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.

Abstract: Cytochrome P450 oxidoreductase (POR) is the obligate electron donor for microsomal cytochrome P450 enzymes. Recessive mutations in the POR gene cause POR deficiency (PORD), a severe metabolic disorder characterized by skeletal malformations, ambiguous genitalia, and adrenal insufficiency. Because global POR knockout is embryonically lethal in mammalian models, the mechanistic study of PORD has historically been restricted to reconstituted biochemical assays or non-steroidogenic cellular backgrounds. Here, we describe complete biallelic POR knockout in human adrenal-derived NCI-H295R and human embryonic kidney HEK293T cells using CRISPR/Cas9. Transcriptomic and mass spectrometric steroid profiling of the adrenal clones revealed a blockade in canonical steroidogenesis, characterized by upstream accumulation of pregnenolone and progesterone, and severe depletion of downstream glucocorticoids and mineralocorticoids. Strikingly, knockout cells maintained low-level synthesis of dehydroepiandrosterone (DHEA) despite the complete absence of canonical CYP17A1 catalytic support, providing critical in vitro validation for the activation of alternative, CYP17A1-independent bypass steroidogenic pathways. Furthermore, utilizing the HEK293T platform, we evaluated the functional complementation of clinically relevant variants (A287P, R457H, P228L, and delP399_E401). Notably, the highly prevalent P228L variant exhibited selective preservation of CYP17A1 activity while severely impairing CYP19A1 aromatase function. A direct comparison between episomal overexpression and endogenous CRISPR prime editing of P228L highlighted critical differences in enzyme efficiency under native regulatory control. Finally, we establish a link between POR loss and altered intracellular Fe(II) storage, indicating perturbed ferroptotic susceptibility. These engineered human cell models provide a highly tractable platform for interrogating mutant-specific pharmacogenomics and developing targeted interventions.

Abstract: Antimicrobial resistance poses a critical global health challenge, necessitating the accelerated discovery of novel antibacterial agents. This study presents a quantitative structure–activity relationship (QSAR)-based multiclass classification framework for predicting the antimicrobial activity of β-lactam, azetidinone, and thiazolidinone derivatives. A chemically diverse library of over 220 compounds was constructed through combinatorial scaffold expansion guided by structure–activity relationship principles, with activity classified as Inactive, Moderate, or Active based on minimum inhibitory concentration (MIC) values. Molecular features were encoded using a fused descriptor set comprising Morgan fingerprints (radius 2 and 3), MACCS structural keys, and eight physicochemical descriptors, yielding a 1,199-dimensional feature vector. Class imbalance was addressed via SMOTE applied exclusively to the training set. Multiple machine learning models were developed and compared, including Random Forest, Gradient Boosting, XGBoost, a stacking ensemble, and a deep neural network with residual connections, batch normalization, and dropout regularization. Hyperparameter optimization was performed using randomized search with stratified 5-fold cross-validation. Model performance was evaluated using accuracy, weighted F1-score, balanced accuracy, and multiclass ROC-AUC. Visualization strategies including t-SNE, PCA, and feature importance analysis confirmed meaningful chemical space organization and robust structure–activity discrimination across all classifiers.

Abstract: Background: Chronic hyperglycemia promotes non-enzymatic glycation of hemoglobin, increasing oxygen affinity, shifting the oxyhemoglobin dissociation curve leftward, and inducing tissue-level pseudohypoxia a state termed glycohypoxia. This meta-synthesis quantitatively validates this concept by estimating the HbA1c-dependent change in P₅₀ and the resulting deficit in oxygen unloading, linking biochemical glycation to microvascular dysfunction. Methods: From six pivotal studies (1984–2012; N = 450 diabetic and control subjects), paired HbA1c and oxygen-release metrics (P₅₀, k, SpO₂–SaO₂ bias) were extracted. Study-specific slopes (ΔP₅₀ mmHg per 1% HbA1c) were pooled via random-effects meta-regression (REML), with sensitivity adjustment excluding 2, 3-DPG compensation. Translational modeling integrated the pooled ΔP₅₀ into the Hill equation for hemoglobin saturation across microvascular PO₂ (20–40 mmHg). Results: The pooled slope was −0.19 mmHg/% HbA1c (95% CI: −0.26 to −0.11; P < 0.001; I² = 45%), indicating a 0.5–1.3% decline in tissue oxygen unloading per 1% HbA1c rise, and a 1.5–3.9% cumulative loss from 6–9%. Independent clinical validation in 261 ventilated type 2 diabetes patients showed higher pulse oximetry versus arterial saturation for HbA1c >7% (SpO₂: 98.0 ± 2.6%, SaO₂: 96.2 ± 2.9%), despite similar PaO₂. The mean SpO₂–SaO₂ bias (1.83 ± 0.55%) correlated with HbA1c (r = 0.307, P < 0.01), confirming pseudonormoxia and leftward ODC shift. Conclusions: Glycohypoxia represents a quantifiable, reversible oxygen-delivery defect driven by HbA1c. Each 1% HbA1c rise translates to measurable hypoxic stress. Efaproxiral (RSR13; ~2.3 mg/kg per 1% HbA1c) could normalize P₅₀ and attenuate related complications by 30–55%, supporting metabolic reoxygenation as a therapeutic frontier in diabetes.

Abstract: Ginger (Zingiber officinale roscoe) is a high-value horticultural crop widely cultivated for its culinary and medicinal applications, yet its production is increasingly constrained by soil-borne diseases. Among these, Fusarium yellows, caused by Fusarium oxysporum f. sp. zingiberi (Foz), is one of the most damaging constraints in ginger-growing regions around the world, leading to progressive yellowing, vascular blockage, and decline in rhizome quality. Members of the Fusarium oxysporum species complex are known to include both pathogenic and non-pathogenic lineages that often co-occur within the same host and environment, complicating disease diagnosis and epidemiological understanding. In this study, we examined Fusarium-like isolates recovered from both symptomatic and symptomless ginger plants within Eumundi, the major ginger production region in Australia, to investigate their genetic diversity, effector gene content, and pathogenic potential. Comparative analyses revealed two genetically and functionally distinct groups: a clonal Foz lineage consistently associated with Fusarium yellows symptoms and characterised by a conserved set of Secreted In Xylem (SIX) effector genes (SIX7, SIX9, SIX10, and SIX12), and a diverse set of F. oxysporum isolates lacking these effectors. The conserved presence and co-localisation of SIX7, SIX10, and SIX12 within a 5 kb region on a 1.4 Mb contig in the Foz lineage is consistent with the retention of a stable lineage-specific effector module, likely associated with accessory genomic regions that may contribute to host specificity and pathogenicity in the Fusarium oxysporum species complex. Patho-genicity assays confirmed that only the Foz lineage induced disease, whereas non-Foz isolates were asymptomatic despite limited colonisation of host tissues. These findings highlight the coexistence of pathogenic and endophytic Fusarium lineages within ginger production systems and support the use of effector-based markers for improved detection and disease management.

Abstract: Background: Rheumatoid arthritis (RA) is a chronic systemic autoimmune disease in which clinically apparent synovitis is preceded by a prolonged preclinical phase characterized by immune dysregulation and autoantibody formation. Growing evidence implicates gut dysbiosis, impaired intestinal barrier integrity, and gut-derived immune priming as upstream contributors to RA pathogenesis, occurring years before overt joint inflammation. In parallel, Ayurveda describes Amavata as a chronic systemic disorder arising from the formation of Ama, a pathogenic burden produced by impaired digestive and metabolic function (Agni), which accumulates silently, disseminates systemically, and later localizes to the joints. Methods: This conceptual review draws on peer-reviewed biomedical and Ayurvedic literature to examine potential functional correspondences between the Ayurvedic construct of Ama in Amavata and contemporary models of gut-derived immune activation in RA, with emphasis on shared temporal and systemic features of disease development. Results: Both frameworks locate disease initiation upstream of overt inflammation and describe a prolonged preclinical phase characterized by systemic pathogenic processes. Ama is interpreted not as inflammation or tissue injury, but as a preclinical, systemic pathogenic state functionally analogous to chronic gut dysbiosis, barrier dysfunction, and immune priming described in RA. The analysis identifies a structural contrast in explanatory logic: Ayurveda integrates multiple upstream processes into a single unifying construct, whereas biomedicine analytically separates them into discrete mechanisms. The proposed mapping is heuristic and non-reductive, without asserting one-to-one equivalence or molecular translation. Conclusions: By situating both Amavata and RA within a shared preclinical, systemic disease framework, this model reinforces the importance of early, preventive intervention targeting metabolic and gut-immune dysregulation prior to irreversible joint damage. The findings demonstrate convergent reasoning across distinct medical traditions and support integrative, systems-oriented perspectives on chronic inflammatory disease initiation.

Abstract: Accurate short-term forecasting of key meteorological variables—air temperature, relative humidity, and wind speed—remains challenging in tropical and sub-Saharan regions due to strong diurnal cycles, seasonal variability, and non-stationary dynamics. To address these limitations, this study proposes a hybrid deep learning model combining Stationary Wavelet Transform (SWT), Multi-Head Attention (MHA), and LSTM networks. First, SWT decomposes meteorological time series into multi-scale components, capturing both low-frequency trends and high-frequency fluctuations while preserving temporal resolution. Then, the attention mechanism dynamically weights the importance of these multi-scale features across time, enhancing the model’s ability to focus on the most relevant patterns and interactions. Finally, LSTM layers model long-term dependencies and nonlinear temporal structures to generate multi-output predictions. The model is trained on hourly data enriched with lagged and statistical features. Experimental results show strong predictive performance (MAE = 1.21, RMSE = 2.01, R² = 94%), with notable improvements in modeling rapid variations, especially for wind speed and humidity. This work represents one of the first integrations of SWT, attention mechanisms, and LSTM for multi-variable forecasting in tropical climates, with practical applications in energy forecasting and precision agriculture.

Abstract: Classical subatomic particles simulations were applied to protons, neutrons and electrons to simulate a complete atom using pseudo potentials to simulate nucleus and electrons distributed around nucleus. Molecular dynamics algorithms were applied to subatomic particles to simulate a complete atom for Hydrogen, Carbon and Uranium atoms that were reported in previous studies. Further analysis of energies of Carbon atom reveals that atom has quantized discrete energy values rather than continuous energies as expected in classical simulations. Changes in atom's total energy were observed to be in step changes going to higher/lower energy states and energy values are observed to be in quantized rather than continuous. Electrons energy profiles was observed during the atom's quantum energy shifts and only one electron got affected that has step change in its energies, for short duration, leaving other electrons unaffected. To observe similar phenomenon in other atoms, Fluorine, Magnesium and Chlorine atom were considered in this study and found to have similar quantized discrete energy values properties. This study implements subatomic particles simulations using classical mechanics that explains why atom's energies are quantized in nature as observed in discrete lines in atomic emission/absorption spectrum for atoms.

Abstract: The structural strength requirements for timber buildings have been significantly tightened in the second generation of Eurocodes (EN 1990:2023, EN 1991-1-7), which poses a particular challenge for solid timber frames with a beam-and-column structure, where the transfer of tensile forces via dowel connections is inherently limited. This article presents an effective two-scale finite element method (FEM) modelling framework for assessing the strength of such frames during column removal. At the connection level, a continuous fracture mechanics model is used, based on a modified quadratic Hashin-type failure criterion, combining non-linear FEM up to peak load with post-peak behaviour defined in accordance with Eurocode 5. At the overall frame level, multi-fibre beam elements accounting for plasticity and damage, non-linear connection elements with six degrees of freedom interaction, and an element erosion method have been implemented. Both levels have been verified against published experimental data. Modelling at the joint level reproduces measured strength and stiffness values with an error of no more than 5% and corresponds to the characteristic values of Eurocode 5, second edition. Modelling at the frame level accurately reflects the non-linearity of the ‘load-displacement’ relationship, the sequence of joint failure, and the axial forces in the chain line under large displacements exceeding 390 mm. The proposed methodology demonstrates high potential for the practical design of structures in accordance with the current Eurocode provisions on reliability.