Search Results (35,181)

The food packaging sector widely relies on polymeric materials, and as sustainability concerns grow, commodity polymers need to be replaced with innovative and more sustainable materials. Thermoplastic polyurethane (TPU) is a versatile elastomeric polymer characterized by flexibility, strength, chemical and abrasion resistance, and biocompatibility. However, it presents some limitations, notably in terms of functional properties (such as barrier properties). The use of nano-sized renewable fillers, such as cellulose nanocrystals (CNCs), may improve these properties, extending the applicability range of TPU. In this work, bio-based TPU nanocomposites were obtained by adding commercial silane-modified cellulose nanocrystals (Si−O−CNC) at different contents (1–5 wt.%). The nanocomposites were produced via melt mixing followed by compression molding and were characterized in terms of their chemical (FTIR), morphological, thermal, mechanical, rheological, wettability, and barrier properties (i.e., water vapor permeability, WVP and oxygen transmission rate, OTR). The presence of Si−O−CNC promoted hydrogen-bonding interactions with the TPU matrix, affecting the microphase separation and organization of the hard segments. These microstructural changes improved thermal stability, reduced WVP and OTR, and increased tensile properties at lower nanofiller contents (1–3 wt.%). At higher contents, partial nanofiller aggregation was observed, leading to a reduction in mechanical performance. Overall, these results suggest that TPU/Si−O−CNC nanocomposites have promising potential as sustainable food packaging materials. Full article

The HEROn magazine was created as an innovation project by the S-TEAM team for the 2024/2025 SUBMERGED season of the FIRST^® LEGO^® League Challenge category. The primary aim of the HEROn project was to implement game-based learning methods to enhance environmental awareness, particularly concerning the protection of our water resources. This initiative is designed to engage individuals from ages 9 to 99 in a creative and enjoyable manner. At the core of the HEROn project is a well-known game that challenges players to find the differences between two photos. This activity not only provides entertainment but also educates participants about the importance of protecting and preserving the aquatic environment. By discovering subtle differences between images, players become more attuned to environmental issues, which promotes a deeper understanding and appreciation of water conservation. The chapters of the HEROn magazine are thoughtfully organised into themes, each focusing on various aspects of water’s importance, its protection, and sustainable usage. Additionally, a random sample of participants was surveyed to gather opinions and feedback on HEROn magazine as part of the project and this research. This feedback is invaluable for assessing the magazine’s impact and for improving future editions to better serve the goals of raising environmental consciousness. The online HEROn questionnaire consisted of 10 items and employed a 5-point Likert scale for responses. Data were collected over a three-month period (28 January–28 April 2025), with 630 Hungarian respondents participating in the survey. The HEROn magazine was generally well received, with mean scores ranging from 4.2 to 4.6. Age-group differences were examined using nonparametric Kruskal–Wallis tests, with Dunn–Bonferroni post hoc comparisons. These analyses show statistically significant differences between adults (30–89) and the younger cohorts for aggregated awareness, design/engagement, and branding measures, while teenagers (9–15) and young adults (16–29) did not differ significantly from each other. The Find-the-Difference game showed the greatest variability across groups, with young adults giving the lowest mean. Full article

In aerial image small-object detection, complex imaging perspectives, arbitrary object orientations, and long-tailed category distributions jointly exacerbate sample imbalance, which significantly degrades detection stability and leads to frequent misclassification of minority categories. To address these challenges, this paper proposes a novel training framework termed SCUD. Specifically, in the label noise suppression strategy (LNSS), a contrastive learning mechanism based on semantic consistency is introduced to constrain the aggregation of similar samples in the feature space, thereby reducing the adverse impact of noisy samples on model optimization. In addition, a scale-aware resampling strategy (SARS) is designed to alleviate noise amplification and overfitting caused by excessive repetition of small objects during training. Furthermore, an adaptive instance selection mechanism (AISM) is developed by jointly modeling prediction uncertainty and global statistical priors, enabling the model to dynamically emphasize learning from informative samples. Extensive experiments are conducted on two publicly available unmanned aerial vehicle (UAV) aerial image datasets to validate the effectiveness of the proposed approach. The proposed method achieves an mAP50 of 70.7% on the DOTA-v1.0 dataset and 88.1% on the DIOR dataset. Notably, the detection accuracy of several rare categories is significantly improved, further demonstrating the effectiveness of the proposed method in addressing sample imbalance in aerial image small-object detection. Full article

We develop a stochastic Stackelberg model of a subscription market with cancellation costs. A representative consumer chooses when to subscribe to and cancel a service as the utility derived from the subscription evolves according to a diffusion process, while the firm selects the subscription fee and cancellation cost to maximize its expected payoff. The consumer’s problem is equivalent to the classical real-options model of entry and exit under uncertainty with adjustment costs and exhibits a two-threshold policy with an inaction band and hysteresis. Unlike the standard formulation, in which the optimal thresholds are characterized implicitly through a system of nonlinear equations, we derive an explicit parametric solution in closed form. This solution reduces the firm’s optimization problem to a two-dimensional unconstrained problem and yields a detailed characterization of the optimal pricing policy. We show that the firm’s strategy exhibits three qualitatively distinct regimes depending on the initial utility level. For small utility levels, the optimal cancellation cost is zero. In an intermediate regime, the firm’s optimal policy induces the consumer to set the entry threshold equal to the initial utility level, resulting in immediate subscription. For sufficiently large utility levels, the firm induces permanent lock-in by setting a high cancellation cost and a low subscription fee: the consumer subscribes immediately and never subsequently unsubscribes. The transition between the latter two regimes is discontinuous and results from competition between two local maxima of the firm’s payoff function. We then extend the model to a heterogeneous population of consumers. The superposition of individual two-threshold subscription strategies generates a Preisach hysteresis operator describing the aggregate dependence of the firm’s revenue on the utility dynamics. The discontinuous regime transition persists under heterogeneity, demonstrating the robustness of the underlying mechanism. The Preisach representation predicts complex history dependence and long-term effects of temporary utility shocks. For a gamma distribution of consumer preferences, the firm’s expected payoff is obtained in closed form in terms of incomplete gamma functions. Full article

Cemented sand and gravel (CSG) dams have been widely applied due to their simple construction and use of local materials. With the increasing occurrence of extreme weather events, temperature has become an important factor affecting the safe operation of dams. To investigate the temperature stress response of CSG dams under low-temperature conditions and achieve cross-scale analysis, an adaptive macro–meso finite element method is proposed. Through an iterative “solution–evaluation–mesh adjustment” procedure, meso-scale modeling is performed in high-stress regions, and the results are compared with those obtained using the conventional submodeling method. The results show that, under low-temperature conditions, temperature gradients and thermal stresses are mainly concentrated near the dam surface, with limited influence on the interior, while hydraulic load remains the dominant controlling factor. The local stress distribution patterns obtained by the two methods are generally consistent, and both can reflect stress concentration near the aggregate–mortar interfaces. The proposed method can characterize local meso-scale responses within a global computational framework, providing a reference for cross-scale analysis of the temperature response and the identification of local unfavorable stress regions in CSG dams. Full article

To explore the feasibility of using recycled glass pumice (microcellular glass pumice aggregate, MGPA) as a substitute for traditional lightweight aggregates and to compare its mechanical performance with that of expanded clay ceramsite, this study systematically investigated the effects of water–cement ratio (0.40–0.46), cement content (330–360 kg/m³), fine MGPA replacement ratio (0–100%), and coarse MGPA replacement ratio (0–100%) on the dry density and compressive strength of lightweight aggregate concrete through an orthogonal experimental design. The results show that the bulk density of coarse MGPA (312 kg/m³) is only 46% of that of ceramsite (678 kg/m³), while its cylinder compressive strength (3.36 MPa) is slightly lower. The range analysis indicates that the dry density of MGPA concrete is primarily influenced by the replacement ratio of coarse aggregate, followed by fine aggregate replacement, water–cement ratio and cement content; the lowest dry density (1445 kg/m³) was obtained with a water–cement ratio of 0.46, cement content of 330 kg/m³, 100% replacement of coarse MGPA, and partial replacement of fine MGPA (mixture S19). For the 28-day compressive strength, the influencing factors rank as coarse aggregate replacement > water–cement ratio ≈ cement content > fine aggregate replacement. In comparison with the ceramsite concrete reference under the respective mix designs tested in this study, the optimal MGPA concrete exhibited only 4.6% higher dry density but achieved a significantly higher compressive strength of 40.0 MPa, compared with 20.5 MPa for the ceramsite mixture. The specific strength (strength/density ratio) of MGPA concrete is about 1.87 times that of ceramsite concrete. Both types of lightweight aggregate concrete reached 77–80% of their 28-day strength at 7 days. Overall, recycled glass pumice is a promising alternative to ceramsite for lightweight concrete, especially when both high strength and low weight are required for precast components, provided that its long-term durability (particularly ASR resistance) is verified in future studies. Full article

To support the achievement of the carbon peaking and carbon neutrality goals and promote the resource utilization of industrial solid waste, a ternary solid waste composite cementitious material was prepared by blending ground granulated blast-furnace slag (GGBFS), fly ash (FA), and carbide slag (CS) with cement. The optimal mix ratio was determined through single-factor experiments and response surface methodology. The synergistic hydration mechanism was elucidated using microstructural characterization techniques, including XRD, FTIR, TG-DTG, and SEM. The composite material was then applied to a semirigid base course, and its mechanical properties and durability were systematically evaluated. The results indicate that the optimal levels of FA, GGBFS, and CS investigated in the single-factor experiments are 20–40%, 30–50%, and 2–6%, respectively. The optimal mix ratio of the ternary solid waste composite is 21.0% FA, 36.3% GGBFS, and 5.7% CS. The underlying microstructural mechanism is that carbide slag creates a highly alkaline environment, which activates the pozzolanic activity of GGBFS and fly ash, leading to the formation of hydration products dominated by C-(A)-S-H gel. With increasing curing age, the gel structure evolves from a loose and disordered state to a dense and ordered state, ultimately forming a compact microstructure based on a highly polymerized C-(A)-S-H gel matrix. The 7-day unconfined compressive strength of the stabilized gravel using the solid waste-based composite cementitious material reached 5.93 MPa, and the 28-day drying shrinkage coefficient was reduced by 18.3% compared with that of cement-stabilized gravel. After 18 freeze–thaw cycles, the compressive strength increased by 2.4%, with the pore structure characterized by a “macropores decreasing, micropores increasing” refinement pattern. After 18 wetting–drying cycles, the cumulative strength loss was 11.26%, outperforming cement-stabilized gravel. Combined with SEM observations, these performance improvements are attributed to the densely intertwined hydration products, particularly C-S-H gel, which effectively fill the voids between aggregate particles and significantly enhance the volume stability, freeze–thaw resistance, and wetting–drying durability of the stabilized gravel. The application of this cementitious material in a semirigid base course demonstrates excellent mechanical and durability properties, providing a theoretical basis and technical support for the widespread application of industrial solid waste in road engineering. Full article

At 1 km resolution, NDVI projections for heterogeneous landscapes can appear spatially coherent in aggregate while concealing substantial class-level prediction weaknesses, a limitation that has received limited systematic attention in the NDVI projection literature. This study applies a four-component assessment workflow to Northeast China (NEC) for 2040 under SSP1-2.6, SSP2-4.5, and SSP5-8.5, integrating multi-stage model selection, land-cover-stratified validation, quantile-regression-based uncertainty characterization, and validation-priority ranking. Among three candidate tree-based models evaluated using spatial block cross-validation, temporal holdout validation, long-jump extrapolation, and climatic perturbation tests, LightGBM showed the most balanced and consistent performance, with spatial CV R² = 0.654 ± 0.123, temporal holdout R² = 0.710, and long-jump R² = 0.671, and was therefore selected for the 2040 projection. Projected regional mean NDVI increased modestly from 0.393 in 2020 to 0.414–0.417 across scenarios, with limited divergence among SSP pathways at this near-term horizon. Class-stratified validation of the 2020 holdout prediction revealed that global model performance masked strong class-level heterogeneity, with R² values ranging from 0.576 for Construction land to −0.886 for Unused land. Water bodies and Unused land exhibited negative R² values, indicating weak class-level predictive support relative to a simple class-mean benchmark. Residual decomposition showed that Water bodies combined high random error with elevated systematic deviation, whereas Unused land was mainly characterized by systematic bias, suggesting different needs for class-specific model improvement. The Uncertainty Risk Index (URI), derived from 95% prediction intervals, was highest in Construction land and lowest in Cropland across all scenarios. Integrating historical residuals with future URI-identified Water bodies, Unused land, and Construction land as the highest-priority classes for future targeted validation. These priorities arise from both limited class representation and intrinsic NDVI-related complexity, including low vegetation signal, mixed-pixel effects, and heterogeneous land-surface composition. These results demonstrate that land-cover-stratified error decomposition and uncertainty-informed priority ranking reveal class-specific projection limitations that aggregate accuracy metrics can conceal. Full article

Traditional grazing maintains essential ecosystem services, yet this activity is rapidly disappearing across Europe. Understanding the geobotanical features of traditionally grazed areas is critical for predicting biodiversity shifts driven by pastoral decline. This study provides a geobotanical characterisation of traditional sheep farms in north-western Spain. We integrated bioclimatic, phytosociological, and biogeographical approaches with spatial autocorrelation analyses, including global Moran’s I, Local Indicators of Spatial Association (LISA), and join-count tests, to assess spatial patterns in vegetation richness and plant community organisation. The results indicate that 28.22% of the studied farms were located in the Castilian Duero sector, 93.45% within the supramediterranean thermotype, and 75.46% within the subhumid ombrotype. A high diversity of vegetation was recorded, with 111 plant communities identified. These include several priority habitats of community interest within the European Union, notably belonging to the phytosociological classes Molinio-Arrhenatheretea, Festuco-Brometea, and Poetea bulbosae. This spatial approach characterises the vegetation mosaics within a fixed buffer around the holdings, although it does not directly measure actual forage use. As a key scientific novelty, this work provides, for the first time, a macro-regional and quantitatively validated integration that explicitly links broad environmental filters with localized pastoral vegetation mosaics. By providing a statistically robust diagnosis of landscape aggregation and segregation, this geobotanical characterisation serves as a fundamental tool for land managers and shepherds, contributing directly to the conservation and sustainable management of endangered traditional pastoral landscapes under changing environmental conditions. Full article

Direct air capture (DAC) requires carbon capture interfaces that operate under highly dilute CO₂ conditions while minimizing thermal and chemical regeneration penalties. Carbonic anhydrase (CA) accelerates the reversible hydration of CO₂ to bicarbonate and is therefore a strong biocatalytic candidate for low-temperature CO₂ capture, but its implementation depends on candidate support materials that combine wet-state accessibility, chemical reactivity, mechanical processability and compatibility with membrane architectures. This study reports the preparation and screening of N-(3-dimethylaminopropyl)-N′-ethylcarbodiimide hydrochloride/N-hydroxysuccinimide (EDC/NHS)-reactive biopolymer composite membranes for future carbonic anhydrase (CA) immobilization. Chitosan particles were precipitated with citrate or tripolyphosphate under high-shear homogenization and compared after lyophilization or convective drying. Chitosan-, shellac-, agarose- and cellulose-acetate-based films plasticized with glycerol and/or polyethylene glycol 400 (PEG-400) were then evaluated by optical microscopy, dry-state penetrometric puncture testing, qualitative EDC/NHS-reactivity mapping and Fourier-transform infrared spectroscopy (FTIR). Freshly precipitated chitosan particles showed dendrite-like high-surface morphologies, while lyophilization preserved porous flocculated aggregates and convective drying produced denser collapsed structures. Neat chitosan showed the highest dry-state puncture force (2.230 ± 0.173 N), whereas chitosan/shellac (0.377 ± 0.044 N) and agarose/chitosan/PEG-400 (0.386 ± 0.038 N) provided the strongest reactive-composite compromise between dry-state puncture resistance and EDC/NHS compatibility. The EDC/NHS reactivity map identified chitosan- and shellac-containing films as the chemically most relevant supports because they provide amine and/or carboxyl functionality, whereas agarose and cellulose acetate alone were not directly suitable for zero-length amidation. FTIR spectra confirmed polymer-specific functional signatures and EDC/NHS-associated changes in carbonyl, amide and C-O/C-O-C regions, especially in shellac- and chitosan-containing composites. The results identify chitosan/shellac as the lead candidate membrane and agarose/chitosan/PEG-400 as a hydration-rich comparator for subsequent carbonic anhydrase immobilization studies. This work should be interpreted as a first-stage materials-screening study of candidate membranes for enzyme immobilization. Full article

The paper examines the macroeconomic and environmental outcomes associated with green financing across 22 emerging markets and developing economies from 2002 to 2024. Driven by the increased policy focus on climate finance as a two-fold tool of sustainability and development, the analysis assesses whether green financing is an economic growth driver. A multi-model structure is used (fixed effects, non-linear (quadratic), threshold, dynamic (lagged), and first-difference specifications) to achieve strength and eliminate model-specific bias. The findings show that green financing exhibits a weak positive association with economic growth in baseline and regime specifications. Still, this relationship is not robust across dynamic and first-difference models. Moreover, there is no indication of non-linearity or a threshold effect (a Green Laffer Curve). Patterns that indicate a weak positive relationship are cross-sectional and not robust to panel estimation; they are therefore aggregation-biased. Conversely, green financing has a low negative correlation with CO₂ emissions, indicating partial environmental efficiency. The results show that climate finance is limited in scale and inefficiently structured, which limits its macroeconomic impact. In general, the paper concludes that green finance, although environmentally applicable, is not sufficient as it currently stands to spur economic growth in emerging economies. Full article

The development of recycled asphalt mixtures combining reduced production temperatures with adequate mechanical performance remains challenging in circular pavement engineering. This study assessed the performance-based redesign of a half-warm mix asphalt (HWMA) produced at approximately 90 °C with a very high reclaimed asphalt pavement (RAP) content and foamed bitumen, using previously validated cold recycled mixture (CRM) and hot recycled mix asphalt (HRMA) formulations as contextual benchmarks. An initial CRM-derived HWMA was evaluated to assess whether cold-recycling design logic could be transferred to half-warm production without added water or cement. Although the mixture showed satisfactory volumetric and moisture-related responses, wheel tracking identified rutting as the governing limitation. The mixture was redesigned by incorporating coarse steel slag aggregate (SSA) to correct the aggregate size distribution, reducing filler content and adjusting the added foamed bitumen while maintaining RAP and SSA at 98% of the aggregate skeleton. The combined redesign reduced the wheel-tracking slope in air from 1.25 to 0.32 mm/10³ cycles and the proportional rut depth in air from 28.1% to 10.4%. Nevertheless, the redesigned HWMA remained less rut-resistant than both benchmarks, confirming the need for further optimisation. It achieved stiffness close to the HRMA benchmark and a fatigue response compatible with base-layer application, although moisture durability requires further validation. Overall, the study demonstrates the feasibility of a sequential performance-based redesign approach for high-RAP HWMA while highlighting the need for systematic optimisation and field validation before broader implementation. Full article

The application of microalgae to soil has gained attention due to their ability to improve soil fertility and sequester C, but the effects of their application on rainfall-induced runoff, sediment, and associated nutrient losses remain unclear. This study investigated the impacts of Chlorella ZJ application on soil properties, C and N accumulation, and the loss characteristics of C and N via runoff and sediment under simulated rainfall at intensities of 50 and 100 mm h⁻¹. The results showed that applying microalgae significantly increased soil pH and the geometric mean diameter (GMD) of aggregates. It also promoted C and N accumulation, which increased by 11.28–23.79% and 13.42–24.62%, respectively, compared to the control. The contents of dissolved organic carbon, dissolved nitrogen, and nitrate nitrogen (NO₃⁻-N) in the crusted soil decreased significantly due to soil disturbance. Under simulated rainfall, intact microalgae crusts reduced sediment loss but did not increase runoff yield. However, they substantially elevated N loss via runoff, with total nitrogen (TN) concentrations (5.85 to 20.31 mg L⁻¹) exceeding surface water quality standards, indicating a high eutrophication risk. Overall, microalgae fertilizers have the potential to sequester C, enhance soil nutrients, and control soil erosion. However, reasonable management measures need to be implemented to prevent N pollution caused by runoff loss during their application. Full article

Respiratory disease classification using lung sound recordings remains challenging due to signal interference, heterogeneous acquisition conditions, and substantial overlap among clinically related acoustic patterns. This study presents a framework for respiratory disease classification using NMF-enhanced log-mel spectrograms and deep neural classifiers. Respiratory sound recordings from two publicly available datasets were harmonized into a unified label space comprising Asthma, Bronchiectasis, Bronchiolitis, COPD, Healthy, Pneumonia and URTI. Following signal standardization and fixed-length segmentation, a non-negative matrix factorization (NMF)-based enhancement stage was applied to increase the salience of respiratory components prior to log-mel spectrogram generation. The proposed classifier was a convolutional recurrent neural network (CRNN) that combined convolutional feature extraction, bidirectional recurrent modelling, and attention-based temporal aggregation. For comparison, RDLINet, a conventional CNN, ResNet, and a YOLO-style backbone were implemented under the same preprocessing and training framework. Experimental results demonstrated that the proposed CRNN achieved the best overall performance, attaining 96.14 ± 0.50% accuracy and 94.05 ± 1.21% Macro-F1 on the unified seven-class cohort. Class-wise analysis, confusion-matrix evaluation, and output-space visualization further showed that the CRNN provided more balanced recognition across disease categories and clearer class separation than competing architectures. These findings indicate that NMF-enhanced spectro-temporal modelling combined with convolutional recurrent learning offers an effective approach for automated multi-class respiratory disease classification. Full article

The long-term stable and efficient operation of dams is crucial. How to accurately assess overall dam safety behavior from discrete monitoring data is the key to realizing real-time monitoring and diagnosis of dam safety. Existing real-time dam safety evaluation methods do not fully consider the temporal similarity and spatial aggregation among anomalies of multiple types of monitoring points, resulting in an insufficiently comprehensive judgment of the overall safety state. This study integrates spatial clustering, temporal similarity analysis, and a spatial influence degree algorithm based on topological correlations among multiple abnormal groups. It converts discrete abnormal points into continuous influence zones and quantifies their spatiotemporal correlations. A comprehensive evaluation method is then proposed for dam operation safety behavior, with spatiotemporal coupling of multiple monitoring points. This method extends dam safety evaluation from single-point judgment to multi-point spatiotemporal collaborative judgment. A case study on a typical engineering case shows that the method achieves a comprehensive score of 96.43, reasonably constructs continuous influence zones of multi-point anomalies, and yields evaluation conclusions consistent with engineering practice. It also exhibits robustness and moderate sensitivity to structural anomaly evolution, providing a feasible way to extend dam safety evaluation from a single point to the entire space. Full article