Search Results (5,302)

This study investigates the acoustic emission (AE) response and damage mode characteristics of ±45° glass fiber-reinforced polymer (GFRP) composites used in wind turbine blade shear webs under quasi-static tensile loading. It aims to establish the relationship between AE features and three typical damage mechanisms—matrix cracking, interfacial debonding, and fiber fracture—to support damage assessment and structural health monitoring. Quasi-static uniaxial tensile tests with synchronous AE monitoring are conducted on specimens with three orientations (0°, 45°, and 90°). AE features are selected using correlation analysis and principal component analysis, and the HAC-initialized K-means clustering method is employed for damage mode identification. The optimal number of clusters is determined to be three, according to the Davies–Bouldin index (DBI) and the Silhouette index (SI). The resulting low-, mid-, and high-frequency clusters are associated with matrix cracking, interfacial debonding, and fiber fracture, respectively. These interpretations are further supported by wavelet-based time–frequency analysis and microscopic fracture surface observations. Full article

Fine-scale accounting of land use carbon budgets and identification of their driving factors provides an essential scientific basis for constructing green and low-carbon territorial spatial systems. This is of great significance for optimizing territorial spatial structure and promoting low-carbon development in urban agglomerations. Taking the Central Guizhou Urban Agglomeration as the study area, this study employed a composite carbon coefficient method to construct a 30 m × 30 m grid-based carbon budget index and quantitatively assessed carbon budget changes induced by land use transitions from 2000 to 2024. POI data and a quantile regression model were further integrated to analyze the dominant spatial characteristics associated with carbon budgets, and a carbon budget monitoring and early-warning index was developed to delineate risk zones. The results show that: (1) From 2000 to 2024, the total area of land use change reached 0.95 × 10⁴ km² in the Central Guizhou Urban Agglomeration, accounting for 17.68% of the total land area, and leading to a net increase of 2.3821 million tons of carbon emissions. This increase was primarily associated with the conversion of cultivated land to construction land, with an accelerated growth rate observed in the later period. (2) The spatial patterns of carbon budgets and carbon emission risk levels exhibit a distinct “core–periphery” structure, with high carbon emission levels concentrated in built-up urban areas and lower levels observed in peripheral ecological land. (3) The expansion of construction land is the dominant contributor to the increase in net carbon emissions; industrial, transportation, and residential spaces exert significant positive driving effects, whereas commercial and service spaces show a negative association. (4) Carbon budget risk zoning based on dominant spatial characteristics identifies Guiyang and Anshun as extremely high-risk areas. The results further suggest that reducing carbon-increment spaces and increasing carbon-reduction spaces may play an important role in territorial carbon budget optimization. The integrated “accounting–driving–monitoring” analytical framework established in this study provides a scientific basis for territorial spatial optimization and carbon emission reduction in mountainous urban agglomerations. Full article

During the excavation of the Qinling Water Conveyance Tunnel, rockbursts influenced by structural planes with varying dip angles occurred frequently, posing a significant threat to personnel and construction safety. This study combines statistical analysis of rockburst cases, numerical simulation, and microseismic monitoring to systematically reveal the influence mechanism of structural plane dip angle on rockbursts. Statistical results indicate that intense rockbursts occurring in the dip angle interval of 0–30° account for 60%. Numerical simulations further demonstrate that dip angles of less than 90° (especially near 10°) induce continuous stress accumulation, leading to large-scale instability. Specifically, the peak acoustic emission (AE) count at a dip angle of 10° is significantly higher than in other configurations, further indicating the highest rockburst risk. Incorporating the influence mechanism of structural plane dip angle into microseismic monitoring analysis significantly improved prediction accuracy. This approach successfully predicted an intense rockburst. Based on these findings, engineering suggestions regarding excavation direction and rockburst early warning optimization are proposed, offering a valuable reference for rockburst mitigation in deep-buried tunnels under similar geological conditions. Full article

Formaldehyde emissions from urea–formaldehyde (UF)-bonded particleboards remain a significant environmental and health concern. This study evaluates the effectiveness of flours as bio-based formaldehyde scavengers in particleboard production. Food-based flours (soy, wheat, green pea) and feed flours (hemp, maize DDGS, feather meal) were incorporated into UF resin at concentrations of 0.3–2.0%. Resin characterization included pH, viscosity, gelation time, solid content, and free formaldehyde, while rheological behavior was monitored at 70 °C and 90 °C. The addition of flour decreased pH from 9.1 to 7.9 and increased viscosity from 414 to up to 1600 cP, depending on flour type and dosage. Free-formaldehyde content was reduced from 0.17% to as low as 0.08%, with the most effective reduction observed for hemp flour. At industrial scale, particleboards produced with 0.5% soy and hemp flours significantly reduced free formaldehyde, with emission values of 3.26 mg/m² and 3.05 mg/m², corresponding to reductions of 66–70% compared to the reference (3.97 mg/m²). Mechanical properties, including density (652–665 kg·m⁻³), bending strength (13.2–14.1 N·mm⁻²), and internal bond (0.42–0.45 N·mm⁻²), were maintained within acceptable limits. While feed flours such as feather meal showed strong scavenging potential, they caused significant viscosity increases (up to 1800 cP), limiting processability. These findings demonstrate that adding low levels of flour, particularly soy or hemp, is an effective, renewable, and low-cost strategy to reduce formaldehyde emissions in UF-bonded particleboards, supporting the production of safer and more sustainable wood-based composites. Full article

This study explored the continuous monitoring of hydrogen-induced cracking (HIC) in high-strength steel tension wires used in metal-based flexible pipes, exposed to a H₂S-saturated aqueous environment, using acoustic emission (AE) techniques. Armor wire samples were subjected to sour conditions under controlled environments for 24 and 96 h. To reinforce and validate the AE findings, a comprehensive characterization was performed, including X-ray microtomography, optical microscopy, and scanning electron microscopy. The experimental results demonstrated that AE techniques effectively monitored the evolution of HIC damage in the armor wire samples, enabling the identification of distinct damage stages and cracking phenomena. These findings confirm that AE can serve as a valuable complementary tool during HIC testing, optimizing test duration and providing insights into the kinetics of the cracking process. Full article

Reducing the environmental impact of animal production is a major challenge in the context of climate change and sustainable agriculture. Although rabbit farming is generally considered less resource-intensive than other livestock systems, it still contributes to emissions of ammonia (NH₃), hydrogen sulfide (H₂S), and methane (CH₄), which can negatively affect air quality and the climate. This study aimed to evaluate whether dietary supplementation with selected natural feed additives could mitigate gaseous emissions and lower the environmental footprint of rabbit production. An experimental feeding trial was conducted in which gaseous emissions from rabbit housing were monitored, and the gas composition of feces was analyzed. Emissions were quantified and expressed as carbon dioxide equivalents (CO₂e) to allow comparative assessment of environmental impact. The inclusion of natural feed additives significantly reduced the emission of gaseous pollutants compared with the control diet, resulting in a lower calculated environmental footprint of the production system. These findings indicate that targeted modification of rabbit diets using natural feed ingredients can be an effective strategy for reducing harmful gaseous emissions and enhancing the environmental sustainability of rabbit farming. Full article

Laser powder bed fusion (LPBF) can fabricate intricate metal components but is prone to defects, such as porosity and cracks, that degrade performance. We present an in situ monitoring framework that fuses structure-borne acoustic emission (AE) and coaxial two-color pyrometry acquired synchronously at 1 MHz. Modality-specific encoders are pretrained separately, their latent representations are exported, and a lightweight feature-level fusion classifier with two binary heads predicts crack-like and porosity-like indications. Evaluation uses a held-out grouped experiment/build-machine-part split with independent Archimedes density and micro-CT ground truth. On the held-out test set, the fused model achieved F1 = 0.974 for crack-like detection and F1 = 0.987 for porosity-like detection, with AUROC = 0.998 and 0.993, respectively. Recall was 1.00 for both heads, corresponding to false-positive rates of 11.18% for crack-like and 0.945% for porosity-like indications. These results support synchronized AE-pyrometry fusion as a promising high-sensitivity in situ screening approach for LPBF. A later matched within-framework ablation campaign was also performed under stricter checkpoint-screening rules to compare AE + PY + Aux, AE + PY, AE-only, and PY-only variants under a common grouped-split protocol. Together, these results support multimodal monitoring while highlighting the need for explicit coupon/geometry-stratified reporting and for separately architecture-optimized unimodal baselines. Full article

Wind energy stands as one of the most technologically mature renewable sources, playing a pivotal role in the mitigation of greenhouse gas emissions. However, wind farms and associated infrastructures increase collision risk for flying organisms. Implementing higher cut-in speeds is a proven mitigation strategy to significantly decrease wildlife mortality rates, particularly for bat species, by preventing turbine operation during low-wind periods of high activity. The suggested, non-standard, increased cut-in speed for wind turbines is generally 5.0 m/s. To test the effectiveness of cut-in speed increase, bat activity was monitored at three wind farms in northern Portugal (Gevancas, Azinheira, and Lagoa de Dom João e Feirão), to characterize spatial and temporal activity patterns and assess the potential associated risk. Ultrasonic acoustic detection was carried out at fixed stations, at heights of 55 m above ground level from March to October. Wind speed data were recorded concurrently using anemometers mounted on meteorological towers. Contradicting recommendations, the results show that significant bat activity might occur at wind speeds above the current curtailment values. Since turbine operation coincides with peak bat activity, it is imperative to implement site-specific mitigation strategies, such as optimized cut-in speeds, to minimize mortality risk. Full article

In order to reach net-zero by 2050, we need to have strong decarbonization policies, especially in hard-to-abate clean-ups like steel (8% of the global emissions), cement (7%), and power generation (30%), and negative emissions through direct air capture (DAC) and bioenergy with carbon capture and storage (BECCS). This review paper summarizes the progress in CO₂ capture, compression, transportation, and storage technologies between 2020 and 2025, including energy penalty (20–40%) and cost (15–30%) reductions, with innovations such as metal–organic frameworks (MOFs), bio-inspired catalysts, ionic liquids, and artificial intelligence (AI)-based optimization. This paper, as a new input into the carbon capture and storage (CCS) field, uses the Weighted Sum Model (WSM) as a multi-criteria decision-making tool to rank the best technologies in the capture, storage, monitoring, and transportation sectors. The weights of the criteria are calculated based on Shannon entropy, and the assessment is performed in three conditions, namely, optimistic, pessimistic, and expected. The weights are computed with sensitivity analysis to make the assessment robust. The viability of key projects, such as Northern Lights (Norway, 1.5 MtCO₂/year), Porthos (The Netherlands, 2.5 MtCO₂/year), Quest (Canada, 1 MtCO₂/year), and Petra Nova (USA, 1.6 MtCO₂/year), is evident, and it is projected that, globally, CCS will reach 49 MtCO₂/year across 43 plants in 2025. The review incorporates socio-economic and environmental justice, including barriers such as high costs ($30–600/MtCO₂), energy penalties (1–10 GJ/tCO₂), and opposition between people (20–40% in EU/US). In comparison with previous reviews, this article has a more comprehensive focus, provides quantitative synthesis through WSM, and discusses the implications for researchers, policymakers, and stakeholders towards achieving faster CCS implementation on the path to net-zero. Full article

Bearings play crucial roles in industrial machinery. Therefore, the continuous monitoring and effective detection of bearing failures are essential to ensure the safety and reliability of motors. Traditional fault diagnosis methods often require information from both the time and frequency domains; however, converting them into a two-dimensional representation significantly increases computational costs. Conversely, utilizing only time-domain features while ignoring frequency-domain features results in incomplete fault information, reducing accuracy under various operating conditions. This study proposes an efficient dual-stream network with soft-gated fusion for bearing fault diagnosis that simultaneously analyzes acoustic emission signals in the time and frequency domains. Our approach employs two separate feature-learning branches: the time-domain branch directly extracts features from the segmented raw acoustic emission signals, and the frequency-domain branch learns features from one-dimensional spectral vectors obtained using the fast Fourier transform. A gated fusion mechanism adaptively balances the contribution of each domain before classifying fault types. The experimental results show that the proposed method significantly reduces the computational cost compared with that of a two-dimensional-representation-based model and improves accuracy over time-only or frequency-only baselines. Full article

Additive manufacturing has recently become a key enabling technology in industrial fields, ranging from customized products for everyday usage to aerospace applications and small-batch industrial tooling. The future prospects extend up to the biofabrication of human organs. Ensuring the quality and repeatability of this process requires a systematic and comprehensive investigation of the underlying physical phenomena. In particular, melt-pool evolution is a critical feature, since irregularities in its spatial profile can influence microstructural evolution and weaken the integrity of the manufactured part. Microscale defects arising from balling and keyhole phenomena, often associated with recoil pressure, can severely degrade the quality of the resulting scanned track. This paper reviews the current state of optical approaches for melt-pool characterization and feature monitoring relevant to industrial laser additive manufacturing for process control and quality improvement, with a special focus on pyrometry and high-speed imaging. A single high-speed camera was generally used in experiments for melt-pool feature extraction, but two cameras were used to bypass emissivity values, which are otherwise difficult to obtain. Mathematical models were introduced to provide complementary information about melt-pool features, while artificial intelligence algorithms were used in other cases to process optical information. New melt-pool imaging databases and classifiers are expected in the near future to enable fast selection of appropriate process parameter windows, eliminating costly trial-and-error experiments. Full article

Hybrid renewable energy systems (HRES) combining photovoltaic, wind, fuel cell, and energy storage technologies are becoming established as viable options for reliable, environmentally friendly distributed electricity generation. In this review, we examine the key architectures, monitoring and forecast approaches, and control systems that improve the efficiency of HRES and facilitate the just-energy transition to low-carbon power generation systems. The main optimization and decision-aware approaches, particularly the evolutionary generation algorithms and machine learning-based prediction models, are addressed with a focus on improving energy allocation, cost minimization, and increased use of clean renewable energy sources. Technical, economic, and environmental performance indicators, such as the levelized cost of energy (LCOE), net present cost (NPC), renewable fraction (RF), and CO₂ emissions reduction, have been compared to demonstrate the feasibility of various system scenarios. This paper evaluates and summarizes recent case studies from around the world and presents the best practices and the challenges they encounter, including resource availability, governance, and economic drivers. The balance of the paper demonstrates that smart forecasting with advanced energy management approaches is crucial for developing sustainable and resilient hybrid distributed power systems for the future. Full article

The large-scale use of compost in arable cropping systems is often limited by the large quantities required to meet the crop’s nutritional needs. Palletization can increase the nutrient density of organic fertilizers and improve their logistical feasibility by reducing storage, transport and application costs. This study evaluated the agronomic and environmental performance of compost pellets derived from pig slurry solids and olive pomace, using them as an alternative nitrogen source for wheat (Triticum aestivum L.) cultivated under Mediterranean conditions. A field experiment was conducted during the 2022–2023 growing season, with four treatments arranged in 24 m² replicated plots: an unfertilized control (C); pelletized compost (PSCOP); fresh pig slurry (PS); and mineral fertilization based on monoammonium phosphate and urea (IN). Excluding the control treatment, all fertilized plots received a uniform nitrogen rate of 150 kg N ha⁻¹. Soil chemical properties and nutrient availability (Pext, NH₄⁺-N and NO₃⁻-N) were evaluated at the beginning and end of the experiment, while wheat yield and grain quality were assessed at harvest. Greenhouse gas (GHG) emissions were monitored throughout the cropping season to evaluate environmental impacts. The results showed that the wheat yields achieved with PSCOP were comparable to those obtained with PS, although they remained lower than those achieved with mineral fertilization. Grain quality was not adversely affected by the application of PSCOP. Furthermore, PSCOP resulted in lower GHG emissions than mineral fertilization, with values closer to those observed in the unfertilized control. These findings suggest that pelletized organic fertilizers such as PSCOP may be a promising way to enhance nutrient circularity and reduce reliance on synthetic fertilizers and maintain crop productivity and limit environmental impact in Mediterranean agricultural systems. Full article

As a significant source of global carbon emissions, the construction industry (CI) urgently needs to promote green transformation with the help of digital twin (DT) against the backdrop of human–machine collaboration and sustainable development advocated by CI 5.0. However, there is still a lack of systematic research on its specific driving mechanism and carbon reduction path. This study uses a systematic literature review (SLR) to explore how five key DT-enabled capabilities, namely, resource management (RM), process optimization (PO), real-time monitoring (R-Tm), sustainable design (SD), and predictive maintenance (PM), influence three performance indicators: efficiency improvement (EI), energy optimization (EO), and cost control (CC). Data from 490 companies were analyzed using partial least squares structural equation modeling (PLS-SEM) and a multilayer perceptron (MLP) with Shapley additive explanation (SHAP). The results show that the PLS-SEM and MLP models showed consistent patterns, with EO exhibiting the strongest predictive performance (Q² = 0.372; R² = 0.3666), followed by EI (Q² = 0.307; R² = 0.3109) and CC (Q² = 0.305; R² = 0.2609); the SHAP results further indicated that RM contributed most to EI (0.242), while PO was the most important driver for both EO (0.304) and CC (0.259). Academically, it introduces a quantitative approach combining PLS-SEM and machine learning. Practically, it highlights the priority of key technologies with cross-dimensional effects and offers guidance for governments to optimize digital resource allocation and carbon performance evaluation, as well as for enterprises to apply DT more effectively. Full article

Achieving urban climate neutrality and interim mitigation targets requires rapid demand-side emission reductions, yet current user-centred interventions remain fragmented, are often concentrated on low-impact actions, and rarely provide a traceable basis for comparing outcomes, validity conditions, and equity implications across contexts. This paper reframes demand-side mitigation as a design problem of “use infrastructures”: integrated configurations of communication, product-technology, services, interaction, and governance that make low-carbon choices practicable within everyday routines. We introduce the Design Evidence Link (DEL) as a traceability device supporting ex ante configuration (selection and orchestration of levers) and ex post verification (monitoring, attribution of outcomes, and trade-off control). Through a design-led comparative analysis of nine international cases in high-impact sectors (household energy, ground mobility, food systems, and circular economy/materials), we derive and consolidate a shared extraction and coding protocol that links determinants (barriers and enablers) to design requirements and decision-grade metrics (carbon impact, adoption, continuity, and equity), explicitly qualifying uncertainty and evidence levels. Cross-case results show that effective interventions rely less on isolated information and more on coordinated action packages that reduce cognitive and economic frictions, enhance data credibility through standards and accountability, and embed follow-up mechanisms that support behavioural continuity. DEL also surfaces recurring validity conditions and failure modes (digital exclusion, trust erosion, rebound, and lock-in), translating them into operational criteria for policy and design. Compared with behaviour-change or theory-of-change framings, DEL focuses on the observable orchestration of integrated conditions of use and on the explicit grading of evidence. It should therefore be read as a structured analytical–operational framework for ex ante and ex post assessment, whose transferability remains conditional on source quality, contextual prerequisites, and the limits of the selected cases. Full article