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While steady-state analysis is useful, it does not consider the inherent transient characteristics of repairable systems' behavior, especially in systems that have relatively short life spans, or when their transient behavior is of special concern such as the motivating example used in this paper, military systems. Therefore, a maintenance policy that considers both transient and steady-state availability and aims to achieve the best trade-off between high steady-state availability and rapid stabilization is essential.

This paper studies the transient behavior of system availability under the Kijima Type II virtual age model. While such systems achieve steady-state availability, and it has been proved that deploying preventive maintenance (PM) can significantly improve its steady-state availability, this improvement often comes at the price of longer and increased fluctuating transient behavior, which affects overall system performance. The authors present a methodology that identifies the optimal PM policy that achieves the best trade-off between high steady-state availability and rapid stabilization based on cost-availability analysis.

When the proposed simulation-based optimization and cost analysis methodology is applied to the motivating example, it produces an optimal PM policy that achieves an availability–variability balance between transient and steady-state system behaviors. The optimal PM policy produces a notably lower availability coefficient of variation (by 11.5%), while at the same time suffering a negligible limiting availability loss of only 0.3%. The new optimal PM policy also provides cost savings of about 5% in total maintenance cost. The performed sensitivity analysis shows that the system's optimal maintenance cost is sensitive to the repair time, the shape parameter of the Weibull distribution and the downtime cost, but is robust with respect to changes in the remaining parameters.

Most of the current maintenance models emphasize the steady-state behavior of availability and neglect its transient behavior. For some systems, using steady-state availability as the sole metric for performance is not adequate, especially in systems that have relatively short life spans or when their transient behavior affects the overall performance. However, little work has been done on the transient analysis of such systems. In this paper, the authors aim to fill this gap by emphasizing such systems and applications where transient behavior is of critical importance to efficiently optimize system performance. The authors use military systems as a motivating example.

This paper explores the school leadership career outcomes, timing and educator evaluation of those who complete the Massachusetts Performance Assessment for Leaders (PAL) in comparison with others who did not. It also compares outcomes for those with different PAL score completion requirements.

Using PAL assessment results and state employment data for years 2015 through 2019, the authors examined trends and timing in PAL completers' career advancement into an initial school leader position (assistant principal or principal), by assessment cohort (based on assessment year and passing (cut) score requirements) and with who never had to complete the assessment for licensure (non-PAL completers). Using regression analysis, the authors evaluated potential race/ethnicity and gender differences in advancement. Using chi-square tests of association, the authors compared non-PAL and PAL completers on their demographic attributes and on retention and promotion from assistant principal and on their educator evaluation scores. The authors also examined differences in advancement based on the cut score requirements and preparation pathways.

PAL completers made steady career advances over time and at faster rates than non-PAL completers. Further, PAL completers subject to higher cut score requirements advanced more quickly than those with lower or no score requirements. PAL completers' gender and race/ethnicity seemed to matter less in career advancement than was found in other studies. In 2019, almost half who advanced were employed in the same districts as they had been in 2014 and were more likely to be new leaders in urban districts. When compared with other career-related measures, PAL completers outperformed non-PAL completers who first became school leaders since 2014: they were more likely to be rated as exemplary on educator evaluation and more likely to be retained or promoted after two years in their first school leader position.

Until now little research has existed on the career effects of licensure assessments. Because it requires candidates to demonstrate proficiency in core areas of school leadership work, the PAL assessment appears to be a superior means of screening initial school leaders (based on rate of hiring) and of signaling future performance (based on subsequent educator evaluation ratings) than other assessment forms (such as the School Leader Licensure Assessment [SLLA] exam).

Every business company deals with the dilemma of how much to invest in long-term (LT) versus short-term (ST) problem (LTvST problem). LT operations increase the reputation of the company, and revenue is rewarded in the future. In contrast, ST operations result in immediate rewards. Thus, every organization faces the dilemma of how much to invest in LT versus ST activities. The former deals with the “what” or effectiveness, and the latter deals with the “how” or efficiency. The role of managers is to solve this dilemma; however, they often fail to do so, mainly because of a lack of knowledge. This study aims to propose a dynamic optimal control model that formulates and solves the LTvST problem.

This study proposes a dynamic optimal control model that formulates and solves the dilemma whether to invest in short- or LT operations.

This model is illustrated as an example of an academic institute that wants to maximize its reputation. Investing in effectiveness in the academy translates into investing in research, whereas investing in efficiency translates into investing in teaching. Universities and colleges with a good reputation attract stronger candidates and benefit from higher tuition fees. Steady-state conditions and insightful observations were obtained by studying the optimal solution and performing a sensitivity analysis.

To the best of the authors’ knowledge, this paper is the first one to explore the optimal strategy when trying to maximize the short and LT activities of a company and solve the LTvST problem. Furthermore, it is applied on universities where teaching is the ST activity and research the LT activity. The insights gleaned from the application are relevant to many different fields. The authors believe that the paper makes a significant contribution to academic literature and to business managers.

The purpose of this paper is to implement a closed loop regulated bidirectional DC to DC converter for an application in the electric power system of more electric aircraft. To provide a consistent power supply to all of the electronic loads in an aircraft at the desired voltage level, good efficiency and desired transient and steady-state response, a smart and affordable DC to DC converter architecture in closed loop mode is being designed and implemented.

The aircraft electric power system (EPS) uses a bidirectional half-bridge DC to DC converter to facilitate the electric power flow from the primary power source – an AC generator installed on the aircraft engine’s shaft – to the load as well as from the secondary power source – a lithium ion battery – to the load. Rechargeable lithium ion batteries are used because they allow the primary power source to continue recharging them whenever the aircraft engine is running smoothly and because, in the event that the aircraft engine becomes overloaded during takeoff or turbulence, the charged secondary power source can step in and supply the load.

A novel nonsingular terminal sliding mode voltage controller based on exponential reaching law is used to keep the load voltage constant under any of the aforementioned circumstances, and its performance is contrasted with a tuned PI controller on the basis of their respective transient and steady-state responses. The former gives a faster and better transient and steady-state response as compared to the latter.

This research gives a novel control scheme for incorporating an auxiliary power source, i.e. rechargeable battery, in more electric aircraft EPS. The battery is so implemented that it can get regeneratively charged when primary power supply is capable of handling an additional load, i.e. the battery. The charging and discharging of the battery is carried out in closed loop mode to ensure constant battery terminal voltage, constant battery current and constant load voltage as per the requirement. A novel sliding mode controller is used to improve transient and steady-state response of the system.

This paper aims at developing an improved method, based on binary search algorithm (BSA) for the steady-state analysis of self-excited induction generators (SEIGs), which are increasingly used in wind energy electric conversion systems. The BSA is also compared with linear search algorithm (LSA) to bring out the merits of BSA over LSA.

All the parameters of SEIG, including the varying core loss of the machine, have been considered to ensure accuracy in the predetermined performance values of the set up. The nodal admittance method has been adopted to simplify the equivalent circuit of the generator and load. The logic and steps involved in the formulation of the complete procedure have been illustrated using elaborate flowcharts.

The proposed approach is validated by the experimental results, obtained on a three-phase 240 V, 5.0 A, 2.0 kW SEIG, which closely match with the corresponding predicted performance values. The analysis is shown to be easy to implement with reduced computation time.

A novel improved and simplified technique has been formulated for estimating the per unit frequency (a), magnetizing reactance (X_m) and core loss resistance (R_m) of the SEIG using the nodal admittance of its equivalent circuit. The accuracy of the predetermined performance is enhanced by considering the SEIG’s varying core loss. Only simple MATLAB programming has been used for adopting the algorithms.

In this paper, a new front end converter (FEC) topology has been proposed for the switched reluctance (SR) motor drive. This study aims to present the performance analysis of FEC-based SR motor drive using various types of control schemes like conventional proportional integral (PI) controller, fuzzy logic controller (FLC) and fuzzy-tuned proportional integral controller (Fuzzy-PI).

The proposed FEC-based SR motor drive with various control strategies is derived for the torque ripple minimization and speed control.

The steady state and the dynamic response of the FEC-based SR motor drive are analyzed using three different controllers under change in speed and loading conditions. The Fuzzy-PI-based control scheme improves the dynamic response of the system when compared with the FLC and the conventional PI controller.

The hardware prototype has been implemented for the FEC-based SR motor drive by using the Xilinx SPARTAN 6 FPGA processor. The experimental verification has been conducted and the results have been measured under steady state and dynamic conditions.

Five-level rectifiers have received widespread attention because of their excellent performance in high-voltage and high-power applications. Taking a five-level rectifier with only four-IGBT for this study, a sliding mode predictive control (SMPC) algorithm is proposed to solve the problem of poor dynamic performance and poor anti-disturbance ability under the traditional model predictive control with the PI outer loop.

First, mathematical models under the two-phase stationary coordinate system and two-phase synchronous rotating coordinate system are established. Then, the design of the outer-loop sliding mode controller is completed by establishing the sliding mode surface and design approach rate. The design of the inner-loop model predictive controller was completed by discretizing the mathematical model equations. The modulation part uses a space vector modulation technique to generate the PWM wave.

The sliding mode predictive control strategy is compared with the control strategy with a PI outer loop and a model predictive inner loop. The proposed control strategy has a faster dynamic response and stronger anti-interference ability.

For the five-level rectifier, the advantages of fast dynamic influence and parameter insensitivity of sliding mode control are used in the voltage outer loop to replace the traditional PI control, and which is integrated with the model predictive control used in the current inner loop to form a novel control strategy with a faster dynamic response and stronger immunity to disturbances. This novel strategy is called sliding mode predictive control (SMC).

This study aims to investigate the factors that drive housing loan default in India based on unique micro-level data drawn from a public sector bank's credit files with a national presence in India. The authors address endogeneity in the loan to value ratio (LTV) while deciphering the drivers of default.

The study uses a probit regression approach to analyze the relationship between the probability of default and the explanatory variables. The authors introduce two instrumental variables to address the issue of endogeneity. The authors also add state-level demographic and several other control variables, including an indicator variable that captures the recent regulatory change. The authors’ analysis is based on 102,327 housing loans originated by the bank between January 2001 and December 2017.

The authors find that addressing the endogeneity issue is essential to specify default drivers, especially LTV, correctly. The nature of employment, gender, socio-religious category and age have a distinct bearing on housing loan defaults. Apart from the LTV ratio, the other key determinants of default are the interest rate, frequency of repayment, prepayment options and the loan period. The findings suggest that the population classification of branch location plays a significant role in loan default. The authors find that an increase in per capita income and an increase in the number of employed people in the state, which reflects borrowers' ability to pay by borrowers, reduce the probability of default. The change in the regulatory classification of loan assets by the Reserve Bank of India did not bear the main results.

The non-availability of the house price index in analyzing the default dynamics in the Indian housing finance market for the period covered under the study has constrained our analysis. The applicability of the equity theory of default, strategic default, borrowers' characteristics and personality traits are potential research areas in the Indian housing finance market.

The study's findings are expected to provide valuable inputs to the banks and the housing finance companies to explore and formulate appropriate strategic options in lending to this sector. It has highlighted various vistas of tailor-making housing loan product offerings by the commercial banks to ensure and steady and healthy growth of their loan portfolio. It has also highlighted the regulatory and policy underpinnings to ensure the healthy growth of the Indian housing finance market.

The study provides a fresh perspective on the default drivers in the Indian housing finance market based on micro-level data. In our analysis, the authors find clear evidence of endogeneity in LTV and argue that any attempts to decipher the default drivers of housing loans without addressing the issue of endogeneity may lead to faulty interpretation. Therefore, this study is unique in recognizing endogeneity and has gone deeper in identifying the default drivers in the Indian housing market not addressed by earlier papers on the Indian housing market. The authors also control for the regulatory changes in the Indian housing finance market and include state-level control variables like per capita GDP and the number of workers per thousand to capture the borrowers' ability to pay characteristics.