Abstract: Inflammation is a normal and essential feature of pregnancy, supporting implantation, placental development, and parturition. When dysregulated, however, inflammatory pathways contribute to major obstetric complications such as preeclampsia, fetal growth restriction (FGR), and preterm birth, which account for substantial maternal and perinatal morbidity and mortality. This review synthesizes current understanding of the maternal–fetal immune interface, examines how inflammation contributes to pregnancy disorders, and explores therapeutic strategies that link pathogenic mechanisms to targeted interventions. The placenta functions as an active immunological hub, coordinating innate and adaptive immune responses to maintain tolerance while protecting against infection. In preeclampsia and FGR, excessive activation—driven by inflammasome signaling, Th1/Th17 polarization, and altered natural killer and macrophage function—impairs placental perfusion, promotes antiangiogenic pathways, and induces systemic endothelial dysfunction. Established therapies, including low-dose aspirin, low-molecular-weight heparin, and antenatal corticosteroids, aim to mitigate inflammation and optimize fetal outcomes, while adjunctive strategies target oxidative stress, nutritional deficits, and the maternal microbiome. Emerging approaches such as cytokine-targeted biologics, inflammasome inhibitors, and mesenchymal stem cell therapies show promise but require rigorous evaluation of safety and efficacy. A deeper understanding of placental immunology and inflammatory signaling is essential to develop precision therapies. Future research should prioritize biomarker validation, pathway-specific interventions, and equitable implementation to reduce inflammation-driven pregnancy complications.

Abstract:

We present a rigorous reformulation of Einstein’s General Relativity using the real Clifford algebra Cl_1,3, constructed from Dirac gamma matrices. In this framework, all geometric and dynamical structures—including the metric, spin connection, curvature, and energy-momentum tensor—are expressed using algebraic operations (symmetrized products, commutators, and traces) of Clifford generators. Rather than invoking the full machinery of differential geometry, we reconstruct the Einstein field equations entirely within an operator algebra framework, while maintaining exact equivalence with the classical theory. The underlying metric structure is assumed through the anticommutation relations defining the Clifford algebra, and is algebraically reconstructed using trace identities. This approach provides a unified representation of both geometry and spinor fields and may offer conceptual and pedagogical advantages in connecting gravity with operator-based formulations. Potential extensions involving bivector sectors and torsion are briefly discussed.

Abstract: Black hole singularities still remain a central challenge in gravitational physics. In this work, we present a geometric interpretation of non-singular black hole cores within teleparallel gravity based on geometric drift vectors. Gravitational effects are encoded in a comoving tetrad framework through a dynamical drift field whose gradients generate torsion rather than spacetime curvature. While the teleparallel equivalent of general relativity reproduces the Schwarzschild behavior in the weak-field regime, nonlinear invariant contributions dominate in the strong-field region, replacing the central singular behavior with a smooth de Sitter–like core. Event horizons emerge as drift horizons associated with the limiting behavior of the geometric flow, and null and timelike trajectories admit analytic extensions across the horizon and central region.

Abstract: Sports science is rapidly changing with new discoveries in molecular biology and artificial intelligence. Modern “omics” tools, such as genomics, proteomics, and metabolomics along with AI-based analytics, help us understand how a child’s body builds muscle, responds to training, and recovers after exercise. These technologies also help identify factors that may increase the risk of injury. Simple genetic tests, including variations like ACE I/D and ACTN3 R577X, provide insights into traits linked to endurance, strength, and muscle performance. Protein and metabolite testing, supported by AI models, can reveal how efficiently the body uses energy or repairs tissues after activity. This review article provides the most recent and up-to-date knowledge regarding modern technologies used for performance enhancement. These scientific tools are not meant to label or limit children. Instead, they help parents and coaches understand each child’s individual needs and support healthier training decisions. AI-driven interpretations can guide choices about training intensity, rest, recovery, and nutrition in a safe and personalized manner. Overall, this paper offers practical guidance for using molecular and AI-driven sportomics responsibly. Our goal is to empower parents and coaches with informed, balanced, and child-centric strategies for enhancing performance.

Abstract: Background: Epigenetic regulation must preserve stable functional states under molecular stochasticity and changing environments, yet an operational model linking context-level signals to measurable chromatin remodeling is limited. Method: This study proposes Epigenetic Teleonomy, a stochastic control framework in which epigenomic observables relax toward an empirically estimated within-subject baseline regime (reference distribution) with lagged mediator-driven inputs and feedback. Results: A local approximation yields mean-reverting dynamics. Simulations illustrate that without effective feedback, diffusion-like drift leads to increasing dispersion, whereas sufficient regulation gain yields bounded fluctuations and recovery. In the isotropic local Ornstein–Uhlenbeck (OU) regime, stationary fluctuations scale with effective diffusion and inversely with return rate (gain). Conclusion: The framework is testable in longitudinal designs by (i) estimating a subject-specific baseline from a stable run-in window, (ii) quantifying deviation using reduced-dimensional proxies, and (iii) fitting gain and diffusion from return-to-baseline statistics.

Abstract: In the age of the Internet of Things (IoT), IoT devices scattered across various locations gather and store data in a decentralized manner to improve computational efficiency. Nevertheless, within IoT networks, factors such as fragile devices, challenging deployment conditions, and unreliable data transmission are raising the likelihood of data gaps, potentially having a substantial impact on the subsequent data processing resulting in failure of the system. Conventional imputation approach relies on using historical trend or sensor fusion techniques to combine information from different sensors to fill in the gaps in where information is missing. Historical trend struggles to capture new or emerging patterns, whereas using sensor fusion, even though it shows promising results, relies on information from multiple sensors from same target environment, making it vulnerable to single-point failures. This article presents an alternative strategy: using sensor-based fusion, but in this case, multiple sensors gather data from different targets independently. The architecture intelligently looks and gathers the sensor information from other location/target (multiple locations), sensing the same environmental information, learns the distribution and correlation and employ algorithm to generate synthetic data for imputing missing information. The study conducted experiments by fusing weather station data from various US locations and comparing the effectiveness of this approach to conventional methods. Further, the proposed synthetic data generation approach outperformed other algorithms when applied to the fused weather station dataset. This innovative approach mitigates the risk of single-point failures and offers a more robust solution for dealing with missing data in IoT networks.

Abstract: The localization of the Sustainable Development Goals (SDGs) has become a central dimension of sustainable urban development, as local governments play an increasingly important role in translating global sustainability agendas into place-based action. This study aims to provide a state-of-the-art assessment of how scholarly research has examined the relationship between local governance and SDG implementation over the period 2018–2025. A mixed-method review approach was employed, combining bibliometric mapping using VOSviewer with qualitative content analysis conducted through NVivo. Based on predefined inclusion criteria, 143 peer-reviewed articles indexed in the Web of Science database were systematically analyzed. The results reveal several dominant thematic clusters, including institutional coordination, sustainable urban planning, data-driven governance, accountability mechanisms, and the growing use of policy tools such as Voluntary Local Reviews (VLRs). The findings indicate an increasing emphasis on performance-based monitoring, participatory governance approaches, and multilevel institutional frameworks supporting the integration of the SDGs into local policy and planning processes. At the same time, persistent challenges are identified, particularly with regard to equity considerations, data inconsistencies, and the limited inclusion of marginalized urban communities in SDG-related decision-making. Overall, this review offers a structured and comprehensive overview of current research on SDG localization in urban governance and identifies key gaps and priorities for future research and policy development aimed at more inclusive, measurable, and context-sensitive pathways to sustainable urban development.

Abstract: Background: TNXB-related classical-like Ehlers-Danlos syndrome (clEDS) is caused by biallelic pathogenic variants in TNXB, encoding the extracellular matrix glycoprotein tenascin-X. Although traditionally classified as a connective tissue disorder based on joint hypermobility and skin findings, accumulating clinical, electrophysiological, and imaging data indicate prominent neuromuscular involvement that likely reflects a central disease mechanism. Methods: A qualitative evidence synthesis was conducted following PRISMA 2020 guidelines. A comprehensive search of PubMed, OMIM, and GeneReviews was performed on January 5, 2026. Data from 18 studies representing 56 individuals with biallelic TNXB variants were synthesized narratively, with findings stratified by assessment method and zygosity. Due to heterogeneity in study designs, assessment methods, and outcome definitions, quantitative meta-analysis was not feasible. Results: Among 56 individuals with biallelic TNXB variants, subjective muscle weakness was reported in only 37% of cases. However, systematic neuromuscular assessment demonstrated objective muscle weakness in 85% of patients examined. Electromyography revealed mixed neurogenic-myopathic patterns in 60%, and muscle imaging abnormalities were present in approximately 50%. A clear dose-effect relationship was observed, with heterozygous individuals exhibiting milder phenotypes correlating with reduced serum tenascin-X levels. Conclusion: Neuromuscular involvement in TNXB-related disorders is frequent, progressive, and mechanistically linked to dysfunction at the muscle-extracellular matrix interface. These findings support the reclassification of TNXB-related disease alongside myopathic Ehlers-Danlos syndrome as a muscle-ECM interface disorder.

Abstract: Older adults with cancer face disproportionately high rates of severe treatment-related toxicities, yet current prediction tools rarely incorporate biomarkers that capture physiological resilience. The hypothalamic–pituitary–adrenal (HPA) axis—central to stress adaptation, immune regulation, and tissue repair—undergoes pronounced age-related alterations, including elevated basal cortisol, reduced dehydroepiandrosterone (DHEA) and its sulphate form DHEAS, and an increased cortisol:DHEA(S) ratio. These changes may impair immune function, delay recovery, and exacerbate vulnerability to treatment toxicity. This narrative review synthesizes mechanistic and clinical evidence linking HPA-axis dysregulation to treatment tolerance in geriatric oncology. Common patterns include blunted diurnal cortisol slopes, elevated evening cortisol, and low DHEA(S), which are associated with fatigue, functional decline, and reduced survival across cancer types. However, their predictive value for acute treatment toxicities remains underexplored due to methodological heterogeneity, lack of age-specific reference ranges, and absence from existing geriatric toxicity models. This review proposes a translational roadmap that prioritizes (1) standardization of salivary cortisol/DHEA(S) protocols; (2) prospective, age-stratified validation studies using standardized toxicity endpoints; (3) interventional testing of behavioral or pharmacological strategies to modulate HPA function; and (4) integration into oncology workflows and electronic decision-support tools. Incorporating endocrine biomarkers into risk prediction could refine treatment stratification, enable targeted supportive care, and ultimately improve outcomes for older patients with cancer.

Abstract: This paper develops a generative AI decision-support and optimisation framework for advancing sustainability and resilience in industrial logistics. The framework combines data aggregation, generative scenario creation, simulation-based evaluation, and multi-objective optimisation to support evidence-based management under tightening European Union sustainability regulations. Building upon the decision-aid lineage of the International Journal of Production Research, it integrates policy variables such as the Carbon Border Adjustment Mechanism (CBAM), the EU Emissions Trading System for maritime transport, FuelEU Maritime, the Digital Product Passport (DPP), and the Corporate Sustainability Reporting Directive (CSRD) directly into logistics-planning equations. Recent studies on digital twins and adaptive optimisation (Longo et al., 2023; Flores-García et al., 2025) highlight the need for AI systems that translate these policies into dynamic cost and carbon trade-offs. The proposed model responds to this need by coupling generative scenario synthesis with traceable optimisation and governance controls consistent with the EU AI Act (European Commission, 2025). An illustrative case from the mining-rope industry demonstrates how global sourcing and transport routes in European, South African, and Chinese configurations can be simulated within the generative environment to evaluate comparative cost, emission, and compliance profiles. Both SME-light and enterprise implementations achieved reduced analysis time and improved transparency of carbon-related decisions. The study contributes a replicable methodology that transforms generative AI from a creative text tool into a quantifiable governance instrument, linking strategic foresight with operational resilience in sustainable logistics networks.

Abstract: We formulate copy time τcopy as a fully specified hypothesis-testing task for quantum dynamics with a conserved global U(1) charge, calibrated by a disturbance budget using the Bures metric. The central point of the paper is methodological: the notion of an information-transfer timescale is defined operationally (state family, measurement class, and error criterion) and then linked to explicit sufficient conditions under which a diffusive heat-kernel envelope controls the receiver’s distinguishability advantage. The frequently invoked Lambert-W−1 structure is shown to be a certificate inversion associated with this envelope and is deliberately demoted to a conditional analytic tool, with an alternative non-diffusive/non-perturbative branch discussed. As a separate, explicitly conditional application, we present a reproducible pipeline that maps τcopy to an infrared coarse-graining scale ΛIR via an explicit window-function construction and then to a minimal Higgs-portal singlet-scalar UV completion. Under stated assumptions (thermal “copy-limited” saturation near the electroweak crossover and a minimal Functional Renormalization Group (FRG) truncation executed in the Supplement), the pipeline yields a benchmark dark mass scale mχ = 58.4(60) GeV. The manuscript is written to separate assumptions from consequences and to supply the scripts required to reproduce every figure, ensuring full academic transparency and reproducibility.

Abstract: Background. Obsessive–compulsive disorder (OCD) is highly heritable, yet the biological routes through which common variants confer risk have not been fully mapped. Glutamatergic dysregulation and faulty synaptic pruning both feature prominently in mechanistic models, but direct, large-scale genetic evaluations of these pathways are scarce.Methods. We analysed summary statistics from a recent genome-wide association meta-analysis of OCD (effective sample = 68 099). Four predefined gene sets were tested: glutamatergic signalling, synaptic pruning in shortened and expanded forms, and two negative-control sets (monoamine and housekeeping genes). Three complementary tools were applied: MAGMA for competitive gene-set enrichment, stratified LD-score regression for partitioned heritability, and S-PrediXcan for transcriptome-wide association signals across six GTEx brain tissues.Results. MAGMA detected no individual genes surpassing the genome-wide threshold. In contrast, LD-score regression showed pronounced and Bonferroni-corrected heritability enrichment for pruning-related sets (shortened set: 1.32-fold, p = 1.45 × 10⁻¹⁰³; expanded set: 1.05-fold, p = 1.13 × 10⁻¹³; pruning genes with glutamatergic overlap removed: 1.06-fold, p = 7.51 × 10⁻¹⁴). Glutamatergic sets were not enriched (all p > 0.84). S-PrediXcan produced modest but significant |Z|-score inflation in the pruning-shortened (1.19-fold, p = 0.044) and glutamatergic (1.06-fold, p = 0.032) collections. Directional TWAS signals pointed to complement and microglial drivers: C4A and the microglial marker TMEM119 both showed positive Z-scores. The negative-control sets were enriched in heritability analyses but not in TWAS, suggesting residual confounding rather than true biological relevance.Conclusions. Across convergent polygenic methods, synaptic pruning genes—independent of glutamatergic overlap—emerge as the principal enrichment signal in OCD. The results fit a model in which excessive microglia- and complement-mediated pruning disrupts cortico-striato-thalamo-cortical circuit maturation, setting the stage for compulsive behaviour. This work illustrates how integrated genomic approaches can refine mechanistic hypotheses and may inform early neurodevelopmental interventions for OCD.

Abstract: Existing methods for fault detection in cloud and quantum systems are powerful but brittle. They struggle with unknown failures, rely on inflexible recovery playbooks, and use fixed quantum error correction (QEC) schemes, a significant problem in diverse multi-cloud settings. To overcome these issues, we introduce \textbf{Intelligent Multi-Cloud Fault Detection with Adaptive Quantum Error Correction}. Our framework is built on three pillars: hierarchical multi-agent learning, adaptive multi-cloud execution, and predictive QEC. Specialized agents learn from experience, while the system adapts to real-time cloud performance and quantum error states. How effective is this approach? Testing on the CloudSim Fault Injection Dataset, Multi-Cloud Performance Benchmark, and IBM Quantum Error Logs shows its real-world impact. We achieved 94.2\% detection accuracy, cutting false positives by 68\%. System availability jumped from 85\% to 96.1\%, and recovery time plummeted from 340s to just 45s. For quantum workloads, the framework reached a 96.7\% success rate with 94.3\% state fidelity. This work offers a more robust and adaptive solution for fault management in today's complex hybrid cloud-quantum environments.

Abstract: Background: Few studies have reported electrocardiogram data collected during the neonatal period. The aim of this study was to evaluate electrocardiogram variations in neonates during the early postnatal period. Methods: Electrocardiogram samples taken during the first hour of life from newborns born at our hospital were analyzed in this prospective observational study. Demographic data and possible electrocardiogram changes were studied. The results were statistically analyzed. Results: A total of 260 patients were included during the study period. Among these, 50% were male (n=130), the mean gestational age was 38.1±1.4 weeks, and the mean birth weight was 3.2±1.4 kg. In the electrocardiograms obtained, low atrial rhythm was detected in 0.3% of the patients (n=1). Right axis deviation was observed in 1.5% of the patients (n=4), and left axis deviation was observed in 1.2% of the patients (n=3). An abnormal P-axis was found in one patient (0.3%), and an abnormal QRS-T angle was found in one patient (0.3%). According to the normogram of Davignon and colleagues, T-wave changes were significantly greater in lead V1 (p=0.02). No statistically significant differences were observed in the other parameters. Conclusion: Different electrocardiogram changes can be observed in the early neonatal period. Further studies are needed to clarify the interpretation of the electrocardiogram findings.

Abstract: MPA occurs in very young children and is prevalent throughout the lifespan of musicians. Childhood presentations are phenotypically similar to adult musicians which raises the question as to whether MPA is innate or acquired and if identified in childhood, the most appropriate way to manage it to forestall MPA as a lifelong problem. An understanding of developmental and psychodynamic psychology and the multifactorial causation of MPA is necessary to develop effective interventions.

Abstract: In this article, the authors analyse the socio-ecological prototype as a model for transforming traditional educational approaches. Innovative technologies and open interaction are becoming increasingly important, even in conventional crafts training. Recent research also suggests that integrating cultural heritage, home learning, and open spaces into educational programs can strengthen and empower communities' self-awareness. The authors' findings are rooted in Urie Bronfenbrenner's (1979) ecological systems theory, a dynamic concept that has transformed our understanding of personality development. This theory suggests that each personality fluid construct evolves through the interaction between the individual and their environment. This environment, as Bronfenbrenner's theory proposes, is not a static backdrop but a dynamic system of relationships and environments, each with its unique impact on the individual.

Abstract: The development and deployment of robust technical solutions for sustainability improvement have become increasingly critical in response to growing environmental and social pressures, while maintaining economic viability, particularly in industrial systems that require multi-stage technology implementation. Identifying such solutions requires the systematic treatment of significant uncertainties that affect sustainability-related decision making. Among these, epistemic uncertainty, arising from incomplete or imperfect knowledge, is inherently subjective and, in principle, reducible. Fuzzy set theory provides an effective and well-established framework for representing and managing epistemic uncertainty in sustainability analysis. In this work, a fuzzy decision-making framework is proposed to support multi-stage technology development and deployment for dynamic sustainability performance improvement in industrial systems. The framework integrates comprehensive sustainability assessment with fuzzy representations of epistemic uncertainty to enable consistent comparison of alternative strategies at each implementation stage. It identifies the most appropriate strategy at each stage while ensuring alignment with long-term sustainability objectives. The proposed approach functions as a decision-support tool for guiding adaptive, stage-wise technology deployment under uncertainty. A case study of a nickel electroplating system is presented to demonstrate the applicability and effectiveness of the methodology.

Abstract: Introduction of livestock into tropical and subtropical forest areas has displaced the orig-inal forest vegetation. Posterior surge of secondary vegetation has served as a source of al-ternative forage to cattle during scarcity periods. The objective of the study was to determine the botanical composition of the diet (BCD) and the productive performance of lac-tating Brown Swiss cows during the dry season. The BCD was determined through microhistological analysis of fecal samples of grazing cows. Grazing areas were sampled to determine morphological and botanical composition (BCP). Forages were sampled to determine their chemical composition. Cow's productive variables were recorded during March, April, and May of 2012. The BCP and BCD consisted of Cynodon plectostachyus and the woody species were Vachellia farnesiana, with smaller proportions of Pithecellobium dulce, Guazuma ulmifolia, and Ficus sp. These forages contributed with 63, 48, and 47% of the dry matter, metabolizable energy, and metabolizable protein requirements of the cows. Therefore, it is concluded that alternative forages contributed significantly to the nutri-tional requirements during periods of pasture scarcity. Understanding the botanical composition of the diet of grazing cows allows for the development of management strat-egies based on the efficient use of local resources.

Abstract: Introduction: Stunting is associated with poor nutritional intake during early childhood. This study evaluated the effect of a daily intake of 510 mL of an oral nutritional supplement for 180 days on linear growth among children with stunting and at-risk of stunting aged 12–36 months. Methods: A community-based, single arm intervention was conducted among 91 children in Kelantan, Malaysia. The children at enrolment had height-for-age Z-scores (HAZ) between <-1.0 SD to >-3 SD based on WHO Growth Standards. Anthropometric measurements were taken at baseline (T0), day-90 (mid-intervention), and day-180 (post-intervention). Nutrient intake was assessed using 24-hour dietary recalls, and compliance was monitored via returned empty sachets. Results: The mean age of the children at baseline was 26.7 ± 6.5 months, with 37 (41%) being stunted and 54 (59%) were at-risk of stunting. After intervention, the linear growth (height-for-age Z-score) was significantly improved over time (p < 0.001) in both stunted and at-risk children. A significant time-by-group interaction (p = 0.014) indicated differential effects between the stunted and at-risk groups. Post-hoc analysis showed HAZ improvements from baseline (T0) to 180 days in stunted and at-risk groups (p < 0.001) with the stunted group having greater mean differences. The number of stunted children declined by 37.8% (p = 0.003). Nutrient intakes of protein, vitamins D, vitamin C, vitamin B-complex, calcium, phosphorus, magnesium, and iron improved significantly. Conclusion: Daily intake of 510 mL of oral nutrition supplement improved linear growth and nutrient intakes. These findings support the potential of targeted supplementation in addressing child growth faltering and micronutrient inadequacies.

Abstract: Conventional image stitching pipelines predominantly rely on homographic alignment, whose planar assumption often breaks down in dual-camera configurations capturing non-planar scenes, leading to visual artifacts such as geometric warping, spherical bulging, and structural deformation. To address these limitations, this paper presents SENA (SEamlessly NAtural), a geometry-driven image stitching approach with three complementary contributions. First, we propose a hierarchical affine-based warping strategy that combines global affine initialization, local affine refinement, and a smooth free-form deformation field regulated by seamguard adaptive smoothing. This multi-scale design preserves local shape, parallelism, and aspect ratios, thereby reducing the hallucinated distortions commonly associated with homography-based models. Second, SENA incorporates a geometry-driven adequate zone detection mechanism that identifies parallax-minimized regions directly from the disparity consistency of RANSAC-filtered feature correspondences, without relying on semantic segmentation or depth estimation. Third, building upon this adequate zone, we apply anchor-based seamline cutting and segmentation, enforcing one-to-one geometric correspondence between image pairs by construction and reducing ghosting, duplication, and smearing artifacts. Extensive experiments on challenging datasets demonstrate that SENA achieves alignment accuracy comparable to leading homography-based methods, while providing improved shape preservation, texture continuity, and overall visual realism.