Search Results (2,416)

Effective waste sorting is central to circular economy goals and sustainable waste management: it maximizes recycling yields, diverts waste from landfills, and reduces the environmental burden of solid waste disposal. Accurate automated sorting using deep learning can achieve this at scale, yet high-performing classifiers are too computationally demanding for the low-cost embedded hardware used in sorting facilities. We propose the KD-Garbage Framework, which applies knowledge distillation to transfer predictive knowledge from a high-capacity teacher model to a lightweight student model, enabling deployment-ready classifiers that approach or exceed teacher-level accuracy without any added inference cost. We also introduce a 15,681-image garbage dataset organized into 13 classes defined by recycling and disposal pathway, assembled from 12 public sources and original photography, with all labels manually verified. Two teacher models were paired with 16 lightweight convolutional neural network (CNN) student architectures and benchmarked on a Raspberry Pi 5 at a minimum throughput of five frames per second. Knowledge distillation reduced misclassification rates by 10–25% across all student architectures. The best-performing student, RegNetY-1.6GF, achieved a balanced accuracy of 0.9129, surpassing both teacher models while sustaining real-time throughput on the target hardware, demonstrating a practical pathway toward scalable, AI-enabled sustainable waste management. Full article

This study examined how exposure to constructive audiovisual news shapes people’s perception of societal issues in the Netherlands. An online experiment was conducted among 575 participants aged 18 to 90 years old. Participants were randomly assigned to watch an audiovisual news item, either constructive or nonconstructive news, about plastic waste in the ocean or the Dutch housing market. The study investigated how these different reporting styles affected participants’ perceptions of the main message, their awareness of the seriousness of the societal issue, and their evaluation of the journalistic quality of the news report. Results showed that, contrary to critics’ concerns, constructive news did not reduce perceived problem awareness: participants across conditions reported similarly high levels of awareness regarding the seriousness of the issues presented. Perceived journalistic quality also remained high in both constructive and nonconstructive conditions, indicating that incorporating constructive elements did not compromise credibility. Furthermore, constructive news appeared to encourage a broader, more solution-oriented perspective, prompting participants to consider opportunities and future prospects. Importantly, this broader perspective did not come at the expense of perceived problem awareness or journalistic quality. Overall, the findings provide empirical support for the value of constructive journalism in the Dutch media context. Full article

The valorization of agri-food by-products represents a key strategy for improving sustainability and promoting circular economy principles in food systems. Pumpkin seed press cake is a protein-rich by-product with potential application in bakery products. The aim of this study was to evaluate the feasibility of using defatted pumpkin seed press cake flour (PPSF) as a major ingredient in cookie formulations and to optimize its incorporation in order to maximize nutritional quality and sensory acceptability. Chemical characterization showed that PPSF has a superior nutritional profile compared to wheat flour, containing 55.75% protein, 8.78% minerals, and 6.15% total dietary fiber, along with significantly higher levels of total phenolics, total carotenoids, and β-carotene (0.26 mg/100 g). Formulation optimization using response surface methodology (RSM) enabled a high inclusion level of 69.61% PPSF, with 41.32% sugar and a baking time of 9 min and 29 s. The developed predictive models for diameter, thickness, overall acceptability, and bending stiffness were highly significant (p < 0.05) with a non-significant lack of fit (p > 0.05), confirming their statistical reliability for exploring the design space. The optimized C-PPSF (defatted pumpkin seed press cake flour) cookies showed a significant nutritional improvement, with protein content increasing from 13.05% to 30.17% and antioxidant capacity (DPPH) rising from 2.90% to 7.10%. While the enriched cookies had a darker color (L* 51.98) and reduced snapping force (39.7 N) due to gluten dilution, they maintained stable geometric parameters and achieved higher sensory scores for aroma, taste, and overall acceptability compared to the control. The main finding of this study is that PPSF can replace a substantial proportion of wheat flour in cookies while maintaining consumer acceptability and significantly improving nutritional quality. The optimized formulation with approximately 70% PPSF shows that this by-product has the potential to serve as a major ingredient in bakery products rather than only as a nutritional supplement. These results confirm that PPSF is a powerful functional ingredient that supports zero-waste manufacturing and provides a foundation for its broader use in bakery formulations within circular economy approaches. Future research should focus on shelf-life stability, bioaccessibility of bioactive compounds, volatile aroma profiling (e.g., GC–MS analysis), and industrial-scale validation of PPSF-based formulations. Full article

Accurate and automated extraction of solid waste sites from remote-sensing imagery constitutes a pivotal demand for contemporary environmental regulation and risk mitigation. However, in high-resolution remote-sensing imagery, solid waste sites are typically represented as a single semantic image object (SIO), which is composed of multiple physical image parcels (PIPs) exhibiting significant variations in scale, morphology, and spectral properties. This intrinsic heterogeneity substantially increases the complexity and uncertainty of multi-class site identification. To address this challenge, this paper proposes WasteMOE Net (WMN), which is developed based on the core concept of modeling the SIO–PIP relationship. WMN adopts a heterogeneous expert selection mechanism combined with a nested mixture-of-experts architecture. It thus enables adaptive perception of complex PIPs across diverse scenarios and their integrated discrimination at the SIO level. In addition, by incorporating the explicit nonlinear representation capability of the KAN network, WMN effectively improves multi-class recognition accuracy while maintaining computational efficiency. Furthermore, this study constructs a high-resolution solid waste site dataset in accordance with the SIO–PIP-aware annotation principle, encompassing five representative categories: tailings ponds (TP), construction spoil sites (CSS), landfill sites (LS), garbage dump sites (GDS), and excavation sites (ES). Experimental results show that WMN achieves mAP50 values of 74.2% (GDS), 63.5% (CSS), 80.9% (ES), 85.4% (TP), and 83.1% (LS) in detection tasks, and 75.4%, 64.1%, 83.0%, 86.7%, and 84.1% for the corresponding categories in segmentation tasks. It achieves competitive performance compared with state-of-the-art methods in both tasks. Further, in a real-world application over Loudi City, China, WMN completed the processing of a 490.67 km² area within 1.34 h. The recognition accuracies for GDS and ES reached 54.8% and 65.3%, respectively. Finally, the proposed method has been successfully integrated into a GIS-based solid waste pollution risk prevention system, which markedly boosts the overall efficiency of environmental monitoring and on-site inspections. Full article

Admiralty brass, commonly used in heat exchangers, is particularly susceptible to corrosion in acidic media such as those used in industrial cleaning. To mitigate this problem, the present study evaluated Musa acuminata (banana) peel and Lupinus mutabilis Sweet (Andean lupine) extracts as sustainable, low-toxicity corrosion inhibitors for admiralty brass in 0.5 M HCl. Six extracts were prepared using different solvents and characterized by qualitative and semi-quantitative phytochemical analyses (phenols, flavonoids, alkaloids). M. acuminata extracts were rich in phenolic compounds, while L. mutabilis extracts contained high levels of quinolizidine alkaloids. A comparative electrochemical screening of the agro-industrial waste-derived extracts revealed that the inhibition efficiency of M. acuminata extracts reached up to 43.6%, whereas the debittering wastewater extract of L. mutabilis (E6) achieved a maximum efficiency of 85.5% at 2000 ppm. A preliminary techno-economic analysis indicated the feasibility of industrial-scale production of the L. mutabilis-based inhibitor, yielding a net present value (NPV) of USD 9.48 million, an internal rate of return (IRR) of 27.3%, and a payback period of 6.7 years. These results demonstrate that agro-industrial residues can be valorized into effective and profitable green corrosion inhibitors, aligning with circular economy and sustainable chemistry principles. Full article

Simultaneous multi-ion detection is important for interpreting leaching, corrosion, hydration, and solidification processes in cement-based materials, because these processes are controlled by coupled ion migration, binding, and precipitation–dissolution reactions. Conventional methods such as pore-solution extraction, ion chromatography, inductively coupled plasma optical emission spectroscopy, and single-ion potentiometric measurements provide useful chemical information, but they generally rely on discrete sampling or isolated ion channels and therefore have limited ability to capture time-aligned multi-ion evolution. In this study, an IoT-based in situ multi-ion detection system was developed by integrating ion-selective electrodes for Cl⁻, Ca²⁺, F⁻, and H⁺ with an ADS1115 analog-to-digital converter, an ESP32 microcontroller, and a voltage amplification module. The system achieved minimum resolvable concentrations of 10⁻⁵ M for Cl⁻ and F⁻ and 10⁻⁴ M for Ca²⁺, while maintaining pH measurement over the range of 2–12. Ten consecutive measurements at 0.01 M showed relative standard deviations below 0.12%, indicating good short-term repeatability under laboratory calibration conditions. Interference and temperature tests showed that Br⁻ and NO₃⁻ affected the chloride channel at high concentrations, Ca²⁺ reduced free F⁻ activity through Ca–F precipitation equilibrium, and the temperature drift of Cl⁻ and F⁻ electrodes changed direction with concentration, whereas the Ca²⁺ response decreased monotonically with increasing temperature. When applied to phosphogypsum–cement hardened pastes, the system captured rapid Ca²⁺ release, low-level F⁻ fluctuation controlled by Ca–F interaction, non-monotonic Cl⁻ release, and alkaline pH evolution on the same time axis. Compared with existing single-ion or offline methods, the proposed system provides synchronized in situ evidence for interpreting coupled ion leaching in cement-based solid-waste systems. Full article

Food waste in school canteens is widely recognized as a significant issue because of its economic consequences, environmental impact, and implications for children’s health. Previous studies have used robust methods to quantify this problem and assess mitigation strategies. This case study of primary school children (6–11 years) used a child-driven approach to measure plate waste and explore reasons for uneaten food and concern about waste. The results indicated that a group of volunteer children (n = 104) directly involved in the assessment were able to evaluate their peers’ food waste, obtaining estimates comparable to those reported in previous studies (mean: 108.4 g per child). The students for whom food waste was measured (n = 443) took part in interviews and proved to be active participants capable of evaluating their own context, although their level of engagement could be further strengthened. Among children who reported leaving food uneaten, a substantial proportion provided specific reasons; nevertheless, generic explanations accounted for 26% of responses for the first course and 35% for the second. Approximately 78.5% of the children demonstrated a high level of sensitivity to food waste, recognizing its direct effects (wasting their parents’ money), indirect effects (waste in a broader sense), and social effects (world hunger/poverty). Establishing a baseline for children’s sensitivity to their own food waste is therefore needed, as it could serve as an indicator of both the urgency and the effectiveness of educational interventions. Full article

Ultrasonic thickness resonance can be effectively used to detect and quantify the level of corrosion in steel nuclear storage containers as well as other corrosion-prone thin-walled structures, such as pipes and storage tanks. Electro-Magnetic Acoustic Transducers (EMATs) have several advantages over more traditional piezoelectric-based transducers; namely, they can be used in a non-contact fashion on robotic platforms, allowing for measurements regardless of surface conditions or temperature. The major challenge of EMAT application is the power required to counteract the low actuation efficiency, which is achieved with a high-power ultrasonic pulse generator and a transformer circuit. Resonance techniques, in which most of the energy is concentrated near structural resonance frequencies, are preferable to improve efficiency of electro-magnetic acoustic measurements. This methodology was applied to 316L stainless steel thin plates subjected to uniform corrosion as well as pitting corrosion imitating different damage scenarios in a nuclear waste container. The resonant peak frequency shift was found to be proportional to the severity of corrosion for minimally corroded samples. However, the complete disappearance of the resonance peak was observed in the samples with severe corrosion damage. The EMAT liftoff distance was studied to quantify its effect on the amplitude, spread, and frequency of resonant peaks. Recommendations for use of EMATs for assessing corrosion damage are presented. The study demonstrates the success of frequency-based detection of corrosion damage in 316L stainless steel used in fabrication of nuclear waste storage containers. Full article

This research examines the hydrothermal corrosion behaviour of ceramic matrix composites (CMCs) under highly alkaline conditions (pH > 11.5) in the framework of a deep geological repository for high-level radioactive waste (HLW). The study focuses on the degradation of alumina powder and fibres, key constituents of an oxide/oxide CMC material. Accelerated ageing experiments were conducted in a highly alkaline aqueous environment (pH > 11.5, T = 70 °C for 220 days and T = 150 °C for 30 days). The research used a cross-disciplinary approach integrating thermodynamic calculations and physicochemical analyses to determine the degradation mechanisms of alumina powder and fibres induced by contact with the aqueous ageing solution. Characterisation of the aged alumina powders and fibres revealed the presence of unaltered alumina, hydrated alumina, amorphous phases, and calcium carbonate precipitates from the aqueous solution. Thermodynamic calculations indicate (1) the hydrolysis of alumina to diaspore and (2) the formation of an aluminosilicate phase and calcium carbonate. However, experimental results reveal kinetic limitations, such as the preferential formation of boehmite over diaspore, and morphology-dependent degradation pathways (protective-layer formation on fibres and partial dissolution of powders). Full article

Precision feeding is an important foundation for improving production efficiency in aquaculture, reducing feed waste, mitigating water pollution, and promoting the intelligent development of aquaculture. Conventional feeding practices remain heavily dependent on operator experience and are typically executed at predetermined times or fixed ration levels. Such approaches frequently result in extensive feeding management, poor adaptability, low feed utilization efficiency, and delayed responses to environmental changes. Advances in machine vision, the Internet of Things, machine learning, deep learning, and automatic control have progressively shifted aquaculture feeding research beyond standalone automatic feeders toward integrated systems encompassing demand perception, intelligent decision-making, precise control, and equipment coordination. This paper reviews the state of the art in precision feeding technologies and equipment in aquaculture. At the technical level, it summarizes advances in feeding demand perception, intelligent feeding decision-making, and precise control and execution. At the equipment level, it reviews the main types, design features, and field application status of precision feeding equipment in intensive aquaculture, pond aquaculture, and offshore aquaculture scenarios. Despite the considerable progress achieved, the practical deployment of precision feeding still faces several limitations. Environmental disturbances, water turbidity, illumination variation, and sensor drift may compromise the reliability of feeding demand perception. Existing decision-making models frequently exhibit limited generalizability across species, growth stages, and aquaculture scenarios. Moreover, insufficient integration of sensing, decision-making, and execution restricts the development of fully closed-loop feeding systems. High initial investment, maintenance costs, and the shortage of skilled personnel further constrain the adoption of precision feeding equipment, particularly in resource-limited regions. On this basis, the main challenges including sensing accuracy, model practicability, closed-loop control, equipment reliability, and standardization, are examined. Future development trends are also discussed, covering multi-source information fusion, synergy between mechanistic models and data-driven methods, system-level closed-loop control, equipment modularization, and industrial application. This review is expected to provide a reference for subsequent research and engineering applications. Full article

Sustainable agricultural technologies are essential to respond to environmental and social pressures, ensuring the maintenance of global food security. Therefore, there is an urgent demand for more sustainable agricultural practices that promote soil quality, as this factor directly impacts the global economy. Agricultural yield is directly associated with soil health and fertility. The use of organic waste serves as a source of essential nutrients for plants, increasing soil organic matter, contributing to the improvement of soil physical and chemical properties, as well as increasing crop yield. Based on this context, this research aimed to evaluate the effects of incorporating organic waste aiming to mitigate the oxidative damage in maize plants grown under different levels of soil fertility (low, average, and high), evaluating soil and plant, more specifically chemical, physiological, biochemical, and morphological responses. In soil, organic waste promoted significant increases in the activities of arylsulfatase and β-glucosidase and improved the chemical parameters, including cation exchange capacity, soil organic matter, base saturation, and sum of bases. The application of organic waste, regardless of fertility level, improved the nutritional status in maize plants, increased concentrations of photosynthetic pigments, maximized the photochemical efficiency and photosynthesis rate. In plant metabolism, the results demonstrated that organic waste promoted significant increases in plant antioxidant defense, including superoxide dismutase, catalase, ascorbate peroxidase, and peroxidase, minimizing the oxidative stress on photosynthetic machinery, especially in plants cultivated on soil with low fertility. Therefore, this research proves that organic waste mitigates the negative impacts associated with nutritional starvation, improves soil health and fertility, favors the maintenance of redox metabolism, and stimulates photosynthesis in maize plants cultivated in low-fertility soil. Full article

Ammonia is an essential high-volume chemical for fertilizer production and other industrial applications, and it is increasingly considered a potential energy carrier; however, its conventional manufacture remains highly energy- and carbon-intensive because it relies predominantly on fossil-based Haber–Bosch (HB) synthesis. This review compares sustainable ammonia-production pathways through the linked dimensions of technology readiness, environmental performance, and economic plausibility across renewable-H₂ HB, biomass- and waste-derived HB routes, electrochemical pathways, photocatalytic and photoelectrochemical systems, plasma-assisted synthesis, biological routes, and chemical looping ammonia synthesis. The analysis reveals a clear divide between pathways that benefit from established industrial infrastructure and those that still depend on unresolved catalytic, materials, or systems-level advances. Renewable-H₂ Haber–Bosch emerges as the most broadly scalable near-term option for large-scale ammonia decarbonization because it combines the highest maturity among low-carbon routes with the strongest techno-economic and life-cycle evidence base. Biomass- and waste-derived Haber–Bosch pathways may become cost-competitive regional complements when low-cost local residues, organic waste, or biomethane is available, feedstock logistics are favorable, and carbon, waste-treatment, or negative-emission credits are included. Overall, sustainable ammonia production is likely to advance through a portfolio of pathways, with near-term progress led by renewable-H₂ HB and longer-term development dependent on improved reactor integration, harmonized assessment methods, and scalable validation. Full article

Agri-food byproducts are increasingly recognized as sustainable feedstocks for high-value bioactive compounds; but their practical valorization requires integrated evidence on recovery conditions; chemical composition; bioactivity; and application readiness. This review critically examines green recovery strategies and chemical profiling platforms for bioactive compounds recovered from peels; pomace; seed residues; hulls; vegetation waters; and pruning waste. Emphasis is placed on how extraction variables shape chemical profiles; extract quality; and reported biological activities. Ultrasound- and microwave-assisted extraction; enzyme- and fermentation-assisted recovery; supercritical fluid extraction; pressurized liquid extraction; pulsed electric field-assisted pretreatment; and green solvent-based extraction are discussed in terms of target-compound selectivity; solvent and energy demand; process safety; scalability; and sustainability-related evidence. Chromatographic; mass-spectrometric; spectroscopic; and metabolomics-based profiling approaches are evaluated for identification; annotation; quantification; fingerprinting; quality-marker selection; and standardization; with confidence levels distinguished according to authentic-standard matching; tandem mass spectrometry evidence; spectral libraries; or fingerprint-level evidence. Circular valorization pathways in food; nutraceutical; cosmetic; pharmaceutical, and biopesticide-related applications are further considered with attention to feedstock heterogeneity; process standardization; stability; safety; regulatory feasibility; scalability; and techno-economic feasibility. Overall; this review provides a linkage-oriented framework for developing standardized; application-readiness-oriented bioactive candidates from agri-food byproducts. Full article

Green prepared dishes are an emerging food-consumption format that links convenience, food safety, and sustainable consumption. In this study, “green” denotes a sustainability-oriented product profile involving food-safety assurance, resource-conscious packaging or sourcing, and waste-reduction potential, rather than formal organic certification. However, existing studies have mainly relied on linear behavioral models and have paid limited attention to nonlinear and asymmetric consumer decision mechanisms. This study integrates the stimulus–organism–response framework with explainable machine learning to predict consumers’ sustainable purchase intention toward green prepared dishes. Based on 805 valid questionnaires collected in Jilin Province, China, predictors were organized into three dimensions: environmental and health cognition, socioeconomic and infrastructural conditions, and sustainable behavioral propensity. The sample represents a regional online consumer profile in Jilin Province rather than a national probability sample. Six classifiers were trained using SMOTE–Tomek resampling and Optuna-based hyperparameter optimization. XGBoost achieved the best predictive performance, with an F1-score of 0.894, an AUC of 0.934, and an MCC of 0.702. Unlike conventional black-box machine learning, the SHAP-based interpretation translated ensemble predictions into transparent feature-level and case-level explanations. Accordingly, the model interpretations are framed as predictive associations rather than causal mechanisms. The study reveals an asymmetric decision pattern in which core behavioral willingness functions as a non-compensatory barrier, while channel convenience, delivery efficiency, and after-sales support facilitate purchase intention among consumers who already show high behavioral readiness. The findings suggest that green prepared-dish strategies should prioritize trust-based advocacy and word-of-mouth, reliable channel design, low-risk trial experiences, and collaborative food-safety governance rather than relying only on short-term traffic acquisition. Full article

This study addresses the pressing issue of utilising plastic from electronic waste as a filler to replace mineral sand. Currently, the use of plastic in construction concrete is limited due to a significant deterioration in the mechanical properties of modified concrete when plastic filler is added at levels exceeding 15–20%. As a result of the research, it was established that the addition of a nano-preparation—fullerene—in amounts as low as 0.001% significantly improves the mechanical properties of concrete with plastic aggregate. Replacing 50% of the mineral aggregate with plastic aggregate, combined with the addition of fullerene at a concentration of 0.01% of the mixing water mass, more than doubles the mechanical properties of the concrete compared to concrete without the nano-additive, with compressive strength increasing by 65.2%, from 16.33 MPa to 26.97 MPa. The impact strength and freeze–thaw resistance of the concrete were also significantly increased. This makes it possible to use concrete with a high plastic aggregate content of up to 50% without a significant reduction in mechanical properties. The use of machine learning and AI data processing methods such as AdaBoost and Random Forest allows for highly accurate prediction of the characteristics of the resulting materials, with a coefficient of determination (R²) for the resulting models close to 1. Full article