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Nowadays, pervasive vision through synthetic aperture radar (SAR) imaging sensors is a crucial part of air-borne and space-borne platforms to reach the concept of internet of multimedia things over satellites and visual flying ad-hoc networks. SAR sensors always need high-performance data reconstruction techniques to provide quality of experience for end-users of the distributed surveillance nodes and ensuring reliable decision-making in the autonomous vehicles. Using prior information in SAR image reconstruction, improves the quality of reconstructed image. Many sparse regularization methods use pre information terms in which the image is sparsely presented based on a predefined dictionary. However, if the desired features of the real image have not a sparse representation based on the predefined dictionary, image reconstruction with enhanced interested features will be failed. Dictionary learning to better adapt with the underlying scene can lead to better image reconstruction. On the other hand, using the idea of dictionary learning in the image reconstruction problem, and considering that the data used to train the dictionary may be incomplete and noisy, will create limitations for this method. In this paper, a new idea based on the use of an overcomplete dictionary consisting of a learned dictionary and a predefined dictionary (pre-learned dictionary) for SAR image reconstruction problem is presented. Developing the necessary mathematical relations and providing the framework for SAR image reconstruction based on the use of the overcomplete pre-learned dictionary, to simultaneously enhance the features considered in advance and the features associated with the image, from incomplete and noisy SAR raw data is presented in this article. We also present an iterative algorithm for solving the corresponding optimization problem. Simulation results based on the real data of TerraSAR-X and the Lynx airborne system show the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

Visualization of numerical results in computer communications is very important such that some very small differences are sometimes crucial, distinguishable, and descriptive for comparison among some state-of-the-art techniques. For the issue of data quality evaluation and compression rates in internet of multimedia things, there are many metrics traditionally, for instance, peak signal-to-noise ratio (PSNR) is strongly able to describe non-sensitive (and relatively ambiguous) results of mean square error and since PSNR is normally between 10 and 100 for most of the lossy techniques, it can plotted with using any graphical/visualization tool. However, the results of compression rates for aggregation techniques may be a little complicated on which using a non-flexible mathematical operator like logarithm may have an unsuitable effect with ignoring the small differences while plotting the results. The aim behind this paper is to introduce a new metric entitled average capacity index (ACI), as a non-linear visualization approach/scaling mechanism, to be usable in evaluating capacity results of data hiding and aggregation algorithms based on bar charts. Some examples with synthetic and real data will show that the proposed metric outperforms the existing conventional tools in terms of statistical measures and visual presentation. [ABSTRACT FROM AUTHOR]

IoT data exchange services are emerging to connect various and distributed IoT data sources, which facilitate data owners exchange their IoT data flexibly. Traditional IoT data exchange services rely on a centralized third-party to negotiate the settlement of trades between data consumers and providers. Such settlement model suffers from issues including single point of failure, extra settlement fee, un-transparency of settlement details, to name a few. New settlement model is imperative to overcome such limitations. Blockchain is an innovative technology that is competent in governing the decentralized network, which poses great opportunity to implement fair settlement in decentralized manner. In this paper, we propose a Blockchain-enabled settlement model for decentralized IoT data exchange services. Firstly, Bitcoin-based time commitment scheme is adopted to build fair and autonomous settlement model. Furthermore, to enhance the accountability of all transactions, an optimized practical Byzantine fault tolerant consensus protocol named ReBFT, is proposed to enable all members involved in the IoT data exchange application achieve identical shared ledger recording all transactions. Finally, experiments are conducted to verify the feasibility of our proposal. [ABSTRACT FROM AUTHOR]

In this study, the impacts to soil thermal properties during and after biocementation via microbially induced calcite precipitation (MICP) method on silty silica sand specimens with varying fines content (0%, 5%, and 15%) were investigated. Firstly, calcium conversion was measured after each pulse; then, the MICP-treated specimens were tested for cementation uniformity. The evolution of thermal conductivity of silty soils with the MICP treatment was assessed using a thermocouple probe. The results show that thermal conductivity of silty saturated sands increased by 17% for specimens treated to 9.7% CaCO3. The improvement in thermal conductivity was attributed to the formation of calcium carbonate bridges binding the soil grains together. The results suggested that the thermal conductivity of silty soil depends on water content, the number of treatment pulses, and the treatment uniformity through the soil specimen. Presence of fines content in the soil was found to play an important role in the distribution and uniformity of biocementation through the soil specimen. However, no statistically significant difference in the thermal conductivity values of MICP-treated specimens with different fines content was observed (p>0.05). The average calcium carbonate content ranged between 10.7% and 7.2% for the soils with 0% and 15% fines content, respectively. The findings of this research could be used to improve the efficiency of geothermal boreholes and other energy geostructures using MICP by improving thermal conductivity of dry and partially saturated soil. [ABSTRACT FROM AUTHOR]

Background and Objectives: During the coronavirus pandemic, the overuse of antibiotics to reduce coinfections and mortality may be contributing to the rise of antimicrobial resistance. In this study, we aim to investigate the antibiotic resistance changes of Acinetobacter baumannii post-COVID-19 pandemic in Northern Iran. Materials and Methods: The current study is a cross-sectional study. Between 2022 and 2023, 2190 clinical samples were collected from patients with healthcare-associated infections (HAIs) at four hospitals in Sari, which served as corona centers after the COVID-19 pandemic. Antimicrobial sensitivity was determined using standard broth macro-dilution, and resistance genes were detected using multiplex PCR. Results: Based on the results co-amoxiclav had a resistance rate of 100%, while piperacillin/tazobactam showed the least resistance rate of 29.82%. In terms of GM MIC values, colistin was the most potent against multi-drug resistant isolates. The frequency of bla OXA-51, ampC, aphA6, and bla NDM genes were 100%, 99.12%, 90.35%, and 69.30% respectively. Conclusion: Our study revealed high multi-drug resistance rates. Piperacillin/tazobactam recommended for treating multidrug resistant Acinetobacter baumannii infections in Northern Iran. [ABSTRACT FROM AUTHOR]

Carotid atherosclerosis is one of the main cardiovascular diseases, widely considered as the main reason for death. Atherosclerosis forms a plaque that impedes blood vessels, and if ruptured, it causes a stroke or heart attack. The treatment protocol for atherosclerosis depends heavily on plaque type, structure, and composition, affecting plaque behavior (stable/unstable) or vulnerability. The fluid–structure interaction between the blood vessels and the blood flow must be examined to study the plaque's behavior. Consequently, this paper aims to reconstruct patient-specific three-dimensional models of the blood vessels for simulation and three-dimensional (3D) Printing, particularly for the carotid artery. In addition, Magnetic Resonance Imaging (MRI) and Computed Tomography (CT) datasets of atherosclerotic vessels are used to reconstruct the 3D model fed into a simulation program to measure the stress, strain, pressure, and velocity to assess the plaque. Five analyses studies were conducted on the constructed blood vessels; Non-pathological Flow in a cylindrical artery, Pathological Flow in a cylindrical artery, Non-pathological Flow in a 2D bifurcating carotid artery, Blood flow analysis in a normal carotid artery, and Blood flow analysis in a stenosed carotid artery. Two validation studies were performed for normal and atherosclerotic arteries. The results agreed with previous well-published work, considering that a 3D realistic model was printed for the vessel. Based on the above, our work provides a simulation environment for predicting atherosclerotic plaque behavior that helps medical specialists choose the proper treatment and preventive medical plans. [ABSTRACT FROM AUTHOR]

Background: Probiotic strains have the potential to modulate immune responses, reduce intestinal inflammation, normalize intestinal mucosal function and decrease allergic reactions. Objective: This study aimed to investigate the effect of oral probiotic supplements containing Bacillus subtilis and Bacillus coagulans spores on clinical symptoms, haematological factors and immune responses to allergic contact dermatitis in dogs induced by dinitrochlorobenzene (DNCB). Methods: DNCB was injected subcutaneously into the scapular region of 20 healthy adult dogs of both sexes, divided into four groups, to induce experimental allergic contact dermatitis. Dogs in Group 1 received food without probiotics or medication. Oral prednisolone was administered to Group 2 for 30 days at a dosage of 0.25 mg/kg every other day. The dogs in Group 3 were treated with a combination of oral prednisolone and probiotics. The dogs in Group 4 were fed daily with a mixture of 109B. subtilis and B. coagulans bacteria for 30 days. The immune system responses and related gene expression were analysed in the treated animals. Results: The administration of probiotics for 30 days resulted in a reduction in clinical symptoms and duration of wound repair. The probiotics treatment also significantly increased the serum bactericidal effects against Staphylococcus aureus and Escherichia coli. It enhanced both the classic and alternative activity of the complement, as well as lysozyme activity. Additionally, the probiotics led to higher total immunoglobulin levels and significant reductions in anti‐trypsin and C‐reactive protein levels. Furthermore, the expression of IgE, induction of interferon‐gamma and IL‐4 genes were also reduced. Conclusions: According to the results, B. subtilis and B. coagulans can be further investigated as a viable alternative to corticosteroids in treating allergic contact dermatitis in dogs. [ABSTRACT FROM AUTHOR]

The effect of spatial variability on the liquefaction prediction and liquefaction-induced ground failure of a soil profile with thinly interbedded layer of sand and clay was examined using simplified procedures and fully coupled two-dimensional (2D) nonlinear dynamic analysis (NDA). The Palinurus Road site in Christchurch, New Zealand, subjected to the 2010 Darfield and 2011 Christchurch events, was selected for the analyses. The liquefaction vulnerability of the site was estimated using one-dimensional (1D) liquefaction vulnerability indices (LVIs), and the results were compared with the results of NDAs and field observations. Spatially correlated random fields conditioned on corrected cone penetration tests (CPTs) were generated based on sand-like and claylike portions of the interbedded soil profile. CPT data were corrected for the thin-layer and transition zone effects. The generated random fields were assigned to the interbedded layer of the stochastic model to examine the effect of spatial variability on the system dynamic response, cross-interaction between layers, and liquefaction-induced diffusion during the evolution of liquefaction and postliquefaction. The potential of sediment ejecta manifestation using the artesian flow potential (AFP) and ejecta potential index (EPI) also was investigated for the stochastic models and compared with those of deterministic models. This study demonstrates the value of advanced 2D NDA modeling with realistic soil spatial variability for understanding inconsistent ejecta predictions from simplified tools. The approach can guide future refinements to assessment procedures and provide insights into key factors controlling manifestation in complex stratified sites. [ABSTRACT FROM AUTHOR]

Background: Pulmonary alveolar microlithiasis (PAM) is a rare autosomal recessive genetic disorder with approximately 1000 known cases worldwide, in which calcium phosphate microliths deposit in the alveolar air spaces. As of writing this report, no definitive conventional therapy exists, and many PAM cases may progress to severe respiratory failure and potential death. Bilateral lung transplantation (BLx) seems to be the most optimal solution; however, this procedure is challenging along with limited reports regarding the outcome in PAM. We report a case of PAM successfully treated with BLx for the first time in Iran. Method: We present the case of a 42‐year‐old female with a longstanding history of cough, not responding to conventional antitussive medication, who was diagnosed as a case of PAM following a hospitalization due to coughing, dyspnea on exertion, and hemoptysis. Despite treatment with corticosteroid and medical treatment, no improvement was achieved and she subsequently developed respiratory and right ventricular failure, with oxygen ventilation dependence. Eventually, she was scheduled for BLx. The operation was successful and during her 2‐year follow‐up, no recurrence or significant postoperative complications has been reported. Conclusion: This case presentation and literature review confirm the effectiveness of BLx as a promising treatment for PAM‐diagnosed patients, improving both life expectancy and quality of life. [ABSTRACT FROM AUTHOR]

Summary: This paper addresses a new robust control scheme for tracking control of the robot manipulators. This algorithm employs time delay estimation (TDE) technique in combination with backstepping control strategy. The suggested control scheme has no dependency on the robot dynamic model and knowing the bound of the inertia matrix is enough to develop this controller. To develop the idea, at first the boundedness of the TDE errors is analyzed by Lyapunov‐Krasovskii approach. Next, the proposed TDE based adaptive backstepping nonsingular terminal sliding mode control (ABNTSMC) algorithm is developed. In this way, a novel adaptive‐robust term is employed to defeat the chattering phenomenon. Moreover, the closed‐loop stability of the system is proven through Lyapunov second approach. Fast transient response, supreme robustness, and chattering‐free as the premier specifications are gained employing the proposed control algorithm. The efficiency of the suggested control scheme is illustrated through some simulations on an industrial robot and the results are compared with some other control algorithms. Finally, the practical effectiveness of the proposed TDE based control algorithm is verified through some experiments on a parallelogram robot manipulator. [ABSTRACT FROM AUTHOR]

This paper presents experimental testing evaluating the ability of a bridge deck deicing system to mitigate concrete deterioration from thermal stresses, frost action, and early-age cracking. Two experimental bridge deck models were constructed with embedded heat exchanger tubing and instrumented with thermocouples and strain gauges. Model 1 evaluated the efficiency of a deicing system in deicing and mitigating concrete deterioration from thermal stresses and frost action in concrete bridge decks, while Model 2 tested the effect of a deicing system on early-age cracking in bridge decks. The models were tested in a cold chamber laboratory under conditions representative of Montana's winter weather, with the system circulating warm fluid through the decks. Results showed the system succeeded in increasing concrete temperatures at all depths. While the system did not always raise temperatures above freezing, the consistent increases suggested that the system could enable deicing and mitigate frost action given certain conditions and higher inlet fluid temperatures. The system also successfully decreased thermal movement strain by up to 40% and reduced thermal shrinkage by decreasing the difference between peak cured and stabilized temperatures. Although the system did not eliminate thermal gradients, the maximum gradient induced was insignificant. Further testing is needed on strength impacts. Overall, the deicing system showed promise to reduce thermally induced deterioration in concrete bridge decks by regulating temperatures. [ABSTRACT FROM AUTHOR]

Natural killer (NK) cells are central components of the innate immunity system against cancers. Since tumor cells have evolved a series of mechanisms to escape from NK cells, developing methods for increasing the NK cell antitumor activity is of utmost importance. It is previously shown that an ex vivo stimulation of patient‐derived NK cells with interleukin (IL)‐2 and Hsp70‐derived peptide TKD (TKDNNLLGRFELSG, aa450‐461) results in a significant upregulation of activating receptors including CD94 and CD69 which triggers exhausted NK cells to target and kill malignant solid tumors expressing membrane Hsp70 (mHsp70). Considering that TKD binding to an activating receptor is the initial step in the cytolytic signaling cascade of NK cells, herein this interaction is studied by molecular docking and molecular dynamics simulation computational modeling. The in silico results showed a crucial role of the heterodimeric receptor CD94/NKG2A and CD94/NKG2C in the TKD interaction with NK cells. Antibody blocking and CRISPR/Cas9–mediated knockout studies verified the key function of CD94 in the TKD stimulation and activation of NK cells which is characterized by an increased cytotoxic capacity against mHsp70 positive tumor cells via enhanced production and release of lytic granules and pro‐inflammatory cytokines. [ABSTRACT FROM AUTHOR]

Lactobacillus probiotic enriched diets can modulate host immune response. In this study, we investigated whether the three species Lactobacillus plantarum, L. bulgaricus and L. rhamnosus up-regulated two T-cells specific antigens and exerted the protective effect when administrated with formalin-killed Streptococcus agalactiae in Nile tilapia. For this purpose, a total of 180 Nile tilapia (average body weight 45.8 ± 22 g) were randomly divided into 6 experimental groups, i.e.: one control group and 5 vaccinated groups: Formalin killed cell (FKC); FKC+ Adjuvant; FKC+ L. plantarum; FKC+ L. bulgaricus and FKC+ L. rhamnosus. All groups were fed with normal commercial pellets and three groups were fed with pellets sprayed with three different lactobacilli. The results showed that the survival rate in the groups of Formalin killed cell (FKC) combined with oral administration of lactobacilli was from 70.0% to 75.0%. Protection in the control group amounted to 31.3% illustrating a significant difference with other experimental groups. In this research, gene expression of CD4 and CD8 which have essential functions in the immune response quantified by qRT-PCR in the head kidney, skin, and spleen was reported in the form of fold change. The analysis of fold change (Mean ± SD) related to CD4 and CD8 at 30- and 60-days post-immunization (dpi) respectively showed an increase in fold change in all probiotic groups compared to the FKC group in two of which it was significant at the level of p<0.05. The results revealed that vaccination with FKC administrated lactobacilli-enriched diets increased the expression of genes related to immune response which can indicate higher protection against Streptococcus agalactiae in the probiotic groups compared with FKC vaccination alone similar to the results observed in FKC coupled with adjuvant vaccination. [ABSTRACT FROM AUTHOR]

In this study, a DNA vaccine was designed based on the aopB gene of type III secretory system of Aeromonas hydrophila using the eukaryotic pCDNA3.1 expression system. Produced DNA vaccine was encapsulated with synthetic polylactic-co-glycolic acid (PLGA) nanoparticles. After evaluating the efficiency and quality of nanoencapsulation, 270 common carp (18.3 ± 2.4 g) were divided into six equal groups in triplicates. Groups A and B (GA & GB) were orally vaccinated with pCDNA3.1-aopB plasmid with or without nanoencapsulation with PLGA, respectively. Groups C and D (GC & GD) were orally vaccinated with pCDNA3.1 plasmid with or without PLGA nanoencapsulation, respectively. Groups E and F received PLGA and PBS, respectively. The serum samples, head kidney, and intestine tissues were taken on days 0, 30, and 60 of the experiment. Hemato-immunological parameters and tissue distribution of the recombinant plasmid were assayed. All groups were also challenged with virulent A. hydrophila bacteria and the survival rate was recorded to determine the vaccination efficacy. The recombinant plasmid was detected in the anterior kidney and intestine of GA and GB. Anti-A. hydrophila antibody titer and the survival rate were significantly increased after the bacterial challenge in GA and GB compared to the control groups (P < 0.05). Nonspecific immune response markers were significantly increased in GA compared to the control group (P < 0.05). Hematological parameters show no significant difference in all groups and sampling time points (P > 0.05). Consequently, the constructed oral DNA vaccine based on the pCDNA3.1-aopB gene, encapsulated with PLGA, showed either sufficient protection or reliable immunogenicity against A. hydrophila infection in common carp. [ABSTRACT FROM AUTHOR]

This study aimed to assess the validity and reliability of the world health organization quality of life questionnaire for people with intellectual disability (WHOQOL-DIS-ID). This was a cross-sectional study of 118 adults with intellectual disability. Internal consistency and temporal reliability were evaluated using Cronbach's alpha and intraclass correlation coefficient (ICC), respectively. Construct validity of the structure was assessed using confirmatory factor analysis. Multiple linear regression analysis was performed to determine the factors associated with the quality of life. Cronbach's alpha for all, but not for discrimination area, as well as ICC for all questions except for 23 and 6, were higher than 0.7. The variables such as periodic check-up, number of friends, entertainment outside the home, parental separation, physical activity, unmet needs score, caregiver's mental health status, and available facilities were the predictors of QOL. The Persian version of the WHOQOL-DIS-ID shows acceptable validity and reliability. [ABSTRACT FROM AUTHOR]

Fast and automated recognizing of abnormal behaviors in crowded scenes is significantly effective in increasing public security. The traditional procedure of recognizing abnormalities in the Web of Thing (WoT) platform comprises monitoring the activities and describing the crowd properties such as density, trajectory, and motion pattern from the visual frames. Accordingly, incorporating real-time security monitoring based on the WoT platform and machine learning algorithms would significantly enhance the influential detection of abnormal behaviors in the crowds. This paper addresses various automatic and real-time surveillance methods for abnormal event detection to recognize the dynamic crowd behavior in security applications. The critical aspect of security and protection of public places is that we cannot manually monitor the unpredictable and complex crowded environments. The abnormal behavior algorithms have attempted to improve efficiency, robustness against pixel occlusion, generalizability, computational complexity, and execution time. Similar to the state-of-the-art abnormal behavior detection of crowded scenes, we broadly classified methods into different categories such as tracking, classification based on handcrafted extracted features, classification based on deep learning, and hybrid approaches. Hybrid and deep learning methods have been found to have more satisfactory results in the classification stage. A set of video frames called Motion Emotion Dataset (MED) is employed in this study to examine the various conditions governing these methods. Incorporating an appropriate real-time approach with considering WoT platform can facilitate the analysis of crowd and individuals' behavior for security screening of abnormal events. [ABSTRACT FROM AUTHOR]

Background: In the context of managing persistent post-concussive symptoms (PPCS), existing treatments like pharmacotherapy, cognitive behavioral therapy, and physical rehabilitation show only moderate effectiveness. The emergence of neuromodulation techniques in PPCS management has led to debates regarding optimal stimulation parameters and their overall efficacy. Methods: this scoping review involved a comprehensive search of PubMed and ScienceDirect databases, focusing on controlled studies examining the therapeutic potential of non-invasive brain stimulation (NIBS) techniques in adults with PPCS. Results: Among the 940 abstracts screened, only five studies, encompassing 103 patients (12 to 29 per study), met the inclusion criteria. These studies assessed the efficacy of transcranial direct current stimulation (tDCS), or repetitive transcranial magnetic stimulation (rTMS), applied to specific brain regions (i.e., the left dorsolateral pre-frontal cortex (DLPFC) or left motor cortex (M1)) for addressing cognitive and psychological symptoms, headaches, and general PPCSs. The results indicated improvements in cognitive functions with tDCS. In contrast, reductions in headache intensity and depression scores were observed with rTMS, while no significant findings were noted for general symptoms with rTMS. Conclusion: although these pilot studies suggest promise for rTMS and tDCS in PPCS management, further research with larger-scale investigations and standardized protocols is imperative to enhance treatment outcomes for PPCS patients. [ABSTRACT FROM AUTHOR]

Diabetes mellitus is one of the most prevalent medical conditions, in both humans and animals. People with diabetes mellitus often experience slower than normal wound healing, making it a serious health concern. This study investigates the effect of M2 differentiated macrophages on full‐thickness wound healing in white Westar rats exposed to streptozocin 70 mg/kg. A full‐thickness skin defect with dimensions of 2 × 2 cm was created on the back of all the animals, and their blood sugar was simultaneously assessed. The monocytes were isolated from blood samples using the plastic adherence method and were exposed to dexamethasone (5–10 μ) for 24 h. Subsequently, they were washed with PBS and incubated in fresh cell culture medium for 5 days. The differentiated M2 cells were injected into four points of the experimental ulcers of the treatment group. Macroscopic and microscopic changes were evaluated and compared over a period of two weeks between the test and control groups. The infusion of these cells a few days after wounding enhances wound healing parameters significantly, as evidenced by an increase in germinating tissue formation, wound contraction, inflammation reduction, and collagen increase in the treated group. [ABSTRACT FROM AUTHOR]

Background: An enhanced aerobic glycolysis ("Warburg effect") associated with an increase in lactic acid in the tumor microenvironment contributes to tumor aggressiveness and resistance to radiation and chemotherapy. We investigated the radiation- and chemo-sensitizing effects of the nonsteroidal anti-inflammatory drug (NSAID) diclofenac in different cancer cell types. Methods: The effects of a non-lethal concentration of diclofenac was investigated on c-MYC and Lactate Dehydrogenase (LDH) protein expression/activity and the Heat shock Protein (HSP)/stress response in human colorectal (LS174T, LoVo), lung (A549), breast (MDA-MB-231) and pancreatic (COLO357) carcinoma cells. Radiation- and chemo-sensitization of diclofenac was determined using clonogenic cell survival assays and a murine xenograft tumor model. Results: A non-lethal concentration of diclofenac decreases c-MYC protein expression and LDH activity, reduces cytosolic Heat Shock Factor 1 (HSF1), Hsp70 and Hsp27 levels and membrane Hsp70 positivity in LS174T and LoVo colorectal cancer cells, but not in A549 lung carcinoma cells, MDA-MB-231 breast cancer cells and COLO357 pancreatic adenocarcinoma cells. The impaired lactate metabolism and stress response in diclofenac-sensitive colorectal cancer cells was associated with a significantly increased sensitivity to radiation and 5Fluorouracil in vitro, and in a human colorectal cancer xenograft mouse model diclofenac causes radiosensitization. Conclusion: These findings suggest that a decrease in the LDH activity and/or stress response upon diclofenac treatment predicts its radiation/chemo-sensitizing capacity. [ABSTRACT FROM AUTHOR]

The Mil‐125(Ti) metal–organic framework nanoparticles were modified with different compounds i.e. polyamidoamine dendrimer (Mil‐Den), chitosan (Mil‐CS) and Mil‐CS‐Den to prepare functional and hydrophilic nanocomposites. These nanocomposites were characterized and applied for modification of polyethersulfone (PES) membrane by blending them in the dope solutions. The mixed matrix PES membranes were manufactured by the nonsolvent‐induced phase inversion method. The introduction of the nanocomposites into the membrane structure led to positive alterations such as the improvement of hydrophilicity and morphology causing the development of permeability and antifouling properties. The bare PES membrane contact angle was decreased from 60° to 34.5° by adding 0.5 wt% of Mil‐CS‐Den nanocomposite. Applications of novel structures with special absorption in 0.5 wt% of Mil‐CS‐Den membrane, caused an increment of porosity, mean pore size and reduction of surface roughness. The permeability was enhanced from 4.2 to 21.43 L/m2 h bar, while fouling was reduced from 48.5% to 13.5% which indicates the strengthening of the antifouling feature of the hydrophobic PES membrane. Also, the flux recovery ratio was enhanced from 66.7% to 89.3% by blending 0.5 wt% of Mil‐CS‐Den. The modified membranes showed an excellent dye separation performance. The attachment of dendrimer and chitosan to the Mil‐125(Ti) surface increased the efficiency of Mil‐125(Ti) in the modification of the PES membrane. The utilization of nanomaterial features can reduce the limitation of membrane processes and reach desirable outcomes. The results of this work indicate the high potential of the modified MOF for the removal of dye contaminants as well as provide a suitable perspective for water purification with the help of membrane processes. [ABSTRACT FROM AUTHOR]

Aims: Investigate the association between oral and dental health (ODH) and metabolic syndrome (MetS) in adults aged 35–70 years. Methods: The study utilized data from the enrollment phase of Dehgolan prospective cohort study in the west of Iran. A cross‐sectional assessment was conducted on a total of 3996 participants, involving a comprehensive oral examination and the assessment of their oral hygiene behavior (ODH). MetS was defined according to the updated National Cholesterol Education Program Adult Treatment Panel III. Logistic regression used to calculate odds ratios (ORs) and 95% confidence intervals (CIs), adjusting for sociodemographic characteristics. Results: MetS was more prevalent among those who not daily brushing and flossing. Participants with missing teeth (MT) show higher prevalent of MetS. Being female, lower age, illiteracy, family history of diabetes, low physical activity, and salt at table were independently associated with increase odds of MetS (p < 0.05). Odds of MetS were significantly decreased with use flossing (OR = 0.75; CI = 0.60–0.93), decayed (OR = 0.83; CI = 0.72–0.97), filled (OR = 0.84; CI = 0.71–0.99), and increased with MT (OR = 1.45; CI = 1.16–1.81) as long as adjusted for ODH factors. When other potential confounder such as sociodemographic, personal and nutritional habits were adjusted, daily flossing was the only factor that still statistically decreased (OR = 0.79; CI = 0.62–0.99) the odds of MetS. Conclusion: Daily flossing was the only factor that independently associated with MetS. Relationship of other ODH factors with Mets were confounded by sociodemographic characteristics of the participants. [ABSTRACT FROM AUTHOR]

The present study investigated the effects of combined and singular oral administration of Bio-Aqua® with different dosages of sodium diformate (NaDF) on biochemical indices, innate immune responses, antioxidant effects, and expressions of immunological related genes of Caspian brown trout (Salmo trutta caspius). Fingerlings Salmo trutta caspius (n = 1800; initial weight 15 ± 3 g) were randomly allocated into five groups (120 fish group-1 in triplicates). Control diet: without any addition, G1, G2, G3, and G4 received diets containing 0.2 g kg−1 commercial probiotic Bio-Aqua® combined with 0, 0.5, 1.0, and 1.5% NaDF to the basal diet for 60 days according to recommended dosages reported in previous studies. Results indicated that serum bactericidal activity (G3 on day 60 and G1 on day 30) and classic complement in all groups (on day 60) (G1 and G2 on day 30) were significantly elevated (P < 0.05). The serum lysozyme, glucose, globulin, and albumin levels showed no significant differences between all groups compared to the control group (P > 0.05). On days 30 and 60 of the sampling, no significant difference was observed in the amount of superoxide disotase (SOD) and catalase (CAT) between the treatments (P > 0.05) but activity of malondialdehyde (MDA) was lower in G1 than the control (P < 0.05). The expression of the immune-regulating genes IL-10, IL-1β, GTP, FATP, and IGF was significantly improved in all probiotic + acidifier–treated groups (P < 0.05). The current findings showed that mixture of Bio-Aqua® and NaDF (1.5% + pro) is beneficial, as it effectively improves some immune parameters and expression of immunological and growth-related genes in Caspian brown trout. [ABSTRACT FROM AUTHOR]

This paper addresses constrained robust optimal control of Cable-Driven Parallel Robots (CDPRs) in detail. In the CDPRs, cables should remain in tension for all movements. Based on this fact, considering tension constraints, a robust optimal control scheme is presented for the cable robots in this study. To develop the idea, at first, the general nonlinear dynamic model of the CDPR is converted to a State-Dependent Coefficient (SDC) linear structure. In this model, uncertainties of the cable robot are included as a vector in the SDC linear structure. Next, a constrained robust optimal control algorithm is proposed based on Hamilton-Jacobi-Bellman (HJB) equations and Karush-Kuhn-Tucker (KKT) conditions using the state-dependent coefficient parameterization form. The proposed control scheme is formed based on the Generalized State-Dependent Riccati Equation (GSDRE), and a new equation is created to counteract with disturbances and uncertainty of the CDPR model. Finally, the stability of the closed-loop system is proved using Lyapunov's second method. Simulation results show the effectiveness of the proposed control algorithm in terms of tracking and coping with the uncertainties and external disturbances. [ABSTRACT FROM AUTHOR]

In the era of smart healthcare tremendous growth, plenty of smart devices facilitate cognitive computing for the purposes of lower cost, smarter diagnostic, etc. Android system has been widely used in the field of IoMT, and as the main operating system. However, Android malware is becoming one major security concern for healthcare, by the serious threat for our medical software assets, like the leakage of private information, the abusing of critical operations, etc. Unfortunately, the existing methods focus on building sustainable classification models, without fully considering system API which is the key to model aging. Compared to the traditional methods, we apply the lifeCycle of API as temporal metric. In addition to the temporal view, the “sizes” of the APPs are utilized as spatial metric in the spatial view. Based on this, we firstly discuss the temporal and spatial metrics together in terms of clustering, and then propose our novel framework-TSDroid. In this framework, we use TS-based clustering algorithm to obtain clustering subsets to enhance the detection capability. We have carried out an experimental verification on three existing excellent methods (i.e., Drebin, HinDroid, and DroidEvolver) and obtain good promotion effects by our framework. [ABSTRACT FROM AUTHOR]

With the continuous increment of city volume and size, a number of traffic-related urban units (e.g., vehicles, roads, buildings, etc.) are emerging rapidly, which plays a heavy burden on the scientific traffic control of smart cities. In this situation, it is becoming a necessity to utilize the sensor data from massive cameras deployed at city crossings for accurate traffic flow prediction. However, the traffic sensor data are often distributed and stored by different organizations or partieswith zero trust,which impedes themulti-party sensor data sharing significantly due to privacy concerns. Therefore, it requires challenging efforts to balance the trade-off between data sharing and data privacy to enable cross-organization traffic data fusion and prediction. In light of this challenge, we put forward an accurate LSH (locality-sensitive hashing)-based traffic flow prediction approach with the ability to protect privacy. Finally, through a series of experiments deployed on a real-world traffic dataset, we demonstrate the feasibility of our proposal in terms of prediction accuracy and efficiency while guaranteeing sensor data privacy. [ABSTRACT FROM AUTHOR]

Purpose: Among all known human coronaviruses, some viruses (e.g., SARS-CoV-2) cause severe pneumonia or even death. With the regard to its spread and the importance of its rapid identification/treatment, and because pAbs are relatively cheap, able to bind to more sites on antigens and even neutralize them, this study was done for the production and purification of anti-SARS-CoV-2 polyclonal antibodies (pAb) in rats. Methods: Viral antigen purification was performed by PEG/NaCl precipitation. The efficiency of the sucrose cushion method was also investigated to produce a purer antigen. Immunization was done and antibody purification was performed by ammonium sulfate precipitation (33%), dialysis, and ion-exchange chromatography. Western blotting and enzyme-linked immunosorbent assay (ELISA) were performed to verify the antibody specificity. All data were analyzed by SPSS software. Results: The results showed that the amount of concentrated virus increased with the increase of PEG concentration. Moreover, the sucrose cushion method increased its purity. Besides, induction of immune response in rats for pAb production with high titers was reached via these antigens and ELISA/western blot results indicated a suitable antibody-antigen interaction. Additionally, it was shown that ion-exchange chromatography could be a suitable technique for IgG purification. Conclusion: Herein, we presented a simple and cheap method for the purification of whole viral particles with relatively high quality. The results verified that these antigens could elicit a good immune response in the rat. The obtained pAbs showed a good specificity toward SARS-CoV-2 antigens. Accordingly, this study proposes a promising method for viral vaccine development. [ABSTRACT FROM AUTHOR]

The appropriate prediction of horizontal earth pressure against retaining walls can lead to a safer and more efficient design of such structures. Retaining walls are among the structures in which the arching phenomenon plays an essential role in distributing horizontal earth pressure. In this research, based on the limit equilibrium condition and considering the arching effect, a new theoretical method is proposed to estimate the earth pressure against a battered wall under translation movement with a cohesive-frictional backfill soil. A parametric study is conducted for assessing the effect of the surcharge pressure, cohesion, internal friction angle, wall–soil friction angle, and wall inclination on lateral earth pressure. In addition, a series of numerical models is employed to implement the finite difference method. The development of shear bands inside the backfill and the changes in earth pressure behind the wall are studied. The outcomes of the suggested analytical solution are compared with the numerical data as well as with some other theories. It is concluded that the proposed method is applicable to a wide range of cohesive-frictional soils retained with walls, covering a variety of batter angles and face frictional conditions. [ABSTRACT FROM AUTHOR]

The aim of the current research is to investigate the position of NGOs in improving the economic, political and social conditions of citizens in Tehran Municipality between 2010 and 2018. The statistical population of this research is the managers and senior experts of the 22 districts of Tehran and their related 121 districts, of which 220 people including the mayors of each district and its districts and senior managers will be selected as a statistical sample in the order of position priority. The required information was also collected through a researcher-made questionnaire whose validity and reliability were confirmed, and the results were obtained based on Friedman’s test, quadratic factor analysis method, and using SPSS and Lisrel software. The obtained results indicate a significant difference in the components of urban management and confirm NGOs’ role in improving urban management (urban social sciences, urban economics and urban politics) in Tehran Municipality from 2018 to 2019. Based on the Friedman test, the significant difference in the components has been investigated. The result of the ranking shows that the urban economy component has the highest rank and is the component in which NGOs play a more effective role. Finally, after performing the exploratory factor analysis, the fitting of the model and second-degree confirmatory analysis were done, the findings of which show that NGOs played an influential role in the component of social sciences, urban economy and urban policy improvement in Tehran Municipality between 2010 and 2018. has it. The main role of NGOs is related to the urban economy component. Considering that providing citizens’ needs, and providing welfare services, are basically infrastructures that are established by municipalities. All of them ultimately lead to the dynamism of the city’s economy and the improvement of the citizens’ well-being. [ABSTRACT FROM AUTHOR]

Cable-driven parallel robots (CDPRs) are a class of parallel robots where the cables are used as the arms of the robot in a redundant kinematic architecture. Such an architecture allows manipulation in a considerably large working area and also operation at high speed and acceleration. However, as cables only support tensile force, the controller should generate positive control effort, which leads to a complex constrained control problem. The positive tension distribution in redundant CDPR is generally maintained via redundancy resolution (RR) methods, which are originally optimization-based. The RR methods are prone to kinematic uncertainty, which brings complexity to the control apart from the original effects of kinematic uncertainty. The constrained control of CDPRs gets more complex in the faulty mode of the actuators. The combined effects of faulty actuators along with the kinematic uncertainty limit the application of RR in practice. To address this, a robust fault-tolerant constrained control scheme is proposed to generate an unidirectional control signal for maintaining positive tension in cables in the presence of actuator fault and model uncertainties, taking advantage of an adaptive finite-time sliding mode control along with a nonlinear adaptive observer. The proposed observer estimates the model uncertainties and the actuator fault, while the controller ensures the convergence of the state variables and compensates for the observer estimation error. The H ∞ asymptotic stability of the proposed observer is ensured through sufficient conditions using linear matrix inequality. Furthermore, Lyapunov's second method is employed to prove the finite-time stability of the system. The performance of the proposed scheme is experimentally validated using a planar CDPR in the presence of actuator fault and model uncertainties. [ABSTRACT FROM AUTHOR]

Objective: To evaluate the effect of Neurotec® herbal capsule (100 mg twice a day for 3 months) on the tinnitus symptoms in comparison with the placebo. Design: A double‐blind randomised clinical trial. Setting: Otolaryngology clinic of Baqiyatallah Hospital. Participants: Patients suffering from subjective tinnitus. Main Outcome Measures: Pure tone audiometry was measured at.5, 1, 2, 4 and 6 kHz frequencies before and after the third month of intervention. In addition, Tinnitus Handicap Inventory (THI) questionnaire as well as visual analogue scale (VAS) for tinnitus loudness, daily annoyance, daily life or sleep disturbance, daily perception and mood alteration were evaluated. Results: Finally, 103 (69 males and 34 females) patients with a mean age of 51.33 ± 13.91 years were analysed. In contrast with control group, patients in intervention group showed a remarkable decrease in THI score after 3 months of treatment (p <.05). Although both groups had improvements in VAS scores, mood disturbance, daily tinnitus perception and daily life alteration scores were only improved in the intervention group. The mean pure tone air and bone conduction were not significantly different between the control and the intervention group at baseline and 3 months after the intervention at.5, 1, 2 and 4 kHz (p >.05). Conclusion: A 3‐month treatment with Neurotec capsules in addition to patient education is of benefit for managing symptoms in patients with chronic tinnitus. [ABSTRACT FROM AUTHOR]

BACKGROUND: Mycobacterium avium subspecies paratuberculosis is the cause of a common disease in dairy herds. Early diagnosis of paratuberculosis infection can improve Johne's disease control programs. OBJECTIVES: This study aimed to compare the sensitivity, and specificity to methods; blood serum ELISA and stool Nested-PCR for the detection of Mycobacterium avium subspecies paratuberculosis infection in dairy cattle. METHODS: A commercial ELISA kit was used to perform the absorbed ELISA test, which was conducted after exposing serum samples to Mycobacterium phlei antigens to limit cross-reactions. Nested-PCR test was performed using nucleotide sequences related to specific MAP gene fragments, i.e. IS900. RESULTS: As a result of the ELISA antibodies kit, out of the total 2203 serum samples, 112 samples were positive (5.08 %) and 2091 samples were negative (94.92 %). The results of Nested-PCR tests of rectal feces showed that out of 59 cows with the positive results in serum ELISA, 47 (79.66 %) samples were positive and 12 (20.34 %) samples were negative. Moreover, out of 31 cattle with a negative result on the ELISA test, 15 (48.38%) and 16 cattle (51.62 %) had positive and negative results, respectively, on the nested PCR tests of the feces samples. CONCLUSIONS: Due to the low sensitivity of PCR compared to ELISA, the positive and negative predictive values, and the accuracy of ELISA test, as well as the high cost and time-consuming nature of PCR and the need for more and more complex facilities than ELISA, the authors concluded that ELISA is a more suitable method for screening and epidemiological studies than PCR. [ABSTRACT FROM AUTHOR]

Mild traumatic brain injury (mTBI), or concussion, can lead to persistent cognitive and functional symptoms that impede quality of life to a varying extent. This condition is referred to as post-concussive syndrome (PCS). Sleep disturbances are part of it but their distribution among different genders remains scarcely investigated. This pilot cross-sectional anonymous web-based survey interviewed volunteer 18–55 years old participants with a recent (i.e., less than 5 years) reported history of mTBI. Questionnaires related to persistent post-concussive symptoms (PPCS; Rivermead post-concussion symptoms questionnaire), sleep quality (Pittsburgh Sleep Quality Index) and daytime sleepiness (Epworth Sleepiness Scale) were administered as part of the survey. Ninety-one participants' responses were analysed (61 female; 30 male); 43% of them suffered from post-concussive syndrome, 60% reported poor sleep quality and 34% experienced excessive daytime sleepiness. The proportion of PPCS was significantly higher in female participants as compared to males (female: 57%; male: 13%; Fisher's exact test p < 0.001). Excessive daytime sleepiness was also significantly more present in females (female: 44%; male: 13%; p < 0.001) whereas poor sleep quality was present in similar proportions between females and males (female: 66%; male: 50%; p = 0.176). Even though based on a relatively small sample, these findings highlight important gender differences that should be accounted for in PPCS medical care and management. [ABSTRACT FROM AUTHOR]

The considerable move towards the use of renewable energy resources has been provided by the digitization of energy systems with the help of virtual power plants (VPPs). However, due to the coincidence of this move with the introduction of new technologies in information and communications, joining these systems raises concerns about the privacy of personal data. The only real-world approach widely used in this case is to anonymize or pseudonymize the information associated with individuals in data received from distributed measurement devices. In this paper, we propose the method of classifying received data packets into different flows and assigning different access levels for each flow. This method makes data pseudonymous. Before this step, the received data, which has a different format, is unionized. To implement this idea, a tuple space flow classification algorithm is parallelized on a CPU cluster using MPI and OpenMP according to different scenarios. The CPU cluster consists of one head node and two computational nodes for packet classification operations. In this research, two scenarios have been used to run the CPU algorithm in parallel. The first scenario uses MPI and the second scenario uses a combination of MPI and OpenMP libraries. Also, the Tuple Space algorithm has been implemented on the computing systems using the mentioned libraries in the form of two scenarios using OpenMP and MPI. According to our results, the increase in the number of processor cores is linearly correlated with the increase in the speed of classification. Furthermore, while MPI uses more memory than OpenMP, it helps to achieve a higher speed of classification. In the combined method, the maximum speed of flow classification can be achieved if the number of processes and threads is equal to the number of processor cores. In other words, when the sum of processes and threads does not outnumber CPU cores, the least classification time and memory usage can be achieved. [ABSTRACT FROM AUTHOR]

This article presents an automated, model-free, data-driven method for the safe tuning of PID cascade controller gains based on Bayesian optimization. The optimization objective is composed of data-driven performance metrics and modeled using Gaussian processes. The safety requirement is imposed via a barrier-like term in the objective, which is introduced to account for operational changes in the system. We further introduce a data-driven constraint that captures the stability requirements from system data. Numerical evaluation shows that the proposed approach outperforms relay feedback autotuning and quickly converges to the global optimum, thanks to a tailored stopping criterion. We demonstrate the performance of the method through simulations and experiments. For experimental implementation, in addition to the introduced safety constraint, we integrate a method for automatic detection of the critical gains and extend the optimization objective with a penalty depending on the proximity of the current candidate points to the critical gains. The resulting automated tuning method optimizes system performance while ensuring stability and standardization. [ABSTRACT FROM AUTHOR]

Nowadays, smart health technologies are used in different life and environmental areas, such as smart life, healthcare, cognitive smart cities, and social systems. Intelligent, reliable, and ubiquitous healthcare systems are a part of the modern developing technology that should be more seriously considered. Data collection through different ways, such as the Internet of things (IoT)-assisted sensors, enables physicians to predict, prevent and treat diseases. Machine Learning (ML) algorithms may lead to higher accuracy in medical diagnosis/prognosis based on health data provided by the sensors to help physicians in tracking symptom significance and treatment steps. In this study, we applied four ML methods to the data on Parkinson's disease to assess the methods' performance and identify the essential features that may be used to predict the total Unified Parkinson's disease Rating Scale (UPDRS). Since accessibility and high-performance decision-making are so vital for updating physicians and supporting IoT nodes (e.g., wearable sensors), all the data is stored, updated as rule-based, and protected in the cloud. Moreover, by assigning more computational equipment and memory in use, cloud computing makes it possible to reduce the time complexity of the training phase of ML algorithms in the cases we want to create a complete structure of cloud/edge architecture. In this situation, it is possible to investigate the approaches with varying iterations without concern for system configuration, temporal complexity, and real-time performance. Analyzing the coefficient of determination and Mean Square Error (MSE) reveals that the outcomes of the applied methods are mostly at an acceptable performance level. Moreover, the algorithm's estimated weight indicates that Motor UPDRS is the most significant predictor of Total UPDRS. [ABSTRACT FROM AUTHOR]

Wellbore instability causes main concerns in the oil and gas industry due to personnel safety and overall expenditure. Several signs such as wellbore spalling, the rate of penetration reduction, pipes sticking, well unnatural wash out, wellbore collapsing, induce fracture could indicate wellbore instability during drilling. A balance between stress concentration near the wellbore and rock strength is the primary condition for a wellbore stability during drilling. Thus, instability due to the failure of wellbore rocks occurs when the intensity of the effective stress exceeds rock strength. In underbalanced drilling (UBD), the wellbore is considered unstable since no pressure or maybe not enough pressure is applied on the well from the drilling fluid, and the pore pressure is considered undesirable stress on the wellbore, so the difference between these two pressures is applied on the wellbore. Consequently, the wellbore instability issue in the UBD approach is of great importance. UBD is mainly used to reduce the damage to geological formation and the risk of drilling fluid loss while increasing the drilling rate. This study has investigated the stability of a well in Iranian Oilfield using FLAC software considering yielded regions (plastic) for different mud weights. The investigation and analysis of the FLAC output plots, especially the plasticity plot, concluded that the ideal and optimal pressure for applying UBD conditions while also ensuring wellbore stability ranges 15.2–16.8 MPa. [ABSTRACT FROM AUTHOR]

Extensive user check-in data incorporating user preferences for location is collected through Internet of Things (IoT) devices, including cell phones and other sensing devices in location-based social network. It can help traveling enterprises intelligently predict users' interests and preferences, provide them with scientific tourism paths, and increase the enterprises income. Thus, successive point-of-interest (POI) recommendation has become a hot research topic in augmented Intelligence of Things (AIoT). Presently, various methods have been applied to successive POI recommendations. Among them, the recurrent neural network-based approaches are committed to mining the sequence relationship between POIs, but ignore the high-order relationship between users and POIs. The graph neural network-based methods can capture the high-order connectivity, but it does not take the dynamic timeliness of POIs into account. Therefore, we propose an Interaction-enhanced and Time-aware Graph Convolution Network (ITGCN) for successive POI recommendation. Specifically, we design an improved graph convolution network for learning the dynamic representation of users and POIs. We also designed a self-attention aggregator to embed high-order connectivity into the node representation selectively. The enterprise management systems can predict the preferences of users, which is helpful for future planning and development. Finally, experimental results prove that ITGCN brings better results compared to the existing methods. [ABSTRACT FROM AUTHOR]

Currently, COVID-19 is circulating in crowded places as an infectious disease. COVID-19 can be prevented from spreading rapidly in crowded areas by implementing multiple strategies. The use of unmanned aerial vehicles (UAVs) as sensing devices can be useful in detecting overcrowding events. Accordingly, in this article, we introduce a real-time system for identifying overcrowding due to events such as congestion and abnormal behavior. For the first time, a monitoring approach is proposed to detect overcrowding through the UAV and social monitoring system (SMS). We have significantly improved identification by selecting the best features from the water cycle algorithm (WCA) and making decisions based on deep transfer learning. According to the analysis of the UAV videos, the average accuracy is estimated at 96.55%. Experimental results demonstrate that the proposed approach is capable of detecting overcrowding based on UAV videos' frames and SMS's communication even in challenging conditions. [ABSTRACT FROM AUTHOR]

Nowadays, WSN (wireless sensor networks) are used in every aspect of life. It consists of tiny sensor nodes that are capable of sensing, aggregating, dispensation, and sending data for end-user queries. Two major constraints in WSN are power consumption and reliability. As we know, power is limited per sensor node, so efficient utilization of power is needed all the way. Another constraint is reliability. Both constraints are in conflict with each other. If we try to increase reliability by sending the same packet on a different path, then it will increase power consumption. Many algorithms have been defined in the past to reduce power consumption and have reliable transmission. Basically, there are two types of reliability mechanisms: packet reliability and occurrence reliability. Package reliability explains about all packages must be reached at the destination. Event reliability ensures that at least one event should be successful in a specific region. This paper proposes an analysis model to evaluate the reliability of wireless sensor networks. In addition, this paper has proposed a reliability technique for events. The proposed algorithm has been simulated, analyzed, and compared with the already-published algorithms, namely BaseModel, SWIA, and ERP techniques. [ABSTRACT FROM AUTHOR]

A convenient, accurate and well-known way toward any supervised task is using Nearest Neighbor approach or its variants. However, there has been little attempt toward improving interpretability by human and providing a classical optimization of Synthetic Reduced Nearest Neighbor. To tackle these issues, this paper provides a novel optimization of Synthetic Reduced Nearest Neighbor based on Expectation Maximization (EM-SRNN). Reduced Nearest Neighbor model consists of a subset of the samples from the trainset that has similar accuracy to Nearest Neighbor. Synthetic Reduced Nearest Neighbor relaxes the model to learn K synthetic samples (or prototypes/centroids) in the space of dataset. Therefore, inspired by EM algorithm for K-means, we propose a novel optimization based on EM algorithm to learn EM-SRNN by iterating over the centroids of the model and assignment of training samples to the centroids. The first step consists of optimizing the position of each centroid based on the assignment of the samples to the centroid and the second step consists finding optimal assignments and labels of the centroids. The EM-SRNN is interpretable since the centroids exist inside the space of training samples. Additionally, the centroids represent the multiple modalities (or sub-clusters) of the classes that are interpretable by human. These properties make this type of interpretability unique, hence, making this model suitable for various studies that are related to interpretability by human, such as image processing and epidemiological studies. In this paper, analytical aspects of problem are explored and it is shown that computational complexity of proposed optimization is linear over size of the trainset. Finally, EM-SRNN shows superior or similar performance when compared with several other interpretable and similar state-of-the-art models, such as trees and kernel SVMs. [ABSTRACT FROM AUTHOR]

The availability of the system is one of the main requirements of a multimedia-based e-health application that carries critical patient health information in the network environment. On the other hand, the Distributed Denial of Service (DDoS) attack is one of the most common attacks on the availability of computer networks which can be devastating for a healthcare system. Therefore, a countermeasure to this attack has to be performed in the early steps of the attack to protect the systems against its damages. Detection methods cannot support this and are only able to detect the attack after it happened. Thus, it is necessary to predict DDoS attacks according to the evidence which the attack makes in the network in the early steps of the attack. Therefore, Prediction approaches can reduce the cost of the attacks compared to detection approaches. In this paper, we propose a new method for prediction of DDoS attack based on Lyapunov Exponent Analysis and Echo State Network (LEAESN). In this method, the future traffic of the network is predicted using the Exponential Smoothing technique, then the time series of the prediction error is calculated based on the difference of this prediction and the observed traffic of the network. As shown in this paper, this time series is chaotic in the presence of attack traffics. To predict the DDoS attack, this time series is predicted using a Recurrent Neural Echo State Network (SCESN), and the attack is detected using Lyapunov exponent analysis on the predicted time series. For the evaluation of LEAESN, we test the method on the Darpa98 dataset which consists of a standard dataset for evaluation of intrusion detection systems. LEAESN has an appropriate ability to predict the DDoS attack. [ABSTRACT FROM AUTHOR]

A series of centrifuge experiments were conducted to identify different internal failure modes of a group of soil-cement (SC) columns (e.g., shearing and tension due to bending) under combined effects of embankment loading and liquefaction-induced lateral spreading. The results were also used to investigate how an increase in the flexural/shear strength of SC columns (using high-strength steel reinforcement bars) can affect their ability to limit settlements or deformations. The centrifuge experiments included two centrifuge tests of liquefiable foundation reinforced by SC columns with and without high-strength steel reinforcement cores during strong earthquake loading. It was found that during and after cracking of the SC columns, shear and tilting failure is the prominent failure mechanism. The test results showed that increasing the flexural capacity of SC columns can reduce the potential for earthquake-induced liquefaction and associated damage. Compared with conventional SC columns, reinforced soil-cement (RSC) columns are more effective in providing confinement for the soil under the embankment during strong shaking events, resulting in lower shaking-induced pore-water pressure and a reduction in the lateral deformation of the soil and embankment. [ABSTRACT FROM AUTHOR]

The prediction of active earth pressure is an essential issue in geotechnical engineering. Luo et al. (2021) proposed a new theoretical method for calculating the active earth pressure on a battered rigid retaining structure under translation mode. The theory of Luo et al. (2021) has some drawbacks which are discussed in this paper. [ABSTRACT FROM AUTHOR]

The high mortality rate and prevalence of cardiovascular disease (CVD) make early detection of the disease essential. Due to its simplicity and low cost, the phonocardiogram (PCG) system is widely used in healthcare applications for the recognition of CVD in multiclass problems. On the basis of the PCG signal, this paper proposes a hybrid method for classifying cardiac sounds with deep extracted features through two‐step learning. For fine‐grained features in Graph Convolutional Networks (GCNs), sampling and prior layers are employed. A PCG signal is divided into equal parts with overlap using the windowing process. L‐spectrograms extract frequency‐domain information from signals to figure out their power spectrum. Furthermore, the deep GCN tries to determine the association between CVD and spectrogram images to recognize CVD signals better. Combining retrieved features with convolutional neural network (CNN) characteristics reveals an image's intrinsic associations. To generate relational feature representations, correlations between clusters and GCN are visualized using a graph structure. CNN's discriminative ability has been enhanced by incorporating GCN attributes. Using Michigan Heart Sound and Murmur Database and PhysioNet/CinC 2016 Challenge results, we are 99.44% and 96.16% accurate, respectively. Through a combination of GCN architecture, CNN design, and deep features, the hybrid model significantly improves CVD classification accuracy. Measuring metrics demonstrate that the proposed approach detects CVD more effectively than previous approaches. [ABSTRACT FROM AUTHOR]

Introduction: Scorpion venom contains various biological compounds. Clinical symptoms in individuals and laboratory animals exposed to scorpion venom depend on the response of the host immune system. The secretion of inflammatory cytokine s is one of the most critical factors involved in the pathogenesis of scorpion venom. Methods: This comparative study aimed to evaluate the expression of inflammatory cytokine IL-6 and anti-inflammatory cytokine IL-10 in rats treated with Hottentotta saulsyi scorpion venom. The venom was obtained from the Razi Vaccine and Serum Research Institute of Ahvaz branch. After determining the H. saulsyi venom LD50, the rats were divided in two groups of test and control (n-12). The test group received 1/3 LD50 dose in 0.5 ml of physiological serum by subcutaneous injection per rat. The exact amount of physiological serum was injected into the control group. After that, cardiac blood samples were taken from rats at 0, 4, 24, and 72 hours after anesthesia. After serum preparation, the levels of IL-10 and IL-6 cytokines were measured in both groups using ELISA assays. Results: The obtained LD50 equaled 1.01 mg/kg of the rat 's body weight. Four hours after experimentally envenomation, the serum levels of IL-6 and IL-10 were significantly increased compared to the control group (P <0.05); but in the taken samples 24 hours after the treatment, there was no significant difference compared to the control group. During 72 hours, the level of these cytokines decreased in the treatment group compared to the control group. Conclusion: Changes in inflammatory and anti-inflammatory cytokines levels during scorpion stings can be used as a novel clinical finding to assess patients' status and perform appropriate therapeutic interventions to reduce scorpion sting complications. [ABSTRACT FROM AUTHOR]

This paper presents the results of nonlinear deformation analyses of an embankment on a spatially variable, liquefiable foundation soil reinforced with spatially variable soil-cement (SC) wall. The objective is to evaluate the merit of employing stochastic modeling approaches, such as spatially correlated random fields, relative to deterministic analysis with uniform properties for the soil and SC wall. Spatial variability, which is specified by mean, coefficient of variation (COV), and scale of fluctuation (SOF), can significantly influence the seismic performance of soils and embankments. The numerical analysis was first validated using data from a dynamic centrifuge test conducted at the Center for Geotechnical Modeling at University of California, Davis. Analyses were performed for different sets of realization of the foundation soil and SC wall as well as the range of the length and strength of SC wall and peak base acceleration. Simulation results of the stochastic models with spatially Gaussian random field were examined and compared with the results of the experiment with an emphasis on the crest vertical displacement, berm horizontal displacement, and contours of shear strain in the embankment, foundation soil, and SC wall. The results revealed that the representative percentile of unconfined compressive strength (qucs) in the range of 35th–50th percentile needed to be used in a uniform model to estimate the median of crest and berm displacement in spatially variable SC wall-supported embankment. [ABSTRACT FROM AUTHOR]