With a population of over 1.5 billion people in 2024, Africa is home to a diverse group of individuals, including the Lesbian, Gay, Bisexual, Transgender, and Queer (LGBTQ) community. However, severe stigmatization and discrimination towards LGBTQ individuals have resulted in healthcare disparities. These disparities are exacerbated by limited access to medical care services, poor health outcomes, and a lack of cultural competence among healthcare providers. Thus, LGBTQ individuals are at a higher risk of sexually transmitted infections and mental health issues. This research commentary sheds light on the healthcare disparities faced by LGBTQ individuals in Africa and highlights the impact of stigmatization on their health. To tackle these disparities and promote healthcare equity for LGBTQ people in Africa, there is a need for increased advocacy for policy and legal reforms that protect their rights and encourage healthcare equity. Fostering education and awareness among healthcare providers, policymakers, and the general public and implementing efficient and sustainable data collection strategies relating to their healthcare needs is also crucial. Hence, a concerted effort fostering collaboration among various stakeholders including healthcare providers, NGOs, government health agencies, and policymakers is essential to advancing inclusive healthcare equity for the LGBTQ population in Africa.

Opportunistic colonization and recurring infection by Pseudomonas aeruginosa are substantial risks to lung functionality for people with underlying respiratory diseases such as cystic fibrosis and chronic obstructive pulmonary disease. The complex metabolic and phenotypic adaptations P. aeruginosa exhibits in response to its environmental conditions make relevant in vitro models of pathogenic populations crucial for identifying and evaluating effective antimicrobial targets. However, an extracellular component that is rarely integrated into these experiment platforms is a spatially extensive, semisolid gel medium representative of biological respiratory mucus layers that P. aeruginosa propagates through via active motility. In this investigation, we examine the applicability of swim plate assays, a qualitative methodology for measuring flagellar swimming motility, as an in vitro platform to study the spatiotemporal development of P. aeruginosa strain PA14. The propagation behavior of PA14 was tracked through timelapse microscopy and studied under different agar gel compositions incorporating methylcellulose as well as native MUC5AC mucin. To aid quantitative characterization of PA14 population expansion, we paired this experimental workflow with a continuum model that would fit density profile fluctuations to changes in PA14 swimming motility and growth kinetics.We observed higher extracellular concentration and production of the phenazine pyocyanin when PA14 populations were grown in swim plate assays supporting the emergence of heterogeneous growth environments within the microbial population. PA14 swim plates exhibited a significantly lower spreading velocity in gels containing 0.30% w/v MUC5AC, which model-to-experiment fitting results determined to be driven by reductions in PA14 swimming motility. Continuum model parameters additionally portrayed PA14 expansion in mucin gels having cell growth outcompeting cell motility, which aligned with experimental assay observations of macrocolonies rapidly developing to high biomass density states. In contrast, PA14 did not show spreading velocity differences in gels containing 0.30% methylcellulose, and fitted parameters did not identify major growth and motility differences when compared to agar only gels. Combined with the resource accessibility of this experimental platform, the swim plate assay as an in vitro model is well suited to investigations of pathogenic community dynamics in gel conditions over more extensive spatial and time scales.

Two novel interface fracture energy measurement techniques, the modified clamped beam bending and the modified cantilever bending, are compared, on a thermal spray coating/substrate system consisting of air plasma sprayed Alumina over Mild steel. Previously, every such geometry was used to determine one value of interface fracture energy from one sample each, which made it mandatory to test materials at high volumes to gather enough statistics. In the present case, multiple values of interface toughness are obtained on the same sample by converting both the geometries to a high throughput condition for the first timeTo precisely control the length of the crack, digital image correlation (DIC) based crack opening displacement (COD) is used as a feedback loop. Energy release rate, G and phase angle, ψ are calculated as a function of load and crack length using finite element simulations for both the techniques. The critical load from the experiments is used to determine the interface fracture energy GC. The interface fracture energy and the standard deviation is found to be geometry dependent, with the modified clamped beam yielding a GC of 56 ± 16 J/m 2, while the modified cantilever results in a GC of 89 ± 5 J/m 2. The role of the phase angle and the mode mixity ratio on the interface fracture energy and the resulting spread in the data is discussed. The advantages and limitations of the two geometries are contextualised for testing a variety of thermal spray coating/substrate combinations and other ceramic/metal interfaces.

As artificial intelligence (AI) systems become increasingly integrated into the workflows, decision-making, and creative processes of human users, a new form of UX relationship has emerged-one characterized by emotional resonance, memory continuity, and behavioral adaptation. We propose the term Adaptive Presence to describe this next-generation paradigm, wherein AI is not experienced as a tool, but as a trusted and evolving teammate.

We investigate the Nuclear Stark Effect induced in hydrogen-like atomic nuclei under super-intense laser fields. Since laser wavelengths are generally larger than nuclear dimensions, direct laser-nucleus interaction is unfeasible. Instead, this effect is induced indirectly through electron oscillations in the laser field, which produce a periodic electric field that shifts the nuclear energy levels. Using perturbation theory, we derive an expression for the energy shift and dynamic polarizability of the nucleus as a function of laser parameters. Our findings reveal that the Nuclear Stark Effect can be controlled by adjusting the laser frequency and intensity, potentially enabling applications in nuclear and quantum optical systems.

Personality significantly influences our attitudes/reactions/choices towards various situations and social interactions, affecting task suitability and team compatibility. Personality trait identification and analysis are vital in personal development, job selection, and performance enhancement. The Myers-Briggs Type Indicator (MBTI) is one of the effective models for understanding individual personality traits. Traditionally, MBTI assessments relied on psychologists, thus introducing subjectivity and bias. After post-pandemic, with increased online interactions using social media platforms, identifying traits without direct intervention through natural interactions is becoming inevitable before formal testing. Recently, deep learning algorithms have shown promise in developing such automated techniques for Personality Recognition; however, they face challenges with existing imbalanced data. We propose an efficient oversampling strategy using the transformer model GPT-2 and Synthetic Minority Oversampling Technique (SMOTE) to address imbalanced data problems with existing MBTI datasets. We introduce a novel lightweight CNN architecture called Dilated Separable Residual Network (DSRNet) that employs depth-wise separable convolutions to minimize computational cost while achieving good accuracy for Personality Recognition.

Chronotopology (CT) is a proposed subfield of Ulyssean Mathematics (UM) that aims to establish a rigorous mathematical structure describing the nature of time, space, and their interactions across dimensional layers. It introduces new constants, operators, and topological constructs for modeling the flexibility and shape of spacetime within and between universes. This paper outlines the fundamental principles and mathematical formalism of CT, its relationship to existing physical phenomena, and its potential applications in high-dimensional physics and topology.

Title:Persistent Opioid–Induced Hiccups: A Case Report and A Systematic Review of Existing LiteratureAuthors: 1. Shaikha Salah Alhaj, MBBS 2. Nada Mansour Abdulhameed, MBBS 3. Mishel Zaichenko 4. Abdul Kareem Abu Ali, MBBS

Bioorthogonal cleavage chemistry (BCC) has been extensively applied to fluorescence-based imaging in cancer diagnostics. Its potential in chemiluminescence imaging is to be explored. In this study, a smart ruthenium (Ru)-catalyzed bioorthogonal activation chemiluminescence (BAC) probe is developed by integrating BCC with a phenoxy-adamantyl-1,2-dioxetane (PAD) for real-time, in vivo imaging of thiol-containing metabolites, particularly hydrogen sulfide (H 2S), associated with thiol dysregulation in the tumor microenvironment. The BAC probe overcomes many limitations existed in other chemiluminescence probes via a highly selective “Ru-locked” mechanism to achieve light-independent, thiol-triggered activation in complex tumor microenvironment. This mechanism enables rapid activation (1 min), high sensitivity (LOD = 0.243 μM), and stable luminescence with a half-life of 18.5 h, as determined in vitro, across a broad emission range (400-800 nm). The probe also demonstrates enhanced selectivity for thiol-containing metabolites, particularly H 2S, and exhibits low toxicity both in vitro and in vivo. In a breast cancer mouse model, the probe successfully visualizes endogenous H 2S with high spatial precision, supporting its utility in tumor localization and image-guided surgery. In addition, the PAD scaffolds are developed via an efficient synthetic route, significantly lowering production costs (300- to 400-fold) and increasing yields from 40% to 95%. Furthermore, our BAC probe holds a broad potential for non-invasive diagnosis and real-time monitoring of thiol dysregulation and pathophysiological processes.

With the development of organic solar cells (OSCs), maintaining the batch stability of photovoltaic donor materials and improving the device stability are becoming a new challenge. Given the successful application of giant oligomeric acceptors, increasing the molecular size while keeping precise molecular structure have been proven to be an effective method. However, the efficient giant oligomeric donors are still less due to a lack of design principles. Here, we innovatively designed and developed “face-to-face” type giant dimeric donors (GDDs), DZ-1 and DZ-2, by covalently tethering BTR-Cl monomer. Using the different rhodanine-based terminals significantly tuned their molecular interaction and thermal-driven assembly capability. Compare to DZ-1, DZ-2 had moderate molecular stacking and compatible miscibility in the blend film, thus realizing a higher PCE of 13.27%. Importantly, the GDDs with increasing molecular size not only improved the Tg, but also suppressed the molecular diffusion in blend films. Furthermore, the ternary OSCs based on PM6:DZ-2:L8-BO achieved an improved PCE of 18.89% and higher device stability, due to the establishment of 3D charge transport channel and suppression of the molecular diffusion. This study provides a new design strategy of giant molecule donors to develop high-performance and stable OSCs.

Introduction: A novel temperature-controlled radiofrequency (RF) catheter enables pulmonary vein isolation (PVI) using very high-power short-duration (vHPSD) ablation, reducing esophageal injury risk but raising concerns about lesion durability in thicker atrial myocardium. This study aimed to assess the efficacy and safety of a hybrid approach that integrates conventional Ablation Index (AI)-guided PVI with vHPSD ablation. Methods: This prospective, single-center study enrolled 160 consecutive patients with atrial fibrillation (AF) between January 2023 and December 2023, who were allocated into two groups. Group 1 (n=80) underwent conventional AI-guided PVI using a 40W setting, while Group 2 (n=80) received a hybrid approach combining 90W and 50W ablation with a temperature-controlled RF catheter (QDOT Micro™, Biosense Webster, Inc., Diamond Bar, CA). Results: Group 2 demonstrated significantly shorter duration for PVI compared to Group 1 (28 ± 11 minutes vs. 35 ± 10 minutes, p < 0.001), with similar rates of first pass isolation (86% vs. 89%, p = 0.63), and acute reconnection (10% vs. 5%, p = 0.23). Complication rates were comparable between the groups (1.3% vs. 1.3%, p = 1.00), with no cases of esophageal or phrenic nerve injury reported. Kaplan-Meier analysis showed no significant difference in freedom from AF at one year (84% vs. 83%, log-rank p = 0.78). Conclusion: The integration of Ablation Index-guided ablation with vHPSD ablation, utilizing a novel temperature-controlled RF catheter, significantly reduces procedural duration while maintaining safety and efficacy comparable to conventional AI-guided PVI.

Persistent atrial fibrillation (PeAF) is associated with a highly significant increase in heart failure, thromboembolism and death and a poorer quality of life. Prognosis improvement requires the sinus rhythm restoration. The first-line Marshall Plan ablation (MPA) appears to be a safe new reproducible strategy for PeAF patients. In this field the discussion is based on the safety and efficacy of this approach. Many elements are in favour of this MPA approach mainly based on randomised recently published studies but also on prospective real life cohort studies. Based on these reports a specific discussion is elaborated on the benefit/risk balance of this strategy. The evidence-based medicine studies are in favour of this strategy and remain at the heart of the debate.

This paper presents a comprehensive study on the use of ensemble Reinforcement Learning (RL) models in financial trading strategies, leveraging classifier models to enhance performance. By combining RL algorithms such as A2C, PPO, and SAC with traditional classifiers like Support Vector Machines (SVM), Decision Trees, and Logistic Regression, we investigate how different classifier groups can be integrated to improve risk-return trade-offs. The study evaluates the effectiveness of various ensemble methods, comparing them with individual RL models across key financial metrics, including Cumulative Returns, Sharpe Ratios (SR), Calmar Ratios, and Maximum Drawdown (MDD). Our results demonstrate that ensemble methods consistently outperform base models in terms of risk-adjusted returns, providing better management of drawdowns and overall stability. However, we identify the sensitivity of ensemble performance to the choice of variance threshold τ, highlighting the importance of dynamic τ adjustment to achieve optimal performance. This study emphasizes the value of combining RL with classifiers for adaptive decision-making, with implications for financial trading, robotics, and other dynamic environments.

Researchers tag and track marine animals to study migration patterns, human impacts on behavior, and behavioral shifts due to climate change. Accurate data collection often relies on tagging individual animals to collect spatio-temporal state estimates of the animal’s geo-position and depth, allowing the measurement of animal motion behaviors and context. Acoustic transmitters are prominent due to their continuous communication without requiring manual retrieval or surfacing to collect data. These transmitters emit underwater acoustic pulses which can be detected by acoustic receivers, or hydrophones. However, the frequent movement of aquatic animals results in high data loss when the animal moves out of the detection range of the stationary hydrophone. Autonomous underwater vehicle (AUV) systems offer a promising solution for localizing acoustic transmitters with higher data resolution over longer periods of time. Such systems deployed in the past have often required multiple hydrophones mounted on a large frame carried by the AUV. This increases AUV drag, limiting the speed at which the AUV can track highly mobile animals such as sharks over large spatial and temporal scales. This work provides an alternative by equipping multiple AUVs with a single compact hydrophone payload, increasing the temporal and spatial resolution and accuracy, with the ability to operate both online and offline. A particle filter algorithm equipped with a hidden Markov model (HMM) behavioral motion model fuses acoustic measurements from multiple AUVs to estimate the acoustic transmitter’s position. Validation using data collected in Santa Elena Bay, Costa Rica, and Long Beach, California, shows a root mean square error (RMSE) of approximately 10 meters for short-term deployments, and a larger simulated dataset shows an RMSE of approximately 15 meters for longer deployments over a much larger geographic area. Using the particle filter with the behavioral motion model fit to historical animal movement data greatly outperforms a baseline random walk motion model. In the absence of such historical data, using the particle filter with a generic velocity motion model also outperforms the baseline model, although not as well as the behavioral motion model. This approach is reasonably robust: it is able to maintain a similar time to convergence with up to 20% of measurements lost.

AbstractThis paper presents a novel approach to cross-cultural AI negotiation agents to facilitate cross-cultural conflict resolution. In multi-party negotiations across different cultural value dimensions, we develop a multi-layered architecture composed of cultural value dimensions, negotiation strategies, and ethical reasoning, which can mediate complex negotiations. Specifically for negotiating with others, we propose a combination of LLMs and symbolic reasoning capabilities to recognize and change accordingly to cultural nuances in the negotiation contexts. We do that through simulated case studies of three different cultural contexts in territorial and resource disputes and show that our system is effective. This shows that culturally aware AI negotiation agents can solve 72% of the scenarios that fail to achieve agreement using only 43% less negotiation timeframes than traditional diplomatic approaches. We explore the implications for international relations, the ethical considerations, and avenues for future research directed at developing AI mediators for complex geopolitical conflicts.

Transdimensional Number Theory (TNT) is a new branch of mathematics that seeks to define arithmetic operations across multiple dimensions, each with its own unique set of rules and behaviors. TNT incorporates the effects of entropy and instability as dimensions increase, allowing us to model the behavior of numbers and operations in a more complex, multi-dimensional framework. This paper introduces TNT's foundations, operators, and potential connections to theoretical physics, as well as its implications for the study of higher-dimensional spaces and the nature of numbers.

Spatial environmental heterogeneity is widely assumed to enhance ecological stability by promoting refugia, biodiversity, and asynchrony. Yet, we lack field experiments testing this fundamental relationship and its underlying mechanisms in naturally-assembled multitrophic systems. To address this gap, we monitored experimental substrates replicating topographic heterogeneity on a rocky shore over three years. Contrary to theory, heterogeneity showed no net effect on community stability due to four counteracting pathways. Heterogeneity increased stability by i) providing refugia that enhanced population stability and ii) boosting species richness, which promoted asynchrony. At the same time, it decreased stability by iii) reducing a dominant non-native species and iv) suppressing consumers, both of which otherwise stabilised community composition. These opposing processes cancelled out the heterogeneity-stability relationship, highlighting the complex and multi-causal nature of this relationship. We caution against the assumption that increasing heterogeneity universally enhances stability, particularly in systems with strong consumer interactions and dominant species.

Global change impacts on biodiversity are highly context-dependent, complicating predictions of ecosystem functioning. We synthesized 18,739 control-treatment paired observations from 680 studies to assess plant diversity responses to simulated global change, identify key mechanisms, and evaluate associated ecological consequences. Our results show that soil texture (sand-to-silt ratio) is the dominant spatial predictor of plant diversity. Elevated CO2 had a neutral effect on species richness, whereas increased precipitation enhanced it. In contrast, warming, drought, and nitrogen addition reduced species richness, with nitrogen addition consistently suppressing Shannon diversity and evenness. Nearly half of all two-driver interactions were additive, although non-additive effects were also common. Contextual factors, including initial diversity, sampling size, experimental duration, and soil properties, mediated plant diversity dynamics under global change. Importantly, shifts in plant diversity altered ecosystem productivity, carbon and water fluxes, and soil biota, underscoring the need to incorporate biodiversity and site-specific context into global change-ecosystem feedback models.

The black stork (Ciconia nigra), recognized as a wetland umbrella species and biological indicator, plays a crucial role in maintaining ecosystem balance and biodiversity conservation. However, it faces significant threats from habitat fragmentation and degradation. This study employed the MaxEnt model and landscape connectivity analysis to evaluate suitable habitats for black storks in China, designed an ecological corridor network, and identified key ecological nodes. The findings reveal that areas of high habitat suitability are primarily located in North China, the northwestern region of Xinjiang, and the middle and lower reaches of the Yangtze River. The ecological corridor network connects regions between North China and the Yangtze River Basin, forming a rectangular network with vertices in Gansu-Qinghai, Shanxi-Beijing-Tianjin-Hebei, the lower Yangtze, and Sichuan-Yunnan Province, respectively, totaling 29,099 kilometers in length. Additionally, four ecological nodes requiring priority protection and management were identified. The study proposes conservation strategies which improve habitat connectivity and ecological functionality to ensure long-term stability of black stork populations. These include prioritizing the protection of highly suitable habitats, enhancing ecological restoration in the Hexi Corridor, and optimizing the management of nature reserves.

Purpose: Leveraging machine learning techniques allows for a deeper understanding and prediction of health issues related to smoking. Identifying specific health markers enables the early detection of potential problems in smokers, facilitating timely interventions. Methods: We analyzed a vast health dataset that included 55,691 records from individuals. Each record contained various health indicators such as blood pressure, cholesterol levels, liver enzymes, and kidney function markers. We focused on the smoking status of participants (whether they were smokers or non-smokers) and used different machine learning models to predict health risks based on these indicators. Results: Our findings showed that the Random Forest model performed the best, achieving an impressive score of 0.907 in distinguishing between health risks for smokers and non-smokers. This model was particularly good at identifying how smoking impacts different health areas, including the heart, liver, and kidneys. Conclusion: This research demonstrates that machine learning tools can be valuable in predicting health issues related to smoking. By combining effective prediction with easy-to-understand insights, these models can help healthcare providers identify at-risk smokers sooner and tailor interventions to improve their health. Our work highlights the potential for personalized approaches to assist smokers in managing their health better. Keyword: Smoking; Machine Learning; Public Health; Predictive Analytics; Risk Assessment.

In this paper, a new four-dimensional fractional-order chaotic system is constructed by introducing a memristor as a feedback term in a fractional-order Lorenz chaotic system. The analytical solution of the system is obtained based on the Adomian decomposition method, and the system is obtained to have an infinite set of equilibrium points. From the Lyapunov exponential spectrum and bifurcation diagram, the fractional-order system exhibits rich dynamical behaviors, such as stable state, multiplicative-period bifurcation, bifurcation mode, chaotic state, etc., under the single-parameter variations are analyzed. The system is found to have double-scroll chaotic attractor, period 1 state attractor, period 2 state attractor of different types under the variation of double parameter analyzed using Lyapunov exponential spectrograms, maximum minimum phase diagrams and time series plots. According to the spectral entropy SE and C 0 complexity map, the system is analyzed to have parameter sensitivity and complex dynamics conversion, and it is concluded that the parameter b has more significant effect on the complexity of fractional-order Lorenz memristor systems, which provides good ideas for the study of fractional-order circuit systems.

We consider the stabilization of star-shaped network with moving boundary. The network consists of a single node with 3 connected arcs. The dynamics on each arc is governed by the wave equation. There is boundary damping at the first two boundary points. At the central node, the states are coupled by algebraic conditions. We show the solution behaviour of star-shaped network with Neumann boundary conditions, that is, the energy of star-shaped network with mixed boundary conditions decrease or not depending on the different range of parameter.

A document by Hongtian Zhang. Click on the document to view its contents.

Objective: To systematically evaluate the efficacy of probiotic intervention in early life for preventing allergic diseases in children. Methods: Databases including Wanfang, CNKI, CBM, Cochrane Library, PubMed, Web of Science, and Embase were searched for randomized controlled trials (RCTs) on the efficacy of probiotic intervention in early life for preventing allergic diseases. Reviewers screened the literature, extracted data, and evaluated the risk of bias in the included studies. The risk of bias for the included studies was assessed in accordance with the guidelines for bias assessment provided in the Cochrane Handbook. Meta-analysis was performed using Stata 11.0. Results: A total of 44 literatures were ultimately included, with 28 on eczema, 21 on asthma, 16 on wheezing, 16 on food allergy, 9 on allergic rhinitis, and 6 on allergic rhinoconjunctivitis. The results showed that probiotic intervention in early life had a significant effect on preventing eczema (RR = 0.79, 95% CI: 0.68 - 0.91, P = 0.001). However, it had no significant effect on preventing asthma (RR = 0.93, 95% CI: 0.81 - 1.07, P = 0.335), wheezing (RR = 0.92, 95% CI: 0.80 - 1.07, P = 0.283), food allergy (RR = 0.79, 95% CI: 0.58 - 1.08, P = 0.139), allergic rhinitis (RR = 1.03, 95% CI: 0.75 - 1.41, P = 0.865), and allergic rhinoconjunctivitis (RR = 0.88, 95% CI: 0.62 - 1.25, P = 0.461). Conclusion: Probiotic intervention in early life has a preventive effect on eczema in children, but no significant effect on preventing asthma, wheezing, allergic rhinitis, and allergic rhinoconjunctivitis. Further research is needed on its mechanism.