Abstract: Organophosphate-induced delayed neuropathy (OPIDN) is a rare, serious neurological consequence of organophosphate poisoning. Unlike acute toxicity, which causes cholinergic crises, OPIDN develops insidiously, often weeks after exposure, leading to progressive sensorimotor deficits. 44-year-old African male pesticide applicator with nine years of organophosphate exposure presented with progressive lower limb weakness, gait disturbances, and paresthesia. The patient exhibited no signs of acute cholinergic symptoms. Neurological examination revealed symmetrical limb weakness, diminished deep tendon reflexes, and distal sensory deficits. Serum cholinesterase levels were decreased. Electrophysiological studies demonstrated axonal degeneration with demyelination, and MRI showed mild spinal cord atrophy. Other causes of neuropathy were excluded. He received supportive care, including physical therapy, pain and spasticity management, antioxidants, vitamins, and off-label intravenous methylprednisolone. Over four months, he regained partial functional improvement, with residual weakness and mild gait disturbance. Chronic low-level organophosphate exposure can cause OPIDN even without acute poisoning. Diagnosis relies on occupational history, neurological examination, and electrophysiological findings. Management is primarily supportive; off-label therapies such as methylprednisolone may reduce neuroinflammation and oxidative stress but are not part of standard care. Early recognition, timely preventive measures, and long-term rehabilitation are essential to improve functional outcomes and quality of life.

Abstract: This article critically examines the conceptual, historical, and epistemological foundations of bioethics as a transdisciplinary field that emerges in response to the ethical tensions produced by technoscientific development. Through an analytical and interpretative approach, the paper revisits the historical events that shaped modern bioethics, and the contemporary challenges that arise from the expansion of biomedical and technological interventions. The analysis highlights the persistent dilemmas involving autonomy, paternalism, vulnerability, and intercultural asymmetries. It also addresses the ethical impact of technoscience on the reconfiguration of life, death, and human nature. The article argues pluralistic and adaptive bioethics capable of sustaining epistemic vigilance and guiding decision-making processes in diverse and complex sociocultural contexts.

Abstract: We present MINT (Multilingual Indic Neural Transformer), a compact 14.7M parameter encoder-decoder architecture for abstractive summarization across seven Indic languages. MINT is designed specifically to operate within the memory envelope of a single commodity NVIDIA T4 GPU (15 GB VRAM), addressing the paradox in which models serving the most resource-constrained communities are themselves the most resource-intensive to deploy. The architecture incorporates Rotary Position Embeddings (RoPE), SiLU feed-forward activations, DropPath regularization, weight tying, and a custom 32,000-token SentencePiece Unigram tokenizer trained over balanced Indic corpora. Training proceeds in two phases on the XL-Sum BBC dataset across Hindi, Bengali, Marathi, Tamil, Telugu, Punjabi, and Urdu: a fluency phase (epochs 1-15) using linear warmup with cosine decay, followed by a refinement phase (epochs 16-25) with a flat low learning rate and a combined coverage-attention entropy loss that jointly penalizes repetition and hallucination. We conduct the first identical-regime comparison in Indic summarization, fine-tuning both IndicBART (440M parameters) and mT5-small (556M parameters) under the same loss function, optimizer, decoding strategy, and data pipeline as MINT’s refinement phase. On the XL-Sum test set, MINT achieves an average ROUGE-1 of 0.1187 at epoch 15, rising to 0.1302 on validation after full refinement, reaching approximately 84.8% of IndicBART’s ROUGE-1 (0.1409) on the six overlapping languages while using only 3.3% of its parameters. A critical methodological contribution of this work is the demonstration that the standard Google rouge_score library returns zero for all Indic scripts due to English centric tokenization; we implement and advocate for whitespace-based ROUGE evaluation as the correct approach. MINT additionally benefits from BERTScore-F1 of 0.8497 (via XLM-RoBERTa-Large) and LaBSE embedding cosine similarity of 0.4306, confirming that generated summaries carry semantic meaning even when surface overlap metrics are modest. All code and checkpoints are publicly released.

Abstract: BackgroundMobile health (mHealth) interventions have shown promise in supporting tuberculosis care, but their association with patient knowledge, attitudes, and practices (KAP) among people with multidrug-resistant tuberculosis (MDR-TB) remains poorly evaluated in high-burden, programmatic settings. We assessed the association between a smartphone-based mHealth application and KAP regarding treatment adherence and adverse events within the V-SMART randomised controlled trial in Vietnam.MethodsThis nested cross-sectional study included 528 patients with MDR-TB (278 intervention, 250 control) enrolled across seven provinces in Vietnam between 2023 and 2025. KAP was measured using a validated questionnaire (Knowledge 0–17, Attitude 0–44, Practice 0–19, total 0–80). Multivariable linear regression adjusted for age, sex, province, education, time on treatment, PHQ-9, stigma, and social support. Dose-response relationships with self-reported app usage were examined in the intervention arm.ResultsThe mHealth intervention was associated with higher total KAP scores (adjusted mean difference 5.0 points, 95% CI 3.3–6.7, p<0.001), with largest gains in practice (+2.2 points) and knowledge (+1.1 points). Clear dose-response relationships were observed: each additional month of app use was associated with a 0.81-point increase in total KAP score (p<0.001).ConclusionThe smartphone-based mHealth intervention was associated with meaningfully higher KAP scores among MDR-TB patients in Vietnam. These findings support integration of mHealth tools into routine programmatic management of drug-resistant tuberculosis in high-burden settings.

Abstract: In this review article, we introduce the problem of team interaction, cover the mathematics, results, discussion, conclusions, and a path forward. To begin, cognitive science assumes a 1:1 relationship between beliefs and actions, whether with games, concepts, preferences, rational choices, eyewitness accounts, or self-reported pain. Unfortunately, it generalizes to reinforcement for generative-AI (gen-AI), a lower form of learning which can not account for higher-level cognition, the resistance of biases to be rectified, the inability to predict successfully without updated information, and supports Planck’s lament that physics evolves one funeral at a time. The problem with 1:1 beliefs-to-reality is that observations of social interaction only produce separable independent and identically distributed (i.i.d.) data, which, by definition, cannot reconstruct the interactions observed. Presently, Gen-AI uses separable i.i.d. data, preventing Gen-AI models from replicating interdependent human systems. Failing to account for interdependence, classical models of teams do not generalize, nor do their models predict advantages. Solving this problem is critical to advancing the science of teams arbitrarily composed of human-AI-machine-robot members. In contrast, based on interdependence, choosing how to “squeeze" uncertainty in our quantum-like (Q-L) model of teams, generalizes (e.g., vulnerability, espionage, time-energy tradeoffs), models self-organization’s ability to provide advantages (e.g., innovation) not possible under command decision-making (viz., authoritarianism), and may solve the hard-to-find connection between mind and reality. Our results suggest that humans have dual cognitive systems, one being cognition and the other embodied, but hidden, interdependence, which Simon was unable to capture and Kahneman had begun to address, our exemplar being Einstein’s decade-long struggle to construct his concept of general relativity. In the future, we propose that coupled tuning “squeezes" interdependent information to produce the advantages we have found over CDM and current AI risks.

Abstract: World models have emerged as a pivotal research direction, with recent breakthroughs in generative AI underscoring their potential for advancing artificial general intelligence. For embodied AI, world models are critical for enabling robots to effectively understand, interact with, and make informed decisions in real-world physical environments. This survey systematically reviews recent progress in embodied world models, under a novel technical taxonomy. We hierarchically organize the field by model architectures, training methodologies, application scenarios, and evaluation approaches, thus offering researchers a clear technical roadmap. We first thoroughly discuss vision-based generative world models and latent space world models, along with their corresponding training paradigms. We then explore the multifaceted roles of embodied world models in robotic applications, from functioning as cloud-based simulation environments to on-device agent brains. Additionally, we summarize important evaluation dimensions for benchmarking embodied world models. Finally, we outline key challenges and provide insights into promising future research directions within this crucial domain. We summarize the representative works discussed in this survey at https://github. com/tsinghua-fib-lab/Awesome-Embodied-World-Model.

Abstract: Robotic assistive devices, such as exoskeletons, are increasingly employed in walking rehabilitation. Therefore, the measurement of both movement kinematics and cognitive workload is important to understand this human-robot interaction in real-world contexts. To address this need this study presents the validation of a framework integrating inertial motion capture (Xsens) and eye-tracking sensor (Pupil Neon) within a Mixed Reality (Meta Quest 3) architecture. We developed an overground dual-task paradigm in which holographic numbers appear in the user’s peripheral vision. This setup actively stimulates visuospatial attention while quantifying kinematic and cognitive output. To validate the framework, the protocol has been tested on 30 healthy subjects across repeated exoskeleton training sessions. Statistical analyses revealed that the Multiple Correlation Coefficient (CMC) and Spectral Arc Length (SPARC), calculated on the shank angular velocity, together with the Step Length Variability exhibited significant time effects (p < 0.01), mapping the transition toward automated gait. Concurrently, pupillometric data demonstrated a measurable reduction in neurocognitive demand; specifically, the Task-Evoked Pupillary Response (TEPR) decreased significantly across progressive training sessions (p < 0.05). With this work, we validated a measurements protocol that aims to provide a novel methodology for objectively evaluating motor and cognitive adaptation in wearable assistive devices.

Abstract: Nano-UAVs weighing under 50g have become useful IoT platforms for GPS-denied navigation, but fitting a neural network into their sub-512kB memory and sub-100mW power budget remains an open engineering problem. PULP-Dronet v3 tackles this with depthwise separable (D+P) blocks and a channel-reduction factor γ. Even so, its most compressed variant (γ = /8, 1.1M MACs) loses 6 percentage points of collision accuracy versus the full model. Methods: We swap the 5×5 first convolution for a 3×3 depthwise + 1×1 pointwise pair, and retrain with cosine-annealing scheduling and per-epoch color-jitter augmentation. Results: At γ = /4 the model has 6409 parameters, needs only 540K MACs, and scores 83.97% collision accuracy with 0.372 steering RMSE on the official benchmark—+2.97pp over the same-γ baseline at 4.4× less compute. The full γ = /1 model (12M MACs) reaches 84%; our model nearly matches it with 22× fewer operations. Conclusions: Factorizing the stem and adjusting the training recipe recovers most of the accuracy lost to aggressive channel reduction, without adding inference cost.

Abstract: Background: Several research has revealed that dehydration remains a major cause of preventable illnesses, particularly among children and older adults. Existing tools such as the WHO IMCI, Gorelick, and Clinical Dehydration Scale (CDS) are limited by population focus and absence of quantitative weighting or digital integration. This study developed and prototyped an evidence-based dehydration-risk prediction model derived from meta-analytic data to enable more objective and universal risk estimation. Methods: Building on our recent systematic review and meta-analysis (Ogbolu et al., 2025), sixteen (16) clinical and demographic predictors were extracted from validated dehydration scales and pooled diagnostic evidence. Heuristic weights (1–4 points) were assigned according to pooled sensitivity and specificity, yielding a total score of 0–42. The total score was transformed to generate continuous probability estimates using logistic regression. The scoring algorithm was embedded within an interactive R Shiny software prototype that supports real-time computation and visualization. Prototype evaluation involved functional verification and usability testing using simulated patient profiles. Results: High-weight predictors, thirst, inability to drink, and lethargy showed the strongest diagnostic value, while modifiers such as age (≥ 65 years) and comorbidity carried lower weights. The cumulative score was transformed into a continuous dehydration-risk probability using a logistic function, reflecting the nonlinear increase in risk with symptom burden. Prototype evaluation of the MetaDehydrate application using simulated profiles demonstrated accurate score computation, consistent probability outputs, sub-second computation latency (< 0.2 s per calculation), and favorable usability feedback. Conclusion: This study presents the design and technical feasibility evaluation of an evidence-informed dehydration risk–scoring algorithm and its implementation as a prototype digital decision-support tool. While no clinical effectiveness was assessed, the findings demonstrate the feasibility of translating pooled diagnostic evidence into a functional, user-interactive application. The tool’s simplicity, limited input requirements, and rapid computation suggest potential utility for future evaluation in community and resource-constrained healthcare settings. Further prospective studies are required to assess effectiveness in real-world and low-resource healthcare settings.

Abstract: We investigate the semiclassical structure of spin-foam transition amplitudes for boundary data that do not admit a real Lorentzian Regge geometry. Considering a fixed triangulation with a single dominant vertex, we demonstrate that when boundary tetrahedra carry mutually incompatible causal orientations, the closure equations have no real solution and the path integral is dominated by a complex Euclidean saddle of the Regge action. In this regime the vertex amplitude acquires a non-oscillatory factor of the form exp(−SE/ℏ), where SE is the Euclidean action evaluated at the complex saddle. We introduce a causal-obstruction criterion based on a convexity argument for the future timelike cone in R ^3,1 , and establish a formal classification of boundary data into three types according to the existence and nature of the saddle-point solutions. We show that SE scales linearly with the spin parameter j in the semiclassical limit, SE = ℏ j C(α)/(8πG), where C(α) is a finite dimensionless geometric constant, providing explicit control over the suppression. Non-degeneracy of the Hessian at the complex saddle is verified after gauge fixing, confirming the validity of the saddle-point approximation. The results constitute a proof-of-concept demonstration that exponentially suppressed, causally confined quantum-geometric transitions emerge as a structural feature of the covariant formulation of loop quantum gravity, without additional postulates.

Abstract: This study aimed to evaluate how specific quantities of green tea and sugar, as well as fermentation temperature, impact variations in kombucha quality parameters. The study used tea quantities ranging from 0.5 to 3.0% w/v, sugar from 3.0 to 6.0% w/v, and fermentation temperatures of 20°C and 26°C. Beverage quality parameters such as pH, volatile acidity, alcohol content, and SCOBY growth were evaluated. At a temperature of 20 ± 2°C, formulations with 6.0% w/v sugar showed no SCOBY growth, and two for-mulations showed volatile acidity above the established maximum limit of 130 mEq L⁻¹. Most formulations had an alcohol content below 0.5% v/v and were classified as non-alcoholic. At 26 ± 2°C, the greatest SCOBY growth occurred, with the highest rec-orded value of 203%. Only two formulations showed an alcohol content above 0.5% v/v, but with values close to the limit. High amounts of sugar do not favor SCOBY growth at either mild or higher temperatures (26°C). Variations in temperature and ingredient quantities influence the production of green tea kombucha that meets safety and quality requirements. These data show the variations in ingredients and temperatures that favor kombucha production, considering its quality and classification.

Abstract: The aim of the study is to determine physico-chemical parameters and eutrophication criteria in the Aegean Sea. The pH, temperature, salinity, dissolved oxygen, turbidity, conductivity, phosphate, ammonium nitrogen, nitrite nitrogen, nitrate nitrogen and chlorophyll-a parameters were determined. The sampling was conducted at 25 stations (Enez, Saros Bay, Gökçeada, Yeniköy, Bozcaada, Babakale, Altınoluk, Ayvalık, Dikili, Çandarlı, Foça, Bostanlı, Urla, Ildır, Çeşme, Sığacık, Kuşadası, Didim, Güllük, Bodrum, Akyaka, Gökova, Datça, Bozburun 1, Bozburun 2) during the spring-summer-autumn seasons of 2022 and 2023. In the Aegean Sea, the mean values were determined as follows: pH 8.05, temperature 21.80 °C, dissolved oxygen 7.86 mg/L, salinity 34.13‰, turbidity 25.15 mg/L, electrical conductivity 50.27 µS/cm, phosphate 9.39 µg/L, ammonium nitrogen 29.59 µg/L, nitrite nitrogen 0.5 µg/L, nitrate nitrogen 1.9 µg/L, and chlorophyll-a 1.76 µg/L.

Abstract: AI cannot be the property of just five or seven companies in the world because its development and evolution has been the work of hundreds of thousands of researchers and scientists who have been working on it for more than two centuries, some for over two millennia as Pythagoras, Euclid, Aristotle, Al-Khwarizmi and many others. They have left us their legacy in the form of the foundations on which AI stands today. AI is not solely the work of technology company CEO’s, as it has been demonstrated that they have used the intelligence, skills, knowledge and innovations of thousands of anonymous programmers and engineers. It is even less likely to be owned by a government that only cares about its own security and the financial and psychological control of its society. Artificial intelligence is a precious legacy of the work of the most important and valuable foundations on the binary system, originally known as Boolean logic and first described in The Mathematical Analysis of Logic a work published in 1847 by George Boole (1815, 1864) and Formal Logic, written by Augustus De Morgan (1806, 1871), to come together as a tool of incalculable mathematical value in the work of John Venn (1834, 1923) of 1894 in his book Symbolic Logic , from which the concepts for the mathematical treatment of sets and the practical application of the Boolean system were consolidated. Another valuable contribution is the research carried out by Santiago F. Ramón y Cajal (1852, 1934) (Spanish histologist) who obtained important results in his research on The Texture of the Nervous System of Man and Vertebrates (1904), results that were key to the application of artificial intelligence in neural networks. John Bardeen (1908, 1991) andWalter Brattain (1902, 1987) invented the transistor at Bell Laboratories in 1947, based on the theoretical work of Carl Ferdinand Braun (1850 - 1918). The name transistor was coined by John R. Pierce (1910, 2002). Other significant precursors include Gottfried Leibniz, Gottlob Frege, Bertrand Russell and Alfred North Whitehead, David Hilbert, Charles Babbage, John Von Neumann, Claude Shannon, Alan Turing, John McCarthy, Edward Feigenbaum, Douglas Lenat, Judea Pearl, Lotfi Zadeh, John Hopfield and Geoffrey Hinton, as well as hundreds of thousands of unknown engineers. Significant contributions have also been made by research laboratories such as Bell Labs and CERN, as well as thousands of academic research universities around the world. The future of second generation AI will be supported by the work of Thomas Fowler, Jan Lukasiewicz, [1] , [2] Alfred Tarski, Stephen Cole Kleene, the Setun project and scientists, universities and laboratories around the world who are carrying out balanced ternary or fuzzy logic research. The AI must be declared for all the above reasons and more: as part of the Cultural and Technological Heritage of Humanity.

Abstract: The blue economy has emerged as a central policy framework for promoting ocean-based economic development while ensuring environmental sustainability. However, the extent to which existing policy frameworks effectively integrate ocean and coastal health into economic decision-making remains limited, reflecting broader challenges in governance, policy coordination, and institutional design. This article examines India’s blue economy through a marine policy lens, focusing on how governance structures, policy instruments, and institutional arrangements shape the treatment of ocean health within economic planning.Using a narrative review approach, this study advances a conceptual reframing of ocean health as economic infrastructure, arguing that ecosystem degradation constitutes a form of infrastructure failure with cascading economic, financial, and social risks. Drawing on interdisciplinary literature and national policy analysis, the paper evaluated sectoral dynamics across fisheries, ports, tourism, and coastal livelihoods, alongside emerging approaches to climate resilience, financial innovation, and marine governance.The analysis identifies key governance challenges, including fragmented institutional mandates, weak policy integration, and limited incorporation of ecological risk into financial and planning systems. These constraints undermine the effectiveness of blue economy strategies and expose ocean-dependent sectors to long-term systemic risk.The paper contributes to marine policy debates by demonstrating that achieving a sustainable blue economy is fundamentally a governance challenge requiring integrated policy frameworks, strengthened institutional coordination, and the incorporation of ecosystem-based risk into decision-making. While grounded in India, the findings offer transferable insights for coastal and ocean-dependent economies seeking to align economic development with long-term ocean sustainability in the Indian-Ocean region.

Abstract: The article explores an epistemological framework for understanding existence, symmetry, complexity, and randomness as emergent phenomena arising when a large-but-finite complex totality is represented through lower-complexity observational subsystems. We propose that existence is not a binary property, but an epistemological category determined by the measure of a system's structural symmetry over time. Chaos, randomness, and infinity are reinterpreted as epistemic markers --- thresholds of comprehension rather than fundamental properties of reality. Through this lens, we examine fractals, cellular automata, and quantum uncertainty, arguing that apparent uncertainty emerges from the compression of finite universal structure into observable forms. The article argues that all localized systems, from particles to cognitive processes, are projections of the universe's total informational structure. This paradigm reframes emergence, not as the accumulation of local interactions, but as the revelation of global coherence through representational compression. By situating existence and complexity within this framework, the manifesto outlines a programme-level foundation for understanding the interconnectedness of phenomena and the unity of the universe as a singular, self-reflective process.

Abstract: Background: Integrins and other cell adhesion molecules play a critical role in migration and homing of leukocytes. This study investigates whether metastatic tumor cells can exploit leukocyte-like rolling and arrest mechanisms during early vascular steps of metastatic dissemination. Methods: B16 melanoma cell adhesion to activated bEnd.3 endothelial monolayers or immobilized VCAM-1 was analyzed under defined shear flow using a parallel-plate chamber. Function-blocking antibodies, divalent cation modulation, pertussis toxin, and low-temperature conditions were used as classical controls. Results: B16-BL6 melanoma cells exhibited robust VLA-4-dependent rolling and arrest on activated endothelial monolayers and on immobilized VCAM-1 under physiological shear stresses (0.7–2 dyn/cm²), independent of chemokine-related Gαi signaling. Conclusions: These findings identify a chemokine-independent mechanism of VLA-4-mediated vascular capture by melanoma cells under shear flow, providing a potential mechanistic basis for early steps in metastatic dissemination.

Abstract: Background: In type 2 diabetes mellitus (T2DM), chronic hyperglycemia induces hemoglobin glycation, elevating oxygen affinity and precipitating glycohypoxia a pseudohypoxic state impairing tissue oxygen unloading. This meta-regression posits hypertension as an adaptive pressor response to sustain oxygen delivery, quantifying HbA1c-linked blood pressure increments and integrating them with oxyhemoglobin dissociation curve (ODC) dynamics. Methods: Aggregated data from three cohorts (CHNS 2011–2015, NHANES 2011–2018, Pu et al. 2012; N=14,838 adults with T2DM) were harmonized, extracting/normalizing slopes for systolic (SBP) and diastolic (DBP) pressure per 1% HbA1c via logarithmic transformation of HRs/ORs. Random-effects meta-regression (REML) pooled estimates, with Hill equation modeling (n=2.7) translating ΔP₅₀ shifts into oxygen-unloading deficits at tissue PO₂ ≈30 mmHg. Sensitivity analyses assessed heterogeneity (I², τ²) and bias. Results: Pooled slopes revealed +2.8 mmHg SBP (95% CI: +1.9 to +3.7; P<0.001; I²=46.3%) and +1.1 mmHg DBP (95% CI: +0.6 to +1.7; P<0.001; I²=41.5%) per 1% HbA1c rise. Each increment induced a −0.19 mmHg P₅₀ leftward shift, reducing oxygen unloading by ≈0.8% and necessitating compensatory perfusion pressure to maintain Q × [O₂] flux. At HbA1c=9%, predicted SBP elevation was +11–12 mmHg, aligning with clinical gradients. Conclusions: Hypertension in T2DM emerges as a quantifiable oxygen-salvaging mechanism against glycohypoxia, with each 1% HbA1c rise exacting a 2–3 mmHg pressor toll via eNOS/NO dysregulation. This framework advocates reoxygenative therapies (e.g., SGLT2 inhibitors, BH₄ supplementation) to avert maladaptive vascular remodeling, reframing glycemic control as integrated metabolic-vascular homeostasis.

Abstract: Rationale: Mother-to-child transmission (MTCT) of HIV remains a critical public health challenge in Nigeria, particularly in resource-constrained regions. Public health education is a cornerstone of the national PMTCT programme, yet limited evidence exists on its awareness, accessibility, usefulness, and acceptance among pregnant women in Cross River State. Understanding these dimensions is essential for optimizing programme design and maternal and child health outcomes. Objectives: The study aimed to assess public health education services in the prevention of MTCT of HIV among pregnant women attending St. Joseph’s Hospital, Ikot Ene. Specifically, it evaluated: (1) awareness of PMTCT programmes, (2) accessibility to services, (3) perceived usefulness, and (4) acceptance of the programmes. Methods: A cross-sectional descriptive survey was conducted with 222 randomly selected pregnant women attending antenatal care. Data were collected using a structured, validated questionnaire covering socio-demographics and PMTCT programme domains. Descriptive statistics summarized responses, and mean domain scores were calculated. Results: Participants were predominantly married (55.0%) with a mean age of 26.4 years; over half had no formal education. Awareness of PMTCT programmes was moderate-to-high (mean score: 64.0%), while accessibility was generally reported as adequate despite systemic barriers (mean score: 75.5%). Perceived usefulness was high (mean score: 68.0%), and programme acceptance was strongest among all domains (mean score: 78.8%). Notably, 40.5% of participants lacked full awareness, and 65.3% had experienced discouragement, highlighting areas for improvement. Conclusion: Public health education programmes significantly contribute to PMTCT knowledge and uptake, yet structural and socio-cultural barriers limit their full effectiveness. Recommendation: Short- and long-term strategies should include culturally tailored education, health system strengthening, community engagement, and continuous monitoring to enhance awareness, accessibility, and utilization. Thus, effective public health education directly reduces MTCT risk, improves neonatal outcomes, and strengthens maternal health, supporting national and global HIV elimination targets

Abstract: Hybrid steel–PVA fiber-reinforced concrete offers promise for enhancing both load-bearing capacity and deformation capacity. However, the coupled effects of fiber parameters and volume-fraction combinations on compressive strength (σc) and peak strain (εc) are still not fully understood. A unified, interpretable, and engineer-ing-oriented quantitative framework is still lacking. This study compiled experimental data from 26 published literature, building a multi-source database consisting of 397 datasets for σc and 203 datasets for εc. Based on this database, a comprehensive ana-lytical framework was proposed, including model prediction, SHAP-based interpreta-tion, Monte Carlo marginalization, synergy gain window determination, and du-al-objective mix proportion optimization. For σc prediction, LightGBM achieved the highest test-set R² (0.9783), whereas CatBoost showed more robust error control (MAE = 2.7409 MPa). CatBoost was therefore selected as the base model for the subsequent interpretation analysis. For εc prediction, Bayesian-optimized CatBoost achieved the best test performance (R² = 0.9659, MAE = 0.0218, RMSE = 0.0358), while the trans-fer-learning model reached a comparable accuracy level (R² = 0.9650). SHAP analysis revealed that σc is mainly governed by matrix mix-proportion factors and steel fiber volume fraction, whereas εc is more sensitive to S/B and PVA-related variables. The mean synergy-gain maps generated via Monte Carlo marginalization and two-dimensional grid evaluation further showed clear differences between the two targets. Positive synergy in σc was highly localized. Its maximum mean synergy gain was 4.7949 MPa at (Steel, PVA) = (1.875%, 2.000%). By contrast, εc exhibited a wider positive-synergy region, with a peak value of 0.0141629 at (0.38%, 1.62%). Therefore, the engineering output of this study is not a single optimal mix point. In-stead, it is a set of candidate windows for different performance targets, together with boundary-risk identification and priorities for experimental validation.

Abstract: We derive an effective gravitational potential \( Φ_{halo} (r)∼-[ln⁡( r/r_*)+1]/r \) from the asymptotic behavior of dark matter halo models. At microscopic scales, the logarithmic term changes sign, producing repulsion that prevents matter from collapsing into a singularity. The corresponding logarithmically corrected Schwarzschild metric yields parameter-free, a priori predictions for the shadows of Sgr A* and M87* that agree with Event Horizon Telescope observations. Six falsifiable predictions for unobserved black holes, particularly NGC315, can discriminate this metric from the Kerr solution; we also make falsifiable predictions for the periastron precession of stars S4711 and S4716 based on the same framework (Appendix D). On galactic scales, the same logarithmic term fits rotation curves of the Milky Way, Andromeda, and NGC2974 using only ordinary matter, and passes the Bullet Cluster lensing test. Tidal effects in the Solar System are far below current experimental limits, ensuring consistency with the equivalence principle and parameterized post-Newtonian tests. We further derive the modified field equations via coarse-grained variation (Appendix B) from the effective action of a quantum vortex background, thus providing a more complete theoretical bridge to the modified Poisson equation and metric used in the main text. This effective theoretical framework indicates that various gravitational phenomena from black holes to galaxies may share a common quantum topological origin. It provides a unified, testable alternative to the dark matter problem, and also points out a potential path for the observable detection of quantum gravity effects.