Abstract: Smart microgrids combining photovoltaic arrays, wind turbines, and battery storage generate telemetry that existing open-source monitoring tools cannot process with per-mechanism energy loss visibility in real time. This paper presents a design, im-plementation, and evaluation of an open-source IoT Monitoring Framework. The framework incorporates a physics-based microgrid simulator, a hierarchical MQTT communication architecture, and a React-based web-based user interface that supports WebSocket-based real-time data visualization. The open-source framework consists of twelve containerized microservices that can be started with a single command: docker compose up -d. The code has been released under the permissive MIT license. All stack performance testing was conducted using a simulated 1 hour test case based on a 100kWp PV system, 10kW wind turbine, and 50kWh battery powered campus mi-crogrid. Average P50 end-to-end latency was 27.2 ms and P99 end-to-end latency was 48.3 ms with 100% message delivery across 5,840 test messages with per-topic analy-sis revealing a 25 ms serialization-order effect in sequential MQTT publishing. Com-parative analysis against ten existing platforms including OpenEMS, VOLTTRON, Eclipse Ditto, and pymgrid confirms that no prior open-source framework unifies physics-based loss telemetry, IoT communication, time-series storage, and real-time visualization in a single reproducible deployment.

Abstract: Artificial intelligence (AI) is increasingly embedded in chemistry teachers’ everyday practices. At the same time, AI generated chemistry content varies in accuracy, representational quality and pedagogical suitability, creating new demands for teachers’ professional judgement. This article presents two practice-oriented contributions designed to support chemistry teachers’ AI integrated practices. First, we introduce a co-design–based LUMA AI Online Forum model, grounded in the Evidence Based Teacher Education (EBTE) framework. The forum brought together chemistry teachers and teacher educators in sessions where AI generated chemistry representations, explanations and experimental suggestions were analysed, critiqued and redesigned through collaborative cycles. AI was approached as an object of pedagogical, disciplinary and ethical evaluation. Second, insights from the co-design process of the forum informed the development of an AI Pedagogical Content Knowledge (AI-PCK) framework. The framework builds on classical PCK by identifying how AI‑integrated chemistry teaching reshapes all five established domains – orientations toward chemistry teaching, curriculum knowledge, knowledge of students’ understanding, instructional strategies and representations, and assessment knowledge – while articulating AI literacy with ethical–epistemic competence as a necessary extension. Together, these contributions offer tools for strengthening chemistry teachers’ pedagogical judgement and professional agency in AI rich educational contexts.

Abstract: Coastal basins are highly dynamic systems susceptible to flooding and erosion, processes intensified by extreme cyclonic events. This study aims to develop a physical-geographic framework for analyzing the multi-hazard geomorphological dynamics of the La Sabana River basin in southern Mexico. The methodology integrates the analysis of the basin's natural and anthropogenic components with morphometric evaluation and multivariate analysis (PCA) at the sub-basin level. The results show a highly efficient drainage network (3.8-5.4 km/km²) and short concentration times (0.98–2.75), which favor a rapid hydrological response and high susceptibility to flooding and erosion. PCA explained 65.8% of the total variance, identifying basin size, drainage organization, and system shape as dominant controls. Critical sub-basins with rapid hydrological response (Tc ≤ 1.5 h) were identified, coinciding with areas of high anthropogenic exposure. It is concluded that integrating morphometric indices through multivariate approaches provides a robust, replicable basis for risk governance and territorial planning in coastal basins.

Abstract: Ageing remains one of biology’s most fundamental unresolved questions. Existing explanations often attribute ageing to stochastic damage accumulation, adaptive programmes, or interacting hallmarks, yet none fully explains why ageing emerges so broadly in organisms that undergo development. Here, I introduce the Double Code Hypothesis of Ageing, which frames ageing as an inevitable consequence of life’s dual inheritance system: the genome and the epigenome. In this manuscript, “code” is used in a computer-science-like sense, closer to source code than to the specialised meaning used in code biology: an organised set of biological instructions whose effects depend on being read, interpreted, maintained, and executed by cellular machinery.I propose that ageing is not merely the progressive accumulation of epigenetic noise within an individual, but the consequence of an inherent instability in a dual inheritance system whose two informational layers must remain functionally aligned across cellular and organismal generations. The relative stability of the genome allows long-term information preservation, whereas the plasticity of the epigenome enables development, differentiation, and adaptation, but also makes this layer vulnerable to cumulative misalignment.This progressive loss of genome–epigenome coordination acts as a natural, non-programmed limit on lifespan in complex organisms. Rather than an encoded death programme, ageing is described as increasing informational disorder emerging from the unresolved tension between genetic stability and epigenetic plasticity. This falsifiable framework offers experimental predictions in model systems such as Schizosaccharomyces pombe and provides a mechanistic explanation for why ageing exists.

Abstract: Data science techniques are increasingly employed to enhance process efficiency, reduce energy consumption and operational costs, enable active process control, ensure consistent product quality, and support predictive maintenance in modern manufacturing systems. A central question arising from recent developments is: How can data models fundamentally transform manufacturing processes, and what are the primary barriers to their widespread adoption? Contemporary manufacturing sectors are progressively integrating data models within digital twin and digital shadow frameworks to enable real-time process optimization and data-informed decision-making. However, the inherent complexity of manufacturing processes—combined with the frequent scarcity of high-quality, balanced datasets—often limits the generalizability and interpretability of purely data-driven models. In practice, quality, contextual relevance, representativeness, and richness of data are significantly more critical than its sheer volume when developing robust and reliable models. This paper provides a comprehensive overview of the application of data modeling in dynamic manufacturing environments. It examines key aspects such as data generation, sampling strategies, data preprocessing and handling, and model development methodologies across steady-state, transient, and generative process regimes.

Abstract: In this paper we used a new and special dispersion relation regarding the particles and antiparticles to investigate the issue of Baryon asymmetry of the Universe. With the new dispersion relation of particles and antiparticles, we found that the number density of antiparticles is greater than that of particles. Since our objective world has a significantly higher number density of particles than that of antiparticles, we then referred to previous work and introduced the CPT-odd leptogenesis and Sphaleron process, which can convert the asymmetry of leptons and antileptons into the asymmetry of baryons and antibaryons, that is, the remaining antileptons can be converted into the remaining baryons. Finally, by comparing with the observed baryon asymmetry data in today's universe, we obtained the concrete value of Q that the only undetermined parameter in the introduced Lorentz violation model, and we found that the obtained value of Q did not exceed the upper bound of Q that was derived from various experimental results in previous work. However, since the value of Q obtained in this paper is so small, it brings a challenge for future experiments to detect it.

Abstract: The mandibular cortical index (MCI) is a valuable screening tool for osteoporosis on dental panoramic radiographs; however, inter-examiner variability remains a significant challenge. This study aimed to evaluate the diagnostic performance and reproducibility of a closed-type generative AI (NotebookLM, Google) compared with eight dentists of varying experience levels. One hundred radiographs were evaluated in two sessions with an interval of at least two weeks. The intra-examiner reliability for the AI was exceptionally high (κ = 0.987), and its processing speed was approximately six times faster than that of the dentists. However, the agreement between the AI and the dentists remained at "slight agreement" or lower (κ < 0.2), statistically rejecting the null hypothesis of diagnostic equivalence. Notably, a "two-level discrepancy" was observed, where the AI interchanged Class 1 (normal) and Class 3 (severe) in over 10% of cases. In contrast, dentists demonstrated a significant learning effect, with inter-examiner agreement improving between sessions. These results suggest that while generative AI offers superior speed and reproducibility, its current decision-making logic deviates fundamentally from human expert criteria. Future integration should focus on hybrid models where AI serves as a standardized feedback tool while dentists provide final confirmatory diagnoses.

Abstract: Renal cell carcinoma encompasses a heterogeneous group of kidney tumors with wide variations in biological behavior, histologic subtype, and clinical aggressiveness. Accurate preoperative characterization is essential for management; however, it remains challenging due to overlapping imaging features and tumor complexity. CT is the most widely used imaging technique for renal mass evaluation, providing broad availability, high spatial resolution, and multiphasic acquisition capabilities. However, its ability to distinguish histologic subtypes and predict tumor aggressiveness remains limited. This review provides an updated overview of renal cell carcinoma epidemiology and evidence supporting CT as an essential imaging modality. It outlines key radiologic features of main histologic subtypes, highlights markers of aggressive behavior, and discusses the relationship between CT findings and the International Society of Urological Pathology (ISUP) grading system. We explore radiomics, summarizing its methodological foundations and applications in characterizing solid renal masses, emphasizing the need for multicenter studies and standardized radiomic workflows to develop accurate, reproducible tools for improving diagnostic accuracy and risk stratification for renal cell carcinoma.

Abstract: Parkinson’s disease (PD) encompasses marked heterogeneity across motor, cognitive, and non-motor domains, reflecting variable balances of neurodegeneration and compensation across distributed brain circuits. Diffusion MRI tractography enables pathway-specific characterization of white matter alterations and offers a framework for linking clinical subtypes to patterns of degeneration and compensation along individual tracts that are often obscured by skeleton-based or connectomic averaging. Although several tract-specific correlational diffusion studies have linked individual pathways to clinical features and progression, much of the literature has relied on group-level skeleton or network representations, limiting generalizability and reproducibility across subtypes. Here, we synthesize tractography-based evidence across PD subtypes—including tremor-dominant, postural instability/gait difficulty, freezing of gait, and cognitive phenotypes—while situating these findings within a complementary multimodal imaging context. We review diffusion models ranging from diffusion tensor imaging to advanced free-water, neurite and fixel-based frameworks, highlighting how these approaches constrain and interpret tract-level findings and help distinguish degenerative processes from adaptive neuroplasticity. Emerging analytical approaches, including harmonization pipelines, radiomic tractometry (the extraction of along-tract microstructural and radiomic features), and machine learning classifiers, further enhance tract-level sensitivity and reproducibility. Cognitive subtypes illustrate how degeneration of posterior association and limbic tracts, in interaction with non-dopaminergic systems such as cholinergic and noradrenergic pathways, shapes clinical progression. Integrating tractography with molecular, genetic, and functional markers enables subtype-specific biomarkers for risk stratification, prognosis, and targeted therapeutic intervention. We propose a conceptual and methodological roadmap for leveraging tractography to refine PD subtype definitions and inform precision neuromodulation and rehabilitation strategies.

Abstract: Introduction: A major health issue in individuals living at high altitude regions is an increase in the number of red blood cells (RBCs). This condition generates a series of physiological alterations, including the nervous system, where damage can occur due to increased blood viscosity. This increased viscosity, in turn, could compromise oxygen uptake, potentially leading to a degree of cognitive impairment. Objective: To determine the association between exposure to chronic hypoxia and sleep quality with the degree of cognitive impairment (IQ) in a young adult population residing at different altitude levels. Methodology: Two hundred apparently healthy subjects of both sexes, aged 21 to 26 years, permanently residing in four cities at different altitudes—Lima, Arequipa, Puno, and La Rinconada (50 participants per location)—were evaluated. Physiological variables such as oxygen saturation (SpO2), blood pressure (BP), heart rate (HR), and hemoglobin (Hb) and hematocrit (Hct) levels were measured. Cognitive impairment was assessed using the Montreal Cognitive Assessment (MoCA), and sleep quality was assessed using the Pittsburgh Sleep Quality Index (PSQI). ANOVA, chi-square, and linear regression models were used to analyze correlations. Results: Hemoglobin (Hb) levels increased gradually with altitude, reaching a maximum of 19.47 ± 3.01 g/dL in La Rinconada, while SpO2 decreased to 81.64% at the same site. Moderate to severe cognitive impairment was a finding exclusive to the La Rinconada population (5100 m), where only 10% of subjects remained unaffected. Regression analysis showed that for each unit increase in Hb, the MoCA score decreased by 0.59 points, indicating that elevated Hb levels were associated with varying degrees of cognitive impairment. No association was found between sleep quality and the degree of cognitive impairment. Conclusions: Chronic exposure to severe hypoxia (>5000 m) is associated with a greater presence of cognitive impairment, while sleep quality is not associated with any degree of cognitive impairment.

Abstract: Background: Physical activity is a recommended first‑line treatment for the management of chronic low back pain, yet adherence to structured exercise often remains poor due to pain, fear, fatigue, and contextual barriers. Snacktivity™, which promotes brief, frequent bouts of movement embedded in daily routines, has emerged as a potentially feasible alternative. However, it remains unclear how, why, and for whom Snacktivity supports engagement in physical activity for people living with chronic low back pain. Objective: To develop and refine programme theories explaining how Snacktivity‑type interventions support physical activity engagement and related outcomes in adults with chronic low back pain. Methods: A realist review was conducted following RAMESES standards. Initial programme theories were developed and iteratively refined through synthesis of quantitative, qualitative, and mixed‑methods evidence from Snacktivity and related sedentary‑reduction interventions in low back pain and other adult populations in order to test developed programme theories. Evidence was analysed to identify context–mechanism–outcome configurations. Results: A total of four studies met the inclusion criteria for Snacktivity-type studies related to low back pain and were included to develop and test the initial programme theories. This was then supported by 38 studies that contributed evidence to programme theory refinement. Five refined programme theories were supported. Snacktivity appears to enable engagement by lowering perceived burden and threat rather than eliminating fear, generating mastery experiences that enhance self‑efficacy, and reducing symptom interference through brief, distributed activity. Education and coaching components supported meaning‑making by reframing movement as legitimate and achievable, while environmental cues and routines promoted habit formation. Psychosocial outcomes (confidence, mood, vitality) and habit formation improved more consistently than performance‑based outcomes, and engagement was sustained even when pain or fatigue persisted. Conclusions: Snacktivity functions as a participation‑enabling intervention rather than traditional exercise prescription. Its effectiveness for chronic low back pain is explained by psychosocial and contextual mechanisms that support psychological safety, mastery, and habit formation. These findings support a shift from dose‑response exercise models toward interventions that prioritise feasibility, meaning, and sustained participation in daily life.

Abstract: Fatigue is a leading contributor to maritime accidents, yet recreational sailors lack the regulatory frameworks and fatigue management tools available to commercial mariners. Peer-reviewed research published in Nature demonstrates that after 17 hours of sustained wakefulness, cognitive performance degrades to a level equivalent to a blood alcohol concentration of 0.05% — the legal driving limit in most countries (Dawson & Reid, 1997). After 24 hours, this rises to 0.10%, well past the threshold for legal intoxication. These findings have been independently replicated (Williamson & Feyer, 2000) and confirmed in field studies aboard racing yachts (Hurdiel et al., 2014). This paper synthesises more than three decades of peer-reviewed research spanning chronobiology, sleep medicine, occupational health, and maritime safety into a biomathematical fatigue model calibrated specifically for pleasure boat passage-making. The model integrates sleep-wake homeostasis, circadian rhythm modulation, sleep fragmentation effects, environmental sleep degradation from sea state, and cumulative multi-day sleep debt into a single framework that outputs impairment as a BAC (blood alcohol concentration) equivalence — an intuitive metric that any sailor can understand. Critically, the model is not merely theoretical. It has been implemented as a freely available, openaccess passage fatigue calculator for mobile and web platforms, making it accessible to the widest possible population of recreational mariners. The application faithfully reproduces every formula, constant, and coefficient described in this paper, allowing sailors to simulate any passage plan — varying crew size, watch schedule, departure time, pre-departure sleep, and sea state — and see the predicted fatigue trajectory hour by hour. The purpose is to bridge the gap between laboratory science and practical seamanship: to give pleasure boat crews the same evidence-based fatigue awareness that professional mariners receive through regulation.

Abstract: Monoclonal antibodies (mAbs) have been the subject of extensive study in recent years due to their recognition as highly promising therapeutic molecules offering high specificity and a low risk of side effects. Monitoring the structure of these molecules is crucial for developing new therapeutics, characterizing interactions with antigens or receptors, and explaining potential changes in activity between antibody production batches. However, commonly used biophysical approaches provide only low spatial resolution information, and conventional structural biology techniques such as crystallography and cryo-electron microscopy (cryo-EM) are difficult to apply to these highly dynamic proteins. Solution nuclear magnetic resonance (NMR) spectroscopy is the method of choice for structural studies of flexible proteins at atomic resolution; however, it has traditionally been limited to low-molecular-weight biological systems. In this review, we present recent advances in NMR spectroscopy and advanced isotopic labeling methods that have enabled the atomic-resolution study of both the crystallizable (Fc) and antigen-binding (Fab) fragments of antibodies. We show how NMR is becoming a powerful tool for investigating full-length mAbs at an atomic level, opening up new possibilities for the characterization and in-depth quality control of therapeutic antibodies in solution.

Abstract: Prolonged contact between oral mucosa and dental amalgam restorations may influence local epithelial homeostasis, although the immunohistochemical profile of clinically non-dysplastic mucosa exposed to long-term restorative materials remains insufficiently defined. This study investigated histopathological remodeling and the expression patterns of cytokeratin 19 (CK19), Ki67, and p53 in oral mucosal specimens adjacent to long-standing amalgam restorations. A total of 108 specimens were retrospectively analyzed, including 78 samples from mucosa in direct contact with amalgam restorations and 30 control specimens without amalgam exposure. Exposed cases were categorized according to contact duration: 5–10 years, 11–20 years, and ≥21 years. Epithelial and stromal changes were semi-quantitatively assessed, and immunohistochemical staining was evaluated using predefined scoring criteria. An exploratory Integrated Epithelial Remodeling Score (IERS), combining basal hyperplasia, inflammatory infiltrate, CK19 distribution, and Ki67 proliferative index, was used to estimate cumulative remodeling intensity. Longer amalgam exposure was significantly associated with increased inflammatory infiltrate, basal epithelial expansion, suprabasal CK19 expression, and higher Ki67 labeling indices (all p &lt; 0.001). CK19 redistribution showed positive associations with both inflammatory intensity and epithelial proliferative activity. IERS values differed significantly among exposure groups (p &lt; 0.001), with more pronounced remodeling in intermediate- and long-duration exposure categories. p53 expression showed statistically detectable but heterogeneous variation. No epithelial dysplasia was observed. These findings suggest that long-term contact with dental amalgam restorations is associated with a coordinated, non-dysplastic remodeling phenotype of the oral mucosa, characterized by inflammatory activation, CK19 redistribution, and reactive proliferative reinforcement. In this context, suprabasal CK19 expression may reflect adaptive epithelial plasticity rather than preneoplastic transformation.

Abstract: This working paper develops a methodological approach for integrating mathematical optimization with a digital twin environment in the analysis of first-mile milk collection systems. The approach combines a mixed-integer linear programming (MILP) model for network design with a digital representation that enables the evaluation of system behavior under changing operating conditions. The optimization model determines the baseline configuration, including the location of collection points, capacity allocation, and producer assignments. This configuration is then embedded into the digital twin, where its performance is examined under a representative perturbation scenario involving a 20% reduction in milk supply. The analysis shows that the baseline configuration, while efficient under nominal conditions, is sensitive to variations in supply, leading to reduced utilization and higher unit costs. Allowing limited operational adjustments within the fixed network structure improves performance, although economic indicators do not fully return to baseline levels. The results also reveal uneven effects across performance dimensions, indicating the presence of trade-offs between economic, operational, and environmental outcomes. The contribution of this study lies in connecting optimization-based design with a digital evaluation environment that enables the assessment of network configurations beyond their initial formulation. The approach provides a structured way to examine how a given design responds to changing conditions without requiring immediate structural modifications. The analysis is illustrative and intended to demonstrate the integration mechanism. Future work will extend this approach through systematic scenario design, quantitative validation, and the incorporation of real-time data.

Abstract: The use of technology-enhanced training for prehospital mass-casualty incident (MCI) preparedness has grown quickly, but there has been no comprehensive overview of how these technologies operate throughout the training process or how competencies are evaluated. This scoping review, conducted as part of the MCIPHER (Mass-Casualty Incident Prehospital Emergency Response) project, followed the Arksey and O'Malley framework and PRISMA-ScR guidelines. We searched seven databases and additional sources, screened 2,105 records, and included 28 studies published from 2015 to 2025. Virtual reality was the most common method (43%), followed by hybrid approaches (29%) and screen-based simulations (21%). We identified five key analytical constructs. Three were derived from the data: the Technology Function Spectrum revealed that half of the studies used dual-purpose platforms for both training and performance assessment; the Data Capture Architecture linked embedded data collection to advanced learning outcomes (L2+); and the Pedagogical Transparency Gap showed that 75% of studies did not specify a training design framework. Two other constructs — the Immersion-Evaluation Paradox and the Scalability-Rigor Tension — suggest areas for future research. Using a modified Kirkpatrick framework with an L2+ (Applied Learning) sub-level, 56% of completed studies demonstrated applied learning through embedded performance assessments. Overall, these findings suggest that investments in MCI preparedness should focus more on measurement capabilities than immersion, incorporate assessment into training platforms, and work to reduce geographic and resource disparities.

Abstract: The growing adoption of electric vehicles (EVs) and the rapid expansion of public charging infrastructure pose new challenges and opportunities for energy systems, particularly in urban settings. This study presents an optimization-based evaluation of different EV charging strategies including direct charging, average-based methods, smart charging, and vehicle-to-grid (V2G) at public parking lots using real-world charging session data. This data-driven model is set to optimize the public EV charging of vehicles in Gothenburg, without sacrificing on the energy requirement while minimizing charging costs for the operators. Results indicate that direct charging scenarios lead to significantly higher peak loads (up to 1286 kW) and costs (around 370 k€), highlighting their inefficiency under unmanaged operation. In contrast, smart charging reduces peak loads by approximately 47% and overall costs by around 74%, showcasing its potential for cost-effective grid-friendly operation. Two different V2G scenarios were tested based on the impact of discharged power accounted for in peak costs, though it enables energy discharge back to the grid, the benefits remain modest under current assumptions due to tight operational constraints and limited incentives. The study emphasizes the value of smart optimization and appropriate market design in enhancing the flexibility and cost efficiency of public EV charging systems.

Abstract: Cranial cruciate ligament (CrCL) rupture in cats is less common than in dogs, and its optimal treatment remains a subject of debate. The aim of this study was to evaluate the application of cranial tibial wedge osteotomy technique (CTWO) as a dynamic stabilization technique in cats with CrCL rupture, describing the technical aspects and clinical outcomes obtained. Five cases with a confirmed diagnosis of CrCL rupture between 2020 and 2024 were included in this study. All patients were treated with CTWO using specific osteosynthesis locking plates for this technique in dogs and a complementary cerclage wire. Radiographic rechecks were performed at 8 and 12 weeks postoperatively and clinical evaluations were performed 24 hours, 8 weeks, 12 weeks and six months postoperatively in every patient. Successful and complete bone healing of the tibial osteotomy was observed in every case. No intraoperative or postoperative complications related to implants or soft tissues were recorded. All cats achieved a complete functional recovery without lameness at the last recheck six months after surgery. The technique was performed without significant technical difficulties, providing adequate stability and favorable clinical outcomes in all cases. Although the use of a cranial tibial wedge osteotomy in combination with a tibial plateau leveling osteotomy (TPLO) was reported by Hoot et al to treat a cruciate ligament rupture in a cat presenting a deformity of the proximal tibia (Hoot et al), to the authors´ knowledge the use of the cranial tibial wedge osteotomy as single technique to treat the CrCL rupture in cats has not been previously reported in the literature. These preliminary results support the use of CTWO as an effective surgical alternative for the treatment of CrCL rupture in cats. However, further studies with a larger number of cases and a longer follow-up are required to evaluate better its clinical application, outcomes and influence on osteoarthritis progression in the long term.

Abstract: The Jiaduoling area is located in the northern segment of the Southwest Sanjiang Metallogenic Belt, a region characterized by complex geological structures and abundant mineral resources. This study systematically identifies the spatial correlation between subsurface magnetic bodies and tectonic structures by utilizing 1:50,000 high-precision aeromagnetic data. Advanced processing techniques—including upward continuation, vertical derivatives, total gradient modulus, and Euler deconvolution—were integrated to refine the structural framework and clarify the mechanisms of fault-controlled mineralization.The results indicate that the aeromagnetic anomaly pattern is predominantly governed by NW-trending faults. Specifically, the deep-seated major fault F1 (with a calculated depth exceeding 3 km) served as the primary migration channel for ore-forming fluids, while secondary faults created localized ore-hosting spaces. Physical property analysis reveals a significant magnetic contrast, where Mesozoic intermediate-acid magmatic rocks act as the essential source for mineralization, providing both material and thermal energy for the formation of porphyrite-type iron deposits.Based on these findings, a three-dimensional "aeromagnetic anomaly-structural framework-mineralization" correlation model was established. Finally, two high-potential metallogenic prospective zones (P1 and P2) were delineated, providing precise geophysical evidence and strategic guidance for regional mineral exploration and the targeting of concealed ore bodies.

Abstract: The Zeta-Minimizer Theorem establishes a variational foundation for the Riemann zeta function by minimizing a phase functional derived from the compressibility factor. Starting from the classical virial expansion, the theorem performs an exact exponential resummation that yields the Euler product form of ζ(s) over a finite helical basis. In a symmetric measure space equipped with non-proper Archimedean conical helices, four geometric constraints—rational signed cosines, positive integer representation dimensions, non-zero integer differences, and prime-modulated exponential decays—force primes to emerge as indivisible cycles in the representation graph, via Hilbert’s irreducibility theorem and Maschke’s theorem. Corollaries include the deductive proof of the Riemann Hypothesis (non-trivial zeros spectrally centered on Re⁡(s)=1/2), stacked phases as stratified orbifolds, emergent layered geometries, bounded prime descent, and dimensional resistance. The three axioms abstract thermodynamic equilibrium conditions purely: strict concavity of entropy on measures, non-vanishing spectral Gibbs minima, and covariance with flux conservation. Number-theoretic structures, complex numbers, polynomials, and quantization itself appear as projected artifacts of the underlying variational optimization. Applications range from atomic stratification (quantized shells arising from phase jumps) and angular-momentum tensors to the fine-structure constant (emergent from cycle sums with β=5 leaps) and covariant mappings to arbitrary conjugate variables via category-theoretic functors and renormalization-group universality. By demoting elementary mathematical constructs to derived descriptions of thermodynamic optimization on the helical manifold, ZMT provides a unified deductive framework for analytic number theory, algebraic geometry, and spectral theory.