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This study aims to evolve an enhanced butterfly optimization algorithm (BOA) with respect to convergence and accuracy performance for numerous benchmark functions, rigorous constrained engineering design problems and an inverse synthetic aperture radar (ISAR) image motion compensation.

Adaptive BOA (ABOA) is thus developed by incorporating spatial dispersal strategy to the global search and inserting the fittest solution to the local search, and hence its exploration and exploitation abilities are improved.

The accuracy and convergence performance of ABOA are well verified via exhaustive comparisons with BOA and its existing variants such as improved BOA (IBOA), modified BOA (MBOA) and BOA with Levy flight (BOAL) in terms of various precise metrics through 15 classical and 12 conference on evolutionary computation (CEC)-2017 benchmark functions. ABOA has outstanding accuracy and stability performance better than BOA, IBOA, MBOA and BOAL for most of the benchmarks. The design optimization performance of ABOA is also evaluated for three constrained engineering problems such as welded beam design, spring design and gear train design and the results are compared with those of BOA, MBOA and BOA with chaos. ABOA, therefore, optimizes engineering designs with the most optimal variables. Furthermore, a validation is performed through translational motion compensation (TMC) of the ISAR image for an aircraft, which includes blurriness. In TMC, the motion parameters such as velocity and acceleration of target are optimally predicted by the optimization algorithms. The TMC results are elaborately compared with BOA, IBOA, MBOA and BOAL between each other in view of images, motion parameter and numerical image measuring metrics.

The outperforming results reflect the optimization and design successes of ABOA which is enhanced by establishing better global and local search abilities over BOA and its existing variants.

Adsorption parameters (e.g. Langmuir constant, mass transfer coefficient and Thomas rate constant) are involved in the design of aqueous-media adsorption treatment units. However, the classic approach to estimating such parameters is perceived to be imprecise. Herein, the essential features and performances of the ant colony, bee colony and elephant herd optimisation approaches are introduced to the experimental chemist and chemical engineer engaged in adsorption research for aqueous systems. Key research and development directions, believed to harness these algorithms for real-scale water treatment (which falls within the wide-ranging coverage of the Sustainable Development Goal 6 (SDG 6) ‘Clean Water and Sanitation for All’), are also proposed. The ant colony, bee colony and elephant herd optimisations have higher precision and accuracy, and are particularly efficient in finding the global optimum solution. It is hoped that the discussions can stimulate both the experimental chemist and chemical engineer to delineate the progress achieved so far and collaborate further to devise strategies for integrating these intelligent optimisations in the design and operation of real multicomponent multi-complexity adsorption systems for water purification.

Since the first moment of the pandemic, national and international travel restrictions are in place to reduce human mobility. This actual situation makes the tourism industry one of the areas most affected by the pandemic. Many microeconomic factors (households and firms) were adversely affected by the pandemic, and this situation brought about macroeconomic contraction. Naturally, governments seek to sustain production and employment by offering financial packages to reduce the negative economic effects of the pandemic. Given such information, the study aims to examine the financial policies implemented by countries to support the tourism industry during the pandemic period.

Content analysis, which is a technique of qualitative research method, was applied in the analysis process of the data. Assessments were made based on data published by the United Nations World Tourism Organization (UNWTO) on the financial and monetary policies implemented by countries to support the tourism industry. The data were analyzed using the MAXQDA qualitative analysis program.

According to the results of the study, countries support the tourism industry financially in terms of credit and liquidity. Also, tourism investments are encouraged by tax breaks and low interest rates.

It is aimed to determine what issues the financial and monetary policies published by the UNWTO focus on to solve the problems in the tourism sector. In this way, it is thought that the study will reveal the problems experienced by tourism enterprises during the pandemic period with a holistic perspective.

Existing Simultaneous Localization and Mapping (SLAM) algorithms have been relatively well developed. However, when in complex dynamic environments, the movement of the dynamic points on the dynamic objects in the image in the mapping can have an impact on the observation of the system, and thus there will be biases and errors in the position estimation and the creation of map points. The aim of this paper is to achieve more accurate accuracy in SLAM algorithms compared to traditional methods through semantic approaches.

In this paper, the semantic segmentation of dynamic objects is realized based on U-Net semantic segmentation network, followed by motion consistency detection through motion detection method to determine whether the segmented objects are moving in the current scene or not, and combined with the motion compensation method to eliminate dynamic points and compensate for the current local image, so as to make the system robust.

Experiments comparing the effect of detecting dynamic points and removing outliers are conducted on a dynamic data set of Technische Universität München, and the results show that the absolute trajectory accuracy of this paper's method is significantly improved compared with ORB-SLAM3 and DS-SLAM.

In this paper, in the semantic segmentation network part, the segmentation mask is combined with the method of dynamic point detection, elimination and compensation, which reduces the influence of dynamic objects, thus effectively improving the accuracy of localization in dynamic environments.

This study aims at extracting and characterizing palm leaf fibers from Elaeis guineensis species of palm trees found in Ethiopia.

The fibers were extracted using three methods: manually, through water retting and chemically with sodium hydroxide. Physical parameters of the extracted fibers were evaluated, including tensile strength, fiber fineness, moisture content, degradation point and functional groups. Its cellulose, hemicellulose and lignin contents were also analyzed.

The results showed that the palm leaf fibers have a comparable fiber strength (170-450 MPa), elongation (0.95-1.25 per cent), fiber length (230-500 mm) and moisture regain (8-10 per cent) to jute, sisal and flax and thus can be used for technical textile application.

The fibers extracted using the water retting method had better properties than the other extraction methods. Its fiber length of 307 mm, cellulose content of 58 per cent, strength of 439 MPa and elongation of 1.24 per cent were the highest for all the extracted fibers. When compared with other fibers, palm leaf fiber properties such as tensile strength (439 MPa), elongation (1.24 per cent), moisture content (7.9-10.4 per cent and degradation point (360-380°C) were consistent with those of jute, sisal and ramie fibers. Hence, palm leaf fibers can be used for technical textile applications such as composite reinforcement.

Changing the product characteristics and demand quantity resulting from the variability of the modern market leads to re-assigned tasks and changing the cycle time on the production line. Therefore, companies need re-balancing of their assembly line instead of balancing. The purpose of this paper is to propose an efficient algorithm approach for U-type assembly line re-balancing problem using stochastic task times.

In this paper, a genetic algorithm is proposed to solve approach for U-type assembly line re-balancing problem using stochastic task times.

The performance of the genetic algorithm is tested on a wide variety of data sets from literature. The task times are assumed normal distribution. The objective is to minimize total re-balancing cost, which consists of workstation cost, operating cost and task transposition cost. The test results show that proposed genetic algorithm approach for U-type assembly line re-balancing problem performs well in terms of minimizing total re-balancing cost.

Demand variation is considered for stochastic U-type re balancing problem. Demand change also affects cycle time of the line. Hence, the stochastic U-type re-balancing problem under four different cycle times are analyzed to present practical case.

As per the authors’ knowledge, it is the first time that genetic algorithm is applied to stochastic U-type re balancing problem. The large size data set is generated to analyze performance of genetic algorithm. The results of proposed algorithm are compared with ant colony optimization algorithm.

This study aimed to propose a performance-oriented approach of automatically generative design and optimization of hospital building layouts in consideration of public health emergency, which intended to conduct reasonable layout design of hospital building to meet different performance requirements for both high efficiency during normal periods and low risk in the pandemic.

The research design follows a sequential mixed methodology. First, key points and parameters of hospital building layout design (HBLD) are analyzed. Then, to meet the requirements of high efficiency and low risk, adjacent preference score and infection risk coefficient are constructed as constraints. On this basis, automatic generative design is conducted to generate building layout schemes. Finally, multi-objective deviation analysis is carried out to obtain the optimal scheme of hospital building layouts.

Automatic generative design of building layouts that integrates adjacent preferences and infection risks enables hospitals to achieve rapid transitions between normal (high efficiency) and pandemic (low risk) periods, which can effectively respond to public health emergencies. The proposed approach has been verified in an actual project, which can help systematically explore the solution for better decision-making.

The form of building layouts is limited to rectangles, and future work can explore conducting irregular layouts into optimization for the framework of generative design.

The contribution of this paper is the developed approach that can quickly and effectively generate more hospital layout alternatives satisfying high operational efficiency and low infection risk by formulating space design rules, which is of great significance in response to public health emergency.

The purpose of this paper is to introduce a novel cross (+) type yoke with hybrid electromagnets and new reluctance modeling to precisely calculate attraction force is given.

The comparison of attraction force and torque analyses between the proposed formulation and the existing formulation in the literature is comparatively presented. For the correctness of the force and torque values calculated in the model created, the system was created in ANSYS Maxwell and its accuracy was proved by making analyses. The maglev carrier system is inherently unstable from the point of view of control engineering. For that, it needs an active controller to eliminate this instability. For the levitation of the carrier system, it is necessary to design a controller in three axes (z, α and β). I-PD controller was designed for the air gap control of the carrier system in three axes and the controller parameters were determined by the canonical method.

While the new formulation proposed in the modeling of the carrier system has a maximum error of 1.03%, the existing formula in the literature has an error of 16.83% in the levitation distance point.

A novel cross-type hybrid carrier system has been proposed in the literature. With the double integral used in modeling the system, it takes a long time to solve symbolically, and it is difficult to simulate dynamic behavior in control validation. To solve this problem, attraction force and inclination torque values are easily characterized by new formulation and besides the simulations are conducted easily. The experimental setup was manufactured and assembled, and the carrier system was successfully levitated, and reference tracking was performed without overshoot.

The paper aims to analyze the relationships between ethical leadership (EL), job satisfaction (JS), affective commitment (AC) and turnover intention (TI) that might make accountants quit withdrawal and become productive and useful in private organizations operating in Istanbul.

Data were collected through an online survey using a simple random sampling methodology, obtained from 153 accountants working in companies in Istanbul. The methodology included descriptive statistics, factor analysis, structural equation modeling and mediation analysis.

Concerning direct relationships between EL, JS, AC and TI, EL has significant positive associations with JS and AC, whereas EL has a significant negative association with TI. JS has a significant positive association with AC, whereas JS has a significant negative association with TI. Also, AC has a weak significant negative association with TI. Given indirect relationships among EL, JS, AC and TI, JS and AC mediate the relationship between EL and TI. Finally, a similarity is found when comparing Generation X and Generation Y in terms of overall JS, AC and TI.

The study is limited solely to companies functioning in Istanbul and incorporates a low number of respondents. Therefore, the results cannot be considered to be accurate for the whole country. The study might guide both private and public organizations in which owners/managers develop strategic plans.

The study fills the gap in research on organizational behavior where little has existed until now that probes the EL–JS–AC–TI links in Turkey. A few studies measure the TIs of accountants. Furthermore, EL and AC are rarely evaluated in the field of accounting in Turkey.

The purpose of this paper is to integrate the proactive role of communities and the use of flood modeling in the implementation of a flood early warning system.

Manual rain gauges were installed in 20 houses of volunteers living within the Bicol River basin to monitor rainfall. Rain information is sent twice daily via SMS message to a receiving computer. The received data are used to run a basin model that was developed in HEC‐HMS, which converts precipitation excess to overland flow and channel run‐off.

Different watershed models were developed for different rainfall events. Geomorphic analysis using 3 s SRTM Digital Elevation Model (DEM) processed in a GIS platform was also done to refine the overland flow. The derived hydrographs were used in the HEC‐RAS hydraulic model which has as main output threshold values for the rain‐flood relationship.

Although SRTM DEM that was used for the geomorphic analysis was sufficient for the purpose of the study, higher resolution DEMs can further improve the mapping of spatial extent of flood areas.

The results are used for the forecast of flood and the timely issuance of flood bulletins.

This study is the first to incorporate the involvement of the community in establishing a flood early warning system. The method can also be used as a prototype for other flood models in other parts of the country.