Search Results (2,236)

The food industry generates large volumes of nutrient-rich by-products that remain underutilised despite their considerable biochemical potential. These materials originate predominantly from the fruit and vegetable, dairy, meat, and fish and seafood sectors and represent a substantial opportunity for sustainable valorisation. Fermentation has emerged as a powerful platform for converting such by-products into high-value ingredients, including bioactive compounds, functional metabolites, enzymes, antimicrobials, and nutritionally enriched fractions. This review synthesises recent advances in microbial fermentation strategies—spanning lactic acid bacteria, filamentous fungi, yeasts, and mixed microbial consortia—and highlights their capacity to enhance the bioavailability, stability, and functionality of recovered compounds across diverse substrate streams. Key technological enablers, including substrate pre-treatments, precision fermentation, omics-guided strain selection and improvement, and bioprocess optimisation, are examined within the broader framework of circular bioeconomy integration. Despite significant scientific progress, major challenges remain, particularly related to substrate heterogeneity, process scalability, regulatory alignment, safety assessment, and consumer acceptance. The review identifies critical research gaps and future directions, emphasising the need for standardised analytical frameworks, harmonised compositional databases, AI-driven fermentation control, integrated biorefinery concepts, and pilot-scale validation. Overall, the evidence indicates that integrated fermentation-based approaches—especially those combining complementary by-product streams, tailored microbial consortia, and system-level process integration—represent the most promising pathway toward the scalable, sustainable, and economically viable valorisation of food industry by-products. Full article

This study investigates the implementation of an attached-growth pilot-scale biofilter for the biological treatment of mixed dairy wastewater derived from real industrial effluents, consisting of equal proportions of raw second cheese whey (SCW) and pudding dessert wastewater (PDW). The biofilter was inoculated with indigenous microorganisms derived from the mixed wastewater stream with initial dissolved Chemical Oxygen Demand (d-COD) concentrations ranged from 1000 to 12,500 mg/L. The removal performance of organic and inorganic components was evaluated at a recirculation rate of 1.0 L/min, resulting in d-COD reductions of up to 92.3% and removal rates reaching 194.6 mg/(L·h). High removal rates were recorded for ammonium (up to 99.9%) and TKN (92.2–98.7%), while nitrate removal varied (29.4–89.3%) and solids removal exceeded 92%. d-COD concentrations of treated effluent consistently met discharge or municipal disposal legislation values, demonstrating the system’s efficiency and stability and proposing it as an ideal solution for wastewater treatment in dairy facilities. Full article

Laboratory and field investigations were used to assess the toxicity of leachate from a closed sanitary landfill on benthic macroinvertebrates in coldwater trout streams located near a landfill in southeastern Minnesota, USA. Field-collected invertebrates were exposed to a range of concentrations (0–100%) of leachate during a series of 24 h and 7-day laboratory toxicity tests. Benthic macroinvertebrates also were collected from two stream sites on either side of the landfill and at a third site downstream to assess potential pollution exposure of the stream communities. Ten different taxa exposed to 100% leachate for 24 h exhibited survival ranging from 0 to 100%, with survivorship not correlated to published invertebrate pollution tolerance values. More extensive 24 h tests with the least tolerant Brachycentrus caddisfly larvae found 100% mortality at leachate concentrations > 70%, with the first mortalities observed after 3 h. Brachycentrus had 100% survival at leachate concentrations < 40%. During 7-day tests, Brachycentrus had 100% survival at all leachate concentrations of 40% and lower, but all Brachycentrus died after 2 days at concentrations of 60% and higher. Instream benthic communities, averaging 12 to 17 different taxa at the various stream sites, were rated from good to excellent based on biotic index values, with intolerant taxa present at all three stream sites. Landfill leachate has not impacted the benthic invertebrate communities in streams nearby, but leachate at higher concentrations has the potential to be toxic to a variety of local taxa. Full article

Modern industrial equipment is a cyber-physical system (CPS) consisting of physical production components and digital controls. Lowering maintenance costs and increasing availability is important to improve its efficiency. Modern methods, based on solving event prediction problem, in particular, prediction of remaining useful life (RUL), are used as a crucial step in a framework of reliability-centered maintenance to increase efficiency. But modern methods of RUL forecasting fall short when dealing with real-world scenarios, where CPS are described by multidimensional continuous high-frequency data with working cycles with variable duration. To overcome this problem, we propose a new method for fault prediction, which is based on extraction of semantic state vectors (SSVs) from working cycles of equipment. To implement SSV extraction, a new method, based on convolutional autoencoder and extraction of hidden state, is proposed. In this method, working cycles are detected in input data stream, and then they are converted to images, on which an autoencoder is trained. The output of an intermediate layer of an autoencoder is extracted and processed into SSVs. SSVs are then combined into a time series on which RUL is forecasted. After optimization of hyperparameters, the proposed method shows the following results: RMSE = 1.799, MAE = 1.374. These values are significantly more accurate than those obtained using existing methods: RMSE = 14.02 and MAE = 10.71. Therefore, SSV extraction is a viable technique for forecasting RUL. Full article

This study is essential for medium- and long-term land-use management, as land-use patterns directly influence local economic and social development. Geographic Information System (GIS) techniques are fundamental tools for analyzing a wide range of geomorphological processes, including relief fragmentation density, relief energy, soil texture, slope gradient, and slope orientation. The present research focuses on the Pesceana river basin in the Southern Carpathians, Romania. It addresses three main objectives: (1) to analyze land-use dynamics derived from CORINE Land Cover (CLC) data between 1990 and 2018, along with the long-term distribution of the Normalized Difference Vegetation Index (NDVI) for the period 2000–2025; (2) to evaluate the basin’s natural potential byintegrating topographic data (contour lines and profiles) with relief fragmentation density, relief energy, vegetation cover, soil texture, slope gradient, aspect, the Stream Power Index (SPI), and the Topographic Wetness Index (TWI); and (3) to assess the spatial distribution of habitat types, characteristic plant associations, and soil properties obtained through field investigations. For the first two research objectives, ArcGIS v. 10.7.2 served as the main tool for geospatial processing. For the third, field data were essential for geolocating soil samples and defining vegetation types across the entire 247 km² area. The spatiotemporal analysis from 1990 to 2018 reveals a landscape in which deciduous forests clearly dominate; they expanded from an initial area of 80 km² in 1990 to over 90 km² in 2012–2018. This increase, together with agricultural expansion, is reflected in the NDVI values after 2000, which show a sharp increase in vegetation density. Interestingly, other categories—such as water bodies, natural grasslands, and industrial areas—barely changed, each consistently representing less than 1 km² throughout the study period. These findings emphasize the importance of land-use/land-cover (LULC) data within the applied GIS model, which enhances the spatial characterization of geomorphological processes—such as vegetation distribution, soil texture, slope morphology, and relief fragmentation density. This integration allows a realistic assessment of the physical–geographic, landscape, and pedological conditions of the river basin. Full article

The study comprises an in-depth characterization of compositional groups of the liquid by-products obtained from the pyrolysis of swine manure at 500 °C, with the aim of providing an alternative and efficient approach for the valorisation of this waste stream, alongside with the production of biogas and char, the latter of which can be further converted into activated carbon. Two samples were considered: de-watered cake and solid product from anaerobic digestion of swine manure. Biocrude oils were fractionated into weak acidic, strong acidic, alkaline and neutral oil fractions. Subsequently, the neutral oil fraction was separated into paraffinic–naphthenic, slightly polar and polar fractions. All fractions were analyzed by GC–MS. The major identified compositional groups were: (i) for de-watered cake: steroids (40.7%), fatty acids, FAs (23.7%) and n-alkenes/n-alkanes (23.3%); (ii) for solid product from anaerobic digestion: FAs (31.0%), phenols/methoxy phenols (26.6%), n-alkenes/n-alkanes (10.8%) and steroids (10.6%). A variety of short-chain FAs (i.e., linear saturated, mono- and di-unsaturated, cis (i-), trans (ai-), isoprenoid, phenyl alkanoic, amongst others) and methyl esters (FAMEs) were identified as well. FA distribution, nC₁₂–nC₂₀, was similar for both manures studied with nC₁₆ and nC₁₈ as major compounds. FAMEs (nC₁₄–nC₂₈, with even carbon number dominance) in the slightly polar fraction of both samples were accompanied by considerable amounts of oleic (nC_18:1) and linoleic (nC_18:2) acids, and corresponding methyl esters. Hydrocarbons, i.e., n-alkenes/n-alkanes, were in the range of nC₁₅–nC₃₄, with nC₁₈ maximizing. Anaerobically digested manure has resulted in (i) an increase in the portion of longer homologues of hydrocarbons and FAMEs and (ii) the appearance of new FAs series of long chain members nC_22:1–nC_26:1, ω-9. The comprehensive analysis of the biocrude oils obtained from the slow pyrolysis of swine manure indicates their potential for use as biodiesel additives or as feedstock to produce value-added materials. Full article

The automotive industry’s shift to a Circular Economy for global sustainability is vital, but it faces challenges when establishing efficient Reverse Supply Chains. Reverse Supply Chain implementation is dependent on multiple barriers and enablers, including eco-nomic, managerial, technological, regulatory, and social domains, thus making single-factor solutions ineffective. The purpose of this review is to conduct a systematic literature review to understand how these interconnected barriers and enablers can collectively shape Reverse Supply Chain implementation and performance, specifically within the automotive sector, which remains little known. The PRISMA framework was utilised, which resulted in 129 peer-reviewed articles being selected for review. Findings showed that the literature focuses primarily on Electric Vehicle batteries within developing economies, particularly China. Reverse Supply Chain implementation is governed not only by isolated barriers but by complex systemic interdependencies between enablers as well. This complex inter-relationship between barriers and enablers can be categorised into five key dimensions: economic and financial; managerial and organisational; technological and infrastructural; policy and regulatory; and market and social. The study reveals two systemic patterns driving the transition: technology–policy interdependence and the conflicting relationship between large-scale production and value extraction. Our findings also presented a research agenda focusing on strategic value creation through material streams of automotive electronics, plastic, and composites with high potential value, and further insights are needed in regions such as the Middle East, Oceania, and the Americas. Organisations should consider Reverse Supply Chain as a strategic approach for securing critical material supplies, while policymakers could leverage the use of digital tools as the foundational infrastructure for subsidies allocation and prevent fraud. Full article

Sustainable finance and carbon risk have attracted substantial interest from both practitioners and scholars. This paper integrates the income-based environmental responsibility framework with financial asset pricing models to investigate how carbon transition risk propagates along value chains and impacts asset returns. By utilizing the Ghosh supply-driven input–output model to quantify downstream value chain carbon emissions as a proxy for the dependence of a company’s revenue streams on high-carbon downstream clients, we construct a novel downstream carbon risk factor (DMC) by sorting stocks into portfolios based on this exposure and forming a factor mimicking long short portfolio. We then integrate this DMC factor into the Fama–French five-factor framework to propose a six-factor model capable of capturing value chain risk transmission. Empirical results of Chinese A-share listed companies demonstrate that firms with high DMC exposure, being vulnerable to carbon transition shocks such as carbon pricing, offer a significant risk premium even after controlling for traditional financial characteristics. This finding provides robust evidence for the carbon premium hypothesis in the world’s largest emerging market and contributes a theoretically grounded and empirically implementable framework for integrating value chain carbon risk into asset pricing analysis. Full article

Corn fermentation in biorefineries produces residual biomass and by-products, particularly corn kernel fiber and outgassed carbon dioxide (CO₂), that have value-added potential for improving sugar and bioethanol conversions. Recovered corn kernel fiber contains lignocellulosic components which can be made accessible by pretreating the biomass with an alkaline sodium carbonate solution made with captured CO₂ and then used as supplemental biomass in corn ethanol production. In this work, different ratios of whole and degermed corn kernel fibers are pretreated and mixed with corn to be evaluated as beneficial ingredients in bioethanol co-fermentation. Sugar yields from enzymatic hydrolysis demonstrate the pretreatment promotes saccharification reaching over 70% total sugar conversion for the whole corn fibers. During co-fermentation, 10 and 20% corn solid loadings significantly increased ethanol yields while additional corn fiber loadings increased sugar yields. Conversion rates and yields were similar between the whole and degermed corn fibers supporting how a single recovery design can benefit multiple corn streams. Full article

This paper presents a comprehensive techno-economic assessment of a community battery energy storage system (BESS) participating concurrently in energy arbitrage and frequency control ancillary services (FCAS) markets, while also providing customer savings through coordinated demand management. The proposed framework employs a mixed-integer linear programming (MILP) model to co-optimize the charging, discharging, and reserve scheduling of the battery under dynamic market conditions. The model explicitly incorporates key operational and economic factors such as round-trip efficiency, degradation cost, market-participation constraints, and revenue from multiple value streams. By formulating the optimization problem within this MILP structure, both the operational feasibility and the economic profitability of the system are evaluated over annual market cycles. Simulation results demonstrate that integrating FCAS participation with conventional energy arbitrage substantially enhances total revenue potential and improves asset utilization, compared with single-service operation. Furthermore, the coordinated management of community demand contributes to additional cost savings and supports local grid reliability. The findings highlight the critical role of co-optimized control and multi-market participation strategies in improving the financial viability and grid-support capabilities of community-scale BESS deployments. Full article

Phosphorus is an essential resource for numerous industrial applications. However, its uneven global distribution makes Europe heavily dependent on imports. Recovering phosphorus from waste streams is therefore crucial for improving resource security. The FlashPhos project addresses this challenge by developing a process to recover phosphorus from sewage sludge, in which phosphorus-rich slag is produced in a flash reactor and subsequently reduced in a Submerged Arc Furnace (SAF). In this process, approximately 250 kg/h of sewage sludge is converted into slag, which is further processed in the SAF to recover about 8 kg/h of white phosphorus. This work focuses on the development of a computational model of the SAF, with particular emphasis on slag behaviour. Due to the extreme operating conditions, which severely limit experimental access, a numerically efficient three-dimensional CFD model was developed to investigate the internal flow of the three-phase, AC-powered SAF. The model accounts for multiphase interactions, dynamic bubble generation and energy sinks associated with the reduction reaction, and Joule heating. A temperature control loop adjusts electrode currents to reach and maintain a prescribed target temperature. To further reduce computational cost, a novel simulation approach is introduced, achieving a reduction in simulation time of up to 300%. This approach replaces the solution of the electric potential equation with time-averaged Joule-heating values obtained from a preceding simulation. The system requires transient simulation and reaches a pseudo-steady state after approximately 337 s. The results demonstrate effective slag mixing, with gas bubbles significantly enhancing flow velocities compared to natural convection alone, leading to maximum slag velocities of 0.9–1.0 m/s. The temperature field is largely uniform and closely matches the target temperature within ±2 K, indicating efficient mixing and control. A parameter study reveals a strong sensitivity of the flow behaviour to the slag viscosity, while electrode spacing shows no clear influence. Overall, the model provides a robust basis for further development and future coupling with the gas phase. Full article

This study presents a statistically robust quality-engineering evaluation of an industrial cathodic electrodeposition (CED) process applied to large electric-charging station components. In contrast to predominantly laboratory-scale studies, the analysis is based on 1250 thickness measurements, enabling reliable assessment of process uniformity, positional effects, and long-term stability under real production conditions. The mean coating thickness was specified at 21.84 µm with a standard deviation of 3.14 µm, fully within the specified tolerance window of 15–30 µm. One-way ANOVA revealed statistically significant but technologically small inter-station differences (F(49, 1200) = 3.49, p < 0.001), with an effect size of η² ≈ 12.5%, indicating that most variability originates from inherent within-station common causes. Shewhart X^¯–R–S control charts confirmed process stability, with all subgroup means and dispersions well inside the control limits and no evidence of special-cause variation. Distribution tests (χ², Kolmogorov–Smirnov, Shapiro–Wilk, Anderson–Darling) detected deviations from perfect normality, primarily in the tails, attributable to the superposition of slightly heterogeneous station-specific distributions rather than fundamental non-Gaussian behaviour. Capability and performance indices were evaluated using Statistica and PalstatCAQ according to ISO 22514; the results (Cp = 0.878, Cpk = 0.808, Pp = 0.797, Ppk = 0.726) classify the process as conditionally capable, with improvement potential mainly linked to reducing positional effects and centering the mean closer to the target thickness. To complement the statistical findings, an AIAG–VDA FMEA was conducted across the entire value stream. The highest-risk failure modes—surface contamination, incorrect bath chemistry, and improper hanging—corresponded to the same mechanisms identified by SPC and ANOVA as contributors to thickness variability. Proposed corrective actions reduced RPN values by 50–62.5%, demonstrating strong potential for capability improvement. A predictive machine-learning model was implemented to estimate layer thickness and successfully reproduced the global trend while filtering process-related noise, offering a practical tool for future predictive quality control. Full article

Wild ginkgo, as an endangered species, holds significant value for genetic resource conservation, yet its practical applications face numerous challenges. Traditional field surveys are inefficient in mountainous mixed forests, while satellite remote sensing is limited by spatial resolution. Current deep learning approaches relying on single-source data or merely simple multi-source fusion fail to fully exploit information, leading to suboptimal recognition performance. This study presents a multimodal ginkgo crown dataset, comprising RGB and multispectral images acquired by an UAV platform. To achieve precise crown segmentation with this data, we propose a novel dual-branch dynamic weighting fusion network, termed dual-branch cross-modal attention-enhanced UNet (DCA-UNet). We design a dual-branch encoder (DBE) with a two-stream architecture for independent feature extraction from each modality. We further develop a cross-modal interaction fusion module (CIF), employing cross-modal attention and learnable dynamic weights to boost multi-source information fusion. Additionally, we introduce an attention-enhanced decoder (AED) that combines progressive upsampling with a hybrid channel-spatial attention mechanism, thereby effectively utilizing multi-scale features and enhancing boundary semantic consistency. Evaluation on the ginkgo dataset demonstrates that DCA-UNet achieves a segmentation performance of 93.42% IoU (Intersection over Union), 96.82% PA (Pixel Accuracy), 96.38% Precision, and 96.60% F1-score. These results outperform differential feature attention fusion network (DFAFNet) by 12.19%, 6.37%, 4.62%, and 6.95%, respectively, and surpasses the single-modality baselines (RGB or multispectral) in all metrics. Superior performance on cross-flight-altitude data further validates the model’s strong generalization capability and robustness in complex scenarios. These results demonstrate the superiority of DCA-UNet in UAV-based multimodal ginkgo crown recognition, offering a reliable and efficient solution for monitoring wild endangered tree species. Full article

This study investigates the valorisation of mixed municipal waste glass (MMWG; EWC 20 01 02) as a sustainable supplementary material in cement mortars. In contrast to most existing studies, which focus almost exclusively on homogeneous container glass, this work addresses a heterogeneous waste stream derived from municipal selective collection, containing flat glass, mirrors, ceramics, porcelain, and metallic residues. Such mixed household glass has not previously been systematically evaluated in cement mortars, thereby addressing a clear research gap. The MMWG was washed, dried, and ground in a Los Angeles drum with corundum abrasives to obtain a fine glass powder (FGP < 63 µm) with a median particle size of approximately 20 µm and a Blaine fineness of 360 m²/kg. Microstructural and chemical characterisation of the milled glass confirmed its highly amorphous nature and angular particle morphology resulting from grinding. In addition, coarse glass granules (0–4 mm) were used as partial replacements for natural sand in mortar mixtures. The incorporation of FGP led to a 4–12% reduction in flowability, attributable to the angular shape and increased specific surface area of the ground-glass particles. At 28 days, mortars containing 5–10% FGP exhibited mechanical properties comparable to the reference mix, while at 56 days their compressive strength increased by up to 8%, indicating delayed pozzolanic activity typical of finely milled, amorphous glass. Mortars containing coarse glass primarily reflected a filler and aggregate-replacement effect. Leaching tests conducted in accordance with PN-EN 12457-4 demonstrated that all mortars, both reference and MMWG-modified, complied with the non-hazardous waste limits defined in Council Decision 2003/33/EC. Minor exceedances of Ba and Cr relative to inert-waste thresholds were observed; however, these values remained within the permissible range for non-hazardous classification and were attributed to ceramic and metallic contaminants inherently present in the mixed glass fraction. Overall, this study demonstrates that mixed municipal waste glass—a widely available yet rarely valorised heterogeneous waste stream—can be effectively utilised as a finely ground supplementary material and as a partial aggregate replacement in cement mortars, provided that particle fineness is adequately controlled and durability-related effects are monitored. The findings extend the applicability of glass waste beyond container cullet and support the development of circular-economy solutions in construction materials. Full article

Precision agriculture has increasingly adopted controlled agricultural microclimates, particularly smart greenhouses, as a strategy to enhance crop yields while maintaining environmental conditions within suitable ranges for each crop. Among the variables that govern the water balance in these systems, air humidity plays a critical role; therefore, accurate humidity forecasting is essential for implementing timely control actions that support productivity levels. However, greenhouse conditions are frequently perturbed by extreme weather events, which lead to nonlinear and non-stationary humidity dynamics. In this context, the aim of this study was to design an optimized evolving fuzzy inference system for humidity forecasting that can adapt to changing and unforeseen situations in agricultural microclimates. A prototyping-based methodology was followed, including phases of communication, quick planning, modeling and quick design, construction of the prototype, and deployment. A hybrid genetic algorithm was used to optimize the parameters of an evolving Mamdani-type fuzzy inference system, extended to handle missing values in online data streams. Thirty independent optimization runs were performed, and the best configuration achieved a mean squared error of 1.20 × 10⁻² in humidity forecasting using one minute of data for three months. The resulting model showed high interpretability, with an average number of 1.35 rules, tolerance for missing values, imputing 2% of the data, and robustness to sudden changes in the data stream with a p-value of 0.01 for the Augmented Dickey–Fuller test at alpha = 0.05. In general, the optimized evolving fuzzy inference system obtained an effectiveness rate greater than 90% and demonstrated adaptability to extreme weather conditions, suggesting its applicability to other phenomena with similar characteristics. Full article