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The increasing pressure on natural resources and the climate has been noted by businesses and governments worldwide, who now face the difficult task of integrating paths of environmental sustainability and economic growth. One promising approach to sustainable development, reducing the pressure on natural resources and solving waste problems is circular economy. From different ways of implementing circular economy, this study focuses on the productization of industrial side streams, which carry a great deal of underutilized potential. In this article, the productization of pulp and paper industry side stream, fiber sludge, for commercial use as a dust-binding agent is studied via a descriptive, in-depth case study. Apart from inconsistent quality, fluctuating supply, cost of storage and logistics, what makes industrial side stream utilization challenging is the lack of knowledge that stakeholders have about side streams and their utilization and the lack of new value chain development for their commercial use. Therefore, it is essential for all parties involved to have a clearer vision of what is being sold for what purposes, through productization. This study found that the productization of side streams follows the same steps as the productization of any other traditional product, and the productized side streams can be added to the company's product portfolio as by-products. [ABSTRACT FROM AUTHOR]

Abstract Successful problem solving relies on the availability of suitable mental representations of the task domain. Especially for more complex problems, there might be a wide variety of possible problem representations, and it might even be beneficial to change them during problem solving. In a first part, we argue that investigating the dynamics of understanding in terms of dynamically changing problem representations is an underexplored aspect of problem solving research, and that most classic tasks even preclude the opportunity of such dynamics to occur. Continuing this theoretical discussion, as an illustrative example of a task designed for the exploration of such representational dynamics, the second part of the paper discusses a novel, complex spatial transformation and problem solving task. In this task, one is asked to repeatedly mentally cross-fold a sheet of paper, and to predict the resulting sheet geometry without the use of external aids. Through its deliberate openness and difficulty, this task requires finding new and more efficient representations – ranging from kinaesthetic and visuospatial imagery to symbolic notions. We present an overview of the task domain and discuss various ways of representing the domain as well as potential dynamics between them. [ABSTRACT FROM AUTHOR]

Abstract: In this paper, we address some computational challenges arising in complex simulation-based design optimization problems. High computational cost, black-box formulation and stochasticity are some of the challenges related to optimization of design problems involving the simulation of complex mathematical models. Solving becomes even more challenging in case of multiple conflicting objectives that must be optimized simultaneously. In such cases, application of multiobjective optimization methods is necessary in order to gain an understanding of which design offers the best possible trade-off. We apply a three-stage solution process to meet the challenges mentioned above. As our case study, we consider the integrated design and control problem in paper mill design where the aim is to decrease the investment cost and enhance the quality of paper on the design level and, at the same time, guarantee the smooth performance of the production system on the operational level. In the first stage of the three-stage solution process, a set of solutions involving different trade-offs is generated with a method suited for computationally expensive multiobjective optimization problems using parallel computing. Then, based on the generated solutions an approximation method is applied to create a computationally inexpensive surrogate problem for the design problem and the surrogate problem is solved in the second stage with an interactive multiobjective optimization method. This stage involves a decision maker and her/his preferences to find the most preferred solution to the surrogate problem. In the third stage, the solution best corresponding that of stage two is found for the original problem. [Copyright &y& Elsevier]

Abstract: Coronectomy is a technique that reduces morbidity of the nerve after operation on high-risk mandibular third molars. This two-part paper aims to give an overview of the technique and a description of common problems that can be encountered during and after operation. In this paper we discuss the technique as an overview and then each type of impaction more specifically. We also explore common problems encountered during the procedure and their subsequent management. [Copyright &y& Elsevier]

In the very few recently published paper by Alavidoost et al. [1] , they proposed a novel fuzzy adaptive genetic algorithm for multi-objective assembly line balancing problem, in continue of their previous presented work by Alavidoost et al. [2] , as a modification on genetic algorithm for assembly line balancing with fuzzy processing times. Despite the fact that both of them are well-written, and completely discussed their contributions, this note looks forward to collate these papers together. Likewise provides the correct order of the figures in [1] matching with their corresponding caption. [ABSTRACT FROM AUTHOR]

This paper provides a corrected formulation to the mixed integer programming model proposed by Aydogan et al. (2012) [1] . They proposed a genetic algorithm to learn fuzzy rules for a fuzzy rule-based classification system and developed a Mixed Integer Programming model (MIP) to prune the generated rules by selecting the best set of rules to maximize predictive accuracy. However, their proposed MIP formulation contains errors, which are described in this technical note. We develop corrections and improvements to the original formulation and test it with non-parametric statistical tests on the same data sets used to evaluate the original model. The statistical analysis shows that the results of the correction formulation are significantly different from the original model. [ABSTRACT FROM AUTHOR]

Highlights: [•] Granular computing aspects of natural computing. [•] Review of different granular soft computing research. [•] Biological motivation, design principles, application areas, open research problems and challenging issues of these models. [ABSTRACT FROM AUTHOR]

Academic networks express the academic relationships among authors, papers and conferences, which offer perspectives and tools to rethink and solve traditional problems in the academic field. Among them, conference ranking is a problem which normally produced by collecting information from different sources and considering many factors. Not like most of the existed ranking methods that based on subjective evaluation, in this paper, we develop a Conference Ranking method based on Academic Network Embedding (CRANE) algorithm to simultaneously rank authors and conferences which improving the accuracy of ranking by embedding the Academic Network. The CRANE uses both conference features like internationality, citations and co-authors, and structure in Academic Network to evaluate the ranking of certain conferences. The theoretical analysis and convergence analysis were provided in this paper. Experiments on datasets of the Digital Bibliography & Library Project (DBLP) and AMiner academic platforms also be conducted. The experimental results show that the CRANE algorithm is superior to other algorithms by comparing with the public academic ranking like the China Computer Federation (CCF) ranking. [ABSTRACT FROM AUTHOR]

Abstract: In this work we present a mathematical model describing the coupled heat and moisture transport in paper. The model is solved numerically and the numerical solution is used to study the interdependency of the moisture and temperature distribution in paper. The results show that variation with temperature of the saturated water vapor concentration and the sorption isotherm parameters are both important for inducing moisture desorption. It is also found that for steep relative humidity ramps moisture sorption generates temperature increments that slow down the sorption process itself. The model is also used to study the moisture gradients in a paper sheet inside a printer from Océ Technologies, which contains a warm print surface. The results predict changes in moisture content of only 0.2%, which suggests that no deformations are induced on the printed sheet. [Copyright &y& Elsevier]

In the framework of a 1D cyclic competition model, the Rock-Paper-Scissors model, where three kinds of generic agents are allowed to mutate, to interact and to move in space, we study the formation of stochastic patterns, where all the agents do coexist. We modelled the problem using an individual-based setting and we used the system size van Kampen expansion to deal with the Master Equation. We have hence been able to characterise the spatio-temporal patterns using the power spectrum of the fluctuations. We proved that such patterns are robust against the intrinsic noise and they can be found for parameter values beyond the ones fixed by the deterministic approach (mean field approximation). We complement such analytical results with numerical simulations based on the Gillespie algorithm. [ABSTRACT FROM AUTHOR]

[Display omitted] • Size-dependent assembly of graphene sheets constructs vertical heat pathways across the graphene paper. • GHGP exhibits both high through-plane thermal conductivity and good compressibility. • The cooling efficiency of GHGP was 2.2 times that of the state-of-the-art commercial TIM. Graphene-based papers have recently triggered considerable interests in developing the application as thermal interface materials (TIMs) for addressing the interfacial heat transfer issue, but their low through-plane thermal conductivity (κ ⊥), resulting from the layer-by-layer stacked architecture, limits the direct use as TIMs. Although various hybrid graphene papers prepared by combining the graphene sheets and the thermally conductive insertions have been proposed to solve this problem, achieving a satisfactory κ ⊥ higher than that of commercial TIMs (>5 W m−1 K−1) remains challenging. Here, a strategy aimed at the construction of heat pathways along the through-plane direction inside the graphene paper for achieving a high κ ⊥ was demonstrated through the simultaneous filtration of graphene sheets with two different lateral sizes. The as-prepared graphene paper presented a hierarchical structure composed of loosely stacked horizontal layers formed by large graphene sheets, intercalated by a random arrangement of small graphene sheets. Due to the heat pathways formed by small graphene sheets along the through-plane direction, the hierarchically structured graphene paper exhibited an improved κ ⊥ as high as 12.6 W m−1 K−1 after a common graphitization post-treatment. In the practical test, our proposed paper as an all-graphene TIM achieved an enhancement in cooling efficiency of ≈ 2.2 times compared to that of the state-of-the-art TIM, demonstrating its superior performance to meet the ever-increasing heat dissipation requirement. [ABSTRACT FROM AUTHOR]

Abstract: We show that the main results in the recent paper [4] by Sadiq Basha (2012) are not real generalizations but particular cases of existing fixed point theorems in the literature. [Copyright &y& Elsevier]

Abstract: The high fidelity generalized method of cells (HFGMC) has been originally developed by as a micromechanical method for periodic multi-phase composite media. A computational implementation of the HFGMC equations has been proposed by to enhance numerical efficiency, still with direct reference to the HFGMC formulation. Later, the same computational implementation is recast as a new method called “finite volume direct averaging micromechanics” (FVDAM), starting by . The current discussion paper has two aims. The first is to show that the FVDAM is not a new method and that it has the same assumptions and identical governing equations as those originally derived by the HFGMC. The only difference is in the solution procedure where intermediate dependent variables, in the form of average displacements at the interfaces, are used instead of directly solving for the unknown micro-variables; the coefficients of the displacement polynomials. Thus, renaming the HFGMC micromodel to FVDAM has not been justified. In fact, () have shown that the same reduction of variables can be achieved by a simple static condensation carried out at the global system of equations instead of introducing intermediate variables. The second aim of this paper is to address misrepresentations in a recent discussion paper by the FVDAM authors claiming, in part, that the HFGMC method using parametric geometry of the subcells should follow their formulation (termed parametric FVDAM). We show that the latter is limited to an incomplete quadratic expansion of the displacement and an approximation in the form of a priori constant Jacobian of the parametric mapping. However, the HFGMC with arbitrary cell geometry, (), has been formulated in a direct and general manner, i.e. retaining the full quadratic expansion of the displacement together with the complete Jacobian. Thus, the parametric FVDAM is a special case of the parametric HFGMC, i.e. when the Jacobian is sampled and evaluated only at one point, namely the origin of the parametric coordinate system. The intended new contribution of to refined micromechanics and progressive damage has been completely ignored by the FVDAM-discussion paper. Therefore, in order to maintain scientific clarity, it is strongly advocated to preserve the original name of the HFGMC method, regardless of the different computational implementations used for solving the governing equations for both orthogonal and parametric geometries of the subcells. [Copyright &y& Elsevier]

Highlights: [•] Input Variable Selection (IVS) helps when processing system inputs is computationally heavy. [•] A framework to accommodate high level perspective of different approaches to IVS is provided. [•] Sensitivity analysis (SA) helps IVS in heterogeneous input variables with time constraint. [•] Event-based SA proves fit by its application in an industrial drilling disaster prevention problem. [ABSTRACT FROM AUTHOR]

Abstract: Several authors have highlighted the role of intuition in expertise. In particular, a large amount of data has been collected about intuition in expert nursing, and intuition plays an important role in the influential theory of nursing expertise developed by Benner [1984. From Novice to Expert: Excellence and Power in Clinical Nursing Practice. Addison–Wesley, Menlo Park, CA]. We discuss this theory, and highlight both data that support it and data that challenge it. Based on this assessment, we propose a new theory of nursing expertise and intuition, which emphasizes how perception and conscious problem solving are intimately related. In the discussion, we propose that this theory opens new avenues of enquiry for research into nursing expertise. [Copyright &y& Elsevier]

Abstract: Although children partition by repeatedly halving easily and spontaneously as early as the age of 4, multiplicative thinking is difficult and develops over a long period in school. Given the apparently multiplicative character of repeated halving and doubling, it is natural to ask what role they might play in the development of multiplicative thinking. We investigated this question by examining children''s solutions to folding tasks, which involved predicting the number of equal parts created by a succession of given folds and determining a sequence of folds to create a given number of equal parts. Analyzing a combination of cross-sectional data and case studies from standardized clinical interviews, we found that children were most successful at coordinating folding sequences with multiplicative thinking when they used a conceptualization of doubling based upon recursion. This conceptualization tended to generate more sophisticated solutions. [Copyright &y& Elsevier]

Abstract: The paper of Huang, Keisler and Linkov (HKL) () has motivated us to this note about the relation between ranking procedures and Multi-Criteria Decision Analysis (MCDA) tools. The key concept in the HKL-paper is in our eyes the ‘trade-off’. A ‘trade-off’ seems necessary, when conflicting indicator values are present. HKL stress that the general application of MCDA is hampered by different terminology. We do not claim to have a solution for that problem. Nevertheless, we think that the discussion is worthwhile and this note presents some of our ideas, which basically imply the need of a broadening of the definition of MCDA. [Copyright &y& Elsevier]

Air combat scenarios present unique problems where the solution is not obvious. An advanced networked simulation environment can be used to train high-level cognitive air combat skills such as problem solving. A problem in the development of principled and construct oriented assessment is related to determining the level of specificity of the assessment. We present a detailed discussion of the definition of critical competencies associated with combat mission problem solving performance, and we describe our approach to develop and implement an embedded performance assessment system that maps outcomes to these competencies. Finally, we discuss the implications for our approach for comprehensive assessment and discuss our goals for an evaluation of the competency-based approach to assessment. [Copyright &y& Elsevier]

The present comment concerns some doubtful results included in the above paper. [ABSTRACT FROM AUTHOR]

The chromatic index , χ ′ (G) , is the smallest integer k for which a graph G has a proper edge coloring. The Classification Problem involves determining whether a graph G is Class 1 ( χ ′ (G) = Δ (G)) or Class 2 ( χ ′ (G) = Δ (G) + 1). It is known that subgraph-overfull graphs must be Class 2. In this paper we are concerned with the Classification Problem for split graphs G [ Q ∪ S ] where Q is a clique and S is an independent set. When Δ (G) is odd, G is known to be Class 1. The original proof presented by Chen et al. (1995) has a minor flaw which we detail in this paper while also clarifying that it does not compromise their result. We prove that their technique can be adapted in a non-trivial way to show that some split graphs with even Δ (G) are also Class 1. We show that to solve the Classification Problem for split graphs it suffices to consider that all vertices in Q have degree Δ (G). Considering the subset X of S of the vertices of degree at most Δ (G) / 2 , we show that if the neighborhood of X has at least ⌊ | Q | / 2 ⌋ vertices, then G is Class 1; in the remaining cases we characterize the subgraph-overfull split graphs. • Correction of a flaw in the proof that odd maximum degree split graphs are Class 1. • Proof that certain split graphs with even maximum degree are Class 1. • Characterization of the split graphs which are subgraph-overfull, and thus Class 2. [ABSTRACT FROM AUTHOR]

For analysing the unidirectional multiple folding of a sheet, this paper proposes two mechanical models: an elastica/elastica-plastica model for a small number of folds (K) and a sliding-layered beam model for a large K. The deformation profiles and applied forces or bending moments during the multiple folding process were predicted via a theoretical model analysis and verified based on corresponding finite-element simulations. Thereafter, the models were used to answer the interesting question: how many times can a piece of paper be folded? It was found that, for an A4 paper sheet, the maximum number of folds is 8; this is consistent with common experience. • Theoretical analysis of the uni-directional multiple folding of a sheet is concerned. • An elastic/elastic-plastic model is applied to solve multi-folding problem with small number of folds K. • A sliding-layered beam model is proposed based on Hill's approach. • Theoretical models are verified by corresponding FE simulations. • The maximum number of folds of a piece of A4 paper is obtained to be 8. [ABSTRACT FROM AUTHOR]

This paper investigates the robust cooperative output regulation problem for heterogeneous lower triangular nonlinear multi-agent systems with an unknown exosystem over jointly connected switching networks. The problem has been studied for the exactly known exosystem over switching networks. However, the existing result for the unknown exosystem is still limited to the static networks. To ensure that all followers acquire the reference trajectory generated by the unknown exosystem through the jointly connected switching networks, by combining a set of auxiliary filtering variables and fixed-time stability theory, an adaptive distributed observer is designed. On the basis of the adaptive distributed observer and the distributed internal model approach, we propose a distributed controller under several standard assumptions to solve the problem. Compared with the similar work subject to the static networks, the controller in this paper is applicable to the more general communication network while weakening the assumptions of the controlled system. • A new distributed observer is designed for relatively general exosystem and networks. • We transform an unknown exosystem into a known exosystem in a fixed time. • We avoided the additional assumptions of articles studying similar exosystems. • We extended similar articles to more general networks and controlled objects. [ABSTRACT FROM AUTHOR]

The main aim of this paper is to present a new multi-attribute decision-making (MADM) approach for solving the problems under the uncertain and complex environment. The key challenges during any MADM problem are how to quantify the objective uncertainty information in the data and how to aggregate such collective information. To answer this, in this paper, we utilize the concept of the bipolar fuzzy information to mark the information in terms of the positive and negative support. To aggregate this different information, we propose some power aggregation operators based on the Aczel-Alsina operators (AAO). The AAOs are the generalized t-norm based operations with an additional parameter to analyze the influence of the expert preferences. Based on these AAO and bipolar fuzzy information, we stated bipolar fuzzy AA power weighted averaging and geometric operators and investigate their features. Later, based on these operators, we establish a MADM algorithm to solve the decision-making problems. The applicability of the stated algorithm is demonstrated through a case study related to quantum computing. The comparative studies and advantages of the study are also analyzed with the various prevailing theories. [ABSTRACT FROM AUTHOR]

Quantum neural network (QNN) is a neural network model based on the principles of quantum mechanics. The advantages of faster computing speed, higher memory capacity, smaller network size and elimination of catastrophic amnesia make it a new idea to solve the problem of training massive data that is difficult for classical neural networks. However, the quantum circuit of QNN are artificially designed with high circuit complexity and low precision in classification tasks. In this paper, a neural architecture search method EQNAS is proposed to improve QNN. First, initializing the quantum population after image quantum encoding. The next step is observing the quantum population and evaluating the fitness. The last is updating the quantum population. Quantum rotation gate update, quantum circuit construction and entirety interference crossover are specific operations. The last two steps need to be carried out iteratively until a satisfactory fitness is achieved. After a lot of experiments on the searched quantum neural networks, the feasibility and effectiveness of the algorithm proposed in this paper are proved, and the searched QNN is obviously better than the original algorithm. The classification accuracy on the mnist dataset and the warship dataset not only increased by 5.31% and 4.52%, respectively, but also reduced the parameters by 21.88% and 31.25% respectively. Code will be available at https://gitee.com/Pcyslist/models/tree/master/research/cv/EQNAS , and https://github.com/Pcyslist/EQNAS. [ABSTRACT FROM AUTHOR]

• The paper gives a novel combination between sparse signal recovery and moving least squares in the field of outlier detection. • This paper proposes a new method to detect outliers in scattered data for approximation. • The idea of sparse signal recovery in the proposed method provides the theoretical guarantee for its effectiveness. • The proposed method can restore the true values corresponding to outliers in high accuracy while outliers are marked. In the scattered data used for function approximation, there may be some data that deviate greatly from their true values, which are called outliers. The existence of outliers significantly affects the accuracy of some approximation methods, such as moving least squares. How to quickly and accurately find these outliers and restore their true values has not been well resolved. In this paper, a new outlier detection and recovery method is proposed for data processing. The method uses the high accuracy of moving least squares quasi-interpolation scheme, its sensitivity to outliers and the sparse distribution of outliers to construct a ł 0 -minimization problem with an inequality constrain. Under certain assumptions, it is proved theoretically that the deviation vector corresponding to the outliers is a solution to the optimization problem. The classical orthogonal matching pursuit algorithm is introduced to solve the optimization problem efficiently. By solving the problem, the outliers are marked, and the deviations are also estimated approximately, which can restore the true values of outliers. The numerical experiments demonstrate that the proposed method has high computational efficiency, very high detection accuracy, and high recovery accuracy for the scattered data used for function approximation, so it is practical. [ABSTRACT FROM AUTHOR]

In this paper, we study the discrete-time control problem for the refrigeration system with unmodeled dynamics. This paper proposes discrete adaptive neural network controllers for two refrigeration systems, including the water membrane evaporator cooling and compression refrigeration systems. The influence of model uncertainty on system performance can be eliminated effectively by designing the neural network and the corresponding discrete-time adaptive updating strategy. Thus, the model parameters in the refrigeration system are not required in our control algorithms. Simulation results show that the proposed strategy can effectively adjust the refrigeration system temperatures. Compared with the traditional PID control strategy, the system response overshoot under our method can be reduced by 0.3%, and the system settling time is reduced by at least 94.3%. • Discrete-time adaptive neural controller is proposed for refrigeration systems. • Uncertainty problems are solved by using the adaptive scheme and neural networks. • Compared with the PID method, the settling time is reduced by at least 94.3%. [ABSTRACT FROM AUTHOR]

Abstract: The Nutrition Care Process and Model (NCPM) provides registered dietitian nutritionists (RDNs) and dietetic technicians, registered (DTRs) a framework to recognize, diagnose, and intervene upon nutrition-related health concerns. Within the NCPM, nutrition assessment is essential to develop a comprehensive evaluation of the client’s nutrition history. The application of critical thinking skills to nutrition assessment is imperative to ensure appropriate acquisition and interpretation of data. The Academy of Nutrition and Dietetics’ Career Development Guide, adapted from the Dreyfus Model of Skill Acquisition, illustrates the progression of critical thinking skills as RDNs and DTRs gain knowledge and experience with practice. The Career Development Guide is characterized by the transition through the following stages: novice, beginner, competent, proficient, and advance practice/expert. The foundation of dietetics knowledge is obtained during the novice and beginner stages. Throughout, the primary objective is introduction of the NCPM and nutrition assessment theory via dietetics education and the application of nutrition assessment in supervised practice. Next, RDNs and DTRs transition to the competent stage of practice. During this phase, entry-level knowledge and skill are applied to patient care settings, and critical thinking skills develop as RDNs and DTRs gain experience. Subsequently, RDNs and DTRs move to the proficient stage as the ability to prioritize attention, generalize, apply problem-solving skills to new scenarios, and identify innovative solutions develops. Some RDNs and DTRs may transition to the advance practice/expert stage, during which critical thinking becomes intuitive. Critical thinking skills are essential to ensure diagnostic accuracy; however, more research is needed to further describe progression of critical thinking skills among RDNs and DTRs. [Copyright &y& Elsevier]

Highlights: [•] The EFFBS algorithm, proposed by Khreich et. al (2010) as an alternative to the Baum-Welch recursions, is reviewed. [•] The EFFBS algorithm suffers from a numerical problem that limits its applicability in estimating HM models. [•] The problem is due to an accumulation of a round-off error that leads the solution to diverge form the correct one. [•] A possible explanation of the reasons of the error is given together with some illustrative examples. [ABSTRACT FROM AUTHOR]

• First survey of relevant research activities on Digital Calibration Certificate-DCC. • A thorough analysis of both the calibration process and the calibration certificate. • Modeling the calibration process, the calibration certificate life-cycle. • Identifying Calibration use-cases and stakeholders. • Advantages and disadvantages of various potential DCC formats. Calibration Certificates (CC) are essential for maintaining the accuracy of measurement instruments, which in turn are necessary to maintain the quality of products and services. Such certificates are mainly used in legal metrology for quality audits, accreditation and examination processes; and also in the operational metrology to adjust measurement results according to device uncertainty. Paper-based CC (PCC) or a digital copy of the paper format has been successfully used for this purpose for many decades. However, the PCC starts to fail short with the emergence of new technologies and applications that require automated creation, processing and updates of the calibration certificates. The last years have witnessed an intense effort on digital transformation in metrology and especially developing Digital Calibration Certificates (DCC). Until recently, we noticed the lack of agreement on the nature and the usage of such important new metrology artifacts as well as some repetition in the work by different researchers. To help reaching a minimum consensus on the subject, we present in this paper, a thorough analysis of the calibration certificate and the calibration process. Besides that, we aim by this work to provide an overview of the carried research on that point to help peer researchers to view the big picture and to avoid wasting their efforts in repeating same work on same ideas. Consequently, they can recognize the peer researchers and organizations working on the same topics, find available resources, identify research gaps and identify the best potential directions of their efforts as well. Finally, our paper aims also to establish a bridge between the metrological concepts and information technology concepts such that more computer science researchers can be involved in solving the problem. [ABSTRACT FROM AUTHOR]

This paper concerns the problem of designing distributed control law for a class nonlinear system in which the measurement outputs of the system are distributed in different subsystems. It leads to a challenge that the states of the original systems cannot be completely reconstructed by any single subsystem. In order to solve this problem, distributed state observers and the distributed observer-based distributed control problem emerges as the times require. However, the distributed observers problem of the nonlinear systems is rarely studied, and the distributed control law formed by distributed nonlinear observers has hardly ever been studied up to now. To this end, this paper develops the distributed high-gain observers for a class of nonlinear systems. Unlike the previous several results, our study has the ability to deal with model uncertainty, and devotes itself to overcoming the problem that the separation principle is not tenable. In addition, based on the state estimate generated by the designed distributed observer, an output feedback control law formed by applying the state estimate is developed. Furthermore, a class of sufficient conditions is proved for guaranteeing the error dynamics of the distributed observer and the state trajectory of the closed-loop system to enter an arbitrary small invariant set around the origin. Finally, the simulation results verify the effectiveness of the proposed method. • This paper proposed distributed-observer-based distributed control law for Lipschitz nonlinear systems with model mismatch. Both the error of distributed observer and the states of the closed-loop system can converge to the arbitrary invariant set. • Compared to the existing literature, this paper overcomes the coexistence of control input, nonlinearity, and uncertainty, while the existing works do not fix the control input into account. Besides, the existence of model mismatch is also deliberated. • This paper includes the observer feedback part based on the system output without relying on the assumptions used in the existing studies concerning distributed-estimation-based distributed control framework. [ABSTRACT FROM AUTHOR]

One important aspect of human behavior understanding is the recognition and monitoring of daily activities. An accurate activity recognition system can improve the quality of life in many key areas. The multi-metric feature extraction and DeepForest classifier designed in this paper effectively had solved the problems of incomprehensive feature extraction and insufficient classifier accuracy. First, this paper extracts a total of 450 dimensional feature vectors using mean, variance, maximum, skewness, minimum, kurtosis, regression of independent variables and sample entropy as feature indicators, so that the features of the original vectors can be presented comprehensively. The dimensionality of the feature set is too large, so PCA is used to reduce the dimensionality of the high-dimensional feature vector. After studying the relationship between the dimensionality reduction and the cumulative contribution rate, it is concluded that the cumulative contribution rate reaches 90% when the dimensionality is 15, which can retain the original features better. Then DeepForest was used as the classifier, and the reduced-dimensional feature set was used as the sample set to divide the training set and test set by 3:1. The model was tested on the test set after training, and the classification accuracy of the test set was 98.202%. Six classifiers, GaussianNB, SVM, K-NN, XGBoost, RandomForest and DecisionTree, were selected as the control experimental group, of which only the RandomForest model reached 97%, while the rest of the control models did not achieve 95% effect, indicating that DeepForest was more accurate in classifying human activities. Next, the generalisation ability of the DeepForest classifier was assessed using Monte Carlo Cross Validation (MCCV), K-fold and its confusion matrix. The MCCV validation used 50% of the data as the training set and 30% of the data as the test set, and set the number of splits to 10, resulting in a mean accuracy of 98.227%. The size of K in the K-fold validation was determined to be 8 based on the number of people conducting the human activity experiment, and the final mean accuracy value obtained was 97.790%. The combined confusion matrix from the K-fold validation (which aggregates the confusion matrix for each classification result) was calculated, and the results showed that the highest accuracy reached 100% for A9 and A14 classifications, where A5 and A10, A15 and A10, A12 and A5 were more likely to be confused, with the highest error rate for A5 classification, which was 4.167%, and the rest of the activity classifications were better. [ABSTRACT FROM AUTHOR]

• Reduce the two-dimensional automated vehicle (AV) trajectory planning to a one-dimensional speed planning problem considering conflicting moving objects (CMOs) in the environment. • Propose a parsimonious and efficient model for the AV trajectory planning problem. • Analyze the conflict areas caused by CMOs in the space-time diagram and reveal upper and lower bounds for feasible trajectories. • Propose a customized dynamic programming algorithm based on the revealed bounds to solve the investigated problem in real time. Trajectory planning for autonomous vehicles (AVs) by considering conflicting moving objects (CMOs) is a challenging problem to AV operations. This paper investigates an AV trajectory planning problem where the AV follows a given spatial path, and the trajectories of CMOs are predictable. With the spatial path fixed, the two-dimensional trajectory planning problem is reduced to a one-dimensional speed planning problem that decides the optimal speeds and accelerations of the AV along the spatial path. This paper first analyzes the conflict area caused by a single CMO in the space-time diagram, which reveals upper and lower bounds to the conflict area. Then a multi-area fusion algorithm is proposed to extend the upper and lower bound analyses to a relatively complex traffic scenario with multiple CMOs. To facilitate the computation of the investigated problem, a customized dynamic programming (DP) algorithm is developed, which employs the revealed upper and lower bounds and arriving time constraints to cut invalid trajectories at each stage. With this, the number of stages and states, as well as the computational time for solving the proposed problem, are largely reduced. A set of numerical experiments are conducted to evaluate the performance of the customized DP-based algorithm. The results show that the proposed customized DP-based algorithm can solve the investigated problem within milliseconds, which enables applications to real-time AV control. This much outperforms a state-of-the-art commercial solver, Gurobi, especially for complex traffic scenarios. We further implemented the proposed method in a real-world case study, and the results show that the trajectory generated by the proposed model has a great potential to enhance the future traffic system. [ABSTRACT FROM AUTHOR]

• To introduce BCOHHS for wrapper-based feature selection • A binary using the X-shaped function of the hybrid, HHO, with chaotic opposition-based initialization and simulated annealing • Evaluated the performance of the BCOHHS using high-dimensional medical data sets Harris Hawks Optimization (HHO) is a relatively new meta-heuristic algorithm that has shown promise in solving various optimization problems. However, HHO suffers from some limitations that can affect its performance. The two main drawbacks of HHO are population diversity and local optima. To overcome these limitations and adapt it to solve feature selection issues, a new meta-heuristic, Chaotic Opposition Harris Hawks Optimization with Simulated Annealing (COHHS), is proposed in this paper. Two main improvements are proposed for the HHO. The first one, the chaotic opposition, is applied at the initialization phase of HHO to improve the population diversity of the search agents. The second one, simulated annealing, is applied to find the optimal solution during each iteration to improve HHO exploitation. The proposed method is a dynamic structure that was originally designed to solve problems with continuous optimization. In this paper, we propose a binary COHHS (BCOHHS) using X-shaped functions to boost the efficiency of feature selection. The KNN classifier is used to evaluate the classification accuracy. The performance of the proposed method is evaluated on nine high-dimensional medical datasets and compared with other optimization methods. The experimental results confirm the superiority of the BCOHHS method over the other methods on the majority of the datasets. [ABSTRACT FROM AUTHOR]

The development of ARX-based lightweight block ciphers has been plagued by the difficulty of theoretical security analysis. SAND solves this problem better by obtaining an equivalent representation based on a synthetic S-box. This paper analyzed SAND in terms of diffusivity and found that it can be optimized. SAND has the issue of slow diffusion after the initial plaintext and key are all 0. On the other hand, it takes at least 11 rounds for SAND to reach full diffusion without AddRoundKey. Thus, this paper proposes a high-diffusion architecture SAND-2 to address the above issues. Firstly, issue 1 is solved by replacing the AND operation with a NAND operation and calling the round function dynamically. Then, in order to solve issue 2, P1 and P2 permutations are introduced into G 0 and G 1 , respectively. The full diffusion speed of SAND-2 is 63.7% increased compared to SAND. In addition, the comparison results of hardware indicators show that the hardware resources of SAND-2 are slightly lower, and the throughput is 10% higher than SAND. Finally, the security analysis shows that SAND-2 reaches the upper bound of the resisting differential analysis in fewer rounds. • SAND-2 diffusion speed is 63.7% increased by SAND. • SAND-2 throughput is 10% higher than SAND. • SAND-2 is more secure than SAND. [ABSTRACT FROM AUTHOR]

This paper studies the high telepresence control for the relay communication based space teleoperation. High telepresence requires real-time, accurate force perception and good position tracking, but all of them are destroyed or affected by time delay. To eliminate the influence of time delay, direct recursive generalized predictive control (DR-GPC) is quoted into this paper to realize position tracking. As for the other two properties, the pseudo position-force (Pseudo-PF) feedback strategy (composed of parameter identification and force reconstruction) is a good way as it can provide real-time and precise force feedback when environment change slowly and time delay is relatively small. However, complex and changeable environment and serious and complex time delay in space teleoperation make the environment force parameter available to force reconstruction is outdated and cannot realize accurate force rebuilding. To solve this problem, we propose a dual regression Pseudo-PF feedback strategy. Compared with the traditional one, its biggest change is the introduction of dual regression that consists of time delay prediction and parameter regression. The former predicts time delay and provides it to the latter to determine the forward prediction steps, while the latter regresses the actual latest and precise environment force parameter according to the forward prediction steps. With the cooperation of time delay prediction and parameter regression, the influence of fast changing environment or serious and complex time delay on force reconstruction can be effectively overcome, so as to realize real-time and accurate force feedback. Finally, DR-GPC and dual regression Pseudo-PF feedback strategies cooperate to achieve high telepresence control, and simulation is given to show its effectiveness. • A dual regression Pseudo-PF feedback strategy is proposed. • High teleopresence control for the relay communication based space teleoperation is first studied. • The introduction of dual regression makes the applicability of this method wider. [ABSTRACT FROM AUTHOR]

This paper studies the efficient methods for option pricing under multivariate rough volatility models. The characteristic functions of the asset log-price, which play important role in the option pricing under the multivariate rough volatility models, are determined by a system of parametric nonlinear fractional Riccati equations. This paper obtains the results on the existence, uniqueness and regularity of the solutions to the parametric nonlinear fractional Riccati equations, proposes a high-order scheme to solve the system and proves the high-order convergence. The option pricing problem is solved by the Fourier-cosine formula with the fast approximation of the characteristic functions. Numerical examples are carried out to confirm the theoretical results and show efficiency of the methods. [ABSTRACT FROM AUTHOR]

In this paper we discuss different transmission operators for the non-overlapping Schwarz method which are suited for solving the time-harmonic Helmholtz equation in cavities (i.e. closed domains which do not feature an outgoing wave condition). Such problems are heavily impacted by back-propagating waves which are often neglected when devising optimized transmission operators for the Schwarz method. This work explores new operators taking into account those back-propagating waves and compares them with well-established operators neglecting these contributions. Notably, this paper focuses on the case of rectangular cavities, as the optimal (non-local) transmission operator can be easily determined. Nonetheless, deviations from this ideal geometry are considered as well. In particular, computations of the acoustic noise in a three-dimensional model of the helium vessel of a beamline cryostat with optimized Schwarz schemes are discussed. Those computations show a reduction of 46% in the iteration count, when comparing an operator optimized for cavities with those optimized for unbounded problems. [ABSTRACT FROM AUTHOR]

To achieve the carbon peaking and carbon neutrality goals, China promoted county-wide photovoltaic projects (CWPVs) in 2021. A large number of distributed photovoltaic (DPV) installations have expanded the scale of prosumer. It also brings the inequity problem of prosumer, distribution network system operator (DSO) and consumer--DSO and consumer bear the costs such as access cost and depreciation of stock investment caused by DPV access. Combined with China's current electricity tariff system and the promotion of photovoltaic scale development, this paper proposes a standby fee model based on the principle of "beneficiary apportionment". In this model, the standby fee mechanism for self-owned power plants is applied to DPV to reduce the implementation cost of the mechanism, and the principle of "beneficiary apportionment" is adopted to ensure the profitability of DPV. This paper takes the CWPV in Hunan Province, China as an example, calculates the standby fee under various electricity metering methods, and determines the most appropriate standby fee by considering factors such as output uncertainty of DPV. Finally, the difficulties in the implementation of standby fee are sorted out. [ABSTRACT FROM AUTHOR]

In developing countries like Indonesia, adding a new unit for hydroprocessing facilities is more efficient than modifying the existing equipment. When the addition occurs, refiners should analyze if the existing hydrogen network can supply enough hydrogen to the new unit before constructing a costly hydrogen plant. Current studies on the hydrogen network have yet to consider pressure drop adequately. Therefore, this paper integrates pressure drop estimation and density prediction into the multi-objective MINLP-based hydrogen network. The multi-objective problem is solved sequentially by adding a minor unit to obtain each configuration's maximum flowrate and total annual cost. The optimal configuration based on the combined objective function is to add 1 PSA and compressor for a hydrogen purity of 0.84 and specified pressure requirements. The pressure drop integration shows an insignificant impact averaging 0.004 m3/s of maximum flowrate difference compared to optimization without pressure drop. • The addition of a new hydroprocessing unit is more efficient than existing refiners. • Multi-objective MINLP-based hydrogen network is used to analyze retrofit options. • The pressure drop integration shows a small impact on the overall. • Network. • The optimal configuration is to add one PSA and one compressor. [ABSTRACT FROM AUTHOR]

Recent papers have emphasized the primary role of cellular information management in biological and evolutionary development. In this framework, intelligent cells collectively measure environmental cues to improve informational validity to support natural cellular engineering as collaborative decision-making and problem-solving in confrontation with environmental stresses. These collective actions are crucially dependent on cell-based memories as acquired patterns of response to environmental stressors. Notably, in a cellular self-referential framework, all biological information is ambiguous. This conditional requirement imposes a previously unexplored derivative. All cellular memories are imperfect. From this atypical background, a novel theory of aging and death is proposed. Since cellular decision-making is memory-dependent and biology is a continuous natural learning system, the accumulation of previously acquired imperfect memories eventually overwhelms the flexibility cells require to react adroitly to contemporaneous stresses to support continued cellular homeorhetic balance. The result is a gradual breakdown of the critical ability to efficiently measure environmental information and effect cell-cell communication. This age-dependent accretion governs senescence, ultimately ending in death as an organism-wide failure of cellular networking. This approach to aging and death is compatible with all prior theories. Each earlier approach illuminates different pertinent cellular signatures of this ongoing, obliged, living process. [ABSTRACT FROM AUTHOR]

Learning the relationship between weak and strong perturbations has been considered a major part of semi-supervised semantic segmentation. We observed two problems with a publicly used perturbation method, which randomly generates a mask with a single large bounding box. The large single bounding box that entirely covers the important object components in an image, hindering the model from capturing partial object information. Furthermore, training the model with a single large bounding box as an image-level perturbation causes the model to be biased towards the shape of the large squared box, rather than the deformable object component shapes. In this paper, we propose Subdivided Mask Dispersion Framework (SMDF) to solve these problems. Our framework disperses the large squared box into small multi-scale boxes, capturing the crucial multi-scaled object components in the image. SMDF achieves state-of-the-art performance on five data partitions of PASCAL dataset and three partitions of Extended SBD dataset. Our extensive ablation studies show the effectiveness of dispersed small multi-scale bounding boxes in semi-supervised semantic segmentation. • We proposed a novel Subdivided Mask Dispersion Framework (SMDF) for semi-supervised semantic segmentation. • We proposed a Subdivided White Mask Disperser (SWMD) that generates small and uniformly distributed bounding boxes. • SMDF outperforms all the semi-supervised semantic segmentation approaches on PASCAL and Extended SBD datasets. • We substantiated the effectiveness of SMDF in different backbone networks and resolutions. [ABSTRACT FROM AUTHOR]

To explore the rich information contained in multi-modal data and take into account efficiency, deep cross-modal hash retrieval (DCMHR) is a wise solution. But currently, most DCMHR methods have two key limitations, one is that the recommended classification of DCMHR models is conditioned only on the objects in different regions, respectively. Another flaw is that these methods either do not learn the unified hash codes in training or cannot design an efficient training process. To solve these two problems, this paper designs Large-Scale Cross-Modal Hashing with Unified Learning and Multi-Object Regional Correlation Reasoning (HUMOR). For the proposed related labels classified by ImgNet, HUMOR uses Multiple Instance Learning (MIL) to reason the correlation of these labels. When regional correlation reasoning is low, these labels will be through "reduce-add" to rectification from max-to-min (global precedence) or min-to-max (regional precedence). Then, HUMOR conducts unified learning on hash loss and classification loss, adopts the four-step iterative algorithm to optimize the unified hash codes, and reduces bias in the model. Experiments on two baseline datasets show that the average performance of this method is higher than most of the DCMHR methods. The results demonstrate the effectiveness and innovation of our method. • We propose multi-object regional correlation reasoning for CMR tasks. • HUMOR conducts effective unified training on hash loss and classification loss. • We tried to simplify the function of unified learning and reduce redundant bias. • Experiments show that our model has obviously better performance. [ABSTRACT FROM AUTHOR]

The exponential stabilization of stochastic neural networks in mean square sense with saturated impulsive input is investigated in this paper. Firstly, the saturated term is handled by polyhedral representation method. When the impulsive sequence is determined by average impulsive interval, impulsive density and mode-dependent impulsive density, the sufficient conditions for stability are proposed, respectively. Then, the ellipsoid and the polyhedron are used to estimate the attractive domain, respectively. By transforming the estimation of the attractive domain into a convex optimization problem, a relatively optimum domain of attraction is obtained. Finally, a three-dimensional continuous time Hopfield neural network example is provided to illustrate the effectiveness and rationality of our proposed theoretical results. • The impulsive control sequence is defined by (mode-dependent) impulsive density. • Estimating attractive domain's size is converted into solving optimization problems. • Introduce a convex set to handle mode-dependent saturated impulsive input strategies. [ABSTRACT FROM AUTHOR]

In this paper, we consider a class of constrained tensor least squares problems which arises in the image restoration. The alternating direction multiplier method (ADMM) is proposed to solve this problem. The convergence analysis of this method is given. The numerical experiments illustrate that the ADMM method is feasible and effective for solving the constrained tensor least squares problem. The simulation experiments of image restoration are also performed. [ABSTRACT FROM AUTHOR]