mohammad rastegar

The stock market, as one of the vital components of the capital market, is an important part of the country's economy that can manage the flow of capital, optimize capital allocation, and thereby contribute to economic growth and development. More accurate prediction of the stock market trend can help investors' decision-making for higher returns by reducing risk. In general, the stock market is constantly changing and many factors influence the trend of this market, so predicting the patterns of movement in the stock exchange requires sufficient information about the past and influencing factors of the market. This article is part of the forecast of the stock market index of Iran, seeking to interpret the model and identify the most influential economic variable on the price index prediction. For this purpose, daily stock market and economic data, during the period 1394-1401 were used. Machine learning models are also used for prediction and the Shapley Additive exPlanations (SHAP) to interpret how to predict and determine the most important variables in the predictive model. Based on results from tree-based ensemble methods, the proposed model in this study, ExtraTrees, performed best based on predictive error criteria. In the study of the feature importance is also based on the ExtraTrees model, in order of the dollar rate (Nima), unemployment rate, dollar rate of market and liquidity, the most important economic variables influencing the forecast model. Also, according to other models used in the research, liquidity is the most effective variable on the stock index trend. Finally, it can be said that the most effective monetary variables on the stock market index in Iran are liquidity and exchange rate variables, so monetary policymakers and stock market investors should be more sensitive to these variables in their decisions.

Wound complications are major morbidities after orthopedic surgery, and thrombo-prophylactic drugs may increase the likelihood of such complications. In this regard, our study has evaluated the possible effects of rivaroxaban on wound complication issues following spinal canal stenosis surgery.

This prospective cohort study was conducted on 40 patients suffering from spinal canal stenosis secondary to degenerative lumbar spine changes. The eligible patients included those patients receiving rivaroxaban to prevent thrombo-emboli post-operatively. The patients were followed up for three months and assessed for postoperative wound-related complications.

None of the patients suffered vascular and thromboembolic complications. Regarding wound complications, these events are mostly limited to the first week post-operatively, including wound dehiscence in 5.0%, serosanguineous discharge in 25.0%, erythema in 35.0%, superficial infection in 10.0%, requiring surgical debridement in 5.0%, cellulitis in 10.0%, and wound induration in 30.0%. Deep infection or hematoma was not reported in our patients. Erythema and wound induration remained 10.0% and 15.0% within the second week, respectively. The hypertrophic scar was a delayed complication that appeared in 15.0% of patients within 1 to 3 months post-operatively.

The main risk profiles related to wound complications, especially infections, were a history of hypertension (HTN), uncontrolled diabetes mellitus (DM), and renal insufficiency. The use of rivaroxaban may be accompanied by temporary and minor wound complications and not with potentially debilitating morbidity in patients undergoing spinal canal stenosis surgery. Therefore, its prescription as a safe thrombo-prophylactic drug in patients undergoing spinal canal stenosis surgery is confidently recommended.

Several treatment approaches are now considered to manage tibial and femoral shaft nonunion after primary surgeries. Double-locking plates with channel bone grafting technology are treatments that surgeons could choose. We aimed to describe our experiences in evaluating the union of bone for these patients after using double-locking plates with channel bone grafting with serial examinations and radiologic studies.

This case study was conducted on 33 patients consisting of 20 femoral nonunion and 13 tibial nonunion cases. They underwent double plate fixation with bone grafting at Sina Hospital, Tehran, Iran, from 2015 to 2020. They were monitored for an average of 60 months after surgery.

Union was achieved in all the patients in a mean of 10.03 months (range: 8-18 months). During patients’ follow-up, no plate and screw breakage, device loosening, deformity, and infection were seen. No additional surgery was needed for any of our patients.

There are various treatment options for nonunion of long bones. This study described the double plating approach for treating femoral and tibial shaft nonunion. The technique of double plate fixation and bone grafting had reasonable union rates in long bone nonunion. The present case series analysis also shows that it is more beneficial to manipulate this promising method for long bone nonunion whenever possible.

Intertrochanteric (IT) fracture is one of the most common fractures in adults. Dynamic hip screw (DHS) surgery is a surgical procedure for IT fracture treatment. This study evaluated the mortality rate and quality of life (QOL) among these patients one year after the surgery.

This cohort study was conducted on 110 patients with IT fractures treated with DHS from 2017 to 2019. A questionnaire was completed for each patient before, during, and after surgery. Preoperative information included demographics, height, weight, body mass index (BMI), smoking, diabetes, variables such as IT fracture classification, injury mechanism, lateral wall, and the American Society of Anesthesiologists (ASA) comorbidity classification. Tip-apex distance (TAD), nail position at the femoral head, and the amount of bleeding during the operation were achieved during and immediately after the surgery. The mortality rate was determined one year after the surgery, and the surviving patients were assessed by the 36-Item Short Form Survey (SF-36) questionnaire.

The mortality rate among patients who underwent DHS was 31.81%. There was no significant difference between living and dead patients regarding demographic information, surgical techniques, and comorbidities. There was no association between patients regarding the average of all areas of physical, mental, and overall health and gender. There was no significant relationship between the mean of physical and mental health with the duration of hospital stay and the amount of bleeding during surgery. A history of diabetes, high blood pressure, and smoking in these patients was not associated with mortality and QOL.

The patient’s age is the most important risk factor for mortality after the DHS surgery.

Several treatment approaches are now considered to manage femoral nonunion following intramedullary (IM) nailing. Augmentation plating over a maintained IM nail was a successful treatment for femur fracture nonunion in recent years. We aimed to describe our experiences in evaluating the union of bone for these patients after plate augmentation with serial examinations and radiologic studies.

This was a case study on 21 patients who had a nonunion or delayed union of the femoral shaft fracture (FSF) after IM nailing. After initial intervention, they were monitored for 20.4 months (range: 12-72 months).

A closed IM nail was used in 18 patients, while the other three patients were shown as non-IM nail cases with a dynamic hip screw (DHS) in one patient, an external fixator in one other patient, and plate fixation in another one that was planned for augmentation plating. After plating, appropriate union appeared in all patients. Fracture union took an average of 7 months ranging from 3 to 12 months.

Augmentation plating for treating FSF nonunion with leaving the nail in place results in excellent and favorable clinical outcomes with the radiographic recovery of the nonunion site in all of the cases with no complication.

In developed societies, residential customers use high-level appliances. The progress in the smart grids and the internet of things have eased the way for home energy management to schedule controllable appliances. Looking to demand increment, demand response strategies aiming at energy management, to achieve goals such as demand reduction and improving reliability, has received attention. A deep review of the existing literature shows the notable efforts put into optimizing the home energy management problem through classic and meta-heuristic optimization algorithms such as game theory, genetic algorithm, and PSO. But, it is worth saying that these algorithms are not pragmatic due to the inherent nature of the home energy management problem. To be more precise, as the environment of the problem changes continuously, these algorithms fail to solve the problem. Hence, some essential assumptions such as considering fixed scenarios are presumed in previous works to enable the conventional algorithm to solve the problem. This is while machine learning addresses this issue by extracting the main features from input data and constructing a general description of the environment. Implementation of machine learning-based algorithms to a home energy management problem requires smart appliances. Hence, in the case of having a smart home, taking the advantage of artificial intelligence for energy management would be feasible and useful. It should be noted that electricity cost reduction can make the demand response program inviting, where customer satisfaction is taken into consideration. Accordingly, customer satisfaction should be considered in the problem formulation. Regarding the mentioned issues, lately, with the remarkable progress in machine learning, novel algorithms evolved for solving optimal decision-making problems such as demand response. Machine learning can be categorized into three main categories, namely supervised learning, unsupervised learning, and reinforcement learning (RL). Among them, reinforcement learning has shown notable performance in decision-making problems. Q-Learning is a model-free RL algorithm that solves nonlinear problems through estimating and maximizing the cumulative reward, triggered by decided actions. The fundamental idea of this algorithm is to identify the best action in each situation. This paper aims to provide a day-ahead demand response program for a smart home. It is done by specifying the quantity of the energy consumption of each appliance, aiming to reduce the electricity cost and user dissatisfaction. In this respect, it is presumed that the smart home is equipped with smart appliances. Moreover, smart meters are installed on appliances to monitor the statuses and receive the command signals from the devices at each hour. These appliances can be divided into three categories, non-responsive, time-shiftable, and controllable loads. Dishwasher and washing machine as time-shiftable loads, EV, air conditioner, and lighting system as controllable loads, and TV and refrigerator as non-responsive loads are taken into account. All in all, we recommend an advanced home energy management system proposing the following contributions: i) Proposing a day-ahead multi-agent Q-Learning method to minimize the electricity cost. ii) Proposing a satisfaction-based framework, which employs a precise model of the customer dissatisfaction functions (i.e., thermal comfort, battery degradation, and desirable operation period). 

In this paper, a multi-agent Q-Learning approach is used to solve the home energy management for a smart home. Q-learning is a popular model-free algorithm among reinforcement learning algorithms, due to the fact that its convergence is proven, and it is feasible to implement, as well. In order to deploy Q-Learning on a home energy management system, first of all, smart home should be formed as a Markov decision process. A Markov decision process consists of four fundamental parameters namely, state, action, reward, and transition probability matrix. Afterward, an agent is trained through experiencing a specific state, taking an action, transition to a new state, and calculating the cumulative reward. By doing so, after visiting a considerable number of states and taking diverse decisions, it will learn gradually to select the optimum action whatever the state is.Another fundamental aspect of this paper is the proposed approach to take customer satisfaction into account. In this paper, a non-linear thermal comfort model, non-linear desirable operation period model, and linear battery degradation model are deployed to consider the customer dissatisfaction, precisely. It should be noted that all simulations have been implemented by python 3.6 programming language without making use of any commercial solver. 

Various case studies have been designed to verify the effectiveness of the proposed method. Scenario 1 is designed to simulate the behavior of a smart home associated with a random manner of energy usage. Scenario 2 is designed to verify the effectiveness of the proposed home energy management system, where Q-Learning is conducted. In this case, battery degradation is overlooked. Scenario 3 is similar to the previous one, where battery degradation is also taken into consideration. Comparing the obtained results indicates that the proposed algorithm has successfully reduced the electricity bill by 31.3% and 24.8% in scenarios 2 and 3, respectively. It is worth saying that customer satisfaction is not violated in mentioned scenarios. Furthermore, in order to evaluate the effect of thermal comfort on the electricity bill, another case study is deployed, where the thermal comfort coefficient is decreased to smaller magnitudes. As expected, the less thermal comfort coefficient, the less electricity bill. The reason behind this is that having a lower thermal comfort coefficient leads to less importance of temperature control compared to the electricity bill.

This paper proposed a method for home energy management, regarding minimizing the electricity bill and user discomfort. In this paper, a multi-agent reinforcement learning via Q-Learning is used to make optimal decisions for home appliances, which are categorized into non-shiftable loads, time-shiftable loads, and controllable loads. Comparing to classic optimization methods, the proposed approach in this paper is capable of modeling more appliances and solving complex problems, due to the inherent nature of the Q-Learning algorithm. Implementing the proposed method in the numerical study section led to a 24.8% electricity bill reduction. The numerical results prove the effectiveness of the proposed approach.

Smart grids have been introduced to address power distribution system challenges. In conventional power distribution systems, when a power outage happens, the maintenance team tries to find the outage cause and mitigate it. After this, some information is documented in a dataset called the outage dataset. If the team can estimate the outage cause before searching for it, the restoration time will be reduced. In line with smart grid concepts, an association rule-based method is presented in this paper to find the outage cause. To do this, we have first combined outage, load, and weather datasets and extracted features. Then, for every cause, the records are labelled main class or others. The association rules are extracted and evaluated. Through these rules, one can determine whether the outage has happened because of a fault in a certain piece of equipment or not. Doing so alongside using smart devices may lead to reliability enhancement.

Energy optimization and consumption analysis is the first step in realizing smart houses. This paper incorporates the uncertainty of the effective parameters and probabilistically optimizes the energy consumption in a building. To this end, probability density functions (PDFs) of building uncertain parameters are modeled by empirical rule method and the energy usage in buildings is optimized. Energy-Plus and MATLAB softwares are respectively used to compute energy consumption in a building and to optimize energy consumption. Energy consumption of the building and the thermal comfort are considered as objective functions of the proposed multi-objective optimization problem. A 12-storey commercial building is used as a case study. The proposed probabilistic method is compared with the deterministic method to assess the proposed method,. The results show a significant difference between the deterministic and probabilistic cases. The maximum difference between the mean optimal values of variable parameters in these cases is about 34%. Finally, sensitivity of the results to the climate changes is also investigated in this paper.

Optical and magneto-optical responses of magnetophotonic crystals (MPCs) with a magnetic defect layer sandwiched between two dielectric Bragg mirrors have been investigated, which have potential application in integrated-optics devices. The Bragg mirrors are periodic multilayered films composed of SiO2 and Ta2O5 coatings. By introducing Ce:YIG magnetic defect layer, the enhancement of rotation angle and transmittance value, have been reported. By using of Finite Element Method (FEM), we have simulated the electric field profile in MPCs and investigated the effect of defect layer thickness on Faraday rotation angles. As a result of localization of light at magnetic defect layer and strong light-matter coupling in MPCs, the magneto-optical responses of these structures, are mostly increased near the PBG edges. The structure with a half wavelength optical thickness of defect layer is most suitable for practical applications.