Stochastic portfolio optimization: A regret-based approach on volatility risk measures: An empirical evidence from The New York stock market.

Portfolio optimization involves finding the ideal combination of securities and shares to reduce risk and increase profit in an investment. To assess the impact of risk in portfolio optimization, we utilize a significant volatility risk measure series. Behavioral finance biases play a critical role in portfolio optimization and the efficient allocation of stocks. Regret, within the realm of behavioral finance, is the feeling of remorse that causes hesitation in making significant decisions and avoiding actions that could lead to poor investment choices. This behavior often leads investors to hold onto losing investments for extended periods, refusing to acknowledge mistakes and accept losses. Ironically, by evading regret, investors may miss out on potential opportunities. in this paper, our purpose is to compare investment scenarios in the decision-making process and calculate the amount of regret obtained in each scenario. To accomplish this, we consider volatility risk metrics and utilize stochastic optimization to identify the most suitable scenario that not only maximizes yield in the investment portfolio and minimizes risk, but also minimizes resulting regret. To convert each multi-objective model into a single objective, we employ the augmented epsilon constraint (AEC) method to establish the Pareto efficiency frontier. As a means of validating the solution of this method, we analyze data spanning 20, 50, and 100 weeks from 150 selected stocks in the New York market based on fundamental analysis. The results show that the selection of the mad risk measure in the time horizon of 100 weeks with a regret rate of 0.104 is the most appropriate research scenario. this article recommended that investors diversify their portfolios by investing in a variety of assets. This can help reduce risk and increase overall returns and improve financial literacy among investors.

Identifying emotional components of event-related potentials in the brain functioning of individuals with contamination obsessions and comparison with healthy control group.

The present study aimed to examine the emotional components of event-related potentials (ERPs) in individuals with contamination OCD and compare them with a healthy control group. A convenience sample of 45 participants was included, consisting of 30 individuals diagnosed with contamination-type OCD and 15 individuals in a healthy control group. Both groups participated in an ERP study where they encountered a computer-based task presenting both contamination and neutral pictures, while their brain activity was recorded. The data were analyzed using repeated measures analysis of variance (RANOVA) with SPSS-24 and Matlab software. Findings suggest that in P3 amplitude, only individuals with OCD exhibited a larger positive amplitude (p < 0.05) in response to contaminated pictures compared to neutral pictures and in N2 amplitude, only individuals with OCD exhibited a larger negative amplitude (p < 0.05) in response to contaminated pictures compared to neutral pictures in the central vertex (Fz). These findings hold promising implications for the development of more targeted and effective treatments for contamination OCD, emphasizing the importance of emotion-oriented approaches to address the unique neural patterns observed in the frontal vertex.

Identification and fixing bottlenecks of a food manufacturing system using a simulation approach.

In manufacturing systems, simulation modeling plays an important role in creating some changes instead of working on real systems. Manipulation in a real system is more costly than manipulation in a simulated model. In this research, we tried to use a simulation approach to recognize and minimize bottlenecks of a production line, which will decrease costs and improve productivity. To achieve our objectives, we chose a case and analyzed its production line. By using our case study strategy, we tried to collect our data and adapt a conceptual model of production processes drawing on an operation process chart (OPC). After that, we created a simulation model of the production processes by using the popular Arena simulation software 13.5. By running the Arena model, some bottlenecks were found. Ultimately, we proposed some solutions to obviate bottlenecks and reduce the total costs of production.

A Fuzzy Crew Rostering Model Based on Crew Preferences and Seniorities considering Training Courses: A Robust Optimization Approach.

Crew scheduling problem is divided into crew pairing problem (CPP) and crew rostering problem (CRP). In this paper, a rostering model is presented to assign crew to pairings in such a way that total weighted preference is maximized. Crew members declare which parings they wish to be assigned and which ones are undesirable for them. A score is calculated in the objective function if a crew member is assigned to his/her preferred pairing, and a penalty is considered if he/she is assigned to an undesirable pairing. Moreover, crew seniorities are considered in calculating total preference. In addition, the model considers standard rules and regulations as well as crew attendance at the required training courses. The model is formulated in such a way that inconsistent crew members are not assigned to a flight. Due to the uncertainty in determining of the seniority weight, this parameter is considered as fuzzy. At the end, the robust counterpart of the nominal model is developed due to the uncertainty of time away from the base (TAFB). In this research, the issue of inconsistent crew in rostering problem is considered for the first time. Moreover, a new scoring mechanism is introduced to calculate desirable and undesirable assignments in the objective function. The proposed CRP is solved using the genetic algorithm (GA), and its performance is verified in comparison with GAMS in some test problems. On average, the optimality gap in GA is only 0.5 percent. Finally, the proposed model is examined with real-world data from Air India Airline. In comparison with the previous research studies, the suggested model (scoring mechanism) reduced the number of undesirable rosters by 61.59%.

Constrained portfolio optimization with discrete variables: An algorithmic method based on dynamic programming.

Portfolio optimization is one of the most important issues in financial markets. In this regard, the more realistic are assumptions and conditions of modelling to portfolio optimization into financial markets, the more reliable results will be obtained. This paper studies the knapsack-based portfolio optimization problem that involves discrete variables. This model has two very important features; achieving the optimal number of shares as an integer and with masterly efficiency in portfolio optimization for high priced stocks. These features have added some real aspects of financial markets to the model and distinguish them from other previous models. Our contribution is that we present an algorithm based on dynamic programming to solve the portfolio selection model based on the knapsack problem, which is in contrast to the existing literature. Then, to show the applicability and validity of the proposed dynamic programming algorithm, two case studies of the US stock exchange are analyzed.

An integration of environmental awareness into flexible supply chains: a trade-off between costs and environmental pollution.

Today, environmental awareness is highly interested in supply chains and logistics networks with regard to sustainable development goals. This proposes a bi-objective linear mathematical model comprising supply chain flexibility dimensions. The proposed model is to integrate environmental considerations into a flexible supply chain as an optimization framework. The first objective function is to minimize the costs, while the second one minimizes the environmental impacts of automotive industry. The goal of this paper is to find a trade-off between the total cost and the environmental pollution with regard to the supply chain flexibility dimensions. We suggest adding four different supply chain flexibility dimensions to the model which are budget for transportation, trained labor team to help the packaging process, number of active plants, and outsourcing the painting process flexibilities to curb harmful emissions from factories while reducing the costs. Six flexibility scenarios are proposed in this study to do the sensitivity analysis. The model is applicable with the use of a real data set derived from an automotive parts factory located in Iran. We use an improved augmented ε-constraint method to address the proposed bi-objective optimization framework. The results show that choosing the model with all flexibility dimensions is the best initiative to promote sustainable development, since it leads to a significant reduction in costs and environmental pollution.

Adjunct Triptorelin in the Treatment of Obsessive-Compulsive Disorder in Clients Receiving Selective Serotonin Reuptake Inhibitors (SSRIs).

Objective: Analog triptorelin is one of the effective agonists for the treatment of reproductive disorders, particularly prostate cancer. Due to results of previous studies, we hypothesized that obsessive-compulsive disorder (OCD can be effectively treated with the long-term administration of a gonadotropin-releasing hormone (GnRH) analog, namely triptorelin. The aim of this study was to evaluate the effectiveness of triptorelin injection in clients with OCD. Method : This randomized single-blind clinical trial was performed on 30 clients with OCD who had a Yale-Brown score of > 17 after 8 weeks of treatment. The participants were randomly assigned into two groups of triptorelin and placebo. The clients in the intervention group were treated with Selective serotonin reuptake inhibitors (SSRIs), including fluoxetine, in addition to triptorelin three times a month for at least 8 weeks. Clients in the control group received injection of distilled water as placebo three times in addition to the routine treatment. The outcome was evaluated by Yale-Brown OCD scale (Y-BOCS) at the baseline, as well as 4, 8, and, 20 weeks after the end of the treatment. Results: The mean scores of Y-BOCS in the intervention and control groups was 30.5 ±67.6 and 30.5 ±67.6, respectively, before intervention, indicating no significant difference between the two groups (P = 0.0.8). The comparison of Y-BOCS scores after the intervention showed a significant difference between the two groups in the scores 4 (P = 0.01), 8 (P < 0.005), and 20 (P < 0.005) weeks after the treatment. With regards to the side effects of the medicine, 6.7% (n = 1) of the clients in the control group developed headache and 66.7% (n = 10) had late period in intervention group. The results revealed a significant difference between the two groups in terms of side effects (P < 0.005). Conclusion: The results of this study showed triptorelin decreased the symptoms of OCD. The effectiveness of triptorelin in the treatment of symptoms in clients with OCD was confirmed in our study. However, due to the limited research addressing this domain, future studies are suggested to clarify this conclusion.

An improved approach for fault detection by simultaneous overcoming of high-dimensionality, autocorrelation, and time-variability.

The control charts with the Principal Component Analysis (PCA) approach and its extension are among the data-driven methods for process monitoring and the detection of faults. Industrial processing data involves complexities such as high dimensionality, auto-correlation, and non-stationary which may occur simultaneously. An efficient fault detection technique is an approach that is robust against data training, sensitive to all the feasible faults of the process, and agile to the detection of the faults. To date, approaches such as the recursive PCA (RPCA) model and the moving-window PCA (MWPCA) model have been proposed when data is high-dimensional and non-stationary or dynamic PCA (DPCA) model and its extension have been suggested for autocorrelation data. But, using the techniques listed without considering all aspects of the process data increases fault detection indicators such as false alarm rate (FAR), delay time detection (DTD), and confuses the operator or causes adverse consequences. A new PCA monitoring method is proposed in this study, which can simultaneously reduce the impact of high-dimensionality, non-stationary, and autocorrelation properties. This technique utilizes DPCA property to decrease the effect of autocorrelation and adaptive behavior of MWPCA to control non-stationary characteristics. The proposed approach has been tested on the Tennessee Eastman Process (TEP). The findings suggest that the proposed approach is capable of detecting various forms of faults and comparing attempts to improve the detection of fault indicators with other approaches. The empirical application of the proposed approach has been implemented on a turbine exit temperature (TET). The results demonstrate that the proposed approach has detected a real fault successfully.

Antipsychotic Drugs and Risk of Developing Venous Thromboembolism and Pulmonary Embolism: A Systematic Review and Meta-Analysis.

BACKGROUND: Antipsychotic (AP) medications are the cornerstone treatment for schizophrenia and some other psychiatric diseases. However, some observational studies suggest that these medications might increase the risk of venous thromboembolism (VTE) and pulmonary embolism (PE). OBJECTIVES: The aim of this study was to assess whether AP medications are associated with the development of VTE or PE, and to assess the risk based on any type of AP drugs, quality of studies and after adjustment of risk factors. DATA SOURCES: To identify relevant studies, we searched PubMed and EMBASE databases up to February 2019. We also searched the reference lists of relevant articles for related studies. STUDY SELECTION: Twenty studies fulfilled the eligibility criteria and were included in our meta-analysis after screening relevant observational cohort and case-control studies. PRIMARY OUTCOME: The primary outcome of our meta-analysis was the occurrence of all VTE or PE only attributed to exposure to AP medications compared with non-exposure to AP medications. RESULTS: Exposure to AP drugs was associated with a significant increase in the risk of VTE (RR 1.53, 95% CI 1.30-1.80, I2 = 85%) and PE (RR 3.69, 95% CI 1.23-11.07, I2 = 90%). In the subgroup metaanalysis, the use of low-potency AP drugs was associated with a higher risk of VTE, (RR 1.90, 95% CI 1.04-3.47, I2 = 78%). CONCLUSION: AP exposure was associated with a 1.5-fold increase in the risk of VTE and a 3.7-fold increase in the risk of PE. Low-potency AP drugs were associated with a higher risk of VTE. However, high heterogeneity among studies limits the generalizability of the results.

Linguistic Z-number weighted averaging operators and their application to portfolio selection problem.

Z-numbers can generate a more flexible structure to model the real information because of capturing expert's reliability. Moreover, various semantics can flexibly be reflected by linguistic terms under various circumstances. Thus, this study aims to model the portfolio selection problems based on aggregation operators under linguistic Z-number environment. Therefore, a multi-stage methodology is proposed and linguistic Z-numbers are applied to describe the assessment information. Moreover, the weighted averaging (WA) aggregation operator, the ordered weighted averaging (OWA) aggregation operator and the hybrid weighted averaging (HWA) aggregation operator are developed to fuse the input arguments under the linguistic Z-number environment. Then, using the max-score rule and the score-accuracy trade-off rule, three qualitative portfolio models are presented to allocate the optimal assets. These models are suitable for general investors and risky investors. Finally, to illustrate the validity of the proposed qualitative approach, a real case including 20 corporations of Tehran stock exchange market in Iran is provided and the obtained results are analyzed. The results show that combining linguistic Z-numbers with portfolio selection processes can increase the tendencies and capabilities of investors in the capital market and it helps them manage their portfolios efficiently.

A decision-making model for performance evaluation and profit sharing in a diagnostic laboratory network.

RATIONALE, AIMS, AND OBJECTIVES: Creating networked business models is one of the innovative approaches that have the ability and potential for meeting market needs. The purpose of this study is to provide a decision-making model for a fair profit sharing among the members of a diagnostic laboratory network while providing a distinctive value for the patients. METHODS: To identify the members of the network of laboratories, a suitable approach to calculate members' efficiency scores is proposed. Then, the network members are classified into three groups based on their performance scores. The three groups help administrators identify eligible members, members who need to improve their performance in order to meet the minimum requirements, and members who do not qualify for admission to the network. Since the performance of the members should play a significant role in the fair profit-sharing mechanism, the fair allocation of profits among network members is done by the use of Shapely value based on the efficiency scores of members. RESULTS: The results show that for such a fair mechanism, the efficiency and sample size (the number of samples [blood and urine] taken from the patients by the laboratories), as the two effective factors, have a decisive role in the share of profit of laboratory units of the network. In the Laboratory Services Network, members receive a number of samples according to their performance. As a result, the sample size received has a direct impact on the net income of each member. CONCLUSION: In conclusion, it is evident that the use of Shapely value may help managers in the process of sharing profits among network members in a fair way, thereby improving network performance. In this way, incentive strategies may be created for the members of the network, and long-term survival of the network may be achieved.

Performance assessment of medical diagnostic laboratories: A network DEA approach.

RATIONALE, AIMS, AND OBJECTIVES: The main purpose of this paper is to measure the efficiency and ranking of medical diagnostic laboratories by applying a network data envelopment analysis (NDEA). METHODS: In this study, each medical diagnostic laboratory is considered as a decision-making unit (DMU), and an NDEA model is utilized to calculate the efficiency of each medical diagnostic laboratory. Therefore, we design a series of four-stage system composed of three main laboratory processes (the pretest process, the test process, and the posttest process). We also consider sustainability criteria in order to cover social, economic, and environmental problems of health care organizations. RESULTS: The results show that three of the 22 considered laboratories are efficient. Therefore, the NDEA approach can lead to performance scores and ultimately real ranking. Also, the average efficiency scores show that the decrease of the reception unit's efficiency results in a decrease of the efficiency of each laboratory. Therefore, the laboratories can increase the number of patients. Along with the intermediate values of the reception unit and the sampling unit, the efficiency of the reception unit increases, which results in an increase for the overall efficiency of each laboratory. CONCLUSION: The proposed model can appropriately help the administrators and managers to identify inefficient units in their laboratory and ultimately improve the laboratory performance.

Two Decades of Speaker Recognition Evaluation at the National Institute of Standards and Technology.

The National Institute of Standards and Technology has been conducting Speaker Recognition Evaluations (SREs) for over 20 years. This article provides an overview of the practice of evaluating speaker recognition technology as it has evolved during this time. Focus is given to the current state of speaker recognition evaluation. Highlights from past SREs and future plans are also discussed.

A portfolio selection model based on the knapsack problem under uncertainty.

One of the primary concerns in investment planning is to determine the number of shares for asset with relatively high net value of share such as Berkshire Hathaway on Stock market. Traditional asset allocation methods like Markowitz theorem gives the solution as a percentage and this ratio may suggest allocation of half of a share on the market, which is impractical. Thus, it is necessary to propose a method to determine the number of shares for each asset. This paper presents a knapsack based portfolio selection model where the expected returns, prices, and budget are characterized by interval values. The study determines the priority and importance of each share in the proposed model by extracting the interval weights from an interval comparison matrix. The resulted model is converted into a parametric linear programming model in which the decision maker is able to determine the optimism threshold. Finally, a discrete firefly algorithm is designed to find the near optional solutions in large dimensions. The proposed study is implemented for some data from the US stock exchange.

Acyclovir Neurotoxicity in a Peritoneal Dialysis Patient: Report of a Case and Review of the Pharmacokinetics of Acyclovir.

BACKGROUND The elderly population in the United States and the world is rapidly increasing. With aging, immunity and kidney function decrease, thus predisposing people to viral illnesses for which there is no effective prophylaxis. Herpes zoster afflicts the elderly and other immunocompromised patients, like those with end-stage renal disease, transplant recipients, and cancer patients, causing significant morbidity and sometimes mortality. Treating herpes zoster becomes problematic when the regular pharmacokinetics of the antiviral drugs are disturbed. CASE REPORT An 83-year-old African American man with end-stage kidney disease (ESRD) and on chronic peritoneal dialysis (PD) developed herpes zoster, for which he received the manufacturer-recommended intravenous dose of acyclovir. Shortly after taking the medication, he developed confusion, disorientation, and visual hallucinations. He was switched from PD to hemodialysis (HD), with successful recovery. Examination of the cerebrospinal fluid for meningitis and imaging studies of the head were negative. Serum levels of acyclovir were elevated. CONCLUSIONS Even when the acyclovir dose is properly adjusted for kidney function based on the current manufacturer's recommendations, it can cause neurotoxicity. Here, we discuss the pharmacokinetics of acyclovir and make some recommendations with regard to dose adjustment in patients with ESRD.

Determining Pro-Oxidant Antioxidant Balance (PAB) and Total Antioxidant Capacity (TAC) in Patients with Schizophrenia.

Objective: Schizophrenia is a disease with unknown etiology. There is evidence suggesting that oxidative damage occurs in schizophrenia. Oxidative damage may arouse from imbalance between oxidant and anti-oxidant factors in cellular and tissue environment. Although it may not be the primary cause, it can worsen the disease and may be a reason of poor response to therapy in these patients. The present study aimed at evaluating the pro-oxidant antioxidant balance (PAB) and total antioxidant capacity (TAC) in serum of schizophrenia patients. PAB is an assay to determine the pro-oxidant load and antioxidant capacity in a single measurement. Method : In this cross- sectional study, patients with diagnosis of schizophrenia, who referred to a psychiatry university hospital (Ibn-e-Sina Hospital) affiliated to Mashhad University of Medical Sciences, were enrolled. Patients' demographic characteristics and laboratory data were recorded from patients' files. Serum PAB and TAC were measured using a special PAB assay and commercial kit, respectively. Data were analyzed using SPSS 16. Results: A total of 84 individuals (42 schizophrenia cases and 42 healthy controls) participated in this study. Controls were age and sex-matched with the patients' group. The mean TAC in the patient and control groups was 0.49±0.04 and 0.51±0.04 nmol/L, respectively (p = 0.16). PAB was higher in patients' group than in controls (127.36±36.44 vs. 118.93±52.34 HK), however, this difference was not statistically significant (p = 0.09). The change was correlated with the chronicity of the disease. Conclusion: Pro-oxidant antioxidant balance was elevated in serum of patients with schizophrenia. These data suggested the occurrence of oxidative stress during the progression of the disease. Lower antioxidant capacity might suggest that patients with schizophrenia could be more susceptible to oxidative stress damage.

A supplier selection model in pharmaceutical supply chain using PCA, Z-TOPSIS and MILP: A case study.

Supplier selection is one of the critical processes in supplier chain management which is associated with the flow of goods and services from the supplier of raw material to the final consumer. The purpose of this paper is to present a novel approach and improves the supplier selection in a multi-item/multi-supplier environment, and provide the importance and the reliability of the criteria by handling vagueness and imperfection of information in decision making process. First, principal component analysis (PCA) method is used to reduce the number of supplier selection criteria in pharmaceutical companies. Next, using the most important criteria resulted from the PCA method, the importance and the reliability of the selected criteria are assessed by a group of decision-maker (DM). Then, the importance value of each supplier with respect to each product is obtained via the Technique for Order Performance by Similarity to Ideal Solution (TOPSIS) based on the concept of Z-numbers called Z-TOPSIS. Finally, these values are used as inputs in a mixed integer linear programming (MILP) to determine the suppliers and the amount of the products provided from the related suppliers. To validate the proposed methodology, an application is performed in a pharmaceutical company. The results show that the proposed method could provide promising results in decision making process more appropriately.

Retailer Stackelberg game in a supply chain with pricing and service decisions and simple price discount contract.

This paper studies the Retailer Stackelberg game in a supply chain consisting of two manufacturers and one retailer where they compete simultaneously under three factors including price, service and simple price discount contract. It is assumed that the second manufacturer provides service directly to his customers, and the retailer provides service for the first product's customers, while the retailer buys the first product under price discount from the first manufacturer. The analysis of the optimal equilibrium solutions and the results of the numerical examples show that if a manufacturer chooses the appropriate range of discount rate, he will gain more profit than when there is no discount given to the retailer. This situation can be considered as an effective tool for the coordination of the first manufacturer and the retailer to offer discount by manufacturer and to provide the service by the retailer. We obtain equilibrium solution of Retailer Stackelberg game and analyze the numerical examples under two cases: a) the manufacturers sell their products to the retailer without price discount contract. b) The first manufacturer sells his products to the retailer with the simple price discount contract. The preliminary results show that the service and the price discount contract can improve the performance of supply chain.

A double-blind, randomized, placebo-controlled trial of saffron stigma (Crocus sativus L.) in mothers suffering from mild-to-moderate postpartum depression.

INTRODUCTION: Numerous adverse effects of antidepressants as well as the attitudes of breastfeeding mothers, who prefer to consume herbal medicine rather than chemical drugs, encouraged us to assess the effects of saffron (Crocus sativus L.) on mothers suffering from mild-to-moderate postpartum depressive disorder. METHODS: A double-blind, randomized, and placebo-controlled trial was conducted on 60 new mothers who had a maximum score of 29 on the Beck Depression Inventory-Second Edition (BDI-II). They were randomly assigned to the saffron (15 mg/Bid) or placebo group. The primary outcome was a change in the BDI-II scores 8 weeks after treatment compared to the baseline. The response and remission rates were considered to be secondary outcome measures. RESULTS: Saffron had a more significant impact on the BDI-II scores than the placebo. The mean BDI-II scores decreased from 20.3 ±â€¯5.7 to 8.4 ±â€¯3.7 for the saffron group (p < .0001) and from 19.8 ±â€¯3.2 to 15.1 ±â€¯5.4 for the placebo group (p < .01). In the final assessment, 96% of the saffron group were in remission compared to 43% of the placebo group (p < .01). The complete response rates were 6% for the placebo group and 66% for the saffron group. CONCLUSION: When administered to treat minor PPD in breastfeeding mothers, saffron had a more significant impact on the BDI-II than the placebo.

Robust network data envelopment analysis approach to evaluate the efficiency of regional electricity power networks under uncertainty.

In this paper, new Network Data Envelopment Analysis (NDEA) models are developed to evaluate the efficiency of regional electricity power networks. The primary objective of this paper is to consider perturbation in data and develop new NDEA models based on the adaptation of robust optimization methodology. Furthermore, in this paper, the efficiency of the entire networks of electricity power, involving generation, transmission and distribution stages is measured. While DEA has been widely used to evaluate the efficiency of the components of electricity power networks during the past two decades, there is no study to evaluate the efficiency of the electricity power networks as a whole. The proposed models are applied to evaluate the efficiency of 16 regional electricity power networks in Iran and the effect of data uncertainty is also investigated. The results are compared with the traditional network DEA and parametric SFA methods. Validity and verification of the proposed models are also investigated. The preliminary results indicate that the proposed models were more reliable than the traditional Network DEA model.