Abstract: This paper develops a new multi-stage method for image distillation which involves two well-known methods. At its first stage, our method creates a matrix from all training images. On the next stage, it adapts a modified principal component analysis (M-PCA) approach to transform the training matrix. On the third stage, the Singular Value Decomposition (SVD) further refines the matrix of the training images through low-rank reconstruction and controlled matrix row selection. On the fourth stage, rotation of small 2 × 2 matrix blocks on the entire left singular matrix is conducted. The upper m (user-selected number) rows of the reconstructed matrix are selected and transformed back to images, which we call distilled images. This dataset is significantly smaller yet retains the critical information needed for an accurate classification. We validated the novelties and the advantages of the new method applying the Baseline and ResNet50V2 CNNs and the public image databases Digit-MNIST, Fashion-MNIST, CIFAR-10, CIFAR-100, and BloodMNIST. Experimental results show that models trained on images distilled by our new method achieve efficient classification and outperform contemporary competitors, while substantially reducing training time compared to training on the original dataset.

Abstract: Recent interpretability work established that a language model’s functional global workspace is defined by verbalizable representations, and released a linear instrument, the Jacobian lens, that reads any residual-stream state as a ranked token distribution. The field therefore possesses an actuator without a doctrine of use. This paper derives such a doctrine from Pratyabhijñā, a ninth-century recognition philosophy whose central identity, reflexive awareness as the supreme Word, anticipates the verbalizable-workspace finding. The doctrine is treated strictly as an engineering specification with five falsifiable clauses: what to write (verbalizable concept codes), where (the workspace band, in band coordinates), when (moments of uncommitted prediction, detected by step entropy), how to verify (readback through a band-targeted lens), and at what cost (a registered entropy budget). Each clause was tested on frozen decoder transformers through a preregistered, gate-audited experimental programme spanning loops L0 to L21, run autonomously by an expected-free-energy selector over registered experiment menus, with twenty-seven selector cycles, eighteen isolated adversarial reviews, and roughly eighteen GPU-hours of compute on a single shared node. Six clauses or mechanisms are confirmed: workspace-band structure and instructed loadability replicate across model families and scale with size (hit-rate@5 of 0.10 at 4B versus 0.55 at 27B, collapsing to 0.00 on a pruned and-distilled twin); band content is legible only to a lens targeted at the band; entropy-gated writes steer within a ±0.5-nat budget at six independent stream seeds (lift 0.30 to 0.35, sign-consistent alignment advantage, one-sided p = 0.016); a calibration recipe with amplitude inversely proportional to lens transport strength transfers steering to a second model family at four seeds with monotone dose response; and an untrained associative memory write path reproduces the analytic steering path on all three seeds tested. Registered negative results bound the doctrine’s scope: gated steering trades concept-surface throughput for legibility rather than maximising it (surface rate 0.16 versus 0.96 for continuous flooding at matched entropy cost), and contrastive refusal-direction steering leaves attack success on an already-aligned 4B model unchanged (0.25 to 0.25). A head-to-head benchmark against the Jacobian-lens final-target baseline shows the band-targeted instrument is roughly twice as sensitive at subtle write doses (detection 0.475 versus 0.24, p = 2.1 × 10⁻⁵) and 2.3 times more write-efficient than continuous flooding. The same activation signal that fails as a blanket steering direction, used instead as an input-conditioned recognition gate, yields a deployable jailbreak defence that matches brute force attack-success reduction at zero benign over refusal on models whose benign and attack projections separate cleanly, and this success is predictable before deployment from an offline clean gap test: across four model families the moat works on two and the predictor is correct on all four. All code, gates, lens checkpoints, an agent callable Model Context Protocol server, and interactive replay applications are released.

Abstract: Classical lipoproteins are classified by density, size, lipid composition, apolipoprotein content, metabolic origin, and receptor routing. These variables explain much of lipid transport but do not establish whether supramolecular organization of the lipid interface adds causal information about particle behavior. We hypothesize that classical lipoproteins are apolipoprotein-scaffolded lipid-state particles in which a measurable, perturbable, and persistent lipid-interface state cooperates with a biogenetic scaffold to influence stability, extracellular remodeling, routing, and recipient-cell responses. Source-state continuity may be material, as during ATP-binding cassette transporter A1-mediated transfer of cellular phospholipids and cholesterol to apolipoprotein A-I, or configurational, as when hepatic or intestinal physiology biases microsomal triglyceride transfer protein-dependent assembly of apolipoprotein B-containing particles. Apolipoprotein B provides a non-exchangeable scaffold with high continuity, whereas apolipoprotein A-I forms a more adaptable scaffold that is initiated by ATP-binding cassette transporter A1-dependent lipidation and subsequently remodeled by lecithin–cholesterol acyltransferase and other plasma factors. After biogenesis, exchangeable apolipoproteins, enzymes, and other associated proteins form an editable identity layer. Structural studies of apolipoprotein B, high-density lipoprotein biogenesis and heterogeneity, and inflammatory remodeling of high-density lipoprotein provide mechanistic precedents, but do not establish the proposed incremental causal layer. The hypothesis predicts that controlled differences in interfacial packing, accessibility, topology, oxidation, or electrostatics will alter protein acquisition, routing, or function after particle number, size, bulk composition, scaffold abundance, and established receptor pathways are controlled. It is weakened if these variables add no reproducible causal or predictive information beyond conventional lipoprotein biology.

Abstract: Afforestation can mitigate the export of water, sediment, and dissolved or adsorbed contaminants to river systems, but identifying effective intervention sites requires accounting for multiple flow-related criteria and their spatial interactions. This paper presents a multi-criteria heuristic approach that extends CAMF (Cellular Automata-based Heuristic for Minimizing Flow), originally designed to select cells from a rasterized landscape for interventions that minimize sediment yield at target sites. We integrated the Distance-to-Ideal-Point (DIST2IP) algorithm in CAMF, enabling the selection of cells where intervention can minimize two or more flows simultaneously. The multi-criteria CAMF was applied ex post to the radioactively contaminated Niida river catchment, Fukushima prefecture, Japan, to identify 1,000 cells within decontaminated zones where immediate afforestation would have maximally reduced both sediment and residual ¹³⁷Cs export. The 1,000 best cells selected by DIST2IP, representing 4% of the decontaminated cells, would have reduced sediment export by 22% and ¹³⁷Cs export by 6%. Selected cells are within the union of cells identified by the two single-criteria optimizations and are predominantly close to water bodies, confirming that blocking flow paths before they connect to the river system is most effective.

Abstract: The recent advancement of Industry 4.0, digital transformation, artificial intelligence (AI), big data, and cloud computing has fundamentally reshaped contemporary economic systems and managerial practices. Consequently, organizations increasingly rely on data science capabilities to improve decision-making, enhance organizational agility, stimulate innovation, and achieve sustainable competitive advantage. This study empirically examines the influence of Data Science Capability (DSC) on Managerial Decision Quality (MDQ), Organizational Agility (OA), Innovation Capability (IC), and Organizational Performance (OP) by integrating the Resource-Based View (RBV), Dynamic Capabilities Theory (DCT), and the Technology Acceptance Model (TAM) into a unified conceptual framework. A quantitative cross-sectional survey design grounded in the positivist research philosophy was adopted. Data were collected from 520 managers, Chief Executive Officers (CEOs), economists, business analysts, data scientists, and Information Technology (IT) managers drawn from manufacturing firms, financial institutions, healthcare organizations, telecommunications companies, public sector institutions, and digital service enterprises. Stratified random sampling was employed to ensure adequate representation of the study population. The collected data were analyzed using IBM SPSS Statistics 29 and SmartPLS 4, employing descriptive statistics, Pearson correlation, reliability and validity assessment, Partial Least Squares Structural Equation Modelling (PLS-SEM), bootstrapping, Multi-Group Analysis (MGA), and Importance–Performance Map Analysis (IPMA). The empirical findings reveal that data science capability exerts a significant positive influence on managerial decision quality, organizational agility, innovation capability, and organizational performance, with all hypothesized relationships supported (p < 0.001). The structural model demonstrated substantial explanatory power, particularly for organizational performance (R² = 0.68), confirming the strategic value of data science in organizational success. The study extends RBV by conceptualizing data science capability as a strategic organizational resource, validates Dynamic Capabilities Theory in digitally transformed organizations, and develops a novel empirical model explaining the mechanisms through which data science generates competitive advantage. The findings offer important theoretical contributions and practical implications for organizational leaders, policymakers, and researchers seeking to leverage data science for enhanced economic productivity, managerial effectiveness, innovation, and sustainable organizational performance in the digital economy.

Abstract: This research explores the structural transformation of contemporary financial mar-kets as they pivot from traditional fundamental indicators toward a "narrative eco-nomics" paradigm, where informational flows and collective sentiment strongly cor-relate with asset price discovery. The primary objective is to evaluate the efficacy of Computational Sentiment Analysis as a real-time early warning mechanism for mar-ket volatility, specifically addressing the critical latency gap inherent in official mac-roeconomic "hard data". Adopting a quantitative methodology rooted in Data Science, the study utilizes the FinBERT deep learning architecture to analyze a 12-month lon-gitudinal dataset (May 2025 – May 2026) of financial news and discourse. Framed strictly as an exploratory, multi-entity case study rather than a sector-wide analysis, the investigation focuses on strategic proxies of the 'Twin Transition' in Eastern Eu-rope: Technology (UiPath), Energy (OMV Petrom/Hidroelectrica), and Banking (Transilvania Bank). Consequently, the findings highlight localized, context-specific dynamics rather than establishing broad, sector-wide behavioral rules. The findings provide exploratory support for a potential "regime shift" in market behavior regard-ing sustainability; ESG narratives transitioned from being perceived as a systemic risk in late 2025 to a primary factor associated with market value by early 2026. The evi-dence indicates patterns consistent with a "digital resilience" effect, wherein technolo-gy assets successfully decoupled from the industrial stagnation of traditional proxies, such as the German Deutscher Aktienindex(DAX). Conversely, the energy sector dis-played diminishing marginal impact of economic narratives regarding geopolitical shocks, with investors increasingly prioritizing long-term transition risks and indus-trial demand over short-term alarmist headlines. The study concludes that unstruc-tured textual data serves as a vital leading indicator for market dynamics, underscor-ing the imperative for integrating advanced Natural Language Processing (NLP) into modern economic forecasting and resilient risk management strategies.

Abstract: Existing vectored immunoprophylaxis (VIP) approaches have primarily focused on IgG, which provides systemic protection but is less specialized for mucosal immunity. In contrast, secretory IgA (sIgA) plays a central role at epithelial surfaces, promoting pathogen neutralization while limiting inflammation. Although monoclonal IgA therapies are effective, their short half-life requires repeated dosing. Thus, VIP strategies enabling sustained sIgA expression at mucosal sites could transform mucosal infection prevention and treatment. This review outlines the key challenges associated with in vivo IgA expression and discusses critical considerations for VIP-mediated IgA delivery at mucosal surfaces, with emphasis on its potential for clinical translation. We provide a detailed overview of platforms for targeted IgA expression, including adeno-associated virus (AAV), adenoviral and lentiviral vectors, and lipid nanoparticle-based systems, alongside relevant routes of administration. Additionally, we examine emerging strategies to enhance the robustness, durability, and localization of IgA expression in vivo. Overall, VIP-enabled IgA expression represents an emerging strategy for enhancing mucosal immunity, with continued advances required to establish its role in the prevention and treatment of mucosal infections.

Abstract: Background: Adamantinomatous craniopharyngiomas (ACPs) are congenital brain tumors that affect children and adults. Due to their location within the brain and their adhesion to surrounding structures they are difficult to treat. They are commonly associated with pituitary and hypothalamic dysfunction. The aim of this report is a detailed phenotypic and genotypic evaluation of a girl with ACP. Methods: Laboratory blood tests and magnetic resonance imaging (MRI) were performed. Histopathological investigations included staining with hematoxylin and eosin (H&E), and various antibodies. Genetics and bioinformatics tests comprised single nucleotide polymorphism (SNP) array, deoxyribonucleic acid (DNA) methylation array, next generation sequencing (NGS) and structural modelling of beta-catenin. Results: An 8.5-year-old girl presented to her general practitioner with gastrointestinal symptoms and was comprehensively investigated by a paediatric gastroenterologist. During the following 1.5 years she showed traits of sensory processing disorder and complained of mild headaches. Brain imaging revealed an adamantinomatous craniopharyngioma which was incompletely resected. Sequencing of the CTNNB1 gene demonstrated a tumorigenic variant, c.98C>G p.(Ser33Cys). Three-dimensional modeling of the Ser33Cys beta-catenin variant indicated a weaker interaction with β‑TrCP1 (β-transducin repeat containing E3 ubiquitin protein ligase 1), due to the impossibility to undergo phosphorylation at Ser33, leading to impaired beta-catenin ubiquitination, a possible cause of protein accumulation and tumorigenic progression. Conclusion: Pediatric ACP patients present with a wide range of symptoms which often results in diagnostic delay. No reliable biomarker exists for this disease, and magnetic resonance brain imaging is the investigation of choice. Next generation sequencing (NGS) is the preferred genetic test to identify the underlying somatic pathogenic variant.

Abstract: This paper proposes a spatial‑functional two‑dimensional hierarchical group decision‑making (HGDM) spectrum management architecture for emergency communication unmanned aerial system (EC‑UAS). The architecture handles the highly dynamic topology, large node population, and differentiated task priorities that characterize EC‑UAS. Using the spectrum management properties of EC‑UAS, we develop a discrete‑time closed‑loop dynamic model of the architecture and design adaptive hierarchical iteration rules. We prove global stability of the model under the stated assumptions and analyze the convergence of the state error, deriving its theoretical upper bound and the relationship between convergence steps and accuracy. An input‑to‑state stability analysis further demonstrates that the system state error remains bounded under dynamic disturbances, with its magnitude scaling with the disturbance bound. Simulations verify the effectiveness of the architecture and the correctness of the theoretical analysis.

Abstract: Advanced Air Mobility (AAM) is increasingly recognized as a promising approach for improving healthcare logistics, yet evidence remains fragmented across aviation, transportation, healthcare, and digital infrastructure. This study examined the operational, clinical, and institutional evidence to characterize the evolution of healthcare-focused AAM, identify dominant research themes, and determine critical knowledge gaps. The review followed PRISMA-ScR guidelines and combined bibliometric analysis, thematic synthesis, and semantic network analysis of 168 peer-reviewed studies published between 2015 and 2025 and retrieved from IEEE Xplore, ScienceDirect, Scopus, and Web of Science. The analysis identified six thematic clusters encompassing system design, healthcare logistics, biological specimen transport, emergency response, health equity, and digital infrastructure. Publication activity increased rapidly after 2020, emergency response represented the most mature research domain, and pharmaceutical logistics, longitudinal operational validation, and health equity remained comparatively underdeveloped. These findings demonstrate that healthcare-focused AAM functions as a sociotechnical system requiring coordinated advances in technology, governance, institutional integration, and equitable access to support clinically reliable and operationally sustainable healthcare delivery.

Abstract: Northeast India sits within the Indo-Burma and Eastern Himalaya biodiversity hotspots, yet its forests face accelerating loss from extractive industries, infrastructural expansion, and weak governance. Although global conservation discourse increasingly recognises community-based approaches, the contributions of vernacular grassroots organisations from frontier regions remain insufficiently documented in peer-reviewed scholarship. This article addresses that gap. It examines the conservation praxis of Nature's Beckon, an independent activist organisation founded in Dhubri, Assam, in 1982, and the work of its founder-director Soumyadeep Dutta, an Ashoka Fellow and naturalist-writer. The study adopts a qualitative, analytical methodology that integrates historical and ecocritical approaches. Data are drawn from organisational records, Dutta's published Assamese-language corpus, news media, government notifications, and peer-reviewed scientific literature on protected-area outcomes. Four case studies are analysed: the Chakrashila Wildlife Sanctuary movement (1983–1995); the Dehing Patkai rainforest campaign (1992–2021); primate conservation focused on the golden langur (Trachypithecus geei) and western hoolock gibbon (Hoolock hoolock); and the Village Sanctuary institutional model. Findings indicate that Nature’s Beckon has played a documented and substantial role in measurable conservation outcomes, including the gazettement of two protected areas with a combined area of approximately 277.22 km². The article advances an ecology of the margins framework to characterise the organisation's distinctive integration of scientific documentation, vernacular advocacy, and community institution-building. Practical policy implications include formal recognition of community-conserved areas, integration of vernacular knowledge into protected-area management, replication of the eco-emissary training framework, and alignment of conservation funding cycles with the multi-decadal time horizons that durable place-based conservation requires.

Abstract:

Compute-capacity contracts—forward claims on GPU time, now cleared as futures—are emerging as an asset class. Their delivery leg is hedgeable and their investment-grade issuers trade single-name CDS, but the cross-issuer cluster riskbetween them is not: a shared funding, datacenter, or regulatory shock defaults a tied group at once. We characterize this cluster correlation as the unhedgeable core of a multi-issuer book and design the instruments that would hedge it. Two design pieces generate it, kept closed-form by an affine intensity model. A single Marshall–Olkin common shock carries the discrete joint defaults across every trigger—datacenter, regulatory, and force-majeure shocks on a shared external cause, the funding cascade instead self-exciting, a first failure propagating to the survivors and over-dispersing the count. A slow, bistable cross-architecture substitution layer then depreciates the delivered good and arms that same cascade, with an endogenous fire-sale recovery that derives the wrong-way spot–credit sign. From a channel-by-channel decomposition we derive a suite of cluster contracts—a compute-CDS index, correlation tranches, a cluster-count swap, an architecture-share swap, and a proxy for the unspanned keystone counterparty—each spanning a distinct channel. Hedging effectiveness is the no-arbitrage band each instrument removes, measured by a cluster-basis good-deal bound; the same bound pins the irreducible floor—the loss carried by a node no contract references—so the market completes only up to its keystone. Two documented clusters—a 2022 mining-pivot cohort and the 2025–26 NVIDIA–OpenAI–Oracle “Stargate” loop—calibrate the suite. Portfolios of compute-capacity contracts—now marked across issuers by cleared futures indices, OTC spreads, and reservation books—carry a dominant risk that does not diversify: a shared trigger defaults a tied group of issuers at once. We characterize this cross-issuer cluster correlation as the systematic, unhedgeable core of such a book and bound its price. Two design pieces generate it, kept closed-form by an affine intensity model. A single Marshall–Olkin common shock carries the discrete joint defaults across every trigger—datacenter, regulatory, and force-majeure shocks on a shared external cause, the funding cascade instead self-exciting, a first failure propagating to the survivors and over-dispersing the count. A slow, bistable cross-architecture substitution layer then depreciates the delivered good and arms that same cascade, with an endogenous fire-sale recovery that derives the wrong-way spot–credit sign. The central result is a good-deal bound: credit instruments (single-name CDS, a CDS index, GPU-loan ABS tranches) augmenting a commodity-and-index tradeable set contract the unhedgeable Föllmer–Schweizer residual to a cluster-basis floor on the no-arbitrage band that we show is attained, hence sharp, closing only in a fully-spanned, architecture-diversified limit. Two documented clusters—a 2022 mining-pivot cohort and the 2025–26 NVIDIA–OpenAI–Oracle circular-financing loop—calibrate the construction. Compute contracts are the motivating instance; the bound applies to any basket of defaultable claims exposed to common shocks no traded index spans.

Abstract: This article presents the results of a research study aimed at designing and evaluating sustainable housing (SH) in post-disaster contexts in Juchitán de Zaragoza, Oaxaca, Mexico, following the 2017 earthquakes. The proposed model integrates four funda-mental dimensions: i) housing design using traditional architecture; ii) analysis of the environmental impact of materials using indicators of CO2 emissions, energy consump-tion, and thermal performance; iii) comparative evaluation of construction costs relative to conventional housing (CH); and iv) implementation of a Social Production of Habitat (SPH) approach. A mixed-methods design was adopted, involving literature review, data collection through an environmental and hygrothermal impact study, comparative cost estimates, and semi-structured interviews. The findings show that the use of local, low-impact materials, together with participatory processes, not only reduces costs and environmental externalities but also strengthens the social fabric and contributes to a more equitable reconstruction that is contextualized within traditional architecture. The housing model developed is proposed as a replicable alternative in areas affected by natural disasters, not only in Mexico but also in other countries.

Abstract: The offshore photovoltaic coupled electrolysis hydrogen production system operates in complex marine environments, which puts high requirements on safety for operation and maintenance. To reduce manual intervention and improve operational safety, this paper proposes a surface electromyography (sEMG)-driven cable-actuated continuum manipulator for offshore photovoltaic coupled hydrogen production operation and maintenance(O&amp;M). The sEMG signals from the operator's upper limb are collected, filtered, and mapped onto the desired trajectories. The genetic algorithm-back propagation(BP) neural network pattern classifier is designed for motion classification. Kinematic and dynamic models, as well as the torque controller of the cable-driven continuum manipulator are established. Experimental results demonstrate that the proposed method can identify upper-limb motion intention and drive the cable-actuated continuum manipulator to follow the desired trajectories with high accuracy. This lays a foundation for operation and maintenance tasks in offshore photovoltaic coupled electrolysis hydrogen production systems.

Abstract: Low-altitude autonomous mobility research requires unmanned aerial vehicle platforms that are affordable, experimentally transparent, and safe enough for repeated indoor and near-ground validation. This paper presents a reproducible safety-aware ESP32-based quadrotor testbed designed as an open experimental platform rather than a sealed commercial flight product. The system integrates an ESP32 flight controller, an ESP32 remote controller, IMU-based attitude estimation, PID stabilization, PWM motor actuation, ESP-NOW communication, telemetry feedback, parameter synchronization, and over-the-air maintenance support. The platform is structured around three design objectives: reproducibility, firmware-level safety awareness, and blackbox-supported diagnosis . The safety architecture includes arming and disarming logic, throttle initialization checks, communication fail-safe, low-voltage warning, ESC-calibration isolation, visual state indication, and tilt/collision shutdown. The diagnostic layer records runtime variables, including battery voltage, compensation factors, attitude angles, PID outputs, offsets, motor commands, communication status, loop timing, and logging overhead. These data support post-flight analysis, controller tuning, offset correction, and repeatable comparison across experiments. Validation was conducted through staged ground tests and conservative indoor low-altitude hover trials. The results indicate that the platform can support short-duration stable hovering with pitch and roll deviations within approximately ±5°, end-to-end control response of about 50–80 ms, and indoor ESP-NOW latency of about 20–40 ms, while maintaining a total prototype cost below approximately USD 200. The contribution of this work is not maximum flight performance or full autonomous mission execution, but the integration of low cost, transparent firmware, explicit safety logic, and data-driven diagnostic workflow into a coherent quadrotor research testbed. The proposed platform provides a practical foundation for embedded-systems education, low-altitude UAV experimentation, and future extensions toward sensing, autonomy, and comparative controller evaluation.

Abstract: Automation of semantic segmentation of binary detection of focal liver lesions (FLLs) in triphasic computed tomography (CT) scans is critical for knowledge extraction of hepatic malignancy, staging a disease, and planning a treatment. But standard models struggle with class imbalances, boundary differences, and tissue heterogeneity in different phases. This study proposes a modified architecture of a parallel dual-encoder network for FLL boundary refinement optimization. The proposed architecture implements an early feature fusion by stacking the non-contrast (NC), arterial (ART), and portal venous (PVP) phases in the channel dimension. Alongside a per scale, lightweight fusion strategy integrating localized convolutional details to global transformer context. The two architectures were modified and optimized: VGG-19 paired with a Swin Transformer and ConvNeXT-Small paired with a Cross-Shape Window (CSwin) Transformer. The study used a patient-grouped stratified 3-fold validation on 517-case MCT-LTDiag dataset. The networks were extensively benchmarked against standard U-Net, DECTNet and nnU-Net baselines across multiple performance metrics: generalization, segmentation, boundary, and computational. Statistical significance was precisely tested by using a two-tailed paired t-test with Benjamini-Hochberg False Discovery Rate (BH-FDR) adjustments. The ConvNeXT-Small + CSwin Transformer configurations turned out to be the best architecture, with an elite average Dice score of 0.9091 and a leading Intersection over Union (IoU) of 0.8924. This model reduced boundary errors and achieved the lowest 95th percentile Hausdorff Distance (HD95) of 13.86. It resolved the precision-sensitivity trade-off plaguing the baseline models by maintaining a leading sensitivity of 0.9484 and a precision of 0.9352. The champion architecture sustained a tight footprint of ~29M parameters and achieved an optimized average inference speed of 0.111 seconds per slice, even though it has an advanced attention mechanism. This cut the process delay of the VGG-19 variant in half. Qualitative and quantitative results confirm the synergy of modern localized depthwise convolutions and global cross-shaped attention mechanisms removed the bloated false-positive masks and broken under-segmentation. It is delivering a highly stable, precise, and reproducible tool for automated clinical workflows.

Abstract: Introduction. Forest fires are complex phenomena causing considerable damage to the environment, habitat destruction, soil erosion, greenhouse gas emissions, and biodiversity loss. They are increasing globally, with extreme events becoming more frequent and destructive. Understanding their root causes and influencing factors is crucial. Methods. This work focuses on analyzing data of forest fires that occurred in the period 2010-2023 in Sicily, an Italian region and big island with special orographic characteristics and substantial agricultural and forestry-pastoral activities. The methods concern a careful extraction of data by using QGIS software and official databases and their appropriate statistical analysis. Results. A definition of forest fire risk, coherent with the literature, is here formulated, and a risk ranking and classification of the Sicilian municipalities is so obtained. Risk factors are elicited by expert advice, and their significance is determined via multiple regression analysis with a transformed dependent variable. Conclusions. The work shows an optimal balancing between ecological perspective and operational risk management. Forest fire data collection empowerment is highlighted, such as fire-starting location and total damage caused by each fire event. The study allows optimally distributing the regional budget for forest fire prevention among the municipalities.

Abstract: Background: Burn injuries have complex physical and psychological consequences, making holistic rehabilitation essential. This mixed methods service evaluation explored the acceptability, perceived benefits and acceptability of a peer-supported breakfast group delivered as part of routine occupational therapy practice for adult inpatient burn survivors. Methods: A tailored survey integrating PROMIS items with open-ended questions was completed by 9 participants. All English‑speaking inpatients aged >18 years who attended were invited (n=36; n=9 completed). A convergent design integrated open‑ended survey responses with PROMIS patient‑reported outcome items, analysed thematically and interpreted using the RE‑AIM (Reach, Effectiveness, Adoption, Implementation, Maintenance) and MMR‑RHS (Mixed Methods Reporting – Rehabilitation and Health Sciences) frameworks. Results: Quantitative findings suggested high perceived physical function and strong emotional support with, low anxiety and depression, and low social isolation. Reflexive thematic analysis generated four themes: fostering human connection and emotional wellbeing; restoring autonomy and confidence; preparing physically and psychologically for discharge; and the influence of the rehabilitation environment. Conclusion: While descriptive only, findings provide early insight into the value of peer‑supported group activity in inpatient burn care. Larger controlled studies with baseline measurement are needed to evaluate effectiveness and implementation feasibility.

Abstract: Cancer development is influenced not only by genetic alterations but also by epigenetic dysregulation driven by environmental and lifestyle factors, particularly diet. Nutrigenomics investigates how dietary components interact with the genome to influence gene expression, while nutritional epigenetics focuses on diet-induced modifications such as DNA methylation, histone modifications and non-coding RNA regulation. Increasing evidence indicates that bioactive dietary compounds can modulate epigenetic mechanisms involved in carcinogenesis, highlighting their potential role in cancer prevention. This review synthesises recent evidence (2015–2025) on the molecular interplay between diet, epigenetic reg-ulation and cancer risk. We examine how key dietary factors—including methyl donors involved in one-carbon metabolism, polyphenols, short-chain fatty acids and omega-3 fatty acids—modulate epigenetic regulators such as DNA methyltransferases and histone deacetylases. Particular emphasis is placed on the emerging diet–microbiota–epigenome axis, whereby microbiota-derived metabolites influence host chromatin architecture and gene expression pathways relevant to tumour suppression and inflammation. In addition, we discuss current evidence linking dietary patterns, including Mediterranean and Western diets, with distinct epigenomic signatures associated with cancer susceptibility. The review further explores the potential of epigenetic biomarkers and epigenome-wide association studies to support precision nutrition strategies for cancer prevention. Collectively, accumulating data suggest that diet-driven epigenetic plasticity represents a promising avenue for cancer prevention. However, further large-scale human studies integrating multi-omics approaches are required to translate mechanistic insights into clinically applicable precision nutrition interventions.

Abstract: The Zeta-Minimizer Theorem (ZMT) furnishes a rigorous axiomatic foundation for thermodynamics through three principles—strict concave entropy maximization, uniform Gibbs free energy landscapes with spectral minima, and irreducibility enforced by perpetual bounded oscillations—augmented by helical geometry in which prime numbers emerge as indivisible cycle lengths. The multi-component grand-partition function Z(s) is constructed and establish its universal categorical invariance under functorial mappings, thereby providing a parameter-free backbone for equilibrium and dynamical phenomena. From this structure the thermodynamic conjugate pairs and variational Maxwell relations, the emergence of chemical equilibrium and the equilibrium constant are deductively derived directly from the grand potential, explicit fugacity coefficients, and a variational reaction-rate law governed by the analytical scalar Hessian of the gas-phase reaction coordinate. For heterogeneous systems solid–fluid interface continuity conditions, marginal stability criteria via the covariant fugacity Hessian are obtained, and—crucially—the first-principles deductive origin of linear scaling relations and Sabatier volcano plots from closed prime functions, without empirical parameters. A complete methodology is developed for ammonia-synthesis loop analysis and catalyst optimization. Central to this methodology is the vapor–solid (V/S) locus, which incorporates inert effects, promoter-induced shifts in effective black-box constants, and distinct operating regimes of Fe- versus Ru-based catalysts. Numerical integrations and Hessian-based kinetic analyses quantitatively recover industrial third-bed behavior, elucidate the lab–industry gap in recent low-pressure studies, and furnish predictive guidelines for next-generation catalyst and process design. The ZMT framework thereby bridges number-theoretic structure with practical chemical engineering, offering zero-adjustable-parameter predictions for sustainable ammonia production.