Analysis of sandwich graphene origami composite plate sandwiched by piezoelectric/piezomagnetic layers: A higher-order electro-magneto-elastic analysis.

This work applies a higher order thickness-stretched model for the electro-elastic analysis of the composite graphene origami reinforced square plate sandwiched by the piezoelectric/piezomagnetic layers subjected to the thermal, electric, magnetic and mechanical loads. The plate is manufactured of a copper matrix reinforced with graphene origami where the effective material properties are calculated based on the micromechanical models as a function of volume fraction and folding degree of graphene origami, material properties of matrix, reinforcement, and local temperature. The governing equations are derived using the virtual work principle in terms of the bending, shear and stretching functions, in-plane displacements, electric, and magnetic potentials. The numerical results including various displacement components, maximum electric, and magnetic potentials are presented with changes of volume fraction, folding degree of reinforcement, electrical, magnetic, and thermal loading. A verification investigation is presented for approve of the methodology, and the solution procedure. The main novelty of this work is simultaneous effect of the thickness stretching and the multi-field loading on the electromagnetic bending results of the sandwich plate. Another novelty of this work is usage of graphene origami nano-reinforcement as a controllable material in a sandwich structure subjected to multi-field loadings. The results show an increase in bending, shear, and stretching deflections with an increase in electromagnetic loads, and folding degree as well as a decrease in volume fraction of reinforcement.

Inflammatory bowel disease evolution in the past two decades: a chronological multinational study.

Background: The multifactorial nature of inflammatory bowel disease (IBD), which manifests differently in individuals creates a need for a better understanding of the behaviour and pattern of the disease due to environmental factors. The current study aimed to study the changes in IBD behaviour, presentation, and characteristics in patients over the past two decades with a goal of improving patients' diagnosis, management and outcomes. Methods: During a 6-month period (1/02/2022-30/07/2022), the information of patients with IBD who attended IBD outpatient clinics of 11 referral centre's in six countries was collected, and based on the first time of diagnosis with IBD, they were allocated as group A (those who were diagnosed more than 15 years ago), group B (those who were diagnosed with IBD between 5 and 15 years ago) and group C (IBD cases who diagnosed in recent 5 years). Then the most prevalent subtypes and characters of the disease are evaluated and compared to make clear if the presenting pattern and behaviour of the disease has changed in the last 2 decades. Findings: Overall 1430 patients with IBD including 1207 patients with ulcerative colitis (UC) (84.5%) and 205 patients with Crohn's disease (CD; 14.3%) included. Mean age of participants at the first time of diagnosis with IBD was 30 years. The extra-intestinal involvement of IBD in groups A and B was more prevalent in comparison with group C. Most of those in groups A & B had academic education but in group C, the most prevalent educational status was high school or diploma (P = 0.012). In contrast to groups A and B, the relative prevalence of medium socioeconomic level in group C had decreased (65%). Relative prevalence of UC subtypes was similar among groups A and B (extensive colitis as most prevalent) but in group C, the most prevalent subtype is left side colitis (38.17%). The most prevalent subtype of CD in groups A and B was ileocolic involvement while in group C, upper GI involvement is significantly increased. The rate of food sensitivity among groups A and B was more than group C (P = 0.00001). The relative prevalence of patients with no flare has increased with a steady slope (P < 0.00001). Relative prevalence of presenting symptoms among patients with UC in group C differs and nowadays the rate abdominal pain (70.7%) and bloating (43.9%) have increased and frequency of diarrhoea (67.4%) has decreased. Interpretation: In the recent 5 years, the pattern of UC presentation has changed. The rate of upper GI involvement in CD and relative prevalence of patients with no disease flare increased and the rate of extra intestinal involvement decreased. Funding: None.

A general electroelastic analysis of piezoelectric shells based on levy-type solution and eigenvalue-eigenvector method.

Eigenvalue-Eigenvector approach as well as Levy type solution are used for electroelastic analysis of a doubly curved shell made of piezoelectric material based on a shear deformable model and piezoelasticity relations. The electroelastic governing equations are derived using virtual work principle. The solution is proposed for a Levy type boundary conditions with two simply-supported boundary conditions and two clamped ones. After derivation of the governing equations, a solution satisfying two simply supported boundary conditions is assumed to arrive a system of ordinary differential equations. The latest governing equations are solved using Eigenvalue-Eigenvector method to satisfy clamped-clamped boundary conditions. The distribution of displacements, rotations, electric potential, strain and stress is presented along the planar coordinate. Accuracy of the proposed solution is justified through comparison with results of previous papers.

Observer-Based Fault-Tolerant Finite-Time Control of Nonlinear Multiagent Systems.

In this article, an adaptive neural containment control for a class of nonlinear multiagent systems considering actuator faults is introduced. By using the general approximation property of neural networks, a neuro-adaptive observer is designed to estimate unmeasured states. In addition, in order to reduce the computational burden, a novel event-triggered control law is designed. Furthermore, the finite-time performance function is presented to improve the transient and steady-state performance of the synchronization error. Utilizing the Lyapunov stability theory, it will be shown that the closed-loop system is cooperatively semiglobally uniformly ultimately bounded (CSGUUB), and the followers' outputs reach the convex hull constructed by the leaders. Moreover, it is shown that the containment errors are limited to the prescribed level in a finite time. Eventually, a simulation example is presented to corroborate the capability of the proposed scheme.

Distributed Adaptive-Neural Finite-Time Consensus Control for Stochastic Nonlinear Multiagent Systems Subject to Saturated Inputs.

In this article, the problem of distributed finite-time consensus control for a class of stochastic nonlinear multiagent systems (MASs) (with directed graph communication) in the presence of unknown dynamics of agents, stochastic perturbations, external disturbances (mismatched and matched), and input saturation nonlinearities is addressed and studied. By combining the backstepping control method, the command filter technique, a finite-time auxiliary system, and artificial neural networks, innovative control inputs are designed and proposed such that outputs of follower agents converge to the output of the leader agent within a finite time. Radial-basis function neural networks (RBFNNs) are employed to approximate unknown dynamics, stochastic perturbations, and external disturbances. To overcome the complexity explosion problem of the conventional backstepping method, a novel finite-time command filter approach is proposed. Then, to deal with the destructive effects of input saturation nonlinearities, the finite-time auxiliary system is designed and developed. By mathematical analysis, it is proven that the mentioned MAS (injected by the proposed control inputs) is semiglobally finite-time stable in probability (SGFSP) and all consensus tracking errors converge to a small neighborhood of the zero during a finite time. Finally, a numerical simulation onto a group of four single-link robot manipulators is carried out to illustrate the effectiveness of the suggested control scheme.

Effects of vitamin D supplementation on depression and some selected pro-inflammatory biomarkers: a double-blind randomized clinical trial.

BACKGROUND: Both augmented inflammatory reaction and low vitamin D status are associated with depression but the magnitude of their relationships is unclear. This study was, therefore, conducted to evaluate the effects of vitamin D supplementation on serum 25(OH)D concentration, depression severity and some pro-inflammatory biomarkers in patients with mild to moderate depression. METHODS: An 8-week double-blind randomized clinical trial (RCT) was performed on 56 (18-60 yrs) patients with mild to moderate depression, randomly assigned to intervention (50,000 IU cholecalciferol 2wks-1) and control (placebo) groups. Serum 25(OH)D, intact parathyroid hormone (iPTH), interlukin (IL)-1ß, IL-6, high-sensitivity C-reactive protein (hs-CRP) and depression severity (Beck Depression Inventory-II) (BDI-II)) were initially and finally assessed. RESULTS: At the end point, statistically significant changes were observed only in intervention group as compared with controls including increased 25(OH)D concentration (+ 40.83 ± 28.57 vs. + 5.14 ± 23.44 nmol L-1, P < 0.001) and decreased depression severity (-11.75 ± 6.40 vs. -3.61 ± 10.40, P = 0.003). No significant within- or between group differences were observed in serum IL-1ß, IL-6 and hs-CRP concentrations. CONCLUSION: Increased circulating 25(OH)D concentrations following 8-week vitamin D supplementation (50,000 IU 2wks-1) resulted in a significant decrease in BDI-II scores in patients with mild to moderate depression. However, this effect was independent of the serum concentrations of the studied inflammatory biomarkers. TRIAL REGISTRATION: The clinical trial registration code was obtained from the Iranian Registry of Clinical Trials (date of registration: 17/09/2018, registration number: IRCT20170926036425N1) and ClinicalTrials.gov (date of registration: 04/12/2018, registration number: NCT03766074).

Comparative Assessment of the Effects of Hydroxyethyl Starch and Normal Saline on Severe Hypotension in Patients with Aluminum Phosphide Poisoning: A Retrospective Study.

Background: Aluminum phosphide poisoning is one of the most common forms of poisoning which requires immediate and urgent treatment. Objective: This study aimed to compare the efficiency of two solutions, including hydroxyethyl starch and normal saline, in treating hypotension in patients with aluminum phosphide poisoning. Methods: This retrospective cohort study was conducted on 35 patients with aluminum phosphide poisoning. We reviewed the profile of 18 patients treated with hydroxyethyl starch and 17 patients treated with normal saline. Within-group and between-group differences in systolic blood pressure before and after treatment were compared using paired t-test and independent t-test, respectively. Results: The mean ± standard deviation (SD) age of the subjects in the starch and normal saline groups was 27.06 ± 9.72 and 27.88 ± 9.08, respectively. The levels of blood pressure in the two groups were not significantly different before the treatment; the mean ± SD of systolic blood pressure in the starch and normal saline groups was 72.67 ± 14.49 and 68.59 ± 8.3, respectively (P=0.313). After the treatment, it was significantly increased to 94 ± 24.45 and 85.18 ± 19.9 in the starch group (P=0.001) and the normal saline group (P=0.004), respectively. However, there was no significant difference between the two groups (P=0.245). Only one person survived in each group. Conclusion: According to the results of this study, although there was no significant difference between the two groups in terms of their effects on hypotension, these treatments could not prevent mortality.

Prescribed Performance Quantized Tracking Control for a Class of Delayed Switched Nonlinear Systems With Actuator Hysteresis Using a Filter-Connected Switched Hysteretic Quantizer.

This article proposes a prescribed adaptive backstepping scheme with new filter-connected switched hysteretic quantizer (FCSHQ) for switched nonlinear systems with nonstrict-feedback structure and time-delay. The system model is subjected to unknown functions, unknown delays, and unknown Bouc-Wen hysteresis nonlinearity. The coexistence of quantized input and actuator hysteresis may deteriorate the shape of hysteresis loop and, consequently, fail to guarantee the stability. To deal with this issue, a new FCSHQ is introduced to smooth the input hysteresis. This adaptive filter also provides us a degree of freedom at choosing the desired communication rate. The repetitive differentiations of virtual control laws and existing a lot of learning parameters in the neural network (NN)-based controller may result in an algebraic loop problem and high computational time, especially in a nonstrict-feedback form. This challenge is eased by the key advantage of NNs' property where the upper bound of the weight vector is employed. Then, by an appropriate Lyapunov-Krasovskii functional, a common Lyapunov function is presented for all subsystems. It is shown that the proposed controller ensures the predefined output tracking accuracies and boundedness of the closed-loop signals under any arbitrary switching. Finally, the proposed control scheme is verified on a practical example where simulation results demonstrate the effectiveness of the proposed scheme.

Effect of hydroxyethyl starch on acidosis in patients with aluminum phosphide poisoning.

BACKGROUND: Given the fact that various studies have reported the positive effects of hydroxyethyl starch therapy in controlling shock, this study aimed to compare the effects of hydroxyethyl starch on modifying acidosis and treating patients with aluminum phosphide poisoning. METHODS: This was a randomized clinical trial that was conducted on 60 patients with aluminum phosphide poisoning. We compared the two groups of patients treated with hydroxyethyl starch and normal saline. Base excess and serum pH of arterial blood gases (ABG) were measured before and after the treatment and compared by t-test. RESULTS: Results showed that arterial pH in the starch group and normal saline group increased by 0.13 and 0.18, respectively, and the difference between the two groups was not statistically significant. The difference in the base excess before and after treatment in the starch group and normal saline group was 6.41 and 5.39, respectively, and the difference between the two groups was not statistically significant. Changes in mean values of arterial pH after the intervention in comparison with before treatment were statistically significant (p<0.05). CONCLUSION: Overall, the results of the present study show that starch is at least as effective as normal saline in treating acidosis in patients poisoned with aluminum phosphide and can be used instead of normal saline, and both of the two treatments could be equally effective.

Epidemiologic profile of deaths due to drug and chemical poisoning in patients referred to Baharloo Hospital of Tehran, 2011 to 2014.

BACKGROUND: In developing countries with high mortality rates, poisoning is one of the most common causes of admission to emergency rooms. To minimize future deaths related to poisoning, the epidemiological profile of deceased individuals is essential. METHODS: The medical records of all dead patients due to poisoning during 2011-2014 in Baharloo Hospital, Tehran, were evaluated. Exclusion criteria include: incomplete records, unknown causes of death, and persons less than 6 years of age. Data analysis was done by means of SPSS at the significance level of P < 0.05. RESULTS: The study included 184 males and 65 females. The mean age range was 37.65 ±â€¯16.78 years. The highest mortality rate was seen in the age range of 21-30 years (30.5%). The most common cause of poisoning was aluminum phosphide (101 cases). The average time of hospitalization was 3.61 days. Most deaths occurred during the first 10 days of admission with intentional poisoning being the most common type (81.5%). CONCLUSION: The outcome of this study indicates that the main cause of death among young people is intentional poisoning with AIP. This study proves that a greater focus when diagnosing mental health patients, as well as an increase in restrictions when accessing lethal drugs and toxins, is crucial.

Higher-Order Thermo-Elastic Analysis of FG-CNTRC Cylindrical Vessels Surrounded by a Pasternak Foundation.

This study analyses the two-dimensional thermo-elastic response of functionally graded carbon nanotube-reinforced composite (FG-CNTRC) cylindrical pressure vessels, by applying the third-order shear deformation theory (TSDT). The effective properties of FG-CNTRC cylindrical pressure vessels are computed for different patterns of reinforcement, according to the rule of mixture. The governing equations of the problem are derived from the principle of virtual works and are solved as a classical eigenproblem under the assumption of clamped supported boundary conditions. A large parametric investigation aims at showing the influence of some meaningful parameters on the thermo-elastic response, such as the type of pattern, the volume fraction of CNTs, and the Pasternak coefficients related to the elastic foundation.

Molecular evidence of role of vitamin D deficiency in various extraskeletal diseases.

BACKGROUND: Role of vitamin D is not only limited to skeletal system but various other systems of the body, such as immune system, endocrine system, and cardiopulmonary system. MATERIALS AND METHODS: It is supported by the confirmations of systems-wide expression of vitamin D receptor (VDR), endocrinal effect of calcitriol, and its role in immune responses. RESULTS: Expression of VDR in various systems, immunoregulatory and hormonal response of vitamin D and deficiency of vitamin D may establish various pathologies in the body. CONCLUSION: This review provides molecular evidence of relation of vitamin D with extra skeletal.

Recursive generalized extended least squares and RML algorithms for identification of bilinear systems with ARMA noise.

Bilinear systems are considered as a particular class of nonlinear systems including the state variables which are typically used for online identification. By using a recursive identification method and the maximum likelihood principle, this paper presents two recursive-based algorithms to identify the parameters of bilinear in parameter systems with ARMA noise. In this regard, recursive generalized extended least squares (RGELS) and recursive Maximum Likelihood (RML) algorithms have been proposed for identification of bilinear systems. These algorithms can be used as an alternative choice in system identification with acceptable performance. The proposed algorithms estimate the correlated noise parameters with high accuracy by making full use of the measurement data. Simulation results indicate that the proposed algorithms are effective for online identification of bilinear in parameter systems with high convergence speed.

Wiener model based GMVC design considering sensor noise and delay.

Even though there is a plethora of literature available for assessing linear control loop performance, they cannot be applied to the nonlinear control loops. In this paper, a nonlinear generalized minimum variance (NGMV) controller based on a single input-single output (SISO) Wiener model is proposed. The NGMV controller's performance is used as a benchmark for a class of nonlinear control loops. The advantage of the proposed method is ability of online parameter estimation of the nonlinear model using common recursive least squares (RLS) method. In real-world applications, sensor and measurement tools force noises and extra delay to the control loop which poses limitations on achievable control performance. Hence, the classic control performance assessment techniques, is not attainable anymore. To handle the limitation caused by sensor delay, the k-step ahead prediction method is utilized. Further, the exponential digital filter is used in order to attenuate impact of the measurement noise on the controller. To show the effectiveness of the proposed method, a simulation test on a pH neutralization process is carried out.

Adaptive neural output-feedback control for nonstrict-feedback time-delay fractional-order systems with output constraints and actuator nonlinearities.

This study addresses the issue of the adaptive output tracking control for a category of uncertain nonstrict-feedback delayed incommensurate fractional-order systems in the presence of nonaffine structures, unmeasured pseudo-states, unknown control directions, unknown actuator nonlinearities and output constraints. Firstly, the mean value theorem and the Gaussian error function are introduced to eliminate the difficulties that arise from the nonaffine structures and the unknown actuator nonlinearities, respectively. Secondly, the immeasurable tracking error variables are suitably estimated by constructing a fractional-order linear observer. Thirdly, the neural network, the Razumikhin Lemma, the variable separation approach, and the smooth Nussbaum-type function are used to deal with the uncertain nonlinear dynamics, the unknown time-varying delays, the nonstrict feedback and the unknown control directions, respectively. Fourthly, asymmetric barrier Lyapunov functions are employed to overcome the violation of the output constraints and to tune online the parameters of the adaptive neural controller. Through rigorous analysis, it is proved that the boundedness of all variables in the closed-loop system and the semi global asymptotic tracking are ensured without transgression of the constraints. The principal contributions of this study can be summarized as follows: (1) based on Caputo's definitions and new lemmas, methods concerning the controllability, observability and stability analysis of integer-order systems are extended to fractional-order ones, (2) the output tracking objective for a relatively large class of uncertain systems is achieved with a simple controller and less tuning parameters. Finally, computer-simulation studies from the robotic field are given to demonstrate the effectiveness of the proposed controller.

Nonlinear system identification based on Takagi-Sugeno fuzzy modeling and unscented Kalman filter.

This paper proposes two novel Kalman-based learning algorithms for an online Takagi-Sugeno (TS) fuzzy model identification. The proposed approaches are designed based on the unscented Kalman filter (UKF) and the concept of dual estimation. Contrary to the extended Kalman filter (EKF) which utilizes derivatives of nonlinear functions, the UKF employs the unscented transformation. Consequently, non-differentiable membership functions can be considered in the structure of the TS models. This makes the proposed algorithms to be applicable for the online parameter calculation of wider classes of TS models compared to the recently published papers concerning the same issue. Furthermore, because of the great capability of the UKF in handling severe nonlinear dynamics, the proposed approaches can effectively approximate the nonlinear systems. Finally, numerical and practical examples are provided to show the advantages of the proposed approaches. Simulation results reveal the effectiveness of the proposed methods and performance improvement based on the root mean square (RMS) of the estimation error compared to the existing results.

A fast iterative recursive least squares algorithm for Wiener model identification of highly nonlinear systems.

In this paper, an online identification algorithm is presented for nonlinear systems in the presence of output colored noise. The proposed method is based on extended recursive least squares (ERLS) algorithm, where the identified system is in polynomial Wiener form. To this end, an unknown intermediate signal is estimated by using an inner iterative algorithm. The iterative recursive algorithm adaptively modifies the vector of parameters of the presented Wiener model when the system parameters vary. In addition, to increase the robustness of the proposed method against variations, a robust RLS algorithm is applied to the model. Simulation results are provided to show the effectiveness of the proposed approach. Results confirm that the proposed method has fast convergence rate with robust characteristics, which increases the efficiency of the proposed model and identification approach. For instance, the FIT criterion will be achieved 92% in CSTR process where about 400 data is used.

Clinical and laboratory findings of rhabdomyolysis in opioid overdose patients in the intensive care unit of a poisoning center in 2014 in Iran / 한국역학회지

OBJECTIVES: The aim of this study was to investigate the clinical and demographic characteristics and some laboratory findings of hospitalized patients with acute opioid toxicity and rhabdomyolysis. METHODS: This cross-sectional study investigated 354 patients hospitalized at Baharloo Hospital in Tehran in 2014 with acute illicit drug toxicity. Data were collected using an investigator-made checklist. The collected data (such as mortality rate, demographic data, and renal function tests, as well as serum biochemical findings) were analyzed by descriptive statistics and the chi-square test. RESULTS: A total of 354 patients were admitted to the hospital in 2014 with acute illicit drug toxicity, including 291 males and 63 females. The total number of patients with rhabdomyolysis was 76 (21.5% of the total), of whom 69 (90.8%) were male and 7 (9.2%) were female. Most cases of rhabdomyolysis were associated with methadone abuse, followed by opium abuse. Rhabdomyolysis was most common in those 20–29 and 30–39 years old, with methadone and opium the most commonly abused illicit drugs. The mean blood urea level was 3.8±1.0 mg/dL, and the mean serum potassium and sodium levels were 3.8±0.3 mg/dL and 140.4±4.0 mg/dL, respectively. Five patients, all of whom were male, passed away due to severe renal failure (6.5%). CONCLUSIONS: Toxicity caused by opioids is associated with clinical complications and laboratory disorders, such as electrolyte disorders, which can lead to lethal or life-threatening results in some cases. Abnormal laboratory test findings should be identified in patients with opioid toxicity in order to initiate efficient treatment.

Clinical and laboratory findings of rhabdomyolysis in opioid overdose patients in the intensive care unit of a poisoning center in 2014 in Iran / 한국역학회지

OBJECTIVES: The aim of this study was to investigate the clinical and demographic characteristics and some laboratory findings of hospitalized patients with acute opioid toxicity and rhabdomyolysis.METHODS: This cross-sectional study investigated 354 patients hospitalized at Baharloo Hospital in Tehran in 2014 with acute illicit drug toxicity. Data were collected using an investigator-made checklist. The collected data (such as mortality rate, demographic data, and renal function tests, as well as serum biochemical findings) were analyzed by descriptive statistics and the chi-square test.RESULTS: A total of 354 patients were admitted to the hospital in 2014 with acute illicit drug toxicity, including 291 males and 63 females. The total number of patients with rhabdomyolysis was 76 (21.5% of the total), of whom 69 (90.8%) were male and 7 (9.2%) were female. Most cases of rhabdomyolysis were associated with methadone abuse, followed by opium abuse. Rhabdomyolysis was most common in those 20–29 and 30–39 years old, with methadone and opium the most commonly abused illicit drugs. The mean blood urea level was 3.8±1.0 mg/dL, and the mean serum potassium and sodium levels were 3.8±0.3 mg/dL and 140.4±4.0 mg/dL, respectively. Five patients, all of whom were male, passed away due to severe renal failure (6.5%).CONCLUSIONS: Toxicity caused by opioids is associated with clinical complications and laboratory disorders, such as electrolyte disorders, which can lead to lethal or life-threatening results in some cases. Abnormal laboratory test findings should be identified in patients with opioid toxicity in order to initiate efficient treatment.

Neuro-adaptive backstepping control of SISO non-affine systems with unknown gain sign.

This paper presents two neuro-adaptive controllers for a class of uncertain single-input, single-output (SISO) nonlinear non-affine systems with unknown gain sign. The first approach is state feedback controller, so that a neuro-adaptive state-feedback controller is constructed based on the backstepping technique. The second approach is an observer-based controller and K-filters are designed to estimate the system states. The proposed method relaxes a priori knowledge of control gain sign and therefore by utilizing the Nussbaum-type functions this problem is addressed. In these methods, neural networks are employed to approximate the unknown nonlinear functions. The proposed adaptive control schemes guarantee that all the closed-loop signals are semi-globally uniformly ultimately bounded (SGUUB). Finally, the theoretical results are numerically verified through simulation examples. Simulation results show the effectiveness of the proposed methods.