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The purpose of this study is to explore the relationship between gender disparities in rural education attainments and agricultural landownership (ALO) in Sub-Sahara Africa with Sustainable Development Goal (SDG) perspective.

This study uses SDG indicators interactions and pairwise correlation analysis.

There is a significant negative association between gender disparities in rural education attainments and ALO in Sub-Sahara Africa. Such negative relationship is not influenced by national economic development and living standards.

The data is limited with 16 Sub-Sahara African countries, and as this is an early output of a number of follow-up studies in the author’s plan, the methodology is relatively simple.

Reducing gender disparity in rural Sub-Sahara Africa especially in ALO requires more integrated approaches which also address other aspects of sustainable development. This is particularly the situation because of the strong male-favored customary practices in rural Sub-Sahara Africa. The prioritization of different dimensions of sustainable development is also important in Sub-Sahara Africa.

Strong awareness of SDGs is important. Further efforts in collecting data for and use data of sustainable development, especially the SDGs, are essential. Emerging trend of studying the interactions across SDGs reflects the future direction of relevant fields.

This paper has high originality because it is an early-stage research in the SDG interactions in Sub-Sahara African countries with the perspective of gender, gender disparity, Sub-Sahara Africa, SDGs, ALO and rural education attainments. This paper has both academic and practical values because of its innovative research thoughts and policy-oriented implications.

As hospital costs continue to rise, increasing attention is being paid to the way these organizations are and should be managed. This attention typically comes in the form of focus on costs of services, quality (often measured through mortality rates) and length of stay. Hospital management has a broad array of choices at their disposal to address these challenges. As service operations, hospitals present a significant opportunity to apply the many tools and techniques from the field of operations strategy to this important industry. The objective of this paper is to use the operations strategy framework to assess the relationship between a set of operational elements and hospital performance in terms of average length of stay (ALOS), so that hospital managers improve the effectiveness and efficiency of patient care of their hospitals.

Using the structural and infrastructural operations strategy framework, this study examines the relationship between several strategic variables and hospital performance. To analyze these relationships the paper employs data from the population of hospitals in New York State. The performance measure is the ALOS for patients, adjusted for the mix and severity of cases in each hospital.

The paper finds that a direct relationship exists between the dependent variable and location, capacity, and teaching status, and failed to find a direct relationship for capital expenditures, salary, and staffing levels. However, the paper did find significant interaction effects between capital expenses and both salary and staffing levels.

There appear to be trade‐offs between capital expenditures and workforce decisions that have significant implications in light of current and expected hospital staffing shortages. The findings indicate that reductions in staff may not be perfectly replaced by corresponding increases in capital expenditures.

This paper further expands the body of research that addresses the important challenges hospitals face from an operations management perspective.

To improve sensorless control of induction motor using Kalman filtering family, this paper aims to introduce a new metaheuristic optimizer algorithm for online rotor speed and flux estimation.

The main problem with unscented Kalman filter (UKF) observer is its sensibility to the initial values of Q and R. To solve the optimal solution of these matrices, a novel alternative called ant lion optimization (ALO)-UKF is introduced. It is based on the combination of the classical UKF observer and a nature-inspired metaheuristic algorithm, ALO.

Synthesized ALO-UKF has given good results over the famous extended Kalman filter and the classical UKF observer in terms of accuracy and dynamic performance. A comparison between ALO and particle swarm optimization (PSO) was established. Simulations illustrate that ALO recovers rapidly and accurately while PSO has a slower convergence.

Using the proposed approach, tuning the design matrices Q and R in Kalman filtering becomes an easy task with a high degree of accuracy and the constraints of time cost are surmounted. Also, ALO-UKF is an efficient tool to improve estimation performance of states and parameters’ uncertainties of the induction motor. Related optimization technique can be extended to faults monitoring by online identification of their corresponding signatures.

Doing business in China

This case was developed for us in an undergraduate strategy course at the point in the course when global strategies are discussed. It might also be used in an undergraduate entrepreneurship class when “diffusion of innovation” is being discussed.

This case describes the experience of a student consulting team from Baylor University working in China during the summer of 2012. The team was charged with the responsibility of determining an entry mode into China for a farm-implement company in Sweden. The students spent most of the summer in three different locations in China interviewing dairy farmers and equipment dealers to identify the proposed customers for the products and their equipment needs. Their findings led them to the conclusion that Alo, the Swedish farm implement company, would have to alter their mode of entry into the Chinese market to be successful. The decision facing Amanda Sherek, the team leader, was how to structure the team’s report to Alo to help them recognize the need for rethinking the company’s original strategy.

At the conclusion of the case discussion, students should be able to: list and explain critical findings of the students that should be involved in developing a strategy for Alo; identify the appropriate global strategy for Alo to use in entering China; relate the theory of “Diffusion of Innovation” to Alo’s situation in China; identify whether Alo was contemplating using a production orientation or the marketing concept for its entry into China; and outline a strategic plan for Alo to enter the Chinese dairy farming industry.

Teaching notes are available for educators only. Please contact your library to gain login details or email support@emeraldinsight.com to request teaching notes.

Generator maintenance scheduling (GMS) is an essential task for electric power utilities as the periodical maintenance activity enhances the lifetime and also ensures the reliable and continuous operation of generating units. Though numerous meta-heuristic algorithms have been reported for the GMS solution, enhancing the existing techniques or developing new optimization procedure is still an interesting research task. The meta-heuristic algorithms are population based and the selection of their algorithmic parameters influences the quality of the solution. This paper aims to propose statistical tests guided meta-heuristic algorithm for solving the GMS problems.

The intricacy characteristics of the GMS problem in power systems necessitate an efficient and robust optimization tool. Though several meta-heuristic algorithms have been applied to solve the chosen power system operational problem, tuning of their control parameters is a protracting process. To prevail over the previously mentioned drawback, the modern meta-heuristic algorithm, namely, ant lion optimizer (ALO), is chosen as the optimization tool for solving the GMS problem.

The meta-heuristic algorithms are population based and require proper selection of algorithmic parameters. In this work, the ANOVA (analysis of variance) tool is proposed for selecting the most feasible decisive parameters in algorithm domain, and the statistical tests-based validation of solution quality is described. The parametric and non-parametric statistical tests are also performed to validate the selection of ALO against the various competing algorithms. The numerical and statistical results confirm that ALO is a promising tool for solving the GMS problems.

As a first attempt, ALO is applied to solve the GMS problem. Moreover, the ANOVA-based parameter selection is proposed and the statistical tests such as Wilcoxon signed rank and one-way ANOVA are conducted to validate the applicability of the intended optimization tool. The contribution of the paper can be summarized in two folds: the ANOVA-based ALO for GMS applications and statistical tests-based performance evaluation of intended algorithm.

Now days, various techniques are used for controlling the plants. New ideas are evolving day by day to get better-quality control for various industrial processes to produce high-quality products. Currently, the focus of this research is being emphasized on application of nature-inspired algorithms in control systems. The purpose of this paper is to apply a nature-inspired algorithm called Ant Lion Optimizer (ALO) for the design of proportional-integrator-derivative (PID) controller for an automatic voltage regulator (AVR) system.

For the design of the PID controller, the ALO algorithm is considered as a designing tool for obtaining the optimal values of the controller parameter. All the simulations are carried out in Simulink/MATLAB environment. A comparative study is carried out with some modern nature-inspired algorithm to describe the advantages of this tuning method.

The proposed method has superiority value in transient and frequency domain analysis than the other published heuristic optimization algorithms. The presented approach has almost no variation in transient response when varying time constants of the system parameter, such as exciter, generator, amplifier and sensor from −50 per cent to +50 per cent. In addition, the close loop system is robust against any disturbances such as input–output disturbances and parametric uncertainty, as the sensitivity values are nearly equal to one.

The proposed method presents the design and performance analysis of proportional integral derivate (PID) controller for an AVR system using the recently proposed ALO.

This study aims to minimize operating cost, adhere to pollution norms and maintain reserve and voltage levels subject to various operational concerns, including non linear characteristics of generators and fuel limitation issues, which are useful for the current power system applications.

Improved control settings are required while considering multiple conflicting operational objectives that necessitate using the modern bio-inspired algorithm ant lion optimizer (ALO) as the main optimization tool. Fuzzy decision-making mechanism is incorporated in ALO to extract the best compromise solution (BCS) among set of non-dominated solutions.

The BCS records of IEEE-30 bus and JEAS-118 bus systems are updated in this work. Numerical simulation results comparison and comprehensive performance analysis justify the applicability of the intended algorithm to solve multi-objective dynamic optimal power flow (DOPF) problem over the state-of-art methods.

Optimal control settings are obtained for IEEE-30 and JEAS-118 bus systems with the objectives of minimizing fuel cost and emission in dynamic environment considering take-or-pay fuel contract issue. The fuzzy supported ALO (FSALO) is applied first time to solve the DOPF problem.

Finding the optimal maintenance schedules is the primitive aim of preventive maintenance scheduling (PMS) problem dealing with the objectives of reliability, risk and cost. Most of the earlier works in the literature have focused on PMS with the objectives of leveling reserves/risk/cost independently. Nevertheless, very few publications in the current literature tackle the multi-objective PMS model with simultaneous optimization of reliability, and economic perspectives. Since, the PMS problem is highly nonlinear and complex in nature, an appropriate optimization technique is necessary to solve the problem in hand. The paper aims to discuss these issues.

The complexity of the PMS problem in power systems necessitates a simple and robust optimization tool. This paper employs the modern meta-heuristic algorithm, namely, Ant Lion Optimizer (ALO) to obtain the optimal maintenance schedules for the PMS problem. In order to extract best compromise solution in the multi-objective solution space (reliability, risk and cost), a fuzzy decision-making mechanism is incorporated with ALO (FDMALO) for solving PMS.

As a first attempt, the best feasible maintenance schedules are obtained for PMS problem using FDMALO in the multi-objective solution space. The statistical measures are computed for the test systems which are compared with various meta-heuristic algorithms. The applicability of the algorithm for PMS problem is validated through statistical t-test. The statistical comparison and the t-test results reveal the superiority of ALO in achieving improved solution quality. The numerical and statistical results are encouraging and indicate the viability of the proposed ALO technique.

As a maiden attempt, FDMALO is used to solve the multi-objective PMS problem. This paper fills the gap in the literature by solving the PMS problem in the multi-objective framework, with the improved quality of the statistical indices.

The purpose of this paper (systematic review and meta-analysis) is to synthesize and analyze studies that assessed Iranian hospital efficiency.

A systematic literature search was conducted using both international (the Institute for Scientific Information, Scopus and PubMed) and Iranian scientific (Magiran, IranMedex and Scientific Information Database) databases. The review included original studies that used the Pabon Lasso Model to examine Iranian hospital performance, published in Persian or English. A self-administered checklist was used to collect data. In total, 12 questions were used for quality assessment.

In total, 34 studies met our inclusion criteria. The fixed-effects meta-analysis indicated that 19.2 percent (95% confidence interval (CI): 15.6–23.2 percent) of hospitals were in Zone 1 (poor performance: low bed turnover rate (BTR) and bed occupancy rate (BOR) and high average hospital stay (ALoS)), 23.7 percent (95% CI: 20.1–27.8 percent) were in Zone 2, 31.7 percent (95% CI: 27.7–36 percent) in Zone 3 (good performance: high BTR and BOR and low ALoS) and 25.4 percent (95% CI: 21.7–29.5 percent) in Zone 4.

Results help Iranian health policymakers to understand hospital performance, which, in turn, may lead to promoting greater awareness and policy attention to improve Iranian hospital efficiency.

This study indicated that most Iranian hospitals had sub-optimal performance. Further studies are required to understand factors that explain the country’s hospital inefficiency.

The purpose of this paper is to find out which algorithm, among Genetic Algorithm (GA), Particle Swarm Optimizer (PSO), the novel Grey Wolf Optimizer (GWO) and the novel Ant Lion Optimizer (ALO), is the best to obtain the optimal value of the nonlinear parameter γ of nonlinear grey Bernoulli model (NGBM(1,1)) under different situations.

The optimization of γ has been attributed to a nonlinear programming problem at first. The convergence, convergence rate, time consuming and stability of GA, PSO, GWO and ALO are compared in the numerical experiments, and in each subcase the criteria are set to be the same. Over 10,000 iterations have been run on the same environment in order to guarantee the reliability of the results.

All the selected algorithms can converge to the same optimal value with sufficient iterations. But the best algorithm should be chose under different situations.

The optimal value of γ seems to exist uniquely due to the empirical results. And there does not exist a best algorithm for all the cases. The researchers and commercial software developers should choose a proper algorithm due to different cases.

The performance of GA, PSO, GWO and ALO to compute the optimal γ of NGBM(1,1) has been compared for the first time. And it is the original work which uses the GWO and ALO to optimize the NGBM(1,1).