Search Results (4,584)

Severity factors are widely used to compress torrefaction temperature–time history into a single scalar descriptor. However, whether such scalar representations are structurally sufficient to describe realised conversion across heterogeneous biomass samples remains unclear. In this study, we evaluated the adequacy of single-scalar severity descriptors using a literature-derived dry torrefaction dataset comprising 154 observations from 7 published studies, covering multiple biomass categories and operating conditions. A severity factor, SF(α), was formulated, and its scaling parameter α was optimised through a systematic α-sweep to maximise its relationship with the experimentally determined extent of conversion (EOC). Based on the optimised formulation, EOC was decomposed into severity-implied conversion (SIC) and conversion offset (CO), separating the dominant severity-controlled trajectory from sample-specific deviations. The optimised formulation (α* = 65.1) showed a strong global correlation with EOC (R2 = 0.8593), confirming that severity captures the main average conversion trend. However, nested model comparisons showed that including CO consistently improved explanatory power for both absolute fuel properties and enhancement ratios, with the greatest gains in enhancement space. SIC and CO accounted for 85.9% and 14.1% of the total variance, respectively, indicating that a non-negligible component of conversion variability was not captured by the single severity descriptor. These results show that, although a single severity scalar is useful for describing dataset-level trends, it does not fully resolve sample-level torrefaction behaviour within the analysed dataset. The SIC–CO framework is therefore proposed not as a new severity index or a pre-measurement predictive model, but as a post hoc diagnostic framework for identifying the explanatory limits of scalar severity representations in biomass torrefaction analysis. Full article

Airports are strategic targets for wastewater-based epidemiology because they concentrate highly mobile populations and may provide early signals of pathogen circulation. However, metagenomic investigations of airport wastewater remain limited, particularly in South America. Here, we present one of the first hybrid-capture target-enriched metagenomic investigations of airport wastewater in Brazil, integrating the detection of human-associated viruses and bacteriophage-derived host signatures to evaluate airports as sentinel surveillance sites. Seven untreated wastewater samples collected from a major Brazilian airport between December 2021 and March 2023 were concentrated, subjected to nucleic acid extraction, and analyzed using hybrid-capture target-enriched next-generation sequencing. Taxonomic analysis identified 615 viral and bacteriophage-associated taxa, including 440 viruses and 175 bacteriophages. Among the viral fraction, 21 human-associated viral taxa representing eight viral families were selected for detailed analysis. Norovirus GII was detected in all samples, while Mamastrovirus 1 and JC polyomavirus were detected in six of seven samples. SARS-CoV-2 and dengue virus type 1 were simultaneously detected in the March, 2023 sample. The bacteriophage fraction comprised 47 host-associated phage groups, with Streptococcus-associated phages predominating across samples. These findings demonstrate that airport wastewater can capture diverse human viral and bacteriophage-derived signatures associated with population mobility, supporting its application in environmental genomic surveillance and early-warning systems for emerging and circulating pathogens. Full article

There is no proven therapy for Long COVID, a post-acute condition characterized by persistent symptoms following SARS-CoV-2 infection. Extracellular vesicles (EVs) are emerging as mediators of disease pathogenesis through their molecular cargo. We investigated whether EV glycosylation is altered in Long COVID plasma and whether these vesicles can be selectively targeted using a glycan-binding affinity resin. Large (100–500 nm) and small (40–200 nm) EVs were isolated from post-acute COVID-19 plasma and analyzed by nanoparticle flow cytometry to assess surface glycosylation. Small EV capture assays were performed using Galanthus nivalis agglutinin (GNA) affinity resin. Plasma miRNA profiles before and after GNA treatment were evaluated using NanoString nCounter analysis, and potential downstream pathway effects were computationally inferred using validated miRNA–mRNA interactions and PROGENy. Mannose-positive large EVs were significantly increased in Long COVID compared to recovered controls (p < 0.05). GNA-mediated small EV capture correlated with mannose-positive EV abundance (r = 0.341, p < 0.05), and seven miRNAs were significantly reduced following treatment. Computational pathway analysis suggested modulation of key signaling pathways, including JAK-STAT, Estrogen, VEGF, and PI3K. These findings suggest a glycan-associated EV signature in Long COVID and support further investigation of lectin-based capture as a potential strategy to target vesicle-associated molecular cargo. Full article

Contemporary buildings and urban spaces are increasingly expected to support psychological well-being—a quality often termed “restorativeness.” Conventional approaches to quantifying restorativeness rely on subjective surveys or coarse green metrics, failing to capture how specific building morphologies and street-level visual configurations shape restorative experiences, particularly for stress-prone groups such as young adults. This study develops a deep-learning-driven framework linking building visual elements to youth-specific perceived restorativeness, using Changsha, China, as a testbed. The framework comprises three AI-powered modules: the TrueSkill algorithm trains a deep learning model to predict six dimensions of youth perception (e.g., beautiful, clean, safe) from pairwise comparisons of street view images; the Mask2Former architecture segments street-level imagery into 18 building and street attributes; and the XGBoost-SHAP pipeline uncovers nonlinear associations and threshold-like patterns between these attributes and the composite Built Environment Restorativeness Index (BERI). Results reveal three key insights: tree coverage shows a sustained positive association without saturation; building density exhibits a weakening association at high levels, suggesting possible saturation; and road proportion follows a bidirectional pattern, shifting from negative to positive beyond a certain range. Spatially, high BERI zones concentrate where ecological assets and diverse building functions co-occur, while youth perception exhibits systematic mismatches (e.g., “beautiful but not clean,” “safe but not lively”), traceable to imbalances in building form, street furniture, and commercial mix. These findings advance AI-assisted evaluation of built environments by shifting from one-dimensional metrics to interpretable, design-relevant diagnostics, offering a replicable evidence base for crafting youth-responsive buildings and streets. Full article

Sustainable CO₂ capture can be achieved using photosynthetic microorganisms such as Arthrospira platensis. This work investigates CO₂ capture and O₂ generation efficiency by employing multiple column-type bubbled photobioreactors with Arthrospira platensis pre-adapted long-term to enhanced CO₂ concentrations. Thirty photobioreactors (10 L each) were placed inside a sealed chamber (2 × 2 × 3 m). Three 12-day experiments under constant illumination (225 μmol/m²·s) and temperature (27 °C) and different CO₂ concentration were conducted at 1.5, 3.0, and 6.0 vol.%. During the experiments, the gas composition within chamber, biomass accumulation, and chemical composition of the culture medium (pH, concentrations of carbonates, bicarbonates, nitrates and phosphates) were monitored. With an increase in CO₂ concentration from 1.5 to 6%, the biomass growth rate increased from 321 to 344 mg/(L·day), while CO₂ capture and O₂ generation efficiency estimated from biomass accumulation changed from 432 to 480 mg/(L·day) and from 371 to 412 mg/(L·day), respectively. Increasing CO₂ concentration effectively suppressed medium alkalinization (pH maintained at 8.75–9.30 at 6.0% CO₂ vs. >9.8 at 1.5% CO₂) and sustained bicarbonate availability. Microscopic analysis confirmed high culture viability (>85% live trichomes) at all studied concentrations. The obtained results can be used for Arthrospira platensis-based CO₂-enhanced biofixation and accumulation of valuable biomass. Full article

Suburban areas increasingly shape contemporary urbanisation, yet public-space dynamics in these environments are weakly represented by conventional urban indicators. This study examines suburban public-space use as a behavioural dimension of urban metabolism, understood here as the observable patterns of human movement, activity, and co-presence occurring within suburban public spaces. It addresses the limited ability of density- or infrastructure-based measures to capture everyday spatial practices in dispersed, car-oriented settings. While urban metabolism research has expanded beyond material and energy flows, empirical evidence linking configurational accessibility with directly observed public-space behaviour in suburban contexts remains limited. To address this gap, we integrate district-scale space syntax analysis with site-scale UAV-based observation across five public spaces in and around Gdańsk, Poland. Based on a dataset comprising 30 standard observation sessions conducted in September and October 2024, spatial syntax indicators (integration and choice) were used to characterise configurational accessibility and support location selection, while UAV monitoring captured traffic intensity, stationary presence, diversity of activities, and temporal rhythms of use. The results reveal distinct behavioural metabolic profiles shaped by interactions between spatial configuration, functional programming, and temporal dynamics. These profiles vary depending on the function of public spaces and dominant modes of movement (pedestrian or vehicular). The study demonstrates that suburban urban metabolism cannot be interpreted through configurational accessibility or residential density alone. By linking space syntax measures with a repeatable UAV observation protocol, the proposed framework supports comparative assessment of suburban public-space performance and informs planning interventions aimed at suburban transformation and improved accessibility. Full article

Background/Objectives: Sapropterin dihydrochloride is an established treatment option for individuals with phenylketonuria (PKU) who demonstrate responsiveness, but uncertainty persists regarding dosing frequency, timing relative to meals, the influence of dietary composition, and efficacy of different formulations. Despite widespread use in the UK, real-world administration behaviours have not previously been characterised. This study aimed to characterise sapropterin administration behaviours among people with PKU in the UK. Methods: A 31-item questionnaire was developed and disseminated via the National Society for Phenylketonuria website and social media channels. The survey captured demographic information, dosing schedules, formulation use, administration techniques, co-ingestion with food, and changes in natural protein tolerance following initiation of generic sapropterin. Results: 124 current sapropterin users completed the survey. Most respondents were caregivers of children or adolescents (68.5% aged 0–18 years). Once-daily dosing was most common (66.1%, n = 82), typically administered at breakfast, followed by twice-daily (32.3%, n = 40) and three-times-daily (1.6%, n = 2). Tablets were the predominant formulation (92.7%, n = 115); 50.4% (n = 58/115) swallowed tablets whole, while the remaining (49.6%, n = 57/115) crushed or dissolved them in water or juice. Nine respondents (7.3%, n = 9/124) used powder sachets. Most participants (75%, n = 93/124) took sapropterin with food, with both low-fat (36.6%, n = 34/93) and high-fat (26.9%, n = 24/93) meals reported. Over a third of participants (33.9%, n = 42/124) tolerated a natural protein intake >30 g/day when this was measured, and a further 15.3% (n = 19) were able to maintain a fully unrestricted protein intake without protein substitute supplementation. The magnitude of protein intake improvement was significantly greater among adults (p < 0.001), those with higher baseline natural protein intake (≥30 exchanges/day) (p < 0.001), and individuals who swallowed sapropterin tablets whole (p = 0.038). Although 71.8% (n = 89/124) were pleased with their increased natural protein allowance, many expressed a desire for further improvement. Conclusions: Substantial heterogeneity in dosing schedules, formulation handling, and co-ingestion practices highlights the absence of standardised guidance. These findings emphasise the need for clearer clinical recommendations to optimise treatment effectiveness and support consistent, equitable care. Full article

Digital business models often emerge rapidly and attract users, yet only some become durable over time. This paper develops the concept of business model establishment to capture the process through which a digital business model becomes organizationally robust, transactionally governable, and economically viable beyond initial growth. Combining the business model literature with Transaction Cost Economics, the paper argues that establishment depends on the co-evolution of governance and monetization. Governance matters because scaling increases the need for measurement, adaptation, and safeguarding mechanisms. Monetization matters because these mechanisms require sustained investment supported by stable, diversified, and economically adequate value capture. The paper applies this framework to fintech, a domain in which digital business models face particular demands around transaction frequency, uncertainty, regulation, and trust. Through qualitative case analysis of Revolut, Klarna, Robinhood, and N26, it illustrates four configurations of establishment defined by varying levels of governance and monetization maturity, contributing to the business model literature by distinguishing establishment from innovation, adaptation, and scaling. Full article

Excessive concentrations of carbon dioxide (CO₂) in the atmosphere lead to adverse environmental effects. Biologically assisted processes that rely on organisms such as microalgae (i.e., Chlorella vulgaris) are common in capturing CO₂ from the atmosphere. Microalgae are rich in proteins, vitamins, minerals, and omega-3 fatty acids. Thus, microalgae production serves both health and environmental sectors. Varying light intensity and temperature are shown to influence algae growth. To quantify algae production under different light intensity and temperature conditions, and monitoring or scaling-up of biological reactors, reliable mathematical models are required. In this work, mathematical models that incorporate light intensity and temperature effects on algae growth in batch and continuous bioreactors are developed. Based on the modeling, the growth rate is maximum at T_opt = 25 °C, reaching the value of μ_max = 0.14 day⁻¹. The growth rate exponentially increases until light intensity (I) reaches around 150 ${\displaystyle \frac{\mathsf{\mu }\mathrm{m}\mathrm{o}\mathrm{l}}{{\mathrm{m}}^2\mathrm{s}}}$, which is approximately the optimal light intensity for Chlorella vulgaris. The effect of T on growth rate is found to be more sensitive than light intensity (I) in both batch and continuous reactor systems. When there are too many parameters in models, uncertainties exist and parameter estimation and model predictions become cumbersome. For these reasons analytical solutions to the models are presented in simplified forms and these models are more practical and easier to implement. The novelty of the work is also the presentation of the models in analytical forms. Analytical solutions to the two reactor models (batch and continuous) will help quantify biomass production as a function of time under the varying light intensity and temperature conditions encountered. Full article

Accurate assessment of postural sway is essential for evaluating balance disorders, rehabilitation outcomes, and fall risk. Traditional laboratory-based motion capture systems provide precise center-of-pressure (CoP) measurements, but are expensive, non-portable, and impractical for widespread clinical use. This study describes the development and testing (reliability and validity) of a portable three-dimensional (3D) camera system (Intel RealSense D415) for quantifying sway and balance. Test–retest reliability was evaluated in healthy adults (n = 10; 6 males, 4 females; mean age 22.3 ± 1.6 years), yielding intraclass correlation coefficients ICC = 0.84–0.86 (95% CI: 0.61–0.95). Concurrent validity, established against a laboratory-based optical motion capture system (Optotrak), demonstrated strong correlations with a mean absolute percentage error of 10.5% relative to Optotrak-derived path length measurements and high levels of agreement. Operating at 30 Hz with end-to-end latency of <40 ms, the RealSense-based system provides a reliable, valid, and portable alternative to lab-based systems. Low-cost markerless motion capture systems based on standard RGB cameras have been validated for postural risk assessment, showing good consistency with gold-standard Vicon systems. These preliminary findings suggest that the system shows promise as a low-cost alternative; however, further validation in clinical populations is required before clinical deployment. Full article

Baseline information on terrestrial wildlife communities and their activity patterns is essential for protected-area management, but such information remains limited for Hunan Zhangjiajie Giant Salamander National Nature Reserve, where conservation attention has historically focused on the Chinese giant salamander and associated aquatic ecosystems. From March 2024 to August 2025, we conducted a camera-trap survey in broad-leaved and coniferous forest habitats of the reserve to document terrestrial mammals and ground-dwelling birds, evaluate taxonomic completeness, and describe diel and seasonal activity patterns. Across 43 camera-trap stations and 16,314 effective camera-trap days, we recorded 59 wildlife species, including 18 mammals and 41 ground-dwelling birds. The assemblage included nationally protected, threatened, and Chinese endemic species, indicating that the reserve’s forest habitats support important terrestrial biodiversity in addition to its aquatic conservation target. Taxonomic completeness curves suggested that the current survey captured most camera-detectable mammal and ground-dwelling bird taxa under the present sampling design, although the results should not be interpreted as a complete inventory of the reserve’s total vertebrate diversity. Annual diel activity analysis of 11 focal species showed clear temporal differentiation among ecological groups: small and medium-sized carnivores were mainly nocturnal, ground-dwelling birds, and red-hipped squirrel were primarily diurnal, and ungulates showed mixed or crepuscular-to-nocturnal tendencies. Seasonal analyses based on bioclimatic periods showed interspecific differences in activity-density distributions between the cool-dry and warm-wet seasons. However, peak-shift reliability analysis indicated that most focal species retained broadly similar main activity peaks across seasons; masked palm civet was the only species showing reliable seasonal displacement of its main activity peak. Pairwise temporal overlap analyses described temporal co-occurrence patterns among selected sympatric species but should not be interpreted as evidence of direct interaction or niche differentiation. Overall, this study provides baseline data on camera-detected terrestrial vertebrates in the reserve and supports long-term monitoring, forest habitat management, and disturbance control for terrestrial mammals and ground-dwelling birds. Full article

Forest carbon assessment requires methods that capture the combined effects of stand structure, site conditions, carbon pools, operational emissions, and circular-economy processes. This study aimed to develop and optimize hybrid machine learning models for predicting the gross CO₂e (carbon dioxide equivalent) balance of Polish forest stands using measurable stand- and site-related variables. The research was based on a primary dataset describing forest management in major Polish macroregions in 2020–2024. After data cleaning and preprocessing, multiple machine learning algorithms, including ensemble, boosting, neural, and hybrid models, were trained, validated, and tested. Model performance was assessed using standard regression metrics, overfitting diagnostics, learning curves, and SHAP (Shapley Additive Explanations). Most models achieved high predictive accuracy, with six of ten algorithms reaching R² values above 0.90 on the test set. The reduction in strongly correlated variables helped limit multicollinearity and excessive overlap between predictors and the target variable, supporting a more reliable interpretation of model performance. The CatBoost algorithm achieved the highest predictive performance on the test set (R² = 0.948), while also recording the lowest root mean squared error (RMSE = 152.242). However, the Decision Tree demonstrated the weakest generalization performance (R² = 0.806) on the test set. SHAP analysis identified tree height as the most influential predictor, followed by tree age, number of trees, species composition, and selected habitat features. The novelty of the study lies in integrating hybrid machine learning, interpretable modelling, and circular-economy-related carbon balance components into a single framework for rapid and operational forest carbon assessment in Polish forest stands. Full article

Biomass valorization plays a vital role in achieving carbon neutrality and circular economy frameworks. Owing to its carbon-rich structure, biomass represents a promising feedstock to produce bio-based hydrocarbons via biological and thermochemical pathways. While biological conversion routes have been extensively studied, their deployment at commercial scale is constrained by high capital costs and low product yields. In contrast, thermochemical conversion technologies are increasingly being explored as viable large-scale biomass valorization routes. This review presents a comprehensive assessment of thermochemical pathways, with particular emphasis on hydrothermal liquefaction (HTL). The review identifies hydrothermal liquefaction (HTL) as a strategically advantageous route for wet and heterogeneous biomass valorization, due to simultaneous yields of liquid biocrude, and solid hydrochar. The review emphasizes the application of biocrude upgradation processes like hydrodeoxygenation under biphasic solvent systems using sulfided NiMo and CoMo catalysts. Further, the review also establishes hydrochar as a tunable functional material rather than a mere byproduct for applications in fields of energy production, soil amendment, and heterogeneous catalysis. The review article examines technology readiness levels of different biomass valorization techniques, and suggests that while combustion, anaerobic digestion, torrefaction, and transesterification are commercially mature, HTL and carbon capture utilization and storage (CCUS)-integrated fuel synthesis pathways remain at intermediate readiness. Additionally, the review carries out an in-depth study on artificial intelligence and machine learning (AI and ML) applications in biomass valorization, where it observes that Tree-based ensemble models, particularly Random Forest and XGBoost, show strong performance for several HTL prediction tasks, while Gaussian Process Regression and neural network–Bayesian optimization approaches provide additional advantages for uncertainty estimation and process-level optimization. Finally, the future research opportunities in biomass valorization and AI/ML application in HTL-process optimization have been identified for improving the bio-based fuel production techniques. Full article

As climate change intensifies, transitioning the construction sector away from fossil fuels is vital to reducing global greenhouse gas emissions and localized urban pollution. This paper assesses the economic feasibility of electrifying construction machinery by developing an Annualized Cost of Ownership framework that incorporates mobile charging solutions, internalizes environmental and public health operational externalities (CO₂, PM_2.5, NO_x, and SO₂), and relies on Monte Carlo simulation with Cholesky decomposition to capture the interdependencies among cost drivers. We analyze twenty distinct models of excavators and wheel loaders—the two largest contributors to construction-machinery emissions—comprising functionally equivalent diesel and battery-electric variants. Our results show that several compact electric models are already cost-competitive even without internalizing environmental and public health operational externalities. When these are accounted for, the economic advantage of electric machinery increases, particularly in denser urban areas where local air pollution damages are severe. While projected battery cost reductions further lower electric ownership costs, the magnitude of this effect is modest. However, the weak penetration of electric construction equipment in the US underscores that targeted policy interventions—such as point-of-sale rebates, green procurement mandates, tax credits, charging infrastructure subsidies, or the creation of low-emission zones and noise ordinances that advantage electric construction machinery—are needed to accelerate market adoption. These measures are particularly critical in densely populated urban areas, where internalizing local air pollution and public health externalities significantly amplifies the economic value of zero-emission machinery. Full article

This study investigated balance control during the half squat by analyzing the relationship between the center of mass (CoM) and the center of pressure (CoP) in five experienced male weightlifters performing segmented squats at five load levels (20–80% 1 RM) across four Power-Based Training (PBT) exercises. The area of the 95% confidence ellipse was quantified using the Vicon motion capture system in conjunction with AMTI force plates. Given the small sample size (n = 5), a dual inference approach was implemented—frequentist repeated-measures analysis of variance (ANOVA) complemented by a unified adaptive Bayesian hierarchical model—to mitigate Type II error in low-power scenarios. Regarding the movement phase, a marked effect on center of pressure (CoP) stability was observed, as evidenced by both statistical approaches (frequentist: F(1.65, 6.59) = 19.44, p = 0.002, ηp² = 0.829; Bayesian: P(β_phase < 0) > 0.999). Although external load did not reach statistical significance in the frequentist analysis (p = 0.177, achieved power = 0.27), the Bayesian model provided moderate evidence of a positive impact (β_load = 0.059, 95% HDI [0.005, 0.115], p = 0.981). The area of the center of mass (CoM) ellipse showed no effects of interest. Limb asymmetries were significant and consistent throughout the experiment (frequentist: 48.01 ± 30.13%; Bayesian: 69.48%, 95% HDI [55.86%, 81.44%], P(AI > 20%) = 1.000) and were not modulated by the experimental condition. CoP-CoM coupling was stronger in the mediolateral direction than in the anteroposterior direction. The findings reveal that phase is the primary factor in postural stability, exerting a modest positive influence discernible only through low-powered probabilistic inference, and that the dual framework strengthens inferential robustness in small-sample biomechanical studies. Confirmatory studies with larger samples are recommended. Full article