Abstract: The outbreak of coronavirus disease 2019 (COVID-19), caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), has emerged as one of the most significant global health crises in recent history. Coronaviruses are a diverse group of RNA viruses classified into alpha, beta, gamma, and delta genera, with SARS-CoV-2 belonging to the beta-coronavirus family. The virus exhibits high transmissibility and causes a wide spectrum of clinical manifestations ranging from mild respiratory symptoms to severe complications such as acute respiratory distress syndrome, multi-organ failure, and death, particularly among elderly and immunocompromised individuals. Structurally, SARS-CoV-2 possesses a large single-stranded RNA genome encoding major structural proteins, including spike (S), envelope (E), membrane (M), and nucleocapsid (N) proteins, which play critical roles in host cell recognition and viral infection. Understanding the molecular mechanisms of virus–host interactions, especially protein–protein interactions (PPIs), is essential for uncovering viral pathogenesis and identifying potential therapeutic targets. Traditional experimental techniques for PPI detection, such as yeast two-hybrid and affinity purification methods, are often expensive, labor-intensive, and prone to inaccuracies. Consequently, computational approaches based on machine learning and deep learning have gained significant attention for efficient and scalable PPI prediction. These methods utilize diverse biological information, including protein sequences, structural features, genomic data, gene ontology annotations, and interaction networks, to model complex biological relationships. This survey provides a comprehensive review of computational approaches for PPI prediction, highlighting both machine learning- and deep learning-based techniques, along with their methodological advancements and performance evaluations. Furthermore, the survey discusses major biological databases and data sources commonly employed in PPI studies, offering insights into current challenges and future directions in computational PPI prediction research.

Abstract: The observable universe has always remained below its own gravitational radius—yet it is not the interior of a black hole. This apparent paradox, derivable from the Friedmann equations, suggests that three-dimensional space is not the fundamental level of physical description. In this work: (1) we derive the global gravitational constraint R_p ≲ R_g valid in every cosmic epoch; (2) we prove with a causal no-go theorem that this constraint does not imply a black hole-type geometry; (3) we show that, within standard physics, the resolution that survives the exclusion of alternatives is holographic: fundamental information resides on a two-dimensional boundary, while the interior volume is an emergent reconstruction. The ingredients of this argument—Friedmann cosmology, covariant entropy bounds, holographic counting—are individually well established. What has been missing is their systematic combination into a closed logical chain. If this chain were trivial, holographic cosmology would already be the dominant paradigm and inflation would be recognized as optional. It is not, which suggests the synthesis itself is the contribution. The framework dissolves what we call the “spacetime island” problem: in standard physics, coordinates are treated as primitives disconnected from the informational language of quantum theory and statistical mechanics. Holographic emergence reconnects them. Giving up one or two “fundamental” dimensions is a gain in parsimony and unification, not a loss. Observable consequences follow. The Gaussianity of the CMB emerges from the central limit theorem applied to boundary degrees of freedom. Primordial gravitational waves are expected to be strongly suppressed (r < 10⁻³); a robust detection at r > 10⁻² would falsify the minimal framework. Recent observations—the absence of predicted dark matter subhalos in high-resolution lensing, the anomalous pressure in cluster mergers—provide independent hints that the standard picture has cracks where this framework offers natural explanations.

Abstract: Sentinel-1 Synthetic Aperture Radar (SAR) is a multi-purpose monitoring satellite suite that, among many applications, provides sea surface wind speeds at high spatial resolution. The overall aim of the study is to quantify the accuracy of the SAR wind products from Copernicus Ocean Wind, called OCN OWI, and from the Technical University of Denmark (DTU) Department of Wind and Energy Systems’ product called DTU SAR. Both products serve as a basis for offshore wind resource mapping for offshore wind energy planning. With the growth in offshore wind farms, offshore wind resource information is highly relevant. However, a comparison between the two products is lacking. This study fills this gap by presenting a comprehensive validation of the two Sentinel-1 wind speed products using wind speed measurements from 18 weather buoys and 10 floating wind lidars in the European Seas. It is the first time a comprehensive wind lidar data set has been used for SAR wind validation. Key findings: OCN OWI vs. lidar (buoy) shows R2 = 0.93 (0.84), root mean square error (RMSE) = 1.18 m/s (1.61 m/s), mean absolute error (MAE) = 0.86 m/s (1.24 m/s), and bias = -0.5 m/s (-0.6 m/s). DTU SAR vs. lidar (buoy) shows R2 = 0.88 (0.84), RMSE = 1.3 m/s (1.6 m/s), MAE = 0.92 m/s (1.22 m/s), and bias = 0.02 m/s (-0.04 m/s). OCN OWI provides a filtered data set, and validation vs. lidar shows R2 = 0.95 and RMSE = 0.88 m/s; however, at the expense of discarding more than 50% of all data. The lidar vs. SAR wind speed statistics outperformed the buoy comparison statistics for all metrics studied. The 3 km Norwegian reanalysis (NORA3) wind speeds vs. lidar (buoy) show RMSE = 1.27 m/s (1.64 m/s) and bias = -0.01 m/s (-0.43 m/s). Lidar wind speed data are more accurate than buoy data and give a more trustworthy validation of SAR wind speed and model wind speeds than buoy data. Lidar data are recommended for validation studies on Geophysical Model Functions on SAR winds. Satellite Global Precipitation Measurement (GPM) Integrated Multi-satellitE Retrievals (IMERG) are collected at the buoy and lidar sites for comparison of SAR-based wind speed accuracy during precipitation. SAR and NORA3 show consistently higher RMSE values vs. buoy and lidar data with increasing precipitation and higher mean wind speeds at higher precipitation rates, but no systematic bias. Creating a precipitation flag for Sentinel-1 SAR-based winds would reduce the number of available samples and potentially lead to biased estimates of the wind resource. Vertical wind profiles at lidar locations are compared to SAR-based wind profile extrapolation, including stability correction.

Abstract: We present GLYBATOMAQ™, a rank-centric and quantum-geometric framework for GLIPR1-focused in silico screening. The framework treats docking as a fixed-protocol comparative oracle and places the main methodological emphasis on auditable rank movement, positive-semidefinite operator geometry, DFT-derived electronic descriptors, QMC-style uncertainty auditing, and MQWalk topology validation. To make the quantum-geometric contribution explicit for quantum-geometry reporting, we introduce Bures/Fubini-Study-style distance controls, local metric and curvature penalties, quantum Fisher information-inspired sensitivity diagnostics, Berry-type gauge-consistency checks, and a candidate-level assembly certificate. Quantum-geometry reporting elements define the reporting schema, connect each mathematical object to a computational decision, and show how DFT, QMC, MQWalk, curvature, and diagnostic penalties are fused without claiming experimental affinity, efficacy, or biomolecular quantum transport. The output is a reproducible leaderboard and audit bundle for GLIPR1-oriented computational hypotheses: rank shifts are accepted only when supported by electronic descriptors, uncertainty-aware energetic evidence, operator-overlap topology, and chemistry-safe HMC/HSX feasibility constraints.

Abstract: A graph G=(V,E) together with a positive real-valued weight-function w:V→R+ or w:E→R+ is called a weighted graph and is denoted by (G;w). In this paper, we introduce the concepts of connectivity, edge-connectivity, and restricted edge-connectivity for a weighted graph (G;w), and we prove general bounds analogous to those in the unweighted case. Furthermore, we study the connectivity and edge-connectivity of the line graph and the P2-path graph of a weighted graph, establishing upper and lower bounds for each of these parameters.

Abstract: Temperature anomalies drive substantial excess mortality, yet existing early warning systems remain limited to regional scales, reliant on linear assumptions, and fail to adequately account for multi-dimensional thermal stress and socioeconomic heterogeneity. This study develops the Planetary Health Axis System–Meteorology (PHAS–M), a framework designed to transform sub-daily weather forecasts into location-specific predictions of the risk of temperature-related excess mortality.PHAS-M employs a Bayesian, prior-informed severity–exposure–vulnerability decomposition coupled with a copula model to capture non-linear mechanisms and spatial variation in adaptive capacity. In validation, it dramatically outperforms existing approaches and surpasses both conventional regression and pure machine learning baselines.This methodology further reveals that the heterogeneity of temperature-induced health risks is attributable to socioeconomic vulnerability, and supports the integration of a broader set of heterogeneous characteristics into predictive climate–health models. The PHAS-M framework provides an interpretable and universal operational tool for decision-makers to better intervene in weather-related health risks.

Abstract: (1) Background: Snacking has become a routine part of how people eat today, with real potential to shape overall diet quality, food choices, and daily nutrient intake. This cross-sectional study aimed to explore snack purchasing and nutritional habits among Greek adults, investigate how consumers perceive the nutritional value of snacks, and understand their attitudes toward nutrition labelling, nutrition claims, and new snack products on the market. (2) Methods: A structured questionnaire was sent out electronically to 1,039 Greek adults. Participants provided information on their soci-odemographic background, health and lifestyle habits, snack consumption and purchasing be-havior, perceptions of snack products, nutrition labelling, and interest in innovative and functional snacks. The data were analyzed using descriptive statistics and Chi-square tests of independence. (3) Results: The most common packaged snack for the average person in the study was a cereal bar, while the least popular non-packaged snack was a bakery cheese pie. Consumers viewed the ap-pearance of the product's packaging as a secondary consideration at the point of purchase, and the most prominent label elements that attracted consumer attention were nutrition, calories, and fat. The claims that consumers found most appealing were "no preservatives" and "sugar-free/no added sugars." A clear preference was shown for snack products that relied mainly on naturally occurring nutrients rather than fortified ingredients, as well as a greater willingness to try new savoury snack options that used familiar/demonstrable Greek ingredients, such as certain olives, nuts, and fruit. Statistically significant relationships have been identified between certain snacking behaviours, at-titudes, and labelling preferences, with respect to age, gender, education level, employment status, BMI, health status, physical activity, and place of residence. (4) Conclusions: Sociodemographic, education level and lifestyle all have an influence on how Greek adult consumers view and use snacks. Interest in nutrition information varies widely between different types of consumers. These findings may be useful in guiding the future development of snacks that meet the nutritional re-quirements of the Mediterranean diet, as well as in creating more targeted nutrition information and consumer education programs.

Abstract: Social service professionals operate in legally sensitive, administratively intensive, and context-dependent environments in which decision-making requires the simultaneous interpretation of regulatory norms, institutional procedures, and individual case circumstances. This paper proposes a conceptual model of a Personal Legal and Social Artificial Intelligence (AI) Assistant intended to support professional decision-making in social services, and demonstrates its functionality through a working prototype. The model is formulated as a domain-specific retrieval-augmented generation (RAG) framework in which a controlled legal and social document corpus is processed through text extraction, chunking, semantic indexing via SentenceTransformer embeddings, top-k retrieval through cosine similarity, and bounded large-language-model reasoning to produce grounded and explainable responses. The proposed framework is informed by three successive prototype versions and by observed sensitivity to corpus scope, document prioritization, and prompt constraints. The current prototype version operates on a prioritized corpus of sixteen Bulgarian normative acts complemented by three supplementary resources, comprising 883 indexed fragments, and uses DeepSeek as the reasoning model accessed through the OpenRouter API. The functionality of the model is validated through a representative use case concerning child protection, in which the prototype identifies the applicable legal provisions, exposes the retrieved documentary evidence, and generates a four-part structured analysis comprising legal qualification, applicable provisions, legal consequences, and recommendations for action. The main contribution lies in the formalization and prototype-level demonstration of a domain-specific AI assistant that combines legal grounding, social-context awareness, and bounded language-model reasoning for trustworthy decision support in regulated social-service practice.

Abstract: The genus Ferula (Apiaceae) is taxonomically challenging because of morphological plasticity, incomplete lineage sorting, and documented discordance between nuclear and plastid datasets. To test marker congruence at the regional scale, we analysed five Ferula species from Central Kazakhstan using the nuclear ITS2 and plastid psbA–trnH loci. Sequences were aligned with MUSCLE and analysed in MEGA X using Maximum Likelihood with 1000 bootstrap replicates and model selection based on the Akaike Information Criterion. Both loci yielded largely congruent topologies, and the two-locus consensus recovered species-level relationships without strongly supported cytonuclear conflict, unlike some previously reported Ferula lineages. These results support the utility of ITS2 and psbA–trnH for regional phylogenetic studies in Ferula and provide additional molecular evidence for species relationships in Central Asian representatives of Ferulinae.

Abstract: Probabilistic graphical models (PGMs) provide a powerful framework for modelling complex systems, but inference over loopy graphs requires approximate methods whose accuracy depends on how factors are clustered in the graphical representation. Existing factor clustering methods rely on the number of variables in a cluster as a proxy for memory cost and informational content---a loose upper bound that leads to suboptimal merging decisions. We address this limitation by proposing an efficient algorithm for estimating the joint entropy of a group of clusters without explicitly multiplying out the constituent factors, thereby avoiding the exponential computational cost that makes exact computation prohibitive. The algorithm integrates naturally with both static and dynamic graph restructuring methods, and reduces to the Kikuchi entropy approximation when applied to the complete graph. Experiments on models with up to 24 variables demonstrate that the algorithm produces accurate (when compared to ideal junction tree performance) entropy estimates across diverse model types, with errors remaining within tight bounds. Scalability is further validated on a substantially larger model defined over 2640 random variables. These results confirm that accurate entropy estimation is achievable wherever reliable probabilistic inference is possible, and that the proposed estimation algorithm yields objectively close approximations, thereby supporting improved clustering decisions in PGM structuring algorithms.

Abstract: Background: Burnout among internal medicine (IM) and internal medicine pediatrics (Med-Peds) residents has reached epidemic levels. While burnout prevalence is well documented, limited research has employed systems-based approaches to identify the specific, contextually grounded work-system factors that drive burnout in these residents. This study aimed to identify and prioritize modifiable work-system factors contributing to burnout among IM and Med-Peds residents using a theory-based, participatory, and data-driven mixed-methods approach. Methods: A sequential mixed-methods design incorporating the National Academy of Medicine's (NAM) systems model of clinician burnout was employed at a single academic medical center across five phases: (1) survey, (2) focus groups, (3) contextual inquiry, (4) modeling and validation, and (5) prioritization and recommendations. IM and Med-Peds residents (N=119) were administered a 25-item survey including demographics, a 2-item Maslach Burnout Inventory (MBI) measuring emotional exhaustion (EE) and deperson-alization (DP), a 2-item Connor-Davidson Resilience Scale, and 21 work-system factor items rated for severity and improvement priority. Five focus groups gathered contextual information. Contextual inquiries (CIs) involving 4–6 hours of in-situ shadowing were conducted with 14 residents. Qualitative data were synthesized into an affinity model, which residents validated and used to rank their five priorities for improvement. Par-ticipants then rated each priority by level of impact and effort to generate prioritized improvement recommendations. Results: The survey response rate was 27% (n=32/119). Of respondents, 56% met criteria for burnout (combined EE and DP score >3), with mean EE of 3.84 (SD 1.19) and mean DP of 3.22 (SD 1.47). Mean resilience score was 8.09 (SD 0.96). The four workplace factors contributing most to burnout by severity were interruptions and distractions (mean 3.75, SD 0.95), excessive workload (3.59, SD 0.84), time pressure (3.38, SD 1.10), and poor work-life integration (3.28, SD 1.37). Focus groups (25%, n=30/119) and CIs (11.7%, n=14/119) provided rich contextual data organized into an affinity model with 167 discrete breakdowns across four major work-system categories. Validation sessions (6.7%, n=8/119) yielded 11 distinct improvement priorities. Impact-effort analysis identified high-impact, low-effort priorities (improve clinic workflows; reduce in-basket workload) and high-impact, high-effort priorities (improve work conditions; improve staffing; reduce patient load; improve Epic Chat and paging). Conclusions: This study demonstrates the feasibility and value of a contextual design approach for identifying actionable work-system factors contributing to resident burnout. By integrating quantitative data with rich qualitative observations, the methodology produces prioritized, context-specific recommendations for targeted improvement. Healthcare systems may utilize this participatory approach to efficiently identify and address the most impactful sources of resident burnout.

Abstract: Symmetry operations are usually studied within the frameworks of group theory, geometry, and operator algebra. In the present work, a Ramsey-theoretic approach to symmetry is developed. Symmetry operations are treated as operators serving as vertices of complete bi-colored graphs, called symmetry graphs (SGs). Two symmetry operators are connected by a maroon edge when they commute and by a teal edge when they do not commute. Thus, the commutation structure of a symmetry group is transformed into a combinatorial object suitable for Ramsey-theoretic analysis. The introduced coloring is generally non-transitive, leading naturally to nontrivial complete bi-colored graphs constrained simultaneously by group-theoretical and combinatorial principles. It is shown that every symmetry graph containing six vertices necessarily contains either a monochromatic commuting triangle or a monochromatic non-commuting triangle as a direct consequence of the classical Ramsey theorem R(3,3)=6. The framework is illustrated for the symmetry groups of the equilateral triangle, regular tetrahedron, crystallographic point groups, infinite Cairo pentagonal tilings, and the triangular Ising ferromagnet. Higher-order structures, including teal quadrangles, second-order graph symmetries, infinite monochromatic cliques, and Lie-algebraic constraints arising from the Jacobi identity, are discussed. The proposed framework establishes a new connection between symmetry theory, Ramsey theory, graph theory, crystallography, and operator algebra.

Abstract: Smartphone-based plant identification increasingly serves as the edge tier of agricultural Internet of Things (IoT) systems, where models must adapt to crowdsourced data under bandwidth, memory, and energy constraints. No prior work has systematically investigated continual learning at the scale of thousands of fine-grained medicinal plant species, nor how retraining frequency affects the cost–performance trade-off in an IoT model-lifecycle setting. We evaluate three continual learning strategies—naïve fine-tuning, experience replay, and Learning without Forgetting—under periodic retraining schedules (updating every K increments), tested on 2,719 species (≥25 images each) from the Viet Medi Species 2026 dataset (310,647 images; 4,799 species total). All three strategies exhibit negative forgetting (performance improvement rather than degradation) in the instance-incremental setting, with naïve fine-tuning and LwF showing the strongest gains. Periodic retraining with K=2 reduces retraining operations by approximately 50% while maintaining performance. A baseline MobileNetV2 model achieves 54.07% top-10 accuracy across 2,719 species and has been deployed via TensorFlow Lite (FP16, ∼11.5 MB) in the Med Herb Lens Android application. Naïve fine-tuning is recommended as the practical default for instance-incremental agricultural IoT deployments.

Abstract: Tele-exercise (TEF) is an option for maintaining the vitality of people who have difficulty exercising outside the home and in confinement situations. The aim of the present study was to determine the effect of TEF in Tai Chi (CT) compared to strength training (ST) on muscle mass and strength in older Mexican adults. The following groups were formed: (i) CT Group (CTG) n=42; (ii) ST Group (STG) n=41; (iii) Control Group (CG) n=40. Skeletal muscle mass index (SMMI), %FM (percentage of fat mass), handgrip strength (HF), and sit-stand test (SST) were assessed before and after the intervention. Both TEF groups showed an increase in SMMI, GTC (+0.73 ± 1 kg/m², p 0.009) and GEF (+0.45 ± 0.9 kg/m², p 0.03) in contrast to the GC (-0.49 ± 0.5, p 0.05). Likewise, both groups showed a significant decrease in %FM (GTC, -4.32 ± 6, p&lt;0.01; GEF, -4.24 ± 6, p&lt;0.01) compared to the GC (2.12 ± 7, p=0.04). An increase was also found in HS: TCG (+4 ± 7, p=0.08) and GEF (+5 ±3, p=0.08), along with a decrease in STST time in both groups (GTC, -2.19±3, p&lt;0.01; GEF, -3.48 ± 3, p&lt;0.001). The tele-exercise in tai chi or strength training has a similar positive effect on SMMI, %FM, HS, and STST, and an option for older adults in confinement situations.

Abstract: The article introduces FOREST, a participatory interface prototype for communicating and negotiating urban heat risk at the scale of the shared courtyard. Instead of treating heat as a one-way disaster message or a purely technical indicator, FOREST translates residents’ images, short texts, sounds, and walking traces into evidence cards that record time windows, location anchors, trigger conditions, and lived consequences. The prototype is framed as a hazard-governance method. It asks how everyday exposure, microclimate difference, and care labor can be made comparable and publicly discussable without scrubbing out uncertainty. What the article adds is a public evidence structure that links heat exposure, vulnerability, and response in a form that can support screening, review, and feedback in community-scale adaptation.

Abstract: Panipuri is a popular street-vended food across South Asia but often lacks regulatory oversight, posing serious public health risks. This study assessed the microbiological quality of panipuri-water sold across District Jaunpur, Uttar Pradesh, India, in the context of a recent typhoid outbreak reported among university students, with the aim of generating hypotheses about potential foodborne risks rather than establishing causality. A total of 150 samples were analysed for Escherichia coli, Staphylococcus aureus, Pseudomonas aeruginosa, and Salmonella typhimurium as per Indian Pharmacopoeia Commission (2022) guidelines. Contamination was widespread, with E. coli detected in 61.3% of samples, S. aureus in 16%, P. aeruginosa in 40%, and S. typhimurium in 36%. Sikara and Kotwali localities showed the highest contamination levels. The presence of ESKAPE pathogens (S. aureus, P. aeruginosa) and indicator organisms confirmed faecal contamination and suggested a potential reservoir of antimicrobial resistance. A strong correlation (r = 0.92, p < 0.001) between E. coli prevalence and total pathogen load revealed poor hygiene conditions. These findings emphasize the urgent need for hygiene training, surveillance, and stricter food safety enforcement for street-vended panipuri, and should be interpreted as hypothesis-generating with respect to any link to the reported typhoid outbreak.

Abstract: Background: Artificial intelligence (AI) is transforming the dental industry by improving diagnostic processes, helping with treatment planning, and increasing the efficiency of patient care through language and advanced image processing. However, the use of AI-powered chatbots in dental practice remains underexplored. This study assessed the potential of chatbots for initial dental symptom assessments and providing accurate triage information to patients. Methods: This cross-sectional study compared the accuracy of three AI systems (ChatGPT, DeepSeek, and a Custom chatbot) with those of 10 dentists for categorizing 100 AI-generated cases based on the American Dental Association guidelines into Emergency, Routine, Urgent, Non-Urgent. Interrater reliability and classification accuracy were analyzed statistically, and ethical standards were observed despite the use of simulated data. Results: ChatGPT achieved the highest accuracy (83%), followed by DeepSeek (82%) and custom chatbot (73%). All chatbots yielded a higher accuracy than the average human accuracy (66%). Conclusion: AI chatbot systems, particularly ChatGPT and DeepSeek, achieved high accuracy for dental triage and outperformed human evaluators. These findings provide valuable insights into the potential role of these systems in supporting clinical decision making in dental care.

Abstract: Light-converting polymer coatings and films are emerging passive photonic materials for spectral engineering in sustainable and protected agriculture. By absorbing ultraviolet or weakly used spectral components and re-emitting in visible bands that overlap with photosynthetic pigments and plant photoreceptor action regions, these materials can modify the radiation environment without additional electrical energy input. This critical review analyses light-converting polymer films and coatings from a materials and coatings perspective, with emphasis on photophysical mechanisms, polymer matrices, luminophore families, coating fabrication routes, optical transparency, photoluminescence, aggregation phenomena, photostability and scalability. The photobiological background is included as a concise framework that justifies the spectral targets of the conversion process. Rare-earth complexes, inorganic phosphors, quantum dots, aggregation-induced-emission systems and organic dyes are compared as candidate luminophores. Particular attention is paid to an author-developed perylene diimide (PDI)-modified poly(methyl methacrylate) (PMMA) solution-cast coating system, used here as a representative case study to discuss dispersion, optical homogeneity and aggregation-related losses. Extrusion, solution casting, spin-coating, dip-coating and sol–gel processing are evaluated as fabrication strategies for laboratory and large-area greenhouse applications. The work concludes by identifying the main gaps that must be addressed before practical deployment: quantitative UV–Vis and photoluminescence characterization, absolute quantum yield, haze and scattering, thickness and morphology mapping, accelerated UV ageing, weathering resistance, toxicity assessment and crop-specific validation.

Abstract: Young barley, derived from the early vegetative stage of Hordeum vulgare L., constitutes a plant-based functional ingredient whose phytochemical profile differs markedly from that of mature grain. Two principal commercial forms exist — dried grass powder and juice-derived products — differing in matrix composition and bioactive compound concentration. This narrative review critically evaluates the current knowledge on the phytochemical composition, biological activity, and translational relevance of young barley preparations considered as a functional plant food. The phytochemical spectrum is dominated by C-glycosyl flavones, particularly saponarin and lutonarin, alongside phenolic acids, chlorophylls, enzymatic antioxidants, vitamins, and minerals. Experimental evidence implicates the modulation of redox homeostasis, inflammatory signaling, and metabolic regulators as the primary biological mechanisms. In vitro studies additionally demonstrate antiproliferative activity in human cancer cell lines and immunomodulatory properties mediated by polysaccharide-rich fractions, extending the biological profile of young barley beyond classical antioxidant activity. Although preclinical models consistently demonstrate antioxidant and metabolic effects, high experimental doses and limited preparation standardization restrict the direct extrapolation to human supplementation contexts. Available clinical trials suggest modest improvements in selected lipid, glycemic, and oxidative stress markers; yet, most are small in scale and brief in duration. Agronomic variables including fertilization strategy and soil composition represent additional, underappreciated sources of phytochemical variability and safety concern. Overall, the current evidence supports the biological plausibility of young barley as a functional plant food; yet, the clinical data remain preliminary. Future research should prioritize preparation standardization, dose–response characterization, and agronomic transparency to strengthen translational reliability.

Abstract: Shape-memory polymers (SMPs) are gaining significant attention for their ability to recover predefined shapes via external stimuli. Among thermally activated systems, biodegradable blends of polylactic acid (PLA) and polycaprolactone (PCL) are particularly promising for biomedical devices and soft actuators. This study develops a thermo-mechanical theoretical model to investigate the shape-memory behavior of a PLA/PCL composite blends under controlled thermal cycling. The framework integrates transient heat transfer, temperature-dependent elasticity, and viscoelastic dynamics to predict temperature evolution, deformation, and internal stress. The thermal response is computed via Newton’s law of convection, while the mechanical transition is described by a sigmoidal temperature and crystallinity-dependent Young’s modulus. Beam bending theory is employed to evaluate the spatial distribution of strain and stress. A parametric sensitivity analysis was performed to evaluate the influence of different parameters including the crystallinity grade, the convective heat transfer coefficient, glass transition temperature, and viscoelastic recovery constant. The theoretical study accurately reproduces the shape-memory cycle, quantifying performance through fixation and recovery ratios. This model provides a robust tool for the rational design and optimization of biodegradable smart polymer structures.