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Ectopic pancreas, also known as heterotopic pancreas, is a rare condition in which the pancreatic tissue is found outside its usual location in the gastrointestinal (GI) tract. It is commonly asymptomatic and benign, and is often discovered incidentally during routine imaging, endoscopy, surgery, or autopsy. However, complications can arise, such as inflammation, bleeding, obstruction, or even malignant transformation, necessitating surgical intervention in some cases. Ectopic pancreas at the ampulla of Vater (EPAV) is an extremely rare condition and a diagnostic and therapeutic nightmare. Most cases have been diagnosed through invasive surgery due to concerns for malignancy, which carries significant morbidity and mortality. In our case, endoscopic snare papillectomy (ESP) was employed to establish a diagnosis. Thus far, only one other case has been reported in which ESP was used to diagnose and resect a pancreatic heterotopia at the ampulla.

Infectious esophagitis (IE) is the leading cause of esophagitis, second only to gastroesophageal reflux disease. Infectious esophagitis is typically observed in immunocompromised individuals due to neutropenia, HIV/AIDS, solid organ malignancies, cancer-directed therapy, or chronic steroid use. The most common causes of IE are herpes simplex virus (HSV), cytomegalovirus (CMV), and Candida albicans . Acute bacterial esophagitis is exceedingly rare, particularly in immunocompetent patients. Herein, we describe a unique case of acute streptococcal esophagitis in a male patient with no pertinent medical history. The patient’s substernal chest pain and odynophagia resolved after antibiotic treatment.

Acute esophageal necrosis (AEN), also known as Gurvits syndrome, is a rare and potentially life-threatening condition characterized by necrosis of the esophageal mucosa. Acute esophageal necrosis is often associated with critical conditions, such as myocardial infarction, diabetic ketoacidosis (DKA), coronavirus disease 2019 (COVID-19) infection, or post-surgical complications. Patients typically present with nausea, hematemesis, acute dysphagia, and melena. Given its high mortality rate, prompt detection with upper endoscopy and early initiation of treatment are crucial. Most cases of Gurvits syndrome are managed conservatively using intravenous fluids, proton pump inhibitors, and antibiotics. Herein, we present a case series of AEN in the setting of DKA. Both patients received supportive care and were discharged in a stable condition.

The problem of formation tracking control for a group of quadcopters with nonlinear dynamics using Barrier Lyapunov Functions (BLFs) is studied in this paper where the quadcopters are following a desired predefined trajectory in a predefined formation shape. The BLFs are employed to formulate the problem of formation trajectory tracking with a predefined accuracy. For this purpose, logarithmic BLFs including both the trajectory errors and the errors between the quadcopters’ distances with the desired ones (for the formation goal) are proposed. The method is firstly developed in a centralized scheme and then extended to a distributed framework using appropriate asymptotically convergent consensus algorithms. Therefore, the asymptotic convergence of the designed distributed algorithm to the centralized one is guaranteed. Moreover, due to the under-actuated feature of a quadcopter system, a general hierarchical scheme is considered for designing the controller. To this end, firstly a formation altitude tracking control is designed and then using the generated control signal, the formation translational tracking control is developed with the assumption of virtual inputs which are then employed to generate desired trajectory signals for the attitude control subsystem. Finally, attitude controllers are designed separately for each agent using the generated desired signals through logarithmic BLFs to consider a predefined accuracy. The efficiency of the proposed method is demonstrated through simulations and comparisons with the similar approaches in MATLAB-Simulink environment.

In light of the advancement of the technologies used in industrial control systems, securing their operation has become crucial, primarily since their activity is consistently associated with integral elements related to the environment, the safety and health of people, the economy, and many others. This work presents a distributed, machine learning based attack detection and mitigation framework for sensor false data injection cyber-physical attacks in industrial control systems. It is developed using the system’s standard operational data and validated using a hybrid testbed of a reverse osmosis plant. A MATLAB/Simulink-based simulation model of the process validated with actual data from a local plant is used. The control system is implemented using Siemens S7-1200 programmable logic controllers with 200SP Distributed Input/Output modules. The proposed solution can be adopted in the existing industrial control systems and demonstrated effective performance in real-time detection and mitigation of actual cyber-physical attacks launched by compromising the communication links between the process and the programmable logic controllers.

Uncertainties in load and solar power forecasting, complex energy storage system (ESS) constraints, and feedback correction pose challenges for very short-term and short-term hybrid power plant scheduling. This paper proposes a two-stage mixed-integer linear programming (MILP)-based energy dispatch engine (EDE). The proposed model ensures optimized scheduling through accurate load and power forecasting, a feedback correction loop, and a set of constraints governing the state of charge (SOC) and state of health (SOH) of the ESS. Such an EDE aims to reduce the plant’s operating costs and the usage of diesel generators (DGs), and minimize the cost of carbon emissions. To test the performance of the developed model, real-time load and photovoltaic (PV) data were used in conjunction with a PV-DG-ESS hybrid plant. The system was evaluated against a heuristic control model and a multistage stochastic control model, with the daily overall electricity and carbon emission costs as evaluation metrics. The test results revealed a 9.2% and 3.5% decrease in daily costs compared to the heuristic and stochastic methods, respectively, and a 29.4% decrease in carbon emission costs.

The load frequency control (LFC) in modern power system like microgrid has turned out to be significantly challenging due to the high penetration of renewable energy sources (RESs) and the consequent reduction of overall system inertia. The inverter-equipped RESs like solar and wind power generation units, besides the load variations can prompt sustained frequency fluctuations in microgrid and further lead to system instability, power outages, and even complete system blackout in the worst case. As a solution to the concerns of intermittent power source integration and resulting microgrid frequency instability, in this work, two robust LFC schemes using conventional linear quadratic gaussian (LQG) and modified LQG with linear quadratic integral (LQI) control schemes are proposed for secondary/battery energy storage system (BESS)-based auxiliary (virtual inertia (VI)) control of islanded/non-linear microgrid. The efficacy of the suggested control strategy is confirmed through MATLAB/SIMULINK simulations and by comparing with other different control schemes under various scenarios of distinct load and RES disturbance input profiles. The simulation results exhibited superior frequency regulation performance for the proposed control mechanisms over other types of control schemes. The proposed control schemes also ensure fast settling of frequency transients and help improve frequency stability under stochastic loads and random RES output power. In addition to the development of an effective robust controller, the vulnerability of the microgrid LFC system towards the denial of service (DoS) attack is analyzed for different control schemes. The vulnerability analysis is performed in the presence and absence of local auxiliary control loop and the remote secondary measurement communication channel is considered as the DoS attack point. The simulation results indicate that the local auxiliary control mechanism can not only help to improve frequency stability but also helps to add cyber-attack resilience to an extent when the secondary control loop is under attack.

Abstract This paper proposes a data‐driven sensor fault detection and isolation approach for the general class of nonlinear systems. The proposed method uses deep neural network architecture to obtain an invariant set of basis functions for the Koopman operator to form a linear predictor for a nonlinear system. Then, the obtained Koopman predictor has been used in a geometric framework for sensor fault detection and isolation purposes without relying on a priori knowledge about the underlying dynamics as well as requiring faulty data, leading to a data‐driven sensor fault detection and isolation framework for nonlinear systems. Finally, the approach's efficacy is demonstrated using simulation case study on a two‐degree of freedom robot arm.

In response to the urgent requirement for rapid, precise, and cost-effective detection in intensive care units (ICUs) for ventilated patients, as well as the need to overcome the limitations of traditional detection methods, researchers have turned their attention towards advancing novel technologies. Among these, biosensors have emerged as a reliable platform for achieving accurate and early diagnoses. In this study, we explore the possibility of using Pyocyanin analysis for early detection of pathogens in ventilator-associated pneumonia (VAP) and lower respiratory tract infections in ventilated patients. To achieve this, we developed an electrochemical sensor utilizing a graphene oxide–copper oxide-doped MgO (GO − Cu − Mgo) (GCM) catalyst for Pyocyanin detection. Pyocyanin is a virulence factor in the phenazine group that is produced by Pseudomonas aeruginosa strains, leading to infections such as pneumonia, urinary tract infections, and cystic fibrosis. We additionally investigated the use of DNA aptamers for detecting Pyocyanin as a biomarker of Pseudomonas aeruginosa, a common causative agent of VAP. The results of this study indicated that electrochemical detection of Pyocyanin using a GCM catalyst shows promising potential for various applications, including clinical diagnostics and drug discovery.

In this study, risk aversion behavior in the Iranian economy has been explained. For this purpose, using various econometric methods, risk aversion parameter and sanctions intensity index in the Iranian economy during the period of April 2007 to March 2020 has been estimated. Then the average individual and social characteristics in Iranian society and inequality index using households' income and expenditur survey data and household income is calculated. The results show that the increase in the average age of the population, rental rates, gender ratio of women in society, the share of the married population in the population, fluctuations in GDP and the intensification of sanctions lead to an increase in risk aversion in the economy. Also, increasing economic growth, years of education, urbanization, household size, increasing income inequality, improving labor market conditions and car ownership rates are factors in improving risk taking in the economy.

Aortic dissection is a relatively uncommon and potentially misdiagnosed disease. Early and accurate diagnosis and appropriate treatment are imperative for patient survival. In this case, we present a 55-year-old male who presented to the Emergency Department with severe abdominal pain and acute neurological deficits who was triaged for stroke protocol. After unexpected findings on physical examination, other diagnostic hypotheses were evaluated, culminating in the diagnosis of aortic dissection. The purpose of this article is to exemplify a patient who presents with noteworthy neurologic clinical features who ultimately was diagnosed with Type B aortic dissection, to help benefit the rapid diagnosis and subsequent treatment for future patients with similar presentations.

This paper presents a model-free fault detection and isolation (FDI) method for nonlinear dynamical systems using Koopman operator theory and linear geometric technique. The key idea is to obtain a Koopman-based reduced-order model of a nonlinear dynamical system and apply the linear geometric FDI method to detect and isolate faults in the system. Koopman operator is an infinite-dimensional, linear operator which lifts the nonlinear dynamic data into an infinite-dimensional space where the correlations of dynamic data behave linearly. However, due to the infinite dimensionality of this operator, an approximation of the operator is needed for practical purposes. In this work, the Koopman framework is adopted toward nonlinear dynamical systems in combination with the linear geometric approach for fault detection and isolation. In order to demonstrate the efficacy of the proposed FDI solution, a mathematical nonlinear dynamical system, and an experimental three-tank setup are considered. Results show a remarkable performance of the proposed geometric Koopman-based fault detection and isolation (K-FDI) technique.

Smart transportation cities are based on intelligent systems and data sharing, whereas human drivers generally have limited capabilities and imperfect traffic observations. The perception of Connected and Autonomous Vehicle (CAV) utilizes data sharing through Vehicle-To-Vehicle (V2V) and Vehicle-To-Infrastructure (V2I) communications to improve driving behaviors and reduce traffic delays and fuel consumption. This paper proposes a Double Agent (DA) intelligent traffic signal module based on the Reinforcement Learning (RL) method, where the first agent, the Velocity Agent (VA) aims to minimize the fuel consumption by controlling the speed of platoons and single CAVs crossing a signalized intersection, while the second agent, the Signal Agent (SA) proceeds to efficiently reduce traffic delays through signal sequencing and phasing. Several simulation studies have been conducted for a signalized intersection with different traffic flows and the performance of the single-agent with only VA, DA with both VA and SA, and Intelligent Driver Model (IDM) are compared. It is shown that the proposed DA solution improves the average delay by 47.3% and the fuel efficiency by 13.6% compared to the Intelligent Driver Model (IDM).

Networked control systems which transfer data over communication networks may suffer from malicious cyber attacks by injecting false data to the transferred information. Such attacks can cause performance degradation of the closed-loop system and the filtering problem. The sequential importance sampling (SIS) particle filtering (PF) methods employ the sequential Monte Carlo approach to estimate the generally non-Gaussian posterior probability density function (pdf) for Bayesian estimation of generally non-linear non-Gaussain systems. In this paper, it is firstly shown that with the normal SIS PF, the injected false data to the networked systems remains stealthy and therefore it is not possible to reduce the degrading effect of the attack on the estimation. However, with a modification in the proposal pdf, a modified SIS PF is then proposed which guarantees the attack detectability where the attacked measurements are incorporated in the particle generation process and thus the particles are updated and make the attack detectable. Using the derived thresholds and under small enough measurement noises, it is also proved that no false alarm occurs. After estimation of the attack value, the posterior pdf conditioned on truly detected attack leads to an estimation equivalent to the attack free SIS PF in terms of estimation bias and estimation covariance error. Finally, the accuracy of the presented concepts is demonstrated for a networked interconnected four-tank system.

IntroductionImmune checkpoint blockade (ICB)-based therapy is revolutionizing cancer treatment by fostering successful immune surveillance and effector cell responses against various types of cancers. However, patients with HER2+ cancers are yet to benefit from this therapeutic strategy. Precisely, several questions regarding the right combination of drugs, drug modality, and effective dose recommendations pertaining to the use of ICB-based therapy for HER2+ patients remain unanswered.MethodsIn this study, we use a mathematical modeling-based approach to quantify the growth inhibition of HER2+ breast cancer (BC) cell colonies (ZR75) when treated with anti-HER2; trastuzumab (TZ) and anti-PD-1/PD-L1 (BMS-202) agents.Results and discussionOur data show that a combination therapy of TZ and BMS-202 can significantly reduce the viability of ZR75 cells and trigger several morphological changes. The combination decreased the cell’s invasiveness along with altering several key pathways, such as Akt/mTor and ErbB2 compared to monotherapy. In addition, BMS-202 causes dose-dependent growth inhibition of HER2+ BC cell colonies alone, while this effect is significantly improved when used in combination with TZ. Based on the in-vitro monoculture experiments conducted, we argue that BMS-202 can cause tumor growth suppression not only by mediating immune response but also by interfering with the growth signaling pathways of HER2+BC. Nevertheless, further studies are imperative to substantiate this argument and to uncover the potential crosstalk between PD-1/PD-L1 inhibitors and HER2 growth signaling pathways in breast cancer.

Financial markets, especially the capital market, may have strong links with other economic sectors. One of the most important aspects of investment is to determine the “optimal investment portfolio”. To date, some research has been conducted to determine the optimal portfolio with” artificial intelligence” and “Fuzzy Logic”. However, we determine the optimal portfolio based on Dynamic Stochastic General Equilibrium (DSGE) model. This study examines the design and calibration of the new Keynesian dynamic stochastic equilibrium model related to an optimal investment portfolio and the effect of shocks such as productivity shocks and foreign exchange earnings’ fluctuation shocks on macroeconomic variables. To this end, we design a DSGE model with sectors of households and firms, government and the central bank, and calibrate the model’s parameters after logarithm–Linearization using seasonal data of 1996-2016 and results of empirical studies. In the designed model, households maintain a portfolio of stocks, cash, securities, and other assets based on risk and return or an optimal portfolio. In the end, we assess the impulse response function of economic variables to shocks of productivity and foreign exchange earnings. Ultimately, the comparison of the present moments in the current study and moments of real data indicates the relative success of the model with regard to the realities of Iranian economy.

Abstract Intrusion detection in supervisory control and data acquisition (SCADA) systems is integral because of the critical roles of these systems in industries. However, available approaches in the literature lack representative flow‐based datasets and reliable real‐time adaption and evaluation. A publicly available labelled dataset to support flow‐based intrusion detection research specific to SCADA systems is presented. Cyberattacks were carried out against our SCADA system test bed to generate this flow‐based dataset. Moreover, a flow‐based intrusion detection system (IDS) is developed for SCADA systems using a deep learning algorithm. We used the dataset to develop this IDS model for real‐time operations of SCADA systems to detect attacks momentarily after they happen. The results show empirical proof of the model’s adequacy when deployed online to detect cyberattacks in real time.

Due to recent increase in deployment of Cyber-Physical Industrial Control Systems in different critical infrastructures, addressing cyber-security challenges of these systems is vital for assuring their reliability and secure operation in presence of malicious cyber attacks. Towards this end, developing a testbed to generate real-time data-sets for critical infrastructure that would be utilized for validation of real-time attack detection algorithms are indeed highly needed. This paper investigates and proposes the design and implementation of a cyber-physical industrial control system testbed where the Tennessee Eastman process is simulated in real-time on a PC and the closed-loop controllers are implemented on the Siemens PLCs. False data injection cyber attacks are injected to the developed testbed through the man-in-the-middle structure where the malicious hackers can in real-time modify the sensor measurements that are sent to the PLCs. Furthermore, various cyber attack detection algorithms are developed and implemented in real-time on the testbed and their performance and capabilities are compared and evaluated.

In this paper, the problem of distributed event-based control of large scale power systems in presence of denial-of-service (DoS) cyber attacks is addressed. Towards this end, a direct current (DC) microgrid composed of multiple interconnected distributed generation units (DGUs) is considered. Voltage stability is guaranteed by utilizing decentralized local controllers for each DGU. A distributed discrete-time event-triggered (ET) consensus-based control strategy is then designed for current sharing in the DGUs. Through this mechanism, transmissions occur while a specified event is triggered to prevent unessential utilization of communication resources. The asymptotic stability of the ET-based controller is shown formally by using Lyapunov stability via linear matrix inequality (LMI) conditions. The behavior of the DGUs subject to DoS cyber attacks are also investigated and sufficient conditions for secure current sharing are obtained. Towards this end, a switching framework is considered between the communication and attack intervals in order to derive sufficient conditions on frequency and duration of DoS cyber attacks to reach the secure current sharing. The validity and capabilities of the presented approach is confirmed through a simulation case study.

This paper presents a new method for robot interception planning based on the proportional navigation. Guidance laws are basically developed for aerospace applications where a pursuer impacts a target using these methods, however in the robotic application it is required to have a smooth grasp between the robot and the goal. In this method, a new term is considered for longitude axis to have the control on the final approaching velocity. The proposed method not only ensures the position and velocity match (also referred to as rendezvous) but also can be used to set the final closing velocity to any desired value. Approaching velocity can be zero for grasping the goal or a specific known velocity for hitting it in a controlled manner. It is shown that the capture region of the proposed approach is wider that other proportional navigation based methods. The proposed method is implemented on Qbot-2 and its performance is experimentally validated.

Connectivity and autonomy are considered two of the most promising technologies to improve mobility, fuel consumption, travel time, and traffic safety in the automated transportation industry. These benefits can be realized through vehicle platooning. A vehicle platoon is composed of a group of connected automated vehicles (CAVs) traveling together at consensual speed, following the leading vehicle (leader) while maintaining a prespecified inter-vehicle distance. This paper reviews the different existing control techniques associated with the transitional platoon maneuvers such as merge/split and lane change. Different longitudinal and lateral vehicle dynamics that are mainly used in the transitional platoon maneuvers are discussed. The most used control algorithms for both longitudinal and lateral control used for transitional platoon maneuvers are reviewed and the advantages and limitations of each control strategy are discussed. The most recent articles on platoon control maneuvers have been analyzed based on the proposed control algorithm, homogeneously or heterogeneously of platoon members, type of platoon maneuver, the aim of control problem, type of implementation, and used simulation tools. This paper also discusses different trajectory planning techniques used in lateral motion control and studies the most recent research related to trajectory planning for automated vehicles and summarizes them based on the used trajectory planning technique, platoon or/and lane change, the type of traffic, and the cost functions. Finally, this paper explores the open issues and directions for future research.

This study attempts to investigate the effect of government spending on regional growth in Iran. The relationship between government spending and economic growth is one of the well-known topics in economic literature. One of the problems of developing countries is the failure to achieve sustainable economic growth, which not only causes economic problems such as recession and unemployment, but also cultural, political and social problems. The government economic stabilization policies can be used to narrow the gap between the potential and realized product and to maintain the product near its potential level. One key issue in the field of regional planning is to study and understand the geographical inequalities in different dimensions. In this paper, using the spatial econometric method, the relationship between government spending and regional growth is estimated by applying the regional data of Statistical Center of Iran during 2001-2017, and Excel and R software’s are used to perform the calculations. This study seeks to explain the growth of different regions using government spending, and to answer these questions: Does government spending have a significant effect on growth in the regions? Do the regions converge in terms of economic growth over time? The results indicate the negative effect of government spending, population growth and human capital on regional growth in Iran. In addition, the statistical significance of spatial correlation coefficient indicates the positive diffusion effects of regional economic growth.

Hemosuccus pancreaticus (HP) is defined as bleeding from the ampulla of Vater through the pancreatic duct. It is a rare complication associated with acute or chronic pancreatitis. The source of bleeding can be from the pancreas itself or surrounding vessels, with the splenic artery most commonly involved. Diagnosing HP is challenging and computed tomography angiography remains the gold standard for diagnosis. We present the case of a 62-year-old male with recurrent pancreatitis complicated with HP. Imaging and endoscopy were consistent with bleeding from the section portion of the duodenum, which resolved without intervention.

The Human Development Index is a widely accepted tool for measuring welfare. Various factors affect the development index. One of these factors is the corruption. Therefore, in the present study, using the quantile regression method, the impact of corruption on human development index in Iran and selected countries of the region in the period 2004- 2017 has been studied. The model estimation results show that at 5% significance level, corruption perception index has a negative impact on human development. The effect of economic freedom on human development is also positive. The high urbanization rate also have a positive effect on the level of human development. The impact of the unemployment rate on human development is also negative. The effects of democracy and real wage rates on human development are also positive.

The cybersecurity of industrial control systems (ICSs) is becoming increasingly critical under the current advancement in the cyber activity and the Internet of Things (IoT) technologies, and their direct impact on several life aspects such as safety, economy, and security. This paper presents a novel semi-supervised dual isolation forests-based (DIF) attack detection system that has been developed using the normal process operation data only and is demonstrated on a scale-down ICS known as the Secure Water Treatment (SWaT) testbed and the Water Distribution (WADI) testbed. The proposed cyber-attack detection framework is composed of two isolation forest models that are trained independently using the normalized raw data and a pre-processed version of the data using Principal Component Analysis (PCA), respectively, to detect attacks by separating-away anomalies. The performance of the proposed method is compared with the previous works, and it demonstrates improvements in terms of the attack detection capability, computational requirements, and applicability to high dimensional systems.

Rosai–Dorfman disease (RDD) is a rare disorder characterized by the proliferation of histiocytes in lymph nodes. It can occur anywhere in the body but commonly involves the cervical area of the neck. Its clinical presentation varies and patients with skin manifestations may develop papules, nodules, plaques, or pustules. Histologically, it typically presents with emperipolesis, where intact lymphocytes are found within histiocytes. The definitive treatment of RDD is not well established given the rarity of the disease and indeed skin lesions can regress spontaneously. Therapeutic treatment options include cryotherapy, radiation, or topical agents such as steroids or retinoids. Here we describe the case of a 24-year-old Hispanic female who presented with skin manifestations which proved to be histologically positive for Rosai–Dorfman disease. The patient clinically improved following the administration of intralesional steroids.

Complete blood counts are frequently collected from cancer patients, but laboratory findings may be misleading. Secondary polycythemia can occur in renal cell carcinoma (RCC) due to erythropoietin (EPO) stimulation. Therefore, complete blood counts should be closely monitored to prevent complications such as thrombosis. We discuss the case of a 47-year-old man with metastatic RCC who presented with secondary polycythemia that improved with chemotherapy. His secondary erythrocytosis was anticipated, but his haemoglobin levels were lower than expected after therapy. This article discusses the treatment and diagnosis of secondary polycythemia in patients with RCC.

The rise in working-age mortality rates in the United States in recent decades largely reflects stalled declines in cardiovascular disease (CVD) mortality alongside rising mortality from alcohol-induced causes, suicide, and drug poisoning; and it has been especially severe in some U.S. states. Building on recent work, this study examined whether U.S. state policy contexts may be a central explanation. We modeled the associations between working-age mortality rates and state policies during 1999 to 2019. We used annual data from the 1999-2019 National Vital Statistics System to calculate state-level age-adjusted mortality rates for deaths from all causes and from CVD, alcohol-induced causes, suicide, and drug poisoning among adults ages 25-64 years. We merged that data with annual state-level data on eight policy domains, such as labor and taxes, where each domain was scored on a 0-1 conservative-to-liberal continuum. Results show that the policy domains were associated with working-age mortality. More conservative marijuana policies and more liberal policies on the environment, gun safety, labor, economic taxes, and tobacco taxes in a state were associated with lower mortality in that state. Especially strong associations were observed between certain domains and specific causes of death: between the gun safety domain and suicide mortality among men, between the labor domain and alcohol-induced mortality, and between both the economic tax and tobacco tax domains and CVD mortality. Simulations indicate that changing all policy domains in all states to a fully liberal orientation might have saved 171,030 lives in 2019, while changing them to a fully conservative orientation might have cost 217,635 lives.

Objective: I aim to develop a Stata program that estimates multistate life table quantities and their confidence intervals while controlling for covariates of interest, as well as adjusting for complex survey designs. Using the Health and Retirement Study (HRS) (2000-2016), I use the new program to estimate US females' total, healthy, and unhealthy life expectancies and their intervals by race/ethnicity at age 52 (the youngest age in the sample), while adjusting for education. Methods: Using the nonparametric bootstrap technique (with replacement), the present study offers and validates an age-inhomogeneous first-order Markov chain multistate life table program. The current proposed Stata program is the maximum likelihood version of Lynch and Brown's Bayesian approach to the multistate life table method, which has been developed in R. I use the estimates from the Bayesian approach to validate the estimates from the unweighted bootstrap approach. I also account for the HRS complex survey design using the HRS baseline survey design indicators (clustering, strata, and sample weights). I utilize the estimates from the unweighted and weighted bootstrap models to evaluate the extent to which ignoring the HRS complex survey design alters the estimates. Results: The health expectancy estimates obtained from the unweighted bootstrap approach are consistent with estimates from the Bayesian approach, which ignores complex survey designs. This indicates that the bootstrap approach developed in the current paper is valid. Also, the results show that ignoring the HRS complex survey design does not meaningfully alter the estimates. Contribution: The paper contributes to the multistate life table methods literature by providing a flexible, valid, and user-friendly program to estimate multistate life table quantities and their variabilities in Stata.

The importance of the security of building management systems (BMSs) has increased given the advances in the technologies used. Since the Heating, Ventilation, and Air Conditioning (HVAC) system in buildings accounts for about 40% of the total energy consumption, threats targeting the HVAC system can be quite severe and costly. Given the limitations on accessing a real HVAC system for research purposes and the unavailability of public labeled datasets to investigate the cybersecurity of HVAC systems, this paper presents a dataset of a 12-zone HVAC system that was collected from a simulation model using the Transient System Simulation Tool (TRNSYS). It aims to promote and support the research in the field of cybersecurity of HVAC systems in smart buildings [1] by facilitating the validation of attack detection and mitigation strategies, benchmarking the performance of different data-driven algorithms, and studying the impact of attacks on the HVAC system.

Objective: The opacity in many banks’ financial reports has brought some difficulties for the analysts and shareholders who are to grasp them. In this research, using ARDL model, we assess the dynamic model of valuation of the shares of the banks listed in Tehran Stock Exchange. Method: For this purpose and subject to the availability of the data, the seasonal data of the four listed banks in Tehran Stock Exchange- which has been extracted by screening method in the years of 2009-2016- has been analyzed. The other contribution of this paper is to choose a competitor model as an auto-regressive ARIMA model in order to assess the accuracy of the ARDL model, and to investigate the effects of political risk of the country on the stock prices while the effects of the sanctions on the Iranian central bank entered the model in the form of a dummy variable. Results: The results show that both of the models have a high capability of prediction of price-to-book (PB) ratio of stocks but the accuracy of the ARDL model is higher In addition, the sanction on the central bank has no significant effect on PB ratio in the long-run term. Finally, depending on the long-term effects of fundamental variables, the “valuation” variable of bank stocks was defined so that its positivity (negativity) signifies overpriced (underpriced) stock.

Purpose: The purpose of this study was to evaluate the sustainable rural quality of life index in Iran during the period of 1991-2016 and to measure the effects of various government investments on this index. Design/methodology/approach: The data were collected from the Statistics Center, the Central Bank and the World Bank. The value of the quality of life indicator was calculated using the software Eviews during the course of the study. The effect of independent research variables including government investment in agriculture, education, health, and rural development using the ARDL model in Microfit software is estimated. Finding: The results of the model estimation in the short run indicate that every 1 percent increase in government investment in agricultural sectors was 0.55 percent, health and treatment 0.54 percent, education 31.3 percent, increase the quality of life respectively. But investment in rural development does not affect quality of life in the short term but investment in rural life does not affect in the short term. In the long run, every 1% increase in investment in agricultural sectors is 0.65%, health and treatment 0.64%, education 45.0% and 32% in rural development in improving quality of life and its sustainability respectively. Research limitations/implications: Access to statistics is very difficult and has been obtained through reviewing all budget rules for the research courses. Practical implications: Strategy for employment to villagers with the support of public policy, based on qualify for investment, jobs, and services in rural areas contributes to the strengthening of agriculture, the changes in agricultural production and natural resources, the creation of jobs in non-farm income and as a result, the increased demand for local agricultural and non-agricultural products. Originality/value: Considering sustainable factors in evaluating quality of life and the effect of government investment in rural life by the applied method are the advantages of this study that has not been studied in previous studies.

Simulators for extracorporeal membrane oxygenation (ECMO) have problems of bulky devices and low-fidelity methodologies. Hence, ongoing efforts for optimizing modern solutions focus on minimizing expenses and blending training with the intensive care unit. This is particularly evident following the coronavirus pandemic, where economic resources have been extensively cut. In this paper, as a part of an ECMO simulator for training management, an advance thermochromic ink system for medical blood simulation is presented. The system was developed and enhanced as a prototype with successful and reversible transitions between dark and bright red blood color to simulate blood oxygenation and deoxygenation in ECMO training sessions.

Despite many advancements in extracorporeal membrane oxygenation (ECMO), the procedure is still correlated with a high risk of patient complications. Simulation-based training provides the opportunity for ECMO staff to practice on real-life scenarios without exposing ECMO patients to medical errors while practicing. At Hamad Medical Corporation (HMC) in Qatar, there is a critical need of expert ECMO staff. Thus, a modular ECMO simulator is being developed to enhance the training process in a cost-effective manner. This ECMO simulator gives the instructor the ability to control the simulation modules and run common simulation scenarios through a tablet application. The core modules of the simulation system are placed in the patient unit. The unit is designed modularly such that more modules can be added throughout the simulation sessions to increase the realism of the simulation sessions. The new approach is to enclose the patient unit in a trolley, which is custom-designed and made to include all the components in a modular fashion. Each module is enclosed in a separate box and then mounted to the main blood simulation loop box using screws, quick connect/disconnect liquid fittings, and electrical plugs. This method allows fast upgrade and maintenance for each module separately as well as upgrading modules easily without modifying the trolley’s design. The prototype patient unit has been developed for portability, maintenance, and extensibility. After implementation and testing, the prototype has proven to successfully simulate the main visual and audio cues of the real emergency scenarios, while keeping costs to a minimum.

This paper investigates the short-term and long-term effects of oil revenues on economic corruption in Iran using Autoregressive Distributed Lags (ARDL) model during 1979-2011. Findings indicate that increase (decrease) in oil revenues by one percent increases (decreases) per capita economic corruption by 0.29 percent in the long run. In addition, increases in government regulations indicator and government size by one percent reduce the per capita economic corruption by 1.95 and 1.63 percent, respectively. Moreover, one percent increase in trade openness results in increase in economic corruption per capita by 1.26 percent.

Power systems are complex systems that have great importance to socio-economic development due to the fact that the entire world relies on the electric network power supply for day-to-day life. Therefore, for the stable operation of power systems, several protection and control techniques are necessary. The power system controllers should have the ability to maintain power system stability. Three important quantities that should be effectively controlled to maintain the stability of power systems are frequency, rotor angle, and voltage. The voltage control in power systems maintains the voltage and reactive power within the required limits and the power factor control enhances the efficiency of power distribution systems by improving load power factors. Among various controls, the frequency control is the most time-consuming control mechanism of power systems due to the involvement of mechanical parts. As the control algorithms of frequency stabilization deliver control signals in the timescale of seconds, load frequency control (LFC) systems cannot handle complicated data validation algorithms, making them more vulnerable to disturbances and cyber-attacks. In addition, the LFC system has extended digital layers with open communication networks and is designed to operate with less human intervention. Moreover, the frequency fluctuation due to load change or cyber-attack in one area affects all other interconnected areas, and thus threatens the stability of the entire network. Due to these circumstances, research activities are still carried out in the field of frequency control and cyber-security. In this paper, a comprehensive review of the cyber-security of the LFC mechanism in the power system is presented. The highlights of the paper include the identification of attack points of different configurations of the LFC system, discussion of the attack strategies, formulation of various attack models, and a brief review of the existing detection and defense mechanisms against cyber-attacks on LFC.

Historically, the mankind has benefited from social capital in his collective life course implicitly and unconsciously. Nowadays, the concept of social capital has been included in the center of economy and community. The communities need to identify the framework of social capital and its consequences in order to overcome recession and deterioration. The relationship among socioeconomic indicators and social capital is one of the most important effects and interactions of social capital. In this article we try to identify the interaction between social capital and human development in the provinces of Iran. To do this, we use vector autoregressive panel data for the period 2000-2009. The results show that social capital has a positive effect on human development in the Iran’s provinces. On the other side, human development will generate social capital strata in the country.

According to international trade theories, countries attempt to produce and export commodities with their abundant resources. Iran has great energy resources and availability of inexpensive energy input has increased energy consumption per capita in manufacturing sector as well as in consuming sector. Relying on low energy price, manufacturers employ energy-intensive methods for producing and exporting commodities. It follows, then, that sensitivity to changes in energy price in Iran is very high. Employing panel-data model, this study seeks to investigate the intensity of the effects of changes in energy price on the export of manufacturing sector. The findings of this paper indicate that energy price and export are negatively related. Furthermore, those sectors that export a larger proportion of their GDP, due to high consumption of energy, are more sensitive to the changes in energy price.

This article conveys an historical analysis on some pavilions representing the Tunisian presence in world fairs from 1851 to 2010. It focuses particularly on their architectural components. It wants to demonstrate that the adoption of architectural Orientalism as a cachet emanated predominantly from an orchestrated policy rather than the application of a fashionable stylistic. Pertinent vectors of the message intended by the empowered people to be transmitted; these pavilions were not simply «exibition architecture»: they were more «architecture of representation» at the service of the capitalist forces, the colonial policies and the overweening ambitions of some leaders.

The correct estimation of gas oil demand function in agriculture industry and calculation of price and income elasticities is important to adopt the price and income policies. Therefore, in the present study the price and income elasticity of gas oil demand were estimated applying non-observable component to the process and creation of a state-space pattern and using maximum likelihood method and the Kalman Filter algorithm. In order to compare the obtained coefficients, ARDL, ECM and FMOLS methods were used. In addition, the data used in this research are collected for the period between 1974 and 2010. The results indicate that the estimated process is random and nonlinear, and its modality is Local Level Model. According to the estimated demand function, the estimated price elasticity of gas oil demand in the short and long term are -0.09 and -0.13, respectively. Also, in the short and long term, the income elasticity is 0.4 and 0.57, respectively. In addition, the coefficient of tractor number in each acre of the area under cultivation, which shows the sensitivity of gas oil demand to equipment and agricultural machines changes, is obtained to be 0.34.

This paper, using an economic analysis of crime, focuses on the relationship between crime and income inequality in Iran. The results indicate that there is a significant relationship between crime and income inequality in Iran. This means that by increasing income inequality in Iran the robbery as a crime will increase as well. In addition the findings reveal that there is a direct relationship between robbery and the proportion of people in urban areas, divorce and unemployment rate, but there is a negative relationship for households’ monthly income in Iran.

This paper presents the development of a Supervisory Control and Data Acquisition (SCADA) system testbed used for cybersecurity research. The testbed consists of a water storage tank’s control system, which is a stage in the process of water treatment and distribution. Sophisticated cyber-attacks were conducted against the testbed. During the attacks, the network traffic was captured, and features were extracted from the traffic to build a dataset for training and testing different machine learning algorithms. Five traditional machine learning algorithms were trained to detect the attacks: Random Forest, Decision Tree, Logistic Regression, Naïve Bayes and KNN. Then, the trained machine learning models were built and deployed in the network, where new tests were made using online network traffic. The performance obtained during the training and testing of the machine learning models was compared to the performance obtained during the online deployment of these models in the network. The results show the efficiency of the machine learning models in detecting the attacks in real time. The testbed provides a good understanding of the effects and consequences of attacks on real SCADA environments.

The world has experienced dramatic growth of population during 5 past years. Nowadays, demographic changes have been identified as one of key factors in development process. In this paper, the effect of age structure of population on economic growth has been examined. To do this, data on 171 countries has been used over period 1966-2004. The results indicate the significant effect of demographic variables on economic growth. Based on results, population growth , the ratio of people under 15 years old to whole population , dependency ratio of young people have negative effect and ratio of people 15-64 years old to whole population, ratio of people over 65 years old to whole population, dependency ratio of elderly people have positive impact on economic growth. The growth rates of different age groups have different effects on economic growth so that the growth rate of people years under 15 years old has the biggest effect and growth rate of active people (15-64 years old) has the smallest effect on economic growth.

Antimicrobial drug resistance rates for Mycoplasma pneumoniae was determined in clinical specimens and isolates obtained during 2011–2012 in Ontario, Canada. Of 91 M. pneumoniae drug-resistant specimens, 11 (12.1%) carried nucleotide mutations associated with macrolide resistance in the 23S rRNA gene. None of the M. pneumoniae specimens were resistant to fluoroquinolones or tetracyclines.

The Economy Affected by Oil Price Shocks when that’s Similar Shocks Didn’t occurred in nearest recent period. In other hands, the relationship between oil price shocks and the Iran economy changed by economic structural changes. For these reasons, present study has been investigate the effects of unforeseen oil price shocks on economic growth during the period 1367.1 -1389.4 using Markov switching model. The Results show that the impact of positive unforeseen oil price shocks on economic growth are lesser and more durable than negative shocks. Also they unable to ensure the high economic growth but they lead to the state of middle economic growth. In return, although negative shocks are not able to keep the economy in a state of low economic growth, but they can be prevented the economy to achieve a status of high economic growth.