Search Results (12,373)

The growing pressure on water resources and the demand for sustainable wastewater treatment have driven interest in low-cost, renewable coagulants for industrial effluent remediation. In this context, chestnut shells (Castanea sativa) were explored as a potential source of natural coagulants through tannin extraction, followed by cationic modification using the Mannich reaction. A Box–Behnken Design was employed to assess the effects of chestnut shell mass, extraction pH, and extraction time on methylene blue decolorization in synthetic wastewater. The quadratic response surface model was not statistically significant, indicating that the tested variables did not collectively explain the observed variation within the investigated range. Consequently, the results were interpreted as exploratory observations within the investigated experimental domain rather than as evidence of optimized or predictive operating conditions. Under the tested conditions, methylene blue removal efficiencies ranged from 20 to 26%, and increasing the coagulant dosage improved decolorization, likely through polymeric bridging. Furthermore, this study demonstrated practical regional industrial integration in Northern Portugal by upcycling agricultural waste to mitigate the impact of textile effluents. This engineering approach aligns with the United Nations Sustainable Development Goals (SDG 6, 9, and 12), suggesting a promising pathway for sustainable industrial scale-up. Full article

Reservoir-bank landslides in canyon-type reservoirs commonly show slow creep and episodic acceleration driven by rainfall infiltration, groundwater fluctuation, reservoir regulation, and engineering disturbance. Sparse coherent targets and the high computational burden of long synthetic aperture radar image stacks limit operational time-series interferometric synthetic aperture radar monitoring (TS-InSAR). Moreover, effectively linking long-term deformation monitoring with mechanism interpretation and short-term prediction remains challenging. This study develops an integrated framework for the Xiaolangdi Reservoir, China, combining Ministack-InSAR, interpretable machine learning, and multi-source deep learning. Sentinel-1A images acquired from 2018 to 2024 were processed using Ministack-InSAR, while random forest (RF) and extreme gradient boosting (XGBoost) combined with Shapley additive explanations (SHAP) were employed to identify the dominant conditioning factors controlling deformation. Based on the identified factors and historical deformation information, multi-source deep learning models were further developed for short-term deformation prediction. Ministack-InSAR improved the spatial continuity of monitoring points (MPs) and preserved phase quality in vegetated reservoir-bank slopes. The RF/XGBoost–SHAP results identified groundwater storage, rainfall, distance to rivers, overburden thickness, and road density as the dominant controls on the spatial variability of deformation. Among the tested prediction models, the multi-source-factor convolutional neural network–long short-term memory (MSF-CNN-LSTM) model achieved the best overall performance, with a mean absolute error (MAE) of 3.0 mm, a root mean square error (RMSE) of 4.5 mm, and a coefficient of determination (R²) of 0.885. These results demonstrate that the proposed framework can effectively integrate deformation monitoring, mechanism interpretation, and short-term prediction, providing practical support for active-zone identification and early warning of reservoir-bank landslides. Full article

Green hydrogen production and storage systems based on renewable energy serve as a core solution to mitigating renewable energy curtailment fluctuations, remedying large-scale long-duration energy storage shortages, and supporting the low-carbon and sustainable development of energy systems. However, complex and dynamic variable operating conditions significantly affect system energy efficiency, operational stability, and comprehensive sustainability performance, restricting the engineering implementation and long-term operational performance of green hydrogen systems. This paper focuses on variable-condition modeling and control technologies for renewable-powered green hydrogen production and storage systems, reviewing major technological innovations and engineering advances reported from 2020 to 2026 while incorporating earlier foundational studies where necessary. It defines the core classification of variable operating conditions and clarifies their influencing mechanisms on system operational performance. In terms of individual equipment and system collaborative regulation, it also summarizes breakthroughs in mechanism-driven, data-driven, and hybrid modeling approaches, as well as the practical engineering applications of hierarchical collaborative control strategies. Based on global megawatt-scale demonstration practices, it further identifies the critical technical bottlenecks limiting the sustainable development and large-scale deployment of green hydrogen systems and outlines promising research and optimization directions for future exploration. The conclusions and prospects of this review provide insightful references for the adaptive optimization, stable and efficient operation, and scaled-up application of green hydrogen systems under variable operating scenarios, thereby facilitating their in-depth integration into emerging power systems and advancing the low-carbon transition and sustainable development of the energy sector. Full article

The convergence of additive manufacturing (AM) and artificial intelligence (AI) is poised to redefine the landscape of modern production; however, the literature remains fragmented across isolated applications, lacking a unified perspective on the engineering impact and practical deployment of these technologies. This review provides a comprehensive and critical synthesis of the state of the art in AI-enhanced AM, systematically covering supervised, unsupervised, and reinforcement learning paradigms, alongside deep-learning-based computer vision, natural language processing, and robotics. In contrast to prior works that focus on singular aspects, this paper consolidates progress across four core engineering domains: (i) lightweight and manufacturable design, (ii) real-time in situ defect detection and process analysis, (iii) energy-efficient process optimization, and (iv) cost-effective build-time estimation with intelligent support minimization. Beyond cataloging these advances, this review identifies key quantitative benchmarks and recurring technical challenges, including data scarcity, poor model generalizability, and the critical gap between offline prediction and real-time closed-loop control. To transcend these isolated successes and enable industrial adoption, we propose a novel, unified closed-loop AI-AM framework that tightly integrates generative design, process planning, in situ production monitoring, and continuous model updating into a cohesive digital thread. Furthermore, a domain-stratified SWOT analysis is compiled, offering a strategic evaluation of strengths, weaknesses, opportunities, and threats across the four application pillars. By bridging the gap between laboratory prototypes and production-ready autonomous systems, this review serves as a definitive reference for researchers and practitioners aiming to navigate, deploy, and advance the rapidly evolving field of AI in additive manufacturing. Full article

Reliability assessment of ship equipment is often constrained by insufficient or unavailable failure data, particularly for highly reliable components with extremely low failure frequencies. To support the use of reference information from similar equipment in subsequent reliability analysis, this study proposes a domain-guided multi-type feature-fusion framework for ship equipment clustering. The proposed framework addresses the heterogeneous nature of ship equipment records by integrating textual, categorical, and numerical attributes into a unified representation. Specifically, equipment names and specification/model information are represented using character-level TF-IDF features; categorical attributes are encoded through One-Hot representation, and numerical attributes are processed using logarithmic transformation and standardization. Six engineering attributes, including equipment name, specification/model information, technical category, measurement unit, number of installations per platform, and reference unit price, are incorporated into the fused feature space. In addition, a domain-knowledge-driven feature-group weighting mechanism is introduced to emphasize attributes that directly reflect functional and technical similarities, especially equipment names and specification/model information. K-Means clustering is then performed in the weighted fused feature space, and the number of clusters is determined by jointly considering the Silhouette Coefficient, Calinski–Harabasz index, Davies–Bouldin index, and cluster-size distribution. Experiments on 1345 practical ship equipment samples show that K = 36 provides a reasonable balance among clustering structure, candidate-set availability, engineering consistency, and stability under random initialization. Compared with the equal-weight scheme, Attribute-Weighted K-Means, K-Prototypes, and Gower-based hierarchical clustering, the proposed framework achieves higher equipment-name and specification/model similarities while maintaining meaningful technical-category consistency. These results indicate that the proposed framework can effectively identify latent functional and technical similarity relationships among ship equipment and provide candidate reference sets for reliability assessment under sparse failure data conditions. Full article

Built-up expansion is reshaping landscapes across Nepal; however, consistent multi-temporal mapping remains challenging due to rugged terrain, fragmented settlements, and heterogeneous land-cover conditions. This study develops and evaluates a multi-product and terrain-informed workflow in Google Earth Engine for mapping built-up expansion across three physiographically contrasting districts of Nepal: Arghakhanchi, Lalitpur, and Chitwan, from 2017 to 2025. Annual predictor stacks were generated by integrating Sentinel-2 spectral bands and derived indices, Dynamic World built-up probabilities, and SRTM-derived elevation and slope variables. ESRI Global Land Cover datasets were used separately for auxiliary cross-product comparison and assessment of the mapped outputs. Preliminary yearly built-up masks were generated using district- and year-specific Random Forest classifications, followed by the post-classification constraints, and were subsequently integrated through cumulative expansion mapping. Accuracy assessment for 2017, 2021, and 2025 yielded overall accuracy values of 86.4–92.4%, built-up F1-scores of 84.7–91.3%, and Kappa coefficients of 0.81–0.91. Between 2017 and 2025, cumulative built-up extent expanded by 8054.65 ha in Chitwan, 2406.20 ha in Arghakhanchi, and 2215.96 ha in Lalitpur; Arghakhanchi recorded the highest proportional increase (117.6%). The mapped expansion was comparatively dispersed in Arghakhanchi, concentrated within metropolitan and peri-urban areas in Lalitpur, and broader and corridor-oriented in Chitwan. Because previously detected built-up pixels were retained in subsequent cumulative outputs, the resulting extents were non-decreasing by construction and did not represent demolition or other land use reversals. Consequently, annual built-up expansion should not be interpreted as net annual land-cover change. The proposed framework provides a practical and transferable approach for comparative built-up expansion monitoring and urban growth assessment across contrasting physiographic settings. Full article

Chromatic confocal studies often report optical design and metrology performance using non-equivalent metrics, limiting reproducibility and practical comparison. We present a design-to-validation study built around a reproducible four-element, three-group all-spherical objective using CDGM catalog glasses. The objective provides an NA of 0.25, a 40 mm working distance, and a nominal 2.100 mm focal shift over 400–700 nm. The workflow separates calibration residuals, independently referenced errors, fixed-position repeatability, and paired-step response, and incorporates manufacturing-tolerance analysis. A monotonic PCHIP calibration produced a 2.31 mm empirical operating interval, while measurement and simulation agreed within 2.90% over their common 440.7–700 nm band. Positions excluded from calibration yielded an uncorrected RMSE of 1.561 µm. Under a predeclared engineering criterion, 1.0 µm centrally and 1.5 µm across the full range were the smallest tested steps that passed. Tolerance analysis identified cemented-group decenter as the dominant sensitivity with assembly refocusing. The principal contribution is a reproducible catalog-glass prescription combined with an error-separated validation protocol that establishes practical performance boundaries without conflating repeatability with absolute accuracy. Full article

The increasing bandwidth demand of artificial-intelligence computing and data-center interconnects has driven silicon photonic optical engines toward higher capacity, compact packaging, and improved scalability. This work presents the packaging design and characterization of a completed 16-channel silicon photonic transmitter module for a 1.6 Tb/s-class optical engine. The module integrates an IMEC-fabricated silicon photonic chip, fiber-array edge-coupling interfaces, wire-bond submounts, PAM4 driver circuits, per-channel MZM heaters, and module-level thermoelectric cooling. A custom submount reduced the highly nonuniform wire-bond distance of 255.98–1788.48 μm to approximately 275.26 μm and provided a simulated packaged-path −3 dB bandwidth of approximately 32 GHz. Fiber-array monitor-port scans showed transverse alignment tolerances of approximately 4–6 μm; after accounting for the nominal 1% monitoring tap and a separately calibrated 4 dB grating-coupler-to-MMF path, the effective packaged input-coupling estimate at 1310 nm was approximately 3.77 dB. All 16 channels were independently verified at 53.125 GBaud PAM4, corresponding to 106.25 Gb/s per lane, and the completed module supports concurrent activation of all lanes. Because the Keysight N1000A optical sampling oscilloscope (Keysight Technologies, Inc., Santa Rosa, CA, USA) accepts one optical input at a time, the eye diagrams and metrics were acquired by sequentially selecting the observed channel and do not constitute a simultaneous 16-channel crosstalk measurement. Across the 16 channels, the measured optical power, OMA, ER, TDECQ, and RLM ranged from −0.09 to 0.30 dBm, 1.141 to 1.267 dBm, 3.037 to 3.055 dB, 3.09 to 3.18 dB, and 0.956 to 0.981, respectively. The measured module-level TEC power was 1.2 W. Using the same nominal 1.6 Tb/s data-rate denominator, the estimated energy consumption is 3.95 pJ/bit for the transmitter electronics excluding the external lasers and TEC and 7.83 pJ/bit when the external laser operating power and measured module-level TEC power are included. These results demonstrate a practical package-level proof of concept for scalable 1.6 Tb/s-class silicon photonic transmitters. Full article

Early-stage selection of pile foundation systems is associated with considerable uncertainty due to limited site information and the need to consider multiple geotechnical, structural, and construction-related factors simultaneously. This study proposes a rule-based decision-support framework, named GeoSupport Decision Platform (GSDP), for transparent and systematic evaluation of pile foundation alternatives across varying engineering conditions. A multicriteria scoring procedure is introduced into the proposed framework to evaluate the recommended pile alternative for the application. The implemented procedure combines compatibility scores and weighting coefficients based on engineering judgement and related to soil type, groundwater conditions, frost depth, load level, vibration/noise restrictions, and site accessibility. The ranked pile alternative list, closely linked to the suitability scores, is an outcome of the framework. The applicability of the GSDP framework is illustrated through two case studies based on construction sites in Kokshetau and Astana, Kazakhstan, representing different geotechnical and construction conditions. For the Kokshetau construction site, the GSDP framework recommends driven piles with a suitability score of 0.975 as the most suitable foundation alternative, followed by bored piles and micropiles, with suitability scores of 0.905 and 0.845, respectively. In contrast, for the Astana case, bored/CFA piles are ranked first with a suitability score of 0.970, followed by micropiles (0.825) and driven piles (0.775). In both cases, the highest-ranked pile alternative corresponds to the pile foundation system adopted at the respective construction site. The results show that the framework generates transparent, interpretable recommendations through a weighted scoring procedure and provides brief explanations that align with the engineering solution chosen for the case study. The contrasting rankings, which demonstrate the ability of the framework to respond to site-specific conditions rather than systematically favoring a single pile type, indicate the GSDP’s practical engineering significance for the preliminary selection of pile foundations when site investigation data are limited. Full article

A flexible part has no unique shape until it is constrained, which makes dimensional inspection difficult. Standard practice clamps it in a dedicated jig and probes it with a coordinate measuring machine or a range sensor. We replace the jig with virtual fixturing. From partial range views of the unfixtured part, the pipeline recovers a coarse pose between the scan and the CAD model using a robust geodesic bilateral curvature algorithm, deforms the model towards the scan by non-rigid registration, and computes deviations along the model surface normal to decide whether the part is in tolerance. On a prismatic part and a game-controller housing, the method is more accurate than optimal-step non-rigid ICP, radial basis function FEM, and coherent point drift, because the generated deformations come from an operator closely related to the proposed method’s own regularizer, those margins favor it by construction and are reported with that caveat. On a generated thin-shell test part the measurement uncertainty of the implementation is measured at about 0.039 mm, dominated by the registration rather than by the sensor. The pipeline is then exercised on a physically scanned injection-molded engine cover, a 612 mm part whose free-state residual against its nominal model is 4.62 mm at the verified global optimum. No independent coordinate-measuring-machine reference was available for that part, so this experiment is reported as a free-state residual and a controlled comparison with and without the feature set, not as a statement of absolute measurement accuracy. Full article

Wood aerogel has emerged as a highly promising substrate for advanced adsorbents due to its green nature, low cost, high porosity, and unique three-dimensional (3D) interconnected network structure. Harnessing forest resources for developing high-performance aerogel materials is crucial for tackling organic dye pollution. This study presents a novel aminated wood-based aerogel engineered through the co-deposition of tannic acid (TA) and polyethylenimine (PEI) on a cellulose skeleton. The fabrication involved a top–down delignification process to create a porous wood aerogel framework, followed by the in situ loading of TA and the grafting of amino-rich PEI, resulting in the final TAPI-DW composite. Benefiting from the abundant active sites deposited on the aerogel’s hierarchically porous surface and the grafted –NH₂ groups, TAPI-DW demonstrated an exceptional adsorption capacity for the anionic azo dye Congo red (CR). The adsorption equilibrium was achieved within approximately 6 h, with a lower pH environment promoting removal efficiency. Coexisting ion experiments indicated that the introduction of Ca²⁺ ions dramatically enhanced the CR adsorption capacity from 168.71 mg·g⁻¹ to 301.14 mg·g⁻¹. This superior capture performance is attributed to the synergistic interplay of the aerogel’s aligned microchannels (derived from the native wood structure) for rapid mass transfer and the intensive chemical interactions, including electrostatic attraction, hydrogen bonding, and π-π stacking between CR molecules and the functional groups (–NH₂ and –OH) on the 3D skeleton. The Freundlich model fitting suggests a complex multilayer adsorption process on this heterogeneous wood aerogel surface. This work establishes a green and sustainable pathway for fabricating high-value biomass aerogel materials that show promise as candidates for the efficient remediation of dye-contaminated water. Further studies on reusability and long-term stability are needed to fully validate their potential for practical application. Full article

Polyhydroxyalkanoates (PHAs) are a structurally diverse family of microbially synthesised, biodegradable polyesters that accumulate as intracellular carbon and energy reserves under conditions of nutrient imbalance. Their combination of adjustable mechanical performance with controlled hydrolytic and enzymatic degradation, and non-toxic degradation intermediates (ex. D-3-hydroxybutyrate) has made them a longstanding candidate biomaterial for human tissue engineering, drug delivery, and resorbable implants. Comparatively, their application in veterinary medicine remains an emerging and fragmented field, despite an arguably stronger practical case: veterinary practice faces acute pressure to replace non-degradable sutures, orthopaedic hardware, and single-use plastics with materials that avoid secondary retrieval surgery, that can be produced at low cost for large-scale animal use, and that align with growing regulatory and consumer demand for sustainable animal healthcare. This review consolidates current understanding of PHA biosynthesis, covering the core: phaA-phaB-phaC pathway, medium-chain-length variants, microbial producers, feedstock flexibility, and metabolic engineering strategies for yield improvement. It also examines material modification strategies, including blending, chemical grafting, surface functionalisation, electrospinning, and additive manufacturing, used to adapt PHAs for specific veterinary form factors. The clinical and preclinical evidence base is presented in detail across wound management, orthopaedic and soft-tissue regeneration, cardiovascular tissue engineering, drug delivery, and surgical devices, with attention to species-specific considerations in companion animals, horses, and food-producing ruminants. This review relies exclusively on peer-reviewed literature for its quantitative claims, while transparently noting where veterinary-specific data are lacking, extrapolated from rodent or human models, or in need of independent verification. Persistent barriers like production cost, batch-to-batch variability, absence of veterinary-specific regulatory pathways, and limited long-term in vivo safety data in large animals are analysed critically, alongside translational opportunities including waste-feedstock valorisation, hybrid PHA/ceramic and PHA/natural-polymer composites, and stimuli-responsive formulations. We conclude that PHAs are scientifically well positioned but institutionally under-validated for veterinary translation, and we outline a concrete research agenda to close this gap. Full article

The second-life utilization of retired power batteries is critically constrained by high cell-to-cell variability in capacity and internal resistance, which severely reduces the usable capacity of repacked modules. To overcome this challenge, this paper presents a multi-stage screening and grouping strategy that balances accuracy with practical efficiency. The method comprises four progressive steps: static Euclidean distance-based pre-screening, 0.1C low-rate reference capacity calibration, 0.5C operating-condition re-screening, and serpentine sorting for final grouping. A total of 389 retired 18650 ternary lithium-ion batteries from a single batch were studied. First, 89 cells were pre-screened using voltage–internal resistance Euclidean distance, from which 16 cells were selected for 0.1C calibration to establish a low-rate reference capacity baseline. Subsequently, 52 cells were re-screened from the remaining 300 and tested at a 0.5C rate. Finally, the 52 cells were assembled into 13 groups via serpentine sorting and uniformly calibrated to 50% SOC. A benchmark conversion coefficient β, defined as the ratio of the mean 0.5C capacity to the mean 0.1C capacity, and a comprehensive consistency index (CQI) were established for evaluation. Results show that the mean 0.1C capacity is 2835.2 mAh with β = 0.9681. After serpentine grouping, the capacity range across the 13 groups is only 16.69 mAh, with a coefficient of variation of 0.0407%—significantly outperforming random grouping—and the CQI reaches 0.985. The proposed method reduces the total capacity testing time from approximately 21.9 days to about 2.5 days, improving efficiency by approximately 88%. In contrast to prior work focusing solely on algorithmic improvements, this study, for the first time, integrates static outlier exclusion, small-sample-rate mapping, and serpentine balanced grouping into a closed-loop engineering workflow, providing a deterministic, rule-based solution for the entire screening-to-grouping pipeline in second-life applications. The method requires neither complex instrumentation nor sophisticated algorithms and exhibits strong robustness against common measurement errors, offering an economical, reliable, and easily replicable engineering solution for retired battery second-life utilization. Full article

Aiming at the problems of high energy consumption, high carbon emissions from data centers and the difficulty of renewable energy accommodation in distribution networks driven by rapid growth in computing tasks, this paper proposes a collaborative trading method for data center–power grid–energy storage systems to improve spatiotemporal flexibility. Firstly, an integrated mechanism model including IT equipment, HVAC cooling systems, delay-tolerant batch tasks and UPS energy storage is established to quantify multi-dimensional internal flexible regulation potential. Secondly, an improved k-means algorithm is adopted for scenario reduction of wind–PV outputs, and a stochastic-robust collaborative trading optimization model considering carbon emission cost is constructed. Multiple practical constraints are incorporated, including power balance, power flow limits, nodal voltage bounds, task service latency and state of charge limits of energy storage. An improved particle swarm optimization with premature-convergence indicator is developed to solve this nonlinear, non-convex, mixed-variable problem. Simulations are carried out on a modified IEEE 33-node test system over a 24 h scheduling horizon. Numerical results demonstrate that compared with the conventional demand-response strategy, the proposed method reduces total operational cost by 10.7%, curtails wind–PV abandoned power, and achieves 28.6% peak-shaving ratio for data center load. Monte Carlo repeated experiments indicate that the improved Particle Swarm Optimization (PSO) reaches a 95% feasible solution rate with an average computation time of 26.8 s for day-ahead dispatch, which satisfies practical engineering requirements. Full article

Ursodeoxycholic acid (UDCA) is the first-line therapy for primary biliary cholangitis and an important active constituent of bear bile. Whole-cell microbial conversion of inexpensive lithocholic acid (LCA) offers a promising alternative to conventional chemical synthesis owing to its high regioselectivity and mild reaction conditions, yet the fermentation process of Fusarium equiseti HG18 (CCTCC M2023160), a natural fungal catalyst for LCA 7β-hydroxylation, has not been systematically engineered for scalable production. This study developed a whole-cell process for UDCA production by F. equiseti HG18. Systematic optimization by single-factor experiments, Plackett–Burman design and Box–Behnken response surface methodology raised the shake-flask UDCA titer from 0.19 to 0.59 mg mL⁻¹. Scale-up in a 3 L stirred-tank bioreactor shortened the fermentation time from 144 h in shake-flask cultivation to 96 h in the bioreactor under optimized operating conditions (pH 8.5, aeration 2.5 L min⁻¹, and agitation 200 rpm). Experiments across a range of initial LCA loadings revealed a progressive decline in UDCA molar yield at elevated substrate concentrations, consistent with substrate-related inhibitory effects, and this loading-dependent behavior was used to design a two-stage fed-batch feeding strategy. Under the optimized condition (2.0 mg mL⁻¹ LCA fed at 0 and 48 h), UDCA titer reached 1.71 mg mL⁻¹, corresponding to a volumetric productivity of 17.8 mg L⁻¹ h⁻¹ and a UDCA molar yield of 41%. This study establishes a laboratory-scale whole-cell bioprocess for UDCA production from LCA using a wild-type fungal catalyst, integrating statistical medium optimization, bioreactor process development, and substrate-feeding strategies. The findings provide a practical framework for improving fungal whole-cell steroid biotransformation and highlight the potential of wild-type fungal platforms for scalable biocatalytic production. Full article