Atria

Real-time adjustments to task difficulty during flight training are crucial for optimizing performance and managing pilot workload. This study evaluated the functionality of a pretrained brain-computer interface (BCI) that adapts training difficulty based on real-time estimations of workload from brain signals. Specifically, an EEG-based neuro-adaptive training system was developed and tested in Virtual Reality (VR) flight simulations with military student pilots. The neuro-adaptive system was compared to a fixed sequence that progressively increased in difficulty, in terms of self-reported user engagement, workload, and simulator sickness (subjective measures), as well as flight performance (objective metric). Additionally, we explored the relationships between subjective workload and flight performance in the VR simulator for each condition. The experiments concluded with semi-structured interviews to elicit the pilots' experiences with the neuro-adaptive prototype. Results revealed no significant differences between the adaptive and fixed sequence conditions in subjective measures or flight performance. In both conditions, flight performance decreased as subjective workload increased. The semi-structured interviews indicated that, upon briefing, the pilots preferred the neuro-adaptive VR training system over the system with a fixed sequence, although individual differences were observed in the perception of difficulty and the order of changes in difficulty. Even though this study shows performance does not change, BCI-based flight training systems hold the potential to provide a more personalized and varied training experience.

Introduction: Lassa fever remains a significant public health threat in West Africa. While vaccine candidates are advancing through the pipeline, efforts in planning timely and sufficient supply have been constrained by limited understanding of demand. This challenge is compounded by the shifting global financing landscape and unclear country-level investment capacity in the vaccine product and the implementation activities required to achieve appropriate coverage. Methods: A demand forecast model for a Lassa fever vaccine was developed. The model includes five implementation scenarios across endemic and non-endemic at-risk countries, accounting for diverse use cases such as integration into routine immunization, targeted geographic campaigns, occupational group campaigns, and reactive use through stockpiles. Key inputs included vaccine characteristics, coverage rates, market entry timing, and policy timelines. Assumptions were developed through literature review and will be refined via workshops and consultations with global, regional, and country experts. Results: Forecasts present a heterogenous vaccine demand picture between 2031 and 2036, driven primarily by differences in countries’ ability to invest. In more conservative scenarios, vaccine demand is initially limited to high-burden regions with gradual scale-up over time, focusing on high-risk groups. Demand is further influenced by product characteristics, such as well as ultra-cold chain capacity. Conclusion: The initial analysis reveals that demand is not solely a function of disease burden but is highly sensitive to country-level prioritization, funding availability, the strategic choices countries make around vaccine introduction. Understanding and anticipating Lassa fever vaccine demand is critical for guiding investment in manufacturing for product developers, in delivery systems for countries, and in policy advocacy for community groups. Demand forecasts must remain flexible, incorporating real-time inputs from country stakeholders and evolving financing landscapes. The ability and prioritisation of countries to invest in both vaccine procurement and implementation strategies will be the primary determinant of demand.

BackgroundMifepristone is a selective progesterone receptor modulator with decades of data demonstrating its potential as a highly effective emergency as well as non-emergency contraceptive. Despite considerable evidence pointing to the potential effectiveness of mifepristone as a non-emergency contraceptive, no systematic review has been conducted to synthesise the available evidence. This systematic review aims to synthesise the current evidence on the use of mifepristone as a non-emergency contraceptive to prevent pregnancy among cisgender girls and women of reproductive age.MethodsWe developed an electronic search strategy in collaboration with the research librarian. We will search five databases (Ovid Medline, CINAHL, EMBASE, Cochrane-Central Trials and Global Health) from inception and identify additional studies using several grey literature search strategies. All databases will be searched from inception, and we planned to complete the search by 30 June 2024. An Ovid Medline search strategy conducted on 24 May 2024 is provided as an example. We will include all studies that involve cisgender girls and/or women of reproductive age (defined as 54 years or younger), which assessed mifepristone as a non-emergency contraceptive to prevent pregnancy. The primary outcome is contraceptive effectiveness. Two independent reviewers will screen studies for eligibility through title, abstract, and full-text review. We will extract data with Covidence software using a Cochrane Effective Practice and Organisation of Care (EPOC)-adapted data-extraction tool and will assess risk of bias using the EPOC risk of bias tool and the Newcastle–Ottawa Scale. If sufficient data are available, we will conduct a meta-analysis using fixed and/or random effect models. However, if we are unable to conduct a meta-analysis, we will present the results narratively using the synthesis without meta-analysis guidelines and the EPOC table recommended for presenting findings without meta-analysis. Grades of Recommendation, Assessment, Development and Evaluation will be used to assess the quality of the evidence. We will report this review according to Preferred Reporting Items for Systematic Review and Meta-Analysis Protocols guidelines.Ethics and disseminationThis review is focused on secondary data and does not require any ethical approval. We aim to publish the review in a peer-reviewed scientific journal to promote knowledge transfer and present results using other knowledge translation mediums.PROSPERO registration numberCRD42024554720.

In concrete, aggregate plays a vital role. The strength and other parameters mainly depend upon the quality of aggregates that we use in the concrete. The aggregates used in concrete are granite stones as coarse aggregates and river sand as fine aggregate, but this leads to severe depletion of natural resources and also sand mining. Sand mining affects the environment and also the ecology system near mining areas. A novel attempt is made by replacing river sand with laterite sand (0% to 100% with an interval of 20%). If the usage of laterite as building material results in good for building construction to some extent sand mining can be avoided. Additional to this to improve the performance of concrete agro waste areca nut fiber (0.5%) was utilised. Since areca nut fiber is one of the waste materials obtained in areca nut farming, which is incorporated in concrete, it would be beneficial for farmers by making areca nut fiber an economical construction material. This novel research aims to investigate the workability, strength properties and microstructural behavior of laterite sand and areca nut fiber-based concrete. The results were found to be in a decrement trend.

Delay to surgery >24 hours has been shown to correlate with mortality rates in patients with hip fracture when left untreated. Many of these patients have multiple comorbidities, including aortic stenosis (AS), and undergo workup for operative clearance, which may delay time to surgery. The purpose of this study was to examine whether preoperative echocardiogram workup affects time to surgery, complications, and mortality after operative fixation for hip fracture. Our institutional hip fracture registry was retrospectively reviewed for inclusion over a 3-year period. Patients who had a preoperative echocardiogram (yECHO) for operative clearance were compared with those who did not (nECHO). Demographic data, time to surgery, overall complication rate, and mortality at 30 days, 90 days, and 1 year were collected. Two cohorts consisted of 136 yECHO patients (45.8%) and 161 nECHO patients (54.2%). Thirty-two yECHO patients (23.5%) had AS. Patients in the yECHO cohort were more likely to have a complication for any cause compared with nECHO patients (25.7% vs. 10.6%, P = 0.01) and have a higher mortality rate at 1 year (38.9% vs. 17.4%, P = 0.001). There was no association found between AS and all-cause complication (P = 0.54) or 30-day (P = 0.13) or 90-day mortality rates (P = 0.79). However, patients with AS had a significantly higher mortality rate at 1 year (45.8% vs. 25.1%, P = 0.03). This study reinforces the benefits of ensuring less than a 24-hour time to surgery in the setting of a hip fracture and identifies an area of preoperative management that can be further optimized to prevent unnecessary prolongation in time to surgery. Patients with known aortic stenosis are not associated with increased 30-day or 90-day mortality or all-cause complications. Surgical delays in the yECHO cohort were attributed to preoperative medical assessments, including echocardiograms and the management of comorbidities. Therefore, the selective utilization of preoperative echocardiograms is needed and should be reserved to ensure they have a definitive role in guiding the perioperative care of patients with hip fracture. III.

Objectives: The primary objectives of this article are to explore and highlight the rich cultural and educational history of Uzbek literature, with a specific focus on the themes of the Motherland, freedom, and independence in Uzbek poetry over the past century. The artist's creative concept, particularly in relation to these themes, will be examined to understand the depth and significance of their contributions. Methods: The methods employed in achieving the objectives involve a detailed analysis of the poet's works, specifically focusing on lyrical scenes from history expressed through beautiful and vivid language. The article delves into the wide and colorful range of themes present in the poet's work, emphasizing pride in being a child of the Motherland. The exploration includes an examination of the poet's use of unique and colorful lines to glorify the pathos of the Motherland, expressing love for the oasis where the poet was born, namely the beautiful nature of Surkhan, and the poet's anthem. Results: The results of the analysis reveal a multifaceted portrayal of the Motherland in the poet's poetry. The symbol of the Motherland is intricately woven with its historical roots, reflecting a harmonious blend of the long past with modernity. The poet's verses celebrate the freedom of the Motherland and express a deep connection to the Uzbek spirit through simple and sincere expressions. The article showcases the poet's ability to convey a sense of national pride and appreciation for the Motherland through the exploration of various themes and historical elements. Conclusion: In conclusion, the article summarizes the findings of the analysis, emphasizing the significance of the themes of the Motherland, freedom, and independence in Uzbek poetry. The poet's contributions to literature are highlighted, showcasing how the artist effectively communicates the pride and love for the Motherland through rich and colorful verses. The article underscores the enduring relevance of these themes and their portrayal in the poet's work, contributing to the cultural and literary heritage of Uzbekistan.

Population-based screening programs for the early detection of breast cancer have significantly reduced mortality in women, but they are resource intensive in terms of time, cost, and workload and still have limitations mainly due to the use of 2D imaging techniques, which may cause overlapping of tissues, and interobserver variability. Artificial intelligence (AI) systems may be a valuable tool to assist radiologist when reading and classifying mammograms based on the malignancy of the detected lesions. However, there are several factors that can influence the outcome of a mammogram and thus also the detection capability of an AI system. The aim of our work is to analyze the robustness of the diagnostic ability of an AI system designed for breast cancer detection. Mammograms from a population-based screening program were scored with the AI system. The sensitivity and specificity by means of the area under the receiver operating characteristic (ROC) curve were obtained as a function of the mammography unit manufacturer, demographic characteristics, and several factors that may affect the image quality (age, breast thickness and density, compression applied, beam quality, and delivered dose). The area under the curve (AUC) from the scoring ROC curve was 0.92 (95% confidence interval = 0.89 - 0.95). It showed no dependence with any of the parameters considered, as the differences in the AUC for different interval values were not statistically significant. The results suggest that the AI system analyzed in our work has a robust diagnostic capability, and that its accuracy is independent of the studied parameters.

Future Road transportation mainly depends upon connected vehicles. Intelligent Transportation Systems bring benefits to the road users through Vehicular Adhoc Networks (VANETs). Since VANET packet contains life critical information, security is inevitable. A rogue node called sybil node can transmit fake messages to its neighbours and disrupt the system, challenging security. Since the nodes are very dynamic, stability is also a major concern. Existing rogue node detection methods do not address this problem suitably. In the proposed work, rouge node detection is implemented in a clustered network which improves the stability of the network. The main aim of this paper is to implement a sybil attack detection method in distributed or coordinated clustered networks using a novel hybridization technique. The cluster head detects the sybil attacker by comparing the received signal strength of packets from each node based on a similarity algorithm, Longest Common SubSequence (LCSS). However, if the sybil attacker launches a power control mechanism, the similarity calculation fails. To overcome this issue, a Change Point Detection(CPD) technique by comparing the changes in mean value of RSS time series from a particular node is proposed. Coordinated attacks can be easily detected in a clustered network as the information regarding the attackers' spreads in the network quickly so that the nodes can avoid connecting to such malicious nodes during their journey. The proposed algorithm shows significant improvement in detection rate, detection delay and false positive for varying vehicle count compared to existing techniques.