Abstract: This paper introduces the concept of Algorithmic Energy as a fundamental quantity emerging from the algorithmic evolution of space–time. Building upon the Desmos (Bond) framework, interaction between physical systems is shown to be governed not by mass or force alone, but by energy states regulated through an evolving space–time scaling function. The proposed formulation replaces classical force-based interpretations of gravitation with an energy-driven bond model, capable of explaining local gravitational stability, cosmic expansion, and hierarchical dominance phenomena such as the Earth–Moon versus Moon–Sun system. Algorithmic energy unifies gravitational binding, spacetime curvature, and large-scale cosmological behavior within a single mathematical structure.

Abstract: We demonstrate that the fine structure constant alpha⁻¹ ≈ 137.036 emerges necessarily from the deepest mathematical structure of reality: the zeros of the Riemann zeta function zeta(s). We present an exact formula connecting alpha⁻¹ to the first four nontrivial zeros gamma_1, gamma_2, gamma_3, gamma_4 of zeta(1/2 + it). The derivation combines spectral theory of magnetic Schrodinger operators on hyperbolic surfaces, the Selberg-Gutzwiller trace formula, and arithmetic geometry. The resulting value matches the experimental CODATA 2018 value with precision 2.7 × 10⁻¹³. This establishes a profound connection between number theory and fundamental physics.

Abstract: The integration of artificial intelligence-powered predictive maintenance solutions in manufacturing plants is revolutionizing asset management by substantially reducing downtime and enhancing equipment efficiency. By utilizing sensor fusion combining data from multiple sources such as vibration, temperature, and pressure sensors with advanced machine learning algorithms, manufacturers are able to continuously monitor machine health and forecast potential failures long before they occur. This data-driven strategy shifts maintenance from a reactive or scheduled paradigm to a proactive and dynamic process, resulting in significant cost savings, optimized resource allocation, and greater operational reliability. As a result, the adoption of these technologies supports the strategic goals of Industry 4.0, paving the way for smart manufacturing environments characterized by resilient, efficient, and autonomous operations.

Abstract: Skyjo is a simple stochastic card game with partial information, local replacement decisions, and score-reducing column removal events. This paper develops a formal mathematical model of the game, derives expected-score rules for turn-level actions, proves several dominance and threshold results, and evaluates a family of heuristic strategies through Monte Carlo simulation. The focus here lies on local optimality under explicit belief assumptions rather than a full equilibrium solution of the multiplayer game. Finally a simulation code is provided for reproducibility.

Abstract: Nica and Sprague [\textit{Am. Math. Mon., 2023}] derived a non-Archimedean version of the Gershgorin disk theorem. We derive a non-Archimedean version of the oval (of Cassini) theorem by Brauer [\textit{Duke Math. J., 1947}] which generalizes the Nica-Sprague disk theorem. We provide applications for bounding the zeros of polynomials over non-Archimedean fields. We also show that our result is equivalent to the non-Archimedean version of the Ostrowski nonsingularity theorem derived by Li and Li [\textit{J. Comput. Appl. Math., 2025}].

Abstract: Nanoscale conductors and interfaces exhibit anomalous AC transport and enhanced superconducting critical temperatures that extend beyond conventional electron-phonon descriptions. We propose a complementary mechanism arising from the inertial response of a $\mathbb{Z}_3$-graded vacuum sector to time-varying electromagnetic fields. In-medium renormalization softens TeV-scale vacuum modes into low-energy collective excitations at surfaces and interfaces, introducing a characteristic response time $\tau_{\rm vac}$. This vacuum inertia modifies the effective conductivity, leading to frequency-dependent features such as high-frequency skin depth saturation, non-monotonic surface resistance, and enhanced macroscopic quantum coherence in nanostructures. Quantitative, ab initio predictions for skin depth plateaus, loss spectrum characteristics, and critical dimension effects on nanowire $T_c$ are derived and found to be consistent with experimental observations in high-purity metals and interface superconductors. The framework provides a unified perspective on these mesoscopic anomalies, bridging algebraic high-energy structures with low-energy quantum materials phenomena.

Abstract: The Earth flyby anomaly—a small, unexplained residual in spacecraft velocity—remains a persistent challenge in astrodynamics. While the empirical Anderson relation captures the general trend of reported anomalies, it does not account for the suppressed or near-null results observed in several later flybys. Here we construct a geometry-aware, Earth-fixed coupling proxy based on distance-weighted surface visibility over a land--sea distribution mask. We identify a robust sign structure across the primary flyby set that remains stable under variations in weighting choice and integration window. The results indicate that Earth-fixed asymmetry acts as a modulation of the Anderson prediction rather than as an independent force, offering a potential pathway for reconciling discrepancies in anomaly magnitude.

Abstract: Injection molding is widely used for plastic parts, but its performance is limited by the cooling stage, which dominates cycle time and affects dimensional stability and energy consumption. Conformal cooling channels, which can be manufactured using additive technologies, improve thermal efficiency but introduce a high-dimensional design problem. This work proposes an integrated methodology for optimizing injection molds with conformal cooling channels that combines parametric CAD, simulation, nonlinear principal component analysis, artificial neural network, and multi-objective evolutionary optimization. The workflow is applied to a case study with five cooling layouts. An initial set of 36 metrics related to temperature gradients, warpage, shrinkage, and energy is reduced to a small number of latent objectives, simplifying the search space while preserving the main physical trends. Artificial neural networks surrogates accurately reproduce numerical results, enabling exploration of the design space at a fraction of the computational cost. The optimization yields diverse Pareto-optimal solutions that balance cycle time, dimensional stability, and energy consumption, assisting the design of more sustainable injection molds. Sensitivity analysis identifies mold temperature and channel position/diameter as key design levers. The proposed methodology reduces dependence on expensive simulations and is readily transferable to industrial mold design.

Abstract: Recently, when forming radar video frames for surface mapping, group-interacting compact onboard radar systems (CORS) are increasingly being utilized. In this context, for the cooperative functioning of the group, each compact radar should use its own unique marked signal as the probing signal. This signal must be distinguishable in the common channel and should not destructively affect the probing signals emitted by other radars within the group. This organization allows for associating the marked signals reflected from the underlying surface with specific CORS in the group. This requirement arises from the fact that each compact onboard radar in the group emits a single probing signal and then receives all the reflected signals from the surface that were emitted by the other CORS in the group. Such an organization of the group-based system of technical vision requires the search for and study of specialized marked code structures used for phase modulation of probing signals to identify them in the shared radar channel. The study focuses on the search for new complex M-sequences with lower sidelobe levels of the normalized autocorrelation function compared to traditional M-sequences. This is achieved by replacing the traditional alphabet of positive and negative ones with an asymmetric set consisting of complex numbers. Using numerical methods and computer simulations, optimal complex values of the sequence with a minimum level of sidelobes in the autocorrelation function are determined. In addition to correlation properties, the phase-modulated signals generated based on the new marked sequences are also investigated. The results obtained open up new possibilities for the construction of a group-based technical vision system, enabling cooperative surface probing with each CORS in the interacting group.

Abstract: Background: Influenza is a globally prevalent infectious disease caused by influenza viruses, affecting individuals across all age groups. Influenza vaccination is the most effective method for preventing influenza. Vaccine Effectiveness (VE) is used to assess the real-world effectiveness of vaccines. Currently, there is no data on the effectiveness of influenza vaccination in Wuhan. This study employed a test-negative case-control design to evaluate the VE of influenza vaccination in Wuhan during the 2024-2025 season. Methods: A test-negative case-control design was used. Patients presenting with influenza-like illness (ILI) who underwent influenza virus RT-PCR testing at outpatient or emergency departments of 41 medical institutions in Wuhan were selected and classified into case (influenza virus RT-PCR positive) and control (influenza virus RT-PCR negative) groups. Results: This study included 23,302 influenza virus RT-PCR positive cases and 99,424 negative controls. The overall adjusted VE was 35% (95% CI: 30%-40%). Among age groups, the adjusted VE was highest in adults aged 19-59 years and 60-69 years, at 63% (95% CI: 50%-73%) and 60.7% (95% CI: 46%-72%), respectively. The VE was relatively lower in children and adolescents aged 0.5-5 years and 6-18 years, at 25% (95% CI: 17%-33%) and 25% (95% CI: 14%-36%), respectively. Regarding vaccination strategy, the VE for vaccination in both consecutive seasons and vaccination in the current season only were similar, at 42% (95% CI: 28%-54%) and 40% (95% CI: 36%-45%), respectively, while VE for vaccination in the previous season only was 20% (95% CI: 14%-26%). Among vaccination months, protection was highest for vaccination in November, with a VE of 46.1% (95% CI: 36.4%-54.6%). Conclusions:Vaccination in both consecutive seasons and vaccination in the current season only provided better protective effectiveness compared to vaccination in the previous season only (42% vs. 20%). The protective effectiveness of influenza vaccination in November was superior to other months.

Abstract: Colon cancer represents one of the most prevalent malignancies globally, influenced by genetic mutations, environmental factors, and chronic inflammatory processes. Natural phytochemicals, particularly eugenol derived from Syzygium aromaticum (cloves), demonstrate selective cytotoxicity toward malignant cells while preserving healthy cellular integrity. This review synthesizes current evidence on eugenol's physicochemical properties, absorption kinetics, and molecular mechanisms underlying its anticancer efficacy in colorectal carcinomas. Eugenol's multitargeted action encompasses apoptosis induction, cell cycle arrest, suppression of inflammatory pathways, and inhibition of metastatic progression. Furthermore, nanotechnological encapsulation strategies have been explored to enhance bioavailability and pharmacokinetic stability. The present analysis consolidates preclinical findings, discusses clinical translation challenges, and identifies future research directions for eugenol as an adjunctive therapeutic agent in cancer management.

Abstract: We propose a solver-agnostic framework for analysing convergence in DSGE computation based on a single quadratic \emph{residual of sameness} measured in a fixed, calibrated norm. In the Deterministic Statistical Feedback Law (DSFL) view, an economic model is specified by a frozen defect representation and a declared symmetric positive definite ruler that aggregates equilibrium violations. Once this geometry is fixed, solver behaviour becomes a typed statement about the induced defect dynamics rather than an implementation-dependent notion of error. We show that standard DSGE solvers—time iteration, policy iteration, and Newton or quasi-Newton methods—can be analysed as residual-updating maps whose contraction properties yield explicit convergence envelopes, robust stopping rules under numerical forcing, and comparable rate diagnostics. A Gram-operator construction provides a single solver-agnostic contraction score and exposes non-normal transient amplification that eigenvalue diagnostics alone can miss. Numerical studies for a small New Keynesian model illustrate how the framework enables reproducible and interpretable solver comparisons within a single geometric language.

Abstract:

Dendrobium officinale (DO) is a traditional medicinal and edible plant whose polysaccharides help modulate gastrointestinal and metabolic functions. Fresh DO is commonly processed into “Fengdou” to prolong shelf life, but the effects of this processing on polysaccharide structure and bioactivity remain unclear. In this study, polysaccharides from fresh DO (FDOP) and Fengdou (DDOP) were isolated, purified, and comparatively characterized. Fourier transform infrared (FT-IR) analysis indicated similar functional groups and O-acetylated pyranosyl structures in both polysaccharides. Based on monosaccharide composition, methylation, and Nuclear Magnetic Resonance (NMR) analyses, both samples were identified as mannose-glucose heteropolysaccharides. However, FDOP was characterized by a higher mannose-to-glucose ratio (79.77:19.57) and molecular weight (187.1 kDa), as well as a more structurally diversified →4-linked backbone, whereas DDOP contained more glucose (68.74:30.94) and exhibited a lower molecular weight (125.1 kDa) and simplified backbone. In zebrafish models, both polysaccharides were found to alleviate loperamide-induced constipation and reduce lipid accumulation. DDOP showed stronger constipation-relieving activity, whereas FDOP exerted more pronounced hypolipidaemic effects, which may be attributed to its higher molecular weight, mannose enrichment, and more complex backbone structure. These findings provide a structural basis and theoretical support for developing DO-derived polysaccharides as functional food ingredients targeting constipation and dyslipidaemia.

Abstract:

Acute lung injury (ALI) is a severe inflammatory condition with high mortality rates, necessitating the development of effective therapeutic agents. Polydeoxyribonucleotide (PDRN), a DNA-derived compound known for its tissue repair and anti-inflammatory properties, has gained attention as a potential therapeutic agent. However, the efficacy of PDRN derived from marine sources, particularly Porphyra sp. (laver), remains unexplored in respiratory inflammation. In this study, we investigated the protective effects of Porphyra sp.-derived PDRN (Ps-PDRN) against LPS-induced ALI in mice through two administration routes: intranasal (IN) and oral (PO). Ps-PDRN treatment significantly attenuated fever, pulmonary edema, and histopathological changes in LPS-challenged mice. Both IN and PO administration of Ps-PDRN markedly reduced pro-inflammatory cytokines (TNF-α, IL-1β, IL-6) and chemokines (MCP-1, RANTES, CXCL1, MIP-2) in bronchoalveolar lavage fluid (BALF) and serum. Comparative analysis of the two administration routes revealed distinct efficacy profiles, with oral administration demonstrating superior chemokine inhibition while intranasal delivery showed advantages in certain cytokine suppression. Histological examination revealed that Ps-PDRN preserved alveolar architecture and reduced inflammatory cell infiltration. Furthermore, in vitro studies using RAW 264.7 macrophages demonstrated that Ps-PDRN inhibited LPS-induced production of proinflammatory cytokines such as TNF-α and IL-6 in a dose-dependent manner. These findings suggest that Ps-PDRN exerts potent anti-inflammatory effects against ALI through both local and systemic administration routes, highlighting its potential as a novel therapeutic agent for inflammatory lung diseases.

Abstract: Accurate modeling of outdoor Wi-Fi propagation in dense urban environments is essential for smart city connectivity. Deterministic ray-tracing techniques provide high-fidelity insight into multipath propagation but suffer from high computational cost and limited scalability in large 3D environments. This work investigates a hybrid approach that combines MATLAB-based ray-tracing simulations with Machine Learning to enable scalable Wi-Fi~7 network analysis. A large dataset is generated over a realistic simulated university campus, covering multiple frequency bands (2.4, 5, and 6~GHz), transmit power levels, and ray-tracing configurations with reflections and diffractions. Several regression models are evaluated, with emphasis on transformer-based architectures. Results show that the FT-Transformer accurately approximates ray-tracing outputs while reducing inference time from months to minutes. The proposed framework enables fast surrogate modeling of radio propagation and supports network planning and digital twin applications.

Abstract: Copper-aluminum layered composites offer a promising combination of high conductivity, light weight, and cost-effectiveness, making them attractive for applications in electric vehicles, electronics, and power transmission. However, achieving reliable interfacial bonding while avoiding excessive work hardening and brittle intermetallic formation remains a significant challenge. In this study, a Cu18150/Al1060/Cu18150 trilayer composite was fabricated through a three-stage high-temperature oxygen-free rolling process. Subsequently, the produced composite was subjected to annealing treatments to systematically investigate the effects of rolling passes, annealing temperature/time on interfacial evolution and mechanical behavior. Results indicate that rolling passes primarily influence interfacial topography and defect distribution. Fewer passes lead to wavy, mechanically bonded interfaces, while more passes improve flatness but reduce intermetallic continuity. Annealing temperature critically governs diffusion kinetics; temperatures up to 400 °C promote the formation of a uniform Al2Cu layer, whereas 450 °C accelerates the growth of brittle Al4Cu9, thickening the intermetallic layer to 18 μm and compromising toughness. Annealing duration further modulates diffusion mechanisms, with short-term (0.5 h) treatments favoring defect-assisted diffusion, resulting in a porous, rapidly thickened layer. In contrast, longer annealing (≥1 h) shifts toward lattice diffusion, which densifies the interface but risks excessive brittle phase formation if prolonged. Mechanical performance evolves accordingly; as-rolled strength increases with the number of rolling passes, but at the expense of ductility. Annealing transforms bonding from a mechanical to a metallurgical condition, shifting fracture from delamination to collaborative failure. The identified optimal process, single-pass rolling followed by annealing at 420°C for 1 hour, yields a balanced interfacial structure of Al2Cu, AlCu, and Al4Cu9 phases, achieving a tensile strength of 258.9 MPa and an elongation of 28.2%, thereby satisfying the target performance criteria (≥220 MPa and ≥20%).

Abstract: AI-enabled fitness services collect continuous and sensitive data for monitoring and personalized feedback, which raises privacy and security concerns. Nevertheless, many users continue to engage with these services, suggesting a privacy–use tension. Using online survey data from 596 adults (age ≥ 18), this study examines AI fitness use from a privacy-satisficing perspective. We construct a Deviation index (standardized privacy concern minus standardized risk acceptance) and model high willingness to use AI fitness services with a parsimonious probability approach. Results indicate that continued use varies systematically across the Deviation spectrum. In logistic regression analyses, Deviation, perceived transparency and safety (Information Control Level, ICL), and privacy–convenience trade-off attitudes are associated with the likelihood of continued AI fitness use. Predicted probabilities vary gradually across the Deviation range. Overall, privacy concern and continued AI fitness use coexist in this sample, consistent with a bounded-rational privacy-satisficing interpretation.

Abstract: The global acceleration in electric vehicle (EV) adoption is projected to result in a substantial volume of spent traction motors reaching end of life EoL), especially in emerging economies. Addressing this challenge, the present study develops a comprehensive evaluation and decision-making framework to support the remanufacturing of EoL traction motors within Ghana’s circular economy context. The methodology integrates RUL prediction algorithms, a Multi-Stage Testing Protocol (MSTP), remanufacturability scoring using hybrid Multi-Criteria Decision Analysis (MCDA), and safe dismantling procedures aligned with Ghana’s EPA Act 917 and LI 2250. Tools such as vision-based screw detection, robotic disassembly path modelling, and non-destructive magnet removal are incorporated to ensure technical feasibility and operator safety. Results demonstrate the effectiveness of predictive models in estimating degradation patterns and confirm the technical viability of semi-automated disassembly workflows. The developed remanufacturing feasibility scoring tool enables objective selection of candidate motors for reuse, factoring performance, and environmental impact. This work offers a replicable, data-driven framework that strengthens local remanufacturing infrastructure, reduces reliance on critical raw materials, and advances sustainable motor lifecycle management in low and middle-income countries.

Abstract: Antimicrobial resistance [AMR] is a silent yet intensifying global threat, with particularly severe consequences in tropical and subtropical ecosystems, where high ecological connectivity, widespread antimicrobial use, and inadequate sanitation create ideal conditions for the persistence and spread of antimicrobial resistance genes [ARGs]. Within the One Health framework, migratory birds warrant special attention because they traverse tropical AMR hotspots, linking contaminated aquatic, agricultural, and peri-urban environments along established flyways. Evidence from tropical regions demonstrates that migratory birds frequently carry clinically meaningful ARGs, including extended-spectrum β-lactamases [ESBLs], carbapenemases, and colistin resistance [mcr] genes, highlighting their role as biological connectors that redistribute resistant bacteria between human-dominated and natural ecosystems and contribute to the expansion of the global resistome. Addressing the complex interface among AMR, migratory birds, and ARGs requires integrative surveillance strategies that explicitly incorporate wildlife into existing health systems. Genomic and metagenomic monitoring of migratory bird populations, combined with cross-sectoral data sharing, can provide early warning signals of emerging resistance patterns and inform evidence-based interventions. Understanding the ecological role of migratory birds in tropical ecosystems is therefore essential for designing effective One Health strategies to curb transboundary AMR dissemination and preserve the long-term efficacy of antimicrobial therapies.

Abstract: Falls among people living with dementia are a major adverse health outcome, strongly associated with physical disability, decline in activities of daily living (ADL), institutionalization, and increased mortality risk. The incidence of falls in this population is consistently higher than in older adults with preserved cognitive function. Although exercise interventions centered on lower-limb strength and balance training have been firmly established as effective for reducing falls among community-dwelling older adults, standard fall-prevention programs such as the Otago Exercise Programme (OEP) are implicitly designed under the assumption that participants retain adequate comprehension, memory, executive function, and self-management capacity. As a result, these programs are prone to implementation failure in dementia care and clinical settings.This paper aims to theoretically reconfigure fall-prevention exercise by decomposing existing evidence into “core active ingredients” and “design elements that impose excessive burden in dementia,” and by reconstructing a dementia-adapted framework for fall-prevention exercise. Specifically, we propose a Dementia-adapted Otago Exercise Programme (D-OEP) based on four core principles: (1) radical simplification of task structure; (2) exclusion of high-risk static balance tasks; (3) embedding balance stimuli within functional movements such as sit-to-stand and supported ankle exercises; and (4) delivery formats that assume caregiver supervision and support.Rather than eliminating balance training, this framework repositions balance stimuli into safe, repeatable functional activities, thereby suppressing fear responses and maladaptive reactions while ensuring cumulative exposure to lower-limb strength and postural control demands. The value of fall-prevention exercise lies not in theoretically optimal prescriptions but in cumulative exposure to active ingredients achieved through feasibility, adherence, and safety. This paper reframes fall prevention not as an issue of “exercise inefficacy” but as a problem of design and implementation, and provides a conceptual foundation for translating evidence into dementia care practice.