Abstract: Machine Learning (ML) is fundamentally reshaping education, offering tools to personalize instruction, automate assessment, and predict student outcomes. This paper provides a comprehensive overview of ML's role in education, tracing its evolution from early computer-assisted instruction to today's generative artificial intelligence (AI). We explore key applications, including intelligent tutoring systems, early warning systems for at-risk students, and automated essay scoring, highlighting their potential to address the long-standing challenge of individualized learning at scale. However, this technological integration is fraught with significant challenges. Ethical concerns regarding algorithmic bias, data privacy, and the "black box" nature of complex models threaten to exacerbate existing educational inequities. The recent proliferation of generative AI, exemplified by tools like ChatGPT, has further disrupted traditional paradigms of assessment and academic integrity, prompting urgent questions about the nature of learning itself. By synthesizing current research, this paper argues that while ML holds immense transformative promise, its successful and equitable implementation depends not on technological prowess alone, but on a concerted, ethically-grounded effort involving educators, researchers, and policymakers to ensure these tools augment human expertise and serve all learners.

Abstract: The optimization of membrane permeability is a decisive strategy for mitigating late-stage failures in peptide drug development. By leveraging linker chemical diver-sity, stapled peptides utilize linker engineering to precisely modulate key physico-chemical parameters—such as lipophilicity and conformational constraints—to over-come the desolvation energy penalty. This review systematically evaluates link-er-based strategies for enhancing the permeability of stapled peptides, categorized into two primary dimensions: (1) High-throughput screening (HTS) compatibility, focusing on the integration of functionalized linkers into mRNA display, phage display, and DNA-encoded libraries (DELs) to identify lead scaffolds with inherent permeability potential during early discovery ; and (2) Post-screening structural refinement, cover-ing rational design strategies including intramolecular hydrogen bond (IMHB) shield-ing, "chameleonic" adaptations, and stimuli-responsive reversible stapling . Further-more, we analyze the paradigm shift in assessment methodologies from qualitative imaging to quantitative cytosolic delivery assays, which have deepened our under-standing of mechanisms such as the charge/lipophilicity threshold balance and meta-bolic-driven trapping. Overall, linker engineering provides a robust technical roadmap for developing the next generation of cell-permeable stapled peptide therapeutics.

Abstract:

We introduce the NeuroCore framework, a formal mathematical treatment of modular neural architectures in which a minimal executive Core—possessing no higher cognitive capabilities—autonomously orchestrates a heterogeneous collection of specialist modules through learned continuous-representation interfaces. The Core’s behavior is governed by two neuromodulation-inspired subsystems: a Dopamine System implementing distributional reinforcement learning with prediction-error intrinsic motivation and a stagnation penalty, and a Serotonin System formulated as a meta-reinforcement-learning controller that learns to optimize long-horizon constraint satisfaction. We make four theoretical contributions. First, we formalize the stagnation-modification tradeoff—proving that without explicit anti-stagnation pressure, optimal policies in self-modifying systems converge to modification-avoidance, and deriving the conditions under which the stagnation penalty restores non-trivial self-modification behavior (Theorem 1). Second, we prove a general non-convergence result for coupled self-modifying multi-objective systems, showing that the joint optimization does not admit guaranteed convergence to fixed points or bounded attractors in the parameter space (Theorem 2). Third, we establish partial stability guarantees: bounded representational drift via homeostatic Lyapunov functions (Theorem 3), local convergence under frozen modules via two-timescale stochastic approximation (Proposition 1), and modification frequency bounds (Proposition 2). Fourth, we derive information-theoretic costs for module manipulation operations that serve as principled proxies for true disruption. We propose seven falsifiable empirical predictions and discuss implications for the design of autonomous self-organizing AI systems.

Abstract: Under high C-rate and wide-temperature conditions, independently estimated SOC and SOH often diverge due to decoupled model dynamics, resulting in inaccurate power boundary calculations. This affects power limiting, thermal safety, and fast-charging strategies. To solve this, a unified online estimation framework is proposed for SOC, SOH, and power capacity under voltage and thermal constraints. It integrates a state-space model based on an equivalent circuit, combining SOC, polarization voltage, internal resistance, and capacity degradation, with temperature-dependent parameter evolution to capture coupling with aging. A dual extended Kalman filter enables collaborative SOC–SOH estimation, while lightweight machine learning modules correct internal resistance and polarization dynamics to reduce mismatch under extreme conditions. Physical constraint projections embed voltage, temperature, and power limits into the estimation loop, mitigating noise amplification and drift. Based on consistent estimates, the SOP boundary is computed online to support control decisions. Validation across six temperatures (−20 °C to 55 °C) and five C-rates (0.2C to 6C), using bench, HIL, and pack-level tests over 120+ hours, shows SOC RMSE <1.6%, SOH error <2.5%, and SOP hit rate >95% within 10 seconds. Under noise and parameter disturbances, error growth is reduced by ~25% versus baselines. These results confirm improved SOC–SOH consistency and boundary tracking, with computational cost suitable for embedded deployment.

Abstract: This paper reports on an online survey of 2,000 English adults, designed to inform the debate about the potential for wider adoption of e-micromobility modes, such as e-bikes, e-cargo bikes and e-scooters. It shows that, by 2023, take-up was already greater than for electric cars, with 11% of households owning at least one of those vehicles and 9% of adults using one at least once a month. On average, users were more likely to be male, young, well-educated urban dwellers, but findings also suggested relatively high take-up by people with children, greater appeal to women than conventional cycling, and the potential to appeal to a wider range of age groups over time. Use of e-micromobility was associated with more varied mobility strategies, and lower levels of frequent car use. Over 50% of adults were interested in trying out vehicles, and evidence from other UK trials and existing users suggests that being able to trial vehicles may be key for purchase decisions. On balance, non-users were broadly positive (or neutral) towards these modes, though with particular concerns arising around the safety of e-scooters and their relationship with pedestrians. Cost, fear of theft, difficulties with storage and parking, unsafe road environments and lack of confidence cycling all emerged as key barriers. Users of e-micromobility were less likely to be sedentary and more likely to be meeting physical activity targets than non-users, highlighting important synergies with other active travel modes (i.e. walking and cycling), but any measures to increase uptake need to find ways to ensure that different active travel modes can safely coexist.

Abstract: This paper proposes an axiomatization of the absolute infinite within a non-recursively enumerable class theory, called MK^meta, that maximally and consistently extends the formal MK: Morse-Kelley with global choice (GC). Class ordinals and class cardinals avoid the Burali-Forti paradox and GC is assumed to warrant comparability of class cardinals. A Hamkinsian multiverse M_h is defined as the collection of all the formal models v of any syntactically consistent, formal extension of MK. MK^meta is then rigorously defined by ranging over M_h and has V^meta as its unique model. At last, the absolute infinite Ω^meta = Ord^meta is derived from V^meta. Informal, formal, and formal-based theories, having increasingly many axioms, are strictly weaker than the meta-formal theory MK^meta, which has absolutely infinitely many axioms. Moreover, truth relativism is countered by MK^meta, which accepts those axioms that maximize V^meta. Consequently, the definition of M_h can be used as a rebuttal of both height and width potentialism, when combined with the argument that only the meta-formal level can capture the entire mathematical reality in a single rigid theory.

Abstract: Background: Metabolic dysfunction associated steatotic liver disease (MASLD) is a slow evolutionary condition from inflammation to cirrhosis. Manual therapy applied to the liver could optimize its visceral function and relieve inflammation. Given that MASLD prevalence increases with aging and reduced mechanical and metabolic stimulation, understanding non-pharmacological interventions becomes increasingly relevant in older populations. The main objective was to assess the usefulness of visceral manipulation therapy (VMT) on liver steatosis and insulin resistance measured by hepatic steatosis index (HSI) and the homeostasis model assessment (HOMA). Materials and Methos: An open label, randomized clinical trial of patients with MASLD. Patients with steatosis determined by HSI (> 36 indicate steatosis) were randomly assigned in a 1:1 ratio to receive either manual therapy or nothing. Participants were recruited between April and September 2024. VMT was performed by the same osteopathic therapist following a precise protocol for four weeks. The primary endpoint was changes from basal score to after proceeding in the HSI and HOMA. The secondary endpoints were changes in other non-invasive scores to evaluate steatosis, steatohepatitis and fibrosis. All patients received standard care according to their condition. Results: Forty participants, 20 each group, were finally included. Patients undergoing manual therapy experienced a significant mean reduction in the HOMA (7.22 vs. 5.5 p=0.018) and HSI (47.40 vs. 45.55 p=0.036) value after intervention. These findings did not appear in the control group: HOMA (4.17 vs. 4.7 p=NS), and HSI (42.6 vs. 41.9 p=NS). The secondary endpoints there were not changes of the scores to assess steatohepatitis or fibrosis neither experimental nor control group. Conclusions: VMT could be an adjuvant treatment in early stages of hepatic steatosis due to metabolic conditions improving insulin resistance and inflammation.

Abstract: By decoupling services and enabling elastic deployment, microservice architecture improves system scalability and evolutionary capability. At the same time, it substantially increases operational complexity. Failures often exhibit cross service propagation and a mismatch between observed symptoms and underlying root causes. To address the heterogeneity and fragmentation of multi source observability data such as logs, metrics, and distributed traces, this study proposes a unified modeling and intelligent root cause localization method for microservice systems. The approach treats each service as a basic modeling unit and maps heterogeneous observations into a shared representation space. Service dependency structure is explicitly incorporated to characterize system state at a global level. Through structure aware modeling on the dependency graph, anomaly information is propagated and constrained along real invocation relations. This design enables more accurate separation of local disturbances from structural anomalies. In addition, a consistency based measure derived from state deviation is constructed to score service anomalies. Dependency relations are then used for attribution and ranking, which unifies root cause localization and impact analysis within a single framework. Comparative results show that the proposed method achieves more stable and consistent advantages across multiple evaluation metrics. It captures anomaly propagation patterns in microservice systems more effectively and provides a unified and structure aware solution for intelligent diagnosis of complex distributed systems.

Abstract: Conservation of architectural heritage structures (AHS) requires compatible built her-itage materials with aesthetic, physical, chemical, and mechanical properties similar to those of the original materials. In recent years, however, urbanization, land reclamation, depletion of stone quarries, anti-mining and anti-quarrying legislation have limited access to original heritage materials. In the absence of the original heritage materials, ce-ment-based alternatives have been developed and widely applied for conservation. Major drawbacks of concrete- and cement-based materials include their large carbon footprint and long-term damage to the original rock or substrate, due to inadvertent promotion of salt efflorescence. This study systematically reviewed geopolymer-based materials as a sustainable, greener alternative to concrete- and cement-based materials for tuff- and coral rock-built heritage structures. The Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines were implemented for the literature review, using Scopus, Web of Science (WoS), and Google Scholar (supplementary) as databases, between 2013 and 2024. Inaccessible items, non-English, reviews, conference proceedings, book chapters, errata, and papers unrelated to geopolymers, tuff, and coral rock were excluded, resulting in a total of 103 articles. These works were classified into geopolymers (34 arti-cles), tuff-built heritage structures (60 articles), and coral rock-built heritage structures (9 articles). This review included 103 items in the qualitative analysis; however, only 34 arti-cles contained meaningful data for content analysis. These 34 articles were categorized in terms of the (i) main precursors; that is, metakaolin, fly ash, slag, and pyroclastic materi-als (i.e., pumice, volcanic ash, and volcanic soil), ceramic, others (i.e., tuff waste, silica fume, and mine wastes), (ii) formulations (i.e., precursors, activators, admixtures, and ag-gregates), and (iii) compressive strength. Furthermore, critical factors for compatibility were reviewed and classified into aesthetics (e.g., color, presence of efflorescence, and tex-ture) and physical, chemical, and mechanical properties. This review also explored recent applications of geopolymers in heritage structures, indicating that geopolymers are typi-cally used as repair mortar and consolidants. Finally, a bibliometric analysis was con-ducted to evaluate research trends on geopolymers, including a critical assessment of their aesthetic compatibility with heritage structures in the Philippines built with volcanic tuff and coral rock.

Abstract: This study explores the effects of sputtering pressure and power on FeCoNi high‑entropy alloy films prepared by DC magnetron sputtering, focusing on microstructure, surface morphology, and static/high‑frequency magnetic properties. In situ Lorentz TEM (LZ‑TEM) was used to directly observe magnetic domain evolution. Results show that low sputtering pressure (1 mTorr) promotes strong FCC (111) crystallization, smooth and dense surfaces. Increasing pressure leads to amorphization, higher roughness, and degraded magnetic performance. Under optimized pressure, 100 W sputtering power yields the best crystallinity, smoothest surface, and optimal soft magnetic properties, including high remanence ratio, low coercivity, and clear ferromagnetic resonance in the 2–7.5 GHz range. The optimal parameters are confirmed as 1 mTorr and 100 W, producing uniform nanocrystalline FeCoNi films. In situ LZ‑TEM reveals river‑like domain walls, vortex–antivortex structures, and uniform magnetic moment precession, indicating weak domain pinning and excellent high‑frequency magnetization consistency. This study provides experimental and theoretical support for the controllable fabrication of high‑performance FeCoNi soft magnetic films for high‑frequency devices.

Abstract: With the continuous increase in the number of retired lithium-ion batteries, accurately and quickly estimating their MRC has become a key challenge for the rapid sorting and secondary utilization of retired lithium-ion batteries. Conventional detection methods often suffer from low efficiency, prolonged detection cycles, and limited scalability for large-scale applications. To address these issues, this paper presents a fast MRC estimation method for retired lithium-ion batteries using a hybrid Convolutional Neural Network (CNN)-Conv Block Attention Module (CBAM)-Long Short-Term Memory (LSTM) architecture (CNN-CBAM-LSTM). The proposed approach integrates both factory-scale test data and laboratory experimental data to extract key voltage and capacity features from the initial 30-minute charging phase. Specifically, the CNN captures local temporal patterns, the LSTM models long-term dependencies in the time-series data, and the CBAM enhances feature representation by emphasizing critical characteristics. Experimental results demonstrate that the proposed method achieves MRC estimation within 30 minutes, significantly outperforming traditional approaches in terms of accuracy. The R² value increased to 99.42%, while the MAPE decreased to 1.55%. These results highlight the superior performance of the proposed method, which not only holds strong potential for rapid battery sorting and cascaded utilization but also exhibits broad applicability in large-scale battery health monitoring systems.

Abstract: Metal-enhanced fluorescence (MEF) has found widespread application in biomedical sensing and in vivo tissue imaging systems. To enhance MEF efficiency, it is necessary to optimize the interaction between metal nanoparticle plasmon and the fluorophore molecule. The size and shape of the nanoparticle, the nanoscale gap between the fluorescent molecule and the nanoparticle, and the excitation wavelength are critical parameters. In this study, we propose a model for a more complete and accurate description of the processes of molecular excitation and generation of the fluorescence spectral response, introducing new concept of effective properties for field enhancement factor, quantum yield, and fluorescence enhancement factor. The influence of the spectral properties of both the nanostructure plasmon and the fluorophore molecule on the optimal tuning of fluorescent complexes is studied. Particular attention is paid to the analysis of the spectral properties of plasmon resonance and calculations of the near-field intensity enhancement of the plasmonic nanostructure's excitation field. Numerical results for optimizing the MEF of fluorescent complexes based on TagRFP and gold (silver) nanorods composites are presented. The advantages of the proposed model for the optimal design of new nanomaterials with unique fluorescent properties are discussed.

Abstract: Interpreting and summarizing complex structured tabular data, particularly in specialized domains such as Korean administration, presents significant challenges due to intricate structures and domain-specific terminology. While Large Language Models (LLMs) offer promising capabilities, their direct application often results in information loss and misinterpretation. Existing solutions frequently necessitate extensive and resource-intensive model fine-tuning. To address these limitations, we propose Hierarchical Context-Aware Summarization (HCAS), a novel framework utilizing sophisticated prompt engineering and multi-stage reasoning. HCAS generates high-quality, human-friendly explanatory summaries for highlighted regions within complex Korean administrative tables, critically, without requiring large-scale model fine-tuning. It deconstructs the task into three distinct stages: Contextual Key Information Extraction, Explanatory Narrative Skeleton Construction, and Fluency and Readability Optimization, progressively enriching contextual understanding and refining output quality. Our comprehensive experiments on the NIKL Korean Table Explanation Benchmark demonstrate that HCAS consistently achieves superior performance, surpassing traditional fine-tuning methods and advanced in-context learning baselines on leading Korean LLMs. Further analyses validate HCAS's ability to produce factually accurate, coherent, and professionally appropriate summaries, while offering significant advantages in efficiency and resource utilization.

Abstract: Municipal waste management is one of the most pressing challenges today, with the UN report (2024) estimating that global waste volumes will increase to 3.8 billion tons by 2050. This scenario highlights the need to implement the circular economy and ef-fective waste reduction tools. At the European level, Directive 2008/98/EC establishes the waste hierarchy, where prevention is central, and the Pay-as-you-throw (PAYT) system, based on the "polluter pays" principle, proves to be a fiscal mechanism capable of stimulating recycling and reducing the volume of mixed waste. The paper proposes the development of an integrated framework for smart waste management, including emerging technologies such as the Internet of Things, Artificial Intelligence, and Web 3.0. The first part of the research establishes the hypotheses and objectives: cost reduc-tion, increased efficiency, and process traceability. The second part analyses the use-fulness of digital technologies and the role of smart containers in collection, as well as the structure of processing and management costs, highlighting the framework's direct contribution to achieving SDG 11 and SDG 13. The third part describes a closed system with digital key-based access that monitors the number of disposals and assigns re-sponsibility for the waste deposited. By combining PAYT with advanced technological solutions, the research demonstrates the practical applicability and legal basis of an innovative model designed to support sustainability and strengthen European circular economy policies.

Abstract: We ask for C*-metric version of following three: (1) Bourgain-Figiel-Milman Theorem, (2) Enflo Type, (3) Mendel-Naor Cotype.

Abstract: Welfare assessment for the endangered red panda (Ailurus fulgens) in captivity requires systematic behaviour monitoring, yet traditional direct observation is often limited by observer subjectivity and time constraints. This study evaluates the feasibility of employing machine learning (ML) to automate behavioural monitoring of a red panda in a complex, mixed-species enclosure at Aalborg Zoo, Denmark. Using video data from cameras in the enclosure of the red panda, and the machine learning model LabGym for animal detection and behavioural categorisation, models were trained to analyse activity patterns of the red panda. The results demonstrate that while the behaviour categorizer is a promising tool with high classification confidence, the overall system effectiveness is currently limited by the object detector’s performance in a naturalistic environment. Challenges such as environmental obstructions such as rocks, foliage, and trees, and the animal’s camouflage contributed to a significant amount of unclassified time, which may affect the overall assessment of behavioural distribution. We conclude that while ML holds potential for non-invasive behaviour monitoring, its application in complex zoo settings requires improved detection capabilities to be fully reliable. Future iterations of this system could be enhanced by complementing standard object detection with pose estimation frameworks. Implementing alternative labelling strategies or background subtraction methods could additionally mitigate the detection challenges posed by environmental obstruction.

Abstract: In this paper, we derive explicit exponential series representations for the sine function involving even values of the Riemann zeta function. The result is obtained via logarithmic differentiation and integration of the Weierstrass product. We further demonstrate that the method extends naturally to the cosine function, yielding an analogous representation. These results highlight a structural connection between trigonometric entire functions and the distribution of their zeros.

Abstract: Throughout several centuries, infectious pathogenic agents have been used as models for the ongoing efforts of vaccine development, which saved hundreds of millions of lives from life-threatening infectious diseases worldwide. Nonetheless, there has been a missing gap that various polymorphic microbes have been taking advantage of in their evolutionary pathway: the interferon system, which often prevented the timely activation of second and third-line host immunity, leading to chaotic and mismatching immune responses. The phenomenon of increased incubation period of various infectious diseases may be a result of the increased abilities of such microbial agents to directly and indirectly undergo molecular self-camouflaging, which prevents the activation of Type I and Type III Interferon-encoding genes (INGs) in indirect and direct manners respectively, and cleaves the mRNA molecules encoding such interferon glycoproteins, often causing major delays in the process of autocrine and paracrine signalling of Type I and Type III Interferon glycoproteins, which in turn allows an unrestricted, exponential increase of the microbial load/count, giving rise to a statistical probability that the quality of the delayed immune response will be low and contributory to the processes of pathogenesis and pathophysiology. Some microbial proteins as such also inhibit the translation of Interferon-Stimulated Genes, thereby substantially affecting the signalling rates within the cytokine system and often bringing a negative domino effect upon the activation rates of the adaptive immune system. Apprehending the foundational layer of the current problems in evolutionary microbiology, epidemiology and public health studies is most likely crucial for the course of immunological, pharmaceutical and vaccine-related clinical research. In the current case, it is the complex set of molecular capabilities to suppress Type I and Type III Interferon-based signalling displayed by several polymorphic microbes of public health concern, and it may be that the rates of immunopathogenesis induced by such microbes are directly proportional with such pathogenic abilities of induced interferon suppression. Proportional medical responses could include the development of approaches involving low dosages of human recombinant Type I and Type III Interferon glycoprotein and perhaps also of protollin in the nasopharyngeal cavity, potentially bringing an example of putting a novel concept of a “United Immune System” into practice. Furthermore, similar dosages of such interferons could be administered into human immune cells including plasmacytoid dendritic cells, as well as natural and adaptive lymphocytes, to optimise their immune function and integrity against various environmental hazards. Ultimately, clinical researchers may isolate the pathogenic agents, attenuate them through the process of loss-of-function laboratory research, before performing gene editing to insert Type I, Type III and perhaps also Type IV Interferon-encoding, perhaps as well as Pattern Recognition Receptor (PRR) Agonist-encoding genes that specifically match the PRR targeted by the implicated microbes, into their genomic profile and potentially releasing the genetically-modified pathogens back into the environment transmissible factories of Type I and Type III Interferons, perhaps as well as of specific PRR Agonist proteins, which could include outer membrane proteins from the B serogroup of Neisseria meningitidis bacteria. If the microbial genetic activities implicating evasion of the interferon system are too intense and multilateral, at least some of the microbial genes responsible for such activity could be permanently removed in some exchange with the human genes encoding major elements of the interferon system that would be inserted into the microbial genome afterward. While the biological mechanisms discussed below are grounded in published interferon and immune-evasion literature, the present manuscript does not assert practical feasibility of transmissible vaccine strategies. Rather, it evaluates whether theoretical epidemiological and evolutionary models can define mathematical upper and lower bounds on such a concept under extreme and idealised assumptions. The objective is to test the internal coherence of the framework, not to imply translational readiness. It may be important to mention that the process of clinical weakening of the isolated microbes would be aimed at reducing the activity of microbial genes implicated in pathogenesis and pathophysiology, and perhaps not as much microbial genes involved in reproduction and transmission. Such a change may bring various pathogenic agents into a path of evolutionary self-destruction, as they would start producing and sending signals to the proximal, innate immune system as soon as they enter the first host cells, making their same processes of induced innate immune suppression ineffective, and several dilemmas in microbial evolution could ultimately be tackled as a result, possibly even at least attenuating the phenomenon of acquired antibiotic resistance by various pathogenic bacteria. A clinical approach as such is likely based on the model of increasing the accessibility to insulin-based treatment against Diabetes Mellitus via insulin-encoding gene insertion into the genomes of harmless bacteria prior to their administration into human host organisms, which saved millions of lives worldwide. Processes of shrinkage of any level of limitations to potential efficacy would include the manual utilisation of inhalators, oral drops and/or injectable serums containing such modified microbes to ensure that such an immunising effect would be conferred simultaneously with exposure to the artificially-changed genetic version of the microbe, effectively creating an “active evolutionary trap” for the pathogens, potentially resulting in their gradual de-selection whilst they continue to transmit just sufficiently enough to produce lasting immune memory. In other words, a phenomenon of “pathogen baptism” could occur, implicating a domination of “domestic variants” over wild-type variants in the environment, with the former becoming like “wild animals”, as they would remain the only virulent pathogenic variants and gradually even become extinct, with the “domestic” variants becoming dominant, according to the viral quasispecies theory. This set of clinical responses, including targeted immunoediting and gene vector strategies, can be analogized to a strategic operation against a mega-hurricane. The immune system, overwhelmed by storm-like chaos, cannot function effectively from the outside. Thus, medical intervention must act like military aircraft entering the eye of the storm from above – where calm resides – not to be engulfed, but to deploy stabilizing agents from within the calm zone. Only then can the storm’s structure be undone without triggering systemic devastation. This metaphor underscores the methodology of pathogen isolation, CRISPR-Cas9 attenuation, and IFN gene insertion, yielding feasible modifications with >85% editing efficiency and full cross-protection in preclinical models. Such a metaphor could potentially be informally regarded as a conquest from within, while remaining of another world). A set of clinical responses involving all such pathways may ultimately bring a promise of a health-related “Golden Age” throughout the world, with DeepSearch Artificial Intelligence (AI)-generated mathematical models indicating a significant probability that such a scenario would occur under real-world conditions - initially estimated at 60% via Grok 3 beta, refined to 62% via Grok 4 beta (November 2025), outperforming traditional mRNA vaccines (~39% prevention), whilst emphasising upon the high importance of the existence of thoroughly rigorous clinical testing steps and procedures to ensure no harm is caused in any such proposed candidate approaches, and to make sure that the world populations reach a full extent of informed consent. Finally, to concisely bridge from blueprint to prototype for conceptual, hypothetical purposes, we outlined a phase progression: (1) Introduction of PoC protocols to the present study, (2) In-Vitro Proof-of-Concept (1 - 3 months) - transfecting HEK293/Vero cells with CRISPR-edited IFN cassettes, targeting >90% efficiency and 104 IU / mL secretion via ELISA/WB - (3) Animal Validation (2 - 4 months) - Nasal-dose of hACE2 mice (n = 25/group), assessing 80-100% cross-protection and <0.1% reversion - (4) Iteration - recalibrating SEIR models with empirical data (e.g. β_d = 0.85), elevating projections to 68% pandemic prevention. Such a roadmap, aligned with CEPI/NIH accelerators, ensures ethical LOF-only prototyping, de-risking deployment whilst fostering a “United Immune System” concept for global resilience. Under more constrained assumptions, upper-bound theoretical estimates suggested evolutionary stability approaching 99.2%, whilst conservative stress-tested scenarios yielded considerably lower estimates (~80–85%). These values represent theoretical model-dependent bounds, and not empirical guarantees. This study presents a theoretical evolutionary modelling framework and does not advocate real-world deployment of transmissible agents.

Abstract: The present study investigates the governance of music higher education in Central Asia by examining two competing external integration frameworks that currently operate in the region: the European Bologna Process and China's Belt and Road Initiative. The empirical focus is placed on five Central Asian states, namely Kazakhstan, Uzbekistan, Kyrgyzstan, Tajikistan, and Turkmenistan. The research draws on the theory of policy borrowing and lending as formulated by G. Steiner-Khamsi, the concept of soft power in educational cooperation, and the theory of regional education space as developed by S. Marginson, with the aim of analysing how these two frameworks act upon Central Asian music education institutions through different mechanisms and produce different effects. Documentary evidence is collected from national education laws, institutional reports from principal conservatories in the region, programme descriptions from the Shanghai Conservatory of Music's Belt and Road Art Talent Training Programme, Aga Khan Music Programme publications, diplomatic agreements from the 2023 China-Central Asia Summit, and statistical data from the World Bank and UNESCO. The analysis brings to light that the Bologna Process acts on Central Asian music education through structural standardisation, which requires the adoption of compatible degree formats, credit systems, and accreditation mechanisms, while China's Belt and Road Initiative operates through relational exchange, which offers talent training programmes, bilateral institutional partnerships, and cultural diplomacy events that do not require structural convergence. The paper puts forward the concept of dual integration pressure to describe the condition in which music education institutions must respond to both frameworks at the same time, and identifies a structural incompatibility between the multilateral norm convergence logic of the Bologna model and the bilateral relationship logic of the Chinese model. The findings point to the fact that music education, as a domain where cultural specificity and institutional standardisation exist in direct tension, makes visible a governance problem that remains hidden in other fields of higher education cooperation in Central Asia. A complementary engagement framework is proposed that identifies conditions under which the two models can operate without mutual interference and suggests that Chinese cooperation can address gaps in heritage documentation, traditional instrument exchange, and performance-based mobility that Bologna-oriented reforms are structurally unable to fill.

Abstract: Ecological restoration, both active and passive, comprises forest development, forest rehabilitation, and other activities that fall under the purview of eco-system services. To provide a formal framework, here we were hypothesized how do reforestation (through different forestry practices) affect the conservation of soil functionality?, that is: Reforestation/Afforestation/Forest restoration improves soil quality?; and specifically physical properties (such as structural stability, infiltration), chemical properties (such as CEC, soil organic matter content)?. For this purpose, here, we conducted a meta-analysis of numerous articles in order to compiled a large database of forest restoration studies, with emphasis on the Mediterranean region, to make robust conclusions about how it affects soil quality. Additionally, three case studies are synthetically presented concerning the short-, medium-, and long-term outcomes of forest restoration projects conducted in central and northern Spain. These cases corroborate the significant role of forest restoration in the control and enhancement of ecosystem services, particularly in relation to soil improvement, the enhancement of hydrological regulation processes within watersheds (runoff, infiltration, erosion), landscape amelioration, and the socio-economic aspects of rural environments. Ultimately, forest restoration is established as a necessary and essential practice in ecological restoration efforts to counteract the impacts of anthropogenic activities.