Elevating metaverse virtual reality experiences through network-integrated neuro-fuzzy emotion recognition and adaptive content generation algorithms

Interactions between individuals and digital material have completely changed with the advent of the Metaverse. Due to this, there is an immediate need to construct cutting-edge technology that can recognize the emotions of users and continuously provide material that is relevant to their psychological states, improving their overall experience. An inventive method that combines natural language processing adaptive content generation algorithms and neuro-fuzzy-based support vector machines natural language processing (SVM-NLP) is proposed by researchers to meet this demand. With this merging, the Metaverse will be able to offer highly tailored and engaging experiences. Initially, a neuro-fuzzy algorithm was developed to identify people's emotional moods from their physiological reactions and other biometric information. Fuzzy Logic and Support Vector Machine work together to manage the inherent ambiguity and unpredictability, which results in a more exact and accurate categorization of emotions. A key component of the ACGA is NLP technology, which uses real-time emotional data to dynamically modify and personalize characters, stories, and interactive features in the Metaverse. The novelty of the proposed approach lies in the innovative integration of neuro-fuzzy-based SVM-NLP algorithms to accurately recognize and adapt to users' emotional states, enhancing the Metaverse experience across various applications. The proposed method is implemented using Python software. This adaptive approach significantly enhances users' immersion, emotional involvement, and overall satisfaction within the augmented reality environment by tailoring information to their responses. The findings show that the SVM-NLP emotion identification algorithm based on neuro-fuzzy, has a high degree of accuracy in recognizing emotional states, which holds promise for creating a Metaverse that is more emotionally compelling and immersive. Stronger human–computer interactions and a wider range of applications, including virtual therapy, educational resources, entertainment, and social media networking, might be made possible by integrating SVM-NLP. These sophisticated systems are around 92% accurate in interpreting the emotions

Impostor phenomenon among urologists in Saudi Arabia

Impostor phenomenon (IP) is the persistent inability to believe that one’s success is deserved or has been legitimately achieved due to one’s efforts or skills. It is associated with burnout, anxiety and depression and can negatively impact the lives of the affected individuals. This study aimed to determine the prevalence of IP among urologists in Saudi Arabia. A cross-sectional study was conducted among practicing urologists and urologists-in-training in Saudi Arabia between November and December 2022. A self-administered questionnaire comprising questions on the sociodemographic characteristics and the Clance impostor phenomenon scale (CIPS) was distributed through email to all registered urologists in the Saudi Commission for Health Specialties database. A total of 155 urologists (143 men and 12 women) were enrolled in this study. The majority of the urologists (44.5%) were consultants, and the prevalence of the impostor phenomenon in this study was 27.7%. Nearly half of the urologists (49.7%) presented moderate levels of the phenomenon, 23.9% of the urologists demonstrated high levels, and 20.6% presented low levels. Only 5.8% of the urologists showed intense levels of the phenomenon. The phenomenon was significantly more prevalent among those in training (p = 0.010) and less prevalent among those with a subspecialty in endourology (p = 0.016). The prevalence of the impostor phenomenon among urologists was 27.7%. It was more commonly seen in resident urologists, and those with a subspecialty in endourology were less likely to be affected by this phenomenon

A novel efficient energy optimization in smart urban buildings based on optimal demand side management

Data availability: The data used for this research and prepatation of this article can be accessed from Brunel University of London repository at: https://doi.org/10.17633/rd.brunel.26049436.v1.Increasing electrical energy consumption during peak hours leads to increased electrical energy losses and the spread of environmental pollution. For this reason, demand-side management programs have been introduced to reduce consumption during peak hours. This study proposes an efficient energy optimization in Smart Urban Buildings (SUBs) based on Improved Sine Cosine Algorithm (ISCA) that uses the load-shifting technique for demand-side management as a way to improve the energy consumption patterns of a SUBs. The proposed system's goal is to optimize the energy of SUBs appliances in order to effectively regulate load demand, with the end result being a reduction in the peak to average ratio (PAR) and a consequent minimization of electricity costs. This is accomplished while also keeping user comfort as a priority. The proposed system is evaluated by comparing it with the Grasshopper Optimization Algorithm (GOA) and unscheduled cases. Without applying an optimization algorithm, the total electricity cost, carbon emission, PAR and waiting time are equal to 1703.576 ID, 34.16664 (kW), and 413.5864s respectively for RTP. While, after applying GOA, the total electricity cost, carbon emission, PAR and waiting time are improved to 1469.72 ID, 21.17 (kW), and 355.772s respectively for RTP. While, after applying the ISCA Improves the total electricity cost, PAR, and waiting time by 1206.748 ID, 16.5648 (kW), and 268.525384s respectively. Where after applying GOA, the total electricity cost, PAR, and waiting time are improved to 13.72 %, 38.00 %, and 13.97 % respectively. And after applying proposed method, the total electricity cost, PAR, and waiting time are improved to 29.16 %, 51.51 %, and 35.07 % respectively. According to the results, the created ISCA algorithm performed better than the unscheduled case and GOA scheduling situations in terms of the stated objectives and was advantageous to both utilities and consumers. Furthermore, this study has presented a novel two-stage stochastic model based on Moth-Flame Optimization Algorithm (MFOA) for the co-optimization of energy scheduling and capacity planning for systems of energy storage that would be incorporated to grid connected smart urban buildings.The research has been partially supported by the Faculty of Informatics and Management UHK excellence project “Methodological perspectives on modeling and simulation of hard and soft systems”

Large expert-curated database for benchmarking document similarity detection in biomedical literature search

Document recommendation systems for locating relevant literature have mostly relied on methods developed a decade ago. This is largely due to the lack of a large offline gold-standard benchmark of relevant documents that cover a variety of research fields such that newly developed literature search techniques can be compared, improved and translated into practice. To overcome this bottleneck, we have established the RElevant LIterature SearcH consortium consisting of more than 1500 scientists from 84 countries, who have collectively annotated the relevance of over 180 000 PubMed-listed articles with regard to their respective seed (input) article/s. The majority of annotations were contributed by highly experienced, original authors of the seed articles. The collected data cover 76% of all unique PubMed Medical Subject Headings descriptors. No systematic biases were observed across different experience levels, research fields or time spent on annotations. More importantly, annotations of the same document pairs contributed by different scientists were highly concordant. We further show that the three representative baseline methods used to generate recommended articles for evaluation (Okapi Best Matching 25, Term Frequency-Inverse Document Frequency and PubMed Related Articles) had similar overall performances. Additionally, we found that these methods each tend to produce distinct collections of recommended articles, suggesting that a hybrid method may be required to completely capture all relevant articles. The established database server located at https://relishdb.ict.griffith.edu.au is freely available for the downloading of annotation data and the blind testing of new methods. We expect that this benchmark will be useful for stimulating the development of new powerful techniques for title and title/abstract-based search engines for relevant articles in biomedical research.Peer reviewe

The Outcomes and Adverse Drug Patterns of Immunomodulators and Thrombopoietin Receptor Agonists in Primary Immune Thrombocytopenia Egyptian Patients with Hemorrhage Comorbidity

Immune thrombocytopenia (ITP) treatment has evolved recently. However, none of the treatments have only benefits without drawbacks. This study aimed to compare the clinical outcomes and adverse drug patterns of Eltrombopag, Romiplostim, Prednisolone + Azathioprine, High Dose-dexamethasone (HD-DXM) (control group), and Rituximab in primary ITP Egyptian patients. All patients were initiated with corticosteroids, HD-DXM, as a first-line treatment for the first month immediately following diagnosis. Four hundred sixty-seven ITP patients were randomly assigned to five groups. The outcome measures were judged at baseline, at the end of treatment (6 months), and after an additional 6-month free treatment period. The follow-up period for which relapse is noted was 6 months after the end of treatment. Eltrombopag and Romiplostim resulted in a significantly higher incidence of sustained response than Rituximab, HD-DXM, and Prednisolone + Azathioprine (55.2% and 50.6% vs. 29.2%, 29.1%, and 18%, respectively; p-value p-value < 0.01). We also describe 23 reports of pulmonary hypertension with Prednisolone+ Azathioprine and 13 reports with HD-DXM. The thrombotic events occurred in 16.6% and 13% of patients who received Eltrombopag and Romiplostim treatment, respectively. Most patients had at least one or two risk factors (92.8% of cases). Corticosteroids are effective first-line therapy in primary ITP patients. However, relapse is frequent. Eltrombopag and Romiplostim are safer and more effective than Prednisolone, HD-DXM, and Rituximab. They might be reasonable beneficial options after a one-month HD-DXM regimen

Therapeutic Outcomes of High Dose-Dexamethasone versus Prednisolone + Azathioprine, Rituximab, Eltrombopag, and Romiplostim Strategies in Persistent, Chronic, Refractory, and Relapsed Immune Thrombocytopenia Patients

Background: Primary immune thrombocytopenia (ITP) is an inflammatory autoimmune disease that can be managed with several treatment options. However, there is a lack of comparative data on the efficacy of these options in different phases of the disease. Aim of the study: This study aimed to evaluate the efficacy of high-dose Dexamethasone (HD-DXM), Prednisolone + Azathioprine, Rituximab, Eltrombopag, and Romiplostim schedules in persistent, chronic refractory or relapsed Egyptian ITP patients with a platelet count ≤30 × 109/L. The primary outcome measure was a sustained increase in platelet counts over 50 × 109/L for an additional 12 months without additional ITP regimens. The study also aimed to identify a suitable treatment regimen with a long remission duration for each phase of ITP. Results: Prednisolone + Azathioprine was significantly more effective in achieving an overall response in persistent patients than Romiplostim, high-dose Dexamethasone, and Rituximab. (90.9% vs. 66.6, [Odds ratio, OR: 5; confidence interval, CI 95% (0.866–28.86)], 45%, [OR: 0.082, CI 95% (0.015–0.448)] and, 25%, [OR: 30, CI 95% (4.24–211.8)], respectively, p-value p-value < 0.01). Conclusions: Finally, Eltrombopag following HD-DXM showed the highest percentage of patients with complete treatment-free survival times of at least 330 days. These findings could help clinicians choose the most appropriate treatment for their patients with ITP based on the phase of the disease. This trial is registered in clinicaltrials.gov with registration number NCT05861297

Interaction-enhanced and time-aware graph convolutional network for successive point-of-interest recommendation in traveling enterprises

Abstract Extensive user check-in data incorporating user preferences for location is collected through Internet of Things (IoT) devices, including cell phones and other sensing devices in location-based social network. It can help traveling enterprises intelligently predict users’ interests and preferences, provide them with scientific tourism paths, and increase the enterprises income. Thus, successive point-of-interest (POI) recommendation has become a hot research topic in augmented Intelligence of Things (AIoT). Presently, various methods have been applied to successive POI recommendations. Among them, the recurrent neural network-based approaches are committed to mining the sequence relationship between POIs, but ignore the high-order relationship between users and POIs. The graph neural network-based methods can capture the high-order connectivity, but it does not take the dynamic timeliness of POIs into account. Therefore, we propose an I nteraction-enhanced and T ime-aware G raph C onvolution N etwork (ITGCN) for successive POI recommendation. Specifically, we design an improved graph convolution network for learning the dynamic representation of users and POIs. We also designed a self-attention aggregator to embed high-order connectivity into the node representation selectively. The enterprise management systems can predict the preferences of users, which is helpful for future planning and development. Finally, experimental results prove that ITGCN brings better results compared to the existing methods