Group membership modulates the hold-up problem: an event-related potentials and oscillations study

The Guanxi mediating role linking organizational justice to contextual performance with age as a moderator

Guanxi, a distinctive Chinese concept, reflects a shared vision of relationships and connections that include mutual understanding, trust, and a deep bond between individuals. Recognized for its potency in shaping the relationships that facilitate business undertakings and access to key resources, Guanxi is postulated as a potential mediator in the nexus between organizational justice and contextual work performance. The depth of Guanxi, intertwined with Chinese culture and values, may be perceived differently across age groups. Specifically, as Chinese millennials usually interact with global paradigms, generational disparities might emerge in valuing these traditional constructs. This study delves into how the dimensions of Guanxi-Ganqing (emotional connection), Renqing (reciprocity), and Xinren (loyalty)-mediate the relationship between organizational justice and contextual work performance, with chronological age as a moderator. The present study includes a convenience sample of 630 Chinese employees, aged 22-67 years, who participated in a quantitative online survey. The findings endorse the mediation role of Guanxi. The total influence of justice was found to be significant, as well as the indirect impacts, that were statistically salient. Although the age-moderated mediation was not wholly substantiated, the age-specific indirect effects of Renqing and Xinren did present significant variances between millennials and those above 42 years. The relevance of this study extends beyond the academic field, shedding light on the cultural dynamics at play within Chinese organizational settings. By unveiling the relationships between Guanxi, organizational justice, and performance, and by elucidating the age-specific variations therein, this research provides insights for organizational leaders and human resource professionals. Based on these findings, businesses can craft targeted interventions that capitalize on the strengths of Guanxi, ensuring fair practices and enhancing performance across diverse age groups. Further, recognizing the unique attributes and values of different generational cohorts can aid in fostering a harmonious, culturally attuned, and efficient workplace environment.

Exploring the dynamics of governance: An examination of traditional governance and governance innovation in the United States professional sports leagues

Leveraging governance structures: Shaping Power Relations and Decision-Making Processes within Organizations. While traditional governance approaches tend to favor hierarchical structures with concentrated authority, the growing demand for increased stakeholder engagement and empowerment has spurred the emergence of innovative governance models. This article examines traditional and evolving governance approaches in major United States professional sports leagues-the National Football League (NFL), Major League Baseball (MLB), National Basketball Association (NBA), and National Hockey League (NHL). Through a review of literature and governance documents, the traditional hierarchal models of the NFL and MLB are analyzed. Their incremental shifts toward more inclusive structures are also explored. In contrast, the NBA's adoption of a franchise model with decentralized authority and the NHL's establishment of a Players' Association are examined as examples of governance innovation. The impacts of these evolving approaches are considered in the context of league operations, labor relations, and overall stakeholder interest representation. This paper shows insights into the dynamics of governance change and the factors influencing shifts toward more collaborative and empowering structures within professional sports organizations.

Investigating cerebral neurovascular responses to hyperglycemia in a rat model of type 2 diabetes using multimodal assessment techniques

To study neurovascular function in type 2 diabetes mellitus (T2DM), we established a high-fat diet/streptozotocin (HFD/STZ) rat model. Electrocorticography-laser speckle contrast imaging (ECoG-LSCI) revealed that the somatosensory-evoked potential (SSEP) amplitude and blood perfusion volume were significantly lower in the HFD/STZ group. Cortical spreading depression (CSD) velocity was used as a measure of neurovascular function, and the results showed that the blood flow velocity and the number of CSD events were significantly lower in the HFD/STZ group. In addition, to compare changes during acute hyperglycemia and hyperglycemia, we used intraperitoneal injection (IPI) of glucose to induce transient hyperglycemia. The results showed that CSD velocity and blood flow were significantly reduced in the IPI group. The significant neurovascular changes observed in the brains of rats in the HFD/STZ group suggest that changes in neuronal apoptosis may play a role in altered glucose homeostasis in T2DM.

An optimal method for diagnosing heart disease using combination of grasshopper evalutionary algorithm and support vector machines

Due to the importance of accurate diagnosis and prompt treatment of this condition, the medical world is searching for a solution for its early detection and efficient treatment. Heart disease is one of the leading causes of death in modern society. With the development of computer science today, this issue can be resolved using computers. Data mining is one of the solutions for diagnosing this illness. One of the cutting-edge disciplines, data mining, can aid in better decision-making in many areas of medicine, including disease diagnosis and treatment. In order to improve diagnosis accuracy, a combination method using the evolutionary algorithms locust and support vector machine has been tested in this study. Use should be made of heart disease. Because of the hybrid nature of this approach, normalization is actually carried out in three steps: first, by using pre-processing operations to remove unknown and outlier data from the data set; second, by using the locust evolutionary algorithm to choose the best features from the available features; and third, by classifying the data set using a support vector machine. The accuracy criterion for the proposed method compared to Niobizin methods, neural networks, and J48 trees improved by 18 %, 30 %, and 24 %, respectively, after implementing it on the data set and comparing it with other algorithms used in the field of heart disease diagnosis.

Brain Source Localization and Functional Connectivity in Group Identity Regulation of Overbidding in Contest

Contests may be highly effective in eliciting high levels of effort, but they also carry the risk of inefficient resource allocation due to excessive effort (overbidding), squandering valuable social resources. While a growing body of research has focused on how group identity exacerbates out-group conflict, its influence on in-group conflict remains relatively unexplored. This study endeavors to explore the impact of group identity on conflicts within and between groups in competitive environments, thereby addressing gaps in the current research landscape and dissecting the involved neurobiological mechanisms. By employing source localization and functional connectivity techniques, our research aims to identify the brain regions involved in competitive decision-making and group identity processes, as well as the functional connectivities between social brain areas. The results of our investigation revealed that participants exhibited activation in the bilateral frontal and prefrontal lobes during the bidding behavior before the group identity task. Subsequently, after the task, additional activation was observed in the right temporal lobe. Results from functional connectivity studies indicated that group identity tasks modify decision-making processes by promoting group norms, empathy, and blurred self-other boundaries for in-group decisions, while out-group decisions after the group identity task see heightened cognitive control, an increased dependence on rational judgment, introspection of self-environment relationships, and a greater focus on anticipating others' behaviors. This study reveals the widespread occurrence of overbidding behavior and demonstrates the role of group identity in mitigating this phenomenon, concurrently providing a comprehensive analysis of the underlying neural mechanisms.

PulmoNet: a novel deep learning based pulmonary diseases detection model

Pulmonary diseases are various pathological conditions that affect respiratory tissues and organs, making the exchange of gas challenging for animals inhaling and exhaling. It varies from gentle and self-limiting such as the common cold and catarrh, to life-threatening ones, such as viral pneumonia (VP), bacterial pneumonia (BP), and tuberculosis, as well as a severe acute respiratory syndrome, such as the coronavirus 2019 (COVID-19). The cost of diagnosis and treatment of pulmonary infections is on the high side, most especially in developing countries, and since radiography images (X-ray and computed tomography (CT) scan images) have proven beneficial in detecting various pulmonary infections, many machine learning (ML) models and image processing procedures have been utilized to identify these infections. The need for timely and accurate detection can be lifesaving, especially during a pandemic. This paper, therefore, suggested a deep convolutional neural network (DCNN) founded image detection model, optimized with image augmentation technique, to detect three (3) different pulmonary diseases (COVID-19, bacterial pneumonia, and viral pneumonia). The dataset containing four (4) different classes (healthy (10,325), COVID-19 (3,749), BP (883), and VP (1,478)) was utilized as training/testing data for the suggested model. The model's performance indicates high potential in detecting the three (3) classes of pulmonary diseases. The model recorded average detection accuracy of 94%, 95.4%, 99.4%, and 98.30%, and training/detection time of about 60/50 s. This result indicates the proficiency of the suggested approach when likened to the traditional texture descriptors technique of pulmonary disease recognition utilizing X-ray and CT scan images. This study introduces an innovative deep convolutional neural network model to enhance the detection of pulmonary diseases like COVID-19 and pneumonia using radiography. This model, notable for its accuracy and efficiency, promises significant advancements in medical diagnostics, particularly beneficial in developing countries due to its potential to surpass traditional diagnostic methods.