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Hybrid metaheuristics is one of the most exciting improvements in optimization and metaheuristic algorithms. A current research topic combines two algorithms to provide a more advanced solution to optimization problems. The present study applies a new approach called HWOA‐TTA which means a hybrid of the whale optimizer algorithm (WOA) and tiki‐taka algorithm (TTA). The hybrid WOA‐TTA combines the exploitation phase of WOA with the exploration phase of TTA. Two stages in the hybridized model are suggested. First, the WOA exploitation phase incorporates the TTA mechanism. Second, a new approach is included in the research phase to enhance the result with each iteration to a set of unconstrained benchmark test functions and engineering design applications. To verify the performance of the improved algorithm, thirteen benchmark functions have been used to compare HWOA‐TTA with the classical intelligent population algorithms (PSO, TTA, and WOA). The hybrid algorithm is applied to two well‐known engineering mathematical models. The experiments show that the HWOA‐TTA outperforms other algorithms. [ABSTRACT FROM AUTHOR]

The rapid advancement of intelligent technology has led to the development of optimization algorithms that leverage natural behaviors to address complex issues. Among these, the Rat Swarm Optimizer (RSO), inspired by rats' social and behavioral characteristics, has demonstrated potential in various domains, although its convergence precision and exploration capabilities are limited. To address these shortcomings, this study introduces the Modified Rat Swarm Optimizer (MRSO), designed to enhance the balance between exploration and exploitation. The MRSO incorporates unique modifications to improve search efficiency and robustness, making it suitable for challenging engineering problems such as Welded Beam, Pressure Vessel, and Gear Train Design. Extensive testing with classical benchmark functions shows that the MRSO significantly improves performance, avoiding local optima and achieving higher accuracy in six out of nine multimodal functions and in all seven fixed-dimension multimodal functions. In the CEC 2019 benchmarks, the MRSO outperforms the standard RSO in six out of ten functions, demonstrating superior global search capabilities. When applied to engineering design problems, the MRSO consistently delivers better average results than the RSO, proving its effectiveness. Additionally, we compared our approach with eight recent and well-known algorithms using both classical and CEC-2019 benchmarks. The MRSO outperformed each of these algorithms, achieving superior results in six out of 23 classical benchmark functions and in four out of ten CEC-2019 benchmark functions. These results further demonstrate the MRSO's significant contributions as a reliable and efficient tool for optimization tasks in engineering applications. [ABSTRACT FROM AUTHOR]

Sitting ability is an important prognostic factor for patients with acute stroke. However, the characteristics of trunk muscle activity and weight-bearing during sitting are unclear. Are trunk muscle activity and weight-bearing during static sitting associated with sitting ability in patients with acute stroke? Trunk electromyography and sitting posturography were performed during static sitting in 20 patients with acute stroke. The electromyography measured the bilateral external oblique and erector spinae muscles. The symmetry of muscle activity between the paralyzed and non-paralyzed side was calculated using the Symmetry Index (SI) raw value and absolute value. The activity of each muscle during static sitting was calculated as the percent reference voluntary contraction (%RVC) normalized by the maximum contraction in the sitting position. Sitting posturography was used to calculate the weight-bearing pressure (%) on the paralyzed and non-paralyzed side, and the raw and absolute values of weight-bearing SI were calculated as a representative value. The trunk impairment scale (TIS) for sitting ability and the Berg balance scale (BBS) for basic balance ability were used as primary outcomes. None of the SI of electromyography for each trunk muscle correlated significantly with clinical performance tests. The %RVC of paralyzed and non-paralyzed external oblique muscle negatively correlated with TIS. The absolute SI of weight-bearing was negatively correlated with BBS. This study showed that the amount, but not the symmetry, of external oblique muscle activity during sitting was associated with sitting ability. We also found that the symmetry of the weight-bearing was associated with sitting ability, not the direction, but the absolute amount of deviation. This suggests that it is important to focus on the amount of external oblique muscle activity and weight-bearing deviation during sitting as a measure of sitting ability in patients with acute stroke. • External oblique muscle activity during sitting was correlated with sitting balance. • Absolute asymmetry of sitting weight-bearing was correlated with balance ability. • Direction of sitting weight-bearing did not correlate with sitting balance. [ABSTRACT FROM AUTHOR]

A novel coupled model integrating Elman-AdaBoost with adaptive mutation sparrow search algorithm (AM-SSA), called AMSSAElman-AdaBoost, is proposed for predicting the existing metro tunnel deformation induced by adjacent deep excavations in soft ground. The novelty is that the modified SSA proposes adaptive adjustment strategy to create a balance between the capacity of exploitation and exploration. In AM-SSA, firstly, the population is initialized by cat mapping chaotic sequences to improve the ergodicity and randomness of the individual sparrow, enhancing the global search ability. Then the individuals are adjusted by Tent chaotic disturbance and Cauchy mutation to avoid the population being too concentrated or scattered, expanding the local search ability. Finally, the adaptive producer-scrounger number adjustment formula is introduced to balance the ability to seek the global and local optimal. In addition, it leads to the improved algorithm achieving a better accuracy level and convergence speed compared with the original SSA. To demonstrate the effectiveness and reliability of AM-SSA, 23 classical benchmark functions and 25 IEEE Congress on Evolutionary Computation benchmark test functions (CEC2005), are employed as the numerical examples and investigated in comparison with some wellknown optimization algorithms. The statistical results indicate the promising performance of AM-SSA in a variety of optimization with constrained and unknown search spaces. By utilizing the AdaBoost algorithm, multiple sets of weak AMSSA-Elman predictor functions are restructured into one strong predictor by successive iterations for the tunnel deformation prediction output. Additionally, the on-site monitoring data acquired from a deep excavation project in Ningbo, China, were selected as the training and testing sample. Meanwhile, the predictive outcomes are compared with those of other different optimization and machine learning techniques. In the end, the obtained results in this real-world geotechnical engineering field reveal the feasibility of the proposed hybrid algorithm model, illustrating its power and superiority in terms of computational efficiency, accuracy, stability, and robustness. More critically, by observing data in real time on daily basis, the structural safety associated with metro tunnels could be supervised, which enables decision-makers to take concrete control and protection measures. [ABSTRACT FROM AUTHOR]

Higher academic performance is almost universally considered a good thing, and most quantitative studies show that performance is positively, although weakly, related to mental health. Simultaneously, however, qualitative studies consistently find that high-performing students and students attending high-performing schools report high levels of stress and other mental health problems. This study investigates a simple explanation for this puzzle – that the relationship between performance and mental health is not linear and is conditional on the performance culture of the school. Data on almost 5000 Swedish students from the Programme for International Student Assessment were used. The results show that the relationship between performance and mental health is generally not linear and that intermediate-performing boys have the best mental health, while both low- and high-performing girls and boys alike have poorer mental health. Although inconclusive, the results also suggest that low-performing students may be vulnerable to a strong school performance culture. [ABSTRACT FROM AUTHOR]

This article proposes the use of a leader white shark optimizer (LWSO) with the aim of improving the exploitation of the conventional white shark optimizer (WSO) and solving the economic operation-based load dispatch (ELD) problem. The ELD problem is a crucial aspect of power system operation, involving the allocation of power generation resources to meet the demand while minimizing operational costs. The proposed approach aims to enhance the performance and efficiency of the WSO by introducing a leadership mechanism within the optimization process, which aids in more effectively navigating the complex ELD solution space. The LWSO achieves increased exploitation by utilizing a leader-based mutation selection throughout each generation of white sharks. The efficacy of the proposed algorithm is tested on 13 engineer benchmarks non-convex optimization problems from CEC 2020 and compared with recent metaheuristic algorithms such as dung beetle optimizer (DBO), conventional WSO, fox optimizer (FOX), and moth-flame optimization (MFO) algorithms. The LWSO is also used to address the ELD problem in different case studies (6 units, 10 units, 11 units, and 40 units), with 20 separate runs using the proposed LWSO and other competitive algorithms being statistically assessed to demonstrate its effectiveness. The results show that the LWSO outperforms other metaheuristic algorithms, achieving the best solution for the benchmarks and the minimum fuel cost for the ELD problem. Additionally, statistical tests are conducted to validate the competitiveness of the LWSO algorithm. [ABSTRACT FROM AUTHOR]

Data Envelopment Analysis (DEA) is one of the widely used methods in measuring the technical efficiency of organizations. Data envelopment analysis is a classical non-parametric technique based on mathematical programming, which is used to compare the evaluation of the efficiency of a set of similar decision-making units. Its significant advantage is that it does not need to determine parametric specifications (such as the production function) to get performance points. Data envelopment analysis is known as a valid and stable tool used in performance evaluation, which provides a single measure of performance for each unit relative to its peers. Even though the number of data envelopment analysis models is constantly increasing and each one has a specialized aspect, the basis of all is a number of main models, among which we can refer to the "Charnes, Cooper and Rhodes" models (1978) as CCR, in which the assumption of constant returns to scale (CRS) has been used in the analysis. There are different approaches to determining the effective factors on efficiency and productivity and how to measure the productivity of production factors. Among the investigated companies in the years 2019 to 2019, it was found that the technical efficiency of the companies decreased, the technological changes of the companies increased, the managerial efficiency of the companies decreased, the efficiency of the scale of the companies decreased, and the overall productivity of the companies faced a decrease. [ABSTRACT FROM AUTHOR]

Purpose: The work aims to propose a comprehensive design framework for additive manufacturing and apply it to a case study of the height adjuster handle of a car. The aim is to provide designers and engineers with a practical example of how the framework can be used to design a complex part for additive manufacturing. The article also aims to demonstrate the potential benefits of additive manufacturing in the automotive industry, such as improved performance, reduced times, and cost savings. Additionally, the article aims to compare the developed framework with existing design for additive manufacturing (DFAM) methodologies and highlight its unique features and advantages in designing the height adjuster handle. Design/methodology/approach: The study used qualitative and quantitative data collection and analysis methods. The first phase of the study involved a systematic review of existing DfAM methodologies and a critical analysis of their strengths and weaknesses. Based on this analysis, a new DfAM framework is developed, which aims to address the limitations of existing frameworks and provide a comprehensive design approach for additive manufacturing. The second phase of the study involved applying the developed framework to a case study of a complex automotive part - a height adjuster handle. The design requirements of the height adjuster handle were identified based on the principles of DfAM, and the part was designed using computer-aided design (CAD) software and optimised topologically using ANSYS software. In the third phase, the performance of the height adjuster handle designed using the developed framework was compared with a part manufactured with injection moulding technology. The comparison was based on various performance criteria, including mechanical properties, dimensional accuracy, and production time and cost. Findings: The findings of the study demonstrate the effectiveness of the developed design framework for additive manufacturing (DfAM) in producing a complex automotive part with improved performance characteristics and reduced lead time. Research limitations/implications: Although the results of the study provide important insights into the effectiveness of the developed DfAM framework for producing a complex automotive part, there are some limitations to the research that should be considered, such as the case study involved the design of a single part, and the results may not be generalisable to other parts or applications. Further research is needed to validate the effectiveness of the DfAM framework for a broader range of automotive parts. Practical implications: The findings of the study have important practical implications for the automotive industry. The developed DfAM framework can be used in the FDM technology. It can be used as a decision aid in the manufacturing of FDM parts in order to improve the efficiency and cost-effectiveness of the production process for complex automotive parts. Originality/value: The value of the study is the development of a novel DfAM framework and the demonstration of its effectiveness in a case study. The proposed framework can be used as a reference for future research. It can also provide practical guidance for industry professionals seeking to improve the efficiency and cost-effectiveness of their additive manufacturing processes. [ABSTRACT FROM AUTHOR]

Introduction: Intelligence in children with autism has been a challenge for long. Previous studies have reported an uneven performance on intelligence tests. Better performance on Raven's Progressive Matrices (RPM) as compared to Wechsler's intelligence scale for children III (WISC-III) has been found in some studies. RPM is quick and easy to administer. Hence, there is a need to understand if RPM is a comparable test to tap the intelligence in children with autism. Aims and Objectives: To find the percentile intelligence quotient (IQ) scores of children with autism and neurotypical children using RPM and WISC III and to compare the performance of these two groups on IQ tests. Materials and Methods: A cross-sectional study including 15 children with autism and normal (neurotypical) children was done after institutional ethics committee approval. The subjects were subjected to WISC-III and RPM. Results: The study shows that neurotypical children perform better on intelligence tests than children with autism. Both tests can be used to measure intelligence in children with autism fairly owing to their correlation. However, RPM showed better scores than WISC-III, for children with autism. Conclusions: RPM can be used as an alternative and an even better test to measure intelligence in children with autism just as the standard WISC-III. Further studies in a larger population of children with autism are warranted to validate our findings. [ABSTRACT FROM AUTHOR]

In the past few years, knowledge graphs (KGs), as a form of structured human intelligence, have attracted considerable research attention from academia and industry. In this very active field of study, a widely explored problem is that of link prediction, the task of predicting whether two nodes should be connected, based on node attributes and local or global graph connectivity properties. The state of the art in this area is represented by techniques based on graph embeddings. However, KGs, especially those acquired using automated or partly automated techniques, are often riddled with noise, e.g., wrong relationships, which makes the problem of link deletion as important as that of link prediction. In this paper, we address three main research questions. The first is about the true effectiveness of different knowledge graph embedding models under the presence of an increasing number of wrong links. The second is to asses if methods that can predict unknown relationships effectively, work equally well in recognizing incorrect relations. The third is to verify if there are systems robust enough to maintain primacy in all experimental conditions. To answer these research questions, we performed a systematic benchmark study in which the experimental setting includes ten state-of-the-art models, three common KG datasets with different structural properties and three downstream tasks: the widely explored tasks of link prediction and triple classification, and the less popular task of link deletion. Comparative studies often yield contradictory results, where the same systems score better or worse depending on the experimental context. In our work, in order to facilitate the discovery of clear performance patterns and their interpretation, we select and/or aggregate performance data to highlight each specific comparison dimension: dataset complexity, type of task, category of models, and robustness against noise. [ABSTRACT FROM AUTHOR]

The goal was to address the problems of slow convergence speed, low solution accuracy and insufficient performance in solving complex functions in the search process of an arithmetic optimization algorithm (AOA). A multi-strategy improved arithmetic optimization algorithm (SSCAAOA) is suggested in this study. By enhancing the population's initial distribution, optimizing the control parameters, integrating the positive cosine algorithm with improved parameters, and adding inertia weight coefficients and a population history information sharing mechanism to the PSO algorithm, the optimization accuracy and convergence speed of the AOA algorithm are improved. This increases the algorithm's ability to perform a global search and prevents it from hitting a local optimum. Simulations of SSCAAOA using other optimization algorithms are used to examine their efficacy on benchmark test functions and engineering challenges. The analysis of the experimental data reveals that, when compared to other comparative algorithms, the improved algorithm presented in this paper has a convergence speed and accuracy that are tens of orders of magnitude faster for the unimodal function and significantly better for the multimodal function. Practical engineering tests also demonstrate that the revised approach performs better. [ABSTRACT FROM AUTHOR]

The employment of conventional optimization procedures that must be repeatedly invoked during the optimization process in real-world engineering applications is hindered despite significant gains in computing power by computationally expensive models. As a result, surrogate models that require far less time and resources to analyze are used in place of these time-consuming analyses. In multi-objective optimization (MOO) problems involving pricey analysis and simulation techniques such as multi-physics modeling and simulation, finite element analysis (FEA), and computational fluid dynamics (CFD), surrogate models are found to be a promising endeavor, particularly for the optimization of complex engineering design problems involving black box functions. In order to reduce the expense of fitness function evaluations and locate the Pareto frontier for MOO problems, the automated multiobjective surrogate based Pareto finder MOO algorithm (AMSP) is proposed. Utilizing data samples taken from the feasible design region, the algorithm creates three surrogate models. The algorithm repeats the process of sampling and updating the Pareto set, by assigning weighting factors to those surrogates in accordance with the values of the root mean squared error, until a Pareto frontier is discovered. AMSP was successfully employed to identify the Pareto set and the Pareto border. Utilizing multi-objective benchmark test functions and engineering design examples such airfoil shape geometry of wind turbine, the unique approach was put to the test. The cost of computing the Pareto optima for test functions and real engineering design problem is reduced, and promising results were obtained. [ABSTRACT FROM AUTHOR]

The fabricated lining is widely used in the underground rail transit, water conveyance system and river‐crossing tunnel. It is generally composed of the precast concrete segments, joints, bolts and reinforcements. Compression and separation often coexist in a single joint, which brings great challenges to the modeling and calculation of the fabricated lining. In this paper, an independent cover isogeometric Reissner‐Mindlin shell model (denoted as the ICI‐R shell model) is proposed for the simulation of the fabricated lining. In the ICI‐R shell model, each segment is regarded as an independent cover, whose geometry is described with the nonuniform rational B‐splines (NURBS) surface, and an isogeometric displacement function is defined based on the Reissner‐Mindlin shell deformation theory. The adjacent segments are connected with the fictitious thin layers, of which the normal and tangential deformations and stresses can be correctly calculated. Within this framework, the difficulties in the continuity‐discontinuity coupling analysis occurring when using the existing shell models are well solved, and the fabricated lining can be simulated expediently. The ICI‐R shell model has the advantages of clear physical meaning, concise formulas and convenient implementation. Benefiting from the strong capability of representing geometries of NURBS and the simplicity of the formulation of the ICI‐R shell model, the parameterized modeling and automatic calculation of the fabricated lining can be easily achieved for simplifying the analysis process and reducing user cost for geotechnical engineers. The successful application in the benchmark tests and engineering example shows the versatility, efficiency and robustness of the ICI‐R shell model. [ABSTRACT FROM AUTHOR]

Design optimization of structural and multidisciplinary systems under uncertainty has been an active area of research due to its evident advantages over deterministic design optimization. In deterministic design optimization, the uncertainties of a structural or multidisciplinary system are taken into account by using safety factors specified in the regulations or design codes. This uncertainty treatment is a subjective and indirect way of dealing with uncertainty. On the other hand, design under uncertainty approaches provide an objective and direct way of dealing with uncertainty. This paper provides a review of the uncertainty treatment practices in design optimization of structural and multidisciplinary systems under uncertainties. To this end, the activities in uncertainty modeling are first reviewed, where theories and methods on uncertainty categorization (or classification), uncertainty handling (or management), and uncertainty characterization are discussed. Second, the tools and techniques developed and used for uncertainty modeling and propagation are discussed under the broad two classes of probabilistic and non-probabilistic approaches. Third, various design optimization methods under uncertainty which incorporate all the techniques covered in uncertainty modeling and analysis are reviewed. In addition to these in-depth reviews on uncertainty modeling, uncertainty analysis, and design optimization under uncertainty, some real-life engineering applications and benchmark test examples are provided in this paper so that readers can develop an appreciation on where and how the discussed techniques can be applied and how to compare them. Finally, concluding remarks are provided, and areas for future research are suggested. [ABSTRACT FROM AUTHOR]

Balancing the exploration and exploitation in any nature-inspired optimization algorithm is an essential task, while solving the real-world global optimization problems. Therefore, the search agents of an algorithm always try to explore the unvisited domains of a search space in a balanced manner. The sine cosine algorithm (SCA) is a recent addition to the field of metaheuristics that finds the solution of an optimization problem using the behavior of sine and cosine functions. However, in some cases, the SCA skips the true solutions and trapped at sub-optimal solutions. These problems lead to the premature convergence, which is harmful in determining the global optima. Therefore, in order to alleviate the above-mentioned issues, the present study aims to establish a comparatively better synergy between exploration and exploitation in the SCA. In this direction, firstly, the exploration ability of the SCA is improved by integrating the social and cognitive component, and secondly, the balance between exploration and exploitation is maintained through the grey wolf optimizer (GWO). The proposed algorithm is named as SC-GWO. For the performance evaluation, a well-known set of benchmark problems and engineering test problems are taken. The dimension of benchmark test problems is varied from 30 to 100 to observe the robustness of the SC-GWO on scalability of problems. In the paper, the SC-GWO is also used to determine the optimal setting for overcurrent relays. The analysis of obtained numerical results and its comparison with other metaheuristic algorithms demonstrate the superior ability of the proposed SC-GWO. [ABSTRACT FROM AUTHOR]

The time-dependent density functional theory (TDDFT) has become the most popular method to calculate the electronic excitation energies, describe the excited-state properties, and perform the excited-state geometric optimization of medium and large-size molecules due to the implementation of analytic excited-state energy gradient and Hessian in many electronic structure software packages. To describe the molecules in condensed phase, one usually adopts the computationally efficient hybrid Quantum Mechanics/Molecular Mechanics (QM/MM) models. Here, we extend our previous work on the energy gradient of TDDFT/MM excited state to account for the mutual polarization effects between QM and MM regions, which is believed to hold a crucial position in the potential energy surface of molecular systems when the photoexcitation-induced charge rearrangement in the QM region is drastic. The implementation of a simple polarizable TDDFT/MM (TDDFT/MMpol) model in Q-Chem/CHARMM interface with both the linear response and the state-specific features has been realized. Several benchmark tests and preliminary applications are exhibited to confirm our implementation and assess the effects of different treatment of environmental polarization on the excited-state properties, and the efficiency of parallel implementation is demonstrated as well. [ABSTRACT FROM AUTHOR]

Sustainable manufacturing has been the subject of debate in the last few years, amongst both researchers and industrialists. Manufacturing activities have been regarded as amongst the most important factors which have caused major damage to planet. The aim of this paper is to scientifically develop a world-class sustainable manufacturing (WCSM) framework. The study uses a structured questionnaire, which was developed using extant literature and pretested to check content validity. The data was collected following a modified version of Dillman’s total design method to improve the quality of response. The psychometric properties of the instrument including reliability and construct validity were assessed using confirmatory factor analysis and multiple regression analysis for hypotheses testing, and partial least squares approach for testing goodness of fit. We find that the scale and items of our theoretical framework meet the criteria. The multiple regression analysis output suggests that all our hypotheses are supported, which further supports the extant literature. Our present study is unique in terms of scope and its contribution to the theory and practice of supply chain management. This study has empirically tested the research calls of various researchers, extended them to WCSM practices and further developed a scale for the measurement of the framework. [ABSTRACT FROM PUBLISHER]

This paper proposes a novel genetic algorithm to deal with the quay crane scheduling problem (QCSP), which is known to be one of the most critical tasks in terminal operations because its efficiency and the quality of the schedule directly influence the productivity of the terminal. QCSP has been studied intensively in recent years. Algorithms in this field are concerned in the solution quality obtained and the required computational time. As QCSP is known to be NP-hard, heuristic approaches are widely adopted. The genetic algorithm proposed is constructed with a novel workload balancing heuristics, which is capable of considering the loading conditions of different quay cranes (QCs) during the reassignment of task-to-QC. The idea is modelled as a fuzzy logic controller to guide the mutation rate and mutation mechanism of the genetic algorithm. As a result, the proposed algorithm does not require any predefined mutation rate. Meanwhile, the genetic algorithm can more adequately reassign tasks to QCs according to the QCs’ loading condition throughout the evolution. The proposed algorithm has been tested with the well-known benchmark problem sets in this field and produces some new best solutions in a much shorter computational time. [ABSTRACT FROM PUBLISHER]

Geomorphic mass flows are often complex in terms of material composition and its evolution in space and time. The simulation of those hazardous phenomena would strongly benefit from a multi‐phase model, considering the motion and—importantly—interaction of phases characterized by different physical aspects including densities, frictions, viscosities, fractions, and their mechanical responses. However, such a genuine multi‐phase model is still lacking. Here, we present a first‐ever, multi‐mechanical, multi‐phase mass flow model composed of three different phases: the coarse solid fraction, fine‐solid fraction, and viscous fluid. The coarse solid component, called solid, represents boulders, cobbles, gravels, or blocks of ice. Fine‐solid represents fine particles and sand, whereas water and very fine particles, including colloids, silt, and clay, constitute the viscous fluid component in the mixture. The involved materials display distinct mechanical responses and dynamic behaviors. Therefore, the solid, fine‐solid, and fluid phases are described by Coulomb‐plastic, shear‐ and pressure‐dependent plasticity‐dominated viscoplastic, and viscosity‐dominated viscoplastic rheologies. They are supposed to best represent those materials. The new model is flexible and addresses some long‐standing issues of multi‐phase mass flows on how to reliably describe the flow dynamics, runout, and deposition morphology of such type of phenomena. With reference to some benchmark simulations, the essence of the model and its applicability are discussed. Key Points: We introduce a new multi‐mechanical, multi‐phase model accounting for the complexity of geomorphic mass flowsSolid, fine‐solid, and fluid fractions can be considered separately, allowing to simulate a broad range of possible material compositionsBenchmark tests on generic landscapes yield plausible results indicating the potential use of the new model for complex real events [ABSTRACT FROM AUTHOR]

In recent years there have been great strides in artificial intelligence (AI), with games often serving as challenge problems, benchmarks, and milestones for progress. Poker has served for decades as such a challenge problem. Past successes in such benchmarks, including poker, have been limited to two-player games. However, poker in particular is traditionally played with more than two players. Multiplayer games present fundamental additional issues beyond those in two-player games, and multiplayer poker is a recognized AI milestone. In this paper we present Pluribus, an AI that we show is stronger than top human professionals in six-player no-limit Texas hold’em poker, the most popular form of poker played by humans. [ABSTRACT FROM AUTHOR]

Auto-scalers for clouds promise stable service quality at low costs when facing changing workload intensity. The major public cloud providers provide trigger-based auto-scalers based on thresholds. However, trigger-based auto-scaling has reaction times in the order of minutes. Novel auto-scalers from literature try to overcome the limitations of reactive mechanisms by employing proactive prediction methods. However, the adoption of proactive auto-scalers in production is still very low due to the high risk of relying on a single proactive method. This paper tackles the challenge of reducing this risk by proposing a new hybrid auto-scaling mechanism, called Chameleon, combining multiple different proactive methods coupled with a reactive fallback mechanism. Chameleon employs on-demand, automated time series-based forecasting methods to predict the arriving load intensity in combination with run-time service demand estimation to calculate the required resource consumption per work unit without the need for application instrumentation. We benchmark Chameleon against five different state-of-the-art proactive and reactive auto-scalers one in three different private and public cloud environments. We generate five different representative workloads each taken from different real-world system traces. Overall, Chameleon achieves the best scaling behavior based on user and elasticity performance metrics, analyzing the results from 400 hours aggregated experiment time. [ABSTRACT FROM AUTHOR]

Abstract In this paper, a local adaptive joint sparse representation (LAJSR) model is proposed for the classification of hyperspectral remote sensing images. It improves the original joint sparse representation (JSR) method in both the signal and dictionary construction phase and sparse representation phase. Given a testing pixel, a similar signal set is constructed by picking a few of the most similar pixels from its spatial neighborhood. The original training dictionary consists of training samples from different classes and is extended by adding spatial neighbors of each training sample. A local adaptive dictionary is built by selecting the most representative atoms from the extended dictionary that are correlated to the similar signal set. In the LAJSR framework, the selected similar signals are simultaneously represented by the local adaptive dictionary, and the obtained sparse representation coefficients are further weighted by a sparsity concentration index vector which aims to concentrate and highlight the coefficients on the expected class. Experimental results on two benchmark hyperspectral data sets have demonstrated that the proposed LAJSR method is much more effective than existing JSR and SVM methods, especially in the case of small sample sizes. [ABSTRACT FROM AUTHOR]

Abstract This paper studies how to perform RGB-T object tracking in the correlation filter framework. Given the input RGB and thermal videos, we utilize the correlation filter for each modality due to its high performance in both of accuracy and speed. To take the interdependency between RGB and thermal modalities, we introduce the low-rank constraint to learn filters collaboratively, based on the observation that different modality features should have similar filters to make them have consistent localization of the target object. For optimization, we design an efficient ADMM (Alternating Direction Method of Multipliers) algorithm to solve the proposed model. Experimental results on the benchmark datasets (i.e., GTOT, RGBT210 and OSU-CT) suggest that the proposed approach performs favorably in both accuracy and efficiency against the state-of-the-art RGB-T methods. [ABSTRACT FROM AUTHOR]

Abstract In this paper a guaranteed equilibrated error estimator is developed for the 3D harmonic magnetodynamic problem of Maxwell's system. This system is recasted in the classical A − φ potential formulation and solved by the Finite Element method. The error estimator is built starting from the A − φ numerical solution by a local flux reconstruction technique. Its equivalence with the error in the energy norm is established. A comparison of this estimator with an equilibrated error estimator already developed through a complementary problem points out the advantages and drawbacks of these two estimators. In particular, an analytical benchmark test illustrates the obtained theoretical results and a physical benchmark test shows the efficiency of these two estimators. [ABSTRACT FROM AUTHOR]

The Lagrange Discrete Ordinates (LDO) equations, developed by Ahrens as an alternative to the traditional discrete ordinates formulation, have been implemented in Denovo, a three-dimensional radiation transport code developed by Oak Ridge National Laboratory. The LDO equations retain the formal structure of the classical discrete ordinates equations but treat particle scattering in a different way. Solutions of the LDO equations have an interpolatory structure such that the angular flux can be naturally evaluated at directions other than the discrete ordinates used in arriving at the solutions, and the ordinates themselves may be chosen in a strategic way for the problem under consideration. Of particular interest is that the LDO equations have been shown to mitigate ray effects at increased angular resolutions. In this paper we present scalar flux solutions of the LDO equations for a small number of test cases of interest and compare the results against flux solutions generated using standard quadrature types. The LDO equations' flux solutions were found to be comparable to those resultant from the standard quadrature types in value; results from the LDO equations were also found to be commensurate with those of standard quadrature types when comparing the flux solutions in the context of the experimental benchmark test case examined. [ABSTRACT FROM AUTHOR]

Abstract Models for simulating interlaminar crack growth in composites under static and fatigue loads are rarely validated with experimental data different from simple standard tests where the crack front propagates with a uniform shape. The underlying reason for not making use of more realistic experimental data, where the crack front shape changes during the delamination growth, is the lack of well-controlled benchmark cases. This work introduces a comprehensive benchmark case consisting of a double cantilever beam (DCB)-like specimen with partially reinforced arms. A complete set of elastic and fracture properties for the material and a detailed description of load-displacement curves and crack front geometries, monitored with X-ray radiography, is provided. The benchmark case generates a rich phenomenology of crack advance in terms of varying crack growth rate and front shape, while being geometrically simple enough to be simulated. [ABSTRACT FROM AUTHOR]

Real-world problems are complex as they are multidimensional and multimodal in nature that encourages computer scientists to develop better and efficient problem-solving methods. Nature-inspired metaheuristics have shown better performances than that of traditional approaches. Till date, researchers have presented and experimented with various nature-inspired metaheuristic algorithms to handle various search problems. This paper introduces a new nature-inspired algorithm, namely butterfly optimization algorithm (BOA) that mimics food search and mating behavior of butterflies, to solve global optimization problems. The framework is mainly based on the foraging strategy of butterflies, which utilize their sense of smell to determine the location of nectar or mating partner. In this paper, the proposed algorithm is tested and validated on a set of 30 benchmark test functions and its performance is compared with other metaheuristic algorithms. BOA is also employed to solve three classical engineering problems (spring design, welded beam design, and gear train design). Results indicate that the proposed BOA is more efficient than other metaheuristic algorithms. [ABSTRACT FROM AUTHOR]

Differential evolution (DE) algorithm is widely used for solving real-parameter optimization problems due to its simplicity and efficiency. However the canonical DE is easy to suffer from the premature convergence. To further improve the performance of the DE, a differential evolution algorithm with dual preferred learning mutation (DPLDE) is proposed. Dual preferred learning mutation simultaneously learns behaviors from the individual with better fitness(BFI) and individual with better diversity(BDI). The learning factor of BFI is self-adaptively and independently adjusted for each individual. The learning factor of BDI is adaptively adjusted at each generation. A total of 26 Benchmark test functions with different characteristics are used for performance comparative experiments. The results show that DPLDE is superior to the eight state-of-the-art improved algorithms in terms of the convergence precision, convergence speed and stability. For the high-dimensional functions, with the same-scale population and maximum number of evolution generations, DPLDE can still get the excellent global optimization performance and has a more prominent advantage. [ABSTRACT FROM AUTHOR]

Abstract The main goal of multi-objective optimization evolutionary algorithms (MOEAs) is to obtain a set of solutions with good diversity and convergence. However, how to concurrently improve the diversity and convergence is a difficult work. To address this problem, an updated strategy based on decomposition is used to maintain the diversity, and the convergence is enhanced by improving the search efficiency. In this paper, an improvement decomposition-based multi-objective evolutionary algorithm using multi-search strategy is aimed at improving the search efficiency. In this work, three search strategies are used to help crossover operators to carry out the local search and global search. This multi-search strategy selects sparse non-dominated solutions to carry out the exploration, and selects convergent solutions to implement the exploitation. In the experiments, the proposed algorithm is compared with seven efficient state-of-the-art algorithms, e.g., NSGAII, MOEA/D, MOEA-DVA, MOEA-IGD-NS, MOEA/D-PaS, RVEA and MPSOD, on twenty-two benchmark functions. Empirical results show that the proposed algorithm can find a set of solutions with better diversity and convergence than six compared algorithms. [ABSTRACT FROM AUTHOR]

Recently, the metaheuristic optimization algorithms inspired by nature and different science branches have been powerful solution methods for unconstrained, constrained, and engineering problems. Various metaheuristic optimization algorithms have been proposed and they have been applied to problems in different fields. This paper proposes a novel optimization method based on a modified version of the Golden Sine Algorithm for solving unconstrained, constrained, and engineering problems. The basic idea behind the proposed modified Golden Sine Algorithm (GoldSA-II) depends on finding the optimum solution field in search space by using the decreasing pattern of the sine function and the golden ratio. The performance of the proposed GoldSA-II is evaluated using 19 unconstrained benchmark functions, five constrained optimization test problems, and five real engineering design problems. The results of the proposed GoldSA-II are compared with best-known optimization algorithms using some well-known criteria. The obtained results show that the GoldSA-II converges more accurately to the global solution in many benchmark functions. [ABSTRACT FROM AUTHOR]

Abstract Recently, co-evolution mechanism is exploited to solve multi-objective optimization problems by using multiple subpopulations on a cooperative manner, such as co-evolutionary multi-swarm particle swarm optimization (CMPSO) based on the multiple subpopulations for multi-objective optimization (MPMO) framework, which is also extended to cooperative multi-objective differential evolution (CMODE). Although their approaches of optimizing each objective with each subpopulation are effective, the evolution and selection methods conducted on external archive are also important for co-evolution, as they significantly impact a lot on the quality and the distribution of final solutions. In this paper, we present a novel multi-objective co-evolutionary algorithm based on decomposition approach (MCEA), also using the subpopulation to enhance each objective. A more powerful DE operator with an adaptive parameters control is run on both of multiple subpopulations and external archive, which helps to improve each objective and diversify the tradeoff solutions on external archive. Moreover, computational resource assignment is also realized between each subpopulation and external archive. Once one objective stops to be enhanced, this objective may find its optimal value and more computational resource can be assigned to evolve other objectives and external archive. By this way, the tradeoff between all the objectives can be well balanced and external archive also has more opportunities for evolution. After evaluating the proposed algorithm on 31 benchmark test problems, such as the ZDT, DTLZ, WFG, UF series problems, the experimental results show that MCEA presents some advantages over two co-evolutionary algorithms (i.e., CMPSO and CMODE) and several state-of-the-art multi-objective evolutionary algorithms (i.e., NSGA-II, SPEA2, MOEA/D-DE, MOEA/D-STM, MOEA/D-FRRMAB and MOEA/D-IR). Graphical abstract Highlights • A novel co-evolution mechanism is realized in each subpopulation and external archive. • Resource assignment is run on each subpopulation and external archive. • Our algorithm presents some advantages over several competitive MOEAs. [ABSTRACT FROM AUTHOR]

The inversion processes of the proposed tubes under axial impact are simulated to investigate the detailed features of crushing process by using the explicit finite element FE code LS-DYNA. To validate the FE results, a new theoretical relation is developed. The validated FE model was then used for the parametric studies, to investigate the maximum crush distance and the effect of impact mass and the geometry parameters (i.e. wall thickness, foam density) on response of free inversion tubes under different strain rate loadings. Finally, a search algorithm combining Latin hypercube design (LHD) points and genetic algorithm is developed to carry out the multiobjective optimisation through the geometrical average method. The optimal design of the foam-filled free inversion tube demonstrates a significant improvement in the crashworthiness over the benchmark designs with the same loading conditions. [ABSTRACT FROM AUTHOR]

Economic dispatch (ED) is of cardinal significance for the power system operation. It is mathematically a typical complex nonlinear multivariable strongly coupled optimization problem with equality and inequality constraints, especially considering the valve-point effects. In order to effectively solve the problem, a simple yet very young and efficient population-based algorithm named across neighborhood search (ANS) is implemented in this paper. In ANS, a group of individuals collaboratively navigate through the search space for obtaining the optimal solution by simultaneously searching the neighborhoods of multiple superior solutions. Four benchmark test cases with diverse complexities and characteristics are firstly employed to comprehensively verify the feasibility and effectiveness of ANS. The experimental and comparison results fully demonstrate the superiority of ANS in terms of the final solution quality, convergence speed, robustness, and statistics. In addition, the sensitivities of ANS to variations of population size and across-search degree are studied. Furthermore, ANS is applied to a practical provincial power grid of China. All the comparison results consistently indicate that ANS is highly competitive and can be used as a promising alternative for ED problems. [ABSTRACT FROM AUTHOR]

Highlights • A new compact teaching-learning based optimization method is proposed. • 32 well-known benchmark tests are conducted for algorithm evaluation. • The algorithm maintains high performance and reduce memory requirement. • The algorithm is used to train two typical function neural networks. Abstract The majority of embedded systems are designed for specific applications, often associated with limited hardware resources in order to meet various and sometime conflicting requirements such as cost, speed, size and performance. Advanced intelligent heuristic optimization algorithms have been widely used in solving engineering problems. However, they might not be applicable to embedded systems, which often have extremely limited memory size. In this paper, a new compact teaching-learning based optimization method for solving global continuous problems is proposed, particularly aiming for neural network training in portable artificial intelligent (AI) devices. Comprehensive numerical experiments on benchmark problems and the training of two popular neural network systems verify that the new compact algorithm is capable of maintaining the high performance while the memory requirement is significantly reduced. It offers a promising tool for continuous optimization problems including the training of neural networks for intelligent embedded systems with limited memory resources. [ABSTRACT FROM AUTHOR]

Abstract This paper proposes a novel optimization algorithm, called Emperor Penguin Optimizer (EPO), which mimics the huddling behavior of emperor penguins (Aptenodytes forsteri). The main steps of EPO are to generate the huddle boundary, compute temperature around the huddle, calculate the distance, and find the effective mover. These steps are mathematically modeled and implemented on 44 well-known benchmark test functions. It is compared with eight state-of-the-art optimization algorithms. The paper also considers for solving six real-life constrained and one unconstrained engineering design problems. The convergence and computational complexity are also analyzed to ensure the applicability of proposed algorithm. The experimental results show that the proposed algorithm is able to provide better results as compared to the other well-known metaheuristic algorithms. [ABSTRACT FROM AUTHOR]

Researchers usually estimate benchmark dose (BMD) for dichotomous experimental data using a binomial model with a single response function. Several forms of response function have been proposed to fit dose-response models to estimate the BMD and the corresponding benchmark dose lower bound (BMDL). However, if the assumed response function is not correct, then the estimated BMD and BMDL from the fitted model may not be accurate. To account for model uncertainty, model averaging (MA) methods are proposed to estimate BMD averaging over a model space containing a finite number of standard models. Usual model averaging focuses on a pre-specified list of parametric models leading to pitfalls when none of the models in the list is the correct model. Here, an alternative which augments an initial list of parametric models with an infinite number of additional models having varying response functions has been proposed to estimate BMD for dichotomous response data. In addition, different methods for estimating BMDL based on the family of response functions are derived. The proposed approach is compared with MA in a simulation study and applied to a real dataset. Simulation studies are also conducted to compare the four methods of estimating BMDL. [ABSTRACT FROM AUTHOR]

In this paper, we propose a novel cuckoo search algorithm with multiple update rules, referred to as a hybrid CS algorithm (HCS). In the presented approach, to overcome the mutual interference among dimensions and enhance the local search capability, two different one-dimensional update rules are integrated into CS framework for acquiring the candidate solutions. Moreover, using the characteristic of occasionally long jumps in Levy distribution, the proper selection between the one-dimensional update rules and Levy flight random walk is achieved by setting a limit value, so as to further enhance the exploration ability. The performance of the presented algorithm is then extensively investigated on 49 benchmark test functions including 11 common instances, 10 instances introduced in CEC 2005, and 28 instances presented in CEC 2013. The experimental results indicate that HCS algorithm is better than other CS variants in terms of solution accuracy and robustness, and it also outperforms the seven state-of-the-art intelligent algorithms. [ABSTRACT FROM AUTHOR]

Highlights • Adoption of Electronic Health Records (EHR) is an emerging challenge in developing countries but not yet documented in Pakistan. • This study evaluates the availability and use of in-patient EHR systems in Peshawar, Pakistan. • We categorized the hospitals into small (<100), medium (100–500) and large (>500) by bed-count and EHR (basic and fully-functional) on computer system capabilities. Out of 58 hospitals, 35 were randomly selected. A pre-tested data collection tool was used. • Only 1 hospital met the requirements of basic EHR, and none qualified for fully functional EHR. However, 9/35 hospitals (26%) had at least one EHR feature. Top reported hurdles in EHR use were high cost (46%), doubt about usefulness (19%), and doctors’ unwillingness to use (19%). • This is the first report on EHR availability in Pakistan, results may serve as a bench mark for improvement. By highlighting the need for EHR adoption, resources may be allocated to develop a national strategy to support the adoption and use of EHR. [ABSTRACT FROM AUTHOR]

Highlights • The MDA-3 algorithm was able to discover some of best solutions for wide range of benchmark testing problems. • The MDA-3 was compared with some of the best algorithms such as DE, EDA, and PSO. • The strength of the algorithm comes from its simplicity and its ability to directly dig in the search space. Abstract Devising ways for handling problem optimization is an important yet a challenging task. The aims are always for methods that can effectively and quickly discover the global optimum for rather complicated mathematical functions that model real-world settings. Typically these functions are too difficult to discover their global optima because they may (1) lack the continuity and differentiability, (2) have multiple local optima, and (3) have complex expressions. In this paper, we address this challenge by offering an algorithm that combines the random search techniques with both an effective mapping and a dynamic adjustment of its search behavior. Our proposed algorithm automatically builds two types of triangular search directives over the unity intervals: principal and marginal. These search directives guide the search within both the effective regions of the search domain that most likely contain the optimum and the marginal regions of the search domain that less likely contain the optimum. During the search the algorithm monitors the intermediate search results and dynamically adjusts the directives' parameters to quickly move the search towards the optimum. Experiments with our prototype implementation showed that our method can effectively find the global optima for rather complicated mathematical functions chosen from well-known benchmarks, and performed better than other algorithms. [ABSTRACT FROM AUTHOR]

Flight catering is a complex food service industry that caters to a wide range of airlines. Meals provided to passengers are a measure of in-flight service on an airplane. In the case of the airline industry, flight catering companies are responsible for preparing the meal that flight crews serve it at a voyaging altitude. This case study examines Emirates Kitchen Flight Catering (EKFC), a jointly owned entity of Dubai Civil Aviation (10%) and the Emirates Group (90%). Based on existing literature, international archival reports, company publications and participant observations of EKFC, the paper examines how EKFC reached the top of the catering supply chain by offering the best-in-class quality services and food to its passengers. The paper addresses the persistent growth of EKFC’s operations and investigates relevant strategies that reinforce EKFC’s fundamental competencies through the application of Supply Chain Operations Reference framework. Offering a combination of on-board excellence, quality, and fresh and delicious food while controlling costs across the supply chain is an important differentiator of EKFC’s flight catering business, thereby setting a benchmark for other businesses. [ABSTRACT FROM AUTHOR]

Simulation of multiphase flows characterized by high density ratios is targeted by using a refined particle method. The proposed method is founded on Moving Particle Semi-implicit (MPS) method which is a projection-based particle method. The proposed method comprises of a decoupled two-step computational algorithm through application of a consistent scheme for coupling the light/heavy phases by incorporating the concept of Space Potential Particles (SPP). The proposed coupling scheme guarantees the continuity of pressure and space (volume conservation) at the phase interface without any need for commonly applied density smoothing/averaging schemes or application of numerical stabilizing terms. Verification of the proposed multiphase particle method is conducted through the simulations of four benchmark tests and the method is shown to possess acceptable accuracy and convergence properties. [ABSTRACT FROM AUTHOR]

According to the sensor location optimization problem in a health monitoring system of an existing bridge, a new optimization algorithm called the Nested-Stacking Genetic Algorithm (NSGA) is proposed. This new algorithm is combined with the traditional Genetic Algorithm (GA) and the Nested-Partitions Algorithm (NPA), which can divide the basic solution domain of the optimization problem into two regions: feasible and unfeasible regions. This division is achieved through the partition operation, and then the GA is used for sampling and selecting. Finally, the optimized results of the discrete variables are obtained through an iterative operation step by step. Subsequently, two benchmark test functions were adopted to test the algorithm. Comparison and analyses of the results were performed by calculating the values of the main control parameters of the new algorithm, such as the crossover and mutation rate, genetic algebra, population size, data volume parameter and the backtrack parameter, through several tentative calculations to the bridge. This new algorithm is found having improved performance compared with the traditional optimization algorithms, and the algorithm is only weakly sensitive to almost all of the parameters and has a strong anti-interference ability. [ABSTRACT FROM AUTHOR]