Search Results (8,921)

Biochar (BC) demonstrates considerable potential for reducing nitrogen emissions. However, it frequently exhibits a limited capacity for the adsorption of NO₃⁻-N, thereby reducing its effectiveness in mitigating N₂O emissions. Nano-biochar (NBC) is attracting attention due to its higher surface energy, but there is a lack of information on enhancing NO₃⁻-N adsorption and reducing N₂O emissions. Accordingly, this study conducted batch adsorption experiments for NO₃⁻-N and simulated N₂O emissions experiments. The NO₃⁻-N adsorption experiments included two treatments: bulk BC and NBC; the N₂O emissions experiments involved three treatments: a no-biochar control, BC, and NBC. N₂O emissions experiments were incorporated into the soil at mass ratios of 0.3%, 0.6%, 1%, and 3%. The results demonstrate that NBC exhibits nearly twice the NO₃⁻-N adsorption capacity compared to bulk biochar (BC), with adsorption behavior best described by a physical adsorption model. The enhanced adsorption performance was primarily attributed to NBC’s significantly increased specific surface area, pore volume, abundance of surface acidic functional groups, and higher aromaticity, which collectively strengthened multiple sorption mechanisms, including physical adsorption, electrostatic interactions, π–π interactions, and apparent ion exchange. In addition, NBC application (0.3–3%) reduced cumulative N₂O emissions by 11.60–54.77%, outperforming BC (9.16–32.65%). NBC treatments also increased soil NH₄⁺-N and NO₃⁻-N concentrations by 2.4–8.2% and 7.3–59.0%, respectively, indicating improved inorganic N retention. Overall, NBC demonstrated superior efficacy over bulk BC in mitigating N₂O emissions and conserving soil nitrogen, highlighting its promise as a sustainable amendment for integrated nutrient management and greenhouse gas reduction in soil. Full article

In this study, we tested a flexible polyurethane (PU) foam, synthesized from bio-based components, for the removal of petroleum-derived fuels from water samples. The PU was synthesized via the prepolymer method through the reaction of PEG 400 with L-lysine ethyl ester diisocyanate (L-LDI), followed by chain extension with 2,5-bis(hydroxymethyl)furan (BHMF), a renewable platform molecule derived from carbohydrates. Freshwater and seawater samples were artificially contaminated with commercial diesel, gasoline, and kerosene. Batch adsorption experiments revealed that the total sorption capacity (S, g/g) of the PU was slightly higher for diesel in both water types, with values of 67 g/g in freshwater and 70 g/g in seawater. Sorption kinetic analysis indicated that the process follows a pseudo-second-order kinetic model, suggesting strong chemical interactions. Equilibrium data were fitted using Langmuir and Freundlich isotherm models, with the best fit achieved by the Langmuir model, supporting a monolayer adsorption mechanism on homogeneous surfaces. The PU foam can be regenerated up to 50 times by centrifugation, maintaining excellent performance. This study demonstrates a promising application of this sustainable and bio-based polyurethane foam for environmental remediation. Full article

Hybrid modeling techniques are increasingly important for improving predictive accuracy and control in biomanufacturing, particularly in data-limited conditions. This study develops and experimentally validates a hybrid deep learning model predictive control (MPC) framework for recombinant P. pastoris fed-batch fermentations. Bayesian optimization and grid search techniques were employed to identify the best-performing hybrid model architecture: an LSTM layer with 2 hidden units followed by a fully connected layer with 8 nodes and ReLU activation. This design balanced accuracy (NRMSE 4.93%) and computational efficiency (AICc 998). This architecture was adapted to a new, smaller dataset of bacteriophage Qβ coat protein production using transfer learning, yielding strong predictive performance with low validation (3.53%) and test (5.61%) losses. Finally, the hybrid model was integrated into a novel MPC system and experimentally validated, demonstrating robust real-time substrate feed control in a way that allows it to maintain specific target growth rates. The system achieved predictive accuracies of 6.51% for biomass and 14.65% for product estimation, with an average tracking error of 10.64%. In summary, this work establishes a robust, adaptable, and efficient hybrid modeling framework for MPC in P. pastoris bioprocesses. By integrating automated architecture searching, transfer learning, and MPC, the approach offers a practical and generalizable solution for real-time control and supports scalable digital twin deployment in industrial biotechnology. Full article

This study analyzes the aromatic profiles of kiwi-based fermented beverages, inoculated with varying proportions of milk kefir grains and incubated under different shaking rates. The experiments were designed using response surface methodology and three consecutive batch cultures were performed under each experimental condition. At the end of each fermentation, the grains were separated from the beverage and reused as the inoculum for fermenting fresh kiwi juice in the subsequent batch. Based on the results, together with the previously determined microbiological and chemical characteristics, two beverages were identified as having broader aromatic profiles, lower contents of sugars, ethanol, and acids, and high counts of lactic acid bacteria (LAB) and yeasts (>10⁶ CFU/mL). These beverages were produced under relatively low agitation rates (38 and 86 rpm) and high inoculum proportions (4.33% and 4.68% w/v) during the second and third batch cultures, respectively. Over 28 days of refrigerated storage, the pH values of both beverages remained relatively stable, and the LAB counts consistently exceeded 10⁶ CFU/mL. Yeast counts, along with the production of ethanol, glycerol, lactic acid, and acetic acid, increased slightly over time. In contrast, the concentrations of citric acid, quinic acid, total sugars, and acetic acid bacteria declined by day 28. Full article

Domestic wastewater discharge is the major source of pollution in Malaysia. Phytoremediation under hydroponic conditions was initiated to treat domestic wastewater and, at the same time, to resolve the space limitation issue by installing a hydroponic system in vertical space at the site. Water hyacinth (WH) was selected in this study to identify its performance of water hyacinth in removing nutrients in raw sewage under batch operation. In the batch experiment, the ratio of COD_initial/plant_initial was identified, and SPSS ANOVA analysis shows that the number of plant size factors was not statistically different in this study. Therefore, four WH, each with an initial weight of 60 ± 20 g, were recommended for this study. Throughout the 10 days of the batch experiment, the average of COD, BOD, TSS, TP, NH4, and color removal was 73%, 73%, 86%, 79%, 77%, and 54%, respectively. The WH biomass weight increased by an average of 78%. The plants have also improved the DO level from 0.24 mg/L to 4.88 mg/L. However, the pH of effluent decreased from pH 7.05 to pH 4.88 below the sewage Standard B discharge limit of pH 9–pH 5.50. Four WH plant groups were recommended for future study, as the COD removal among the other plant groups is not a statistically significant difference (p < 0.05). Furthermore, the lower plant biomass is preferable for the high pollutant removal performance due to the fact that it can reduce the maintenance and operating costs. Full article

Arsenic contamination in groundwater is a significant public health concern, with As(III) posing a greater and more challenging risk than As(V) due to its higher toxicity, mobility, and weaker adsorption affinity. Fe-Mn-based adsorbents offer a promising solution, simultaneously oxidizing As(III) to As(V), enhancing its adsorption. This study evaluates an Fe-Mn nanocomposite across typical batch (20 mg of adsorbent), fixed-bed column (28 g), and pilot-scale (2.5 kg) studies, bridging the gap between laboratory and real-world applications. Batch experiments yielded maximum adsorption capacities of 6.25 mg/g and 4.71 mg/g in a synthetic matrix and real groundwater, respectively, demonstrating the impact of the water matrix on adsorption. Operational constraints and competing anions led to a lower capacity in the pilot (0.551 mg/g). Good agreement was observed between the breakthrough curves in the pilot (breakthrough at 475 bed volumes) and the fixed-bed column studies (365–587 bed volumes) under similar empty bed contact times (EBCTs). The Thomas, Adams–Bohart, and Yoon–Nelson models demonstrated that lower flow rates and extended EBCTs significantly enhance arsenic removal efficiency, prolonging the operational lifespan. Our findings demonstrate the necessity of continuous-flow experiments using real contaminated water sources and the importance of optimizing flow conditions, EBCTs, and pre-treatment in order to successfully scale up Fe-Mn-based adsorbents for sustainable arsenic removal. Full article

This study examines the effects of polyester and denim microfibers (MFs) on aerobic granular sludge (AGS) over a 42-day period. Treatment performance, granulation, and microbial community changes were assessed at 0, 10, 70, 210, and 1500 MFs/L. Reactors with 70 MFs/L achieved rapid granulation and showed improved settling by day 9, while 0 and 10 MFs/L reactors showed delayed granule formation, which was likely due to limited nucleation and weaker shear conditions. Severe clogging and frequent maintenance occurred at 1500 MFs/L. Despite > 98% MF removal in all reactors, treatment performance declined at higher MF loads. Nitrogen removal dropped from 93% to 68%. Phosphate removal slightly increased in reactors with no or low microfiber loads (96–99%), declined in reactors with 70 or 210 MFs/L (92–91%, 89–88%), and dropped significantly in the reactor with1500 MFs/L (86–70%, p < 0.05). COD removal declined with increasing MF load. Paracoccus (denitrifiers) dominated low-MF reactors; Acinetobacter (associated with complex organic degradation) and Nitrospira (nitrite-oxidizing genus) were enriched at 1500 MFs/L. Performance decline likely stemmed from nutrient transport blockage and toxic leachates, highlighting the potential threat of MFs to wastewater treatment and the need for upstream MF control. Full article

Air quality monitoring IoT is one of the approaches to achieving a sustainable future. However, the large area of IoT and the high number of monitoring microsites pose challenges for device maintenance to guarantee quality of service (QoS) in monitoring. This paper proposes a novel maintenance programming model for a large-area IoT containing 1500 monitoring microsites. In contrast to classic device maintenance, the addressed programming scenario considers the division of appropriate microsites into batches, the determination of the batch maintenance date, vehicle routing for the delivery of maintenance services, and a set of hard constraints such as QoS in air quality monitoring, the maximum number of labor working hours, and an upper limit on the total CO₂ emissions. Heuristics are proposed to generate the batches of microsites and the scheduled maintenance date for the batches. A genetic algorithm is designed to find the shortest routes by which to visit the batch microsites by a fleet of vehicles. Simulations are conducted based on government open data. The experimental results show that the maintenance and transportation costs yielded by the proposed model grow linearly with the number of microsites if the fleet size is also linearly related to the microsite number. The mean time between two consecutive cycles is around 17 days, which is generally sufficient for the preparation of the required maintenance materials and personnel. With the proposed method, the decision-maker can circumvent the difficulties in handling the hard constraints, and the allocation of maintenance resources, including budget, materials, and engineering personnel, is easier to manage. Full article

Ruminant livestock contribute significantly to methane emissions, necessitating sustainable mitigation strategies. Essential oils (EOs) show promise for modulating ruminal fermentation, but their synergistic effects remain underexplored. Two 24 h in vitro experiments evaluated the synergistic effects of EO blends on rumen microbial fermentation. Exp. 1 screened five oils using two triad combinations. Triad 1 tested 10 combinations of thyme (THY), peppermint (PPM), and cinnamon leaf (CIN) oils. Triad 2 tested 10 combinations of anise (ANI), clove leaf (CLO), and peppermint (PPM) oils. Each blend was tested at 400 mg/L, using batch culture methods measuring: pH, ammonia-N (NH₃-N), and volatile fatty acids (VFAs). The two most effective blends, designated as T1 and T2, were selected for Exp. 2 to assess total gas and methane (CH₄) production using pressure transducer methods. All treatments were incubated in a rumen fluid–buffer mix with a 50:50 forage-to-concentrate substrate (pH 6.6). In Exp. 1, data were analyzed according to the Simplex Centroid Design using R-Studio. In Exp. 2, an analysis was conducted using the MIXED procedure in SAS. Mean comparisons were assessed through Tukey’s test. The results from Exp. 1 identified CIN+PPM (80:20) and ANI+CLO (80:20) as optimal combinations, both increasing total VFAs while reducing acetate/propionate ratios and NH₃-N concentrations. In Exp. 2, both combinations significantly reduced total gas and CH₄ productions compared to the control, with CIN+PPM achieving the greatest methane reduction (similar to monensin, the positive control). Specific essential oil combinations demonstrated synergistic effects in modulating rumen fermentation and reducing methane emissions, offering potential for sustainable livestock production. Further in vivo validation is required to optimize dosing and assess long-term effects on animal performance. Full article

Bioabsorbable polymer stents (BPSs) were developed to address the long-term clinical drawbacks associated with permanent metallic stents by gradually dissolving over time before these drawbacks have time to develop. However, the polymers used in BPSs, such as polylactic acid (PLA), have lower mechanical properties than metals, often requiring larger struts to provide the necessary structural support. These larger struts have been linked to delayed endothelialisation and an increased risk of stent thrombosis. To address this limitation, this study investigated the incorporation of high-strength basalt fibres into PLA to enhance its mechanical performance, with an emphasis on optimising the processing conditions to achieve notable improvements at minimal fibre loadings. In this regard, PLA/basalt fibre composites were prepared via twin-screw extrusion at screw speeds of 50, 200, and 350 RPM. The effects were assessed through ash content testing, tensile testing, SEM, and rheometry. The results showed that lower screw speeds achieved adequate fibre dispersion while minimising the molecular weight reduction, leading to the most substantial improvement in the mechanical properties. To examine whether a second extrusion run could enhance the fibre dispersion, improving the composite’s uniformity and, therefore, mechanical enhancement, all the batches underwent a second extrusion run. This run improved the dispersion, leading to increased strength and an increased modulus; however, it also reduced the fibre–matrix adhesion and resulted in a notable reduction in the molecular weight. The highest mechanical performance was observed at 10% fibre loading and 50 RPM following a second extrusion run, with the tensile strength increasing by 20.23% and the modulus by 27.52%. This study demonstrates that the processing conditions can influence the fibres’ effectiveness, impacting dispersion, adhesion, and molecular weight retention, all of which affect this composite’s mechanical performance. Full article

Rolling bearings are indispensable but also the most fault-prone components of rotating machinery, typically used in fields such as industrial aircraft, production workshops, and manufacturing. They encounter diverse mechanical stresses, such as vibration and friction during operation, which may lead to wear and fatigue cracks. From this standpoint, the present study combined a 1-D convolutional neural network (1-D CNN) with a long short-term memory (LSTM) algorithm for classifying different ball-bearing health conditions. A physics-guided method that adopts fault characteristics frequencies was used to calculate an optimal input size (sample length). Moreover, grid search was utilized to optimize (1) the number of epochs, (2) batch size, and (3) dropout ratio and further enhance the efficacy of the proposed 1-D CNN-LSTM network. Therefore, an attempt was made to reduce epistemic uncertainty that arises due to not knowing the best possible hyper-parameter configuration. Ultimately, the effectiveness of the physics-guided optimized 1-D CNN-LSTM was tested by comparing its performance with other state-of-the-art models. The findings revealed that the average accuracies could be enhanced by up to 20.717% with the help of the proposed approach after testing it on two benchmark datasets. Full article

This paper presents an integrated computational framework for predicting temperature fields in glulam beam–column connections under fire conditions, combining finite element modeling, automated parametric analysis, and deep learning techniques. A high-fidelity heat transfer finite element model was developed, incorporating the anisotropic thermal properties of wood and temperature-dependent material behavior, validated against experimental data with strong agreement. To enable large-scale parametric studies, an automated Abaqus model modification and data processing system was implemented, improving computational efficiency through the batch processing of geometric and material parameters. The extracted temperature field data was used to train a DeepONet neural network, which achieved accurate temperature predictions (with a L2 relative error of 1.5689% and an R² score of 0.9991) while operating faster than conventional finite element analysis. This research establishes a complete workflow from fundamental heat transfer analysis to efficient data generation and machine learning prediction, providing structural engineers with practical tools for the performance-based fire safety design of timber connections. The framework’s computational efficiency enables comprehensive parametric studies and design optimizations that were previously impractical, offering significant advancements for structural fire engineering applications. Full article

Four machine learning models (Decision Tree Regressor, Linear Regression, XGBoost Regression, K-Neighbors Regressor) were developed to predict the outcomes of a composting process based on key input parameters, including Ambient Temperature, mixture composition, and initial feedstock volume. The models were trained on data from 88 composting batches, monitoring temperature evolution, and compost yield. Performance evaluation demonstrated high accuracy in predicting compost maturity, process duration, and final product quantity. These predictive models could optimize composting operations by enabling real-time adjustments, improving efficiency, and enhancing resource management in sustainable waste processing. Full article

The development of efficient and sustainable water recycling systems is essential for long-term human missions and the establishment of space habitats on the Moon, Mars, and beyond. Humidity condensate (HC) is a low-strength wastewater that is currently recycled on the International Space Station (ISS). The main contaminants in HC are primarily low-molecular-weight organics and ammonia. This has caused operational issues due to microbial growth in the Water Process Assembly (WPA) storage tank as well as failure of downstream systems. In addition, treatment of this wastewater primarily uses adsorptive and exchange media, which must be continually resupplied and represent a significant life-cycle cost. This study demonstrates the integration of a membrane-aerated biological reactor (MABR) for pretreatment and storage of HC, followed by brackish water reverse osmosis (BWRO). Two system configurations were tested: (1) periodic MABR fluid was sent to batch RO operating at 90% water recovery with the RO concentrate sent to a separate waste tank; and (2) periodic MABR fluid was sent to batch RO operating at 90% recovery with the RO concentrate returned to the MABR (accumulating salinity in the MABR). With an external recycle tank (configuration 2), the system produced 2160 L (i.e., 1080 crew-days) of near potable water (dissolved organic carbon (DOC) < 10 mg/L, total nitrogen (TN) < 12 mg/L, total dissolved solids (TDS) < 30 mg/L) with a single membrane (weight of 260 g). When the MABR was used as the RO recycle tank (configuration 1), 1100 L of permeate could be produced on a single membrane; RO permeate quality was slightly better but generally similar to the first configuration even though no brine was wasted during the run. The results suggest that this hybrid system has the potential to significantly enhance the self-sufficiency of space habitats, supporting sustainable extraterrestrial human habitation, as well as reducing current operational problems on the ISS. These systems may also apply to extreme locations such as remote/isolated terrestrial locations, especially in arid and semi-arid regions. Full article

Deep learning approaches for pork freshness grading typically require large datasets, which limits their practical application due to the high costs associated with data collection. To address this challenge, we propose BBSNet, a lightweight few-shot learning model designed for accurate freshness classification with a limited number of images. BBSNet incorporates a batch channel normalization (BCN) layer to enhance feature distinguishability and employs BiFormer for optimized fine-grained feature extraction. Trained on a dataset of 600 pork images graded by microbial cell concentration, BBSNet achieved an average accuracy of 96.36% in a challenging 5-way 80-shot task. This approach significantly reduces data dependency while maintaining high accuracy, presenting a viable solution for cost-effective real-time pork quality monitoring. This work introduces a novel framework that connects laboratory freshness indicators to industrial applications in data-scarce conditions. Future research will investigate its extension to various food types and optimization for deployment on portable devices. Full article