Abstract: This study investigated the relationships between ambivalent sexism, social roles, and body compassion in Albanian and Italian women. The participants were 251 Albanian and 280 Italian women who completed validated measures assessing hostile and benevolent sexism, social roles transcendence and link to social roles, and three subdimensions of body compassion (defusion, common humanity, and acceptance). Path analyses indicated excellent model fit across samples. In Albanian women, hostile sexism negatively predicted social roles transcendence and positively predicted a link to social roles, both of which were associated with lower body compassion. Benevolent sexism was positively associated with social roles transcendence, which in turn was related to higher body compassion. In contrast, Italian women showed a different pattern: benevolent sexism positively predicted a link to social roles, while social roles transcendence and link to social roles were both negatively related to defusion. Age positively predicted defusion and acceptance, highlighting a possible protective effect. Explained variance was higher in the Italian sample, particularly for the link to social roles. Overall, findings suggest that sexist attitudes and adherence to stereotyped social roles influence women’s body compassion differently across cultural contexts, revealing ambivalent and sometimes contradictory associations. The study highlights the need for culturally sensitive approaches in promoting positive body image.

Abstract: In contemporary research on natural bioactive compounds, increasing emphasis is placed on the development of efficient and sustainable extraction technologies. This study aimed to develop and optimize an innovative extraction process for wild cyclamen (Cyclamen purpurascens Mill.) tubers to maximize the yield of total extractives using a Box-Behnken design. The effects of four extraction parameters were evaluated on the system response. A second-order polynomial model accurately described the extraction process, yielding a coefficient of determination of 0.919. The liquid-to-solid ratio was identified as the dominant factor affecting the extraction efficiency compared to the other factors investigated. The optimal extraction conditions were as follows: extraction time of 15.5 min, 13% (v/v) ethanol, liquid-to-solid ratio of 13.5 mL/g, and extraction temperature of 34 °C, resulting in a yield of 53.44%. The optimized process yielded a significant saponin content of 16.19 g/100 g, while the levels of phenolic compounds (132.52 mg GAE/100 g) and flavonoids (12.04 mg QE/100 g) were also quantified. UHPLC–ESI–MS/MS analysis confirmed the presence of triterpene saponins, flavonoids, and terpenoids. DPPH, ABTS⁺, and CUPRAC assays indicated the antioxidant potential of the extract, while the minimum inhibitory concentration assay showed antibacterial activity against Staphylococcus aureus and Escherichia coli. The established chemical profile and observed biological activities provide the basis for further evaluation of wild cyclamen tubers as a source of bioactive secondary metabolites.

Abstract: This paper presents the development and implementation of a psychological model aimed at predicting the mental states of Air Traffic Controllers (ATCOs) within an Exploratory research project, entitled CODA (The Controller Adaptative Digital Systems Assistant), within the SESAR 3 Joint Undertaking and European Union’s Horizon Europe research and innovation programme. The proposed model aims to advance human–machine collaboration in air traffic management by enabling the precise prediction of critical operator cognitive and affective states, including mental workload, fatigue, stress, and attentional engagement. By formally integrating core cognitive processes—namely perception, comprehension, and decision-making—within its architecture, the model provides a principled framework for the continuous monitoring and real-time adaptation of support systems. Such adaptive capabilities are intended to optimize the allocation of assistance provided by artificial agents, thereby strengthening human–system coordination and contributing to enhanced operational safety and efficiency within the complex and highly dynamic environment of air traffic control. To estimate the parameters of the model, several air traffic simulations were conducted with expert controllers. In these simulations changes to traffic situations were introduced. Those changes could affect the controllers' mental states. The results of these changes were observed in the measured verbal and psychophysiological dependent variables. This paper presents results that partially validate the initial parameters of the models. These results will contribute to a future improvement of the model by refining the parameters of the proposed formulas for calculating mental workload, fatigue, stress, and vigilance in the air traffic control task.

Abstract: Artificial intelligence has achieved enormous visibility in recent years, mostly thanks to the success of deep learning and its generative AI applications. Still, current state-of-the-art models remain brittle and struggle to provide reliable predictions under settings that differ, often even marginally, from those that generate their training data. The issue is only compounded when one attempts to enable machines to continually learn from data, in an imitation of humans’ lifelong learning experience. While recognizing this issue under various names (e.g., ‘overfitting’ or ‘model adaptation’), traditional machine learning seems unable to address it in radical ways. We argue that a real breakthrough requires a proper mathematical treatment of the ‘epistemic’ uncertainty stemming from a forcibly partial knowledge of the world, which in turn links to both the continual learning from new data and the injection of knowledge in a neurosymbolic sense. Our position supports the creation of a new continual learning paradigm designed to provide worst-case guarantees on model predictions throughout the learning process, coupled with the extension of neurosymbolic AI under epistemic uncertainty, as the two main channels to reduce the latter via additional knowledge.

Abstract: Lipolytic enzymes play a crucial role in the food industry, aiding in cheese ripening, ester hydrolysis, and flavor production. Utilizing waste residues through low-cost, energy-efficient methods enables the synthesis of high-value commercial compounds. This research assessed lipolytic enzyme production by Serratia marcescens 11E using solid-state fermentation (SSF), with residual cheese whey and vegetable wax as solid supports. After 48 hours, the crude extract exhibited higher activity toward 4-nitrophenyl acetate (0.743 U/mg) than 4-nitrophenyl palmitate (0.125 U/mg), indicating esterase activity. Biochemical analysis indicated optimal conditions at pH 9.0 and 30 °C, with a secondary activity peak at 70 °C, suggesting the presence of isoforms supported by SDS-PAGE bands at 35 and 40 kDa. Size-exclusion chromatography separated two peaks with specific activities of 17 and 26 U/mg, demonstrating the potential of lipid-rich industrial waste for producing thermally stable esterases, thus advancing more sustainable biotechnological application processes.

Abstract: This paper investigates the causal relationship between artificial intelligence (AI) investment, smart city governance infrastructure, and urban total factor productivity (TFP) across ten leading digital economies over the period 2010--2026. Drawing on a novel panel dataset that integrates ICT capital expenditure, digital infrastructure indices, Global Innovation Index scores, and the United Nations E-Government Development Index, we estimate dynamic System Generalized Method of Moments (GMM) models combined with Spatial Durbin specifications and machine-learning-based regime clustering. Our results indicate a statistically and economically significant positive association between AI investment and urban TFP: a ten percent increase in AI investment (as a share of GDP) is associated with approximately 1.5 percent higher TFP, conditional on digital infrastructure endowment and innovation capacity. We further document an inverted-U (EKC-type) relationship between AI intensity and employment polarization, suggesting that economies surpassing a threshold AI investment level of approximately 5.2 percent of GDP begin to experience convergence in skill demand. Spatial spillover effects are quantitatively important, with indirect TFP effects accounting for roughly one-third of total impacts. These findings are robust across alternative specifications, sub-period analyses, and a jackknife leave-one-out procedure. Our study contributes to the emerging literature on AI-driven urban transformation by providing causal panel evidence and a tractable theoretical framework, and offers policy implications for economies at different stages of digital transition.

Abstract: Bangladesh confronts a structural paradox: it is among the world’s most climate-vulnerable nations while simultaneously depending on resource-intensive industries — chiefly ready-made garments and agro-aquatic value chains — whose linear production logic accelerates the very environmental degradation that threatens its development gains. Direct transplantation of Scandinavian circular economy models is poorly matched to Bangladesh’s conditions of land scarcity, dense population, fragmented infrastructure, and constrained regulatory capacity. This article proposes an alternative conceptual framework, industrial symbiosis and deltaic resource optimization, which re-engineers circular economy principles around five resource streams intrinsic to Bangladesh’s geography and industrial structure: (1) structural valorization of textile residues (jhut) into high-performance composite building materials; (2) aquavoltaic systems integrating floating photovoltaics with pond-based aquaculture; (3) coastal seaweed bio-refineries producing biofuels and blue carbon credits; (4) integrated mangrove-shrimp cultivation generating premium organic seafood and carbon market revenue; and (5) decentralized urban anaerobic digestion combined with jute-based biopolymer manufacturing. Drawing on a narrative review of peer-reviewed studies, technical reports, and policy documents, the article synthesizes technical performance data, economic projections, and institutional barriers for each pathway. Evidence indicates that all five pathways are technically feasible with existing technologies and that pilots already demonstrate promising performance when embedded in supportive governance environments. The dominant barriers are institutional rather than technological: fragmented regulation, chronic under-enforcement of existing mandates, inadequate access to climate finance, and incentive structures that allow linear industrial models to externalize environmental costs. The article concludes with a phased implementation roadmap and targeted policy recommendations emphasizing coherent national strategy, enforcement capacity, and systematic engagement with global climate finance instruments.

Abstract: The Arteni volcanic complex (Armenia) represents a distinctive volcanic landscape characterized by well-preserved pyroclastic deposits, rhyolitic domes, extensive obsidian flows, and significant archaeological evidence. This study aims to evaluate the geoheritage value of the complex and to develop a scientifically grounded geotouristic trail model based on the targeted selection of representative viewpoints. Field-based investigations were integrated with semi-quantitative viewpoint assessment and GIS-supported spatial analysis, including morphometric, viewshed, and accessibility analyses. The results allowed the identification of key viewpoints (VP1–VP9), effectively representing the principal stages of volcanic evolution, including explosive eruptions, lava flow emplacement, and dome formation. Spatial analysis demonstrates that the selected view-points enable the development of a coherent, accessible, and scientifically meaningful geotouristic route while balancing scientific representativeness with visitor accessibility and safety. In addition, the widespread occurrence of obsidian and associated archaeological artifacts highlights the combined geological and cultural significance of the area. The proposed approach provides a transferable framework for the development of scientific geotourism in volcanic regions and contributes to geoheritage conservation, geoeducation, and sustainable regional development.

Abstract: Bauhinia variegata is a plant with considerable application potential owing to its combined ornamental, edible, aromatic, and medicinal values. However, research on this species remains limited and superficial both domestically and internationally, and systematic investigation of the volatile organic compounds (VOCs) emitted from its flowers is still lacking. Through integrated metabolome and transcriptome analyses, this study provides the first comprehensive characterization of the VOC composition, floral scent profile, key aroma components, and the molecular mechanisms underlying VOC variation during anthesis in Bauhinia variegata floral buds and fully opened flowers. A total of 1,214 volatile compounds were identified across buds and flowers, including 239 odor-active compounds and 37 differential odor-active compounds. Flavor statistics revealed that the floral scent profile of Bauhinia variegata is dominated by fruity, sweet, floral, green, woody, herbal, citrus, phenol, fresh, and spicy. Compared to floral buds, the majority of differential odor-active compounds were markedly up-regulated in fully opened flowers, notably including key floral aroma constituents such as phenylacetaldehyde, rose oxide, beta-ocimene, (Z)-beta-ocimene, 2-methylbenzaldehyde, and melon heptenal. Conversely, (R)-(+)-citronellal, which possesses defensive functions, and the bitter-tasting compound 1-methyl-4-nitro-benzene were significantly down-regulated in flowers, reflecting an ecological strategy shift from a defense-oriented mode at the bud stage to an attraction-oriented mode at anthesis. The up-regulation of Phenylalanine/histidine ammonia-lyase, Acyl-CoA synthetase, and Squalene synthetase genes, together with the down-regulation of Copper amine oxidase, O-methyltransferase, and Aldo/keto reductase genes, synergistically promoted the accumulation of floral aroma compounds such as phenylacetaldehyde and facilitated the floral transition. This study provides an important theoretical foundation for understanding the ecological interactions between Bauhinia variegata floral scent and its pollinators, as well as the molecular mechanisms governing floral scent formation. Furthermore, it contributes to the application of Bauhinia variegata in landscape beautification, edible flower utilization, and fragrance development.

Abstract: Background: Thoracic surgery is associated with severe post-operative pain caused by chest wall manipulation and intercostal nerve injury. Multimodal analgesia with non-opioid agents such as lidocaine, ketamine and magnesium might be beneficial for pain control and reduce opioid consumption. Methods: In this prospective cohort study, we recruited 118 consecutive patients who underwent lung resection via thoracotomy from January 2019 to January 2021 at Hospital Universitari de Girona Doctor Josep Trueta. The primary outcome was total intravenous morphine consumption within the first 24 h post-operatively. Multi-variable linear regression modelling was used to determine the adjusted association between lidocaine, ketamine and magnesium administration and morphine consumption in the first 24 h after surgery. Statistical analysis was performed using Wilcoxon’s rank-sum and Fisher’s exact tests. Results: In total, 71 patients received lidocaine, ketamine and magnesium intraoperatively (LKM) and 47 patients did not receive this regimen (non-LKM group). The LKM group had a higher prevalence of hypertension and higher proportions of patients undergoing lobectomy and pneumonectomy. Morphine consumption within 24 h post-operatively was lower in the LKM group than in the non-LKM group (median [interquartile range], 2 [2–6] mg vs. 5 [3–8] mg; p = 0.001). No drug-related adverse events were observed. After multi-variable risk adjustment, lidocaine, ketamine and magnesium use was associated with significantly decreased total intravenous morphine consumption within 24 h post-operatively (−1.76, 95% confidence interval = −3.40 to −0.12, p = 0.03). Conclusions: Lidocaine, ketamine and magnesium use was associated with lower 24-h morphine consumption in our prospective cohort.

Abstract: Cereals are a staple component of the Nigerian diet; however, their contamination with heavy metals raises serious public health concerns. This study evaluated the concentrations, contamination levels, and associated non-carcinogenic and carcinogenic risks of cadmium (Cd), chromium (Cr), copper (Cu), lead (Pb), and zinc (Zn) in commonly consumed cereals; maize, millet, sorghum, and wheat sold in Wunti Market, Bauchi State, Nigeria. Composite samples were collected and analyzed using atomic absorption spectrophotometry after acid digestion. Contamination factors (CF) and standard human health risk assessment models were employed to estimate exposure via ingestion, inhalation, and dermal contact for both adults and children. The results indicated that Cd and Pb concentrations in all cereal samples exceeded recommended permissible limits, whereas Cu and Zn remained within acceptable thresholds. Cadmium showed particularly high contamination factors, especially in maize and millet, indicating significant environmental accumulation. Exposure assessments revealed that children had higher estimated intake levels than adults across all exposure pathways, reflecting their greater vulnerability. Although ingestion pathways suggested low non-carcinogenic risk overall, inhalation and dermal exposure routes demonstrated elevated hazard quotient (HQ) and hazard index (HI) values, particularly for Cd, Cr, and Pb. Furthermore, the estimated total carcinogenic risk for both adults and children surpassed the acceptable threshold established by regulatory agencies, with maize contributing the highest risk levels among the cereals studied. These findings suggest that both consumption and handling of contaminated cereals may pose substantial long-term health risks, especially for children. The study underscores the need for routine monitoring of heavy metal contamination in food products, adoption of safer agricultural practices, and stricter regulation of agrochemical use.

Abstract: Adapting language models to specialized domains remains challenging under limited computational resources. We introduce CoDES (Context-efficient Domain Ensemble System), a framework that improves small language model performance through domain-specific fine-tuning and weighted parameter ensembling. CoDES combines parameter-efficient adaptation via Low-Rank Adaptation (LoRA) with completion-only supervision, and merges two fine-tuned models through weighted parameter averaging to improve robustness and accuracy. We evaluate CoDES on two biomedical question answering benchmarks, MedMCQA and MedQA. On MedMCQA, the ensemble achieves 74.8\% accuracy, approaching a 72B-parameter model (77.1\%) while consuming 2.5 times less energy. Consistent improvements on MedQA further demonstrate the framework's generalizability across datasets and examination styles. Taken together, these results show that targeted domain adaptation combined with model ensembling provides a practical pathway for deploying competitive language model systems under realistic resource constraints.

Abstract: The Thai durian industry is one of most important agricultural exports of the country, evidenced by its rapid growth and expanding demand in international markets. De-velopment of more advanced inspection technologies is therefore crucial for the in-dustry to ensure competitiveness toward global standards. This research aimed to de-velop an integrated decision support system (DSS) for selecting appropriate non-destructive testing (NDT) technologies for durian quality inspection. The study integrated Multi-Criteria Decision Analysis (MCDA), Analytic Hierarchy Process (AHP), Technique for Order Preference by Similarity to Ideal Solution (TOPSIS), and Techno-Economic Analysis (TEA) to evaluate five NDT technologies, including Near-Infrared Spectroscopy (NIR), Hyperspectral Imaging (HSI), Acoustic Response Analysis (RSA), Electrical Impedance Spectroscopy (EIS), and X-ray Imaging. The evaluation criteria consisted of three dimensions: technical performance, economic viability, and operational feasibility. Results revealed that Near-Infrared Spectroscopy (NIR) has the highest suitability for large-scale industrial implementation, achieving the highest weighted score (4.57) and ranked first in the TOPSIS analysis with a Closeness Coefficient of 0.91. The findings suggested that selection of NDT technologies must balance technical accuracy with economic and operational viability. The proposed DSS framework can support the de-velopment of smart agro-industry systems and contribute to the sustainable advance-ment of Thailand’s durian export sector.

Abstract: Atopic dermatitis (AD) is a chronic, relapsing inflammatory skin disease characterized by substantial clinical and immunological heterogeneity. Traditionally considered a disorder of epidermal barrier dysfunction primarily, AD is now increasingly recognized as a complex systemic inflammatory condition involving dysregulated immune responses, epithelial-derived signaling, neuroimmune interactions, and diverse molecular endotypes. Advances in molecular immunology have significantly expanded current understanding of the cytokine networks underlying disease pathogenesis and have accelerated the transition toward precision medicine approaches in AD. This narrative review summarizes current evidence regarding the immunopathogenesis of AD, with particular emphasis on the interplay between classical and emerging cytokines, biomarker development, and recent therapeutic innovations. Classical type 2 cytokines, including interleukin (IL)-4 and IL-13, remain central drivers of allergic inflammation and epidermal barrier impairment, whereas emerging mediators such as IL-31, IL-33, IL-22, thymic stromal lymphopoietin (TSLP), and OX40/OX40L signaling pathways contribute significantly to chronic inflammation, neuroimmune activation, epidermal remodeling, and pruritus. Comparative analysis of these cytokine pathways highlights the molecular heterogeneity of AD and supports the identification of distinct immunological endotypes. The review additionally discusses current and emerging biomarkers associated with disease severity, therapeutic responsiveness, and inflammatory profiling, including cytokine signatures, serum biomarkers, and transcriptomic approaches. Furthermore, major therapeutic advances involving biologic agents and Janus kinase (JAK) inhibitors are examined within the context of mechanism-based and biomarker-guided therapeutic strategies. Importantly, this review proposes a conceptual precision medicine framework integrating immunopathogenesis, cytokine profiling, molecular endotyping, and targeted therapeutic innovation in AD. Continued advances in biomarker discovery, multi-omics technologies, and individualized therapeutic algorithms may further refine disease stratification and improve personalized management strategies for patients with AD.

Abstract: Under global climate change, shifts in the suitable distribution of forest vegetation have become an important issue in ecology and biogeography. Birch forests are widely distributed across cold-temperate, temperate, and montane regions in China, but different birch forest types may vary in their environmental adaptations and spatial responses to climate change. In this study, three representative birch forest vegetation types in China, namely Betula utilis forest, Betula albosinensis forest, and Betula ermanii krummholz, were selected for comparative analysis. Based on vegetation distribution records and environmental variables, an optimized MaxEnt model was constructed using ENMeval to identify current suitable distribution patterns, key environmental drivers, and future habitat changes under climate change scenarios.The results showed that the three birch forest types differed markedly in current suitable distribution patterns. Betula utilis forest was mainly concentrated in the Qinling Mountains, Betula albosinensis forest showed a broader montane distribution pattern, and Betula ermanii krummholz was restricted to high-altitude or high-latitude cold habitats. Climatic factors were the dominant drivers of suitability, but the key environmental variables differed among the three vegetation types, indicating niche differentiation along temperature, precipitation, and elevation gradients. Under future climate scenarios, the suitable habitats of the three types showed type-specific changes in area, spatial stability, and centroid migration. Betula utilis forest and Betula albosinensis forest mainly exhibited regional spatial adjustment and partial expansion, whereas Betula ermanii krummholz showed stronger dependence on high-elevation cold habitats and more limited spatial adjustment capacity. These findings indicate that different birch forest vegetation types in China do not respond uniformly to climate change. The study provides a vegetation-type-specific basis for identifying stable suitable areas, potential expansion areas, and climate-sensitive habitats, and can support adaptive management and conservation planning for montane forest vegetation under future climate change.

Abstract: This study examined the mechanical and hygrothermal response of cement mortars in-corporating recycled low-density polyethylene (LDPE) as a partial replacement of fine ag-gregate at levels of 2.5%, 5.0%, and 7.5% by weight of sand. A reference mixture without polymer was also prepared. The experimental program included determining density, workability, and compressive strength at 7 and 28 days. Based on the mechanical results, the formulation containing 2.5% LDPE was selected for further hygrothermal assessment in 1:5 scale chambers coated with conventional and modified mortars. The incorporation of LDPE progressively decreased both density and compressive strength. At 28 days, the reference mortar reached 5.243 MPa, whereas the mixture with 2.5% LDPE attained 1.461 MPa. Hygrothermal monitoring showed no substantial improvement in indoor tempera-ture regulation; however, the LDPE-modified coating maintained higher indoor relative humidity than the conventional system during the test period. These results indicate that recycled LDPE can be incorporated into cement-based mortars at low replacement levels for exploratory non-structural applications. However, the resulting decrease in mechani-cal strength restricts its wider applicability and highlights the necessity for further verifi-cation to satisfy performance standards tailored to specific applications.

Abstract: Activation-loop mutations in receptor tyrosine kinases alter catalytic-state equilibria by changing the balance between local flexibility, global allostery, and inhibitor-induced conformational selection. Here we analyze the c-Kit D816V substitution as a model for how mutation-induced entropy loss can convert a regulated kinase into a rigidified active-like enzyme that resists type II inhibition. Using vibrational-entropy mapping, normal-mode analysis, molecular dynamics simulations, docking, dimer-interface energetics, and in silico saturation mutagenesis, we identify a conformational rigidification mechanism centered on Val816. The substitution replaces the wild-type Asp816-centered hydrogen-bond network with a Val816-Asn819 hydrophobic seam that propagates rigidity through the juxtamembrane latch, N-lobe wedges, and activation loop. This architecture is predicted to pre-organize the catalytic spine and restrict access to the inactive DFG-out state. Although imatinib and dasatinib retain favorable calculated binding energies, D816V imposes a larger predicted energetic penalty on imatinib than on dasatinib, and the rigidified pocket is predicted to impair the conformational collapse required for productive type II inhibition, thereby uncoupling binding from functional shutdown. Saturation mutagenesis separates hydrophobic D816 substitutions into a rigid/stabilizing thermodynamic regime and N819 substitutions into a flexible/destabilizing regime, indicating that activation-loop variants can be classified by the conformational states they impose rather than by position alone. Residual local disorder near residue 816 and energetically permissive mutant-wild-type heterodimerization suggest additional mechanisms for signaling adaptability. These results support a testable biophysical model in which conformational entropy loss, rather than increased flexibility, drives D816-centered c-Kit activation and type II inhibitor resistance.

Abstract: The functioning of complex natural structures, such as living systems, still lacks a generally accepted theoretical basis with respective empirical experimental verification for decades. We propose a class of experiments to test whether such systems could be subject to an unknown ordering principle that cannot be captured by known physical laws. We hypothesise that the quantum mechanical uncertainty principle enables ordering phenomena in nearly chaotic systems in the sense of a strong emergence principle, which would not be expected when they are modelled conventionally, as several authors have already formulated in various forms. To account for the harsh conditions prevailing in living systems that may preclude fragile macroscopic quantum coherence, our hypothesis does not require such coherence at all, contrary to earlier related proposals. To test this hypothesis, two virtually identical and sufficiently complex experimental setups should be compared. One setup will operate with deterministic pseudo-random number generators at key sensitive points, while the other one will use quantum-based physical random-number generators, the two setups being otherwise identical. Existing artificial neural networks are proposed as possible test objects, and their performance under identical training conditions can be used as a quantitative benchmark. As this working hypothesis extends far beyond artificial networks, a successful outcome of such an experiment could have significant implications for many other branches of science.

Abstract: Sequence information can specify molecular components, but specification is not equivalent to cellular state realization. A gene product contributes to living function only when the cell occupies a physical state in which gradients persist, compartments remain intact, diffusion and phase organization remain compatible with execution, and perturbations can be recovered without loss of viability. This gap defines a state-realization problem: what physical architecture constrains the feasible state space within which molecular programs can be executed, stabilized, reversed, or transformed? From this problem, I derive a set of substrate requirements: self-bounded aqueous interfaces, selective permeability, electrochemical asymmetry, tunable continuous physical variables, cross-scale coupling, recursive interaction with protein and information-memory systems, and measurable recovery dynamics. Lipid-organized boundary systems satisfy these requirements in an exceptionally integrated way in modern aqueous cellular life. I therefore propose the Central Homeorhetic Principle (CHP): cellular identity, robustness, and fate transitions are constrained by a distributed homeorhetic state architecture, with lipid-organized boundary systems occupying a privileged but nonexclusive substrate position. CHP is not a rejection of the Central Dogma, nor a claim that lipids alone determine phenotype. It is a complementary constraint framework that asks how molecular information becomes physically executable and dynamically sustainable. The proposed mechanism is a distributed constraint-sensing-enactment loop in which boundary-state variables are sensed, evaluated through thresholds, converted into regulatory responses, and recursively remodeled by execution and memory systems. The framework yields testable predictions concerning temporal precedence of boundary-state shifts, threshold-like fate transitions, recovery kinetics, state degeneracy, protocell persistence, and state-trajectory restoration. It is falsifiable if boundary-state variables consistently follow rather than precede commitment, fail to alter fate thresholds under controlled perturbation, or add no predictive power beyond molecular profiles.

Abstract: Road traffic accidents represent a critical road safety problem whose severity depends on the complex interaction of multiple factors. The study of these factors and their interrelationship has therefore long been a focus of scientific literature. The objective of this study is to analyze the factors that determine the severity of road accidents, identifying the most important ones and their correlations. An accident dataset incorporating variables related to infrastructure, location, time, and vehicle type was used to predict the Injury Severity Index (ISI), applying Association Rules to identify latent correlations and an Optimized Decision Tree (CART) model for hierarchical risk classification. The results reveal that the Type of Collision is the primary predictor of severity; collisions with objects or pedestrians showed a 100% confidence in resulting in low severity, while maximum severity is associated with heavy traffic and head-on or side-impact collisions. Critical scenarios were also identified during the early morning hours and in rural areas, primarily linked to trucks. The combined use of both tools provides a solid scientific basis for designing interventions on highly vulnerable road segments and during high-risk time periods.