Non-cooperative Behavior Research Articles

Effects of acepromazine, xylazine and propofol on spinal reflexes in healthy dogs.

In the neurological examination, it is crucial to identify the possible location of the lesion in order to determine the appropriate treatment process. In aggressive animals, chemical restraint may be necessary due to their non-cooperative behaviour. However, sedatives may distort the results of examinations. Therefore, a drug should be found that has minimal impact on the examination results. To investigate the effects of acepromazine, xylazine, and propofol on spinal reflexes in healthy dogs. In a randomized, blinded study, ten native adult mixed-breed dogs were participated in three groups with a 1-week washout period between each group. Before performing each step, the spinal reflexes were evaluated. Then, in the first group, acepromazine (0.05mg/kg, IM), in the second group, xylazine (1mg/kg, IM), and in the third group, propofol (3mg/kg, IV for initial bolus and 0.1mg/kg/min for maintenance) were injected for sedation. The spinal reflexes were reevaluated at maximum sedation and at 15, 30, and 45 min thereafter. Acepromazine increased the patellar reflex and decreased the panniculus reflex. Xylazine increased the cranial tibial reflex and decreased the panniculus reflex, while propofol decreased the withdrawal, and extensor carpi radialis reflexes, and suppressed the palpebral and gag reflexes. The drugs used in the present study did not have a significant impact on the most important reflexes evaluated in neurological examinations. Among the drugs, acepromazine has the least effects compared to other drugs, making it a suitable choice for sedation.

Cross-functional group decision making with heterogeneous cooperation for digital transformation in supply chain resilience

Supply chain resilience plays a critical role in gaining competitive advantages for companies. The resilience of supply chains can be achieved by leveraging emerging digital technologies to realize digital transformation. It is necessary to select an appropriate digitalization technology under such background. The wide-spanning of digital transformation and technology selection needs cross-functional integration of various expertise. However, in the process of making decisions by leveraging expert wisdom, differences in experts’ willingness to cooperate lead to difficulties in reaching a consensus. The existing literature fails to incorporate both non-cooperation and proactive-cooperation into the consensus reaching process. Thus, in this study, we introduce a cross-functional multi-attribute group decision making model for digitalization technology selection. To manage potential non-cooperative behaviors in the group consensus reaching process, the proposed model allows experts to have proactive cooperation, i.e., making more contributions than recommended feedback suggestions provided by the moderator. Proactive cooperation can make up for the loss caused by the non-cooperative behaviors of experts. A knowledge mining method is proposed to mine academic and practical preferences for attributes. Two consensus mechanisms are put forward for the meso decision-making process in functional teams and the macro decision-making process in the whole group, respectively. An illustrative example regarding the technology selection in shipbuilding industry is provided to verify the applicability of our model. Numerical experiments suggest that our model will improve the efficiency of consensus reaching process.

New Trust Based Smart Data Forwarding Mechanism in Vehicular Delay Tolerant Network

Vehicular Delay Tolerant Network (VDTN) is the growing field with a considerable possibility to handle future wireless application’s requirements. Using vehicles for the data communication purpose can be contemplated as a substitute for the wired and wireless systems. This paper proposes a trust-based data forwarding mechanism for Vehicular Delay Tolerant Network (VDTN). Data forwarding in VDTN requires every vehicular or stationary node to participate in data forwarding. But some time malicious nodes show non-cooperative behaviour in data forwarding. Therefore, malicious nodes must be identified specifically to accelerate the data forwarding. A threshold based social skeleton membership process along with new trust-based data forwarding mechanism is proposed in this paper. We reveal that the social skeleton members perform better in data forwarding in terms of data delivery ratio, data delay and data overhead. Simulation results demonstrate that the trust-based data forwarding improves the data delivery ratio with trust-based data forwarding approach. After simulation a comparative analysis of two different scenarios is presented with epidemic routing, prophet routing and spray and wait routing.

Large-scale consensus in incomplete social network with non-cooperative behaviors and dimension reduction

Obtaining a consensus solution is a formidable challenge for large-scale decision-making (LSDM) in social network (SN). The reaching of large-scale consensus in SN is hindered by serious difficulties, including complex decision information, incomplete social relations, a multitude of decision-makers (DMs), and non-cooperative behaviors. This paper introduces a novel three-stage consensus framework that systematically addresses these challenges by data preprocessing, dimension reduction, and optimization modeling. Firstly, the cloud model is applied to convert the probabilistic linguistic information into numerical information, facilitating computational analysis. Meanwhile, an improved t-norm trust propagation method that incorporates the impact of opinion similarity is developed, ensuring the completeness of SN. Secondly, an improved Louvain algorithm is designed to divide large group into cohesive subgroups, enhancing the manageability of LSDM. On this basis, a three-stage consensus optimization that considers non-cooperative behaviors is proposed, which boasts threefold benefits: (i) Assures the synchronous achievement of local and global consensus. (ii) Implements self-adaptive management mechanism of non-cooperative behaviors. (iii) Provides acceptable adjusted opinions for subgroups and DMs. Finally, detailed numerical experiments and comparative analyses are given to demonstrate the effectiveness of the proposed method.

The role of the family’s ethnicity and correlates in social workers’ risk perceptions: Evidence from a vignette study in Hungary

Studies have shown that children from some ethnic minority families are disproportionately represented in the child protection system. Ethnic-based treatment is problematic because it challenges whether child protection decision-making is based on the principle of’the best interests of the child’. Roma children are also overrepresented in the child protection systems of many European countries, but little research has explored why this is so. The present study seeks to address this gap by examining child welfare workers’ perceptions of risk at the early stages of child protection decision-making. The main research question is whether they perceive higher levels of risk if the parents are Roma. Method: We use data from a vignette-based survey conducted among child welfare workers (N = 600) in Hungary in 2018. The factors influencing social workers’ risk perceptions are analysed using multilevel ordinal logistic regressions. The regression models of risk perception include case variables (harm to child, parents’ ethnicity, mother’s education, parents’ employment, housing conditions, living environment, parental alcoholism, mother’s willingness to cooperate), and the individual characteristics of the social worker. Ethnicity is examined individually as well as in interaction with other case characteristics. Results: Findings show that physical harm to the child, parental alcoholism, a messy living environment, and the non-cooperative behaviour of the mother increase the level of risk perceived by child welfare workers. There is no evidence of a statistical association between parental socioeconomic status and the social worker’s perception of risk. Family ethnicity also does not have a statistically significant impact on risk perception (main effect). However, some case characteristics affect risk perception differently when the family is of Roma origin (interaction effects). Physical abuse has a strong effect on the social worker’s perception of risk, but this effect is more modest when the family is of Roma origin. Likewise, the mother’s non-cooperative behaviour is a risk-increasing factor, but this effect disappears for Roma families. Conclusions: Although, no ethnic bias was found in social workers’ perception of risk, our findings indicate that the perception of risk for Roma families is less dependent on the circumstances of the case than for majority families. In some cases, this may lead to an unjustified overestimation of risk or, conversely, an underestimation when the family is Roma.

A Relative Projection-Based Multiattribute Group Decision-Making Model With Noncooperative Behavior Management and Application to NEV Supplier Selection.

This article reports a novel consensus model where a group of internal and external experts evaluate alternatives under multiple attributes and provide mutual evaluations. First, different from previous studies, the cognitive and interest conflicts of internal and external experts are considered simultaneously. But interest conflict is emphasized for internal experts, and cognitive conflict is mainly considered for external experts. Second, we explore the categorization and management methods of noncooperative behaviors (NCBs) of experts. The relative projection-based indexes are proposed for the first time to measure the degrees of cognitive and interest conflicts by using multiattribute preference matrices (MAPMs) and the weight vectors of attributes. A group of experts are divided into three categories and the corresponding management strategies are developed. Third, we investigate the consensus mechanism among experts with cognitive and interest conflicts. For reaching an acceptable consensus level, an adjustment process is proposed to revise some local entries in MAPMs and mutual evaluation matrix (MEM). A penalty mechanism is further established to dynamically update the weights of experts. An algorithm is designed to capture the consensus reaching process in multiattribute group decision making, where internal and external experts are distinguished by proposing a parameter. Finally, the high-performance battery supplier selection of new energy vehicle is studied to illustrate the proposed model. The results reveal that the efficiency of reaching consensus can be enhanced by using the developed model with effective management of NCBs.

The adaptive consensus reaching process with the ER-PSO interaction mechanism and its application in public charging stations evaluation

BackgroundPublic charging stations (PCSs) have become integral public infrastructure in China, responding to the considerable and rapidly growing demand. Evaluating the service quality of PCSs by a diverse user base has become essential for the effective management of PCSs. ObjectivesIn the group evaluation of PCSs' service quality, which encompasses broad public interests, consensus reaching process (CRP) is commonly utilized to manage group conflicts and achieve evaluation consensus. The intricate interactions among decision makers (DMs) significantly influence the evolution and resilience of collective viewpoints. Against this backdrop, this research aims to develop an adaptive CRP to enhance the discriminatory power of group consensus and improve consensus robustness in the presence of non-cooperative behavior. MethodologyWith the above objectives, this study proposes an adaptive CRP supported by a novel interaction mechanism, consisting of two stages: (1) In the interaction network generation stage, based on the Erdős-Rényi (ER) random network model, innovative interaction signals are generated to guide the generation of an ER-based interaction network, enhancing the dissemination of dominant viewpoints, and constraining the influence of minority opinions. (2) In the decision opinion interaction stage, based on the Particle Swarm Optimization (PSO) algorithm, utilizing novel dual individual fitness functions, the PSO algorithm is employed to formulate the expected updated belief, driving the internal evolution and convergence of opinions. The proposed adaptive CRP is implemented in the group evaluation of PCSs' service quality in Nanjing. FindingsComparative analysis reveals that the proposed CRP offers significant advantages in consensus efficiency, the discriminatory power of group consensus, and consensus robustness in the presence of non-cooperative behavior compared to existing CRPs. NoveltyDiffering from previous CRP research primarily focuses on minimizing adjustment scales, this study emphasizes the adaptive evolution and convergence of opinions, providing a more suitable description of group opinion dynamics. Additionally, from a methodological perspective, this research utilizes the ER network model and the PSO algorithm to support the adaptive CRP at both structural and preference levels, enhancing consensus discriminatory power and robustness.

An adaptive consensus model for managing non-cooperative behaviors in portfolio optimization for large companies

An adaptive consensus model for managing non-cooperative behaviors in portfolio optimization for large companies

A Consensus Model with Non-Cooperative Behavior Adaptive Management Based on Cognitive Psychological State Computation in Large-Scale Group Decision

A Consensus Model with Non-Cooperative Behavior Adaptive Management Based on Cognitive Psychological State Computation in Large-Scale Group Decision

Multi-stage type-α constrained minimum-cost consensus for linguistic Z-number large-scale decision-making

As an extension of group decision-making in terms of scale and uncertainty, linguistic Z-number large-scale decision-making (LZ-LSDM) is emerging as a prominent research topic in the field of decision science. The unique structure of LZ-LSDM presents new challenges for both clustering analysis and consensus building. Minimum-cost consensus (MCC) based on the optimization principle is widely recognized as an effective tool for managing the consensus-reaching process. However, there is a scarcity of literature that addresses the study of MCC within the context of LZ-LSDM, as well as the application of MCC in the identification and treatment of non-cooperative behaviors. To this end, this study proposes a punishment strategy-driven multi-stage type-α constrained MCC model for LZ-LSDM problems. First, a similarity constraint-based clustering method with linguistic Z-numbers is proposed. Given the clustering results, a type-α constrained MCC (α-CMCC) model with personalized feedback constraints is designed to provide a personalized solution for visualizing opinion adjustment and preventing over-adjustment. Based on the optimal solution obtained by α-CMCC, the identification rule for non-cooperative behaviors is proposed. We conclude three punishment strategies—namely, pure, mixed, and cross—to address non-cooperative behaviors by arranging and combining commonly used punishment approaches. Finally, we illustrate the feasibility and validity of the model through a case study designed to facilitate consensus among an online patient community on knowledge-based recommendations. An exhaustive comparative analysis reveals the advantages and features of the proposed consensus model.

Antisocial Personality Disorder as a Form of Adaptive Strategy

Background: in contemporary domestic psychiatry the evolutionary explanations of antisocial personality disorder are practically lacking. The aim of study: the analysis of the significant concepts of the evolutionary approach to understanding the origins and typology of antisocial, or dissocial, personality disorder. Material and methods: a narrative review of contemporary English-language psychiatric and psychological literature using all available search engines using the keywords “antisocial psychopathy” and “evolutionary psychiatry and psychology”. Discussion and conclusion: in English-language psychiatric and psychological literature the affective component of antisocial traits, emphasized only in ICD-10, is called “primary psychopathy,” while the behavioral component of antisocial traits, associated with “sensation seeking” and “hyperactivation,” is called “secondary” psychopathy. Evolutionary and ecological approaches emphasize the adaptive role of aggressive and antisocial behavior, viewing such behavior as a “high-risk strategy” in social and sexual competition, as “costly” but potentially adaptive (rather than as “behavioral dysfunctions”). The understanding of persistent antisocial behavior as a social “trickster” strategy exploiting the trust and cooperative behavior of others is based on the R. Axelrod-W. Hamilton “iterated” prisoner’s dilemma. In the Prisoner’s Dilemma evolutionary model, the player’s payoff is determined by the effects on “fitness”: survival and fertility. Participating in several interactions with individuals, an antisocial “deceiver” with high mobility is able to exist, manifesting itself with a fairly low frequency in the population. Non-cooperative behavior in both animals and humans reduces the fitness of others and includes aggressive behavior, deception, betrayal, predation and social parasitism. From the point of view of the concept of “economic games,” people with subclinically expressed primary psychopathic traits (emotional coldness, manipulativeness, Machiavellianism) are represented as “strategists” with cooperation partners: they give with reciprocity to those partners whom they consider “highly valuable” (with the potential for great benefits in future), and only “take”, manipulating and deceiving, from those of “low value”. In a broad sense, evolutionary psychologists and psychiatrists find in the characteristics of antisocial individuals a predominance of a set of properties of “fast” life strategies, while the concept of “fast strategies” is also used to explain geographical differences in the prevalence of aggressive behavior.

Intelligent Surveillance of Airport Apron: Detection and Location of Abnormal Behavior in Typical Non-Cooperative Human Objects

Most airport surface surveillance systems focus on monitoring and commanding cooperative objects (vehicles) while neglecting the location and detection of non-cooperative objects (humans). Abnormal behavior by non-cooperative objects poses a potential threat to airport security. This study collects surveillance video data from civil aviation airports in several regions of China, and a non-cooperative abnormal behavior localization and detection framework (NC-ABLD) is established. As the focus of this paper, the proposed framework seamlessly integrates a multi-scale non-cooperative object localization module, a human keypoint detection module, and a behavioral classification module. The framework uses a serial structure, with multiple modules working in concert to achieve precise position, human keypoints, and behavioral classification of non-cooperative objects in the airport field. In addition, since there is no publicly available rich dataset of airport aprons, we propose a dataset called IIAR-30, which consists of 1736 images of airport surfaces and 506 video clips in six frequently occurring behavioral categories. The results of experiments conducted on the IIAR-30 dataset show that the framework performs well compared to mainstream behavior recognition methods and achieves fine-grained localization and refined class detection of typical non-cooperative human abnormal behavior on airport apron surfaces.

Remanufacturing Mode Selection Based on Non-cooperative Behavior Management in Group Consensus Reaching Process

Remanufacturing Mode Selection Based on Non-cooperative Behavior Management in Group Consensus Reaching Process

A tolerance index based non-cooperative behaviour managing method with minimum cost in social network group decision making

This paper introduces a novel consensus theoretical framework designed to effectively manage non-cooperative behaviour in social network group decision making (SNGDM). It addresses the challenge by considering both individuals’ willingness to adjust preferences and the associated costs of achieving consensus. To deal with this issue, the personalised individual semantics (PIS) model is employed to handle original evaluation matrices by converting linguistic terms into numerical values based on experts’ personalised opinions. Subsequently, a tolerance index (TI) is defined to reflect the willingness of experts to adjust their preferences. An improved minimum cost (MC) feedback model based on TI is established. The novelty of the proposed approach is that its integration of individual preference adjustment willingness and consensus efficiency, effectively preventing groupthink. In addition, a maximum group consensus degree optimisation model is proposed to detect non-cooperative behaviour of experts. To ensure an optimal solution for the minimum cost feedback model, a weight update method is proposed, considering the trust relationship between experts. A detailed analysis regarding the selection of tolerance thresholds to prevent over-penalisation of weights of non-collaborators is reported. Finally, comprehensive numerical and comparative analyses are presented to validate the proposed method.

Challenges Confronted, During Data Collection Concerning Nurses and Paramedics in Bahawalpur, Pakistan

Data collection is one of the initial steps in almost all research practices. This study describes the challenges and issues faced by a researcher, in a developing country like Pakistan, during the execution of time-use research on nurses and paramedics, working in public sector hospitals of District Bahawalpur. We collected the data from the year 2018 to 2021, in three phases from 364 participants. Obstacles faced included: non-cooperative behavior of health department officials, non-response, reluctance, disinformation and misinterpretation on behalf of participants during working hours or after working hours and limited resources regarding funds and time. This paper also highlights the social and religious restrictions & obligations of being a female, Muslim researcher in fieldwork. It also includes the problems faced during COVID-19 confinement. This article may help novice researchers, especially from developing countries who are planning to execute field research on nurses and paramedics.

A Blast from the Past: Shaping the Moscow–Beijing–Pyongyang Axis in 2023

The Beijing–Pyongyang–Moscow triangle is far from being a new invention. An inherent problem with such a political construct is that each side is pursuing its own ambitions at the expense of others, making it fragile from within. The recent geopolitical reality has summoned the triangle back from the past of the Cold War when it was first formed. But even if the emerging alliance appears to be stable from the outside, the three parties’ different aims and non-cooperative behaviour result in fragility of this construct as it was years ago.

A dynamic dual-trust network-based consensus model for individual non-cooperative behaviour management in group decision-making

In group decision-making, trust relationships are the basis of interactions among decision makers (DMs) and play an important role in maintaining cooperation. However, DMs from different backgrounds may use the trust relationship to influence the opinion adjustment of other individuals and adopt non-cooperative behaviours, which will also consume trust among DMs, both of which are ignored by extant consensus models. To explore the mutual influence of trust relationships, consensus processes, and non-cooperative behaviour, a consensus model for individual non-cooperative behaviour management in group decision-making under a dynamic dual-trust network is proposed. First, to represent the trust relationship among DMs in a more realistic way, a dynamic dual-trust network based on familiarity-based trust and similarity-based trust is developed. This approach can comprehensively model the interpersonal relationship and opinion similarity among DMs, while dynamically updating according to their interactions. Second, a minimum adjustment consensus model based on a dual-trust relationship is proposed. This model can provide adjustment suggestions to DMs to retain initial opinions as much as possible while considering the impact of trust relationships on opinion adjustments. Subsequently, a non-cooperative behaviour management method based on non-cooperative willingness and trust risk is designed to classify the behaviour of DMs and then adjust their trust relationship and weight accordingly. Subsequently, an illustrative example is provided to demonstrate the effectiveness of the proposed method. Finally, the validity of the model is verified through a simulation and comparative analysis.

Consensus adjustment for multi-attribute group decision making based on cross-allocation

Consensus adjustment is crucial in group decision making (GDM), aiming to reduce discrepancies in decision makers’ (DMs’) judgments for final decision results. The consensus adjustments made by DMs are interactive rather than independent. However, few studies investigate the consensus adjustment problem from the perspective of interactions among DMs. This study first constructs a model to determine the total minimum consensus adjustment under the requirements of consensus threshold and individual-collective judgment bias. Then, a consensus adjustment approach based on cross-allocation scheme is proposed to address the inconsistency between individual and collective minimum adjustments, which concerns all DMs’ individual favorable allocation results under the total minimum consensus adjustment. The cross-allocation scheme ensures unanimous support for the same allocation result among all rational DMs, thereby eliminating the discrepancies among them. Subsequently, the optimization model-based minimum consensus judgment penalty is formulated to handle the non-cooperative behaviors of DMs. Furthermore, we show how to determine the unique consensus adjustment result for the non-cooperative DMs using the cross-allocation scheme. Therefore, our study contributes by developing a new GDM method that conducts consensus adjustment allocation by considering interactions among DMs. Finally, a case study is employed to demonstrate the specific application of the new method and discuss its efficiency. Meanwhile, comparison analysis and simulation experiments are provided.

Communication behavior recognition using CNN-based signal analysis

This article explores the technology of recognizing non-cooperative communication behavior, with a specific emphasis on analyzing communication station signals. Conventional techniques for analyzing signal data frames to determine their identity, while precise, do not have the ability to operate in real-time. In order to tackle this issue, we developed a pragmatic architecture for recognizing communication behavior and a system based on polling. The method utilizes a one-dimensional convolutional neural network (CNN) to segment data, hence improving its ability to recognize various communication activities. The study assesses the reliability of CNN in several real-world scenarios, examining its accuracy in the presence of noise interference, varying lengths of interception signals, interferences at different frequency points, and dynamic changes in outpost locations. The experimental results confirm the efficacy and dependability of the convolutional neural network in recognizing communication behavior in various contexts.

Taguchi Method Based Node Performance Analysis of Generous TIT- for-TAT Cooperation of AD-HOC Networks

Ad-Hoc networks have an adaptive architecture, temporarily configured to provide communication between wireless devices that provide network nodes. Forwarding packets from the source node to the remote destination node may require intermediate cooperative nodes (relay nodes), which may act selfishly because they are power-constrained. The nodes should exhibit cooperation even when faced with occasional selfish or non-cooperative behaviour from other nodes. Several factors affect the behaviour of nodes; those factors are the number of packets required to redirect, power consumption per node, and power constraints per node. Power constraints per node and grade of generosity. This article is based on a dynamic collaboration strategy, specifically the Generous Tit-for-Tat (GTFT), and it aims to represent an Ad-Hoc network operating with the Generous Tit-for-Tat (GTFT) cooperation strategy, measure statistics for the data, and then analyze these statistics using the Taguchi method. The transfer speed and relay node performance both have an impact on the factors that shape the network conditions and are subject to analysis using the Taguchi Method (TM). The analyzed parameters are node throughput, the amount of relay requested packets produced by a node per number of relays requested packets taken by a node, and the amount of accepted relay requested by a node per amount of relay requested by a node. A Taguchi L9 orthogonal array was used to analyze node behaviour, and the results show that the effect parameters were number of packets, power consumption, power constraint of the node, and grade of generosity. The tested parameters influence node cooperation in the following sequence: number of packets required to redirect (N) (effects on behaviour with a percent of 6.8491), power consumption per node (C) (effects on behaviour with a percent of 0.7467), power constraints per node (P) (effects on behaviour with a percent of 0.6831), and grade of generosity (ε) (effects on behaviour with a percent of 0.4530). Taguchi experiments proved that the grade of generosity (GoG) is not the influencing factor where the highest productivity level is, while the number of packets per second required to redirect also has an impact on node behaviour.