Search Results (922)

Humic substances have long been interpreted as high molecular weight macromolecules, yet this view largely reflects the translation of instrumental responses into ontological claims. Size exclusion chromatography measures hydrodynamic behavior relative to calibration standards, not molecular architecture; nuclear magnetic resonance reports chemical environments and dynamic regimes, not polymeric identity; fractal scaling describes morphology, not bonding. Conflating these levels of description has sustained artificial dichotomies and obscured the role of interaction-driven organization. Here, humic substances are reinterpreted as operationally defined, interaction-dominated states of organic matter that emerge under specific boundary conditions of pH, ionic strength and hydration, recognizable through measurable dynamic signatures (accessibility, confinement, hysteresis, relaxation) rather than inferred molecular architecture. The State Resilience Index (SRI) is proposed as a quantitative descriptor of how strongly a humic-like state resists, or recovers from, controlled perturbations, expressing “humic behavior” as an observable property of system response rather than a presumed molecular identity. Reframing humic substances from entities to states resolves long-standing conceptual tensions without discarding their practical utility, provided operational clarity, functional focus, and contextual limitation are explicitly maintained. The future of humic-substances research is not to resolve a molecular identity for humic fractions, but to quantify when and why organic matter behaves humic-like. Full article

Turmeric (Curcuma longa L.) is one of the most important spice crops and a valuable medicinal plant, but it is seriously affected by various types of diseases such as fungal, bacterial, nematode and viral diseases. In this paper, a complete mathematical model of the turmeric plant disease dynamics is developed under a fractal-fractional model in this context, encompassing all four types of pathogens and associated treatment classes. The fractal-fractional Caputo derivative operator captures memory effects and, through its fractal exponent, a genuine deformation of the classical memory kernel, allowing the underlying biological dynamics to be represented more flexibly than under the classical integer-order derivative; we do not, however, claim that this kernel deformation corresponds to demonstrated self-similarity or spatial heterogeneity in the turmeric plant–pathogen system. We show the positivity and boundedness of the solutions, calculate the next-generation matrix approach-based basic reproduction number ${\mathcal{R}}_0$ and investigate the local and global stability of both disease-free and endemic equilibria by Lyapunov functionals. A sensitivity analysis of ${\mathcal{R}}_0$ is conducted to determine the most important parameters influencing disease transmission and control. The existence and uniqueness of solutions and Ulam-Hyers stability of solutions are established by fixed point theory. For the associated integer-order system, we formulate an optimal control problem is formulated with three time-dependent controls: the prevention effort ($u_1$), the enhancement of treatment ($u_2$), and the care management ($u_3$), and the optimality conditions are derived via Pontryagin’s maximum principle. Numerical simulations are conducted with three different fractal-fractional operators, namely Caputo, Caputo-Fabrizio and Atangana-Baleanu. A deep neural network is developed and trained to approximate the solution of the integer-order system. The third-layer deep neural network consists of neurons of sizes 80, 32, and 24, with activation functions of logistic sigmoid, radial basis and hyperbolic tangent, respectively, and is trained to approximate the system dynamics with the fourth-order Runge-Kutta method as a reference. The DNN is found to be very accurate in predicting the values with Nash-Sutcliffe Efficiency between 0.79 and 0.99 and Theil Inequality Coefficient around ${10}^{-2}$ in all 11 compartments, and hence proved capable of being a good surrogate modelling tool for the ODE systems. The present work contributes towards SDG 2 (Zero Hunger) and SDG 3 (Good Health and Well-being) by laying a mathematical basis for integrated disease management in turmeric cultivation for sustainable agriculture and food security. Full article

Falls in older adults are a leading cause of injury, and the time spent on the floor afterwards is the stronger predictor of outcome. We present a low-cost system that detects the sustained lying posture following a fall. An FLIR Lepton 3.1R (160 × 120 px) and an int8-quantized FOMO detector run fully on an OpenMV RT1062 board; no image data leaves the node. Ten healthy adults, none in the training data, followed a structured posture protocol, yielding 3034 labelled frames. No fall, real or simulated, was recorded: lying is a proxy for a post-fall state, and claims are restricted accordingly. The detector produced an output on only 66.3% of labelled frames, with a strong class dependence (75.6% lying, 51.7% standing). End-to-end, binary lying-posture recognition reached a sensitivity of 0.723 (95% CI 0.637–0.812) and an F1 of 0.801. Every sustained lying bout of at least 20 s was flagged, but the deployed alert rule also produced roughly one hundred false alerts per hour of non-lying activity. Low-resolution thermal sensing is therefore a workable basis for long-lie detection; the limiting factors are the detector’s class-dependent miss rate and the alert logic, not the posture classifier. Full article

Polyhydroxyalkanoates (PHAs) are a structurally diverse family of microbially synthesised, biodegradable polyesters that accumulate as intracellular carbon and energy reserves under conditions of nutrient imbalance. Their combination of adjustable mechanical performance with controlled hydrolytic and enzymatic degradation, and non-toxic degradation intermediates (ex. D-3-hydroxybutyrate) has made them a longstanding candidate biomaterial for human tissue engineering, drug delivery, and resorbable implants. Comparatively, their application in veterinary medicine remains an emerging and fragmented field, despite an arguably stronger practical case: veterinary practice faces acute pressure to replace non-degradable sutures, orthopaedic hardware, and single-use plastics with materials that avoid secondary retrieval surgery, that can be produced at low cost for large-scale animal use, and that align with growing regulatory and consumer demand for sustainable animal healthcare. This review consolidates current understanding of PHA biosynthesis, covering the core: phaA-phaB-phaC pathway, medium-chain-length variants, microbial producers, feedstock flexibility, and metabolic engineering strategies for yield improvement. It also examines material modification strategies, including blending, chemical grafting, surface functionalisation, electrospinning, and additive manufacturing, used to adapt PHAs for specific veterinary form factors. The clinical and preclinical evidence base is presented in detail across wound management, orthopaedic and soft-tissue regeneration, cardiovascular tissue engineering, drug delivery, and surgical devices, with attention to species-specific considerations in companion animals, horses, and food-producing ruminants. This review relies exclusively on peer-reviewed literature for its quantitative claims, while transparently noting where veterinary-specific data are lacking, extrapolated from rodent or human models, or in need of independent verification. Persistent barriers like production cost, batch-to-batch variability, absence of veterinary-specific regulatory pathways, and limited long-term in vivo safety data in large animals are analysed critically, alongside translational opportunities including waste-feedstock valorisation, hybrid PHA/ceramic and PHA/natural-polymer composites, and stimuli-responsive formulations. We conclude that PHAs are scientifically well positioned but institutionally under-validated for veterinary translation, and we outline a concrete research agenda to close this gap. Full article

Teachers’ beliefs about Education for Sustainable Development (ESD) shape how they perceive and enact ESD as an educational task, yet remain empirically understudied, particularly among pre-service primary school teachers. This study investigates ESD-related belief patterns among pre-service primary school teachers specialising in Natural and Social Sciences in Germany, addressing both the structural configuration of shared beliefs and how these configurations reflect a fundamental tension between ESD as a political programme and ESD as an independent educational concept. Using Q-methodology, 31 pre-service teachers from nine German universities sorted 31 statements about ESD. Factor analysis identified three structurally distinct, yet partially overlapping, viewpoints: ESD as a Whole-School Task of Fostering Autonomy, ESD as a Critical-Transformative Educational Concept, and ESD as an Individual Teaching Responsibility. The viewpoints share a general-pedagogical belief core centred on autonomy, real-world relevance, and cross-curricular responsibility, while explicitly political-critical dimensions of ESD are absent from this shared core. Across the viewpoints, the political and normative orientation of ESD is related in different ways to educational claims of autonomy, independent judgement, and non-indoctrination. The findings have implications for pre-service teacher education and contribute to closing a methodological gap in ESD belief research by revealing how individual ESD-related beliefs are configured and related within distinct viewpoints. Full article

Ensuring that artificial intelligence contributes to sustainable, equitable education requires more than aggregate accuracy—it requires verifying that predictive systems serve all learners fairly, including across geographic regions. We audit a dropout-prediction pipeline built on the Open University Learning Analytics Dataset (OULAD) for disparities across gender, disability, and geographic region, using a student-level train/test partition to prevent the same student’s records from contaminating both sets. Logistic regression and random forest classifiers attain approximately 0.85 accuracy and 0.91–0.92 AUC overall, yet region-stratified recall (true-positive rate) ranges from 0.55 in Wales to 0.80 in the West Midlands Region, an equal-opportunity gap of 0.25 that is corroborated by region-specific AUC, by a random forest classifier, and, for actual withdrawals, by a likelihood-ratio test showing region predicts being missed by the classifier beyond what the Index of Multiple Deprivation (IMD) explains. A region-isolation test shows that excluding region as a model predictor is associated with a significantly narrower gap in both model families (0.13–0.19 without region versus 0.25–0.28 with region), an association not explained by IMD band alone; because the bootstrap 95% confidence interval on this difference ($[-0.003,0.173]$) narrowly includes zero, we treat the attribution to region specifically as suggestive rather than conclusively established. A naive region-specific decision-threshold mitigation, evaluated correctly on a held-out validation set, does not improve the gap; a shrinkage-regularized version recovers a modest, observed reduction (0.24 to 0.17) on the held-out test set, without a formal uncertainty interval for this difference, at the cost of a near-doubling of regional false-positive rates. Because our analysis is retrospective and several predictors are computed over the full module presentation, these findings support methodological lessons for the design and auditing of future systems rather than direct claims about the performance or fairness of an operational, real-time early-warning intervention; we report them, including the mitigation failure, as evidence that dropout-prediction systems audited only for aggregate accuracy, without a properly validated regional fairness assessment, risk under-serving or unevenly burdening students in specific regions, working against rather than for the aims of SDG 4. Full article

Indoor biophilic design may support student well-being and learning, yet evidence relevant to higher education interior environments has not undergone systematic synthesis with formal quality appraisal. This systematic mixed-methods review followed the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) 2020 and searched Scopus and the Web of Science Core Collection. We screened 1601 unique database records and 59 supplementary records and included 63 empirical studies. We appraised study quality using Mixed Methods Appraisal Tool (MMAT) 2018 and integrated findings through a convergent integrated approach. The evidence base was recent but uneven, with interior design elements forming the largest category; ambient conditions were largely decontextualized, and spatial attributes were dominated by window views. Perceptual outcomes were assessed most often and had the highest positive rate. Physiological and cognitive findings were less consistently positive, while cognition contained the most negative findings. Most studies adopted a restorative paradigm, whereas a protective paradigm appeared in 25% of studies but in two-thirds of those assessing cognition. Current evidence supports cautious use of indoor plants, green walls, and window views of nature in classrooms for perceptual and short-term affective benefits. It does not yet support transferable claims about sustained cognition, non-classroom settings, ambient conditions, or spatial attributes beyond window views. Full article

Mariculture is expected to support sustainable food systems and coastal economies, yet its environmental performance varies markedly among production systems. Life-cycle assessment (LCA) links farm operations with upstream and downstream processes, but inconsistent functional units, system boundaries, allocation rules, impact categories, and ecological-process accounting limit cross-study comparison. This study presents a narrative synthesis of 83 studies identified through a systematic search of the Web of Science Core Collection and retained after structured relevance screening and full-text verification. The main findings are as follows: Fed finfish and shrimp systems were primarily influenced by feed supply chains, farm energy use, survival, and nutrient emissions. Bivalve and seaweed systems shifted attention toward hatcheries, infrastructure, vessels, yield, post-harvest handling, and carbon or nutrient accounting. Integrated systems and emerging technologies showed mitigation potential, but their benefits depended on uptake efficiency, electricity mixes, infrastructure lifetimes, and allocation choices. Overall, the evidence supports a transition from isolated product footprints toward transparent, regionalized, and prospective assessments. Future research should prioritize inventory transparency, regionalized impact assessment, nutrition-sensitive functional units, separate reporting of ecosystem services and conventional environmental burdens, and explicit boundaries for environmental claims. Full article

Rapid urban population expansion in semi-arid environments poses considerable challenges for sustainable planning, particularly where demographic growth occurs under environmental constraints. This study examines the statistical association between climatic variability and population change in the Soran district center, Kurdistan Region of Iraq, and develops an exploratory framework for forecasting future demographic trends. It uses the official annual population series compiled by the Statistics Directorate of the Soran Independent Administration for 2010–2024 (15 observations) together with monthly meteorological records for 2015–2024 from the Soran agro-meteorological station. Two properties of the demographic record govern what can be inferred from it. The 2010–2023 values are inter-censal estimates reproduced to within 0.10% by a single declining-growth rule, so goodness-of-fit statistics obtained on that segment measure agreement with an interpolation rule rather than forecasting skill; and the 2024 value derives from the 2024 census round, representing a level shift of +18.4% against a 2.0% per year trend—178 residual standard deviations—rather than a year of growth. Detrended, lagged Spearman correlations across twelve climate–lag combinations yielded no association surviving Holm–Bonferroni correction; because the pre-2024 values are administratively smoothed, this is reported as an inability of the available data to resolve a climate–demography association rather than as evidence of independence. Eight specifications—naïve, drift and linear-trend benchmarks, a second-order polynomial, a log-linear trend, an AIC-selected ARIMA, and two multilayer perceptrons—were compared under identical rolling-origin cross-validation, producing seven one-step-ahead forecasts each. On the inter-censal segment a random walk with drift attained a mean absolute percentage error of 0.087%, better than every fitted specification; on the census fold every specification erred by at least 11,663 persons. The reported 2025–2030 projection is therefore anchored on the 2024 census level with growth extrapolated from its 2011–2023 trend, giving 93,342 residents in 2025 and 101,691 in 2030 (scenario range 100,395–104,064), reproduced to within 488 persons by an ARIMA(0,2,2) with a 2024 step term. Polynomial and log-linear trends fitted across the discontinuity project 2025 values are below the observed 2024 population and are reported as specification failures. The study demonstrates both the potential and the limitations of demographic forecasting in data-scarce semi-arid urban settings and shows that the provenance of an administrative population series materially constrains the claims such a study can make. Full article

Emerging-market exporters encounter global sustainability governance not as a treaty or a target but as a product rule: an energy performance threshold, a restricted-substance limit, and a recyclability declaration. Those rules are published, and publication is not the same as access. A firm with a regulatory affairs department can establish which version of a measure is in force, which products it covers and what evidence the destination market will accept; the small- and medium-sized enterprises that dominate emerging-market export bases usually cannot, so rules written to raise environmental standards can exclude the firms least equipped to read them. This paper asks whether emerging digital technologies can convert the transparency infrastructure of the trading system into sustainable business capability. We develop GRACE, a WTO-informed decision-support system that resolves regulatory versions and timelines before interpretation begins, ranks evidence by the authority of its source, declines to state any obligation that no official passage supports, and escalates to human experts when the record is incomplete. Evaluation covers 214 held-out notification families under family-level splits and five seeds, against direct prompting, generic retrieval-augmented generation, a domain-adapted single agent with the same retriever and supervision, an always-on version of the same agent set, a hierarchical-audit baseline and a frozen frontier model, with 72 cases scored blind by trade-law assessors. Against the domain-adapted single agent, it raises citation support from 87.4% to 92.6%, a 5.2-point gain (95% bootstrap CI for the difference [3.6, 6.9]), lifts requirement-action coverage from 82.9% to 88.4%, reduces unsupported claims by 45.3%, and matches the always-on pipeline at 36.7% fewer tokens; blind expert scores are 4.16 of 5 against 3.60. Within the evaluated stack, interpretive capacity, not information supply, is what converts environmental regulation into sustainable business practice. Full article

This narrative review synthesizes and evaluates the current scientific evidence on Bactris guineensis (L.) H.E. Moore, an underutilized tropical palm native to the Caribbean region and Central America. This review integrates current evidence on the nutritional composition, phytochemical profile, biological activities, and technological properties of B. guineensis to identify current knowledge, technological advances, and remaining research gaps. The available literature indicates that B. guineensis contains a bioactive matrix rich in thermally stable cyanidin-3-rutinoside, oligomeric proanthocyanidins, carotenoids, α-tocopherol, and phenolic acids. Optimized extracts exhibit notable antioxidant capacity (e.g., ORAC: 6690–14,688 µmol Trolox equivalents/100 g fresh weight (FW)), alongside preliminary in vitro evidence of selective cytotoxicity against cancer cell lines and antiviral activity. Technologically, the reported thermal and storage stability of its anthocyanins supports further investigation of their use in functional beverages, fermented dairy matrices, and cosmetic formulations. Furthermore, the review outlines theoretical, hypothesis-generating perspectives for advanced delivery systems and circular bioeconomy strategies, explicitly distinguishing these conceptual frameworks from experimentally validated data. Despite these significant findings, current evidence remains largely restricted to in vitro and pilot-scale studies. Major gaps persist regarding gastrointestinal bioaccessibility, pharmacokinetics, systematic toxicology, and agronomic standardization. Addressing these limitations through rigorous in vivo validation will be essential to substantiate health claims and enable the sustainable industrial exploitation of this tropical bioresource. Full article

Super-liquid-repellent materials hold significant promise for minimizing food residue, suppressing interfacial fouling, and enhancing the cleanability of food-contact surfaces. However, prevailing evaluation paradigms—centered on static contact angle, roll-off angle, and dry abrasion—fail to forecast long-term service performance in complex food-processing environments. Unlike idealized probes, real food matrices comprise proteins, polysaccharides, lipids, surfactants, and microbiota, driving interfacial behavior that is time-dependent, multicomponent-coupled, and dynamically evolving. Consequently, traditional static metrics are inadequate across temporal, chemical, and mechanical dimensions. Furthermore, while “fluorine-free,” “edible,” or “bio-based” labels offer design cues, they cannot substitute for rigorous, lifecycle-resolved assessments encompassing fabrication, aging, migration, and end-of-life impacts. This review delineates the fundamental mismatch between current evaluation frameworks and operational food environments, exposing latent safety and sustainability risks obscured by superficial green claims. We subsequently discuss a dynamic evaluation framework featuring multidimensional metrics and a tiered screening workflow, shifting the paradigm from endpoint-focused assessment to a process-based evidentiary chain. Finally, we outline future trajectories, emphasizing the transition from passive repellency to active fouling modulation and the co-design of performance, safety, and sustainability. This work provides a conceptual blueprint for updating evaluation standards and accelerating the industrial translation of food-contact super-liquid-repellent materials. Full article

This study investigates a sustainable vehicle-routing problem in which a heterogeneous fleet serves geographically dispersed customer demands from a central distribution facility. The problem simultaneously minimizes transportation costs and CO₂ emissions, with deliveries performed by either in-house or externally rented vehicles. A bi-objective mixed-integer programming (MIP) model is formulated, and two lexicographic anchor solutions are generated using opposite objective-priority orderings. A tailored Problem Space Search (PSS) metaheuristic is evaluated on five application-informed simulated datasets containing 10–50 nodes. Six parameter configurations combining m ∈ {10, 20} and β ∈ {0.15, 0.20, 0.25} are evaluated using 30 random seeds. For cases where CPLEX certifies primary-objective optimality, the mean PSS deviation ranges from 0.00% to 7.81%, while the best PSS run remains within 3.20% of the optimum in every case. On the 50-node instance, each PSS run improves the time-limited CPLEX primary incumbent under both priority orderings, although unresolved CPLEX gaps preclude near-optimality claims. PSS also improves the embedded heuristic in most cases, while increasing m generally improves solution quality at additional computational cost. The results demonstrate the computational effectiveness of PSS for the sustainable fleet-assignment and routing instances examined. Full article

The present systematic review set out to reassess whether claims of energy-efficient deep learning in transportation infrastructure are supported by direct sustainability evidence. Deep learning models run in transportation systems on edge devices with a limited energy budget, and the literature labels them “green” or “energy-efficient”; the share of studies that support the label with measurement had not been quantified. Following PRISMA 2020, the Scopus, IEEE Xplore, and Web of Science databases were searched for the period from January 2020 to June 2026. Included were 721 studies applying Green AI techniques: pruning, quantization, knowledge distillation, lightweight architectures, TinyML, and dedicated accelerators. The review covers the transport domains of roads and ADAS, railway, connected and autonomous vehicles, and sensor networks. Studies were classified by the strongest efficiency evidence they report: a direct sustainability metric (energy, power, power efficiency, battery life, CO₂) or computational proxies. A direct metric is reported by 58 studies (8.0%); the share is a lower-bound estimate. Full-text verification of a stratified random sample of 34 Tier 2 studies found one study with a direct metric not stated in its abstract (2.9%); the sample-adjusted estimate of the share is 10.7% (95% confidence interval 8.5 to 21.8%). The evidence levels differ: 48 studies (6.7% of the corpus) report power or energy measured on the target hardware, two derive battery life from a measured energy budget, seven report modelled or simulated values, and one a macro-level CO₂ estimate. Railway contributes three studies. The largest measured reduction in energy per inference is 1961.8-fold (0.005 J on an FPGA against 9.77 J on a 95 W CPU); the largest modelled factor in the corpus is approximately 2400-fold (a memristor accelerator against an embedded GPU). Measured and modelled values are distinguished throughout the text. The studies that measure show that rigorous reporting is feasible; from the evidence presented follows the recommendation that an efficiency claim in transportation AI be supported by a direct metric measured or explicitly modelled on a named target platform. Full article

Sustainability transitions depend on public willingness to carry the costs they impose, yet recognition of climate risk does not necessarily extend to support for measures that place those costs directly on households. This exploratory cross-sectional study examines that distinction among urban respondents in Adana, Mersin, and Osmaniye in Türkiye’s Mediterranean Region. The analytic dataset comprised 214 adult urban respondents. Because no sampling frame or respondent-level inclusion probabilities are available, the achieved sample is treated as an unweighted non-probability sample and no claim of population representativeness is made. Principal-axis exploratory factor analysis with oblimin rotation and parallel analysis identified three dimensions: personal climate–environmental risk, support for cost-bearing environmental action, and an exploratory environmental concern. An in-sample three-factor confirmatory sensitivity model showed acceptable fit (comparative fit index (CFI) = 0.970, Tucker–Lewis index (TLI) = 0.958, root-mean-square error of approximation (RMSEA) = 0.057, standardized root-mean-square residual (SRMR) = 0.057). Personal risk (mean (M) = 8.70) and environmental concern (M = 8.62) were much higher than cost-bearing support (M = 4.27). The within-person risk–cost support gap was 4.43 points (95% confidence interval (CI) 3.99–4.87; d_z = 1.36). Risk and concern were not significantly correlated with cost-bearing support, and their addition to a heteroskedasticity-robust (HC3) adjusted model increased explained variance by only 1.2% (p = 0.210). Provincial differences were substantial: Mersin had the widest gap, whereas Osmaniye had the highest cost-bearing support. The results do not estimate public opinion for the provinces; rather, they show that climate-policy legitimacy cannot be inferred from high risk perception alone. Policy-specific research should test fairness, effectiveness, trust, revenue use, and distributive safeguards in representative samples. For sustainability governance the implication is direct: the social pillar of sustainability—perceived fairness, affordability, and distributive protection—has to be designed into climate and environmental measures rather than assumed to follow from environmental awareness. Full article