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Adaptive behavior requires learning about the structure of one’s environment to derive optimal action policies, and previous studies have documented transfer of such structural knowledge to bias choices in new environments. Here, we asked whether people could also acquire and transfer more abstract knowledge across different task environments, specifically expectations about cognitive control demands. Over three experiments, participants performed a probabilistic card-sorting task in environments of either a low or high volatility of task rule changes (requiring low or high cognitive flexibility respectively) before transitioning to a medium-volatility environment. Using reinforcement learning modeling, we consistently found that previous exposure to high task rule volatilities led to faster adaptation to rule changes in the subsequent transfer phase. These transfers of expectations about cognitive flexibility demands were both task- (Experiment 2) and stimulus- (Experiment 3) independent, thus demonstrating the formation and generalization of environmental structure knowledge to guide cognitive control.Statement of RelevanceWe investigated whether structural knowledge of one task environment can be transferred to guide cognitive control strategies in new environments. Past research has found that while learning generally improves subsequent performance, it does so only for the learned task (“near transfer”) and has little or no generalizability to novel task rules and stimuli (“far transfer”). However, recent studies suggest that learning more abstract, structural task features (e.g., cognitive maps) allows for that knowledge to be applied to new environments. Here, we took a critical additional step and showed that people can acquire and transfer expectations about cognitive control demands (specifically cognitive flexibility) across different task environments. To our knowledge, this is the first demonstration of people’s ability to extract and re-use cognitive control learning parameters that transcend specific stimuli and tasks. This transfer of learned cognitive flexibility is particularly noteworthy because such flexibility is impaired in several common psychiatric conditions.

Collaboration is critical to organizations and difficult when work is distributed. Prior research has indicated that when individuals are distributed, organizations respond by structuring their work to decrease reciprocal interdependence, reduce the complexity of tasks that individuals perform, or accept moderate inefficiencies. Yet in an increasing number of organizations—location-independent organizations—employees are fully distributed, exist without a physical office, and engage in reciprocally interdependent work. To understand how these distributed organizations collaborate, I undertook an inductive multiple-case study. I identify two patterns of collaboration, an asynchronous orientation and a real-time orientation, and reveal the specific enabling practices for each, with a focus on asynchronous-oriented organizations. This research contributes to the distributed work literature by detailing three novel practices that enable effective collaboration for reciprocally interdependent work without geographic or temporal alignment and to the organizational design literature by identifying distinct approaches to distributed collaboration. This study also engages with the future-of-work conversation by providing empirical grounding that enhances our understanding of the theory, boundary conditions, and nuance of the phenomenon of distributed organizations, specifically location-independent organizations.

The impact of user presence on both collaboration and search task performance within virtual reality is not fully understood. The purpose was to review existing literature and determine what research has been conducted on user presence in virtual reality (VR), as well as what research has been conducted on how user presence in virtual reality influences a user’s communication abilities and performance on search tasks. The need for alternative means of pilot training, specifically the possibility of utilizing virtual reality, and the importance of understanding collaboration and search task performance for this training method were outlined. Future research into the efficacy of VR as a training tool for pilots should aim to fill in this gap and seek to understand how user presence in VR influences both communication and search task performance in a virtual cockpit environment.

Typical censoring models have mass points at the upper or lower tails, or at both tails, of an otherwise continuous outcome distribution. In contrast, we consider a censoring model with a mass point in the interior of the outcome distribution. We refer to this mass point as “bunching” and use it to estimate model parameters. For example, economic theory suggests that, for increasing marginal income tax rates, many taxpayers will report income exactly at the threshold where the tax rate increases. This translates into a censoring model with bunching at the threshold. The size of this mass point of taxpayers can be used to estimate an elasticity parameter that summarizes taxpayers’ responses to taxes. In this article, we introduce the command bunching, which implements new nonparametric and semiparametric identification methods for estimating elasticities developed by Bertanha, McCallum, and Seegert (2021, Technical Report 2021-002, Board of Governors of the Federal Reserve System). These methods rely on weaker assumptions than what are currently made in the literature and result in meaningfully different estimates of the elasticity.

A metering roundabout where traffic is controlled by traffic lights with phase times influenced by queue detector occupancy might be the solution to enhance performance when there are unbalanced traffic flows at roundabouts. There have, however, been minimal studies on how the distance of the queue detector from the stop line affects signal phase time durations and the queuing lengths. This research, therefore, seeks to develop a Cuckoo Search/Local search Algorithm using parameters such as arrival volumes, conflicting volumes, detector distance and phase time to investigate the relationship of signal setting, detector location and queuing formulations. Also, some additional statistical tests were performed for the fitness of the data. In order to conduct solid model calibrations and validations, model output data was compared against the AIMSUN model. The results from the analyses demonstrated that the queue detector distance can affect phase time durations and vehicle queuing lengths on the controlling approach as well as queuing lengths on the metered approach. This study showed that, based on the study for the Old Belair Road roundabout, the total queue length (controlling + metered) will be minimized from 689 to 499 m by the proposed methods when the detector is relocated at 210 m from the roundabout stop line, giving longer phase green times and resulting in decreased intersection queuing lengths.

Pure-compression shells have been the central topic in the form-finding of shells. This study investigated tension-compression mixed-type shells by utilizing a NURBS-based isogeometric form-finding approach that analyses Airy stress functions to expand the possible plan geometry. A complete set of smooth version graphic statics tools is provided to support the analyses. The method is validated using examples with known solutions, and a further example demonstrates the possible forms of shells that the proposed method permits. Additionally, a guideline to configure a proper set of boundary conditions is presented through the lens of asymptotic lines of the stress functions.

Firms use aggregate data from data brokers (e.g., Acxiom, Experian) and external data sources (e.g., Census) to infer the likely characteristics of consumers in a target list and thus better predict consumers’ profiles and needs unobtrusively. The authors demonstrate that the simple count method most commonly used in this effort relies implicitly on an assumption of conditional independence that fails to hold in many settings of managerial interest. They develop a Bayesian profiling introducing different conditional independence assumptions. They also show how to introduce additional observed covariates into this model. They use simulations to demonstrate that in managerially relevant settings, the Bayesian method will outperform the simple count method, often by an order of magnitude. The authors then compare different conditional independence assumptions in two case studies. The first example estimates customers’ age on the basis of their first names; prediction errors decrease substantially. In the second example, the authors infer the income, occupation, and education of online visitors of a marketing analytic software company based exclusively on their IP addresses. The face validity of the predictions improves dramatically and reveals an interesting (and more complex) endogenous list-selection mechanism than the one suggested by the simple count method.

Mobile app users are often exposed to a sequence of short-lived marketing interventions (e.g., ads) within each usage session. This study examines how an increase in the variety of ads shown in a session affects a user's response to the next ad. The authors leverage the quasi-experimental variation in ad assignment in their data and propose an empirical framework that accounts for different types of confounding to isolate the effects of a unit increase in variety. Across a series of models, the authors consistently show that an increase in ad variety in a session results in a higher response rate to the next ad: holding all else fixed, a unit increase in variety of the prior sequence of ads can increase the click-through rate on the next ad by approximately 13%. The authors then explore the underlying mechanism and document empirical evidence for an attention-based account. The article offers important managerial implications by identifying a source of interdependence across ad exposures that is often ignored in the design of advertising auctions. Furthermore, the attention-based mechanism suggests that platforms can incorporate real-time attention measures to help advertisers with targeting dynamics.

Inaccurate stereotypes—perceived differences among groups that do not actually differ—are prevalent and consequential. Past research explains stereotypes as emerging from a range of factors, including motivational biases, cognitive limitations, and information deficits. Considering the minimal forces required to produce inaccurate assumptions about group differences, we found that locally adaptive exploration is sufficient: An initial arbitrary interaction, if rewarding enough, may discourage people from investigating alternatives that would be equal or better. Historical accidents can snowball into globally inaccurate generalizations, and inaccurate stereotypes can emerge in the absence of real group differences. Using multiarmed-bandit models, we found that the mere act of choosing among groups with the goal of maximizing the long-term benefit of interactions is enough to produce inaccurate assessments of different groups. This phenomenon was reproduced in two large online experiments with English-speaking adults ( N = 2,404), which demonstrated a minimal process that suffices to produce biased impressions.

Accurate pressure control and fast dynamic response are vital to the pneumatic electric braking system (PEBS) for that commercial vehicles require higher regulation precision of braking force on four wheels when braking force distribution is carried out under some conditions. Due to the lagging information acquisition, most feedback-based control algorithms are difficult to further improve the dynamic response of PEBS. Meanwhile, feedforward-based control algorithms like predictive control perform well in improving dynamic performance. but because of the large amount of computation and complexity of this kind of control algorithm, it cannot be applied in real-time on single-chip microcomputer, and it is still in the stage of theoretical research at present. To address this issue and for the sake of engineering reliability, this article presents a logic threshold control scheme combining analogous model predictive control (AMPC) and proportional control. In addition, an experimental device for real-time measuring PEBS multi-dynamic parameters is built. After correcting the key parameters, the precise model is determined and the influence of switching solenoid valve on its dynamic response characteristics is studied. For the control scheme, numerical and physical validation are executed to demonstrate the feasibility of the strategy and for the performance of the controller design. The experimental results show that the dynamic model of PEBS can accurately reflect its pressure characteristics. Furthermore, under different air source pressures, the designed controller can stably control the pressure output of PEBS and ensure that the error is within 8KPa. Compared with the traditional control algorithm, the rapidity is improved by 32.5%.

The present research demonstrates how consumer responses to negative and positive offers are influenced by whether the administering marketing agent is an artificial intelligence (AI) or a human. In the case of a product or service offer that is worse than expected, consumers respond better when dealing with an AI agent in the form of increased purchase likelihood and satisfaction. In contrast, for an offer that is better than expected, consumers respond more positively to a human agent. The authors demonstrate that AI agents, compared with human agents, are perceived to have weaker intentions when administering offers, which accounts for this effect. That is, consumers infer that AI agents lack selfish intentions in the case of an offer that favors the agent and lack benevolent intentions in the case of an offer that favors the customer, thereby dampening the extremity of consumer responses. Moreover, the authors demonstrate a moderating effect, such that marketers may anthropomorphize AI agents to strengthen perceived intentions, providing an avenue to receive due credit from consumers when the agent provides a better offer and mitigate blame when it provides a worse offer. Potential ethical concerns with the use of AI to bypass consumer resistance to negative offers are discussed.

Retail is rapidly evolving to construct virtual environments for consumers. Online product images, videos, and virtual reality (VR) interfaces enliven consumer experiences and are a source of product information. Because consumers are unable to physically touch products in these digital environments, this research examines vicarious touch, or the observation of a hand in physical contact with a product in a digital environment. Across eight studies, the authors use images, GIFs, and VR to show that vicarious touch affects consumers’ psychological ownership and product valuation due to the active nature of product touch, which results in a felt sense of body ownership of the virtual hand. This is termed the “vicarious haptic effect.” Results demonstrate that it is not enough to show a hand in an advertisement; the hand must be touching a product. The vicarious haptic effect is strongest for people who become highly stimulated by an immersive VR experience (i.e., measured via the elevation in heart rate). The vicarious haptic effect is attenuated if the viewed interaction does not represent a diagnostic hand movement. The authors discuss theoretical and managerial implications for digital product presentation to encourage feelings of product ownership and valuation.

Sexual satisfaction has been robustly associated with relationship and individual well-being. Previous studies have found several individual (e.g., gender, self-esteem, and attachment) and relational (e.g., relationship satisfaction, relationship length, and sexual desire) factors that predict sexual satisfaction. The aim of the present study was to identify which variables are the strongest, and the least strong, predictors of sexual satisfaction using modern machine learning. Previous research has relied primarily on traditional statistical models which are limited in their ability to estimate a large number of predictors, non-linear associations, and complex interactions. Through a machine learning algorithm, random forest (a potentially more flexible extension of decision trees), we predicted sexual satisfaction across two samples (total N = 1846; includes 754 individuals forming 377 couples). We also used a game theoretic interpretation technique, Shapley values, which allowed us to estimate the size and direction of the effect of each predictor variable on the model outcome. Findings showed that sexual satisfaction is highly predictable (48–62% of variance explained) with relationship variables (relationship satisfaction, importance of sex in relationship, romantic love, and dyadic desire) explaining the most variance in sexual satisfaction. The study highlighted important factors to focus on in future research and interventions.

Previous work using the numerical comparison task has shown that an empty set, the nonsymbolic manifestation of zero, can be represented as the smallest quantity of the numerical magnitude system. In this study, we examined whether an empty set can be represented as such under conditions of automatic processing in which deliberate processing of stimuli magnitudes is not required by the task. In Experiment 1, participants performed physical and numerical comparisons of empty sets (i.e., empty frames) and of other numerosities presented as framed arrays of 1 to 9 dots. The physical sizes of the frames varied within pairs. Both tasks revealed a size congruity effect (SCE) for comparisons of non-empty sets. In contrast, comparisons to empty sets produced an inverted SCE in the physical comparison task, whereas no SCE was found for comparisons to empty sets in the numerical comparison task. In Experiment 2, participants performed an area comparison task using the same stimuli as Experiment 1 to examine the effect of visual cues on the automatic processing of empty sets. The results replicated the findings of the physical comparison task in Experiment 1. Taken together, our findings indicate that empty sets are not perceived as “zero,” but rather as “nothing,” when processed automatically. Hence, the perceptual dominance of empty sets seems to play a more important role under conditions of automatic processing, making it harder to abstract the numerical meaning of zero from empty sets.

Research on the Beauty-is-Good stereotype shows that unattractive people are perceived to have worse moral character than attractive individuals. Yet research has not explored what kinds of moral character judgments are particularly biased by attractiveness. In this work, we tested whether attractiveness particularly biases moral character judgments pertaining to the moral domain of purity, beyond a more general halo effect. Across four preregistered studies ( N = 1,778), we found that unattractive (vs. attractive) individuals were judged to be more likely to engage in purity violations compared with harm violations and that this was not due to differences in perceived moral wrongness, weirdness, or sociality between purity and harm violations. The findings shed light on how physical attractiveness influences moral character attributions, suggesting that physical attractiveness particularly biases character judgments pertaining to the moral domain of purity.

Attentional resources are limited, and resistance to interference plays a critical role during cognitive tasks and learning. Previous studies have shown that participants find it difficult to avoid being distracted by global visual information when processing local details. In this study, we investigated an innovative approach for enhancing the processing of local visual details by middle-school adolescents. Two groups completed a classic global/local visual search task in which a predefined target could appear at the global or local level, either with or without a frame. The results from the no-frame display group provided a direct replication in adolescents of previous findings in adults, with increasing number of interferent stimuli presented in the display adversely affecting detection of local targets. In addition, by varying the numbers of distractors inside and outside the frame, we showed that distractors only interfered with the processing of local information inside the frame, while the deleterious impact of increases in distracting information was prevented when the distractors were outside the frame. These findings suggest that when a frame delimits an attentional area, the influence of an increasing number of distractors present outside the frame is eliminated. We assume that application of a frame allows for efficient delimitation of attention deployment to a restricted topographical visual area in adolescents. These results evidence that processing of local details can be improved without modifying the structure of the stimuli, and provide promising clues for optimising attentional resources during time-absorbing visual searches. Applicable implications in the educational field are discussed.

This paper presents a morphing blade design for wind turbine application with flexibility in chord-wise bending while providing sufficient stiffness to carry the aerodynamic loads. The NACA64 profile is selected for the camber morphing blade demonstrator. A corrugation concept is chosen because it is relatively easy to manufacture and provides sufficient stiffness to resist deformation due to the aerodynamic loads (through the provision of effective stringers) while providing the required flexibility for chord-wise bending. A mechanical actuation mechanism is employed to achieve the desired morphing angle and increase the stiffness of the morphing airfoil section to resist aerodynamic loading. The design of a morphing blade demonstrator is presented together with the manufacturing process. Finally, an experimental study is conducted where the morphing angle is measured for increasing actuation load and compared with FE analysis showing good agreement between the experimental results and results from the finite element analysis in addition to achieving the desired morphing angle.

Despite a rising interest in artificial intelligence (AI) technology, research in services marketing has not evaluated its role in helping firms learn about customers’ needs and increasing the adaptability of service employees. Therefore, the authors develop a conceptual framework and investigate whether and to what extent providing AI assistance to service employees improves service outcomes. The randomized controlled trial in the context of tutoring services shows that helping service employees (tutors) adapt to students’ learning needs by providing AI-generated diagnoses significantly improves service outcomes measured by academic performance. However, the authors find that some tutors may not utilize AI assistance (i.e., AI aversion), and factors associated with unforeseen barriers to usage (i.e., technology overload) can moderate its impact on outcomes. Interestingly, tutors who significantly contribute to the firm's revenue relied heavily on AI assistance but unexpectedly benefited little from AI in improving service outcomes. Given the wide applicability of AI assistance in a variety of services marketing contexts, the authors suggest that firms should consider the potential difficulties employees face in using the technology rather than encourage them to use it as it is.

When searching for a target object (e.g., a friend at a party), we engage in a continuous “look- identify” cycle in which we use known features (e.g., hair color) to guide attention and eye gaze towards potential targets and then to decide if it is indeed the target. Theories of attention refer to the information about the target in memory as the “target” or “attentional” template and typically characterize it as a single, fixed, source of information. However, this notion is challenged by a recent debate over how the target template is adjusted in response to linearly separable distractors (e.g., all distractors are “yellower” than an orange target). While there is agreement that the target representation is shifted away from distractors, some have argued that the shift is “relational” (Becker, 2010) while others have argued it is “optimal” (Navalpakkam & Itti, 2007; Yu & Geng, 2019). Here, we propose a novel resolution to this debate based on evidence that the initial guidance of attention uses a coarse code based on “relational” information, but subsequent decisions use an “optimal” representation that maximizes target-to-distractor distinctiveness. We suggest that template information differs in precision when guiding sensory selection and when making identity decisions during visual search (Wolfe, 2020a, 2020b).

Relationship partners affect one another’s health outcomes through their health behaviors, yet how this occurs is not well understood. To fill this gap, we present the Dyadic Health Influence Model (DHIM). The DHIM identifies three routes through which a person (the agent) can impact the health beliefs and behavior of their partner (the target). An agent may (a) model health behaviors and shape the shared environment, (b) enact behaviors that promote their relationship, and/or (c) employ strategies to intentionally influence the target’s health behavior. A central premise of the DHIM is that agents act based on their beliefs about their partner’s health and their relationship. In turn, their actions have consequences not only for targets’ health behavior but also for their relationship. We review theoretical and empirical research that provides initial support for the routes and offer testable predictions at the intersection of health behavior change research and relationship science.

This paper investigates statistical reproducibility of the [Formula: see text]-test. We formulate reproducibility as a predictive inference problem and apply the nonparametric predictive inference method. Within our research framework, statistical reproducibility provides inference on the probability that the same test outcome would be reached, if the test were repeated under identical conditions. We present an nonparametric predictive inference algorithm to calculate the reproducibility of the [Formula: see text]-test and then use simulations to explore the reproducibility both under the null and alternative hypotheses. We then apply nonparametric predictive inference reproducibility to a real-life scenario of a preclinical experiment, which involves multiple pairwise comparisons of test groups, where different groups are given a different concentration of a drug. The aim of the experiment is to decide the concentration of the drug which is most effective. In both simulations and the application scenario, we study the relationship between reproducibility and two test statistics, the Cohen’s [Formula: see text] and the [Formula: see text]-value. We also compare the reproducibility of the [Formula: see text]-test with the reproducibility of the Wilcoxon Mann–Whitney test. Finally, we examine reproducibility for the final decision of choosing a particular dose in the multiple pairwise comparisons scenario. This paper presents advances on the topic of test reproducibility with relevance for tests used in pharmaceutical research.

Two papers have appeared evaluating interferences in glucose meters. These studies are method comparisons with the added information of the medication(s) taken by the subjects. This paper contrasts a traditional interference study with the method comparison protocols. Unlike the advice in CLSI EP7, a substance that interferes should be reported even if the level of interference is clinically acceptable. The evidence of no clinically important interference in the method comparison protocol is very weak, and there is no possibility to detect statistically significant interferences. I provide an example where vitamin C at a therapeutic level was within clinical error limits, but when the concentration was at levels used to treat cancer, there was bias well above clinically acceptable limits.

In recent years, wind energy conversion systems (WECSs) have been growing rapidly. Due to various advantages, a permanent magnet synchronous generator (PMSG) is an appealing solution among different types of wind generators. As wind power penetration level in the grid increases, wind power impacts the grid and vice versa. The most essential concerns in the system are voltage sag and swell, and grid code compliance, particularly for low voltage ride-through (LVRT) and high voltage ride-through (HVRT) capability, is a pressing necessity. This paper presents a parallel capacitor (PC) control strategy to enhance the LVRT and HVRT capability of PMSG. Furthermore, this study presents a method for the sizing of a PC system for the reduction of the overvoltage of the DC-link during voltage sags and swell. Fast Fourier transform analysis is used to determine the total harmonic distortion (THD) for the injected current into the grid. The obtained results illustrate the effectiveness of the proposed system in keeping the DC-link voltage below the limit, power quality improvement, and increasing the LVRT and HVRT capability. Models of wind turbine, PMSG, and PC control system are built using MATLAB/SIMULINK software.

In this article, we describe qregsel, a community-contributed command that implements a copula-based sample-selection correction for quantile regression recently proposed by Arellano and Bonhomme (2017, Econometrica 85: 1–28). The command allows the user to model selection in quantile regressions by using either a Gaussian or a one-dimensional Frank copula. We illustrate the use of qregsel with two examples. First, we apply the method to the fictional dataset used in the Stata Base Reference Manual for the heckman command. Second, we replicate part of the empirical application of the original article using data for the United Kingdom that cover the period 1978–2000 to compare wages of males and females at different quantiles.

While computational fluid dynamics (CFD) can solve a wide variety of fluid flow problems, accurate CFD simulations require significant computational resources and time. We propose a general method for super-resolution of low-fidelity flow simulations using deep learning. The approach is based on a conditional generative adversarial network (GAN) with inexpensive, low-fidelity solutions as inputs and high-fidelity simulations as outputs. The details, including the flexible structure, unique loss functions, and handling strategies, are thoroughly discussed, and the methodology is demonstrated using numerical simulations of incompressible flows. The distinction between low- and high-fidelity solutions is made in terms of discretization and physical modeling errors. Numerical experiments demonstrate that the approach is capable of accurately forecasting high-fidelity simulations.

Item does not contain fulltext Measurement and production of traditional prosthetic sockets are time-consuming, labor-intensive, and highly dependent on the personnel involved. An alternative way to make prostheses is using computer-aided design (CAD) and computer-aided manufacturing (CAM). Fused Filament Fabrication (FFF) may be an alternative to make low-cost prosthetic sockets. This study investigates the tensile properties of potential printing materials suitable for FFF according to ISO527 (Standard Test Method for Tensile Properties of Plastics). To ensure that FFF-printed sockets are safe for patient usage, the structural integrity of the 3D-printed prosthesis will be investigated according to ISO10328 (International Standard Structural Testing of Lower Limb Prostheses). Tough PLA was the most suitable print material according to ISO 527 testing. The Tough PLA printed socket completed 2.27 million cycles and a static test target value of 4025 N. Future research remains necessary to continue testing new potential materials, improve print settings, and improve the socket design for the production of FFF-printed transtibial prosthetic sockets. FFF using Tough PLA can be used to create transtibial prostheses that almost comply with the International Standard for Structural Testing of Lower Limb Prostheses.

Wearable electrocardiogram (ECG) systems should be comfortable, non-stigmatizing, and capable of producing high-quality data. Many different designs of wearable textile ECG systems have recently emerged. Some of them are not considered to be smart garments, whereas most of the others present only the electronic side of the system. Our research work introduces a comprehensive study for an improved single-lead ECG smart shirt to identify automatically premature ventricular contraction as a common form of arrhythmia. For artifact-free results, Marvelous Designer is implemented to design our optimized relaxed slim fit shirt. In addition, a weft-knitted fabric of 80% nylon–20% spandex is used to manufacture the outer part of the shirt. Moreover, lightweight and small size electronic components are integrated to the outer part via low-resistance dry textile electrodes and 100% cotton fabric as an inner layer for easy transmission of weak ECG signals.

Background To manage the public health risk posed by COVID-19 and assess the impact of interventions, policymakers must be able to closely monitor the epidemic's trajectory. Methods Here we present a simple methodology based on basic surveillance metrics for monitoring the spread of COVID-19 and its burden on health services in Scotland. Results We examine how the dynamics of the epidemic have changed over time and assess the similarities and differences between metrics. Discussion We illustrate how our method has been used throughout the epidemic in Scotland, explore potential biases and conclude that our method has proven to be an effective tool for monitoring the epidemic's trajectory.

In this article, the problem of adaptive finite-time control is studied for a category of nonstrict-feedback nonlinear time-delay systems with input saturation and full state constraints. The fuzzy logic systems are applied to model the unknown nonlinear terms in the systems. Then, a novel tan-type barrier Lyapunov function is adopted to overcome the problem of full state constraints. By utilizing the finite-time control theory and the backstepping technique, a finite-time fuzzy adaptive controller is designed. The controller can guarantee that the tracking error is adjusted around zero with a small neighborhood in a finite time and all the signals in the closed-loop system are bounded. Finally, two simulation examples are included to verify the validity and feasibility of the control scheme.

Non-linearities and actuator faults often exist in practical systems which may degrade system performance or even lead to catastrophic accidents. In this article, a fault-tolerant compensation control strategy is proposed for a class of non-linear systems with actuator faults in simultaneous multiplicative and additive forms. First, radial basis function neural network is employed to approximate the system non-linearity. The approximation is achieved by only one adaptive parameter, which simplifies the computation burden. Then, by means of the backstepping technique, an adaptive neural controller is developed to cope with the adverse effects brought by the system non-linearity and actuator faults in multiplicative and additive forms. Meanwhile, the proposed control design scheme can guarantee that the considered closed-loop system is stable. The novelty of the article lies in that the system non-linearity, the additive actuator faults, and the multiplicative actuator faults that often exist in practical engineering are catered for simultaneously. Furthermore, compared with some existing works, the approximation of the system non-linearity is achieved by only one adaptive parameter for the purpose of reducing the computation burden. Therefore, its applicability is more general. Finally, a numerical simulation and a comparative simulation are carried out to show the effectiveness of the developed controller.

Assembly quality of aero-engine casing plays a key role in the whole aero-engine, since it is directly related to the final function and dynamic performance. However, during the design phase, the tolerance analysis is usually conducted independently without any consideration of the effect on the dynamic characteristic. This paper aims to integrate manufacturing precision with dynamic performance instability together. First, the 3-D tolerance model of the aero-engine casing is constructed based on the Jacobian-Torsor theory. The target deviation from the tolerance model is defined as the input variable into the vibratory governing equation. Then, the effect of 3-D assembly deviation on the natural frequency is studied. The corresponding frequency distributions for different vibration modes are illustrated. Finally, the mapping relationship between assembly tolerance and fluctuation ratio of natural frequency is established through the 3-D fitted surface. Under the given constraint of performance stability, the optimized tolerance zone is obtained. This work provides a significant guidance for performance improvement and tolerance design in the aero-engine casing assembly.

This paper investigates dynamic event-triggered control for systems subject to asymmetric actuator saturation. The asymmetric saturation could severely degrade the performance of systems, which always exists in control engineering. A dynamic event-triggered scheme considering the character of asymmetric saturation is proposed to reduce triggered number of events, under the premise that the closed-loop system subject to asymmetric actuator saturation is asymptotically stabilized. Sufficient conditions are derived to stabilize the system and the minimum inter-event time interval is calculated to exclude Zeno behaviour. An optimization problem is solved to estimate the contractive invariant set as the stable region of the system. A numerical example is given to illustrate the theoretical results.

The reliability of door sealing plays an important role in maintaining the quality of vehicle noise, vibration, and harshness (NVH). To improve the reliability of door sealing, a novel sealing process has been proposed. The factors influencing the door sealing quality have been investigated. The relationships between the peeling force, peeling speed, and peeling time have been calculated using the five-variable rheological theory. The maximum stress versus time curve and the distribution of stress and strain at the moment of debonding have been obtained by nonlinear solid cohesive coupling dynamics simulation. The rolling process of the door sealing strip has been developed, and automatic rolling equipment for door sealing has been designed and manufactured. The factors influencing the peeling force have been determined by a peeling test of the door sealing strip. The results show that the peeling force increases with an increase in the peeling speed and time. The C-shaped structure at the front end of the base material during the stripping process makes the peeling angle sharp. In addition, the uneven stripping speed causes the stripping line to become irregular. The peeling force increases with the increase in rolling pressure and failure time. The optimum rolling pressure was 70 N/m2 and the optimum aging time was 24 h.

Objective: This study explores whether “future” lighting systems that provide greater control and opportunity for circadian synchronization are acceptable to participants in the role of patients. Background: Tunable, dimmable light emitting diode systems provide multiple potential benefits for healthcare. They can provide significant energy savings, support circadian synchronization by varying the spectrum and intensity of light over the course of the day, address nighttime navigation needs, and provide user-friendly control. There is an emerging understanding of the important visual and nonvisual effects of light; however, important questions remain about the experience and acceptability of this “future” lighting if we are to adopt it broadly. Methods: Volunteer participants (34) performed a series of tasks typical of patients, such as reading or watching a video, in a full-scale simulated inpatient room. Each participant conducted these tasks under 12 lighting conditions in a counterbalanced order that included varying illuminance levels, correlated color temperatures (CCTs), and in a few conditions, saturated colors. The participants rated each lighting condition on comfort, intensity, appropriateness, and naturalness. Results and Conclusions: The participants found that conditions with CCTs of 5,000 K and higher were significantly less comfortable and less natural than conditions with lower CCTs. Conditions with lighting distributed in multiple zones in the patient room were viewed more favorably than a traditional overbed configuration. The participants in this simulated patient study reacted negatively to colored lighting on the footwall of the room but found a mixture of warmer and cooler luminaire CCTs acceptable.

This paper studies the leader-following state consensus problem for heterogeneous linear multi-agent systems under fixed directed communication topologies. First, we propose a consensus protocol consisting of four parts for high-order multi-agent systems, in which different agents are allowed to have different gain matrices so as to increase the degree of design freedom. Then, we adopt a state linear transformation, which is constructed based on the incidence matrix of a directed spanning tree of the communication topology, to equivalently transform the state consensus problem into a partial variable stability problem. Meanwhile, the results of the partial variable stability theory are used to derive a sufficient and necessary consensus criterion, expressed as the Hurwitz stability of a real matrix. Then, this criterion is further expressed as a bilinear matrix inequality condition, and, based on this condition, an iterative algorithm is proposed to find the gain matrices of the protocol. Finally, numerical examples are provided to verify the effectiveness of the proposed protocol design method.

Reasonable and accurate forecasts can be used by the highway maintenance management department to determine the best maintenance timing and strategy, which can keep the highway performing well and maximize its social and economic benefits. A Grey–Markov combination model is established in this paper to predict highway pavement performance accurately based on the Grey GM (1, 1) model (a single-variable Grey prediction model with a first-order difference equation) and revised by the Markov model. The advantages of the short-term forecast Grey model and the probabilistic Markov model, which considers the fate of pavement performance prediction, are comprehensively applied to the combined forecasting model. The Grey GM (1, 1), Grey–Markov model and Liu-Yao model are adopted to predict the pavement condition index (PCI) based on the actual PCI values measured in Shanxi, Chongqing, and Shaoguan. The average relative errors of the above three models’ predicted values in Shanxi are 0.73%, 1.18%, and 0.67%, respectively, from 2012 to 2014. Thus, the prediction errors of the three models are relatively close. The average relative errors of the prediction values predicted by the three models are 3.89%, 0.67%, and 0.50%, respectively, from 2015 to 2019. The latter two errors are more minor than the Grey GM (1, 1) model. Two other regions have similar conclusions. The results show that the prediction accuracy of the combination Grey–Markov prediction model established in this paper is feasible to predict asphalt pavement performance in China.

The liberalization of markets for goods and assets that took place beginning in the 1980s, alongside a strengthening of the resulting transnationalization of capital, did not alter the basic hierarchical organization of the global capitalist system. This new dependency, here called “dependency 4.0,” is based on the rent seeking that marks the contemporary wealth accumulation process and ongoing technological progress. Brazil’s incorporation into the international division of labor is emblematic of this type of subordination. A liberalização dos mercados de bens e ativos que teve lugar a partir dos anos 1980 e o fortalecimento da transnacionalização do capital que resultou daí não alteraram o pressuposto fundamental da prevalência de uma organização hierárquica no sistema capitalista mundial. Um novo tipo de dependência a relacionar países centrais e periféricos, dependência 4.0, estaria assentada no rentismo que marca hoje o processo de acumulação e na natureza do progresso tecnológico em curso. O caso do Brasil—a história de sua inserção na divisão internacional do trabalho—é emblemática desse novo tipo de subordinação.

Regular inspection of tunnel surfaces is an important practice from both a safety and tunnel asset management perspective. However, inspection for cracking and spalling is still predominantly a manual task, which is time consuming, subjective, and exposes on-foot staff to risk. This presentation will explore the use of 3D laser scanning technology and artificial intelligence to automate the inspection process with a Canadian metro case study being presented.

In conventional Class II treatment, distalisation of the maxillary arch usually requires multiple phases of tooth movement during which anchorage loss can occur. In order to solve this issue, a rational and simple technique has been developed. Instead of using intraoral distalisers along with palatal mini-implants, Ni-Ti superelastic loops are used to obtain molar distalisation while buccal interradicular miniscrews (BIM), preferably placed between the roots of upper premolars, supply the necessary anchorage. Once the distalisation of molars and second premolars is performed, miniscrews are placed between the roots of first molars and second premolars after removing the previous miniscrews. Elastic chains or tie-backs, which go from the new miniscrews to the hooks of a 0.019×0.025-inch stainless-steel archwire, produce the retraction of incisors, canines and first premolars with optimal control of anchorage. Clinical cases are shown to illustrate the technique.

The spacecraft relative motion trajectory planning is one of the enabling techniques for autonomous proximity operations, especially in the increasingly complicated mission environments. Most traditional trajectory planning methods focus on improving the performance criteria in the deterministic conditions, whereas various uncertain elements in practice would significantly degrade the trajectory performance. Considering the uncertainties underlying the collision avoidance constraints, this paper suggests a model predictive control based online trajectory planning framework in which the obstacle information in higher-precision would be consistently updated by the onboard sensor. To improve the computational efficiency of the online planning framework, the rotating hyperplane (RH) technique is utilized to transform the nonlinear ellipsoidal keep-out zone constraints into convex formulations. And the concept of rotation window is introduced to eliminate the unexpected mismatch between the spacecraft motion and hyperplane rotation in the conventional RH method, which in sequence improves the RH method’s capability for multiple obstacle avoidance problem. Moreover, a three-dimensional (3-D) extension strategy is proposed to simplify the computation procedure when applying the RH method for a 3-D collision avoidance problem. Numerical simulations are carried out to validate the performance of the proposed online trajectory planning framework in addressing the uncertain collision avoidance constraints.

The partial potential impact fraction describes the proportion of disease cases that can be prevented if the distribution of modifiable continuous exposures is shifted in a population, while other risk factors are not modified. It is a useful quantity for evaluating the burden of disease in epidemiologic and public health studies. When exposures are measured with error, the partial potential impact fraction estimates may be biased, which necessitates methods to correct for the exposure measurement error. Motivated by the health professionals follow-up study, we develop a Bayesian approach to adjust for exposure measurement error when estimating the partial potential impact fraction under the main study/internal validation study design. We adopt the reclassification approach that leverages the strength of the main study/internal validation study design and clarifies transportability assumptions for valid inference. We assess the finite-sample performance of both the point and credible interval estimators via extensive simulations and apply the proposed approach in the health professionals follow-up study to estimate the partial potential impact fraction for colorectal cancer incidence under interventions exploring shifting the distributions of red meat, alcohol, and/or folate intake.

Minimization is among the most common methods for controlling baseline covariate imbalance at the randomization phase of clinical trials. Previous studies have found that minimization does not preserve allocation randomness as well as other methods, such as minimal sufficient balance, making it more vulnerable to allocation predictability and selection bias. Additionally, minimization has been shown in simulation studies to inadequately control serious covariate imbalances when modest biased coin probabilities (≤0.65) are used. This current study extends the investigation of randomization methods to the analysis phase, comparing the impact of treatment allocation methods on power and bias in estimating treatment effects on a binary outcome using logistic regression. Power and bias in the estimation of treatment effect was found to be comparable across complete randomization, minimization, and minimal sufficient balance in unadjusted analyses. Further, minimal sufficient balance was found to have the most modest impact on power and the least bias in covariate-adjusted analyses. The minimal sufficient balance method is recommended for use in clinical trials as an alternative to minimization when covariate-adaptive subject randomization takes place.

Under the International Safety Management (ISM) Code, shipping companies are required to build a Safety Management System and develop procedures that will regulate the incident analysis process. By these procedures, the shipping companies develop an incident analysis form and use it on all ships within their fleets. Although these forms serve a common purpose, their structure shows some differences between companies. Thus, time may be wasted when transferring the data or insufficient data may be obtained. This paper examines incident analysis forms used for tanker ships whose hazardous cargos make them of particular importance. The features of incident analysis forms used on tankers of a shipping company were specified, and the fuzzy analytic hierarchy process (FAHP) method presented their importance levels. Expert opinion was collected by a questionnaire. The results revealed that the highest priorities belong to the sections allocated for details about the ship and environmental conditions and root cause analysis technique. The least essential sub-criteria were related to the structure and variety of the forms. Based on the study results, which captured the current practice and the sector’s expectations, tanker shipping companies’ existing incident analysis forms could be revised. The revised documents would help to achieve complete information and prevent loss of time in information exchange between companies. Bringing a common approach to the analysis of incidents in tanker shipping would increase safety in the shipping industry.

Scholars have used both quantitative and qualitative approaches to empirically study nonprofit roles. Mission statements and program descriptions often reflect such roles, however, until recently collecting and classifying a large sample has been labor-intensive. This research note uses data on United Ways that e-filed their 990 forms and supervised machine learning to illustrate an approach for classifying a large set of mission descriptions by roles. Temporal and geographic variation in roles detected in mission statements suggests that such an approach may be fruitful in future research.

The nonprofit starvation cycle raises a fundamental question about the impact of overhead spending on nonprofit program outcomes and organizational effectiveness. We test this relationship using data on the U.S. nonprofit arts and cultural subsector from 2008 to 2018. Contrary to the conventional wisdom that lower overhead is better, the results of different model specifications consistently show an optimal level of overhead captured by an inverted U-shaped relationship between the overhead cost ratio and program outcomes. Increased spending on overhead can improve program outcomes up to the optimal point. Beyond this point, further spending on overhead can yield negative returns. The study contributes to correcting the unrealistic expectations surrounding overhead costs and argues for investing more in building sustainable nonprofit capacity. We discuss the implications of our findings for nonprofit charity monitors, donors, scholars, and other stakeholders concerned about nonprofit accountability and effectiveness.

Transit signal priority (TSP) and freight signal priority (FSP) allow transportation agencies to prioritize signal service allocations considering the priority of vehicles and, potentially, decrease the impact signal control has on them. However, there have been no studies to develop guidelines for implementing signal control considering both TSP and FSP. This paper reports on a study conducted to provide such guidelines that employed a literature review, a simulation study, and a decision tree algorithm based on the simulation results. The guideline developed provides recommendations in accordance with the signal timing slack time, the proportion of major to minor street hourly volume, hourly truck volume per lane for the major street, hourly truck volume per lane for the minor street, the proportion of major to minor street hourly truck volume, the proportion of major to minor street hourly bus volume, the volume-to-capacity ratio for the major street, and the volume-to-capacity ratio for the minor street. The guideline developed was validated by implementing it for a case study facility. The validation result showed that the guideline works correctly for both high and low traffic demand.

Train dwell time is a complicated component and depends on many factors. One of the dominant factors is passenger volume. This study used actual train movement data and passenger demand data from London Underground, UK, to estimate the number of passengers and train dwell times at each station, and then evaluated train dwell times from a different perspective. Considering the various characteristics of stations, it is complicated to evaluate dwell time. Therefore, data envelopment analysis (DEA) was introduced to evaluate the dwell time at each station in relation to passenger volume at that station. The study investigated whether the dwell time spent at stations is efficient when considering the number of passengers that the stations can serve. The results showed that, in low-passenger-volume stations, the dwell time efficiency score is low and increases relative to the increase in passenger volume. For high-passenger-volume stations, interactions between passengers are more relevant and have a strong influence on dwell time. Passenger movement direction is a key factor to classify stations. This research proposes that stations should be classified according to their characteristics, and points out the challenge at any station with the same characteristics as Victoria station which has high passenger volume with bi-directional flow, and where trains arriving are crowded. This characteristic would result in high interactions between passengers, thus making a long dwell time. The station has to handle high passenger volume and also has to keep the dwell time within the threshold.

Recognition of prior learning features at the top of the policy agenda in many countries because recognition of prior learning (RPL) has proven effective in some parts of the world; and there is much policy learning taking place. Therefore, many countries want to gain the advantages associated with RPL. It brings benefits to individual end-users and countries as a whole. Whether RPL should be identically implemented everywhere is more questionable. This paper proposes a reflexion based on experience: It looks at what RPL is about, at what it took to implement the validation des acquis de l’expérience (VAE) system in France and its potential transferability. While it is important to learn from one another, transporting a system from one national context to another is unlikely to work because the necessary social and cultural context is never transferred along with the technical apparatus. This is not new, but this paper emphasises the need for appropriate adaption whenever systems are borrowed.

Virtual reality (VR) has a high potential to facilitate education. However, the design of many VR learning applications was criticized for lacking the guidance of explicit and appropriate learning theories. To advance the use of VR in effective instruction, this study proposed a model that extended the cognitive-affective theory of learning with media (CATLM) into a VR learning context and evaluated this model using a structural equation modeling (SEM) approach. Undergraduate students ( n = 77) learned about the solar system in a VR environment over three sessions. Overall, the results supported the core principles and assumptions of CATLM in a VR context (CATLM-VR). In addition, the CATLM-VR model illustrated how immersive VR may impact learning. Specifically, immersion had an overall positive impact on user experience and motivation. However, the impact of immersion on cognitive load was uncertain, and that uncertainty made the final learning outcomes less predictable. Enhancing students’ motivation and cognitive engagement may more directly increase learning achievement than increasing the level of immersion and may be more universally applicable in VR instruction.