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There is continuing concern about the paucity of social science graduates who have the quantitative skills required by academia and industry. Not only do students often lack the confidence to explore, and use, statistical techniques, the dominance of qualitative research in many disciplines has also often constrained programme-level integration of more quantitative material. However, whilst the topic of statistical literacy is relatively well researched within the more general educational literature, the evidence-base with respect to the effectiveness of teaching and learning of quantitative research methods in the social science remains somewhat limited. This paper describes the development, integration and evaluation of a series of student-led inquiry-based quantitative workbooks within a sociology/social policy undergraduate degree. It outlines how the workbooks were constructively aligned within a "methods spine" and offers some insight into quantitative teaching and learning generally. The paper also discusses some of the opportunities and challenges of taking both an aligned and IBL approach to the teaching of quantitative methods. In doing so it adds to growing evidence that 'problem-based pedagogies' tend to increase educational gain over and above more didactic approaches to learning and teaching. It highlights three key findings: programme-level approaches to curriculum design can be crucial in improving quantitative skills, particularly where they are tailored to student needs; a general indifference to quantitative methods is likely to be due to a process of disenfranchisement that happens before and during students' engagement with university; and, meaningfully engaging students as partners in the process of designing, integrating and evaluating curricula can help to overcome some of the barriers associated with the learning and teaching of quantitative skills.

The issue of poor statistical literacy amongst undergraduates in the United Kingdom is well documented. At university level, where poor statistics skills impact particularly on social science programmes, embedding is often used as a remedy. However, embedding represents a surface approach to the problem. It ignores the barriers to learning that students bring to class, which may not always be addressed solely through embedding, such as, mathematics anxiety. Instead, embedding can only work within a much deeper pedagogic model that places students at its heart, as active participants in learning. This paper examines the development of such a model within a large sociology programme, where there was an implementation of a range of pedagogic strategies to support the development of students' statistical literacy.

This paper examines the cloud computing for education (CCE) literature, and analyzes if the research is developing scientifically with adequate empirical validation. All aspects of empirical investigations covered in the literature are shown as weak, hence, the necessary scientific development of CCE requires extending its scope of interest, and involving scholars synergistically to create and maintain a “common research agenda.” Background: A need to develop research on CCE has been recognized, and considerable efforts made to create an accurate understanding of the development of its scope of interest, in terms of supporting pedagogical developments and processes for better quality of studies. Research Questions: This paper has three main aims: 1) to evaluate the scope of interest in the literature for CCE with specific reference to pedagogy and educational processes; 2) to analyze the characteristics of papers, specifically empirical studies, from the various points of view of the daily improvement activities of teachers and learners at all levels of education; and 3) to identify eventual research gaps to consider and stimulate new topics or further investigations. Methodology: This systematic mapping study review followed a rigorous, replicable process to collect and analyze representative studies of CCE. Findings: Differences are found across geographic areas in applying CCE infrastructure and technologies in educational institutions; few studies address CCE’s impact on pedagogic processes. The scope of interest in CCE is only partially covered; with empirical research being very shallow. Suggestions are made for more effective research on concerning the production and use of content. [ABSTRACT FROM AUTHOR]

This article presents the author's response to the article "The Sociology of Teaching Graduate Statistics" by Timothy Patrick Moran. Moran takes on the admirable task of trying to improve the teaching graduate statistics. Like many good teachers, he makes controversial and thought-provoking statements designed to promote discussion and debate. First, the author was struck that a paper about teaching graduate statistics makes sweeping statements while offering virtually no statistics to back them up. While noting that "teaching statistics literature concerns itself with undergraduate-level issues," Moran nonetheless feels free to assert that "when teaching statistics, sociological thinking is largely abandoned." The author states that his own suspicion is that there is too much diversity in the teaching of graduate statistics to make such generalizations. He added that certainly, students need to learn how to use statistics appropriately, and it may be a useful pedagogical device for an instructor to review the great debates of the past. But, it is not as though there is a lack of contemporary writing on statistical controversies.

Scholarship on teaching undergraduates increasingly emphasizes the benefits of providing students with an active role in their education whereby instructors are more aptly described as facilitators of knowledge rather than merely providers of it. Additionally, recommendations from the American Sociological Association aimed specifically at the undergraduate sociology curriculum argue that students must engage as practitioners of sociology at each level of their program development. In short, undergraduates should do sociology--not just read or write sociology. Applying this recommendation to teaching statistics, I suggest organizing a course around a student-led research project in which students generate the topics, questions, and data that are then used to complete a substantive research paper. Students are given the opportunity to be actively engaged in all stages of the research and data-gathering process. Students also present their work in student roundtables, further legitimating their important contributions as researchers.(Contains 1 table.)

A significant amount of attention has been given to the predictors of academic achievement in higher education. However, the vast majority of articles have centred on entrance criteria and the learning approaches or personal habits of students. Investigations into how achievement depends on student efforts, being almost invariably based on subjective and unavoidably imprecise student self-evaluations, do not generally help the university determine how it can actually promote academic achievement. In this article, the authors construct models for the academic achievement of economics students in various subjects at their institution. These models include students' previous scores and objective information about their studies during the year, including marks for home assignments and tests; subjective information from the students is not used. The predictive power of these models is high, and the authors use them to formulate how the university can enhance academic achievement and improve the quality of studies: for example, improving student feedback; tailoring subjects to complement each other; determining the need for additional classes; identifying students who are in danger of failing; and giving instructors feedback on the efficacy of activities such as home assignments or the format of examination papers and marking. (Contains 1 figure.)

The effect of the substrate on the cyclic bending reliability of screen printed silver traces is studied in this paper. The silver screen printed test samples in this study were three different plastic films and three varieties of paper sheets. The specimens with line widths of 250, 500, 1000, and 2000 \mu\mboxm were subjected to cyclic bending, and their electrical behavior was monitored with dc resistance measurements in situ. In this study, a 20% increase in resistance was defined as the failure criterion, and the reliability of each test population was assessed by a statistical Weibull analysis. The results showed that the reliability of the traces was highly dependent on the substrate thickness in all the studied materials. The traces on a 125- \mu\mboxm-thick polyethylene terephthalate substrate were found to have the lowest characteristic lifetimes of about 1000 bending cycles, whereas the traces with the same geometry on polyethylene terephthalate with a thickness of 50 \mu\mboxm endured hundreds of thousands of bending cycles. Similarly, decreasing the substrate thickness improved the reliability of the printed traces on paper substrates. This relationship was due to the reduced mechanical strain on the trace surface when using thinner substrates. [ABSTRACT FROM PUBLISHER]

In this paper, we discuss ways collaborative research groups can be used and what students appear to learn about social science reasoning and quantitative literacy from such groups. Data are from field observations, pre- and post-semester surveys in both collaborative and traditional courses, end of the semester student comments, and student work on semester-long group-based projects. The findings illuminate teaching and learning processes, cognitive and personal learning outcomes, and remaining challenges. Students resist having to work in groups, while simultaneously becoming attached to their group and acknowledging its importance. In the end, many students see the importance of both social science reasoning and quantitative skills and demonstrate the ability to articulate key components of both. Challenges that remain include more specific measures of particular skills, potential group conflicts, and active learning within a culture of consumerism and entitlement. (Contains 2 tables and 3 footnotes.)

This article presents the author's response to the comments provided by Farkas, Hardy, Williams, and Zipp regarding his article on the teaching of statistics in graduate programs. The author mentions two issues: Conceptually, what are the needs for making changes in the graduate statistics curriculum? And practically, what are the merits of various proposed changes, and how do they implement them? The author completely agrees with Professors Hardy and Zipp that the teaching of statistics in graduate programs needs to become an institutionally based discussion, considered more broadly within the context of both the other courses they expect graduate students to take and their prior training as undergraduates. The author believes that as improvements are made, more can be found such that the teaching of quantitative methodology begins to mirror the scholarship of statistics itself--that is, more like an ongoing process of development and advancement. To this end, it is obvious that the conversation between the five of them has made valuable use of these pages.

This article presents the author's response to Timothy Patrick Moran's article "The Sociology of Teaching Graduate Statistics." Since 1972, the author has taught the required graduate-level social statistics course in three different departments. During this time, he has seen the truth of the concerns that Moran expresses at the beginning of his article; the foundation course must cover more ground than ever before, and computer software has become much easier to use. However, the author has drawn different implications from these trends than the ones suggested by Professor Moran. His decisions about how to teach the foundation course have been driven by his judgment of what the students most need to learn during their first year of graduate social statistics. The author mentions that his response has been to focus the course around statistical techniques that will be broadly useful for the work the students are preparing to do on their dissertations and in their part published articles. In addition, some of Professor Moran's assumptions are questionable. Sociological theory is at best peripheral and usually irrelevant to understanding these controversies. Further, it is unlikely that students will be able to understand the issues argued about and the positions taken by various factions as these controversies developed historically.

As we enter our second century, it is an appropriate time for sociologists to take stock of where we have been and where we are going. Although most of this reflection appears to focus on substantive matters, Timothy Patrick Moran is right in arguing that their gaze ought to extend to how they teach graduate statistics. This article presents the author's response to Moran's article "The Sociology of Teaching Graduate Statistics." Reading Moran's article, for the most part, the author finds a lot to recommend. Two points are clearly uncontroversial: Graduate statistics courses should emphasize the importance of sociological theory in all phases of the research process--both Moran and him are hopeful that this already happens--and these courses should be sure to include an appropriate dose of statistical theory. Moran suggests, and he may very well be correct, that the need to cover more and more advanced techniques has driven these more basic topics out of the curriculum. The author mentions that perhaps this is the best way to characterize one of Moran's biggest contributions: bringing the sociologist back into statistics. The author adds that Moran makes an excellent point; the typical statistics course is taught as if the techniques are not problematic at all, that they have no history and that their acceptance is universal.

This article presents a response to Timothy Patrick Moran's article "The Sociology of Teaching Graduate Statistics." In his essay, Moran argues that exciting developments in techniques of quantitative analysis are currently coupled with a much less exciting formulaic approach to teaching sociology graduate students about quantitative analysis. The author mentions that to begin to remedy this problem, Moran suggests they infuse the teaching of statistics with theory, history, and debate, coupling this "substantive" approach with the technical training that now appears to their primary interest. The author finds much to applaud in his proposal. She mentions that the classes Moran describes are much more fun to teach and more like the statistics/methods training she received as a graduate student. She believes that implementing Moran's methods/statistics curricula will require more than a change in how they teach statistics. It will require some fundamental changes in how they think about "basic" graduate training in sociology. (Contains 2 footnotes.)

Contributions: The underrepresentation of women in computer science (CS) majors has long been a focus of attention in many academic documents, the majority of them from the United States and Europe. There is, however, a lack of information about educational interventions (EIs) for women in computing in Latin America. The contribution of this article is to cover this gap and describe what researchers in Latin American countries have been publishing about the recruitment and retention of women in the CS field. Background: Many EIs targeting female students at different educational levels—K-12, undergraduate, and graduate—have been undertaken to increase the participation of women in computing in Latin America. However, descriptions of these activities rarely are included in international academic databases. Research Questions: This literature mapping addresses two main research questions (RQ) about the topic of women in computing in academic publications in Latin American countries: RQ1) what are the characteristics of the publications about women in computing in Latin America? and RQ2) what are the published interventions to recruit and retain women in computing in Latin America? To answer RQ1, six subquestions were created covering year, language, country of origin, document type, and professional track. Furthermore, for RQ2, two subquestions were created involving educational level and the use of software development with a female theme. Methodology: This investigation used the systematic literature mapping process. To achieve a broad coverage of papers, the following sources were included: Scopus, Web of Science, Google Scholar, EBSCO, the proceedings of the Latin American Women in Computing Conference (LAWCC), and those of the Women in Technology (WIT) workshop colocated with the annual conference of the Brazilian Computer Society (SBC). The included papers were published in the last decade (2010–2020) and written in English, Portuguese, or Spanish. Findings: The literature mapping encompasses 197 academic documents, 48.2% of which were written in Portuguese, 28.7% in English, and 23.1% in Spanish. The papers originated from 15 of the 20 Latin American countries. Brazil and Costa Rica have the highest number of publications overall. The documents describe initiatives to increase the participation of women in computing majors that cover the entire educational spectrum, from K-12 to graduate programs, but papers targeting populations in higher education have started to appear recently. [ABSTRACT FROM AUTHOR]

The antagonistic behavior in the crowd usually exacerbates the seriousness of the situation in sudden riots, where the antagonistic emotional contagion and behavioral decision making play very important roles. However, the complex mechanism of antagonistic emotion influencing decision making, especially in the environment of sudden confrontation, has not yet been explored very clearly. In this paper, we propose an Emotional contagion-aware Deep reinforcement learning model for Antagonistic Crowd Simulation (ACSED). First, we build a group emotional contagion module based on the improved Susceptible Infected Susceptible (SIS) infection disease model, and estimate the emotional state of the group at each time step during the simulation. Then, the tendency of crowd antagonistic action is estimated based on Deep Q Network (DQN), where the agent learns the action autonomously, and leverages the mean field theory to quickly calculate the influence of other surrounding individuals on the central one. Finally, the rationality of the predicted actions by DQN is further analyzed in combination with group emotion, and the final action of the agent is determined. The proposed method in this paper is verified through several experiments with different settings. We can conclude antagonistic emotions play a critical role in the decision making of the crowd through influencing the individual behavior in the riot scenario, where individual behaviors are primarily driven by emotions and goals, rather than common rules. The experiment results also prove that the antagonistic emotion has a vital impact on the group combat, and positive emotional states are more conducive to combat. Moreover, by comparing the simulation results with real scenes, the feasibility of our method is further confirmed, which can provide good reference to formulate battle plans and improve the win rate of righteous groups in a variety of situations. [ABSTRACT FROM AUTHOR]

Defines a nonsimulation game as a game dealing with a mathematical or scientific topic which is superior to the typical simulation in that it provides an optimal mix of competition and cooperation. Describes two variations of Allen's 1969 Equations nonsimulation game, which may be used to teach various statistical concepts. (JDH)

States that regardless of the content or level of a statistics course, five goals to reach are: (1) overcoming fears, resistances, and tendencies to memorize; (2) the importance of intellectual honesty and integrity; (3) understanding relationship between deductive and inductive inferences; (4) learning to play role of reasonable critic; and (5) learning to handle complexities in a systematic fashion. (JDH)

Self-supervised learning has shown value for uncovering informative movement features for human activity recognition. However, there has been minimal exploration of this approach for affect recognition where availability of large labelled datasets is particularly limited. In this paper, we propose a P-STEMR (Parallel Space-Time Encoding Movement Representation) architecture with the aim of addressing this gap and specifically leveraging the higher availability of human activity recognition datasets for pain-level classification. We evaluated and analyzed the architecture using three different datasets across four sets of experiments. We found statistically significant increase in average F1 score to 0.84 for pain level classification with two classes based on the architecture compared with the use of hand-crafted features. This suggests that it is capable of learning movement representations and transferring these from activity recognition based on data captured in lab settings to classification of pain levels with messier real-world data. We further found that the efficacy of transfer between datasets can be undermined by dissimilarities in population groups due to impairments that affect movement behaviour and in motion primitives (e.g. rotation versus flexion). Future work should investigate how the effect of these differences could be minimized so that data from healthy people can be more valuable for transfer learning. [ABSTRACT FROM AUTHOR]

Many real-world optimization problems involve uncertainties, and in such situations it is often desirable to identify robust solutions that perform well over the possible future scenarios. In this paper, we focus on input uncertainty, such as in manufacturing, where the actual manufactured product may differ from the specified design but should still function well. Estimating a solution’s expected fitness in such a case is challenging, especially if the fitness function is expensive to evaluate, and its analytic form is unknown. One option is to average over a number of scenarios, but this is computationally expensive. The archive sample approximation method reduces the required number of fitness evaluations by reusing previous evaluations stored in an archive. The main challenge in the application of this method lies in determining the locations of additional samples drawn in each generation to enrich the information in the archive and reduce the estimation error. In this paper, we use the Wasserstein distance metric to approximate the possible benefit of a potential sample location on the estimation error, and propose new sampling strategies based on this metric. Contrary to previous studies, we consider a sample’s contribution for the entire population, rather than inspecting each individual separately. This also allows us to dynamically adjust the number of samples to be collected in each generation. An empirical comparison with several previously proposed archive-based sample approximation methods demonstrates the superiority of our approaches. [ABSTRACT FROM AUTHOR]

Cooperative co-evolution (CC) is an evolutionary algorithm that adopts the divide-and-conquer strategy to solve large-scale optimization problems. It is difficult for CC to specify a suitable subpopulation size to solve different subproblems. The population diversity may be insufficient to search for the global optimum during subpopulations' evolution. In this paper, an adaptive method for enhancing population diversity is embedded in a contribution-based CC. In CC, there are two kinds of subpopulation: the convergent or stagnant subpopulations and the non-convergent and non-stagnant subpopulations. A method is proposed in the paper to evaluate the convergent and stagnant subpopulations' contributions to improving the best overall objective value, which is different from the contribution evaluation on the non-convergent and non-stagnant subpopulations. In each co-evolutionary cycle, the new CC adaptively determines to select a subpopulation, which can make a greater contribution to improving the best overall objective value, between the above two kinds of subpopulation to undergo evolution. When a convergent or stagnant subpopulation is selected to undergo evolution, the subpopulation is re-diversified to enhance its global search capability. Our experimental results and analysis suggest that the new CC algorithm can improve the performance of CC and serves as a competitive solver for large-scale optimization problems. [ABSTRACT FROM AUTHOR]

This paper compares three modern and two classical multiobjective optimizers (MOOs) as applied to real-world problems in electromagnetics. The behavior of sophisticated optimizers on simple test functions has been studied exhaustively. In contrast, the algorithms here are tested on practical applications, where the function evaluations are computationally expensive, making the convergence rate a crucial factor. The examples considered include the optimization of a narrowband slot antenna, a mushroom-type electromagnetic bandgap structure, and an ultrawideband Vivaldi antenna. Another popular topic in the literature is in comparing classical MOOs on electromagnetics problems. The modern optimizers chosen in this paper are state of the art and each has a distinct design philosophy. This paper introduces two unique MOOs to the electromagnetics community: BORG, an auto-adaptive genetic algorithm and the Multi-Objective Covariance Matrix Adaptation Evolutionary Strategy (MO-CMA-ES), an extension of the popular single-objective CMA-ES. These algorithms are compared to the Multi-objective Evolutionary Algorithm based on Decomposition (MOEA/D), a Chebysheff scalarization algorithm, and two classical MOOs. This paper will study the behavior of these algorithms on problems in electromagnetics with a limited number of function evaluations using five distinct metrics and will provide useful guidelines and recommended optimizer settings. [ABSTRACT FROM AUTHOR]

The main goal in group testing is to recover a small subset of defective items from a larger population while efficiently reducing the total number of (possibly noisy) required tests/measurements. Under the assumption that the input-output statistical relationship (i.e., channel law) is known to the recovery algorithm, the fundamental as well as the computational limits of the group testing problem are relatively better understood than when these statistical relationships are unknown. Practical considerations, however, render this assumption inapplicable, and “blind” recovery/estimation procedures, independent of the input-output statistics, are desired. In this paper, we analyze the fundamental limits of a general noisy group testing problem, when this relationship is unknown. Specifically, in the first part of this paper, we propose an efficient scheme, based on the idea of separate-decoding of items (where each item is recovered separately), for which we derive sufficient conditions on the number of tests required for exact recovery. The difficulty in obtaining these conditions stems from the fact that we allow the number of defective items to grow with the population size, which in turn requires delicate concentration analysis of certain probabilities. Furthermore, we show that in several scenarios, our proposed scheme achieves the same performance as that of the corresponding non-blind recovery algorithm (where the input-output statistics are known), implying that the proposed blind scheme is robust/universal. Finally, in the second part of this paper, we propose also an inefficient combinatorial-based scheme (or, “joint-decoding”), for which we derive similar sufficient conditions. [ABSTRACT FROM AUTHOR]

Particle swarm optimization (PSO) has two salient components: 1) a dynamical rule governing particle motion and 2) an interparticle communication topology. Recent practice has focused on the fully connected topology (Gbest) despite earlier indications on the superiority of local particle neighborhoods. This paper seeks to address the controversy with empirical trials with canonical PSO on a large benchmark of functions, categorized into 14 properties. This paper confirms the early lore that Gbest is the overall better algorithm for unimodal and separable problems and that a ring neighborhood of connectivity two (Lbest) is the preferred choice for multimodal, nonseparable and composition functions. Topologies of intermediate particle connectivity were also tested and the difference in global/local performance was found to be even more marked. A measure of significant improvement is introduced in order to distinguish major improvements from refinements. Lbest, according to the experiments on the 84 test functions and a bi-modal problem of adjustable severity, is found to have significant improvements later in the run, and to be more diverse at termination. A mobility study shows that Lbest is better able to jump between optimum basins. Indeed Gbest was unable to switch basins in the bi-modal trial. The implication is that Lbest’s larger terminal diversity, its better ability to basin hop and its later significant improvement account for the performance enhancement. In several cases where Lbest was not the better algorithm, the trials show that Lbest was not stuck but would have continued to improve with an extended evaluation budget. Canonical PSO is a baseline algorithm and the ancestor of all contemporary PSO variants. These variants build on the basic structure of baseline PSO and the broad conclusions of this paper are expected to follow through. In particular, research that fails to consider local topologies risks underplaying the success of the promoted algorithm. [ABSTRACT FROM AUTHOR]

When solving constrained optimization problems (COPs) by evolutionary algorithms, the search algorithm plays a crucial role. In general, we expect that the search algorithm has the capability to balance not only diversity and convergence but also constraints and objective function during the evolution. For this purpose, this paper proposes a composite differential evolution (DE) for constrained optimization, which includes three different trial vector generation strategies with distinct advantages. In order to strike a balance between diversity and convergence, one of these three trial vector generation strategies is able to increase diversity, and the other two exhibit the property of convergence. In addition, to accomplish the tradeoff between constraints and objective function, one of the two trial vector generation strategies for convergence is guided by the individual with the least degree of constraint violation in the population, and the other is guided by the individual with the best objective function value in the population. After producing offspring by the proposed composite DE, the feasibility rule and the $\boldsymbol {\varepsilon }$ constrained method are combined elaborately for selection in this paper. Moreover, a restart scheme is proposed to help the population jump out of a local optimum in the infeasible region for some extremely complicated COPs. By assembling the above techniques together, a constrained composite DE is proposed. The experiments on two sets of benchmark test functions with various features, i.e., 24 test functions from IEEE CEC2006 and 18 test functions with 10 dimensions and 30 dimensions from IEEE CEC2010, have demonstrated that the proposed method shows better or at least competitive performance against other state-of-the-art methods. [ABSTRACT FROM AUTHOR]

The aim of this paper is to mitigate the problem of high power demand peak and load oscillations in the operation of a large population of thermostatically controlled loads (TCLs) operated by model predictive control (MPC) at the TCL level. Two desynchronized MPC schemes are introduced: 1) adding random delays in reference signals and 2) extra penalizations on MPC objective functions. For characterizing and validating the proposed desynchronization MPC schemes, a partial differential equation (PDE) model is developed to represent the evolution of the operational states of the TCLs controlled by MPC in a population. The focus of this paper is put on the control of cooling fans in server racks of datacenters, whereas the proposed approach is applicable to other types of TCLs. Numerical simulation studies are carried out and the obtained results confirm the validity and the applicability of the developed approach. [ABSTRACT FROM AUTHOR]

Accessible Summary: What is already known?: The nurse–patient relationship in mental health care is an important focus of mental health nursing theories and research.There is limited evidence about which factors influence nurse‐sensitive patient outcomes of the nurse–patient relationship. This hinders the development, planning, delivering, and quality assurance of the nurse–patient relationship in nursing practice and nursing education. What this paper adds to existing knowledge?: To our best knowledge, this is the first study to examine associations between nurse‐sensitive patient outcomes of the nurse–patient relationship and a range of patient characteristics and relationship‐contextual factors.In this study, we found that gender, age, hospital characteristics, nurse availability when needed, nurse contact, and nurse stimulation were associated with the scores on the nurse‐sensitive patient outcome scale. What are the implications for practice?: Having insight into the factors associated with nurse‐sensitive patient outcomes of the nurse–patient relationship can help nurses, nursing students, nursing management and also patients to enhance the nurse–patient relationship, trying to influence outcomes of nursing care. Introduction: The lack of evidence on patient characteristics and relational‐contextual factors influencing nurse‐sensitive patient outcomes of a nurse–patient relationship is a possible threat to the quality and education of the nurse–patient relationship. Aim: To measure nurse‐sensitive patient outcomes of the nurse–patient relationship and to explore the associations between nurse‐sensitive patient outcomes and a range of patient characteristics and relational‐contextual factors. Method: In a multicenter cross‐sectional study, 340 inpatients from 30 units in five psychiatric hospitals completed the Mental Health Nurse‐Sensitive Patient Outcome Scale. Descriptive, univariate and Linear Mixed Model analyses were conducted. Results: Overall, patient‐reported outcomes were moderate to good. Female participants, nurse availability when needed, more nurse contact and nurse stimulation were associated with higher outcomes. Age differences were observed for some of the outcomes. Outcomes also varied across hospitals but were not related to the number of times patients were hospitalized or to their current length of stay in the hospital. Discussion: The results may help nurses to become more sensitive and responsive to factors associated with nurse‐sensitive patient outcomes of the nurse–patient relationship. Implications: The nurse‐sensitive results can support nurses in designing future nurse–patient relationships. [ABSTRACT FROM AUTHOR]

In this paper, the pinning process of pinned photodiodes (PPDs) is described by a new analytical model, assuming the PPD to be composed of inner and junction regions. There are two definitions of the pinning potential: maximum conduction band potential variation ($\Delta \varphi _{M}$) and maximum electron quasi-fermi level potential variation ($\Delta $ FP $_{M}$). The output of the previous pinning potential models is only an approximation of $\Delta \varphi _{M}$. In this paper, a comprehensive model is proposed in which both aforementioned definitions of pinning potential can be achieved analytically. The proposed model is a system of equations relating PPD’s main characteristics such as $\Delta \varphi _{M}$ and equilibrium full-well capacity to other main PPD parameters such as PPD spatial dimensions, impurity dopant concentrations, maximum inner region potential, and remnant carrier population at $\Delta \varphi _{M}$. The proposed model is verified by two previously reported experiment-based data. [ABSTRACT FROM AUTHOR]

As a well-known engineering practice, concurrent engineering (CE) considers all elements involved in a product’s life cycle from the early stages of product development, and emphasizes executing all design tasks simultaneously. As a result, there exist various complex design problems in CE, which usually have many design parameters or require different disciplinary knowledge to solve them. To address these problems and enable concurrent design, different methods have been developed. The original problem is usually divided into small subproblems so that each subproblem can be solved individually and simultaneously. However, good decomposition, optimization, and communication strategies among subproblems are still needed in the field of CE. This paper attempts to study and analyze cooperative co-evolution (CC) based design optimization in CE by employing a parallel CC framework. Furthermore, it aims to develop new concurrent design methods based on parallel CC to solve different kinds of CE problems. To achieve this goal, a new novelty-driven CC is developed for design problems with complex structures and a novel concurrent design method is presented for quasi-separable multidisciplinary design optimization (MDO) problems. The efficacy of the new methods is studied on universal electric motor design problems and a general MDO problem, and compared to that of some existing methods. Additionally, this paper studies how the communication frequency among subpopulations affects the performance of the proposed methods. The optimal communication frequencies under different communication costs are reported as experimental results for both proposed methods on the test problems. Based on this paper, an effective self-adaptive method is proposed to be used in both optimization schemes, which is able to adapt the communication frequency during the optimization process. [ABSTRACT FROM PUBLISHER]

In this paper, we define a novel census signal temporal logic (CensusSTL) that focuses on the number of agents in different subsets of a group that complete a certain task specified by the STL. CensusSTL consists of an “inner logic” STL formula and an “outer logic” STL formula. We present a new inference algorithm to infer CensusSTL formulas from the trajectory data of a group of agents. We first identify the “inner logic” STL formula and then infer the subgroups based on whether the agents’ behaviors satisfy the “inner logic” formula at each time point. We use two different approaches to infer the subgroups based on similarity and complementarity, respectively. The “outer logic” CensusSTL formula is inferred from the census trajectories of different subgroups. We apply the algorithm in analyzing data from a soccer match by inferring the CensusSTL formula for different subgroups of a soccer team.

Note to Practitioners—The method described in this paper can be used to discover subgroups of agents and their behavior patterns based on spatial–temporal data of all agents in the group. The subgroups are formed by agents that behave similarly or complementarily for a certain task. The behavior patterns of the subgroups are expressed in the form of signal temporal logic statements. These are logical statements about the number of agents in the subgroups that are in certain states during a certain time interval. For example, if there are more than two blue agents in the region, then within 2 h, there will be more than three red agents in the same region. The behavior patterns inferred using this method can be used in: 1) analysis of group behavior while performing a task, e.g., in sports tactical analysis and 2) prediction of future group behavior under similar situations. [ABSTRACT FROM AUTHOR]

This paper proposes a united-based imperialist competitive algorithm (UICA) for compensatory neural fuzzy systems. The original imperialist competitive algorithm (ICA) comprises numerous empires in the population, and each empire comprises one imperialist and some colonies. Each country represents a feasible solution in the empire, and the more favorable solutions become imperialists, taking over less favorable solutions (i.e., colonies). In the ICA, each colony moves toward its relevant imperialist according to an assimilation policy. This paper proposes a UICA that focuses on this assimilation policy to explore the characteristics of the colonies. The assimilation policy consists of three major parts in this paper. In the first part, a colony searches for the best previous position of the colony. In the second part, the colony faces the best-so-far imperialist. In the third part, the colony moves toward the corresponding imperialist of the colony. The proposed UICA was applied to nonlinear system problems, and the experimental results indicated that the proposed UICA is effective. [ABSTRACT FROM PUBLISHER]

With the advent of electric vehicles (EVs), issues connected to the electric vehicle charging scheduling (EVCS) problem, which is $\textsf {NP}$ -hard, have become important. In previous studies, EVCS has been mainly formulated as a constrained shortest-path problem; however, such formulations have not involved variables such as charging rates, traffic congestion, scalability, and waiting time at charging station (CS), that need to be considered in practical settings. Earlier research has also tended to focus on the strengths of particular evolutionary optimization algorithms like differential evolution (DE) or particle swarm optimization (PSO) over others or traditional mathematical programming methods, with only a limited study of hybrid approaches. In this paper, fast and slow charging options at a station are considered in the EVCS problem for practical use. In previous studies, EVs have been considered to have fixed speeds; however, in order to mitigate CS congestion and thus waiting times at CSs, dynamic speed control of EVs has been considered in this work. This work also investigates the scalability of different EVCS solutions. A hybrid approach using PSO and the Firefly algorithm (FFA) with a Lévy flights search strategy is designed and implemented to solve the EVCS. Also, different hybrid methods variants of PSO and FFA have been evaluated in this paper to find the best performing hybrid variant. Experimental results validate the effectiveness of our approach on both synthetic and the real-world transportation networks. [ABSTRACT FROM AUTHOR]

Mobile edge computing (MEC) is an emerging computing paradigm that decreases the computing time and extends the lifespan of user equipments (UEs). In MEC, the computational tasks are offloaded from UEs to the base station (BS) at the edge of the network for processing. However, MEC cannot cope with environments where there are no BS or where communication facilities have been destroyed. In this paper, we study the problem of minimizing the energy consumption of UAV equipped with MEC servers as a mobile base station to serve users. The problem involves user offloading decision, UAV location and allocation with computational resources, and is a hybrid optimization problem with continuous and discrete variables. To address this problem, we propose a hybrid nature-inspired optimization algorithm (HNIO) and its version for discrete optimization, where HNIO incorporates mutation and population diversity detection mechanisms to boost its global optimization capability, and we design a probabilistic selection-based coding strategy for the discrete optimization version. The experimental study is conducted based on ten cases with different numbers of UEs. Comparing HNIO with several other state-of-the-art optimization algorithms, it is concluded from the Friedman and Wilcoxon’s test of the experimental results that HNIO shows better precision and stability in nine out of the ten cases with higher number of UEs. [ABSTRACT FROM AUTHOR]

Food is one of the basic essential elements for human beings. Its quality is directly related to our physical, emotional, and social well-being. An electronic nose is a non-destructive instrument and is an effective solution for quick and easy determination of food quality. In this paper, an integrated soft E-nose methodology focusing on performance enhancement and complexity reduction is proposed and implemented for the effective classification of beef quality and prediction of microbial population in beef. The soft E-nose methodology is tested on 18 available datasets. Ours is a four-step approach that includes validated data collection, signal processing with a moving average filter and principal component analysis for feature reduction, efficient pattern recognition using machine learning and deep learning techniques like support vector machine, k-nearest neighbors, artificial neural network, extreme learning machine, and deep neural network for classification and regression, and an output stage for classification and prediction of the microbial population of beef. This methodology could provide accuracy greater than 98% using k-nearest neighbors, artificial neural network methods, and a correlation coefficient of greater than 0.98 using support vector machine and artificial neural network methods. The proposed methodology is validated using 10-fold cross-validation. [ABSTRACT FROM AUTHOR]

The population of electric vehicles (EVs) has grown rapidly over the past decade due to the development of EV technologies, battery materials, charger facilities, and public charging services. Many governments have implemented plans to ban fossil fuel vehicles considering the significance of EVs in reducing greenhouse gas emissions. However, due to the battery material characteristics and charger power limitations, the EV charging process requires more time than is needed to fill a non-EV with fuel at a gasoline station, causing drivers to experience range anxiety and impeding the promotion of EVs. Hence, the battery swapping station (BSS) model has been proposed as an alternative method. Recently, researchers have studied the BSS approach by proposing various operation systems and optimization methods, and BSS service operators have successfully implemented swapping at commercial and private stations. This paper reviews the state-of-the-art BSS literature and business models, where the BSS offers a recharged battery to an incoming EV with a low state-of-charge. First, four operation modes are presented: a single BSS, multiple BSSs, an integrated BSS and battery charging station (BCS), and multiple BSSs and BCSs. Then, the BSS decision scenarios are surveyed in relation to five operational areas, i.e., charging schedule, service policy, construction and planning, dispatching and routing, and power management, where the scenarios are compared in terms of the BSS mode, decision maker, EV category, number of battery types, vehicle to grid, and focus and objective. Finally, the survey concludes with a discussion of several future research directions for EV BSSs. [ABSTRACT FROM AUTHOR]

The capability of traffic-information systems to sense the movement of millions of users and offer trip plans through mobile phones has enabled a new way of optimizing city traffic dynamics, turning transportation big data into insights and actions in a closed-loop and evaluating this approach in the real world. Existing research has applied dynamic Bayesian networks and deep neural networks to make traffic predictions from floating car data, utilized dynamic programming and simulation approaches to identify how people normally travel with dynamic traffic assignment for policy research, and introduced Markov decision processes and reinforcement learning to optimally control traffic signals. However, none of these works utilized floating car data to suggest departure times and route choices in order to optimize city traffic dynamics. In this paper, we present a study showing that floating car data can lead to lower average trip time, higher on-time arrival ratio, and higher Charypar-Nagel score compared with how people normally travel. The study is based on optimizing a partially observable discrete-time decision process and is evaluated in one synthesized scenario, one partly synthesized scenario, and three real-world scenarios. This study points to the potential of a “living lab” approach where we learn, predict, and optimize behaviors in the real world. [ABSTRACT FROM AUTHOR]

The failure rate is essential in power system reliability assessment and thus far, it has been commonly assumed as constant. This is a basic approach that delivers reasonable results. However, this approach neglects the heterogeneity in component populations, which has a negative impact on the accuracy of the failure rate. This paper proposes a method based on risk functions, which describes the risk behavior of condition measurements over time, to compute individual failure rates within populations. The method is applied to a population of 12 power transformers on transmission level. The computed individual failure rates depict the impact of maintenance and that power transformers with long operation times have a higher failure rate. Moreover, this paper presents a procedure based on the proposed approach to forecast failure rates. Finally, the individual failure rates are calculated over a specified prediction horizon and depicted with a 95% confidence interval. [ABSTRACT FROM AUTHOR]

Core power control of a nuclear reactor during power maneuvering transients is an important issue in a nuclear power plant. This paper, for the first time, presents an optimized PID controller for a nuclear research reactor with a new model of the reactor core, which is based on the fractional neutron point kinetics (FNPK) equations. This model preserves the main dynamic properties of the neutron movement in which the relaxation time related to a quick change in the neutron flux includes a fractional order, acting as an exponent of the relaxation time, to show the dynamic characteristics of the nuclear reactor in the best way. The physical justification of the fractional exponent is associated with nonFickian impressions from the neutron diffusion equation point of view. In addition, in this paper, for the first time, feedback reactivity effects are considered for FNPK model. Genetic algorithm (GA) is used to optimize gains of the PID controller. The objective function is according to the minimization of the integral of time-weighted absolute error (ITAE). The simulation result demonstrates that this optimized control method has satisfactory performance and stability during output-tracking process for higher values of fractional order. [ABSTRACT FROM AUTHOR]

Operational indices optimization is crucial for the global optimization in beneficiation processes. This paper presents a multitasking multiobjective evolutionary method to solve operational indices optimization, which involves a formulated multiobjective multifactorial operational indices optimization (MO-MFO) problem and the proposed multiobjective MFO algorithm for solving the established MO-MFO problem. The MO-MFO problem includes multiple level of accurate models of operational indices optimization, which are generated on the basis of a data set collected from production. Among the formulated models, the most accurate one is considered to be the original functions of the solved problem, while the remained models are the helper tasks to accelerate the optimization of the most accurate model. For the MFO algorithm, the assistant models are alternatively in multitasking environment with the accurate model to transfer their knowledge to the accurate model during optimization in order to enhance the convergence of the accurate model. Meanwhile, the recently proposed two-stage assortative mating strategy for a multiobjective MFO algorithm is applied to transfer knowledge among multitasking tasks. The proposed multitasking framework for operational indices optimization has conducted on 10 different production conditions of beneficiation. Simulation results demonstrate its effectiveness in addressing the operational indices optimization of beneficiation problem. Note to Practitioners—Operational indices optimization is a typical approach to achieve global production optimization by efficiently coordinating all the indices to improve the production indices. In this paper, a multiobjective multitasking framework is developed to address the operational indices optimization, which includes a multitasking multiobjective operational indices optimization problem formulation and a multitasking multiobjective evolutionary optimization to solve the above-formulated optimization problem. The proposed approach can achieve a solution set for the decision-making. The simulation results on a real beneficiation process in China with 10 operational conditions show that the proposed approach is able to obtain a superior solution set, which is associated with a higher grade and yield of the product. [ABSTRACT FROM AUTHOR]

In recent years, we have witnessed the explosion of large-scale data in various fields. Classical statistical methodologies, such as linear regression or generalized linear regression, often show inadequate performance on heterogeneous data because the key homogeneity assumption fails. In this paper, we present a flexible framework to handle heterogeneous populations that can be naturally grouped into several ordered subtypes. A local model technique utilizing ordinal class labels during the training stage is proposed. We define a new “progression score” that captures the progression of ordinal classes, and use a truncated Gaussian kernel to construct the weight function in a local regression framework. Furthermore, given the weights, we apply sparse shrinkage on the local fitting to handle high dimensionality. In this way, our local model is able to conduct variable selection on each query point. Numerical studies show the superiority of our proposed method over several existing ones. Our method is also applied to the Alzheimer’s Disease Neuroimaging Initiative data to make predictions on the longitudinal clinical scores based on different modalities of baseline brain image features. [ABSTRACT FROM AUTHOR]

In this paper, we propose a hybrid genetic algorithm (GA) for the optimal shape design of an axially symmetric dual-reflector antenna by combining the GA with the moving least square (MLS), which enhances the convergence rate and the global search performance. The MLS is used to construct local interpolation functions from non-uniform sample data and to estimate new superior positions. By combining these superior positions in the next generation, the MLS-GA shows better search performance for the global optimum and a faster convergence rate than those of the conventional GA. To verify the proposed MLS-GA, we applied it to the optimal shape design of the DRA at the W-band. [ABSTRACT FROM AUTHOR]

Dense stereo image matching in remotely sensed images is a challenging problem, though it has been studied for more than two decades, due to occlusions, discontinuities, geometric, and radiometric distortions. A novel multiobjective fitness function-based dense stereo matching approach using genetic algorithms (GAs) is proposed in this paper. The proposed method is useful for estimating dense disparity map with an improved number of inliers for a stereo image pair, despite the constraint of finding correct disparity at depth discontinuities. In this paper, the steps of GA, such as initialization of the population, fitness function, and crossover and mutation operation, are designed and implemented to effectively deal with the problem of dense stereo image matching. To initialize the population, a Scale Invariant Feature Transform (SIFT) descriptor is computed for each pixel and multiple-size window-based matching is performed, using the similarity measures: 1) Euclidean distance and 2) spectral angle mapper. The generated disparity maps are pruned to choose a suitable subset using the designed fitness functions, considering the constraints related to stereo image pair, such as epipolar constraint, which encodes the epipolar geometry and the similarity measure that is useful to decide accuracy of the correspondences. The two objective functions are the number of inliers computed using the fundamental matrix and an energy minimization function, considering discontinuities and occlusions. The usefulness of this approach for remotely sensed stereo image pairs is demonstrated by improving the number of inliers and favorably comparing with state-of-the-art dense stereo image matching methods. [ABSTRACT FROM AUTHOR]

Brain storm optimization (BSO) is a swarm intelligence optimization algorithm inspired by the collective behavior of human beings in solving problems. This paper provides a conceptual overview and detailed explanation of the BSO algorithm. For the first time, the effectiveness of BSO in electromagnetic applications is demonstrated using a Yagi–Uda antenna design and a Luneburg lens antenna design, and this effectiveness is further validated via optimization, prototyping, and measurement of a dual-band slotted patch antenna design. In addition, a novel binary version of BSO (BBSO) for optimizing discrete problems is proposed, and the process of transforming the concept of BSO into a binary language is discussed in detail. A method for selecting algorithm parameter values for BBSO is shown. Finally, BBSO is applied successfully to an array thinning example and a pixelated patch antenna design. [ABSTRACT FROM AUTHOR]

This paper proposes a method for visualizing combinatorial search spaces named low-dimensional Euclidean embedding (LDEE). The proposed method transforms the original search space, such as a set of permutations or binary vectors, to $\pmb {\mathbb {R}}^{k}$ (with ${k} {=} {2}$ or 3 in practice) while aiming to preserve spatial relationships existing in the original space. The LDEE method uses the t-distributed stochastic neighbor embedding (t-SNE) to transform solutions from the original search space to the Euclidean one. In this paper, it is mathematically shown that the assumptions underlying the t-SNE method are valid in the case of permutation spaces with the Mallows distribution. The same is true for other metric spaces provided that the distribution of points is assumed to be normal with respect to the adopted metric. The embedding obtained using t-SNE is further refined to ensure visual separation of individual solutions. The visualization obtained using the LDEE method can be used for analyzing the behavior of the population in a population-based metaheuristic, the working of the genetic operators, etc. Examples of visualizations obtained using this method for the four peaks problem, the firefighter problem, the knapsack problem, the quadratic assignment problem, and the traveling salesman problem are presented in this paper. [ABSTRACT FROM AUTHOR]

Conventional evolutionary algorithms (EAs) are not well suited for solving expensive optimization problems due to the fact that they often require a large number of fitness evaluations to obtain acceptable solutions. To alleviate the difficulty, this paper presents a multitasking evolutionary optimization framework for solving computationally expensive problems. In the framework, knowledge is transferred from a number of computationally cheap optimization problems to help the solution of the expensive problem on the basis of the recently proposed multifactorial EA (MFEA), leading to a faster convergence of the expensive problem. However, existing MFEAs do not work well in solving multitasking problems whose optimums do not lie in the same location or when the dimensions of the decision space are not the same. To address the above issues, the existing MFEA is generalized by proposing two strategies, one for decision variable translation and the other for decision variable shuffling, to facilitate knowledge transfer between optimization problems having different locations of the optimums and different numbers of decision variables. To assess the effectiveness of the generalized MFEA (G-MFEA), empirical studies have been conducted on eight multitasking instances and eight test problems for expensive optimization. The experimental results demonstrate that the proposed G-MFEA works more efficiently for multitasking optimization and successfully accelerates the convergence of expensive optimization problems compared to single-task optimization. [ABSTRACT FROM AUTHOR]