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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: Previous research has shown that attentional bias toward game-related stimuli is a significant factor in the etiology, maintenance, and severity of internet gaming disorder (IGD). Therefore, interventions targeting attentional bias toward game-related stimuli can potentially ameliorate this disorder. The present research aims to examine the effectiveness of online Attentional Bias Modification (ABM) training in reducing gamerelated attentional bias and the severity of IGD in adolescents. Methods: In this quasi-experimental study, 33 adolescents with a DSM-5 diagnosis of IGD were sampled and randomly assigned to an experimental group (n=17) and a control group (n=16). The experimental group received online ABM, while no intervention was delivered to the control group. Attentional bias and IGD severity in these two groups were measured at pretest and posttest phases and then at 2 months follow-up via a modified Stroop test and internet gaming disorder-20 (IGD-20) questionnaire. Two-way repeated measures analysis of variance and Fisher's Least Significant Difference (LSD) were implemented to analyze the data using SPSS software, version 26. Results: Game-related attentional bias and the severity of IGD were significantly decreased in the participants of the experimental group (P<0.05). The reductions were also maintained at the 2-month follow-up, whereas such reductions were not evident in the control group at any stage (P>0.05). Conclusion: Given our findings, it can be concluded that online ABM can be an auxiliary or standalone treatment for adolescents with IGD; further research is necessary to understand its mechanisms of effect. [ABSTRACT FROM AUTHOR]

Summary: Although the effectiveness of discrete wavelet transform (DWT) for analyzing time‐varying measurements has been studied for mechanical and civil engineering applications, a few fundamental questions still need to be answered, such as how to choose an optimal decomposition level (DL) for a specific task. Selection of the optimal DL still remains a question that has not been adequately explored and not thoroughly investigated. Vast majority of wavelet based methods in mechanical and civil engineering applications have been associated with nonstationary measurements and do not offer detail information on how DL should be selected or linked to useful features that can identify anomalies in a structure. This study proposes a new detailed framework for choosing the optimal DL to guarantee an effective wavelet analysis for time‐varying structural responses. The approach proposed in this paper uses various wavelets and theoretical levels to decompose the signal and to identify those aspects that interact to affect the DL, such as data characteristics, frequency band features, noise reduction, similarity, sharing information, and reconstruction quality. To show the effectiveness of our proposed method, we considered a comparative study using various mother wavelets on El Centro earthquake and acceleration responses from shaking table results of physical experiment. Experimental results show that the optimal DL for El Centro earthquake is 4 and for the acceleration data is 6. These results obviously demonstrate the stability and the robustness of the proposed method in the analysis of contaminated time‐varying signals. [ABSTRACT FROM AUTHOR]

Implementation of efficient vibration control schemes for seismically excited structures is becoming more and more important in recent years. In this study, the influence of different control schemes on the dynamic performance of a frame structure excited by El Centro wave, with an emphasis on reaching law based control strategies, is examined. Reaching law refers to the reachable problem and criteria for the sliding state of a control system. Three reaching laws are designed to present different sliding mode control strategies by incorporating a state space model that describes structural dynamic characteristics of a frame structure. Both intact and damaged structures are studied by using the aforementioned control strategies. The influence of different structural damage extents, control locations and reaching law based control methods are further investigated. The results show that the structure can be well controlled using the sliding mode strategy when the induced structural damage extent does not exceed the standard percentage for considering the structure was damaged, which is 20% reduction in structure stiffness, as reported in the literature. The control effectiveness is more satisfactory if the control location is the same as the direction of external excitation. Furthermore, to study the chattering phenomenon of the sliding mode control method, approximation and detail components extracted from the phase plots of the sliding mode control system are compared via wavelet transform at different scales. The results show that for the same type of control law, the system behaves with similar chattering phenomenon. [ABSTRACT FROM AUTHOR]

Hysteresis is a natural phenomenon that widely exists in structural and mechanical systems. The characteristics of structural hysteretic behaviors are complicated. Therefore, numerous methods have been developed to describe hysteresis. In this paper, a review of the available hysteretic modeling methods is carried out. Such methods are divided into: a) model-driven and b) data-driven methods. The model-driven method uses parameter identification to determine parameters. Three types of parametric models are introduced including polynomial models, differential based models, and operator based models. Four algorithms as least mean square error algorithm, Kalman filter algorithm, metaheuristic algorithms, and Bayesian estimation are presented to realize parameter identification. The data-driven method utilizes universal mathematical models to describe hysteretic behavior. Regression model, artificial neural network, least square support vector machine, and deep learning are introduced in turn as the classical data-driven methods. Model-data driven hybrid methods are also discussed to make up for the shortcomings of the two methods. Based on a multi-dimensional evaluation, the existing problems and open challenges of different hysteresis modeling methods are discussed. Some possible research directions about hysteresis description are given in the final section. [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]

Introduction: Cardiovascular diseases (CVDs) are the leading cause of death in the world. Many modifiable risk factors have been reported to synergistically act in the development of CVDs. We aimed to compare the predictive power of anthropometric indices, as well as to provide the best cut-off point for these indicators in a large population of Iranian people for the prediction of CVDs and CVD risk factors. Methods: All the data used in the present study were obtained from Khuzestan comprehensive health study (KCHS). Anthropometric indices, including BMI (body mass index), WC (waist circumference), HC (hip circumference), WHR (waist-to-hip ratio), WHtR (waistto-height ratio), ABSI (a body shape index), as well as CVD risk factors [dyslipidaemia, abnormal blood pressure (BP), and hyperglycaemia] were recorded among 30 429 participants. Results: WHtR had the highest adjusted odds ratios amongst anthropometric indices for all the risk factors and CVDs. WC had the highest predictive power for dyslipidaemia and hyperglycaemia [area under the curve (AUC) = 0.622, 0.563; specificity 61%, 59%; sensitivity 69%, 60%; cut off point 87.95, 92.95 cm, respectively], while WHtR had the highest discriminatory power for abnormal BP (AUC = 0.585; specificity 60%; sensitivity 65%; cut-off point 0.575) and WHR tended to be the best predictor of CVDs (AUC = 0.527; specificity 58%; sensitivity 64%; cut-off point 0.915). Conclusion: In this study, we depicted a picture of the Iranian population in terms of anthropometric measurement and its association with CVD risk factors and CVDs. Different anthropometric indices showed different predictive power for CVD risk factors in the Iranian population. [ABSTRACT FROM AUTHOR]

Introduction: Irritable bowel syndrome (IBS) is a functional disease with no exact laboratory or imaging findings. IBS is more common in areas with a history of psychological trauma and war. This study aims to report the prevalence and possible determinants of IBS in southwestern Iran, an area with a notable history of war. Methods: We randomly enrolled 1849 permanent residents in 29 cities aged 20 to 65 years. A validated for Farsi version Rome III criteria and a questionnaire, including demographic data and health history, were administered to each subject. Participants who fulfilled the Rome III criteria were categorized into three groups: Diarrhea dominant (IBS-D), Constipation dominant (IBS-C), and Mixed type (IBS-M). Results: The total prevalence of IBS was 3.2%, with 70% of subjects being of Arab descent (P = 0.004). IBS was more common in females, singles, illiterate subjects, and people younger than 30 years; however, none of these differences were statistically significant. People with depression, anxiety, self-report of psychological disorders, and very low socioeconomic status had a significantly higher prevalence of IBS (P < 0.05). After multivariable logistic regression analysis, very low socioeconomic status had an independent role in IBS predictivity (OR: 2.28, 95% CI: 1.01–5.15). Conclusion: This study shows a higher prevalence of IBS symptoms in a population-based study in the region compared to counterparts in other regions of Iran. Considering the higher prevalence of self-reported psychological disorders, further studies are recommended to focus on the exact diagnosis of mental disorders and their influence on IBS. [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]

Computational data of a complex problem could be mapped and solved by artificial intelligence (AI) for postprocessing purposes and numerical time-saving. A fuzzy adaptive network as a hybrid AI system has already been used for CFD data machine learning. However, no investigation has been done for the ant colony optimisation (ACO) performance evaluation compared to the adaptive network. As a state-of-the-art method, the ACO and fuzzy system are combined and tuned to estimate the flow in a reactor. The liquid-phase velocity in the y-direction is predicted using a combination of fuzzy system and ACO in a large domain. The findings further indicate that, in addition to the CFD dataset and Navier-Stokes’s equations, employing an ant colony optimisation structure as a training technique may give an accurate approximation of two-phase flow. Furthermore, for numerical methods, a continuous link between input and output datasets may be a significant data source for the AI mechanism to train the physical process, incredibly bubbly flow. The ant colony optimisation shows a more accurate prediction than the adaptive network. As far as authors know, there are no such investigations to develop a new AI algorithm and performance analysis for fluid dynamics problems. [ABSTRACT FROM AUTHOR]

Introduction: 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. Conclusion: 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]

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]

Objective: After the COVID-19 outbreak, corona anxiety has become prevalent all over the world. To understand and treat this type of anxiety, researchers have examined its relationship with attentional bias, a phenomenon closely associated with other types of anxiety. The dot-probe task is a common instrument used for the evaluation of attentional bias. However, the psychometric properties of this instrument, when used for the assessment of attentional bias towards coronarelated stimuli, are unknown. This study aimed to evaluate the psychometric properties of the COVID-19 dot-probe task to see whether its application in COVID-19 studies is justified. Methods: A total of 362 Iranian adults completed the COVID-19 dot-probe task and Corona Anxiety Disease Scale (CADS), 146 of whom repeated this procedure after two weeks to provide test-retest data. Split-half reliability, the Cronbach a, intraclass correlation coefficient of testretest scores, and associations between COVID-19 dot-probe task and CADS were calculated using SPSS v. 26. Results: The study results indicated that the standard version of the COVID-19 dot-probe task lacks internal consistency, test-retest reliability, and criterion validity, whereas the response-based version of the instrument promotes all of these psychometric properties to an acceptable level. Conclusion: COVID-19 dot-probe task is a psychometrically sound instrument for evaluating corona-related attentional bias and investigating its role in the mechanism of corona anxiety, only if the response-based method of computation is used for calculating the measures of attentional bias. [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]

[Display omitted] • The enhanced design of an array of the piezoelectric harvester was introduced. • (α) manage the problem of lowering the power between the resonance frequencies of the array. • The working bandwidth broadened by a factor of 26 compared to the conventional design. • The output power increased by a factor of 31 compared to the conventional design. • The effect of harvester parameters on the broadband was investigated. Converting the wasted ambient vibration to electric power, using a piezoelectric energy harvester, is a promising strategy for powering a sensor network for medical and industrial applications. The designed harvester must satisfy the wideband natural frequency of ambient vibration. Conventional array harvester generates peak power at the cantilever's resonance frequencies and low power between them. This challenge can be managed by controlling the inclination angle of the tip masses (α). It is observed that the range of α from 45 ° to 53.3 ° generates an output power of 2 - 3 m W in the frequency range of 1 9 to 29 H z. For the second mode shape, the output power has been measured to be 9 m W in the frequency range from 290 to 330 Hz. Comparing the proposed design with conventional designs, the working bandwidth broadband is increased by a factor of 26, and the output power is increased by a factor of 31. The proposed approach is modeled using finite element analysis and analytical methods. The FEM model is validated by experimental results. [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]

• Investigated uncertainties in FE model updating from measurement and modeling errors. • Employed information entropy measure criterion for optimum sensor placement in system identification. • Presented a novel nonlinear FE model updating framework using EKF and UKF. • Investigated uncertainties in nonlinear FE model updating framework. • Used information entropy measure criterion to determine optimal response utilization in FE model updating. In this work, we developed a framework for identifying frame-type structures regarding the measurement uncertainty and the uncertainty involved in inherent and structural parameters. The identification process is illustrated and examined on a one-eight-scale four-story moment-resisting steel frame under seismic excitation using two well-known recursive schemes: the Extended Kalman filter (EKF) and Unscented Kalman Filter (UKF) methods. The nonlinear system equations were assessed by applying a first-order instantaneous linearization approach through the EKF method. In contrast, the UKF algorithm employs several sample points to estimate moments of random variables' nonlinear transformations. A nonlinear transformation is applied to distribute sample points to derive the precise mean and covariance up to the second order of any nonlinearity. Accordingly, it is theoretically expected that the UKF algorithm is more capable of identifying the nonlinear systems and determining the unknown parameters than the EKF algorithm. The capability of the EKF and UKF algorithms was assessed by considering a 4-story moment-resisting steel frame with several inherent uncertainties, including the material behavior model, boundary conditions, and constraints. In addition to these uncertainties, the combination of acceleration and displacement responses of different structural levels is employed to evaluate the capability of the algorithms. The information entropy measure is used to investigate further the uncertainty of a group of established model parameters. As highlighted, a good agreement is observed between the results using the information entropy measure criterion and those using the UKF and EKF algorithms. The results illustrate that using the responses of fewer levels placed in the proper positions may lead to improved outcomes than those of more improperly positioned levels. [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 novel hybrid strategy (HJDEA) based on Jaya and differential evolution algorithm is proposed. • A noise insensitive objective function is established based on adjacent acceleration correlation function. • An attractive combination of HJDEA and gradient search method is proposed to further improve performance. • Alterations of both stiffness and mass parameters are considered in the damage model. • Studies on shear-type frame, cantilever beam and ASCE benchmark structure are conducted to verify the effectiveness of the proposed methods. To solve the optimization-based structural damage identification problem, a novel hybrid algorithm based on Jaya and differential evolution algorithm (HJDEA) is proposed to detect, locate and quantify structural damages by effectively incorporating the powerful local exploitation capacity of Jaya algorithm and global exploration capability of differential evolution. Meanwhile, Hammersley sequence initialization and Lévy flight search mechanism are introduced into HJDEA to further improve convergence rate and refining the quality of the best solution. Four different algorithms, genetic algorithm, particle swarm optimization, Jaya and the proposed HJDEA are employed for comparative study. In addition, the objective function is established by adjacent acceleration correlation function so as to avoid false identification caused by defining improper reference point. The performance of the proposed damage identification strategy based on HJDEA and adjacent acceleration correlation function is investigated with numerical examples involving an 8-DOF lumped mass model and a cantilever beam, as well as an experimental study of the ASCE benchmark structure under white noise excitation. Results show that the proposed hybrid identification method is accurate, efficient and robust in the identification of the damage existence, location and severity of stiffness and mass parameters even with partial output-only responses and 20% noise-polluted measurements. [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.