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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]

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]

The rapid development of information technologies provided unprecedented big data environments for condition monitoring and degradation analyses. However, the available big data sets are often sparse with a limited number of observations per recorded unit. For example, in many healthcare systems, data are collected from a large number of patients, but the available observations from each patient are quite limited. Unfortunately, most of the existing approaches for data-driven degradation modeling may not work well in this scenario as they either pool the information from the population or require rich historical observations in each unit. To address the challenges in “sparse data environments,” this paper proposes a structural degradation modeling framework (SDM). The SDM is inspired by the recommender system, which provides recommendations about specific items for the user. In addition, it is also tailored to the needs of degradation modeling. In particular, the framework takes into consideration: 1) the available data from the unit of interest; 2) the population characteristics; 3) the relationship between the available units; and 4) the precision of the available units. Simulation studies and a case study that involves the Alzheimer’s disease (AD) neuroimaging initiative data set are conducted, which shows satisfactory performance of the proposed method. Note to Practitioners—This paper proposes a framework for modeling and predicting the degradation level and/or condition of units with time. Our framework is particularly useful where many units have missing and/or limited degradation observations. Essentially, our proposed method integrates two important ideas: 1) leveraging the available data from the unit of interest to improve the modeling fitting of the individual unit over the observed time domain and 2) considering the relationship between the available units to extract proper and accurate population characteristics to address the challenge of limited observations. The proposed approach is validated via simulation studies as well as a healthcare case study based on AD. In the future research, we will further explore the extension of the proposed method such as considering more generic degradation models and optimal parameter tunings. [ABSTRACT FROM AUTHOR]

In recent years, imbalanced utilization of medical resources is widely concerned within the tiered Chinese hospital system. Reverse referral, as a measure of promoting patient flows from upper level hospitals (ULHs) to lower level hospitals (LLHs), has demonstrated its advantages on alleviating ULH workload and balancing resource utilization between ULHs and LLHs. Nevertheless, it remains unclear on how to control the reverse referral decision process at the operational level. In this paper, we consider an ULH-dominant setting at which the LLH must accept patient referrals from the ULH whenever it has available beds. We focus our attention on an easy-to-implement threshold policy for the ULH to make the reverse referral decision. To investigate, we first formulate an analytically tractable queueing model for a simplified reverse referral process. We then investigate a more general patient flow control model, for which we analyze the patient population dynamics with a Markov chain process, and apply the concept of state-dependent Markovian arrival process to generate an infinitesimal generator of the system. We use RG factorization to compute the system performance measures. We next formulate a threshold optimization problem with the objective of maximizing the ULH profit. Simulation experiments are performed, which conclude that the threshold control policy is insensitive to the service time distribution. Finally, we report real-world inspired numerical studies, from which we generate insights into effective adjustment of the control threshold in response to the system parameters and discuss potential hindrance from the LLH incorrectly informing its real-time resource availability to the ULH. Our work is the first that applies systems engineering to the real-time reverse referral decision problem in China. It provides the novel perspective of resource balancing to patient flow control studies in the care transition management literature. Note to Practitioners—It is recognized, in almost all countries with insufficient health care delivery infrastructure, that the care coordination is a key strategy to improve the effectiveness, safety, and efficiency of the health care system. Through communicating/sharing knowledge, multiple care organizations work together to create a proactive care plan for the patients to meet their needs and preferences, especially dealing with transitions of care. With the promotion of population care management, practitioners throughout the world have increasingly acknowledged the importance of patient flow streamlining, resource alignment within each population, and linking to community care resources from acute care facilities. However, applying care coordination in everyday routines of a medical practice can be overwhelming, even when it is obvious that the changes will improve patient care and provide efficiency. In this paper, we investigate an easy-to-implement patient flow control policy in making reverse referral decisions for patients receiving postdischarge care. We expect to improve provider’s profit with improved operational management. Through our analyses, we want to stress the importance of applying systems engineering and mathematical modeling to study care coordination and interorganization patient flow control. We also want to advocate knowledge sharing and accountability establishment, as a well-functioned partnership between hospitals at different tiers may very much rely on them. [ABSTRACT FROM AUTHOR]

An improved solution for drug distribution is presented in this paper. It is divided into two parts: i) a multi-operator evolutionary algorithm in charge of calculating the initial delivery routes and ii) an ambient intelligence-based support system able to tracing the merchandise along the distribution route. The first one establishes the routes to be followed by the vehicles, based on a proposal of estimation of the travel times. The second one is formed by a system able to recognize and trace the drugs inside each vehicle. A laboratory experimentation has been conducted in order to demonstrate the adequacy of the route calculator. In addition, a field experimentation has been carried out by implementing the traceability system in a delivery van which of the drug distributor in the city of Bilbao. [ABSTRACT FROM AUTHOR]

The emerging collective motions of swarms of interacting agents are a subject of great interest in application areas ranging from biology to physics and robotics. In this paper, we conduct a careful analysis of the collective dynamics of a swarm of self-propelled heterogeneous, delay-coupled agents. We show the emergence of collective motion patterns and segregation of populations of agents with different dynamic properties; both of these behaviors (pattern formation and segregation) emerge naturally in our model, which is based on self-propulsion and attractive pairwise interactions between agents. We derive the bifurcation structure for emergence of different swarming behaviors in the mean field as a function of physical parameters and verify these results through simulation. [ABSTRACT FROM PUBLISHER]

In this paper, we propose new memetic algorithms (MAs) for the multiobjective flexible job shop scheduling problem (MO-FJSP) with the objectives to minimize the makespan, total workload, and critical workload. The problem is addressed in a Pareto manner, which aims to search for a set of Pareto optimal solutions. First, by using well-designed chromosome encoding/decoding scheme and genetic operators, the nondominated sorting genetic algorithm II (NSGA-II) is adapted for the MO-FJSP. Then, our MAs are developed by incorporating a novel local search algorithm into the adapted NSGA-II, where some good individuals are chosen from the offspring population for local search using a selection mechanism. Furthermore, in the proposed local search, a hierarchical strategy is adopted to handle the three objectives, which mainly considers the minimization of makespan, while the concern of the other two objectives is reflected in the order of trying all the possible actions that could generate the acceptable neighbor. In the experimental studies, the influence of two alternative acceptance rules on the performance of the proposed MAs is first examined. Afterwards, the effectiveness of key components in our MAs is verified, including genetic search, local search, and the hierarchical strategy in local search. Finally, extensive comparisons are carried out with the state-of-the-art methods specially presented for the MO-FJSP on well-known benchmark instances. The results show that the proposed MAs perform much better than all the other algorithms. [ABSTRACT FROM PUBLISHER]

This paper considers minimizing makespan for a blocking flowshop scheduling problem, which has important application in a variety of modern industries. A constructive heuristic is first presented to generate a good initial solution by combining the existing profile fitting (PF) approach and Nawaz–Enscore–Ham (NEH) heuristic in an effective way. Then, a memetic algorithm (MA) is proposed including effective techniques like a heuristic-based initialization, a path-relinking-based crossover operator, a referenced local search, and a procedure to control the diversity of the population. Afterwards, the parameters and operators of the proposed MA are calibrated by means of a design of experiments approach. Finally, a comparative evaluation is carried out with the best performing algorithms presented for the blocking flowshop with makespan criterion, and with the adaptations of other state-of-the-art MAs originally designed for the regular flowshop problem. The results show that the proposed MA performs much better than the other algorithms. Ultimately, 75 out of 120 upper bounds provided by Ribas [“An iterated greedy algorithm for the flowshop scheduling with blocking”, OMEGA, vol. 39, pp. 293–301, 2011.] for Taillard flowshop benchmarks that are considered as blocking flowshop instances are further improved by the presented MA. [ABSTRACT FROM AUTHOR]

Medical test selection is a recurring problem in health prevention and consists of proposing a set of tests to each subject for diagnosis and treatment of pathologies. The problem is characterized by the unknown risk probability distribution across the population and two contradictory objectives: minimizing the number of tests and giving the medical test to all at-risk populations. This article sets this problem in a general framework of chance-constrained medical test rationing with unknown subject distribution over an attribute space and unknown risk probability but with a given sample population. A new approach combining decision-tree and Bayesian inference is proposed to allocate relevant medical tests according to the subjects’ profile. Case studies on screening of hypertension and diabetes are conducted, and the performance of the proposed approach is evaluated. Significant savings on unnecessary tests are achieved with limited numbers of subjects needing but not receiving necessary tests. Note to Practitioners—Whether a medical test is needed for all subjects in health prevention? Is it possible to reduce unnecessary tests without jeopardizing the goal of screening at-risk populations? This article attempts to answer these questions by proposing a data-driven approach combining decision trees for subject profiling, Bayesian inference for unknown probability distribution estimation, and combinatorial optimization for test allocation. The application of this approach to a real-case study reduces the number of electrocardiogram (ECG) tests by 90% while keeping the number of hypertensive subjects needing but not receiving ECG tests small (five out of 230). A significant cut of unnecessary tests is also achieved in a second case study of diabetes screening. This approach allows decision-makers to better balance the cost-saving and the level of public health objective. Furthermore, the combination with decision trees makes the practical implementation quite straightforward. [ABSTRACT FROM AUTHOR]

Improving the efficiency and sustainability of distribution networks (DNs) is nowadays a challenging objective both for large networks and microgrids connected to the main grid. In this context, a crucial role is played by the so-called network reconfiguration problem, which aims at determining the optimal DN topology. This process is enabled by properly changing the close/open status of all available branch switches to form an admissible graph connecting network buses. The reconfiguration problem is typically modeled as an NP-hard combinatorial problem with a complex search space due to current and voltage constraints. Even though several metaheuristic algorithms have been used to obtain—without guarantees—the global optimal solution, searching for near-optimal solutions in reasonable time is still a research challenge for the DN reconfiguration problem. Facing this issue, this article proposes a novel effective optimization framework for the reconfiguration problem of modern DNs. The objective of reconfiguration is minimizing the overall power losses while ensuring an enhanced DN voltage profile. A multiple-step resolution procedure is then presented, where the recent Harris hawks optimization (HHO) algorithm constitutes the core part. This optimizer is here intelligently accompanied by appropriate preprocessing (i.e., search space preparation and initial feasible population generation) and postprocessing (i.e., solution refinement) phases aimed at improving the search for near-optimal configurations. The effectiveness of the method is validated through numerical experiments on the IEEE 33-bus, the IEEE 85-bus systems, and an artificial 295-bus system under distributed generation and load variation. Finally, the performance of the proposed HHO-based approach is compared with two related metaheuristic techniques, namely the particle swarm optimization algorithm and the Cuckoo search algorithm. The results show that HHO outperforms the other two optimizers in terms of minimized power losses, enhanced voltage profile, and running time. Note to Practitioners—This article is motivated by the emerging need for effective network reconfiguration approaches in modern power distribution systems, including microgrids. The proposed metaheuristic optimization strategy allows the decision maker (i.e., the distribution system operator) to determine in reasonable time the optimal network topology, minimizing the overall power losses and considering the system operational requirements. The proposed optimization framework is generic and flexible, as it can be applied to different architectures both of large distribution networks (DNs) and microgrids, considering various types of system objectives and technical constraints. The presented strategy can be implemented in any decision support system or engineering software for power grids, providing decision makers with an effective information and communication technology tool for the optimal planning of the energy efficiency and environmental sustainability of DNs. [ABSTRACT FROM AUTHOR]

Social robots are coming to our homes and have already been used to help humans in a number of ways in geriatric care. This article aims to develop a framework that enables social robots to conduct regular clinical screening interviews in geriatric care, such as cognitive evaluation, falls’ risk evaluation, and pain rating. We develop a social robot with essential features to enable clinical screening interviews, including a conversational interface, face tracking, an interaction handler, attention management, robot skills, and cloud service management. Besides, a general clinical screening interview management (GCSIM) model is proposed and implemented. The GCSIM enables social robots to handle various types of clinical questions and answers, evaluate and score responses, engage interviewees during conversations, and generate reports on their well-being. These reports can be used to evaluate the progression of cognitive impairment, risk of falls, pain level, and so on by caregivers or physicians. Such a clinical screening capability allows for early detection and treatment planning in geriatric care. The framework was developed and implemented on our 3-D-printed social robot. It was tested on 30 older adults with different ages, achieved satisfying results, and received their high confidence and trust in the use of this robot for human well-being assessment. Note to Practitioners—This article is motivated by the goal of using a social robot to perform geriatric well-being assessment through clinical screening interviews. In order to conduct clinical screening interviews, the social robot needs the following essential features: having a verbal conversational interface, adapting to different types of clinical screening interviews, scoring and evaluating answers, having nondirective listening responses, and enabling directive listening responses. The proposed general clinical screening interview management (GCSIM) model demonstrates these capabilities on the social robot. The robot can give structured clinical screening interviews with different question–answer sheets. This will help advance assistive technologies for use by geriatric physicians, nurses, and social service professionals to keep older adults healthy, safe, and independent at home. Robots will become more and more essential in working alongside geriatric practitioners to help monitor older adults at home and to provide early detection and warning of cognitive/mental health problems, falls’ risk, and so on. This early detection property can improve quality-of-care and help older adults remain living at home. [ABSTRACT FROM AUTHOR]