This paper presents a new competitive approach to provide Reliability for distribution system customers. The model is based on the Cournot game and utilizes the Nash equilibrium concept to find the output of the problem. Reliability in the proposed framework is an ancillary service and the customers who participated in the program must pay for Reliability provision. The proposed model also considers regulatory concerns of Reliability insuring the average Reliability of the system is not incurred. Based on the proposed model, customers will compete for their Reliability enhancement considering all the constraints related to the network, regulator and each customer.  The expected outage time for each customer is considered the Reliability index in this paper. The model is investigated in a sample case study and the results show how a customer would behave if they participated in the Reliability Improvement program of distribution systems. Our results also show that there would exist a high motivation for both parties (utility and customers) to implement the proposed model for the Reliability enhancement of the distribution system.

Background: In spite of many advantages in Lenke classification of adolescent idiopathic scoliosis, the latest study of Richards et al showed that the King classification is better than had been reported recently and the Lenke classification system of adolescent idiopathic scoliosis is less reliable than previously reported.Methods: In this study, we performed a multi-surgeon comparison of these two classification systems. After teaching and discussing all available data of these classification systems with four spine surgeons, a pilot classification was performed. Then, they independently evaluated preoperative radiographs (standing posteroanterior, lateral, and two supine side-bending views) of 99 patients with adolescent idiopathic scoliosis. The results were determined by calculating the average percentage of intraobserver and interobserver agreement. Reliability was quantified using kappa statistics.Findings: The King classification demonstrated good intraobserver and interobserver Reliability, with an intraobserver agreement of 85.8% (kappa coefficient, 0.80). Interobserver percentage of agreement averaged 80.8% (kappa coefficient, 0.74). The complete Lenke classification, combining curve type, lumbar modifier, and sagittal thoracic modifier, demonstrated good Reliability for both intraobserver and interobserver measurements. The intraobserver percentage of agreement averaged 85.8% (kappa coefficient, 0.82). The interobserver percentage of agreement averaged 80.8% (kappa coefficient, 0.77).Conclusion: In this study, with each investigator performing the radiographic measurements, the King and Lenke classifications were almost similar (the Lenke classification had slightly better results). Such better results might be due to more training of this complex classification system. So, because of greater coverage of idiopathic scoliosis curve and usefulness of Lenke classification system, we prefer using this classification system in our center.

The regulatory schemes currently used for Reliability Improvement have weaknesses in the provision of quality services based on the customers’ perspective. These schemes consider the average of the service as a criterion to incentivize or penalize the distribution system operators (DSOs). On the other hand, most DSOs do not differentiate electricity services at the customer level, due to the status of the electricity grid and lack of adequate information about customers’ preferences. This paper proposes a novel Reliability insurance scheme (RIS), which enables the electricity consumers to determine their desired Reliability levels according to their preferences and pay corresponding premiums to the DSO. The DSO can use the premiums to improve Reliability or reimburse consumers. To design efficient insurance contracts, this paper uses utility function to estimate customers’ viewpoints of electricity energy consumption. This function measures the customers’ satisfaction of electricity energy consumption. The proposed utility based Reliability insurance scheme (URIS) may create a free-riding opportunity for the DSO, in which low quality service is provided and the collected premiums are used to pay the reimbursements. To prevent free-riding opportunity, this paper incorporates the proposed URIS and reward/penalty schemes (RPSs). The results show that the success of the proposed Reliability scheme increases as the grid flexibility increases.

The stability transformation method (STM) of chaos feedback control is a simple and efficient approach to implement the convergence control of the first order Reliability method (FORM). The robustness and efficiency of the STM are depended on selected parameters of this algorithm such that involuntary matrix and step size in structural Reliability analysis. When involuntary matrix and step size are inappropriately selected, the STM iterative formula can be prematurely and slowly converged. In this paper, the STM scheme was improved based on a simple dynamical step size and the involuntary matrix equals to unit matrix. The proposed dynamical step size can be computed using the new and previous results of the FORM iterative formula. The efficiency and robustness of improved STM was compared with the STM and finite-step length methods from five numerical and mechanical examples taken through the literature. The results impales that the improved STM not only ensured the numerical stability of nonlinear mechanical problems but also is more efficient than the STM to approximate the failure probability.

The Big Five Inventory (BFI) is one of the most widely used personality questionnaires, but very few studies have been conducted on its psychometric properties in Iranian population. This study was carried out with the aim of studying the psychometric properties of the BFI questionnaire. For this purpose, 390 university students in Tehran (210 girls and 180 boys) with an age range of 18 to 56 years (M = 27.52; SD = 8.79) were selected using the convenience sampling method and completed BFI and NEO-FFI questionnaires. Cronbach's alpha, multitrait-multimethod matrix, and confirmatory factor analysis were used to estimate the psychometric properties of the questionnaire. The result of the confirmatory factor analysis showed that 15 of the 44 items did not have a strong factor loading (higher than 0.40) on the corresponding factors and were therefore removed from the questionnaire. The fit indices showed that the Persian version of 29 questions (BFI-29) with 5 factors has a good fit with the data. The Reliability analysis also showed that the range of Cronbach's alpha coefficients of the factors is from 0.70 to 0.79. The lowest Cronbach's alpha coefficient was related to agreeableness factor and the highest was related to conscientiousness. The findings from the multitrait-multimethod matrix showed that the BFI-29 questionnaire has good convergent and divergent validity with the NEO-FFI questionnaire. In general, based on the results of the present study, it can be said that the BFI-29 questionnaire has desirable psychometric properties and whenever a short tool for personality measurement is needed, this questionnaire can be used.

In recent years, Many manufacturing industries for promoting their efficiency have tended to use the automatic manufacturing systems. Expanding automatic systems and to increase their complexity are representing the necessity of studying a proper functional quality and using reliable equipment in such systems more than ever. In this direction, the technique of fault tree analysis (FTA), along with using other techniques such as failure mode and effect analysis (FMEA) reveals the incorrect performance states (modes) in system in order to know these modes exactly may prevent their occurance and increase their function quality. In this study, the approaches may increase the Reliability of performance in an industrial robot are studied by FTA technique as a case study to show Improvement in performance of equipments on automatic systems to reduce their destruction (fault) during the work, and finally access to an automatic manufacturing systems with high Reliability.

Towards realizing an intelligent networked society, enabling low-cost low-energy connectivity for things, also known as the Internet of Things (IoT), is of crucial importance. While the existing wireless access networks require centralized signaling for managing network resources, this approach is of less interest for future generations of wireless networks due to the energy consumption in such signaling and the expected increase in the number of IoT devices. Then, in this work, we investigate leveraging machine learning for distributed control of IoT communications. Towards this end, first, we investigate low-complex learning schemes that are applicable to resource-constrained IoT communications. Then, we propose a lightweight learning scheme which enables the IoT devices to adapt their communication parameters to the environment. Further, we investigate analytical expressions presenting the performance of a centralized control scheme for adapting communication parameters of IoT devices and compare the results with the results from the proposed distributed learning approach. The simulation results confirm that the Reliability and energy efficiency of IoT communications could be significantly improved by leveraging the proposed learning approach.

In this paper, the system Reliability in the design stage and before production is improved. The proposed algorithm is based on the fact that the chosen structure should be practical in reality. First, possible structures for the system of interest are designed then the optimum structure is chosen for production. The proposed approach to select the optimal alternative is to determine the effective criteria for selecting process, then using analytic network process as a robust and useful technique for multi-attribute decision making.

Distribution system Reliability programs are usually based on Improvement of average Reliability indices. They have weakness in terms of distinguishing between Reliability of different customers that may prefer different level of Reliability. This paper proposes a new framework based on game theory to accommodate customers’ Reliability requests in distribution system Reliability provision. To do this, distribution Reliability equations are developed so that it is recognized how game theory is suitable for this purpose and why conventional methods could not provide customer Reliability requirements appropriately. It would be shown that customer participation in distribution system Reliability provision can make conflict of interest and leads to a competition between customers. So, in this paper a game theoretic approach is designed to model possible strategic behavior of customers in distribution system Reliability provision. The results show that by implementing the proposed model, distribution utilities would have the capability to respond to customers’ Reliability requirements, such that it is beneficial for both utility and customers.

The nature of electro-optical equipment in various industries and the pursuit of the goal of reducing costs demand high Reliability on the part of electro-optical systems. In this respect, Reliability Improvement could be addressed through a Reliability allocation problem. Subsystem Reliability must be increased such that the requirements as well as defined requisite functions are ensured in accordance with the designers’ opinion. This study is an attempt to develop a multiobjective model by maximizing system Reliability and minimizing costs in order to investigate design phase costs as well as production phase costs. To investigate Reliability Improvement feasibility in the design phase, effective feasibility factors in the system are used and the sigma level index is incorporated in the production phase as the Reliability Improvement difficulty factor. Thus, subsystem Reliability Improvement priorities are taken into consideration. Subsystem dependency degree is investigated through the design structure matrix and incorporated into the model’ s limitation together with modified criticality. The primary model is converted into a single-objective model through goal programming. This model is implemented on electro-optical systems, and the results are analyzed. In this method, Reliability allocation follows two steps. First, based on the allocation weights, a range is determined for the Reliability of subsystems. Afterward, Improvement is initiated based upon the costs and priorities of subsystem Reliability Improvement.