Search Results (3,329)

The build-up of carbon monoxide (CO) in confined spaces can pose serious risks to human health. One method of mitigating CO in air is catalytic oxidation. Low-temperature catalytic oxidation of CO is desirable to enhance energy efficiency and sustainability. Noble metal catalysts have been shown to be active catalysts for CO oxidation at low temperatures. In addition, photocatalysis has been demonstrated as a method to degrade harmful gas-phase compounds into less toxic ones at room temperature. In this study, the effects of platinum (Pt) loading and oxidation state on a TiO₂ (P25) support were investigated in a light-emitting diode (LED)-based flow reactor for the room-temperature catalytic and photocatalytic oxidation of CO in air. It was found that the catalytic activity increased with Pt loading up to 5 wt%, and catalytic activity was significantly higher when the Pt was reduced (Pt⁰) on the catalyst compared to oxidized platinum (Pt²⁺, Pt⁴⁺). Visible light absorption increased with increasing platinum loading on TiO₂, and the catalyst activity for room-temperature CO oxidation increased with exposure to both ultraviolet (UVA) and visible light. Platinum is responsible for the adsorption of CO and the activation of adsorbed oxygen and CO, and it interacts with the photogenerated electrons upon exposure to light to enhance the photocatalytic activity of Pt/TiO₂ catalysts for the room-temperature oxidation of CO. Full article

Aiming at tight farming schedules, slow straw decomposition, and severe soil-borne disease risks in the practical maize straw returning production of China’s wheat–maize double cropping zones, this study developed a field-adapted in situ rapid high-temperature straw composting technology matched with a special composite microbial inoculant. Post-harvest summer maize straw collected from the field was crushed to 3–5 cm; the inoculant group T and water control CK were arranged with three biological replicates. Raw materials were adjusted to 65% moisture and loosely stacked into trapezoidal piles equipped with layered temperature–humidity sensors covered by plastic film for continuous monitoring. After formula and pile structure optimization, the pile temperature exceeded 50 °C within 8 h and stayed at 58–63 °C for 9 days, limiting the composting cycle to within 15 days. Cellulose and lignin degradation reached 56.25% and 50.39%, respectively; available P and K rose by 12.33% and 14.69%, free amino acids doubled; the C/N ratio dropped to 18:1 and the GI exceeded 130%. High temperature enriched functional flora of Bacillus subtilis, Aspergillus niger and actinomycetes, whereas pathogenic Fusarium abundance decreased to less than 1/31 of the initial level. This technology can bring approximately 400 yuan of potential additional benefit per mu, providing an efficient and labor-saving practical candidate for straw returning in regions with a high multiple-cropping index. Full article

Recent studies have witnessed significant advances in unified multi-weather image restoration, which aims to handle diverse weather degradations within a single model. In this work, we observe that rain, haze, and snow restoration exhibit substantial differences in both degradation characteristics and learning dynamics, making straightforward joint optimization prone to performance imbalance and ineffective knowledge transfer. To this end, we propose UMWIR-Net, a unified multi-weather image restoration network equipped with an Asymmetric Task Collaborative Learning strategy. ATCL consists of Intra-Task Difficulty Optimization and Inter-Task Contribution Scheduling. Specifically, Intra-Task Difficulty Optimization jointly models the remaining restoration error and recent learning progress to dynamically estimate the optimization difficulty of each weather task, thereby assigning larger weights to slowly converging and under-optimized tasks. Inter-Task Contribution Scheduling measures the directional influence of a source-task update on the validation objective of a target task, constructs an asymmetric task-contribution matrix, and accordingly promotes tasks that provide stronger transferable knowledge while compensating those that benefit less from collaborative learning. In this manner, different weather restoration tasks collaborate selectively and asymmetrically, allowing the model to exploit complementary knowledge across tasks and improve overall restoration performance. Furthermore, UMWIR-Net adopts a wavelet-based Transformer backbone to capture low- and high-frequency information, enabling effective modeling of both global structures and local details for diverse weather restoration. Extensive experiments on multi-weather image restoration datasets show that UMWIR-Net achieves state-of-the-art performance and delivers more balanced restoration quality across rain, haze, and snow removal. Full article

The Dafosi well field is a typical Huang-Long Jurassic low-rank coalbed methane (CBM) field. Clarifying its CBM geochemical characteristics and the mechanisms of its formation is of significant importance for deepening the understanding of the formation mechanisms of low-rank CBM in China and for the scientific assessment of its resource potential. A total of eight gas emission samples from six coalbed methane wells in the Dafosi coalfield were collected, along with 22 coal samples from the 4^# coal seam. Detailed analyses of microscopic coal petrographic components, gas chemical compositions, and carbon isotopes were performed. By integrating data from the 20 relevant literature sources on coalbed gas composition and isotopic characteristics within the study area, a comprehensive dataset comprising 28 sets was utilized to examine the carbon isotope characteristics and genesis types of both CH₄ and CO₂ in the coalbeds, as well as elucidate the mechanism behind CH₄ carbon isotope depletion. The findings indicate that in the primary 4^# coal seam’s microscopic petrographic composition, the organic matter content is considerably higher, averaging 93.2%. Among these, the inertinite group is dominant, averaging 68.2%; the vitrinite group is the next most abundant, averaging 22.8%. The CBM composition is predominantly CH₄, with concentrations varying from 68.753% to 98.006%, averaging 80.276%. N₂ concentrations range from 1.259% to 29.926%, averaging 17.476%. CO₂ concentrations vary from 0.04% to 2.380%, averaging 1.032%. The average concentration of heavier hydrocarbons C₂ and above is less than 0.078%, indicative of typical dry gas characteristics, C₁/C_1~n > 0.999. The concentration of CH₄ and N₂ was negatively correlated. δ¹³C₁ ranges from −87.200‰ to −62.400‰, averaging −75.802‰. CH₄ is composed of secondary biogenic gas with dominant content and a small amount of thermogenic gas. δ¹³C_CO2 ranges from −41.693‰ to −7.065‰, averaging −20.016‰. CO₂ is an organic gas, mainly derived from thermal degradation and microbial degradation of organic matter. The mechanism responsible for the light carbon isotopic composition of δ¹³C₁ lies in the fact that most of CH₄ is produced by CO₂ reduction, and a small amount is produced by acetic acid fermentation. In the gas generation process of these two pathways, biogenic methane will eventually enrich light carbon isotopes, resulting in light δ¹³C₁. Full article

(1) Background: Whether spatial predictive patterns in non-communicable disease mortality persist after accounting for socio-demographic development and biomarkers remains understudied in East Africa. (2) Methods: This study used heterogeneous graph transformer (HGT) models and other techniques to model spatial patterns in cause- and sex/age-specific mortality (hypertensive heart disease [HHD], ischaemic heart disease [IHD], stroke, and diabetes), incorporating risk factors and socio-demographic development (SDI), using data from the Global Burden of Disease (GBD) study, 1990–2023, across Burundi, Kenya, Rwanda, Tanzania, and Uganda. (3) Results: HGT achieved higher performance than OLS spatial lag benchmarks (R² 0.948–0.970 vs. 0.194–0.376). Spatial predictive patterns were disease-specific. Stroke was the only disease with consistent positive spatial structure (SDI-only: 0.645%, 95% CI [0.380, 0.907]), with spatial structure strengthening after 2015. HHD exhibited severe and stable degradation (Risk-only: −137.892%, 95% CI [−181.908, −96.380]), driven by the interaction between metabolic risk covariates and geographic adjacency. Diabetes showed consistently severe degradation (SDI + Risk: −201.941%, 95% CI [−257.349, −150.082]). IHD patterns were weak and unstable. Sex disaggregation revealed stronger stroke spatial signals, indicating latent sex-specific patterns masked by aggregation. GBD measurement uncertainty contributed less than 0.025% of result variance, with model randomness dominating. (4) Conclusions: Spatial predictive patterns in NCD mortality in East Africa are disease-specific. Stroke shows emerging cross-border spatial structure after 2015, while HHD and diabetes reflect country-specific determinants. Sex-disaggregated graph construction reveals latent spatial heterogeneity invisible to aggregate models, supporting disease-specific, sex-stratified regional health strategies. Full article

Urban rivers face increasing degradation from wastewater, runoff, and land-use change, yet multi-season tropical estuarine datasets remain scarce. This study compared physicochemical water quality, metals, and salinity dynamics over one year in the Juan Díaz and Pacora rivers, two contrastingly urbanized Panama Bay watersheds. Juan Díaz showed higher nutrient concentrations, lower dissolved oxygen, and greater variability than Pacora. Dissolved oxygen in Juan Díaz fell below 5 mg/L in five of nine campaigns and ammonia nitrogen exceeded 3.7 mg/L in all three dry-season campaigns, versus two of seven campaigns below 5 mg/L in Pacora. Total dissolved solids exceeded 500 mg/L in four of nine Juan Díaz campaigns, all in the wet season, but stayed compliant in Pacora. Total nitrogen and total phosphorus were up to 10-fold and seven-fold higher, respectively, in Juan Díaz during the dry season. Cu exceeded its USEPA criterion in two of three detections; Cd was detected once, in Juan Díaz, below its saltwater criterion (0.0079 mg/L). Salinity confirmed stronger tidal influence in Juan Díaz (up to 24.7 g/kg) than Pacora (0.03–2.74 g/kg). PCA separated the two rivers along a nutrient–oxygen gradient explaining 53.6% of variance. Overall, Juan Díaz shows greater degradation, while Pacora remains less contaminated but requires continued monitoring amid rapid urbanization. Full article

A Lyapunov-based framework for stability analysis and synthesis of adaptive neural-network (NN) controllers for a class of uncertain second-order nonlinear systems (SNS) with bounded external perturbations and unmodelled dynamics is presented. Online learning is employed for the reconstruction of the plant nonlinearity with the use of a radial-basis-function (RBF) network whose weights are adapted using a direct adaptation law deduced from a single composite Lyapunov function. The proposed controller couples the weight update to a persistent robustifying action, while the closed-loop stability is guaranteed throughout the learning transient, in contrast to schemes that guarantee stability after learning has converged. Using a composite Lyapunov function in the filtered tracking error and the weight-estimation error, we prove that all closed-loop signals are uniformly ultimately bounded (UUB) and that the tracking error converges to an explicitly characterized residual set whose radius is governed by the network reconstruction accuracy, the disturbance bound and the design gains. A $\sigma$-modification ensures parameter boundedness without persistency of excitation, and a robustness theorem shows that bounded parametric perturbations of the plant preserve stability and enlarge the ultimate bound only gradually (a graceful degradation, rather than a loss of the guarantee). The open-loop plant (a forced double-well Duffing oscillator) is characterized by means of equilibrium and Jacobian analyses. A bifurcation diagram and the largest Lyapunov exponent are presented, which show a chaotic regime (with ${\lambda }_1\approx 0.17$). Numerical experiments indicate that the proposed controller is able to suppress the chaotic motion with a small value of the ultimate bound, and maintain a smooth reference motion with a small and constant RMS error of order ${10}^{-3}$, which is approximately 26 times less than the RMS error obtained with a tuned fixed-gain baseline, and the theoretical dependence of the ultimate bound on the disturbance and the design gains is confirmed by sensitivity sweeps. Full article

‘Genetic zipper’ technology—based on CUAD (Contact Unmodified Antisense DNA) biotechnology, briefly CUADb—represents a step forward in eco-friendly pest control. This unique innovative approach is based on a fundamentally new biological mechanism—a two-step DNA containment (DNAc) mechanism. DNAc employs short, unmodified antisense DNA molecules to selectively degrade target pre-rRNA and/or rRNA in insect pests recruiting up-regulated RNase H1 and RT-RNase H during DNAc, disrupting protein synthesis and causing the down-regulation of kinases due to ATP insufficiency and ultimately leading to high mortality rates. Demonstrating exceptional speed and precision, this technology enables the design of effective and selective DNA pesticides (oligonucleotide pesticides) for no less than 15% of known insect pests in a single day. In this review, we highlight the simplicity and global applicability of this technology using case studies involving 12 economically significant pest species, including hemipterans and one spider mite, from five continents. These oligonucleotide pesticides, computationally predicted via the DNAInsector web tool, are supposed to offer 80–90% efficacy against target pests within one–two weeks under laboratory or field conditions. Their action is primarily non-systemic, requiring direct contact. Oligonucleotide pesticides are environmentally safe, biodegradable, and highly specific, reducing risks to non-target organisms. The ‘genetic zipper’ technology not only provides a powerful tool for researchers and practitioners but also opens a new era in DNA-programmable pest management, where personalized, algorithm-driven pesticides can be easily created and applied for sustainable agriculture. Full article

In natural language understanding, intent recognition plays a central role in human–computer interaction. However, in elderly-care scenarios, acoustic signals are often affected by atypical speech patterns, slower speaking rates, and environmental noise, making audio information less reliable and reducing the effectiveness of conventional text–audio fusion methods. To address this problem, we propose a prompt-guided and quality-aware text–audio intent recognition framework. Specifically, a χ²-based intent prototype soft prompt is introduced to enhance the semantic representation of text. Then, a residual-free text-guided cross-attention module is designed to refine degraded acoustic features using textual semantics as reliable guidance. In addition, a dynamic fusion gate is developed to adjust the contributions of text and audio based on modality reliability and intent-related information. Experiments on the MIntRec dataset with simulated acoustic degradation show that the proposed model achieves 60.90% accuracy, 60.80% weighted F1, and 57.90% macro-F1, outperforming several competitive baselines. These results indicate that the proposed framework can improve the robustness of intent recognition under challenging acoustic conditions in elderly-oriented interaction scenarios. Full article

Sn-3Ag-0.5Cu-xBi alloys (SAC305-xBi) represent promising lead-free alternatives for low-temperature soldering. Low Bi concentrations can strengthen SAC-based solders through solid-solution strengthening, refining β–Sn grains and transforming needle-like Ag₃Sn phases into equiaxed morphologies. However, excessive Bi alloying may induce precipitation of brittle Bi particles, cause microstructural instability and interfacial degradation, thereby weakening the solder joint performance. As such, the concentration of Bi in the SAC305 alloys should be carefully controlled. In this work, the microstructure and corrosion behavior of Sn-3Ag-0.5Cu-xBi solder alloys (SAC305-xBi, where x = 0, 1, 2 and 4 wt. %) were investigated. Attention has been paid to the influence of low Bi concentration on the microstructure, morphology, and chemical composition of the phases present in the solder alloys before and after corrosion exposure. The alloys were prepared by induction melting of Sn, Ag, Cu and Bi lumps under Ar gas. The microstructure of the SAC305 and SAC305-1Bi alloys represented a hypoeutectic microstructure with dendritic (Sn) grains and the ternary eutectic, consisting of (Sn), Cu₆Sn₅ and Ag₃Sn, located in inter-dendritic regions. In the SAC305-2Bi and SAC305-4Bi alloys, a segregation of (Bi) particles was observed in addition to dendritic (Sn) and ternary eutectic. The (Bi) particles were located at the (Sn)Ag₃Sn interface in the inter-dendritic spaces of the (Sn) solid solution. The corrosion resistance of the as-cast alloys was studied in aqueous NaCl electrolyte (3.5 wt. %) using electrochemical methods. Open circuit potentials of the alloys were found to increase with increasing concentration of Bi. The highest corrosion current was found for the SAC305-1Bi alloy. It was observed that micro-galvanic cells at the Sn-Ag₃Sn interface were the initiating factors of corrosion in the SAC305-1Bi alloy. The corrosion activity of the SAC305-1Bi alloy is related to the high density of fine Ag₃Sn particles. The higher fraction of Ag₃Sn particles provided a dense network of local galvanic interaction sites, leading to the acceleration of the corrosion rate. The presence of discrete Bi precipitates in the SAC305-2Bi and SAC305-4Bi alloys, on the other hand, partially reduced the risk of galvanic corrosion. Since Bi has a higher standard electrode potential compared to Sn, the Bi/Ag₃Sn and Bi/Cu₆Sn₅ couples were less prone to corrosion. The corrosion mechanism of the SAC305-xBi alloys is discussed, and results are compared to previously studied SAC-Bi alloys. Full article

Chronic coastal pollution can drive progressive mangrove degradation, yet its spatiotemporal trajectories and post-disturbance recovery remain poorly quantified from satellite observations. In this study, Landsat time-series imagery and the LandTrendr algorithm implemented on Google Earth Engine (GEE) were used to characterize mangrove responses to a kaolin pollution event in Tieshan Port, Guangxi, China. NDVI, NDMI, and NBR trajectories were first compared to identify the most sensitive indicator of contamination-induced stress, and LandTrendr was then applied to extract the timing, magnitude, duration, and spatial extent of mangrove disturbance and recovery. Results showed that kaolin contamination imposed persistent chronic stress on mangroves from 2017 to 2021, with degradation first occurring near Langen Village and then expanding northward across the port. Moderate and severe degradation were mainly distributed along tidal creeks and patch edges, indicating strong spatial control by local hydrodynamics and geomorphology. Among the tested indices, NDVI provided the earliest and clearest response to contamination, whereas NDMI and NBR showed delayed or less consistent responses. The disturbance mapping achieved an overall accuracy of 86.5% with a Kappa coefficient of 0.73. Recovery remained limited after pollution discharge ceased, suggesting persistent environmental constraints on mangrove regeneration. These findings demonstrate that Landsat–LandTrendr trajectories provide an effective framework for monitoring chronic pollution-driven mangrove degradation and recovery in coastal wetlands. Full article

Remaining useful life (RUL) estimation is a fundamental task in Prognostics and Health Management (PHM), supporting condition-based and predictive maintenance of engineering systems. Data-driven methods contribute to many effective strategies for RUL prediction. However, two problems need to be solved when they are used in industrial applications: (1) The amount of data under one working condition is limited, and data from different working conditions suffer from domain discrepancies. These methods are constrained by distribution differences in data under different working conditions. (2) There is an urgent need to quickly achieve prediction with much less computing resources. To address these issues, a lightweight RUL prediction method called a knowledge transfer-based heterogeneous distillation network is proposed by combining knowledge distillation and transfer learning. First, the adversarial training mechanism is introduced for the extraction of domain-invariant features. Subsequently, a heterogeneous knowledge distillation framework is further designed for remaining useful life prediction, in which a bi-directional long short-term memory model serves as the teacher network and a compact fully connected network acts as the student model. The teacher model is used to learn informative degradation patterns and guide the training of the lightweight student model through knowledge transfer. Results obtained on the N-CMAPSS dataset verify that the proposed method achieves promising effectiveness and strong generalizability, reducing the average RMSE and MAE by 44.83% and 41.30%, respectively. Full article

Coptotermes formosanus is an economically significant termite species with a highly organised caste system, in which division of labour underpins colony function. Although gut microbiota is widely recognised for its roles in host nutrition and adaptation, much less is known about how these microbial communities are structured across castes. To explore caste-related gut bacterial variation in C. formosanus, we analysed the community structure of both workers and soldiers using high-throughput amplicon sequencing targeting the bacterial 16S rRNA gene. Although both castes were dominated by Bacteroidota and Spirochaetota, which together accounted for 78.87% to 85.78% in workers and 63.31% to 84.38% in soldiers, significant caste-associated differences were evident. Workers showed significantly higher bacterial richness, as indicated by observed ASVs, Chao1 indices, and Faith’s PD. Further clear caste-associated bacterial community was revealed by beta-diversity analysis. Differential taxonomic analysis revealed distinct caste-associated enrichment patterns. In addition, co-occurrence network analysis indicated a caste-associated interaction structure, with soldier-biased taxa forming a dense, highly connected module while worker-biased taxa contributed to local structure and bridging positions. Furthermore, chemoheterotrophy and fermentation were predicted to be enriched in workers, whereas nitrate reduction, aerobic chemoheterotrophy, aromatic compound degradation, and phenotypes related to biofilm formation, stress tolerance, and mobile elements were predicted to be relatively enriched in soldiers. Moreover, qPCR analysis further showed caste-associated differences in dominant gut protists, with Pseudotrichonympha in workers significantly higher than in soldiers and positively correlated with Azobacteroides. These results provide clear evidence of caste-associated differentiation in gut microbial composition and offer a foundation for identifying novel microbial targets for termite pest management. Full article

Near-surface temperature inversions are prevalent during the cold months in Ulaanbaatar city, Mongolia, and significantly degrade urban air quality by trapping hazardous pollutants within a shallow atmospheric boundary layer. This study investigates spatiotemporal variability, physical mechanisms, and long-term evolution of near-surface temperature inversions over Ulaanbaatar by integrating 25 years (2000–2024) of ground-based meteorological and radiosonde observations, with high-resolution Weather Research and Forecasting (WRF) model simulations for 2012–2023. Our results demonstrate the four-dimensional data assimilation (FDDA) grid nudging effectively captures localized topographic influences in the WRF simulations, showing a strong agreement with radiosonde observations (R² = 0.783, p < 0.000). Near-surface temperature inversions are strongly controlled by the Siberian High, with the highest frequency occurring from December to February, when up to 67% of morning observations exhibit inversion conditions. A pronounced diurnal cycle was identified, with inversion intensity peaking at 5.6–6.8 °C during the early morning hours (02:00–08:00 LST) before reaching a minimum around 14:00 LST. Spatially, the strongest inversions occur along the low-lying Tuul River valley, where the planetary boundary layer is compressed to below 350 m and wind speeds decrease to less than 2.4 m·s⁻¹, creating persistent atmospheric stagnation. Despite these favorable conditions for inversion formation, long-term observations indicate that regional warming (+2.0 °C) and the urban heat island effects have reduced inversion frequency by 31%, inversion thickness by 170 m, and inversion intensity by 0.9 °C over the past 25 years. These findings demonstrate the strong coupling between regional complex terrain, and boundary layer thermodynamics, highlighting the need to incorporate urban ventilation corridors and topography-informed planning into climate adaptation and winter air-quality management strategies. Full article

As timber construction continues to expand toward taller and larger buildings, ensuring structural resilience under fire conditions requires advanced fire–structural performance assessment approaches. Such assessments generally integrate fire models to define thermal exposure, heat transfer models to predict temperature evolution within structural members, and structural models to evaluate fire-induced structural response. While substantial progress has been achieved in fire and thermal modelling, the structural modelling component, particularly constitutive representation of timber behaviour at elevated temperatures, remains comparatively less developed. This paper presents Wood^ST, a temperature-dependent plastic–damage constitutive modelling approach developed to advance the structural assessment of timber structures under fire conditions. The key modelling components of Wood^ST are briefly introduced, and its capabilities are demonstrated through applications to representative timber structural systems, including bending members (LVL, glulam w/o openings, OSB-web I-joists), axially loaded compression members considering stability effects, complex bolted timber connections and assemblies (light wood frame and hybrid timber–concrete floors) involving multiple interacting components and contact behaviour. The presented applications demonstrate the capability of Wood^ST to capture fire-induced material degradation, nonlinear response, instability, and structural interaction across multiple scales. These advances support performance-based fire design and contribute to the development and implementation of design codes and standards (e.g., CSA O86 and ISO TC92), while facilitating innovation in timber products and structural systems. Full article