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As the Internet of Things (IoT) landscape expands, new devices with various functionalities are continuously being integrated into the IoT ecosystem. When traditional systems, which involve human interaction, are replaced by devices, it becomes crucial to upgrade the conventional authorization and authentication systems. This is essential to establish a new access control system designed to manage accessibility of multiple devices. Traditional device identification approaches often struggle to accommodate the dynamic behaviors exhibited by IoT devices. In response, this paper introduces an innovative approach that leverages enhanced behavioral features to generate a representation of device behavior. This representation is then employed to train machine learning models for classifying devices based on their behaviors. Furthermore, this paper also considers special scenarios where the access management system lacks access to full network traffic data. In such cases, device identification is achieved based on HTTPS features and user agent information. We conducted experimental analyses using real data from state-of-the-art IoT device profiling datasets. The performance results indicate that the extracted behavioral-based features have the capability to identify multiple IoT devices with various functionalities and vendors. [ABSTRACT FROM AUTHOR]

Introduction: Gadolinium-based T1-weighted MRI sequence is the gold standard for the detection of active multiple sclerosis (MS) lesions. The performance of machine learning (ML) and deep learning (DL) models in the classification of active and non-active MS lesions from the T2-weighted MRI images has been investigated in this study. Methods: 107 Features of 75 active and 100 non-active MS lesions were extracted by using SegmentEditor and Radiomics modules of 3D slicer software. Sixteen ML and one sequential DL models were created using the 5-fold cross-validation method and each model with its special optimized parameters trained using the training-validation datasets. Models' performances in test data set were evaluated by metric parameters of accuracy, precision, sensitivity, specificity, AUC, and F1 score. Results: The sequential DL model achieved the highest AUC of 95.60% on the test dataset, demonstrating its superior ability to distinguish between active and non-active plaques. Among traditional ML models, the Hybrid Gradient Boosting Classifier (HGBC) demonstrated a commendable test AUC of 86.75%, while the Gradient Boosting Classifier (GBC) excelled in cross-validation with an AUC of 87.92%. Conclusion: The performance of sixteen ML and one sequential DL models in the classification of active and non-active MS lesions was evaluated. The results of the study highlight the effectiveness of sequential DL approach and ensemble methods in achieving robust predictive performance, underscoring their potential applications in classifying MS plaques. [ABSTRACT FROM AUTHOR]

In the era of rapidly expanding graph-based applications, efficiently managing large-scale graphs has become a critical challenge. This paper introduces an innovative graph reduction technique, Node-Centric Pruning (NCP), designed to simplify complex graphs while preserving their essential structural properties, thereby enhancing the scalability and maintaining performance of downstream Graph Neural Networks (GNNs). Our proposed approach strategically prunes less significant nodes and refines the graph structure, ensuring that critical topological properties are maintained. By carefully evaluating node significance based on advanced connectivity metrics, our method preserves the topology and ensures high performance in downstream machine learning tasks. Extensive experimentation demonstrates that our proposed method not only maintains the integrity and functionality of the original graph but also significantly improves the computational efficiency and preserves the classification performance of GNNs. These enhancements in computational efficiency and resource management make our technique particularly valuable for deploying GNNs in real-world applications, where handling large, complex datasets effectively is crucial. This advancement represents a significant step toward making GNNs more practical and effective for a wide range of applications in both industry and academia. [ABSTRACT FROM AUTHOR]

Supply chain cybersecurity has become a critical concern for organizations due to the increasing frequency of cyber threats that endanger sensitive information, disrupt operations, and cause financial harm. This survey article presents the outcomes of a comprehensive study aimed at deepening our understanding of the challenges and best practices in supply chain cybersecurity. It provides a comprehensive review of critical applications that are susceptible to cyber threats across various sectors of the supply chain. The literature review identifies two distinct categories of approaches utilized to secure the supply chain: traditional and innovative methods. Both categories are extensively examined, providing valuable insights into the current state of supply chain cybersecurity. The findings of this study serve as a valuable resource for organizations seeking to enhance their cybersecurity strategies and fortify their resilience against evolving cyber threats. Furthermore, this research contributes to the knowledge base of supply chain management by facilitating the development of robust and efficient supply chain cybersecurity frameworks. By understanding vulnerabilities and best practices, organizations can proactively tackle cybersecurity challenges and safeguard their supply chains effectively. This survey article empowers organizations with practical insights and guidance to enhance their cybersecurity posture in the dynamic landscape of supply chain operations. [ABSTRACT FROM AUTHOR]

Persistent malware use techniques, such as obfuscation, process injection, and system call abuse to evade security mechanisms and avoid detection throughout their compromise. Malware analysis and memory forensics must have proper skill for fighting them. To show the limitation of current memory forensics, we introduce an adversarial technique to remove the forensics evidence required to identify malware, called parent process termination (PPT). PPT neither creates a new malware nor does it manipulate the features of a running process like malware obfuscation techniques, which abuse the parent–child relationship. In PPT, the malware process creates child processes for a malicious purpose and then terminates. This termination, letting the operating system (OS) reuses the parent process's resources and thus erases all trace of it, while leaving its children to perform anomalous activities. To show PPT's applicability in Windows OS, we run and analyze selected malware samples in a controlled environment. We implement PPT and show how this technique benefits from current memory forensics tools being unable to identify the exited processes. The forensics analysis proves behaviour of the PPT adversarial technique run in different malware executions. Our experiments show PPT successfully removes forensics evidence to identify the source of malicious activity. We hope these results can shed light on the future design of memory forensics tools and better-informed choices by users. [ABSTRACT FROM AUTHOR]

Background: Communication and mutual understanding among healthcare providers is a significant concern within the healthcare system, and enhancing empathy is one way to foster effective communication and mutual understanding. The aim of this research is to evaluate and compare the impact of story reading on the level of empathy in medical students at Mazandaran University of Medical Sciences. Methods: The study employed an intervention educational design (a quasi-experimental), with a convenience sample of 51 medical students selected as the statistical population. The process of story reading was conducted over six two-hour virtual sessions in the students' classroom, spanning six weeks. Selected stories were discussed in an online virtual class under the supervision of an instructor, focusing on story elements. To assess students' empathy in this educational program, the Davis General Empathy Questionnaire was administered before each of the six sessions, after, and one week later at the end of the course. Statistical analysis of the collected data was performed using repeated measures analysis of variance and Bonferroni's post hoc test through SPSS version 28 software, with a significance level set at 0.05. Results: The findings revealed that 27 participants (58.7%) were female students, with the remaining being male students, having an average age of 19.5 ± 0.86 years. The level of general empathy among the students significantly increased after the intervention compared to before the intervention (P<0.001). Furthermore, the analysis of variance with repeated measures indicated a significant effect of the story reading program on enhancing empathy in terms of emotional and cognitive transfer among students in the intervention group (P<0.001). Conclusions: The research findings suggest that the story reading program effectively enhances the overall sense of empathy among medical students at the University of Medical Sciences. Therefore, implementing this method in universities, higher education centers, libraries, and psychology centers for teaching empathy can be valuable in fostering empathy skills and improving healthcare. [ABSTRACT FROM AUTHOR]

The battle between malware analysts and malware authors is a never-ending challenge with the advent of complex malware such as polymorphic, metamorphic, and packed malware. A malware packer uses various techniques combined with file encryption to harden against reverse engineering of the program and hinder the analysis of program behaviors. In any case, substantial elements have emerged after more than a decade of continuous research in malware packer detection, such as multi-packing. Newly modified packers have this persistent problem, which demands new concepts and techniques. This study aims to provide a systematic and comprehensive review of run-time packers' mitigation techniques. We provide different types of packers and propose a malware packer handling life cycle for AV engines. Furthermore, we deliver a modern malware packers classification features set by examining the feature engineering in the packing handling life-cycle, such as feature extraction techniques in machine learning approaches. Also, we present extensive related works and discuss each work's benefits and weaknesses to address this problem, with a particular emphasis on packers identification techniques, to aid in unpacking malware. Finally, we identify the current gaps in knowledge and provide ideas about future work. [ABSTRACT FROM AUTHOR]

This research addresses a critical need in the ongoing battle against malware, particularly in the form of obfuscated malware, which presents a formidable challenge in the realm of cybersecurity. Developing effective antivirus (AV) solutions capable of combating packed malware remains a crucial endeavor. Packed malicious programs employ encryption and advanced techniques to obfuscate their payloads, rendering them elusive to AV scanners and security analysts. The introduced research presents an innovative malware packer classifier specifically designed to adeptly identify packer families and detect unknown packers in real-world scenarios. To fortify packer identification performance, we have curated a meticulously crafted dataset comprising precisely packed samples, enabling comprehensive training and validation. Our approach employs a sophisticated feature engineering methodology, encompassing multiple layers of analysis to extract salient features used as input to the classifier. The proposed packer identifier demonstrates remarkable accuracy in distinguishing between known and unknown packers, while also ensuring operational efficiency. The results reveal an impressive accuracy rate of 99.60% in identifying known packers and 91% accuracy in detecting unknown packers. This novel research not only significantly advances the field of malware detection but also equips both cybersecurity practitioners and AV engines with a robust tool to effectively counter the persistent threat of packed malware. [ABSTRACT FROM AUTHOR]

Self-Compacting Concrete (SCC) is a unique type of concrete that can flow and fill spaces without the need for vibrating compaction, resulting in a dense and uniform material. This article focuses on estimating the compressive strength of SCC utilizing Artificial Neural Networks. Specifically, the study employs multilayer perceptrons with back-propagation learning algorithms, which are commonly used in various problem-solving scenarios. The study covers essential components such as structure, algorithm, data preprocessing, over-fitting prevention, and sensitivity analysis in MLPs. The input variables considered in the research include water, fine aggregate, super-plasticizer, fly ash, coarse aggregate, ground granulated blast furnace slag, limestone powder, viscosity-modifying admixtures, cement, silica fume, and rice husk ash. The target variable is the compressive strength. Through a sensitivity analysis, the study evaluates the relative importance of each parameter. The results indicate that the AI-based model accurately predicts the compressive strength of self-compacting concrete. [ABSTRACT FROM AUTHOR]

Design and construction of offshore wind turbines (OWT) in near-fault sites is more in demand worldwide. However, very limited studies are available that assess the effects of near-field ground motions on the response of OWTs. This paper describes a numerical study on the response of OWTs to pulse-like near-field earthquakes in liquefiable soils by performing a series of advanced fully coupled nonlinear dynamic analyses using FLAC-3D. The simple anisotropic sand (SANISAND) constitutive model was adopted for soil to consider liquefaction. Two types of foundations commonly used for OWTs, namely the monopile and suction bucket were modeled. One of the characteristics of near-field ground motions is containing a significant vertical component, which is mostly in the range of high frequencies. Meanwhile, OWTs can be sensitive to vertical ground motion in terms of having a high natural frequency in the vertical direction. Therefore, focusing on the frequency content of input seismic motion, a comparison was made between the response of OWT structures to horizontal and vertical components of earthquakes in the far-field and near-field conditions. The mechanisms behind the obtained results were elaborated, and some suggestions were made for the design of OWTs under near-field conditions. According to the results, liquefaction and tilt are the major potential risks associated with the near-field earthquakes in the horizontal direction. However, in the vertical direction, the excessive amplification of acceleration and upthrow of the rotor-nacelle-assembly (RNA), are the major issues to be considered. Under near-field conditions, a suction bucket foundation can be a better option in terms of lower amplification factor of vertical acceleration, although its aspect ratio should be selected with special care to the rotation response. [ABSTRACT FROM AUTHOR]

Background: Neonatal resuscitation training in a simulated delivery room environment is a new paradigm in pediatric medical education. The purpose of this research is to highlight team-based simulation as an effective method of teaching neonatal resuscitation to senior pediatric residents. Methods: In an intervention educational study, we evaluated the impact of team-based simulation training in the development of neonatal resuscitation. A team consisting of a three-person group of senior pediatric residents performed neonatal resuscitation on a low-fidelity newborn simulator based on the stated scenario. Video-based structured debriefing was performed and followed by the second cycle of scenario and debriefing to evaluate the feasibility of conducting team-based simulation training in a lesser-resourced environment. Evaluation criteria included megacode scores which is a simulation performance checklist, pre-and post-test scores to evaluate residents' knowledge and confidence, the survey checklist as a previously developed questionnaire assessing residents' satisfaction, and debriefing from live and videotaped performances. Four months after the end of the training course, we measured the behavioral changes of the residents by conducting an OSCE test to evaluate post-training knowledge retention. Mean ± SD was calculated for megacode, satisfaction (survey checklist), and OSCE scores. Pre- and post-program gains were statistically compared. The first three levels of Kirkpatrick's training effectiveness model were used to evaluate the progress of the program. Results: Twenty-one senior residents participated in the team-based simulation. The mean ± SD of the megacode score was 35.6 ± 2.2. The mean ± SD of the overall satisfaction score for the evaluation of the first level of the Kirkpatrick model was 96.3 ± 3.7. For the evaluation of the second level of the Kirkpatrick model, the pre-posttest gain in overall confidence score had a statistically significant difference (P = 0.001). All residents obtained a passing grade in OSCE as an evaluation of the third level. Conclusions: Team-based simulation training in neonatal resuscitation improves the knowledge, skills, and performance of pediatric residents and has a positive effect on their self-confidence and leadership skills. There is still a need to investigate the transfer of learning and abilities to real-life practice, and further research on cost-effectiveness and impact on patient outcomes is warranted. [ABSTRACT FROM AUTHOR]

Compression index is an effective assessment of primary consolidation settlement of clayey soils, but the process of obtaining compression index is time-consuming and laborious. Thus, in the present study, we developed two classical tree-based techniques: random forest (RF) and extreme gradient boosting (XGBoost), to predict the compression index of clayey soils. To establish these two models, we collected an available dataset--including 391 consolidation tests for soils--from previously published research. The dataset consists of six physical parameters, including the initial void ratio, natural water content, liquid limit, plastic index, specific gravity, and soil compression index. The first five parameters are the models' inputs while the compression index is the models' output. We trained both two tree-based models using 90% of the entire dataset and used the remaining 10% to assess the well-trained models, which is consistent with the published research. Several statistical metrics, such as coefficient of determination (R2), root mean squared error (RMSE), mean absolute error (MAE), and mean absolute percentage error (MAPE), are the criteria for assessing the models' performance. The results show that the RF model has better accuracy in predicting compression index compared with the XGBoost model because it outperforms the XGBoost model both on the training and testing datasets. The performance of the RF model is R2 of 0.928 and 0.818, RMSE of 0.016 and 0.025, MAPE of 7.046% and 10.082%, and MAE of 0.012 and 0.020 on the training and testing datasets, respectively. The sensitivity analysis reveals that the initial void ratio has a significant impact on the compression index of clayey soils. [ABSTRACT FROM AUTHOR]

Nowadays, the Internet of Things (IoT) concept plays a pivotal role in society and brings new capabilities to different industries. The number of IoT solutions in areas such as transportation and healthcare is increasing and new services are under development. In the last decade, society has experienced a drastic increase in IoT connections. In fact, IoT connections will increase in the next few years across different areas. Conversely, several challenges still need to be faced to enable efficient and secure operations (e.g., interoperability, security, and standards). Furthermore, although efforts have been made to produce datasets composed of attacks against IoT devices, several possible attacks are not considered. Most existing efforts do not consider an extensive network topology with real IoT devices. The main goal of this research is to propose a novel and extensive IoT attack dataset to foster the development of security analytics applications in real IoT operations. To accomplish this, 33 attacks are executed in an IoT topology composed of 105 devices. These attacks are classified into seven categories, namely DDoS, DoS, Recon, Web-based, brute force, spoofing, and Mirai. Finally, all attacks are executed by malicious IoT devices targeting other IoT devices. The dataset is available on the CIC Dataset website. [ABSTRACT FROM AUTHOR]

Calcium carbonate-enriched marl is supposed to lose the bearing capacity while subjected to an increase/decrease in the moisture content and under the effect of multiple freeze–thaw (F-T) cycles. Investigating different weight percentages and curing periods, combination of ordinary Portland cement (OPC) and lignosulfonate is introduced as an efficient method to improve the mechanical strength of the marl soil under the effect of F-T cycles. In this regard, compaction, unconfined compressive strength, and direct shear tests were conducted. It was observed that although the addition of OPC (as the sole binder) can enhance the shear strength, the performance of samples under the action of F-T was not significantly improved. However, samples containing lignosulfonate showed a rectified behavior. Microstructural investigations exhibited the development of new intensity peaks for the calcium-aluminate-silicate-hydrate (C-A-S–H) products and elaborated a denser structure with a lower porosity, keeping the soil particles closer. Next, different intelligent approaches of machine learning (ML) were employed to provide cost-effective and accurate speedy tools. Among eight machine learning algorithms and advanced ensemble models, comparative study revealed the efficiency of gradient boosting model with coefficients of determinations of up to 98.5% for the prediction of UCS. Feature importance analysis suggested the duration of treatment and the cement content as the main contributing factors to the UCS. Highly accurate and efficient EPR-based models with coefficients of determination of higher than 99% were also proposed for the prediction of shear strength and shear stress parameters. [ABSTRACT FROM AUTHOR]

Cement-grouted injections are increasingly employed as a countermeasure material against liquefaction in active seismic areas; however, there is no methodology to thoroughly and directly evaluate the liquefaction potential of saturated sand materials reinforced by the cement grout-injected micropiles. To this end, first, a series of 1 g shaking table model tests are conducted. Time histories of pore water pressures, excess pore water pressure ratios (ru), and the number of required cycles (Npeak) to liquefy the soil are obtained and modified lower and upper boundaries are suggested for the potential of liquefaction of both pure and grout-reinforced sand. Next, adopting genetic programming and the least square method in the framework of the evolutionary polynomial regression technique, high-accuracy predictive equations are developed for the estimation of rumax. Based on the results of a three-dimensional, graphical, multiple-variable parametric (MVP) analysis, and introducing the concept of the critical, boundary inclination angle, the inclination of micropiles is shown to be more effective in view of liquefaction resistivity for loose sands. Due to a lower critical boundary inclination angle, the applicability range for inclining micropiles is narrower for the medium-dense sands. MVP analyses show that the effects of a decreasing spacing ratio on decreasing rumax are amplified while micropiles are inclined. [ABSTRACT FROM AUTHOR]

Compacted granular columns are frequently used to mitigate construction difficulties over soft soil deposits through a combination of densification, increasing load-bearing capacity and improvement in consolidation features. The behaviour of granular columns under vertical loading has been well recognised; however, this research is aimed to investigate the behaviour of granular column-reinforced soft soil subjected to shear stress. In this contribution, large-scale direct shear tests were performed, and the influence of undrained shear strength of clay, diameter of granular column and relative density of aggregates was examined. In general, the use of granular column has been shown to improve the shear resistance of the composite ground, which was found to be much significant at lower undrained shear strength of the soft clay. Increasing density of aggregates from Dr = 50% to 90% caused a large portion of the shear stress to be sustained by the accumulated friction of aggregates as the internal friction angle of the treated clay increased from 0 to 28°. In addition, test results revealed that the use of highly compacted column yields a lowered shear displacement at failure, which was indirect evidence of stiffness improvement of the composite ground. [ABSTRACT FROM AUTHOR]

Introduction: Pragmatics refers to how language is used in social communication. Pragmatics has different dimensions. This study investigated the turn-taking, topic maintenance, and duration of topic maintenance in children with Cochlear implants (CIs) and normal-hearing children matched with chronological age and language age. Materials and Methods: In this cross-sectional study, pragmatics were compared in 15 CI children, 15 normal-hearing children matched with chronological age, and 15 normal-hearing children matched with language age. Pragmatic skills of subjects such as turn-taking, topic maintenance, and duration of topic maintenance in verbal conversation were observed and measured in two groups including CI children with age-matched peers and CI children with language-matched normal-hearing children. The children's conversations were transcribed and the data were analyzed based on the normality of their distribution by independent-sample t-test and Mann-Whitney U tests. Results: Findings showed no significant difference between the mean of turn-taking, topic maintenance, and duration of topic maintenance skills of CI children with their hearing peers (P>0.05). Also, the results obtained by comparing the mean of turn-taking, topic maintenance, and duration of topic maintenance skills in CI children with normal-hearing children matched with language age did not show a significant difference (P>0.05). Conclusion: Children with CIs achieved a level of skills in turn-taking, topic maintenance, and duration of topic maintenance, which was similar to age-matched and language-matched normal-hearing children. Probably, the duration of auditory experience after cochlear implantation, age of implantation of the prosthesis, age of diagnosis of hearing loss, age of rehabilitation intervention, and family follow-up have been effective in achieving these skills. [ABSTRACT FROM AUTHOR]

Introduction: The emergence of a novel coronavirus (COVID-19) in late December 2019 and its rapid global spread has led World Health Organization (WHO) to introduce it as a very dangerous pandemic. People with underlying disease and a history of organ transplantation are at higher risk for COVID-19 disease compared with healthy people. Objectives: In the present study, clinical and laboratory manifestations in the patients with COVID-19 with a history of kidney transplantation has been investigated. Patients and Methods: This study conducted on 103 COVID-19-positive kidney transplant patients as a descriptive epidemiological study. Clinical and laboratory symptoms of hospitalized renal transplanted patients have been assessed. Statistical analysis of the collected data conducted using SPSS (Statistical Package for Social Sciences, version 22). Results: This study consisted of 103 COVID-19 patients with a history of kidney transplant, of which 64 males (62.1%) and 39 females (37.9%) with an average age of 48.5 ± 13.1 years. The most common clinical manifestations were headache (67%) and shortness of breath (66%). Elevated lactate dehydrogenase (LDH) levels, erythrocyte sedimentation rate (ESR), and C-reactive protein (CRP) has been observed in 100%, 98.1% and 93.2% of patients, respectively. In 12.6% and 41.7% of patients, the degree of lung involvement was above 75% and 50%-75%, respectively. Moreover, 79.6% of patients has been discharged after improved, while 20.4% of patients died. Conclusion: We found, kidney transplantation may increase COVID-19-related mortality when compared to COVID-19-related mortality in the general population. [ABSTRACT FROM AUTHOR]

Introduction: Acute kidney injury (AKI) is prevalent in the coronavirus disease-2019 (COVID-19). There are little data on the relationship between renal dysfunction and COVID-19 prognosis. Objectives: The aim of this research was to investigate the effects of AKI in COVID-19 patients hospitalized to the Golestan and Razi hospitals in Ahvaz, Iran. Patients and Methods: In this retrospective cohort study, a total of 194 COVID-19 patients were included, consisting of 79 patients with AKI and 115 patients without AKI. Primary and secondary outcomes were compared between the two groups. Results: According to the findings, mortality was significantly different between the two groups, and mortality was higher in the AKI group (P < 0.001). The mean length of hospital stay was statistically significantly higher in the AKI group (P = 0.024). Moreover, there was a significant correlation between intensive care unit (ICU) admission and the study group (P < 0.001). Staging of AKI group were seen as; stage I (49.37%), stage II (36.71%), and stage III (13.92%). No significant correlation was observed between outcome and the stages of AKI (P = 0.496). Furthermore, 14 patients (17.72%) needed renal replacement therapy (RRT) in the AKI group. Conclusion: Although AKI is a common finding in COVID-19 patients, most patients were in stage I disease, which returned to normal after COVID-19 treatment. According to our research, COVID-19 rarely leads to serious and persistent kidney injury. However, the risk of death is increased in COVID-19 patients with AKI. Therefore, it is necessary to evaluate the renal function tests during the course of disease. [ABSTRACT FROM AUTHOR]

As part of the incident response process, the memory forensics tools extract forensic artifacts and display them. Many memory forensics analysis tools are being developed to address the challenges of modern cybercrimes. Investigations are successful when they have an accurate analysis provided by a memory forensics tool that consumes resources reasonably. This paper presents a comparative analysis of three dominant memory forensics tools: Volatility, Autopsy, and Redline. We consider three malware behaviour scenarios and evaluate the forensics capabilities of these tools in each. We also experimentally measure the CPU and memory consumption of each for memory analysis in other operational states. We find that Volatility provides the most accurate memory analysis. Our measurements show that Redline consumes more CPU resources, and Autopsy needs more memory resources to analyze a memory image file. The results of our investigation will hopefully lead to tool improvement and more informed choices by users. [ABSTRACT FROM AUTHOR]

A compromised Smart Grid, or its components, can have cascading effects that can affect lives. This has led to numerous cybersecurity-centric studies focusing on the Smart Grid in research areas such as encryption, intrusion detection and prevention, privacy and trust. Even though trust is an essential component of cybersecurity research; it has not received considerable attention compared to the other areas within the context of Smart Grid. As of the time of this study, we observed that there has neither been a study assessing trust within the Smart Grid nor were there trust models that could detect malicious attacks within the substation. With these two gaps as our objectives, we began by presenting a mathematical formalization of trust within the context of Smart Grid devices. We then categorized the existing trust-based literature within the Smart Grid under the NIST conceptual domains and priority areas, multi-agent systems and the derived trust formalization. We then proposed a novel substation-based trust model and implemented a Modbus variation to detect final-phase attacks. The variation was tested against two publicly available Modbus datasets (EPM and ATENA H2020) under three kinds of tests, namely external, internal, and internal with IP-MAC blocking. The first test assumes that external substation adversaries remain so and the second test assumes all adversaries within the substation. The third test assumes the second test but blacklists any device that sends malicious requests. The tests were performed from a Modbus server's point of view and a Modbus client's point of view. Aside from detecting the attacks within the dataset, our model also revealed the behaviour of the attack datasets and their influence on the trust model components. Being able to detect all labelled attacks in one of the datasets also increased our confidence in the model in the detection of attacks in the other dataset. We also believe that variations of the model can be created for other OT-based protocols as well as extended to other critical infrastructures. [ABSTRACT FROM AUTHOR]

Nowadays, due to the increase in the amount of traffic and the number of axles, the use of additives in asphalt concrete has become common. In recent decades, one of the goals of engineers in asphalt technology has been to increase the bearing capacity and improve the tensile strength of asphalt mixtures. One of these effective additives in this matter is fiber. Fibers mainly contribute to the reinforcement of asphalt mixtures. Accordingly, in this research, the properties of asphalt mixtures reinforced with 5-component fibers and glass fibers have been investigated. 5-component fibers are added in 0.05, 0.075, and 0.01% of the weight of stone materials, and glass fibers are added in 0.1, 0.2, and 0.3% of the weight of stone materials in asphalt mixtures and finally, based on performance characteristics including Marshall resistance, indirect tension, moisture sensitivity, dynamic creep, and resilient modulus are the most optimal among the above percentages. According to the results of this research, 5-component fibers have the best results in many of the mentioned characteristics at 0.075 percent and glass fibers at 0.2 percent. [ABSTRACT FROM AUTHOR]

Background: Students of medical sciences are a highly vulnerable group during COVID-19 pandemic who may experience a wide range of challenges and stresses. The aim of this study was to investigate the perceived challenges caused by COVID-19 outbreak in students of medical sciences. Materials and Methods: A qualitative study was carried out based on conventional qualitative content analysis following Graneheim and Lundman from December 2020 to February 2021. The participants were 17 students of medical sciences and data gathering was done through semistructured interviews. Results: Data analyses revealed 5 categories and 12 subcategories. The extracted categories were perceived fear of contracting coronavirus, social limitation, changes in education, neglecting health protocols, and worrying news and information overload. Conclusions: The findings indicated that students of medical sciences had been facing challenges and the COVID-19 had affected the psychological, social, and academic functioning of the health-care students. It is important to take measures to improve their mental health. These measures can prevent medical complications in these students, especially during clinical internship. [ABSTRACT FROM AUTHOR]

The advances of Internet of Things (IoT) have had a fundamental impact and influence on sharping our rich living experiences. However, since IoT devices are usually resource-constrained, lightweight block ciphers have played a major role in serving as a building block for secure IoT protocols. In CHES 2015, SIMECK, a family of block ciphers, was designed for resource-constrained IoT devices. Since its publication, there have been many analyses on its security. In this paper, under the one bit-flip model, we propose a new efficient fault analysis attack on SIMECK ciphers. Compared to those previously reported attacks, our attack can recover the full master key by injecting faults into only a single round of all SIMECK family members. This property is crucial, as it is infeasible for an attacker to inject faults into different rounds of a SIMECK implementation on IoT devices in the real world. Specifically, our attack is characterized by exercising a deep analysis of differential trail between the correct and faulty immediate ciphertexts. Extensive simulation evaluations are conducted, and the results demonstrate the effectiveness and correctness of our proposed attack. [ABSTRACT FROM AUTHOR]

The current study was aim to the effects of n-6 and n-3 polyunsaturated fatty acids (PUFA, or palmitic acid sources) on expression of pro-inflammatory cytokines in lambs vaccinated against foot and mouth disease (FMD) virus. A total of fifteen Sangsari male lambs with an age of 6 ± 1 months and body weight of 42 ± 1 kg were randomly assigned to one of three experimental treatments as follows: 1) PO: received calcium soap of palm oil fatty acid (FA) in the diet as source of palmitic acid (16:0), 2) SO: received calcium soap of sunflower oil FAs in the diet as source of linoleic acid (n-6 18:2) and 3) LO: received calcium soap of linseed oil FA in the diet as source of α-linolenic acid (n-3 18:3). The lambs were offered an iso-energetic and iso-nitrogenous diet for 28 days (including 21 days of adaptation and 7 days of sampling). The lambs were individually housed and had access to water ad libitum. The expression of interleukin-1β (IL-1β) mRNA was lower in LO when compared with PO and SO (P<0.05). The expression of tumor necrosis factor-α (TNFα) mRNA was higher in PO when compared with SO and LO and the lowest expression of TNFα mRNA was measured in LO (P<0.05). Lower concentrations of serum cholesterol and triglyceride (TG) were measured in vaccinated lambs on LO diet when compared with vaccinated lambs on palmitic FA diet (P<0.05). There was no significant difference between SO and LO groups in serum cholesterol or TG levels. There were no significant differences between treatments (P>0.05) in serum level of total proteins and albumin. The findings from the current study showed feeding α-linolenic acid diet following vaccination against FMD resulted in a decrease in serum levels of cholesterol and TG due to higher n-3 PUFA intake and suppressed the pro-inflammatory cytokine expression (IL-1 and TNFα). [ABSTRACT FROM AUTHOR]

Background and purpose: Clinical supervision supports learners and paves the way for effective and efficient learning in clinical settings. This study aimed to explain the responsibilities of clinical supervisors in clinical education wards to improve the professional skills of medical students. Materials and methods: In this qualitative study, we used the conventional content analysis approach. The sample consisted of 16 faculty members of medical sciences and medical graduates of Iranian universities. Purposeful sampling and semi-structured interviews were used to collect data. The Graneheim and Lundman method (2004) analyzed the data. Results: From the analysis of interviews, 2 themes, 8 categories, and 18 subcategories were obtained. "Clinical supervisor responsibilities" as a theme includes the categories: "Creating motivation in learner", "Learner's need recognition", "Performance evaluation", "Creating learning opportunities", and "Professional ethics education". And, the sub-categories were: "Creating a supportive atmosphere", "Task assignment","Understanding training needs", "Understanding individual needs", "Periodic evaluation", "Proper feedback', "Reduce work stress", "Learner engagement' , "Learning Facilitation", "Attention to the patient's treatment", and "Ethical observance in relation to patients". As the second theme "Clinical supervisor characteristics" included the categories of: "Scientific competence", "Leading role", and "Ethical model". Their sub-categories are clustered as: "Knowledge of educational concepts", "Mastery of professional concepts", "Effective communication skills", "Understanding managerial concepts", "High resilience", "Career commitment ", and "social commitment". Conclusions: The clinical supervisor will improve the professional skills of medical students, which will improve the quality of services provided, train efficient graduates, and provide a safe and relaxing environment that leads to patient satisfaction. [ABSTRACT FROM AUTHOR]

Objective: The role of unconscious factors in the motivational process, both in decision making and education, has increasingly been noticed. The effectiveness of commercial, non-commercial, and educational messages, as well as the structures of educational advertisements in neuromarketing, are discussed because the advertising audience has complex emotions. It is essential to determine the impact of motivational factors. In this regard, the present systematic review study was conducted to identify motivational factors related to the effectiveness of neuromarketing advertising. Methods: In this systematic review study, English full-text articles published on Science Direct, Scopus, Google Scholar, and Emerald databases from 1990 to 2021 were searched with the keywords of "neuromarketing", "commercial and non-commercial messages", "ad effectiveness", "behavior", and "attitude". Afterward, the qualified articles were reviewed. Results: Firstly, 900 articles were identified from different databases, of which 300 met the inclusion criteria. A total of 210 articles were deleted because they lacked experimental studies. Accordingly, 90 articles were selected for the final review. In total, 37 factors were identified related to the effectiveness of advertising in neuromarketing. Conclusion: The two most influential factors in the research were attention and positive emotions. The greatest emphasis on the effectiveness of advertising is on attention-drawing stimuli and provoking positive emotions. Memory and negative emotions were identified as two other critical factors among the influencing factors. [ABSTRACT FROM AUTHOR]

In this paper, new β-NaFeO2@SrFe12O19 magnetic nanocomposite was prepared using co-precipitation route accompanied by thermal decomposition at 950 °C and its structural, morphological, optical and magnetic properties investigated by FT-IR, XRD, FE-SEM, EDS, VSM, and TGA. XRD and FT-IR predict the successfully preparation of β-NaFeO2@SrFe12O19. SEM and EDS represented the well dispersion of SrFe12O19 into β-NaFeO2 nanoparticles, also agglomeration was happened. VSM show saturation magnetization of 4.19 emu/g with coercivity of 1023 Oe, predicts hard magnetic material with ferromagnetic property. In addition, it was investigated against antibacterial activity. It is active against both Escherichia coli and Staphylococcus aureus strains. [ABSTRACT FROM AUTHOR]

Android has become the target of attackers because of its popularity. The detection of Android mobile malware has become increasingly important due to its significant threat. Supervised machine learning, which has been used to detect Android malware is far from perfect because it requires a significant amount of labeled data. Since labeled data is expensive and difficult to get while unlabeled data is abundant and cheap in this context, we resort to a semi-supervised learning technique, namely pseudo-label stacked auto-encoder (PLSAE), which involves training using a set of labeled and unlabeled instances. We use a hybrid approach of dynamic analysis and static analysis to craft feature vectors. We evaluate our proposed model on CICMalDroid2020, which includes 17,341 most recent samples of five different Android apps categories. After that, we compare the results with state-of-the-art techniques in terms of accuracy and efficiency. Experimental results show that our proposed framework outperforms other semi-supervised approaches and common machine learning algorithms. [ABSTRACT FROM AUTHOR]

This paper presents a probabilistic seismic hazard assessment (PSHA) for Guilan Province, an influential, highly populated and strategic district to the south of the Caspian Sea in northern Iran. In this study, the peak ground acceleration (PGA) and spectral acceleration (SA) over bedrock are estimated for the whole area of the province. NGA-West2 and the EMME project attenuation relationships are applied and combined using the logic tree method. Seismic hazard assessment is then carried out to establish the maps of seismic hazard distribution based on PGA values for two hazard levels as suggested by the Iranian seismic code of practice. Finally, deaggregation analysis is performed to determine the magnitude and distance pair that most prominently contributes to the seismic hazard for each district, which is of great importance for the region, as this has not been done before. Comparing the results with those suggested by the Iranian seismic code of practice and those obtained by previous studies, a general agreement seems to exist, although some violations are observed for some areas, especially in the southeastern part of the province, highlighting a need for revision of the Iranian seismic code of practice. [ABSTRACT FROM AUTHOR]

Secure data deduplication, as it can eliminate redundancies over encrypted data, has been widely developed in cloud storage to reduce storage space and communication overheads. Among them, the convergent encryption has been extensively adopted. However, it is vulnerable to brute-force attacks that can determine which plaintext in a message space corresponds to a given ciphertext. Many existing schemes have to sacrifice efficiency to resist brute-force attacks, especially for cross-domain deduplication, which is inevitably contrary to practical applications. Moreover, few existing schemes consider protecting the message equality information (i.e., whether two different ciphertexts correspond to an identical plaintext). To address the above challenges, in this paper, we propose an efficient and privacy-preserving big data deduplication scheme for a two-level multi-domain architecture. Specifically, by generating a random tag and a constant number of random ciphertexts for each data, our scheme not only ensures data confidentiality under multi-domain deduplication but also resists brute-force attacks. By allowing only the agent and cloud service provider to perform intra-deduplication and inter-deduplication, respectively, our scheme can protect the message equality information from disclosure as much as possible. Detailed security analysis shows that our scheme achieves privacy-preservation for both data content and the message equality information and data integrity while resisting brute-force attacks. Furthermore, extensive simulations demonstrate that our scheme significantly outperforms the existing competing schemes, especially the computational cost and the time complexity of the duplicate search. [ABSTRACT FROM AUTHOR]

The convergence-confinement method via the ground reaction curve (GRC) is used as the common practice of tunnel design which demands accurate determination of the stress state and material strength behavior in different zones around the tunnel section. Besides, formation of the excavation/blast-induced damaged zone (EDZ/BDZ) adds more complexity to the problem due to variation of elasticity modulus of the rock mass in this zone. As a result, advanced numerical methods via finite element/difference commercial packages or user-coded, semi-numerical techniques are required to develop the GRC, which demands a high degree of proficiency and knowledge of computational plasticity and geomechanics. In this study, a new, simple, and accurate method is proposed for prediction of GRC of circular tunnels constructed in the damaged, elastoplastic rock masses obeying softening in the plastic zone. The effects of deterioration caused by the drilling/blast in the EDZ were taken into account by assuming a reduced and varying Young's modulus using the disturbance factor, in the form of Hoek–Brown failure criterion and the Geological Strength Index (GSI). Besides, effects of intermediate principal stress and the exponential decaying dilation parameter are taken into account thanks to adoption of the unified strength criterion (USC) as the material strength criteria. To do so, genetic algorithm (GA) via the method of evolutionary polynomial regression (EPR) is used to find a relationship between a number of 19 affecting parameters on the GRC as the input, and the internal support pressure as the target of prediction. Verification analysis was performed to verify the validity of the results using field measurements data as well as other advanced numerical studies found in the literature. Lastly, variation of the support pressure with simultaneous changes in the affecting input parameters was investigated using multivariable parametric study. [ABSTRACT FROM AUTHOR]

Social interactions through online social media have become a daily routine of many, and the number of those whose real world (offline) and online lives have become intertwined is continuously growing. As such, the interplay of individuals' online and offline activities has been the subject of numerous research studies, the majority of which explored the impact of people's online actions on their offline activities. The opposite direction of impact—the effect of real‐world activities on online actions—has also received attention but to a lesser degree. To contribute to the latter form of impact, this paper reports on a quasi‐experimental design study that examined the presence of causal relations between real‐world activities of online social media users and their online emotional expressions. To this end, we have collected a large dataset (over 17K users) from Twitter and Foursquare, and systematically aligned user content on the two social media platforms. Users' Foursquare check‐ins provided information about their offline activities, whereas the users' expressions of emotions and moods were derived from their Twitter posts. Since our study was based on a quasi‐experimental design, to minimize the impact of covariates, we applied an innovative model of computing propensity scores. Our main findings can be summarized as follows: (a) users' offline activities do impact their affective expressions, both of emotions and moods, as evidenced in their online shared textual content; (b) the impact depends on the type of offline activity and if the user embarks on or abandons the activity. Our findings can be used to devise a personalized recommendation mechanism to help people better manage their online emotional expressions. [ABSTRACT FROM AUTHOR]

Among the important effects of the liquefaction, the settlement of sands due to the cyclic loading is considered in this paper. In this regard, an experimental program is conducted to investigate the excess pore water pressure ratio (EPWP) and the settlement (presented as the volumetric strains) of Anzali sand. The soil used in experiments is poorly graded sand, SP, in the Unified Soil Classification System. To conduct the study, a series of shaking table tests was performed on the studied sand under different conditions. During the experiments, excess pore water pressures were measured in two ways: by the pressure transducers and standpipe piezometers installed at the same levels, and surface settlements were measured by using three linear variable differential transformers, which were installed at different locations over the soil surface. The influence of different factors is studied in details. Saturated sand specimens were tested in the following categories: (i) tests at different input excitations; (ii) tests at different time durations of acceleration; and (iii) tests at different relative densities. The generation of the excess pore pressure and volumetric strains were measured for each of the tests. Results were analyzed to investigate the influences of the variable parameters. As the main novelty and the contribution to the field, effects of duration of the excitation, frequency and density indexes on the post-cyclic settlements and density changes, and also dilative behavior of liquefying sand under different frequencies are parametrically investigated. In addition, evolutionary polynomial regression-based models, which use the best features of conventional regression models, the genetic programming technique, and the least square method, are, also, proposed for the accurate prediction of the pore water pressure and post-cyclic settlements based on the results of shaking table tests. [ABSTRACT FROM AUTHOR]

Non-conventional materials are often applied to dissipate the energy of dynamic loads and mitigate liquefaction. In this study, the effect of Lightweight Expanded Clay Aggregate (LECA) on the mitigation of liquefaction was examined using shaking table tests. LECA, a common lightweight material, was added to saturated sand in a range of 0–20% to study the variation of excess pore water pressure (EPWP) generation, as well as the acceleration response of the soil. Based on the obtained results, the addition of 5% and 10% LECA to saturated sand resulted in a significant decrease in the generation of EPWP, while higher LECA content led to an increase in the generation of EPWP. The mechanisms behind such behaviors contradict the effects of increasing the damping of the soil mixture and reducing the effective initial stress as a function of overall mixture density. Adding 5 and 10% LECA reduces EPWP because aggregates accelerate the dissipation of EPWP during and after vibration. As the percentage of LECA increases by 15% and 20%, excess pore pressure ratio increases. The cause of this may be the reduction of effective initial stress leading to reduced soil density in higher-LECA contents. 10% LECA is presented as the threshold for changing the liquefaction behavior of sand-LECA mixtures. This study concludes that adding LECA to sand samples increases the rate of dissipation of EPWP, but also leads to a significant decrease in initial effective stress. [ABSTRACT FROM AUTHOR]

Compacted granular columns are commonly used to support embankments over soft soils. Using a reinforcement layer under the embankment causes the total stress to be further transferred to the column rather than soft soil, thus reducing total deformations of the subsoil. In this paper two dimensional (2D) numerical analysis was used to study the influence of stone columns and basal geosynthetic on deformations and stability of an embankment over soft deposit by means of Plaxis 2D finite element code. Unit cell to plane strain conversion approach was applied to transform columns into equivalent walls thus allowing to simulate a full embankment over a group of columns. Comprehensive parametric analysis was then performed to investigate the role of different critical parameters on embankment behavior. Results showed that the use of stone columns yielded the total deformations of the subsoil to significantly reduce, while its influence was less remarkable as a high stiffness geogrid was placed under the embankment. It was also found that the stone column length was the most influential parameter on the embankment total deformations, so that increasing columns length from 0.25Hs to 0.75Hs reduced the vertical and horizontal deformations by about two and five times, respectively. In addition, the use of a high stiffness basal geogrid caused the stability of the embankment to remarkably improve as the value of safety factor at the end of construction increased from 1.25 to about 1.9. [ABSTRACT FROM AUTHOR]

Malicious online advertisement detection has attracted increasing attention in recent years in both academia and industry. The existing advertising blocking systems are vulnerable to the evolution of new attacks and can cause time latency issues by analyzing web content or querying remote servers. This article proposes a lightweight detection system for advertisement Uniform resource locators (URLs) detection, depending only on lexical‐based features. Deep learning algorithms are used for online advertising classification. After optimizing the deep neural network architecture, our proposed approach can achieve satisfactory results with false negative rate as low as 1.31%. We also design a novel unsupervised method for data clustering. With the implementation of AutoEncoder for feature preprocessing and t‐distributed stochastic neighbor embedding for clustering and visualization, our model outperforms other dimensionality reduction algorithms by generating clear clusterings for different URL families. [ABSTRACT FROM AUTHOR]

Aim: Bone and mineral disturbances happen commonly in kidney transplant recipients and are affiliated with a high risk of fracture. This study investigates the currency of osteoporosis in transplant patients 2 years after the operation and evaluates possible and related risk factors. Method: The study was performed as a census on all patients who underwent kidney transplantation two years ago. 75 eligible people attended to participate in the research project were divided into three groups based on densitometry results: normal, osteopenia and osteoporosis. Other variables included body mass index (BMI), gender, age, dietary supplementation of calcium, and vitamin D. Quantitative variables of blood pressure, and serum calcium level were also compared in different groups. The relationship between qualitative variables was examined using the Chi-square test, and the mean of each quantitative variable was examined using the Mann-Whitney nonparametric test. Results: Findings revealed that out of 75 eligible people, 30.7% belonged to the age group over 50 years, 69.3% belonged to the age group under 50 years, 57.3% are women, and 42.7% are men. In the present study, based on bone densitometry, participants were divided into normal, osteopenia and osteoporosis. The patient's' body mass index was measured, and there is the highest frequency at the level of BMI 20-25. Conclusion: Current retrospective research has demonstrated that the bone mineral density value, is positively correlated with the female gender and negatively correlated with the Age, Calcium level and BMI and had no significant relation with patients' blood pressure levels. [ABSTRACT FROM AUTHOR]

Blockchain‐based applications provide many promising opportunities to overcome the challenges associated with the Internet of Things (IoT) ecosystems (eg, centralized architecture, data integrity, and reliability). In particular, blockchain technology offers many desirable features for IoT infrastructures, such as decentralization, trustworthiness, trackability, and immutability. However, while logging all transactions in a distributed blockchain ledger provides transparency, it also makes it possible to compromise user's privacy, thus posing a grand challenge to IoT architects and implementers. Over the past years, a set of solutions have been proposed for various scenarios, to address these privacy issues. In this paper, we survey these solutions, classify, and analyze their advantages and disadvantages. We also introduce an evaluation framework to evaluate the quality of the privacy‐preserving based on an adjustable weighting scheme. Finally, we rate the analyzed solutions based on their privacy ranks, and hope our evaluation can shed light on the future design of privacy‐preserving solutions applicable for blockchain‐based IoT platforms. [ABSTRACT FROM AUTHOR]

With the advances in computing powers and increasing volumes of data, deep learning's emergence has helped revitalize artificial intelligence research. There is a growing trend of applying deep learning techniques to image processing, speech recognition, self-driving cars, and even health-care. Recently, several deep learning models have been employed to detect a cyber threat such as network attack, malware infiltration, or phishing website; nevertheless, they suffer from not being explainable to security experts. Security experts not only do need to detect the incoming threat but also need to know the incorporating features that cause that particular security incident. To address this issue, in this paper, we propose a deep embedded neural network expert system (DeNNeS) that extracts refined rules from a trained deep neural network (DNN) architecture to substitute the knowledge base of an expert system. The knowledge base later is used to classify an unseen security incident and inform the final user of the corresponding rule that made that inference. We consider different rule extraction scenarios, and to prove the robustness of DeNNeS, we evaluate it on two cybersecurity datasets including UCI phishing websites dataset and Android malware dataset comprising more than 4000 Android APKs from several sources. The comparison results of DeNNeS with standalone DNN, JRip, and common machine learning algorithms show that DeNNeS with the retraining uncovered samples scenario outperforms other algorithms on both datasets. Furthermore, the extracted rules approximately reproduce the accuracy of the neural network from which they are derived. DeNNeS achieves an outstanding accuracy of 97.5 % and a negligible false positive rate of 1.8 % about 2.4 % higher and 3.5 % lower than the rule learner JRip on the phishing dataset. Moreover, DeNNeS outperforms random forest (RF), which produces the highest results among decision tree (DT), support vector machine, k-nearest neighbor, and Gaussian naive Bayes. Despite smaller training data in the malware dataset, DeNNeS achieves an accuracy of 95.8 % and F 1 s c o r e of 91.1 % , much higher than JRip and RF. [ABSTRACT FROM AUTHOR]

First impounding of the earth-fill dams is considered as one of the most critical stages of these earth structures during their lifespan such that it demands careful monitoring of the dam's safety. In this study, behavior of Siahoo dam, an earth-fill dam in Iran, is evaluated by numerical modeling at the stage of first impounding to (1) back-analysis and calibration of the numerical model using data of instrumentation measurements, gathered during the dam construction. (2) Determine the optimized plan of the dam's first impounding so that the dam safety is secured against hydraulic fracturing. (3) Predict the displacements of the dam in the future under seepage and consolidation using the calibrated numerical model. (4) Analyze the dam under seismic loading condition. The material properties were chosen through a two-step procedure based on numerical simulation of laboratory tests in the software and calibration between the stress–strain analysis of the dam and instrumentation measurements at the end of construction stage, so that the real behavior of the dam can be represented. To find the maximum rate of dam's first impounding in safe condition, a sensitivity analysis was performed assuming eight number of impounding scenarios. Having performed the steady-state seepage analysis, seismic loading was applied using the equivalent linear method and the permanent displacements were predicted using the method of Newmark sliding blocks. Liquefaction analysis was also performed to recognize liquefiable zones of the foundation. Finally, stress redistribution analysis was carried out to estimate the post-earthquake displacements. [ABSTRACT FROM AUTHOR]

There are some ambiguities in understanding of the effect of non-plastic fine content (silt) on the liquefaction behaviour of sand in the literature. Reviewing the results of field and laboratory studies suggests that increasing the amount of non-plastic fine content in sand can lead to improvement or deterioration of liquefaction resistance. In this study, the effect of non-plastic silt on liquefaction resistance of Anzali sand is investigated using shaking table tests. According to the results, for the relative density of 30%, liquefaction resistance was found to be initially decreasing by increasing the silt content until the limiting fine content (LFC) as 15% and increasing thereafter. However, for the relative density of 60%, liquefaction resistance showed a continuously decreasing trend by increasing the silt content. Finally, comparison of the result through the limiting bounds of pore pressure obtained in this study with that of other researchers showed an appropriate consistency. [ABSTRACT FROM AUTHOR]

Background: Colonoscope is a diagnostic tool of choice to examine the lower part of the gastrointestinal tract, which includes the colon and the beginning of the ileum. Colonoscopes are used for diagnostic and therapeutic purposes. Colonoscopy is also accompanied by abdominal pain. Accordingly, the purpose of this study was to evaluate the analgesic effect of Ziziphora tenuior essential oil and lubricant gel in patients undergoing colonoscopy using a randomized double-blind clinical trial. Materials and Methods: In this clinical trial, 100 patients who were candidates for colonoscopy were selected through convenience sampling and were randomly divided into two groups. The control group (n = 50) used lubricant gel (placebo) and the treatment group (n = 50) used the gel containing Ziziphora tenuior essential oil (the compounds of the Ziziphora tenuior essential oil were identified by injection of the sample into a gas chromatography-mass spectrometry (GC-MS) device). The patients' satisfaction and pain were assessed using a standard questionnaire and the collected data were analyzed by descriptive and inferential tests (t test). Results: The GC analysis revealed 42 different compounds in the essential oil. Six of the identified compounds comprised more than 60% of the compounds presented in the oil including pulegone (26.65%), alpha-terpinyl acetate (9.53%), geraniol (7.11%), menthone (5.74%), thymol (5.51%), and alpha-terpineol (3.24%) in decreasing order. The mean value of pain and the duration of colonoscopy in the control group (6.56 + 1.89) were significantly different from the corresponding value for the case group (4.25 + 1.2) (p < 0.0001). Conclusion: The use of the mix of Ziziphora tenuior essential oil and lubricant gel as a safe and inexpensive analgesic drug during colonoscopy is recommended. [ABSTRACT FROM AUTHOR]

Introduction: Thymoglobulin is a lymphocyte-depleting polyclonal antibody, administered for induction therapy at the time of kidney transplantation to reduce the risk of acute allograft rejection. The appropriate dosage and duration of therapy is controversial. The higher dosages are associated with infection and malignancy. Objectives: In this study efficacy and safety of lower dosage (in comparison with previous studies) of thymoglobulin in kidney transplant recipients was evaluated. Patients and Methods: In this clinical trial, 106 adult kidney transplant recipients, were randomized before transplantation in two groups (case and control). The case group (53 patients) were received induction therapy with thymoglobulin (1.5 mg/kg/d for 3 days) and the control group (53 patients) were received non-induction regiment. Delayed graft function (DGF), glomerular filtration rate (GFR), acute allograft rejection and thymoglobulin complications were evaluated during the first post-transplantation year. Results: Around 106 kidney transplant recipients were enrolled (71 or 66.98% deceased donor) to the study. No significant statistical differences were found in GFR at the time of discharge from hospital (P = 0.399) and at 1 year (P = 0.851) and acute allograft rejection (P = 0.304) between two groups. Graft survival (73.5% in case group versus 81.1% in control group, P = 0.392) at month 12th was similar among groups. Additionally, no significant differences of acute allograft rejection in recipient from deceased or living donor between two groups were detected. There was a higher incidence of DGF in the control group (26.4%) than the thymoglobulin group (5.8%) and the difference was statistically significant (P = 0.004). Thrombocytopenia (17% versus 49.1%, P < 0.001) and leukopenia (11.3% versus 50.9%, P < 0.001) were also significantly higher in the case group. Conclusion: While the incidence of DGF was reduced in thymoglobulin group, the short-term acute allograft rejection rate was not reduced compared to the control group. However, our results require further consideration with larger samples. [ABSTRACT FROM AUTHOR]

An elastoplastic, dynamic, finite-difference method was applied to study the effects of nonlinear seismic soil–pile interaction on the liquefaction potential of marine sand with piles. The developed model was well validated using the centrifuge test. The results showed that acceleration, bending moment, and excess pore water pressure complied well with centrifuge test results. The effect of different affecting parameters on liquefaction potential was investigated using parametric study. Using a sensitivity analysis, the pile embedment parameter was shown to be the most influential parameter. Finally, applying the evolutionary polynomial regression technique, a new model for predicting the liquefaction potential was presented. [ABSTRACT FROM AUTHOR]