The Role of Xanthan Gum in Predicting Durability Properties of Self-Compacting Concrete (SCC) in Mix Designs

This study comprehensively investigates the rheological properties of self-compacting concrete (SCC) and their impact on critical parameters, including the migration coefficient, penetration depth of chlorine ions, specific electrical resistance, and compressive strength. A total of 43 mix designs were meticulously examined to explore the relationships between these properties. Quantitative analysis employed a backpropagation neural network model with a single hidden layer to accurately predict the resistant and durable characteristics of self-compacting concrete. The optimal number of neurons in the hidden layer was determined using a fitting component selection method, implemented in MATLAB software(2021b). Additionally, qualitative analysis was conducted using sensitivity analysis and expert opinions to determine the priority of research additives. The main contributions of this paper lie in the exploration of SCC properties, the utilization of a neural network model for accurate prediction, and the prioritization of research additives through sensitivity analysis. The neural network model demonstrated exceptional performance in predicting test results, achieving a high accuracy rate using 14 neurons for predicting parameters such as chlorine penetration depth, compressive strength, migration coefficient, and specific electrical resistance. Sensitivity analysis revealed that xanthan gum emerged as the most influential additive, accounting for 43% of the observed effects, followed by nanomaterials at 35% and micro-silica at 21%

Dual Target Optimization of Two-Dimensional Truss Using Cost Efficiency and Structural Reliability Sufficiency

The main contribution of this study is to open a discussion regarding the structural optimization associated with the cost efficiency and structural reliability sufficiency consideration. To do so, several various optimization approaches are investigated to deliberate both cost and reliability concerns. Particularly, particle swarm optimization is highlighted as a reliable optimization approach. Accordingly, an illustrative example is rendered to compare the feasibility of the considered optimization approaches. The feasibility of the investigated approaches is evaluated using the cost and reliability analysis. For the considered example, it was observed that the PSO optimization algorithm has multiple advantages such as easy realization, fast convergence, and promising performance in nonlinear performance optimization. The PSO optimization algorithm can be successfully applied in various fields of civil engineering. This popularity is due to the understandable performance of the PSO as well as its simplicity. In this paper, first, the literature on the subject has been described by two-dimensional truss analysis using the finite element method and optimized using the PSO particle swarm algorithm. A comparison of the results with this reference indicates the accuracy of this particle swarm algorithm in truss optimization. Indeed, this study ignites two main insights in structural optimizations assessment. The first illustration is related to how to establish a framework for structural system reliability analysis associated with the different degrees of indeterminacies. And the second illustration is related to making a decision problem concerning the structural optimization while both cost and reliability metric are two main parameters for the construction point of the view

Classification of Seismic Vulnerability Based on Machine Learning Techniques for RC Frames

Seismic vulnerability means the inability of historical and monumental buildings to withstand the effects of seismic forces. This article presents a classification model to specify the damage state of the Reinforced Concrete (RC) frames based on a collection of datasets from the damaged buildings in Bingol earthquake of Turkey for use in the learning process of the algorithm. The proposed model uses two classifiers including the redundancy and also the construction quality of the buildings to estimate the class of damage from four categories including none, light, moderate and severe. The available database of the considered earthquake includes the information of 27 damaged RC buildings which are published in the literature. The model provided a simple structure for engineers to predict the class without complex calculations in which it needs a few steps to determine the class of damage for RC frames. The results show that the presented model can estimate the class of each input vector with an acceptable error

Reliability-based analysis of horizontal curve design by evaluating the impact of vehicle automation on roadway departure crashes and safety performance

Roadway departure (RwD) crashes are significant safety concerns, especially at horizontal curves. The design of these curves plays a crucial role in mitigating RwD crashes. Thus, a thorough understanding of the interaction between driver behavior, vehicle automation, and geometric design is vital. Substantive safety, which emphasizes the inherent safety in a road\u27s design and function, serves as the foundation of our approach. Building on this, the study employs a safe system approach to investigate the performance of horizontal curves under both non-automated and partially automated conditions, using a reliability-based analysis focusing on Stopping Sight Distance as the primary driver demand. Factors including Perception-Brake Time and Take-Over Time for automated vehicles are examined. The analysis covers horizontal curves, characterized by their geometric design and crash data. Our findings highlight a shift in the performance of horizontal curves under automation, emphasizing the need to consider automation in roadway design within the safe system approach. This study demonstrates how a reliability-based analysis can guide designers in making informed decisions regarding the geometric design of horizontal curves to reduce RwD crashes. To enhance transportation safety in the era of increasing automation, ongoing exploration of the relationships between driver behavior, automation, and road design is indispensable

Quantification and Reduction of Uncertainty in Seismic Resilience Assessment for a Roadway Network

The nation’s transportation systems are complex and are some of the highest valued and largest public assets in the United States. As a result of repeated natural hazards and their significant impact on transportation functionality and the socioeconomic health of communities, transportation resilience has gained increasing attention in recent years. Previous studies on transportation resilience have heavily emphasized network functionality during and/or following a scenario hazard event by implicitly assuming that sufficient knowledge of structural capacity and environmental/service conditions is available at the time of an extreme event. However, such assumptions often fail to consider uncertainties that arise when an extreme hazard event occurs in the future. Thus, it is essential to quantify and reduce uncertainties to better prepare for extreme events and accurately assess transportation resilience. To this end, this paper proposes a dynamic Bayesian network-based resilience assessment model for a large-scale roadway network that can explicitly quantify uncertainties in all phases of the assessment and investigate the role of inspection and monitoring programs in uncertainty reduction. Specifically, the significance of data reliability is investigated through a sensitivity analysis, where various sets of data having different reliabilities are used in updating system resilience. To evaluate the effectiveness of the model, a benchmark problem involving a highway network in South Carolina, USA is utilized, showcasing the systematic quantification and reduction of uncertainties in the proposed model. The benchmark problem result shows that incorporating monitoring and inspection data on important variables could improve the accuracy of predicting the seismic resilience of the network. It also suggests the need to consider equipment reliability when designing monitoring and inspection programs. With the recent development of a wide range of monitoring and inspection techniques, including nondestructive testing, health monitoring equipment, satellite imagery, LiDAR, etc., these findings can be useful in assisting transportation managers in identifying necessary equipment reliability levels and prioritizing inspection and monitoring efforts

System reliability analysis of the scoliosis disorder.

BACKGROUND: Scoliosis is a spine abnormal deviation, which is an idiopathic disorder among children and adolescents. As a matter of the fact, distribution of loads on the patient\u27s spine and load-carrying capacity of the vertebral column are both random variables. Therefore, the probabilistic approach may consider as a sophisticated method to deal with this problem. METHOD: Reliability analysis is a probabilistic-based approach to consider the uncertainties of load and resistance of the vertebral column. The main contribution of this paper is to compare the reliability level of a normal and scoliosis spinal. To do so, the numerical analyses associated with the inherent random parameters of bones and applied load are performed. Then, the reliability indices for all vertebrae and discs are determined. Accordingly, as the main innovation of this paper, the system reliability indices of the spinal column for both normal and damaged backbone systems are represented. RESULTS: Based on the required reliability index for normal spinal curvature the target system reliability level for scoliosis disorder is proposed. CONCLUSION: Since the proposed target reliability index is based on the strength limit state of the vertebral column, it can be considered as a reliability level for any proposed treatment approaches

Effect of nanoclay contents on properties, of bagasse flour/reprocessed high density polyethylene/nanoclay composites

The effect of nanoclay contents on the physical and mechanical properties of bagasse flour/ reprocessed high density polyethylene (rHDPE)/ nanoclay composites was investigated. The bagasse flour content was constant at 50%, the maleic anhydride content was constant at 3%, and the nanoclay (Cloisite 30B) content was set at three different levels: 0%, 2%, and 4%. The materials were mixed in a co-rotating twin-screw extruder; afterwards, the specimens were fabricated using an injection molding method. The water absorption and mechanical properties, such as flexural and tensile strength, flexural and tensile modulus, and notched impact strength, were measured. The nanoclay dispersion was examined by X-ray diffraction. The results indicated that tensile and flexural modulus increased with an increase in nanoclay content. Also By increasing the nanoclay content at 2 wt.%, the tensile and flexural strengths of the composite were increased. However, the addition of 4 wt.% nanoclay resulted in reductions of these properties. Water absorption decreased with increasing nanoclay content. The structural examination of the bagasse polymer composite with X-ray diffraction showed that the nanoclay was distributed as an intercalated structure in the polymer matrix, and the d-spacing of layers decreased with increasing nanoclay content. Scanning electron microscopy (SEM) showed that 2% nanoclay samples with lower and more uniform pores compared at 4% nanoclay samples, respectively

The prevalence of sleep disturbances among physicians and nurses facing the COVID-19 patients: a systematic review and meta-analysis

Abstract: Background: In all epidemics, healthcare staff are at the centre of risks and damages caused by pathogens. Today, nurses and physicians are faced with unprecedented work pressures in the face of the COVID-19 pandemic, resulting in several psychological disorders such as stress, anxiety and sleep disturbances. The aim of this study is to investigate the prevalence of sleep disturbances in hospital nurses and physicians facing the COVID-19 patients. Method: A systematic review and metanalysis was conducted in accordance with the PRISMA criteria. The PubMed, Scopus, Science direct, Web of science, CINHAL, Medline, and Google Scholar databases were searched with no lower time-limt and until 24 June 2020. The heterogeneity of the studies was measured using I2 test and the publication bias was assessed by the Egger’s test at the significance level of 0.05. Results: The I2 test was used to evaluate the heterogeneity of the selected studies, based on the results of I2 test, the prevalence of sleep disturbances in nurses and physicians is I2: 97.4% and I2: 97.3% respectively. After following the systematic review processes, 7 cross-sectional studies were selected for meta-analysis. Six studies with the sample size of 3745 nurses were examined in and the prevalence of sleep disturbances was approximated to be 34.8% (95% CI: 24.8-46.4%). The prevalence of sleep disturbances in physicians was also measured in 5 studies with the sample size of 2123 physicians. According to the results, the prevalence of sleep disturbances in physicians caring for the COVID-19 patients was reported to be 41.6% (95% CI: 27.7-57%). Conclusion: Healthcare workers, as the front line of the fight against COVID-19, are more vulnerable to the harmful effects of this disease than other groups in society. Increasing workplace stress increases sleep disturbances in the medical staff, especially nurses and physicians. In other words, increased stress due to the exposure to COVID-19 increases the prevalence of sleep disturbances in nurses and physicians. Therefore, it is important for health policymakers to provide solutions and interventions to reduce the workplace stress and pressures on medical staff

Global prevalence of polypharmacy among the COVID-19 patients : A comprehensive systematic review and meta-analysis of observational studies

BACKGROUND: Polypharmacy has traditionally been defined in various texts as the use of 5 or more chronic drugs, the use of inappropriate drugs, or drugs that are not clinically authorized. The aim of this study was to evaluate the prevalence of polypharmacy among the COVID-19 patients, and the side effects, by systematic review and meta-analysis. METHODS: This study was performed by systematic review method and in accordance with PRISMA 2020 criteria. The protocol in this work is registered in PROSPERO (CRD42021281552). Particular databases and repositories have been searched to identify and select relevant studies. The quality of articles was assessed based on the Newcastle-Ottawa Scale checklist. Heterogeneity of the studies was measured using the I 2 test. RESULTS: The results of meta-analysis showed that the prevalence of polypharmacy in 14 studies with a sample size of 189,870 patients with COVID-19 is 34.6% (95% CI: 29.6-40). Studies have shown that polypharmacy is associated with side effects, increased morbidity and mortality among patients with COVID-19. The results of meta-regression analysis reported that with increasing age of COVID-19 patients, the prevalence of polypharmacy increases (p < 0.05). DISCUSSION: The most important strength of this study is the updated search to June 2022 and the use of all databases to increase the accuracy and sensitivity of the study. The most important limitation of this study is the lack of proper definition of polypharmacy in some studies and not mentioning the number of drugs used for patients in these studies. CONCLUSION: Polypharmacy is seen in many patients with COVID-19. Since there is no definitive cure for COVID-19, the multiplicity of drugs used to treat this disease can affect the severity of the disease and its side effects as a result of drug interactions. This highlights the importance of controlling and managing prescription drugs for patients with COVID-19

Identification of suitable drug combinations for treating COVID-19 using a novel machine learning approach: The RAIN method

COVID-19 affects several human genes, each with its own p-value. The combination of drugs associated with these genes with small p-values may lead to an estimation of the combined p-value between COVID-19 and some drug combinations, thereby increasing the effectiveness of these combinations in defeating the disease. Based on human genes, we introduced a new machine learning method that offers an effective drug combination with low combined p-values between them and COVID-19. This study follows an improved approach to systematic reviews, called the Systematic Review and Artificial Intelligence Network Meta-Analysis (RAIN), registered within PROSPERO (CRD42021256797), in which, the PRISMA criterion is still considered. Drugs used in the treatment of COVID-19 were searched in the databases of ScienceDirect, Web of Science (WoS), ProQuest, Embase, Medline (PubMed), and Scopus. In addition, using artificial intelligence and the measurement of the p-value between human genes affected by COVID-19 and drugs that have been suggested by clinical experts, and reported within the identified research papers, suitable drug combinations are proposed for the treatment of COVID-19. During the systematic review process, 39 studies were selected. Our analysis shows that most of the reported drugs, such as azithromycin and hydroxyl-chloroquine on their own, do not have much of an effect on the recovery of COVID-19 patients. Based on the result of the new artificial intelligence, on the other hand, at a significance level of less than 0.05, the combination of the two drugs therapeutic corticosteroid + camostat with a significance level of 0.02, remdesivir + azithromycin with a significance level of 0.03, and interleukin 1 receptor antagonist protein + camostat with a significance level 0.02 are considered far more effective for the treatment of COVID-19 and are therefore recommended. Additionally, at a significance level of less than 0.01, the combination of interleukin 1 receptor antagonist protein + camostat + azithromycin + tocilizumab + oseltamivir with a significance level of 0.006, and the combination of interleukin 1 receptor antagonist protein + camostat + chloroquine + favipiravir + tocilizumab7 with corticosteroid + camostat + oseltamivir + remdesivir + tocilizumab at a significant level of 0.009 are effective in the treatment of patients with COVID-19 and are also recommended. The results of this study provide sets of effective drug combinations for the treatment of patients with COVID-19. In addition, the new artificial intelligence used in the RAIN method could provide a forward-looking approach to clinical trial studies, which could also be used effectively in the treatment of diseases such as cancer