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Abstract In the context of civil engineering applications, vibration responses are complex, exhibiting variations in time and space and often containing nonlinearity and uncertainties not considered during data collection. These responses can also be contaminated by various sources, impacting damage identification processes. A significant challenge is how to effectively remove noise from these data to obtain reliable damage indicators that are unresponsive to noise and environmental factors.This study proposes a new denoising algorithm based on discrete wavelet transform (DWT) that addresses this issue. The suggested method offers a strategy for denoising using distinct thresholds for positive and negative coefficient values at each band and applying denoising process to both detail and trend components. The results prove the effectiveness of the technique and show that Bayes thresholding performs better than the other techniques in terms of the evaluated metrics. This suggests that Bayes thresholding is a more accurate and robust technique for thresholding compared to other common techniques.

Total ankle arthroplasty (TAA) is a motion-preserving treatment for end-stage ankle arthritis. An effective tool for analyzing these implants’ mechanical performance and longevity in silico is finite element analysis (FEA). An FEA in ABAQUS was used to statically analyze the mechanical behavior of the ultra-high-molecular-weight polyethylene (UHMWPE) bearing component at varying dorsiflexion/plantarflexion ankle angles and axial loading conditions during the stance phase of the gait cycle for a single cycle. The von Mises stress and contact pressure were examined on the articulating surface of the bearing component in two newly installed fixed-bearing TAA implants (Wright Medical INBONE II and Exactech Vantage). Six different FEA models of variable ankle compressive load levels and ankle angle positions, for the varying subphases of the stance phase of the gait cycle, were created. The components in these models were constrained to be conducive to the bone–implant interface, where implant loosening occurs. Our results showed that the von Mises stress and contact pressure distributions increased as the compressive load increased. The highest stress was noted at dorsiflexion angles > 15°, in areas where the UHMWPE liner was thinnest, at the edges of the talar and UHMWPE components, and during the terminal stance phase of the gait cycle. This static structural analysis highlighted these failure regions are susceptible to yielding and wear and indicated stress magnitudes that are in agreement (within 25%) with those in previous static structural TAA FEAs. The mechanical wear of the UHMWPE bearing component in TAA can lead to aseptic loosening and peri-implant cyst formation over time, requiring surgical revision. This study provides ankle replacement manufacturers and orthopedic surgeons with a better understanding of the stress response and contact pressure sustained by TAA implants, which is critical to optimizing implant longevity and improving patient care.

Owing to the low measuring range of traditional fiber Bragg grating (FBG) sensors and the complexity, imprecision, and lack of practicality in existing large-strain sensors, this paper introduces a novel type of large-strain sensor based on pre-relaxation and continuous sensing technology. This design aims to realize the whole-process strain monitoring of prestressed fiber reinforced polymer (FRP) plates. Static tensile tests were conducted on 9 large-strain sensor specimens. The effects of pre-relaxation degree, section number, pre-tension time, and prestressing level were evaluated. The results reveal that, the pre-relaxed sensor, proved its efficacy in capturing the strain pertinent to the post-tensioned operational state of the FRP plate, and a sensing performance of up to 18,923 με can be facilitated and further extended by a pre-relaxation and continuous sensing technique. Moreover, it is recommended that during the fabrication of the large-strain sensor, two pre-tensions be applied, with a prestressing level between 115% and 125% of the pre-relaxation degree.

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.

Structural damage identification based on structural health monitoring (SHM) data and machine learning (ML) is currently a rapidly developing research area in structural engineering. Traditional machine learning techniques rely heavily on feature extraction, where weak feature extraction can lead to suboptimal features and poor classification performance. In contrast, ML-based methods, particularly deep learning approaches like convolutional neural networks (CNNs), automatically extract relevant features from raw data, improving the accuracy and adaptability of the damage identification process. This study developed a time–frequency-based data-driven approach aiming to improve the effectiveness of traditional data-driven structural damage identification approaches for large complex structures. Firstly, the structural acceleration signals in the time domain were converted into two-dimensional images via the Gram angle difference field (GADF). Subsequently, the characteristic feature in the image data was studied by convolutional neural networks (CNNs) to predict the structural damage conditions. An experimental study on a scale model of a cable-stayed bridge was conducted to identify the damage of stay cables under the moving vehicle load on the main girders. The CNN was employed to extract the characteristic features from the time-varying monitoring data of vehicle–bridge interactions. The CNN parameters were optimized to conduct the structural damage classification task. The performance of the proposed method was evaluated by comparing it with various traditional pre-trained networks. The effect of environmental noise on the prediction accuracy was also investigated. The numerical results show that the ResNet model has the best performance in terms of damage identification accuracy and convergence speed, achieving higher accuracy and faster convergence compared to the other four traditional networks. The method can accurately identify damage on bridges using insufficient sensors on the bridge deck, which has valuable potential for application to real-world bridges with monitoring data. As the Signal-to-Noise Ratio (SNR) decreases from 20 dB to 2.5 dB, the prediction accuracy of ResNet decreases from 86.63% to 62.5%, which demonstrates the robustness and reliability in identifying structural damage.

Quick and accurate structural damage detection is essential for maintaining the safety and integrity of infrastructure, especially following natural disasters. Traditional methods of damage assessment, which rely on manual inspections, can be labor-intensive and subject to human error. This paper introduces a hybrid deep learning model that combines the capabilities of ResNet50 and GoogLeNet, further enhanced by a convolutional block attention module (CBAM), proposed to improve both the accuracy and performance in detecting structural damage. For training purposes, a diverse dataset of images depicting both structural damage cases and undamaged cases was used. To further enhance the robustness, data augmentation techniques were also employed. In this research, precision, recall, F1-score, and accuracy were employed to evaluate the effectiveness of the introduced hybrid deep learning model. Our findings indicate that the hybrid deep neural network introduced in this study significantly outperformed standalone architectures such as ResNet50 and GoogLeNet, making it a highly effective solution for applications in disaster response and infrastructure maintenance.

This review article provides a detailed overview of the advanced applications of piezoelectric material in electrochemical processes. Energy harvesting, a potential topic, aims to transform ambient energy sources, including heat, light, and mechanical motion, into useful energy that would otherwise be wasted. This review focuses on an innovative and developing field that intends to connect-directly energy-harvesting devices and materials to the electrochemical systems. To regulate electrochemical processes in presence of piezoelectric materials, electrochemical reactions are dominantly influenced by produced electric charge under mechanical stress. In detail, the applications of piezoelectric material electrochemistry involved for dye degradation, hydrogen production, self-charging power cells, nanogenerator and water splitting is discussed under the umbrella of piezocatalysis. A very focused data is presented for piezo electrochemical hybrid systems from the future perspective and the potential applications for current enhancement induced by internal piezoelectric effect of the systems are also highlighted.

Abstract 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.

This paper highlights the advancement in wind energy harvesting using piezoelectric materials to produce sustainable power generation. It is a highly encouraging, fascinating, and challenging method to capture energy from piezoelectric materials. The purpose of this research is to evaluate the principal characteristic groups that affect energy harvesting performance and to provide recommendations for further improvement. Piezoelectric energy harvesters (PEH) can provide electricity for low-power electronic devices, which additionally possess the potential to boost self-powered, autonomous devices. The objective of this article is to provide recommendations for wind energy harvester modeling techniques. After establishing the fundamental idea of Piezoelectric Wind Energy Harvesters (PWEHs), it is next examined how well these devices function structurally and where their research stands in relation to various phenomena, including vortex-induced vibration, flutter, and galloping. A cantilever beam connected with a tip body is the typical component of a galloping piezoelectric harvester for wind energy collection. Wind energy has been turned into mechanical vibrations and ultimately into electrical power via the flutter phenomena. Fluttering-based wind energy harvesters are a new technology that provides an effective replacement for conventional wind turbines. The future development trend for PWEHs has been anticipated. The most current developments in strategies and approaches for wind energy harvesting using piezoelectric materials are also discussed in this paper. First, this paper highlights various piezoelectric energy harvesting materials, then it shows various wind energy harvesters’ design. After that this paper displays various types of wind energy harvesters and their applications. Finally, it highlights some challenges, future development, and recommendations.

This paper reviews self-powered medical devices integrated with advanced energy harvesting technologies. This article aims to explain the advantages of integrating self-powered medical devices with advanced energy harvesting technologies, outlining the transformation in healthcare system and patient experience. In today's world, we focus more on consuming energy harnessed from the environment and human body. This approach lowers down our emphasis on conventional power sources like batteries, power packs etc. As a result, the devices used in the medical sector have a longer lifespan, maintain continuous functioning, and improve patient comfort and mobility. Integrating advanced energy harvesting technologies (i.e., piezoelectric, thermal, solar, and electromagnetic) with medical devices plays a pivotal role in revolutionizing the healthcare sector. But there is still some research and development needed to enhance these technologies. This paper will set out by introducing some self-powered medical devices commonly used in healthcare, followed by their advantages, benefits, and challenges that the healthcare practitioners face. This review also discusses their biocompatibility factor which is crucial to use. Then there are examples of a few advanced energy harvesting methods that are being used which include: piezoelectric, solar, thermal, triboelectric and electromagnetic. As we go further, we will come across a table consisting of a comparison between these advanced energy harvesting technologies and their examples in the healthcare sector. The last section is future perspective and the conclusion highlights the transformative potential of this integration, followed by a future recommendation for advancing this field.

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 AttitudesTest) 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.

This paper provides a comprehensive review of the recent progress made in energy harvesting systems for wearable technology. An energy-harvesting system would be a useful strategy to address the issue of powering wearable electronic devices. This review presents different wearable energy harvesting methods based on the human body's heat and mechanical energy. To achieve continuous operation and high performance, reduce the requirement for external sources of energy, and enhance the lifespan of wearable devices, the invention of a sustainable and compatible power supply is required. In the human body, heat and mechanical motions are the two reliable and readily available energy sources. This study highlights the most recent research and advancements in energy harvesting from the human's mechanical motion and heat source. This article provides a detailed overview of the different wearable energy harvesters, their fabrication, working, and output results, which include piezoelectric, electrostatic, triboelectric, electromagnetic, thermoelectric, solar and hybrid wearable energy harvesters. The second part defines wearable energy harvesting using smart systems and artificial intelligence technology. Then the comparison of these energy harvesters is analyzed. Hybrid wearable energy harvesters provide the maximum power densities because they use two combined energy conversions. The advantages, limitations, and future perspectives of wearable energy harvesting technology are also discussed. Lastly, the wearable energy harvesters' market, and general developing and manufacturing cost of each wearable device is also presented functioning as a point of reference to comprehend the cost factors that are taken into account during the development and manufacturing processes.

In this study, we investigate the use of natural additives (biopolymers) resistant to scouring. To this end, three natural substances, Kathira, sodium alginate, and guar gum, have been utilized as additives resistant to scouring, and we examine their mechanical performance, resistance to scouring, and the properties of fresh concrete including slump test, setting time, and ultimately shrinkage test. For this purpose, a total of 12 cylindrical specimens with dimensions of 15 by 30 cm were prepared for 28-day compressive strength test, and 12 cylindrical specimens with dimensions of 15 by 30 cm were prepared for 28-day indirect tensile strength test. Additionally, 12 concrete beams with dimensions of 10 by 10 by 35 cm were fabricated for a 28-day flexural strength test. All laboratory specimens were submerged in lime-saturated water for hydration for a period of 28 days for maintenance and preservation. The results indicate that all three biopolymers improve resistance to scouring, and, two substances enhance compressive, tensile, and flexural strength. Furthermore, all of them lead to a reduction in concrete shrinkage.

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.

This paper provides a short review of sustainable hybrid energy harvesting and its applications. The potential usage of self-powered wireless sensor (WSN) systems has recently drawn a lot of attention to sustainable energy harvesting. The objective of this research is to determine the potential of hybrid energy harvesters to help single energy harvesters overcome their energy deficiency problems. The major findings of the study demonstrate how hybrid energy harvesting, which integrates various energy conversion technologies, may increase power outputs, and improve space utilization efficiency. Hybrid energy harvesting involves collecting energy from multiple sources and converting it into electrical energy using various transduction mechanisms. By properly integrating different energy conversion technologies, hybridization can significantly increase power outputs and improve space utilization efficiency. Here, we present a review of recent progress in hybrid energy-harvesting systems for sustainable green energy harvesting and their applications in different fields. This paper starts with an introduction to hybrid energy harvesting, showing different hybrid energy harvester configurations, i.e., the integration of piezoelectric and electromagnetic energy harvesters; the integration of piezoelectric and triboelectric energy harvesters; the integration of piezoelectric, triboelectric, and electromagnetic energy harvesters; and others. The output performance of common hybrid systems that are reported in the literature is also outlined in this review. Afterwards, various potential applications of hybrid energy harvesting are discussed, showing the practical attainability of the technology. Finally, this paper concludes by making recommendations for future research to overcome the difficulties in developing hybrid energy harvesters. The recommendations revolve around improving energy conversion efficiency, developing advanced integration techniques, and investigating new hybrid configurations. Overall, this study offers insightful information on sustainable hybrid energy harvesting together with quantitative information, numerical findings, and useful research recommendations that progress and promote the use of this technology.

Background: Air pollution in large cities is one of the main difficulties that have harmful effects on humans and cause various diseases including cancers. So, present study with aim of determination the relation between air pollution and Incidence rate of two most common cancer in Tehran were performed. Methods: at first, hourly data of pollutants including of CO, NO2, O3, SO2, PM10 were taken from the environmental protection agency Tehran and Air Quality Control Company and Archived documents of Disease data were acquired from Iran University of Medical Sciences .Then, descriptive studies were performed on criteria air pollutants and dependent variable . Finally, inferential tests such as Pearson correlation coefficient and linear regression were used to determine the relationship between air pollution and the incidence rate of Lung and Hematologic Neoplasms. Results: Results showed there was significant relationship between PM10/O3 and among men and women. The correlation of coefficients between PM10 and O3 with Number of cases of disease in men and women were 0.904, 0.882, 0.924, 0.931, 0.856, 0.949, 0.976, 0.860 respectively. Conclusion: According to the results, there were positive relationship between PM10/O3 and incidence rate of Lung and Hematologic Neoplasms among men and women. Due to the high level of ozone and airborne particles and their health consequences, Continuous and further prevention and control measures should be developed to alleviate the situation of the two air pollutants.

Introduction: Occlusion stability following orthodontic treatments is a daunting challenge for orthodontists and this stability is maintained using retainer devices after the active phase of treatments. Although Hawley retainer has a wide application in retention objectives, the use of Essix retainers has been on a rise in recent years. The present study aimed to compare occlusal settling in orthodontic patients after using Essix and Hawley retainers using the T-Scan III technique for bite recordings in the retention period.Materials and Methods: In this observational-analytical trial, 46 orthodontic patients (24 for Hawley and 22 for Essix retainers) were selected at the end of the active phase of the treatment. The patients used Hawley and Essix retainers at both jaws for 1 year, and the occlusal settling (occlusal contacts) was recorded by the T-Scan III technique at the initial time (T0), as well as 6 months (T1) and 1 year (T2) after retention. The intragroup and intergroup changes (percent) that occurred in occlusal contacts in different time intervals were statistically analyzed by paired t-test and student t-test, respectively.Results: In the Hawley retainer group, the occlusal contacts significantly increased in the posterior teeth (7.708±13.5; P=0.008) and significantly decreased in the anterior teeth (7.416±11.791; P=0.005). On the other hand, in the Essix retainer group, occlusal contacts decreased in the posterior teeth (10.19±14.884; P=0.008) and significantly increased in the anterior teeth (10.381±18.591; P=0.005). Conclusion: AS evidenced by the obtained results, both Hawley and Essix retainers caused changes in the occlusal contacts and relative vertical movement of posterior teeth in the patients after 6 months and 1 year of retention, compared to the initial time. Nonetheless, Hawley retainer seemed to ring about more posterior changes (settling) than Essix retainer. Due to the increase in posterior tooth contact and the decrease in anterior tooth contact with the use of Holly retainer, this retainer can be a better choice in deep bite cases. Furthermore, due to the reduction of posterior contacts in the first 6 months in the Essix license plate, it is suggested that the full-time use of this retainer be shortened.

Background and Aim: Attention Control Training is a new cognitive intervention that improves emotion regulation by reducing attentional bias towards negative information. Given the significant association between death anxiety and emotion dysregulation and death-related attentional bias, this study aimed to assess attention control training as an intervention for reducing death anxiety. Methods & Materials: In this clinical trial, the Convenience Sampling Method sampled 50 university students in Tehran and was randomly assigned to 2 experimental and 1 control groups. Attention control training was delivered using death-related stimuli for the 1st experimental group and negative stimuli unrelated to death for the 2nd experimental group. The control group didn’t receive any interventions. In 3 measurements of pretest, posttest, and 3 months follow-up, death-related attentional bias, death anxiety, and sensitivity to mortality salience were assessed, respectively, using the Modified Stroop Test, Multidimensional Fear of Death Scale (MFODS), and Propensity to Moral Disengagement Scale (PMDS) after inducing Mortality Salience condition. Ethical Considerations: This study was registered at the Iranian Registry of Clinical Trials (Code: IRCT20190406043181N1) and was approved by the research ethics committee of Shahid Beheshti University of Medical Sciences (Code: IR.SBMU.MSP.REC.1398.539). Results: The study showed that after completing attention control training, the 1st experimental group’s death-related attentional bias, death anxiety, and sensitivity to mortality salience were significantly reduced (P0.05). Conclusion: Based on the results of this study, it can be concluded that attention control training is capable of significantly reducing death-related attentional bias, and this reduction in attentional bias leads to decreased death anxiety and sensitivity to mortality salience

Bridge vortex-induced vibration (VIV) refers to the vertical resonance phenomenon that occurs in a bridge when pulsating wind passes over it and causes vortices to detach. In recent years, VIV events have been observed in numerous long-span bridges, leading to fatigue damage to the bridge structure and posing risks to driving safety. The advancement of technologies such as structural health monitoring (SHM), machine learning, and big data has opened up new research avenues for the intelligent identification of VIV in bridges. Machine learning algorithms can accurately identify the VIV events from historical data accumulated by SHM systems, thus providing an effective method for VIV recognition. Nevertheless, the existing identification methods have limitations, particularly in their applicability to bridges lacking historical VIV data. This study introduces an adaptive VIV recognition method in the main girders of long-span suspension bridges based on Transfer Component Analysis (TCA). The method can accurately identify VIV patterns in real-time or in historical data, even when specific VIV data are not available for the target bridge. The proposed method exhibits suitability for multiple long-span bridges. Experimental validation is performed using the SHM datasets from two long-span suspension bridges. The results show that the proposed VIV identification method can recognize more VIV samples compared to the benchmark model. When using sensor 1 data of bridge B as the source domain to identify the VIV of the L-section of bridge A, the F1 score of the TCA-based method is 0.836, while the F1 score of the benchmark model is 0.165. In the other 11 cases, the F1 score of the proposed model is higher than 0.8, which demonstrates the method’s robust generalization capabilities.

Background: With the increase in recent droughts and shortage of drinking water resources led to planning to desalinate water and set up mobile stations. The purpose of this study is to determine the microbial and physicochemical quality of water discharged from desalination plants and tankers in Qom. Methods: This descriptive cross-sectional study was conducted for one year with monthly sampling of water from domestic water purifiers and mobile tankers. Samples were analyzed for microbial contamination and physicochemical quality (pH, TDS, turbidity and chlorination) according to standard methods. The data were analyzed using SPSS and Excel software and compared with the standards. Results: Positive values of fecal coliform contamination was 13.34% in tankers and 4.35% in domestic water purifiers. The pH, TDS, turbidity was in accordance with the national and WHO standards. T-TEST statistical analysis showed, microbial quality of water had a significant relation with variables of dewatering containers (P

Abstract Background Chronic kidney disease (CKD) is a growing global health problem with faster progression in developing countries such as Iran. Here we aimed to evaluate the prevalence and determinants of CKD stage III+. Methods This research is part of the Khuzestan Comprehensive Health Study (KCHS), a large observational population-based cross-sectional study in which 30,041 participants aged 20 to 65 were enrolled. CKD was determined with estimated glomerular filtration rate (eGFR) less than 60 ml/min/1.73m2, based on two equations of Modification of Diet in Renal Disease (MDRD) and Chronic Kidney Disease Epidemiology Collaboration (CKD-EPI). The multivariate logistic regression was used to evaluate the CKD stage III+ determinants. Results Prevalence of CKD stage III+ is estimated to be 7.1, 5.5, and 5.4% based on MDRD, CKD-EPI, and combination of both equations, respectively. More than 89% of CKD subjects aged higher than 40 years. In regression analysis, age more than 40 years had the strongest association with CKD stage III+ probability (OR: 8.23, 95% CI: 6.91–9.18). Higher wealth score, hypertension, High-Density Lipoprotein levels less than 40 mg/dl, and higher waist to hip ratio were all associated with CKD stage III+ while Arab ethnicity showed a protective effect (OR: 0.69, 95% CI: 0.57–0.78). Conclusion Our findings provide detailed information on the CKD stage III+ and its determinants in the southwest region of Iran. Due to strong association between age and CKD stage III+, within a few decades we might expect a huge rise in the CKD prevalence.

Abstract 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.

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.

Background: Diabetes mellitus is a widespread disease disrupting cognitive function. We investigated the effect of eight-week high-intensity interval training (HIIT) and caffeine supplementation on Brain-derived neurotrophic factor (BDNF) and glial line-derived neurotrophic factor (GDNF) in a rat diabetic model. Methods: In this experimental study, streptozotocin-induced diabetic rats were randomly divided into: control (C), diabetic (D), diabetic+caffeine (D+CA), diabetic+training (D+T) and diabetic+training+caffeine (D+T+CA) groups. Training groups underwent a high-intensity interval training program (5 sessions a week over 8 weeks). The supplement groups were administered with 7mg caffeine/100gr body weight for 5 days a week before each exercise session throughout the experimental period. The rat hippocampus and brainstem were removed 48 h after the last training session and blood samples were taken from left ventricle. The levels of glucose, BDNF and GDNF were measured by ELISA assay. Data were analyzed using two-way ANOVA test. Results: Streptozotocin-induced diabetes increased blood glucose (p 0.05). We also observed a significant interaction between treatments regarding GDNF changes (P=0.024); yet, the interaction between caffeine and HIIT on BDNF did not reach the significance level (P=0.074). Conclusion: Based on the findings, HIIT increased BDNF and GDNF levels in rat diabetic model, but caffeine ingestion had no significant effect on neurotrophic factors. However, caffeine seems to blunt HIIT-induced increase in neurotrophic factors which remains to be further investigated.

Abstract Background Self-care behaviors and positive changes in lifestyle are essential for successful hypertension control. We used a behavioral model based on the theory of planned behavior to assess which factors influence self-care behaviors for controlling hypertension. Methods In this cross-sectional study, five hundred patients with at leastaone-year history of diagnosed hypertension participated in this study. The data collection tool was designed based on the theory of planned behavior. Structural equation modeling was used to estimate the main parameters. Results For self-care behaviors, ninety-six (19.2%) and forty-five (9.1%) participants had good knowledge and acceptable behavior(≥8 out of 10 points). Having perceived behavioral control regarding quitting smoking and alcohol intake was associated with the patient’s intention and behavior [b:1.283 ± .095 and b:1.59 ± .014 (p

The study discussed the question of succession of two conditions in Arabic grammar, namely that two conditions exist on one answer in the pronunciation, and the answer mentioned for the first condition, and the answer to the second condition is deleted is indicated by the first condition and its answer, or if the answer to the second condition is dispensed with the answer of the first condition, then no An answer to the second originally, The researcher presented the position of the ancients regarding the succession of the two conditions, especially in those concerned with the interpretation of the Qur’an and its significance, and then moved to clarify the position of the modern scholars, and suggested what was most likely from their views, and concluded the results that the conclusion guaranteed.تناول البحث بالدراسة مسألة توالي شرطين في النحو العربي، وهي أن يتوارد شرطان على جواب واحد في اللفظ ويكون الجواب المذكور للشرط الأول ، وجواب الشرط الثاني محذوف مدلول عليه بالشرط الأول وجوابه ، أو أن يكون جواب الشرط الثاني مستغنى عنه بجواب الشرط الأول ، فلا جواب للثاني أصلا، وقد عرض الباحث موقف القدماء من توالي الشرطين ولا سيما في الذي عُنوا بتفسير القرآن وبيان دلالاته، ومن ثم انتقل لبيان موقف العلماء المحدثون، ورجّح ما كان راجحا من آرائهم، وخلص إلى نتائج تكفلت الخاتمة ببيانها.

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%).

Energy harvesting from piezoelectric materials is quite common and has been studied for the past few decades. But recently, there have been a lot of new advancements in harnessing energy via piezoelectric materials. In this regard, several studies were carried out in analytical chemistry. This paper provides a detailed review of different piezoelectric materials, their structures, their fabrication processes, and their applications in analytical chemistry. Detection of the various gases percentage in ambient air is a valuable analytical chemistry technique. Additionally, the benefits of using piezoelectric materials, i.e., crystal for gas and liquid chromatography, virus detection including COVID-19 virus detection, water determination, trace metal analysis and the ability to measure micro weights with quartz crystal with some other applications are also described in this review. Energy harvesting is incredibly important and must be implemented on a large scale. So, developing self-powering devices can resolve the problems, and piezoelectric materials are gaining interest day by day because these materials help in energy generation.

Total ankle replacement (TAR) is the replacement of a damaged arthritic ankle joint with a prosthetic implant to eliminate the source of resultant pain and swelling. Historically, however, the ankle joint has been one of the most difficult joints to analyze and replicate for successful replacement due to its complex anatomy and multiplanar motion. Ankle fusion, the standard of care for end stage ankle arthritis, has excellent functional outcomes but results in loss of motion at the joint. TAR was first attempted in the 1970s and by the early 1990s, prosthesis designs more closely mirrored the natural anatomy of the ankle and TAR was no longer considered an experimental procedure. Although the outcomes of TAR have significantly improved over this short period of time, there are still many areas that warrant further investigation including (1) optimal patient selection, (2) number of prosthesis components, (3) bearing type, (4) revision rates and causes and (5) comprehensive finite element models (FEM) of the ankle. The main goal of this paper is to present a literature review on the background and evolution of TAR, the current state of practice and prosthesis types and challenges and directions for future improvement.

Hysteresis is a non−unique phenomenon known as a multi−valued mapping in different fields of science and engineering. Accurate identification of the hysteretic systems is a crucial step in hysteresis compensation and control. This study proposes a novel approach for simulating hysteresis with various features that combines the extreme learning machine (ELM) and least−squares support vector machine (LS−SVM). First, the hysteresis is converted into a single−valued mapping by deteriorating stop operators, a combination of stop and play hysteresis operators. Then, the converted mapping is learned by a LS−SVM model. This approach facilitates the training steps and provides more accurate results in contrast to the previous experimental studies. The proposed model is evaluated for several hystereses with various properties. These properties include rate−independent or rate−dependent, congruent or non-congruent, and symmetric or asymmetric problems. The results indicate the efficiency of the newly developed technique in terms of accuracy, computational cost, and convergence rate.

Intelligent and resilient infrastructure and smart cities make up a rapidly emerging field that is redefining the future of urban development and ways of preserving the existing infrastructure against natural hazards...

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.

Pisha sandstone is a special kind of soft rock in the semiarid region along the up and middle reaches of Yellow River and Loess Plateau and is severely eroded by wind, rainfall and gravity force. Pisha sandstone region is called “the most severe water loss and soil erosion in the world” and referred to as the “Earth Cancer” by local residents and experts both at home and abroad. The plan for the control of Pisha sandstone started in the 1950s. Since then a series of projects have been implemented, both engineering and vegetation measures have been developed and widely used to protect Pisha sandstone from erosion. Since seabuckthorn was introduced as a viable approach for soil erosion protection and its implementation resulted in a breakthrough to control Loess Plateau, it was considered as an important and efficient measure. Then the seabuckthorn flexible dam was proposed for coarse sediment retention and fine sediment deposition. And then a new comprehensive control model using consolidation materials combined with vegetation was proposed and promising results were achieved both in laboratory and field demonstrations. In the future, the complex erosion mechanism and ecological carrying capacity should be studied further and more efficient and practical comprehensive measures shall be developed. Moreover, monitoring systems shall be used to predict and detect the changes in the Pisha sandstone slope and structure. Keywords: Water loss and soil erosion, Land degradation, Ecological restoration, Seabuckthorn, Comprehensive measure

In this data article, natural (NL) and manganese oxide-modified LECA (MML) adsorbents were applied for adsorptive removal of Tetracycline (TC) from aqueous solution. The used adsorbents was characterized using fourier transform infrared (FTIR) spectroscopy, scanning electron microscopy (SEM), X-ray diffraction (XRD) and X-ray fluorescence spectroscopy (XRF). The chemical analysis of XRF data revealed increased chemical composition of Mn as MnO to 8.96 wt%. The SEM patterns were illustrated the extent of surface and enhanced porosity in MML with Mn. In optimum operational conditions, maximum removal percentage of TC was achieved at 51.5 and 99.4% using NL and MML, respectively. The maximum adsorption capacities obtained from Langmuir modeling were 6.89 and 9.24 for NL and MML, respectively. The modeling of the adsorption kinetics revealed that TC adsorption by both NL and MML adsorbents was best-fitted with a pseudo-first-order model (R2 = 0.978). The isotherm studies of TC adsorption by MML showed that the Freundlich isotherm was the most appropriate model, with a higher coefficient of determination. The obtained data was illustrated that high competitive capacity of chloride and hardness ions compared with other ions against TC adsorption. Keywords: Tetracycline, Light weight expanded clay aggregate, Adsorption, Manganese oxide, Aqueous solutions

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.

The phenomenon of hysteresis in engineering systems has been with us for ages and has been attracting the attention of many investigators for a long time [...]

A restoring-force model is a versatile mathematical model that can describe the relationship between the restoring force and the deformation obtained from a large number of experiments. Over the past few decades, a large body of work on the development of restoring-force models has been reported in the literature. Under high intensity cyclic loadings or seismic excitations, reinforced concrete (RC) structures undergo a wide range of hysteretic deteriorations such as strength, stiffness and pinching degradations. These characteristic behaviors can be described by the multi-parameter Bouc-Wen-Baber-Noori (BWBN) model, which offers a wide range of applicability. This model has been applied for the response prediction and modeling restoring-force behavior in structural and mechanical engineering systems, by adjusting the distribution range of this model’s parameters. However, a major difficulty in utilizing the multi-parameter BWBN model is the parameters’ identification. In this paper, a deep neural network model is used to estimate the hysteresis parameters of the BWBN model. This model is one of the most versatile and widely used general hysteresis models that can describe the hysteretic behavior of RC columns. The experimental data of the RC columns used in this paper are collected from the database of the Pacific Earthquake Engineering Research Center (PEER). Firstly, the hysteretic loop obtained from a physical experiment is described by the BWBN model, and the parameters of the BWBN model are identified via a genetic optimization algorithm. Then a neural network is established by a backpropagation (BP) algorithm for associating the identified BWBN model parameters with physical parameters of the RC column. Finally, the regression analysis of the identified parameters is carried out to obtain the regression characteristics of the RC columns. The trained neural network model can directly identify the parameters of BWBN model based on the physical parameters of RC columns, and is effective and computationally efficient for multi-parameter BWBN model identification. The proposed approach overcomes the difficult problem of identifying the parameters of BWBN model and provides a promising approach for a wider application of this multi-parameter hysteresis model.

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.

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.

Damage detection of civil and mechanical structures based on measured modal parameters using model updating schemes has received increasing attention in recent years. In this study, for uncertainty-oriented damage identification, a non-probabilistic structural damage identification (NSDI) technique based on an optimization algorithm and interval mathematics is proposed. In order to take into account the uncertainty quantification, the elastic modulus is described as unknown-but-bounded interval values and the proposed new scheme determines the upper and lower bounds of the damage index. In this method, the interval bounds can provide supports for structural health diagnosis under uncertain conditions by considering the uncertainties in the variables of optimization algorithm. The model updating scheme is subsequently used to predict the interval-bound of the Elemental Stiffness Parameter (ESP). The slime mold algorithm (SMA) is used as the main algorithm for model updating. In addition, in this study, an enhanced variant of SMA (ESMA) is developed, which removes unchanged variables after a defined number of iterations. The method is implemented on three well-known numerical examples in the domain of structural health monitoring under single damage and multi-damage scenarios with different degrees of uncertainty. The results show that the proposed NSDI methodology has reduced computation time, by at least 30%, in comparison with the probabilistic methods. Furthermore, ESMA has the capability to detect damaged elements with higher certainty and lower computation cost in comparison with the original SMA.

Magnetorheological elastomeric (MRE) material is a novel type of material that can adaptively change the rheological property rapidly, continuously, and reversibly when subjected to real-time external magnetic field. These new type of MRE materials can be developed by employing various schemes, for instance by mixing carbon nanotubes or acetone contents during the curing process which produces functionalized multiwall carbon nanotubes (MWCNTs). In order to study the mechanical and magnetic effects of this material, for potential application in seismic isolation, in this paper, different mathematical models of magnetorheological elastomers are analyzed and modified based on the reported studies on traditional magnetorheological elastomer. In this regard, a new feature identification method, via utilizing curvelet analysis, is proposed to make a multi-scale constituent analysis and subsequently a comparison between magnetorheological elastomer nanocomposite and traditional magnetorheological elastomers in a microscopic level. Furthermore, by using this “smart” material as the laminated core structure of an adaptive base isolation system, magnetic circuit analysis is numerically conducted for both complete and incomplete designs. Magnetic distribution of different laminated magnetorheological layers is discussed when the isolator is under compressive preloading and lateral shear loading. For a proof of concept study, a scaled building structure is established with the proposed isolation device. The dynamic performance of this isolated structure is analyzed by using a newly developed reaching law sliding mode control and Radial Basis Function (RBF) adaptive sliding mode control schemes. Transmissibility of the structural system is evaluated to assess its adaptability, controllability and nonlinearity. As the findings in this study show, it is promising that the structure can achieve its optimal and adaptive performance by designing an isolator with this adaptive material whose magnetic and mechanical properties are functionally enhanced as compared with traditional isolation devices. The adaptive control algorithm presented in this research can transiently suppress and protect the structure against non-stationary disturbances in the real time.

Magnesium/aluminum layered double hydroxide (LDH) nanoparticles were synthesized by hydrolyzing urea and used to remove metronidazole (MN) from aqueous solution. The surface morphology images of the LDH nanoparticles showed that the adsorbent surface consisted of hexagonal nanosheets with a diameter of 200–1000 nm. The MN removal efficiency was strongly dependent on the solution pH ranging from 3 to 9. The addition of nitrate, sulfate, and carbonate did not remarkably affect MN adsorption, while hardness slightly improved MN removal efficiency. The adsorption isotherm data could be well described using the Sips equation. The analysis of kinetic data showed that the adsorption of MN onto LDH closely followed the Avrami model and that several kinetic processes may control the rate of sorption. The adsorption process was non-spontaneous and exothermic in nature. The maximum Langmuir adsorption capacity was 62.804 mg/g, demonstrating that LDH is an efficient adsorbent that can be used for the removal of MN compounds.

Automatic crack identification for pipeline analysis utilizes three-dimensional (3D) image technology to improve the accuracy and reliability of crack identification. A new technique that integrates a deep learning algorithm and 3D shadow modeling (3D-SM) is proposed for the automatic identification of corrosion cracks in pipelines. Since the depth of a corrosion crack is below the surrounding area of the crack, a shadow of the crack is projected when the crack is exposed under light sources. In this study, we analyze the shadow areas of cracks through 3D shadow modeling (3D-SM) and identify the evolving cracks through the shape analysis of the shadows. To denoise the 3D images, the connected domain analysis is implemented so that the shadow groups of the evolving cracks can be retained and the scattered shadow groups that occur due to insignificant defects can be eliminated. Moreover, a novel deep neural network is developed to process the 3D images. The proposed automatic crack identification method successfully processes the 3D images efficiently and accurately diagnoses the corrosion cracks. Experimental results show that the proposed method achieves satisfactory performance with 93.53% accuracy and a 92.04% regression rate.

Background and Objectives: Population growth and limited access to fresh water and need to use bottled water for drinking, chemical and biological quality of the water exacerbates the need for review. The aim of this study was to evaluate chemical and microbial quality in multi-brand bottled water was consumed in the country. Materials and Methods: A descriptive cross-sectional study carried out in stages, the first brand of bottled water available 6 recognized and sampling was conducted. The chemical and bacteriological quality of water parameters include cL-, nitrate,Mg+2, Ca+2, hardness, Na+,No2-, pH , TDS, EC, F-, total coliforms and fecal coliforms in the samples were tested. The average value of each property, with standard and were compared with the amounts listed on the label. Data analysis using the software SPSS, descriptive statistics and analysis of variance (p

Background: Suitable budget allocation of organizational activities can deliver the resources distribution, enabling operational monitoring and providing expected access to the cost results. The aim of this study was to calculate the cost of final units in one of the Armed Forces Hospitals. Materials and Methods: One of the Armed Forces Hospitals selected in 2013.Data gathered from all cost centers to costs calculation and evaluated based on theory of Activity-Based Costing analysis. General wards and intensive care units, operating rooms, dental, emergency and dialysis were considered as final units. Cost calculation performed using Excel software version 2007. Total cost of hospital was 244,416 million rails in 2013 which direct and indirect allocated costs were 52% and 48%, respectively. The CCU, surgery ward, ICU and operating rooms had the highest and clinics and dental had the lowest in case of costs. More than 50 percent of the direct costs of all the units have been spent on manpower except the operating room. After labor costs, medication costs accounted for the largest percentage of direct spending units. Conclusion: Paying attention to human resources in terms of number and distribution of all units especially in the final units, also considering medications use particularly in middle units and consumables in overhead ones especially can be result in reduction and adjusting hospital costs.

Recent advancements in sensor technology have resulted in the collection of massive amounts of measured data from the structures that are being monitored. However, these data include inherent measurement errors that often cause the assessment of quantitative damage to be ill-conditioned. Attempts to incorporate a probabilistic method into a model have provided promising solutions to this problem by considering the uncertainties as random variables, mostly modeled with Gaussian probability distribution. However, the success of probabilistic methods is limited due the lack of adequate information required to obtain an unbiased probabilistic distribution of uncertainties. Moreover, the probabilistic surrogate models involve complicated and expensive computations, especially when generating output data. In this study, a non-probabilistic surrogate model based on wavelet weighted least squares support vector machine (WWLS-SVM) is proposed to address the problem of uncertainty in vibration-based damage detection. The input data for WWLS-SVM consists of selected wavelet packet decomposition (WPD) features of the structural response signals, and the output is the Young’s modulus of structural elements. This method calculates the changes in the lower and upper boundaries of Young’s modulus based on an interval analysis method. Considering the uncertainties in the input parameters, the surrogate model is used to predict this interval-bound output. The proposed approach is applied to detect simulated damage in the four-story benchmark structure of the IASC-ASCE SHM group. The results show that the performance of the proposed method is superior to that of the direct finite element model in the uncertainty-based damage detection of structures and requires less computational effort.

A cable-supported bridge is usually a key junction of a highway or a railway that demands a higher safety margin, especially when it is subjected to harsh environmental and complex loading conditions. In comparison to short-span girder bridges, long-span flexible structures have unique characteristics that increase the complexity of the structural mechanical behavior. Therefore, the system safety of cable-supported bridges is critical but difficult to evaluate. This study proposes a novel and intelligent approach for system reliability evaluation of cable-supported bridges under stochastic traffic load by utilizing deep belief networks (DBNs). The related mathematical models were derived taking into consideration the structural nonlinearities and high-order statically indeterminate characteristics. A computational framework is presented to illustrate the steps followed for system reliability evaluation using DBNs. In a case study, a prototype suspension bridge is selected to investigate the system reliability under stochastic traffic loading based on site-specific traffic monitoring data. The numerical results indicated that DBNs provide an accurate approximation for the mechanical behavior accounting for structural nonlinearities and different system behaviors, which can be treated as a meta-model to estimate the structural failure probability. The dominant failure modes of the suspension bridge are the fracture of suspenders followed by the bending failure of girders. The degradation of suspenders due to fatigue-corrosion damage has a significant effect on the system reliability of a suspension bridge. The numerical results provide a theoretical basis for the design on cable replacement strategies.

Structural control and health monitoring scheme play key roles not only in enhancing the safety and reliability of infrastructure systems when they are subjected to natural disasters, such as earthquakes, high winds, and sea waves, but it also optimally minimize the life cycle cost and maximize the whole performance through the full life cycle design. In this scheme, system identification is regarded as a major technique to identify system states and related parameter variables, thus preventing degradation of structural or mechanical systems when unexpected disturbances occur. In this paper, three different strategies are proposed to identify general hysteretic behavior of a typical shear structure subjected to external excitations. Different case studies are presented to analyze the dynamic responses of a time varying shear structural system with the early version of Bouc-Wen-Baber-Noori (BWBN) hysteresis model. By incorporating a “Gray Box” strategy utilizing an Intelligent Parameter Varying (IPV) and Artificial Neural Network (ANN) approach, a Genetic algorithm (GA), and a Transitional Markov Chain Monte Carlo (TMCMC) based Bayesian Updating framework system identification schemes are developed to identify the hysteretic behavior of the structural system. Hysteresis characteristics, computational accuracy, and algorithm efficiency are further discussed by evaluating the system identification results. Results show that IPV performs superior computational efficiency and system identification accuracy over GA and TMCMC approaches.