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

The intention to adopt photovoltaic systems: integrating behavioral theories with mediation-moderation analysis

The swift advancement of photovoltaic (PV) technology, coupled with the increasing emphasis on sustainable energy solutions, has generated interest in understanding the factors that influence consumers' choices to purchase PV systems. This research aims to identify key areas where the intention to acquire photovoltaic systems might be improved. The purchasing intention of Photovoltaic systems is influenced by various individual theories, including the Theory of Planned Behavior, the Model of Technology Acceptance (TAM), and the Theory of the Diffusion of Innovations (DOI). The data we collected came from an online survey that was distributed in May 2024 to a cohort of 1200 persons who are members of a paid panel managed by Credamo.com. The eligible participants were limited to individuals who owned homes in China and did not have solar panels. We use Amos and SPSS for data analysis. We choose China since it is one of the major RPV marketplaces globally, offering numerous purchasing opportunities. Since most of the study on China's adoption of PV systems has been theoretical and not cumulative, an additional purpose is to establish a foundation for future research endeavors. The present study introduces a comprehensive framework that aims to explain consumer interest in residential photovoltaics by incorporating aspects from Technology Acceptance Model, Theory of Planned Behavior, and Diffusion of Innovation. The study results demonstrate a robust and favorable impact of the Theory of Planned Behavior, the Technological Acceptance Model, and the Diffusion of Innovations on the intention to purchase photovoltaic systems. As anticipated by the DOI (Diffusion of Innovation) theory, the findings indicate that the innovativeness of DOI is positively associated with the inclination to engage in discussions about a potential purchase of a photovoltaic (PV) system. The results indicate that both the total effect and direct effect are statistically significant (S.E: 0.276, P: 0.000) accounting for 34 % of the overall contribution. Consumer novelty seeking had a significant beneficial impact on interest and also had favorable indirect impacts through perceived relative advantage. The TPB variables explained 31 % of the variance in interest, taking into account household limitations. The inclusion of social curiosity in the model revealed that the belief in the personal benefits of solar panels had the most powerful direct and overall impact on interest. This was demonstrated by the significant (S.E: 0.313, P: 0.000) direct and total effect on the intention to purchase a PV system for household use. The analytical findings demonstrate a direct and favorable relationship between the Technology Acceptance Model, also known as the TAM, and the intention to acquire photovoltaic (PV) systems in China. The findings demonstrate a 35 % variation and emphasize the significance (S.E: 0.070, P: 0.000) of improving the perceived simplicity of use and perceived usefulness of PV systems in order to encourage customer adoption.

Artificial intelligence in stroke rehabilitation: From acute care to long-term recovery

Stroke is a leading cause of disability worldwide, driving the need for advanced rehabilitation strategies. The integration of Artificial Intelligence (AI) into stroke rehabilitation presents significant advancements across the continuum of care, from acute diagnosis to long-term recovery. This review explores AI's role in stroke rehabilitation, highlighting its impact on early diagnosis, motor recovery, and cognitive rehabilitation. AI-driven imaging techniques, such as deep learning applied to CT and MRI scans, improve early diagnosis and identify ischemic penumbra, enabling timely, personalized interventions. AI-assisted decision support systems optimize acute stroke treatment, including thrombolysis and endovascular therapy. In motor rehabilitation, AI-powered robotics and exoskeletons provide precise, adaptive assistance, while AI-augmented Virtual and Augmented Reality environments offer immersive, tailored recovery experiences. Brain-Computer Interfaces utilize AI for neurorehabilitation through neural signal processing, supporting motor recovery. Machine learning models predict functional recovery outcomes and dynamically adjust therapy intensities. Wearable technologies equipped with AI enable continuous monitoring and real-time feedback, facilitating home-based rehabilitation. AI-driven tele-rehabilitation platforms overcome geographic barriers by enabling remote assessment and intervention. The review also addresses the ethical, legal, and regulatory challenges associated with AI implementation, including data privacy and technical integration. Future research directions emphasize the transformative potential of AI in stroke rehabilitation, with case studies and clinical trials illustrating the practical benefits and efficacy of AI technologies in improving patient recovery.

Employing artificial bee and ant colony optimization in machine learning techniques as a cognitive neuroscience tool

Higher education is essential because it exposes students to a variety of areas. The academic performance of IT students is crucial and might fail if it isn't documented to identify the features influencing them, as well as their strengths and shortcomings. The student academic prediction system needs to be enhanced so that teachers can forecast their students' performance. Numerous studies have been conducted to increase the prediction accuracy of IT students, but they encountered difficulties with unbalanced data and algorithm tuning. To address these issues, the study proposed different machine learning (ML) algorithms that handled imbalanced data by applying the synthetic minority oversampling technique (SMOTE) and employing hyperparameter tuning algorithms to enhance prediction during the training process. The ML models we used were decision tree (DT), k-nearest neighbor, and XGBoost. The models were fine-tuned by applying Ant colony optimization (ACO) and artificial bee colony optimization techniques. Subsequently, these optimization techniques further enhanced the performance of the models. After comparing them, the results showed that SMOTE and ACO combined with the DT model outperformed other models for academic prediction. Additionally, the study utilized the Kendall Tau correlation coefficient technique to analyze the correlation between features and identify factors that positively or negatively impact student success.

Transforming brain cancer biomarker research with patinformatics and SWOT analysis

Brain cancer heterogeneity imposes significant challenges in diagnosis, causing high mortality. The lack of timely diagnosis intensifies these challenges, underscoring the need for improved diagnostics. Recent advancements in biomarker discovery have led to biomarker detection at ultra-low concentrations via multiplexing with biosensors, offering a promising avenue for the timely detection of brain cancer. Serving as a comprehensive resource, this review highlights the crucial role of primary biomarkers in brain cancer diagnosis via integration of patinformatics and SWOT analysis, thereby facilitating timely diagnosis and informed decision making. Furthermore, we aim to outline recent advances in brain cancer prognostics and management strategies, ultimately improving patient outcomes.

Advancing neurological disorders therapies: Organic nanoparticles as a key to blood-brain barrier penetration

The blood-brain barrier (BBB) plays a vital role in protecting the central nervous system (CNS) by preventing the entry of harmful pathogens from the bloodstream. However, this barrier also presents a significant obstacle when it comes to delivering drugs for the treatment of neurodegenerative diseases and brain cancer. Recent breakthroughs in nanotechnology have paved the way for the creation of a wide range of nanoparticles (NPs) that can serve as carriers for diagnosis and therapy. Regarding their promising properties, organic NPs have the potential to be used as effective carriers for drug delivery across the BBB based on recent advancements. These remarkable NPs have the ability to penetrate the BBB using various mechanisms. This review offers a comprehensive examination of the intricate structure and distinct properties of the BBB, emphasizing its crucial function in preserving brain balance and regulating the transport of ions and molecules. The disruption of the BBB in conditions such as stroke, Alzheimer's disease, and Parkinson's disease highlights the importance of developing creative approaches for delivering drugs. Through the encapsulation of therapeutic molecules and the precise targeting of transport processes in the brain vasculature, organic NP formulations present a hopeful strategy to improve drug transport across the BBB. We explore the changes in properties of the BBB in various pathological conditions and investigate the factors that affect the successful delivery of organic NPs into the brain. In addition, we explore the most promising delivery systems associated with NPs that have shown positive results in treating neurodegenerative and ischemic disorders. This review opens up new possibilities for nanotechnology-based therapies in cerebral diseases.

Neuroimaging Findings of Psychosis in Alzheimer's Disease: A Systematic Review

BACKGROUND

Previous studies on neuroimaging findings in Alzheimer's disease (AD) patients with hallucinations and delusions have yielded inconsistent results. We aimed to systematically review neuroimaging findings of delusions and hallucinations in AD patients to describe the most prominent neuroimaging features.

METHODS

We performed a comprehensive search in three online databases, including PubMed, Scopus, and Web of Science in June 2023. We included studies that reported neuroimaging features of AD patients with delusion, hallucination, or psychosis.

RESULTS

After the screening, 34 studies with 2241 AD patients were eligible to be included in our qualitative synthesis. On the basis of the included studies, there are significant changes in the volume and perfusion levels of broad brain areas, including the hippocampus, amygdala, insula, cingulate, occipital, frontal, prefrontal, orbitofrontal, temporal, and parietal cortices in these patients. Moreover, AD patients with psychosis, hallucinations, or delusions reflected different EEG waves compared to AD patients without these disorders.

CONCLUSION

The results of our review provided evidence about the neuroimaging alterations in AD patients suffering from psychosis, hallucinations, and delusions using different imaging methods. AD patients with psychosis, hallucinations, or delusions have significant differences in the volume and perfusion levels of various brain regions along with alterations in EEG waves and biological molecules compared to patients with only AD.

The role of sirtuin 1 in ageing and neurodegenerative disease: A molecular perspective

Sirtuin 1 (SIRT1), an NAD+-dependent deacetylase, has emerged as a key regulator of cellular processes linked to ageing and neurodegeneration. SIRT1 modulates various signalling pathways, including those involved in autophagy, oxidative stress, and mitochondrial function, which are critical in the pathogenesis of neurodegenerative diseases. This review explores the therapeutic potential of SIRT1 in several neurodegenerative disorders, including Alzheimer's disease (AD), Parkinson's disease (PD), Huntington's disease (HD), and Amyotrophic Lateral Sclerosis (ALS). Preclinical studies have demonstrated that SIRT1 activators, such as resveratrol, SRT1720, and SRT2104, can alleviate disease symptoms by reducing oxidative stress, enhancing autophagic flux, and promoting neuronal survival. Ongoing clinical trials are evaluating the efficacy of these SIRT1 activators, providing hope for future therapeutic strategies targeting SIRT1 in neurodegenerative diseases. This review explores the role of SIRT1 in ageing and neurodegenerative diseases, with a particular focus on its molecular mechanisms, therapeutic potential, and clinical applications.

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.