Distance Preferences Research Articles

A two-stage preference driven multi-objective evolutionary algorithm for workflow scheduling in the Cloud

The workflow scheduling problem considered difficult in the Cloud becomes even more challenging when multiple scheduling criteria are used for optimization. It is much harder to maximize the conflicting interests of users, service providers and the environment, and to obtain “triple win” solutions. This paper presents a two-stage preference driven multi-objective evolutionary algorithm (tsp-MOEA) to address the workflow scheduling problem involving multiple roles. We formulate it as a multi-objective optimization problem with preferences for the first time by considering the special preferences of different participants in decision-making. In particular, a preference distance strategy is introduced in the first stage to speed up the arrival of solutions around the decision makers’ region of interest (ROI); and a special preference region ranking strategy is also designed to focus on searching in the ROI in the second stage. Moreover, the two-stage transition is completed adaptively, which can more fully find a variety of elite solutions. In addition, an elite learning strategy is adopted to guide the global evolution of the population to further enhance the quality of the solution. Extensive experiments demonstrate that the proposed tsp-MOEA approach outperforms recent state-of-the-art algorithms, and can provide significantly better solutions for scientific workflow scheduling in the Cloud.

Widely targeted metabolomics unveils baicalin-induced hippocampal metabolic alternations in a rat model of chronic unpredictable mild stress

Baicalin has various neuroprotective effects in models of nervous system disease. Our study has shown baicalin could alleviate depressive-like behaviors in a neuroendocrine mouse model. But the systematic metabolic characteristic and particular targets of baicalin in regulating depressive behaviors have never been investigated. Therefore, this study aims to reveal the hippocampal metabolic profiling of chronic unpredictable mild stress (CUMS) induced depressive rats and the potential metabolic variations after baicalin treatment. We first used the sucrose preference test and open field test to access the antidepressant effects of baicalin. Then, metabolites of the hippocampus after baicalin therapy were monitored by widely-targeted metabolomics based on ultra-performance liquid chromatography-tandem mass spectrometry technology. Finally, the potential mechanism associated with neurogenesis obtained from metabolomics was verified by immunohistochemistry. The results showed that baicalin(40,80 mg/kg) could significantly alleviate depressive behaviors induced by CUMS as demonstrated by an increase in sucrose preference and movement distance and stand-up times in open field test. In the metabolomic analysis, a total of 733 metabolites were identified after baicalin treatment including 15 differential metabolites such as organic acid and its derivatives, heterocyclic compounds, fatty acid, bile acids, amino acid and its metabolites, and so on. Enrichment for differential metabolites showed that the differential metabolites might be involved in the process of folate and cofactor biosynthesis, cholesterol metabolism, primary bile acid biosynthesis, tyrosine metabolism and dopaminergic synapse. Moreover, immunohistochemical analysis confirmed baicalin could facilitate hippocampal neurogenesis of depressive rats in CUMS model. These results suggested baicalin might exert antidepressant effects through regulating the differential metabolites which might play a crucial role in inhibiting oxidative stress and improving neurogenesis. Our findings wish to discover the potential mechanism of baicalin on depression from the metabolomics perspective and promote its clinical application.

The effects of race and class on community-level stigmatization of opioid use and policy preferences.

With opioid use and overdose rates continuing to plague minority communities in the U.S., we explored whether a geographic community's racial composition and social class affect how opioid use in the community is stigmatized and what policy preferences arise in response. We use case vignettes in a randomized, between-subjects study (N=1478) with a nation-wide survey. The vignettes describe a community where opioids are harmfully used, varying whether the community was (1) wealthy or poor, (2) predominantly Black or White and (3) facing prevalent use of painkillers or heroin. We tested how these variables affect public stigmatization of opioid use (measured with ratings of responsibility, dangerousness, sympathy, concern, anger, and disappointment) preferred levels of social distance from communities with opioid use (measured with responses to questions about living, working, and interacting in the community), and policy preferences for responding to opioid use (measured with levels of support for providing a safe-consumption site in the community, treating drug use in the community punitively, treating drug use in the community as an illness, and funding drug treatment in the community through income redistribution). Compared to wealthy communities with opioid use, poor communities with opioid use were less stigmatized in terms of responsibility, sympathy, concern, anger, and disappointment; they were also met with less support for punitiveness, more support for treating drug use as an illness, and preferences for greater social distance. Compared to White communities with opioid use, Black communities with opioid use were less stigmatized in terms of responsibility, and they were met with stronger preferences to not live and work there and with reduced support for using income redistribution to provide drug treatment for people in the community. Poor-Black communities with opioid use were also perceived to be more dangerous than both poor-White and wealthy-Black communities with opioid use. These results point to class- and race-based territorial stigma affecting how communities with opioid use are judged and whether policies for providing communities with treatment are supported.

Spatial decision-making in acorn dispersal by Eurasian jays around the forest edge: Insights into oak forest regeneration mechanisms

Seed dispersal is key to forest regeneration and often depends on the behavior of seed scatter-hoarding animals. However, our understanding of the decision-making of scatter-hoarders and how vegetation structure at different spatial scales affects dispersal patterns remains limited. We studied seed dispersal at the forest edge across multiple spatial scales (habitat, sub-habitat, microsites) between and within habitats with distinct vegetation structure. Our model system is a mixed-oak forest dominated by holm oak (Quercus ilex) and an adjacent shrubland dominated by the shrub Retama sphaerocarpa. For three years, we tracked the movement of acorns by a keystone scatter-hoarder, the Eurasian jay (Garrulus glandarius). At the coarse scale, jays did not show a preference for caching acorns either in the forest or in the shrubland when the source tree was within the forest. However, when the source tree was on the forest edge or was an isolated oak tree in the shrubland at < 200 m from the forest, jays showed a preference for caching in the forest. Conversely, when isolated oak trees were > 200 m from the forest, all acorns were dispersed in the shrubland. Dispersal distances were shorter within the forest than within the shrubland. At an intermediate scale, within the shrubland jays preferred to cache acorns under large retamas while avoiding gaps. In the forest, jays also avoided gaps and preferred certain woody species and vertical vegetation structures, although these preferences varied across feeders. At the finest scale, jays selected stones as cache microsites. Thus, the selection of acorn caching sites followed a hierarchical and selective top-down process across different spatial scales. The position of the source trees in the landscape determined habitat preference and dispersal distance. We detected different preferences in response to similar vegetation structure and composition, indicating that the decision-making process is flexible at an intermediate scale that generates diverse spatial patterns of dispersal. This study sheds light on the intricate decision-making process of seed caching by scatter-hoarders, and the consequences for the spatial patterns of seed dispersal as well as the expansion and regeneration of oak forests.

Schwierige Texte in Kritik und Vermittlung

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Geometric determinants of the postrhinal egocentric spatial map

Animals use the geometry of their local environments to orient themselves during navigation. Single neurons in the rat postrhinal cortex (POR) appear to encode environmental geometry in an egocentric (self-centered) reference frame, such that they fire in response to the egocentric bearing and/or distance from the environment center or boundaries. One major issue is whether these neurons truly encode high-level global parameters, such as the bearing/distance of the environment centroid, or whether they are simply responsive to the bearings and distances of nearby walls. We recorded from POR neurons as rats foraged in environments with different geometric layouts and modeled their responses based on either global geometry (centroid) or local boundary encoding. POR neurons largely split into either centroid-encoding or local-boundary-encoding cells, with each group lying at one end of a continuum. We also found that distance-tuned cells tend to scale their linear tuning slopes in a very small environment, such that they lie somewhere between absolute and relative distance encoding. In addition, POR cells largely maintain their bearing preferences, but not their distance preferences, when exposed to different boundary types (opaque, transparent, drop edge), suggesting different driving forces behind the bearing and distance signals. Overall, the egocentric spatial correlates encoded by POR neurons comprise a largely robust and comprehensive representation of environmental geometry.

Unveiling the mystery: Does the traffic control policy in Beijing trigger a rebound effect in household electric vehicles?

Despite the strong support for electric vehicles (EVs) from the Chinese government, their environmental benefits are not well known, especially as they could be offset by potential rebound effects. This paper examines whether EVs could trigger rebound effects under special traffic control policies in Beijing and identifie potential driving factors of rebound effects at the residential level. The data was collected through a survey of 368 Beijing residents who own private vehicles. It was found that EVs have not caused a significant rebound effect at present. However, the potential rebound risk of promoting EVs cannot be ignored. On the one hand, EVs may increase travel demand among certain groups, especially for the single-EV household, whereas the considerable increase in travel mileage may cause higher energy consumption. On the other hand, purchasing additional EVs as a fallback option would result in resource waste. The scenario analysis found that if an EV is used to replace an internal combustion engine vehicle (ICEV), the rebound effect will highly likely occur (72.17 % ± 62.01 %) and the original energy-saving goal cannot be achieved; while if households with an ICEV are allowed to buy an additional EV, there is a risk of a rebound or even backfire effect (the rebound effect is 6.64 % ± 24.43 %). The driving factors of rebound effects included family size, commuting distance, and individual preference for pro-environmental elements. In particular, environmental awareness could easily lead to a counterproductive result, resulting in rebound effects.

Optimal scale selection based on multi-scale single-valued neutrosophic decision-theoretic rough set with cost-sensitivity

The selection of optimal scale has always been the essential problem of multi-scale system. However, most of the current studies only consider the consistency of the system, and ignore the cost information. Therefore, based on the ranking methods of single-valued neutrosophic number, this paper constructs a multi-scale dominant single-valued neutrosophic system. Furthermore, the test cost is defined according to the relative distance preference degree, and the risk cost of Bayesian theory introduced by decision-theoretic rough set is used as the decision cost of accepting, delaying and rejecting decisions. Therefore, we establish a multi-scale dominant single-valued neutrosophic decision-theoretic rough set based on test and decision cost. In addition, we also propose a scale updating algorithm to find out all consistent scales. Afterwards, we further raise an optimal scale selection algorithm based on the minimum total cost criterion. Finally, the algorithm is verified and analyzed by UCI data sets. The algorithm and model proposed in this paper further expand the application of single-valued neutrosophic rough set in multi-scale system, and provide a reference for subsequent research in this field.

Velocity-based model for pedestrian dynamics considering direction preferences

To reflect the direction preferences of the individual moving process, a velocity-based model is developed based on the Collision Free Speed (CFS) model. Our model used the local optimal direction selection algorithm based on a greedy algorithm that considers individual direction preferences for greater headway distance and dynamic willingness to turn during movement. The headway distance is calculated based on the principle of conflict avoidance, while gaussian distribution is used to fit individual willingness to turn and the optimal velocity (OV) function is used to determine the magnitude of the velocity. The concept of competition coefficient is proposed to quantitatively describe individual variability of direction preferences. The lane formation phenomena are reproduced in the bidirectional flow. In high-competition bottleneck, flow-width relation and pedestrian concentration fit better in the experimental data of our new model compared with the original CFS model. Furthermore, we analyze the competition coefficient and reproduce the typical phenomenon of fast-is-slow.

ARASsort: A new sorting based multiple attribute decision‐making algorithm

AbstractMultiple attribute decision‐making (MADM) tools can effectively support the decision analysts in selecting the best alternative among many, ranking the alternatives in decreasing or increasing order of preference, or allocating the alternatives into pre‐defined ordered classes/categories. Even though the literature provides the analyst with precious sorting‐based MADM tools such as PROMSORT, UTADIS, AHPSort, TOPSISsort, and so forth, the majority of the methods can be found complex and hard to be understood by the researchers and practitioners who are not familiar with the mathematical notions and computations of MADM (distance calculation, threshold and preference function determination, and so on). To provide a simpler but powerful MADM tool aiming at sorting the alternatives into classes, this study proposes a sorting‐based additive ratio assessment algorithm which is called ARASsort. For limiting (interval‐based) and central (reference‐based) profiles describing the categories, we have developed two algorithms: ARASsort‐lp and ARASsort‐cp, respectively. Their applicability was shown in two examples: green supplier evaluation and economic freedom evaluation of countries. The validity of algorithms was presented by demonstrating the class assignment similarities between the results obtained by ARASsort, VIKORsort, and TOPSISsort. The findings show that ARASsort works well because it shows a higher level of class assignment similarities with the other methods.

Relacja pomiędzy wymiarami kultury, a ich znaczeniem dla wyboru stylu w konflikcie.

This paper proposes a broad perspective for studying the influence of cultural dimensions and their influence on the process of choosing conflict management styles Definition of both culture and that of conflict are presented. Analytical review of research on the conflict styles and their relation with dimensions of culture are presented in this article. Within the paper following cultural dimensions are considered in the context of conflict management styles: individualism and collectivism, high and low power distance as well as high and low uncertainty avoidance. All models of cultural dimensions are considered within the cultural dimensions framework of Hofstede (1981). In case of individualism there is preference for an avoiding style of conflict management and in collectivist cultures typical choice is that of compromising and obliging conflict style. In case of high power distance avoiding conflict style is likely to be chosen by members of High Power cultures. Few studies cover the problem of power distance and conflict style preference, however, we may assume that the individual will chose conflict style that allows him to maintain either low or high power distance typical for his own culture. Finally the paper shows connection of low uncertainty avoidance and the choice of collaborative and compromising conflict styles. The implications of conflict resolution styles across cultures for the future research and implementation in organizations as well as in international relations are discussed.

Adaptation of a robotic dialog system for medication reminder in elderly care

Social robots can assist older adults in their daily life. Verbal conversation is a natural and convenient way for older adults to interact with social robots. However, most of the existing conversation-based robot services, such as medication reminders, are rule-based systems. These systems require many hand-crafted rules and a significant amount of expert knowledge, therefore they cannot adapt to older adults’ characteristics and dialog history. There are many reinforcement learning (RL) based methods for task-oriented dialogues, but they mainly focus on completing the tasks through text-based conversations. Those methods cannot be directly used for elderly care applications involving human–robot interactions (HRI). Considering the above shortcomings, we proposed a dialog system adaptation method (DSAM) for social robots. The DSAM is based on reinforcement learning which considers the characteristics of older adults, the dialog history and user preference to adapt the dialog policy and improve the dialog module. We implemented DSAM in our custom-made ASCCBot social robot. To evaluate DSAM, we firstly tested the dialog agent which was trained by a user simulator with different settings. The results show that the obtained agent achieves a good result with the desired dialog flow compared to the baseline agent. Based on the obtained dialog policy, the adaptation process is evaluated. The results show that with a good success rate, the number of dialog turns is decreased and the NLU module performance is improved by the adaptation process, which proves the effectiveness of DSAM. We also tested DSAM with human subjects. The results show that the average adaptation success rate is 94.7% and the preference distance reaches 0 after 6 rounds of adaptation while creating reminders successfully with a limited amount of user feedback. • A robotic dialog adaptation system for medication reminder in elderly care. • Reinforcement learning based methods for task-oriented dialogues. • Characteristics of older adults, dialog history and user preference considered in adaptation. • User simulator and user modeling combined to adapt the dialog agent.

Route Planning for Autonomous Driving Based on Traffic Information via Multi-Objective Optimization

Route planning for autonomous driving is a global road planning method based on a given starting point and target point combined with current traffic flow information. The optimal global route can reduce traffic jams and improve the safety and economy of autonomous vehicles. The current optimization method of route planning for autonomous driving only considers a single objective or a chain of single objectives, which cannot meet the requirements of drivers. In this paper, we devise a general framework for the route planning method based on multi-objective optimization. Different from planning optimization based on not only traffic information, the framework considers travel time, distance, cost and personal preference, but focuses more on vehicle status and driver requirements. We use an improved depth-first search algorithm to find the optimal route. The evaluations of our method on real-world traffic data indicate the feasibility and applicability of the framework. Our study contributes to a better understanding of route planning and reveals that exploitation of personal preference can more flexibly configure the corresponding route according to the driver’s requirements.

Validating the feeling thermometer as a measure of partisan affect in multi-party systems

Affective polarization is increasingly studied comparatively, and virtually all studies that do so operationalize it using the feeling thermometer. Yet this survey instrument has not yet been validated in a multi-party context. We argue that for the thermometer to be a valid measure of partisan affect also in multi-party systems, it needs to capture sentiment towards partisans and to correlate with other measures of affective polarization. Using panel study fielded throughout Israel’s elections in 2019–2020, we show that both requirements hold. We use text analysis to substantiate that thermometer scores reflect sentiment towards party supporters, and demonstrate that they go hand-in-hand with preferences for social distance and discrimination in economic games. We discuss implications for the study of affective polarization.

The Perception and Effect of Aggressive Dog Breeds on Human Social Interactions

Abstract During social interactions, one must be able to interpret social signals that convey information and respond appropriately to interactants. Several factors have been shown to influence decisions of interpersonal distance preferences; however, research has yet to determine the associative effects companion animals may have on social information processing during intended interactions. Previous research ( Briones et al., 2022 ) distinguished between various breeds of dogs perceived to be higher or lower in aggression, and further showed that people had a greater tendency to interact with dogs perceived to be lower in aggression. The current studies (Study 1 n = 174; Study 2 n = 229) were an extension of that research and used computer-simulated avatar interactions to assess the influence of dog breeds and other factors on actual proxemic behavior during goal-related social interactions. Results showed that during social interactions, (a) interactants associated with dog breeds perceived higher in aggressiveness elicited larger interpersonal spaces; (b) closer interpersonal distances are maintained with friends than strangers; (c) greater affinity for dogs, in general, was related to closer proxemic distances; and (d) dog handlers taking the perspective of the approaching interactant results in greater interpersonal distances.

Protective Effect of Ulinastatin on Cognitive Function After Hypoxia.

Ulinastatin (UTI) has neuroprotective properties. Neurologic insults, including hypoxia and use of anesthetic agents, cause postoperative cognitive dysfunction and alter gamma-aminobutyric acid (GABA) function. This study aimed to assess whether UTI could preserve learning and memory using a zebrafish hypoxic behavior model and biomarkers. Zebrafish (6-8months of age and 2.5-3.5cm long) were divided into eight groups as follows: phosphate-buffered saline (PBS) control, hypoxia + PBS, UTI (10,000, 50,000, and 100,000 units/kg), and hypoxia with UTI (10,000, 50,000, and 100,000 units/kg) groups. The endpoints of the T-maze experiment included total time, distance moved, and frequency in target or opposite compartment. We also measured the degree of brain infarction using 2,3,5‑triphenyltetrazolium chloride staining, assessed SA-β-galactosidase activity, and examined GABAA receptor expression using real-time polymerase chain reaction. In a dose-dependent manner, UTI affected learning and memory in zebrafish. Despite hypoxia, 100,000 units/kg of UTI preserved preference (time and distance) for the target compartment. More than 50,000 units/kg of UTI also showed reduced hypoxia-induced brain infarction, decreased SA-β-galactosidase levels, and upregulated GABAA receptors. This study demonstrated that the location of the GABAA receptor is affected by hypoxia or UTI.

Genetic Polymorphism Can Play a Major Role in Distance Preference by Elite Stayers

Genetic Polymorphism Can Play a Major Role in Distance Preference by Elite Stayers

Nurses' Willingness and Demand for Internet +home Care Services and Its Influencing Factors in Different Levels of Hospitals in China - A Nationwide Survey.

PurposeThe present study aimed to investigate nurses’ willingness and demand for Internet +home care services in different levels of hospitals in China and analyze the influencing factors.Participants and MethodsFrom October 1 to December 31, 2021, 5714 nurses from 15 hospitals in five regions of China were recruited in this cross-sectional study using a two-stage random sampling method. A self-designed questionnaire with good reliability and validity was used to measure nurses’ willingness and demand for Internet +home care services. χ2 test, Welch t-test, and multiple linear regression analyses were used to analyze the data.ResultsNurses were highly willing to provide Internet +home care services. Statistical differences were found in the willingness to provide Internet +home care services and the preference for service distance, service platform, and single service fee between nurses in different levels of hospitals (both P<0.05). The willingness to provide “catheter maintenance service” and “rehabilitation nursing service” of nurses in different levels of hospitals were statistically significant (both P<0.05). Nurses’ demand for Internet +home care services increased with the level of their hospital. Multiple linear regression showed that professional title, educational level, monthly family income, and mortgage or car loan influence nurses’ demand for Internet +home care services.ConclusionNurses’ willingness and demand for Internet +home care services vary with the level of their hospitals. It is recommended that government and hospitals regulate the service items, the service distance, single service fee, and other contents according to nurses’ willingness and demand and establish relevant laws and regulations to ensure the steady and orderly development of the Internet +home care services.

Alterations of monoamine neurotransmitters, HPA-axis hormones, and inflammation cytokines in reserpine-induced hyperalgesia and depression comorbidity rat model

BackgroundPain and depression often occur simultaneously, but the mechanism of this condition is still unclear.MethodsThe aim of this study was to examine the alterations of monoamine neurotransmitters, hypothalamic–pituitary–adrenal (HPA) axis hormones, and inflammation cytokines in hyperalgesia and depression comorbidities. The reserpine-induced “Sprague Dawley” (SD) rat models were used, and the concentrations of monoamine neurotransmitters serotonin (5-HT), norepinephrine (NE), dopamine (DA), and their metabolic products 5-hydroxyindoleacetic acid (5-HIAA), Homovanillic acid (HVA), 3,4-Dihydroxyphenylacetic acid (DOPAC) in raphe nucleus region were tested by High Performance Liquid Chromatography (HPLC). Serum levels of Adrenocorticotropic Hormone (ACTH), Cortisol (CORT), and inflammatory cytokines interleukin (IL)-1β, IL-6, tumor necrosis factor (TNF)-α, interferon (IFN)-γ, IL-4, IL-10 were assessed by enzyme linked immunosorbent assay.ResultsRepeated reserpine injection induced hyperalgesia and depressive behaviors with decreased sucrose preference and horizontal movement distance, and increased immobility time in forced swimming test. The concentrations of 5-HT and NE in raphe nucleus, and ACTH and CORT in serum were elevated in the model group. And the model group showed increases in serum IL-1β and IL-6, and decrease in serum IL-10.ConclusionMore research in these areas is needed to understand the pathogenesis of the disease, so as to find more and better therapeutic targets.

Personal socio-cultural preferences modulate neural correlates of decisions to socialize with powerful persons.

Social power differences fundamentally shape the behavioral interaction dynamics of groups and societies. While it has long been recognized that individual socio‐cultural preferences mitigate social interactions involving persons of power, there is limited empirical data on the underlying neural correlates. To bridge this gap, we asked university student participants to decide whether they were willing to engage in social activities involving their teachers (higher power status), classmates (equal power status), or themselves (control) while functional brain images were acquired. Questionnaires assessed participants' preferences for power distance, uncertainty avoidance, and cultural intelligence. As expected, participants generally accepted more social interactions with classmates than teachers. Also, left inferior frontal activity was higher when accepting than when rejecting social interactions with teachers. Critically, power distance preferences further modulated right lateral frontoparietal activity contrasting approach relative to avoidance decisions towards teachers. In addition, uncertainty avoidance modulated activity in medial frontal, precuneus, and left supramarginal areas distinguishing approach decisions towards teachers relative to classmates. Cultural intelligence modulated neural responses to classmate approach/avoidance decisions in anterior cingulate and left parietal areas. Overall, functional activities in distinct brain networks reflected different personal socio‐cultural preferences despite observed social decisions to interact with others of differential power status. Such findings highlight that social approach or avoidance behaviors towards powerful persons involves differential subjective neural processes possibly involved in computing implicit social utility.