outputs and available experimental results. [ABSTRACT FROM AUTHOR]

In this paper, a structural health monitoring (SHM) system is proposed to provide automatic early warning for detecting damage and its location in composite pipelines at an early stage. The study considers a basalt fiber reinforced polymer (BFRP) pipeline with an embedded Fiber Bragg grating (FBG) sensory system and first discusses the shortcomings and challenges with incorporating FBG sensors for accurate detection of damage information in pipelines. The novelty and the main focus of this study is, however, a proposed approach that relies on designing an integrated sensing-diagnostic SHM system that has the capability to detect damage in composite pipelines at an early stage via implementation of an artificial intelligence (AI)-based algorithm combining deep learning and other efficient machine learning methods using an Enhanced Convolutional Neural Network (ECNN) without retraining the model. The proposed architecture replaces the softmax layer by a k-Nearest Neighbor (k-NN) algorithm for inference. Finite element models are developed and calibrated by the results of pipe measurements under damage tests. The models are then used to assess the patterns of the strain distributions of the pipeline under internal pressure loading and under pressure changes due to bursts, and to find the relationship of strains at different locations axially and circumferentially. A prediction algorithm for pipe damage mechanisms using distributed strain patterns is also developed. The ECNN is designed and trained to identify the condition of pipe deterioration so the initiation of damage can be detected. The strain results from the current method and the available experimental results in the literature show excellent agreement. The average error between the ECNN data and FBG sensor data is 0.093%, thus confirming the reliability and accuracy of the proposed method. The proposed ECNN achieves high performance with 93.33 % accuracy (P%), 91.18 % regression rate (R%) and a 90.54 % F1-score (F%). [ABSTRACT FROM AUTHOR]

Seismic response prediction is a challenging problem and is significant in every stage during a structure's life cycle. Deep neural network has proven to be an efficient tool in the response prediction of structures. However, a conventional neural network with deterministic parameters is unable to predict the random dynamic response of structures. In this paper, a deep Bayesian convolutional neural network is proposed to predict seismic response. The Bayes-backpropagation algorithm is applied to train the proposed Bayesian deep learning model. A numerical example of a three-dimensional building structure is utilized to validate the performance of the proposed model. The result shows that both acceleration and displacement responses can be predicted with a high level of accuracy by using the proposed method. The main statistical indices of prediction results agree closely with the results from finite element analysis. Furthermore, the influence of random parameters and the robustness of the proposed model are discussed. [ABSTRACT FROM AUTHOR]

Over the past two decades, an increasing number of large-scale structures have been built around the world. Constructing these structures has been a time consuming and highly expensive process. Thus, providing a structural health monitoring system to guarantee their proper functionality is important. In recent years, the advancement of technology and artificial intelligence methods based on signal processing and machine learning has attracted the attention of researchers. The challenges currently exist in the field of structural health monitoring to identify and classify damages to achieve high accuracy in a health-monitoring program. The presence of noise in measurement, various exciting load types, and varying environmental conditions cause difficulty in the practical identification and classification of damage in structures. Recent studies have employed finite element modeling to test the effectiveness of proposed methods for identifying damages in structures. However, detecting damage in real-world structures as mentioned above, presents unique difficulties, and the effectiveness of the proposed methods for damage detection in real-world structures remains uncertain. In order to improve the performance of damage detection methods and increase the accuracy of these methods as much as possible, the most important action is to identify damage sensitive data in the structure. The next challenge is to choose a high performance algorithm for damage identification and classification. One of the advanced algorithms, which has a very high ability to extract the desired features from the measured data, is the XGBoost algorithm. This algorithm has recently attracted the attention of researchers and has been used in different fields. So far, the ability of this algorithm has not been examined in the field of damage detection in order to extract desirable features. This article deals with the identification, classification, and severity of damages in the SMC benchmark bridge, which is an existing megastructure in the real world, as well as the IASC-ASCE benchmark structure, whose responses were taken under applied loads in the laboratory environment. First, using the XGBoost algorithm, the importance of the features extracted from the sensors' data is evaluated, and then the features, which are effective in the damage detection process, are selected. The results of this algorithm indicate that only by selecting 6 features from a large volume of data, the best performance can be achieved and selecting more does not help increase efficiency. In the next step, the Stacking method, which is a hybrid machine learning algorithm for damage classification, is evaluated and compared with some conventional machine learning algorithms that have been used in previous studies. The Stacking method stands out as the top performer with an average accuracy rate of 93.1%, leading to the conclusion that it is the most effective approach. Finally, by applying the presented algorithm to the two mentioned structures, its validation is appraised. [ABSTRACT FROM AUTHOR]

One of the primary goals for the researchers is to create a high-quality sensor with a simple structure because of the urgent requirement to identify biomolecules at low concentrations to diagnose diseases and detect hazardous chemicals for health early on. Recently graphene has attracted much interest in the field of improved biosensors. Meanwhile, graphene with new materials such as CaF2 has been widely used to improve the applications of graphene-based sensors. Using the fantastic features of the graphene/CaF2 multilayer, this article proposes an improvement sensor in the sensitivity (S), the figure of merit (FOM), and the quality factor (Q). The proposed sensor is based on the five-layers graphene/dielectric grating integrated with a Fabry–Perot cavity. By tuning graphene chemical potential (µc), due to the semi-metal features of graphene, the surface plasmon resonance (SPR) waves excited at the graphene/dielectric boundaries. Due to the vertical polarization of the source to the gratings and the symmetry of the electric field, both corners of the grating act as electric dipoles, and this causes the propagation of plasmonic waves on the graphene surface to propagate towards each other. Finally, it causes Fabry–Perot (FP) interference on the surface of graphene in the proposed structure's active medium (the area where the sample is located). In this article, using the inherent nature of FP interference and its S to the environment's refractive index (RI), by changing a minimal amount in the RI of the sample, the resonance wavelength (interferometer order) shifts sharply. The proposed design can detect and sense some cancers, such as Adrenal Gland Cancer, Blood Cancer, Breast Cancer I, Breast Cancer II, Cervical Cancer, and skin cancer precisely. By optimizing the structure, we can achieve an S as high as 9000 nm/RIU and a FOM of about 52.14 for the first resonance order (M1). Likewise, the remarkable S of 38,000 nm/RIU and the FOM of 81 have been obtained for the second mode (M2). In addition, the proposed label-free SPR sensor can detect changes in the concentration of various materials, including gases and biomolecules, hemoglobin, breast cancer, diabetes, leukemia, and most alloys, with an accuracy of 0.001. The proposed sensor can sense urine concentration with a maximum S of 8500 nm/RIU and cancers with high S in the 6000 nm/RIU range to 7000 nm/RIU. Also, four viruses, such as M13 bacteriophage, HIV type one, Herpes simplex type 1, and influenza, have been investigated, showing Maximum S (for second resonance mode of λR(M2) of 8000 nm/RIU (λR(M2) = 11.2 µm), 12,000 nm/RIU (λR(M2) = 10.73 µm), 38,000 nm/RIU (λR(M2) = 11.78 µm), and 12,000 nm/RIU (λR(M2) = 10.6 µm), respectively, and the obtained S for first resonance mode (λR(M1)) for mentioned viruses are 4740 nm/RIU (λR(M1) = 8.7 µm), 8010 nm/RIU (λR(M1) = 8.44 µm), 8100 nm/RIU (λR(M1) = 10.15 µm), and 9000 (λR(M1) = 8.36 µm), respectively. [ABSTRACT FROM AUTHOR]

A rapidly expanding global population and a sizeable portion of it that is aging are the main causes of the significant increase in healthcare costs. Healthcare in terms of monitoring systems is undergoing radical changes, making it possible to gauge or monitor the health conditions of people constantly, while also removing some minor possibilities of going to the hospital. The development of automated devices that are either attached to organs or the skin, continually monitoring human activity, has been made feasible by advancements in sensor technologies, embedded systems, wireless communication technologies, nanotechnologies, and miniaturization being ultra-thin, lightweight, highly flexible, and stretchable. Wearable sensors track physiological signs together with other symptoms such as respiration, pulse, and gait pattern, etc., to spot unusual or unexpected events. Help may therefore be provided when it is required. In this study, wearable sensor-based activity-monitoring systems for people are reviewed, along with the problems that need to be overcome. In this review, we have shown smart detecting and versatile wearable electrical sensing mediums in healthcare. We have compiled piezoelectric-, electrostatic-, and thermoelectric-based wearable sensors and their working mechanisms, along with their principles, while keeping in view the different medical and healthcare conditions and a discussion on the application of these biosensors in human health. A comparison is also made between the three types of wearable energy-harvesting sensors: piezoelectric-, electrostatic-, and thermoelectric-based on their output performance. Finally, we provide a future outlook on the current challenges and opportunities. [ABSTRACT FROM AUTHOR]

• Propose a novel deep learning model based on Runge-Kutta recurrent neural network (RKRNN) with prior knowledge to realize structural system identification and seismic response prediction. • Formulate a partition training strategy to train the proposed neural network to improve the efficiency of training. • Utilize three numerical examples to valid the feasibility of RKRNN model including a linear three degrees of freedom system, a nonlinear single degree of freedom system with Bouc-Wen hysteresis and a simply supported bridge. • Site monitoring data from a bridge located in California has been used to further validate the proposed approach. In the seismic analysis of structural systems, dynamic response prediction is an essential problem and is significant in every stage during the structural life cycle. Conventionally, response analysis is carried out by numerical analysis. However, when the structural parameter is unknown, the establishment of a numerical model will be difficult. Enlightened by the Runge-Kutta (RK) numerical algorithm, this paper proposes a novel recurrent neural network named Runge-Kutta recurrent neural network (RKRNN) to realize the seismic response prediction. A partition training strategy is formulated to train the proposed neural network and to improve the efficiency of training. The proposed model can be trained by using a limited number of samples. Three numerical examples are utilized to validate the feasibility of RKRNN model including a linear three degrees of freedom (DOFs) system, a nonlinear single DOF system with Bouc-Wen hysteresis, and a numerical reinforced concrete bridge model. Additionally, the site monitoring data from a real-world bridge is utilized to further validate the proposed network. The results show that the proposed RKRNN model can effectively and efficiently predict the structural response under seismic load and exhibits robustness to noise, with good potential for applications in engineering practice. [ABSTRACT FROM AUTHOR]

This study proposes FastCrackNet, a computationally efficient crack-detection approach. Instead of a computationally costly convolutional neural network (CNN), this technique uses an effective, fully connected network, which is coupled with a 2D-wavelet image transform for analyzing and a locality sensitive discriminant analysis (LSDA) for reducing the number of features. The algorithm described here is used to detect tiny concrete cracks in two noisy adverse conditions and image shadows. By combining wavelet-based feature extraction, feature reduction, and a rapid classifier based on deep learning, this technique surpasses other image classifiers in terms of speed, performance, and resilience. In order to evaluate the accuracy and speed of FastCrackNet, two prominent pre-trained CNN architectures, namely GoogleNet and Xception, are employed. Findings reveal that FastCrackNet has better speed and accuracy than the other models. This study establishes performance and computational thresholds for classifying photos in difficult conditions. In terms of classification efficiency, FastCrackNet outperformed GoogleNet and the Xception model by more than 60 and 80 times, respectively. Furthermore, FastCrackNet's dependability was proved by its robustness and stability in the presence of uncertainties produced by network characteristics and input images, such as input image size, batch size, and input image dimensions. [ABSTRACT FROM AUTHOR]

In recent years, the application of structural control strategies to attenuate the dynamic response of civil engineering structures subjected to human-induced and environmental loads has once again received much attention. The hardware involved in providing effective control, the stochastic nature of the dynamic excitation, the number of distinct variables required to determine the dissipating forces, and the nature of the control approach are a few of the parameters involved in determining the efficacy of structural control systems. However, the primary parameter to determine the overall performance of a smart adaptive structure is the implementation of a control algorithm that would conveniently counteract the external known and/or unknown excitation by providing additional force input through control devices. The control algorithm to be adopted should be robust, multi-purpose, and simple to design and implement. Moreover, the control algorithm should provide the flexibility in selecting performance objectives for a holistically effective response reduction. This paper focuses on providing a comprehensive review of control algorithms implemented in structural control engineering. This article first provides an overview of the fundamental concepts pertaining to the vibration control of structures subjected to dynamic excitations. Next, an exhaustive review of different types of control algorithms that feed the input signal to active-type control devices is conducted. Thereafter, important benchmark applications in the field of structural control in civil engineering are presented. Finally, conclusions are presented on the advantages and drawbacks of each control strategy and recommendations are presented for further research in this area. [ABSTRACT FROM AUTHOR]

Identifying structural damage is an essential task for ensuring the safety and functionality of civil, mechanical, and aerospace structures. In this study, the structural damage identification scheme is formulated as an optimization problem, and a new meta-heuristic optimization algorithm, called visible particle series search (VPSS), is proposed to tackle that. The proposed VPSS algorithm is inspired by the visibility graph technique, which is a technique used basically to convert a time series into a graph network. In the proposed VPSS algorithm, the population of candidate solutions is regarded as a particle series and is further mapped into a visibility graph network to obtain visible particles. The information captured from the visible particles is then utilized by the algorithm to seek the optimum solution over the search space. The general performance of the proposed VPSS algorithm is first verified on a set of mathematical benchmark functions, and, afterward, its ability to identify structural damage is assessed by conducting various numerical simulations. The results demonstrate the high accuracy, reliability, and computational efficiency of the VPSS algorithm for identifying the location and the extent of damage in structures. [ABSTRACT FROM AUTHOR]

Background: Little is known regarding the impact of quantity and quality of sleep on the incidence of cardiovascular disease. The aim of this study was to investigate the possible independent association of late bedtime and premature coronary artery disease (PCAD). Methods: Between October 2016 and November 2019, we conducted a cross-sectional population-based study on 30 101 participants aged 20--65 years in Khuzestan Comprehensive Health Study (KCHS). Data on major risk factors of cardiovascular disease, habit history, physical activity, and sleep behavior was gathered and participants underwent blood pressure, anthropometric, and serum lipid and glucose profile measurements. PCAD was defined as documented history of developing obstructive coronary artery disease before 45 years in men and before 55 years in women. Results: Of a total of 30 101 participants (64.1% female, mean age: 41.7 ± 11.7 years) included in this study, 1602 (5.3%, 95% confidence interval: 5.1%--5.6%) had PCAD. Late bedtime was reported in 7613 participants (25.3%, 95% confidence interval: 24.9%--25.8%). Age-sex standardized prevalence for PCAD and late bedtime were 3.62 (3.43-3.82) and 27.8 (27.2--28.4), respectively. There was no significant difference (P = 0.558) regarding prevalence of PCAD between those with late bedtime (5.5%, 95% CI: 4.9%--6.0%) and those with early bedtime (5.3%, 95% CI: 5.0%--5.6%). However, after adjustment for potential confounders, late bedtime was independently associated with PCAD (OR = 1.136, 95% CI = 1.002--1.288, P = 0.046). Conclusion: In this study, late bedtime was significantly associated with presence of PCAD. Future prospective studies should elucidate the exact role of late bedtime in developing coronary atherosclerosis prematurely. [ABSTRACT FROM AUTHOR]

Background: In 2017, the American College of Cardiology/American Heart Association (ACC/AHA) provided a new guideline for hypertension prevention and management. We aimed to update the prevalence, awareness, control, and determinants of hypertension based on this guideline in Khuzestan province, southwest of Iran, and to estimate the number of people who are eligible for non-pharmacologic and pharmacologic intervention. Methods: This population-based cross-sectional study was conducted in Khuzestan, a large province in the southwest of Iran. Comprehensive information about the potential relating factors of hypertension was collected, blood pressure was measured, and anthropometric measurements were obtained. Moreover, the dietary pattern was evaluated in 2830 individuals, using a qualitative food frequency questionnaire. Results: Among 30,506 participants, 30,424 individuals aged 20–65 years were eligible for the study. In comparison with the previous guideline released by the Joint National Committee (JNC8), the prevalence of hypertension in Khuzestan dramatically increased from 15.81 to 42.85% after implementation of the ACC/AHA guideline, which was more dominant in the male population and the 45–54 age group. The sex and age adjustment of the hypertension prevalence was estimated to be 39.40%. The percentage of hypertension awareness, treatment, and control were 45.85%, 35.42%, and 59.63%, which dropped to 22.72%, 26.37%, and 28.94% after implementation of new guideline, respectively. Conclusions: In the ACC/AHA guideline, a higher number of individuals with the pre-hypertension condition were shifted into the hypertension category and the level of awareness, treatment, and control were dramatically decreased, which highlight a great need to expand the public health infrastructure for further managing the substantial increased burden on healthcare system. However, further studies with population over 65 years are required to estimate the eligibility for antihypertensive treatment in this province after implementation of new guideline. [ABSTRACT FROM AUTHOR]

[Display omitted] • Proposes a novel deep learning model that utilizes both 1DCNN and RNN variants. • Uses the attention mechanism to improve the performance of the 1DCNN-RNN variant. • Uses raw acceleration time-series type of sequential data as the input data. • Compares the proposed model with equivalent dl -based model architectures. • Moreover, examines the environmental variable which affects structural response. In recent years, by emerging deep learning (DL) based algorithms, researchers have been exploring dl -based models to identify structural damage through data-driven approaches. dl -based data-driven techniques using autonomous feature extraction from raw sequential data are more robust under environmental variations. Extraction of robust features while considering the sequential dependencies will significantly improve the accuracy of damage identification by these techniques. In this regard, various architectures of dl -based models have been proposed. This study presents a novel dl -based model that utilizes both one-dimensional convolutional neural network (1DCNN) and recurrent neural network (RNN) variants using an attention mechanism. Attention mechanism improves the performance of the 1DCNN-RNN variants model precisely when its input data is raw acceleration time-history, as a kind of sequential data. The IASC-ASCE phase II and Qatar University grandstand simulator benchmarks are used to evaluate the proposed model by comparing its performance with dl -based neural network architectures that could be equivalent to this combination. Moreover, the environmental variable which affects structural response is also examined. Results demonstrate that the CNN-ATT-biGRU model architecture has the best accuracy and appropriate training time and model size among nine compared architectures. [ABSTRACT FROM AUTHOR]

This work presents an experimental and a numerical studies on the effect of the phenomenon of porosity on the mechanical properties of Glass Fiber Reinforced Polymer (GFRP). In a first part, material elaboration, as well as its characterization using a three-point bending test to extract the basic mechanical properties of the material, is considered. In a second part, a finite element model is created to simulate the problem of air bubbles broadly. Several cases of different shapes and sizes are simulated. The results show a significant effect on the reduction of load in both tensile and bending cases as the size of the bubbles increases. Furthermore, the second part includes the application of the Artificial Neural Network-Enhanced Jaya Algorithm (ANN-E JAYA) to predict the reduction of the tensile load as a function of different crack lengths obtained from an Extended Finite Element Method (XFEM) model. Next, to verify the accuracy of provided application , a comparison is made with two other applications such as Artificial Neural Network-Jaya Algorithm (ANN-JAYA) and Artificial Neural Network-Particle Swarm Optimization (ANN-PSO). The results of the three algorithms show good convergence, with a slight increase in accuracy for ANN-E JAYA. MATLAB code and data used in this article can be found at https://github.com/Samir-Khatir/GFRP-ANN-E-JAYA.git. [ABSTRACT FROM AUTHOR]

This work presents an experimental and a numerical studies on the effect of the phenomenon of porosity on the mechanical properties of Glass Fiber Reinforced Polymer (GFRP). In a first part, material elaboration, as well as its characterization using a three-point bending test to extract the basic mechanical properties of the material, is considered. In a second part, a finite element model is created to simulate the problem of air bubbles broadly. Several cases of different shapes and sizes are simulated. The results show a significant effect on the reduction of load in both tensile and bending cases as the size of the bubbles increases. Furthermore, the second part includes the application of the Artificial Neural Network-Enhanced Jaya Algorithm (ANN-E JAYA) to predict the reduction of the tensile load as a function of different crack lengths obtained from an Extended Finite Element Method (XFEM) model. Next, to verify the accuracy of provided application , a comparison is made with two other applications such as Artificial Neural Network-Jaya Algorithm (ANN-JAYA) and Artificial Neural Network-Particle Swarm Optimization (ANN-PSO). The results of the three algorithms show good convergence, with a slight increase in accuracy for ANN-E JAYA. MATLAB code and data used in this article can be found at https://github.com/Samir-Khatir/GFRP-ANN-E-JAYA.git. [ABSTRACT FROM AUTHOR]

A correction to the article "Impact of 2017 ACC/AHA Guideline on Prevalence, Awareness, Treatment, Control and Determinants of Hypertension: A Population-Based Cross-Sectional Study in Southwest of Iran" by Fatemeh Sadeghi, Bahman Cheraghian, Zahra Mohammadi et al. which appeared in a 2021 issue is presented.