Search Results (2,696)

This paper investigates an inverse problem of recovering the spatial component of a source term for a degenerate ultraslow diffusion equation with a Hadamard–Caputo fractional derivative. Nonlocal boundary conditions of the Samarskii–Ionkin type are imposed. A distinctive feature of the problem is that the system of eigenfunctions of the associated spectral problem, although complete and minimal, does not form an unconditional basis in $L_2(0,1)$. Hence the standard eigenfunction Fourier expansion cannot be applied directly. To overcome this difficulty, we construct a special auxiliary system from normalized linear combinations of asymptotically close eigenfunctions and prove that it forms a Riesz basis. Expansion with respect to this basis reduces the inverse problem to a cascade system of fractional differential equations for the modal coefficients. Using spectral asymptotics, estimates for the Le Roy function, and two-sided Riesz basis inequalities, we prove the existence, uniqueness, and conditional Lipschitz stability of a strong generalized solution and rigorously justify the convergence of the resulting series. We also quantify the high-frequency instability of the inverse mapping in the weaker $L_2$ data topology. An analytical two-mode illustration shows explicitly how neglecting the coupling between asymptotically close spectral modes changes the reconstructed source coefficient. Full article

The construction industry’s dependence on ordinary Portland cement (OPC) makes low-carbon binder systems an urgent priority; yet, the nonlinear interactions among multiple supplementary cementitious materials (SCMs) and nanomaterials complicate rational mix design. This study fuses explainable artificial intelligence (XAI) with a hierarchy of closed-form mathematical formalisms and an experimental durability programme for a quaternary sustainable concrete in which OPC is partially replaced by sugarcane bagasse ash (SCBA, 40 kg/m³), ground granulated blast furnace slag (GGBS, 60 kg/m³), natural zeolite (20 or 40 kg/m³) and nano-silica (0–20 kg/m³) at a constant water–binder ratio of 0.45. Thirteen mixes were tested for compressive and flexural strength, rapid chloride penetration (RCPT) and sulfuric acid resistance at 7, 28 and 56 days. The optimum blend (12 kg/m³ nano-silica) reached 45.0 MPa at 28 days, 49.5% above the control, while reducing chloride charge by 70% and acid mass loss by 65%. Information theoretic discrimination among three competing hydration kinetics laws selects the hyperbolic rate model with an Akaike weight of 1.000 (ΔAIC_c > 32), showing the blend raises the ultimate strength ceiling by 46% while delaying half-strength by only two days. Within this mix series, effective binder (k-value) analysis indicates that, at low dosage, one kilogram of nano-silica contributes 28-day strength broadly comparable to that of several tens of kilograms of OPC (a dataset-specific, dose-dependent estimate rather than a general mass equivalence), and three independent estimators—the experimental peak, the response surface stationary point (12.8 kg/m³) and the marginal efficiency zero (13.2 kg/m³)—converge on an optimum nano-silica dosage of 3.0–3.3% of binder. Principal component analysis compresses the six-dimensional strength–durability response into a single latent statistical axis (interpreted as an indicator of pore connectivity) carrying 91.5% of the variance, and a Fickian error function solution seeded by Berke–Hicks conversion of RCPT charge projects a 3.4-fold extension of the chloride-initiation service life (36.7 versus 10.8 years at 50 mm cover). Six machine learning models were benchmarked; extremely randomized trees performed best (R² = 0.9905, RMSE = 0.920 MPa; leave-one-out R² = 0.986; bootstrap 95% CI on R² [0.981, 0.996]), and SHAP force plot attributions were triangulated with Sobol global sensitivity indices (curing age 75.4%, nano-silica 23.1% of output variance) and response surface significance tests. The optimized mixes cut embodied CO₂ by 26–32% and improve eco-strength efficiency 2.1-fold; grey relational analysis over six strength, durability and carbon criteria ranks the 12 kg/m³ nano-silica mixes first. The framework demonstrates how interpretable machine learning, information theoretic model selection, diffusion theoretic service-life projection and experimental durability evidence can be unified into a transparent, physically validated basis for sustainable concrete mix design. Full article

Molecular dynamics simulations examined the roles of molecular structure, thermodynamic compatibility, intermolecular association, and mixture composition in the supercritical CO₂ extraction of ten crude oil components at 363.15 K and 15 MPa. Single-component extraction ratios ranged from 80.70% for n-hexane to 5.85% for 2-naphthol. Compounds of similar size differed widely, indicating that topology, aromaticity, and polar functional groups were more informative than molecular size alone. CO₂ solubility parameters obtained from MD agreed with estimates derived from NIST data, and δ_CO2 = 7.28ρ_r captured their reduced density dependence over 303.15–363.15 K. Extraction generally decreased with increasing oil–CO₂ solubility parameter difference. In binary systems evaluated using oil boundaries determined by the half density criterion, higher fractions of nonpolar partners were associated with increased total extraction ratios, whereas higher fractions of polar partners were associated with decreases. Multicomponent systems showed redistribution that depended on the overall composition, and relative diffusion coefficients qualitatively reflected mobility differences. The gas–oil interaction competition factor, R_comp, decreased from 1.985 to 0.087 in the same order as the extraction ratios. Both the thermodynamic and energetic correspondences persisted after excluding 2-naphthol. Configurations and radial distribution functions showed that association in nonpolar hydrocarbons was dominated by dispersion interactions, whereas polar and aromatic components exhibited additional hydrogen bonding, aromatic stacking, and dipolar or electrostatic organization. Local CO₂ enrichment near polar sites alone did not explain overall extraction. Overall, the selective transfer of individual components was consistent with a balance between local CO₂–oil association and collective oil–oil cohesion that depended on mixture composition, while molecular organization may regulate the accessibility of favorable CO₂ contact sites. Full article

The use of phase change materials (PCMs) in cementitious mortars is a promising strategy for passive thermal regulation and thermal energy storage (TES) in buildings, but its practical implementation remains constrained by PCM leakage and its effects on physical and mechanical performance. This study investigates the use of recycled clay brick waste as a dual-function component in eco-efficient mortars, serving as a partial replacement for fine aggregate and as a porous carrier for paraffin-based PCM. The experimental program comprised three stages: selection of an eco-efficient reference mortar, impregnation of recycled ceramic aggregates using thermal and vacuum-assisted procedures, and evaluation of PCM-modified mortars through fresh-state, physical, mechanical, thermophysical, direct thermal exposure, thermoregulation, and infrared thermography tests. Thermal impregnation at 15 wt% PCM provided the most favorable balance between PCM incorporation and stability against surface accumulation and mass loss and was selected for mortar production. Compared with REFeco, PCM incorporation reduced water absorption by approximately 10% and caused compressive and flexural strength losses below 10%. PCM15 exhibited the most favorable thermophysical balance, increasing volumetric specific heat by 14.9% and thermal inertia by 8.5%, while reducing thermal diffusivity by 10.8%. Under direct flame exposure, PCM25 produced the greatest thermal buffering effect, delaying the attainment of 200 °C on the rear face by approximately 4 min and reducing maximum estimated heat flux by approximately 16% relative to REFeco. Overall, recycled clay brick waste demonstrated potential as a PCM carrier for eco-efficient cementitious mortars with thermal energy storage functionality. Full article

Nanostructured tungsten–copper (W–Cu) composites are promising materials for high-heat-flux components and advanced thermal management; however, their processing is limited by a high positive enthalpy of mixing of ~35.5 kJ/mol and weak interfacial bonding. This study provides a thermodynamic and kinetic justification for a technological route intended for 70W–30Cu and 75W–25Cu (wt.%) composites using high-energy mechanical activation and spark plasma sintering (SPS). CALPHAD-type calculations identified a critical copper activity plateau a_Cu ≈ 0.33 at 950 °C in the W-rich range, which favors the retention of submicron grains of 200–300 nm by limiting the chemical potential driving force for coarsening. Kinetic modeling via DICTRA predicted the formation of metastable interfacial diffusion zones with a characteristic width of 20–90 nm during short SPS holding times of 150–300 s, enabling the transition from mechanical interlocking to metallurgical bonding. Based on these calculations, a processing window of 950–1050 °C is proposed to achieve a target relative density of ≥97% and electrical conductivity of 35–45% IACS. The results provide a predictive framework for the experimental synthesis of nanostructured pseudoalloys with optimized conductive networks and reinforced tungsten skeletons. Full article

Digital agriculture (sensors, Internet of Things, machine learning, robotics, digital twins) has for fifteen years been sustained by massive investment promising more productive, precise, and sustainable agriculture, yet adoption remains heterogeneous and systemic impact limited despite intense scientific output. The dominant explanation treats this as a diffusion deficit and calls for more farmer training, advisory support, and subsidy; this reading leaves the structure of research itself unexamined and does not, on its own, explain why the gap between announced and delivered performance persists across technology generations. We argue that, alongside diffusion-side and demand-side factors, the paradox has a further, under-recognised upstream component: research design oriented towards technological artefacts rather than guaranteed functions. We develop the case for this complementary reading using Stahel’s performance economy framework and the Product-Service Systems literature. Four contributions follow: a tripartite typology of function (service-, specification-, and process-function) articulated hierarchically explains why research centred on specification-function alone cannot guarantee a service-function; current research is characterised, on the reading developed here, by three cumulative biases and three blind spots, including data governance and the under-representation of the social sciences; a four-axis reorientation agenda is proposed; and the framework is distinguished from Agricultural Innovation Systems, Responsible Research and Innovation, and mission-oriented research by specifying the functional level these leave indeterminate. Full article

Reliable optical sensing of smoke and flames in high-rise buildings is challenging due to weak early cues, vertical smoke diffusion, facade occlusions, nighttime illumination, and fire-like urban interferences. We propose CDU-YOLO, a scene-aware real-time detection framework built upon YOLOv8n. Rather than relying on indiscriminate network scaling, task-oriented integration of existing modules is introduced: dynamic point-sampling (DySample) to preserve blurred boundaries of distant micro-targets, an enlarged receptive field (UniRepLKNet) to capture large-scale vertical propagation, and a dynamic bounding-box regression loss (WIoU) to handle occlusions. Experiments on a custom high-rise fire dataset and two public datasets demonstrate 94.9% mAP@0.5 and 56.7% mAP@0.5:0.95. In a dedicated flame-only size-stratified evaluation, CDU-YOLO improves AP@0.5 for small flames from 79.6% to 91.7% and reduces their miss rate from 25.2% to 11.3% relative to YOLOv8n. Under a unified desktop protocol (RTX 3080, PyTorch FP16, $640\times 640$, batch size 1, no TensorRT), end-to-end throughput increases from 41 FPS to 55 FPS. A separate Jetson Orin NX deployment benchmark reaches 92 FPS using TensorRT FP16. The explicit introduction of an “others” category during training contributes to reducing false positive predictions against fire-like distractors. These results support the use of CDU-YOLO as a supplementary visual sensing component for early situational awareness. Nevertheless, residual misses on small and ultra-distant flames, continuous video-stream validation, and long-term field testing remain to be addressed before safety-critical online deployment. Full article

Metallized pellets are spherical iron-bearing burden materials obtained by treating iron ore pellets through processes such as direct reduction. They contain a certain proportion of metallic iron and mainly consist of metallic iron, incompletely reduced oxides, gangue, and other nonmetallic components. They are one of the commonly used iron-bearing materials in electric smelting furnaces. The melting process of metallized pellets not only affects the melting efficiency of the charge but is also accompanied by slag–metal separation and gangue separation, which is directly related to mass transfer, heat transfer, and production efficiency during the smelting process. However, existing studies have mainly focused on the melting behavior of pellets in a single-phase molten pool, while studies on gangue separation, interfacial migration, and slag–metal separation during pellet melting at the slag–metal two-phase interface remain rare. Because this region involves complex interfacial heat transfer, fluid flow, and interfacial interactions, investigating only the overall melting process of pellets is insufficient to reveal the actual gangue separation mechanism. Therefore, a systematic investigation of the melting and separation processes of pellets at the slag–metal interface is necessary. Based on the principle of similarity, a water–oil–ice three-phase physical model was employed in this study, in which water, silicone oil, and ice balls containing dyed silicone oil samples were used to simulate molten iron, slag, and pellets, respectively. The dyed silicone oil is specially designed to simulate the gangue in the pellet. Visualization experiments were conducted to investigate the evolution of pellet melting morphology, oil droplet (gangue) release behavior, and diffusion characteristics in the oil layer under different initial oil droplet positions and static or parallel flow conditions. The results show that the initial position of the oil droplet and the parallel flow significantly affect the local melting behavior of the ice ball and the oil droplet release process. In particular, the release time of the oil droplet located above the ice ball is significantly longer than that of the oil droplet located below the ice ball. The parallel flow significantly changes the melting sequence of the ice ball and the oil droplet release path by enhancing convective heat transfer in the lower region of the ice ball. According to the initial position of the oil droplet and the flow conditions, the oil droplet release process can be classified into five typical separation types, including (1) lateral release of the upper oil droplet after the ice shells on both sides melt through, (2) release of the upper oil droplet through a hole at the bottom of the upper hemisphere, (3) direct release of the lower oil droplet through a local hole, (4) two-stage release of the lower oil droplet controlled by interfacial constraint, and (5) single-stage release of the oil droplet on the downstream flow side driven by parallel flow. The oil droplet release time in the parallel flow cases is shorter than that in the static conditions. The diffusion behavior of the oil droplet after entering the oil layer is weakly affected by the parallel flow and is mainly characterized by inertial diffusion along the initial release direction, followed by spreading toward the surrounding area. This study reveals the slag–metal separation mechanism during pellet melting at the slag–metal interface under the combined control of flow, ice shell morphology, and interfacial interactions, providing experimental evidence for optimizing the melting and separation behavior of pellet charges in electric smelting furnaces and related smelting processes. Full article

Persistent late-time variation can remain in physics-informed neural network (PINN) solutions after the governing transient has effectively decayed, while conventional error norms do not reveal whether this variation has a systematic temporal–frequency structure. This study develops an offline temporal–spectral diagnostic and postprocessing workflow for a one-dimensional advection–diffusion benchmark. A high-accuracy analytical reference and three fixed-resolution finite-difference baselines are used to assess a PINN whose architecture is selected by a fully supervised neural architecture search and whose parameters are trained with progressive temporal windowing. Candidate late-time intervals are selected without using the reference solution by applying the Bayesian information criterion (BIC) to a breakpoint model for the inter-reconstruction sensitivity; the selected field is subsequently reconstructed by retaining a prescribed fraction of its temporal spectral energy and is evaluated independently through reference-error and physics-consistency measures. For $[t_{\mathrm{cut}},t_{\max }]=[1.8,5]$, the zero-frequency component contains $0.9999996$ of the raw-field energy, so the $q=0.95$ reconstruction retains only the temporal mean. This projection reduces the final-time spatial error norm from $2.70\times {10}^{-3}$ to $1.06\times {10}^{-3}$, a factor of approximately $2.5$, while changing the discrete governing-equation residual by less than $0.3\%$ over the filtered window. Mean-removed tests for $q=0.90,0.95,0.99$ show that the discarded fluctuation is dominated by low-frequency approximation error rather than high-frequency noise. The result supports the proposed selection–validation workflow for this controlled benchmark but does not establish a universally transferable filter. Full article

Single-frame infrared small and dim target detection underpins military early warning and space surveillance. However, existing review literature lacks a classification system based on architectural paradigms and standardized cross-architecture benchmarking. This paper systematically reviews deep learning methods from two complementary perspectives: classification based on architectural paradigms and quantitative empirical evaluation. Within our proposed analytical framework, we categorize existing methods into seven architectural paradigms—detector-based, U-Net-based, GAN-based, Transformer-based, diffusion-based, Mamba-based, and hybrid models. Each paradigm is defined by two inseparable components: the “task formulation” and the “computational architecture.” The task formulation specifies how the detection problem is transformed into a mathematical problem (e.g., bounding box regression, pixel-level segmentation, conditional denoising generation, etc.), while the computational architecture specifies which computational units are used to implement this formulation (e.g., CNN convolutions, self-attention mechanisms, state space models, etc.). For each paradigm, we analyze the potential conflicts between these paradigms and the physical characteristics of infrared weak targets, as well as the corresponding optimization pathways. Building upon this foundation, through a comprehensive survey of current algorithms, we have selected over 20 representative methods and established a cross-architecture performance comparison benchmark using five metrics (IoU, nIoU, Pd, Fa, and Params) across three datasets (NUAA-SIRST, NUDT-SIRST, and IRSTD-1k). Through three types of diagnostic experiments—performance benchmarking, evaluation of generalization capability across datasets, and robustness testing against synthetic interference sources—this study reveals cross-paradigm patterns, suggesting that within the settings and datasets examined, the primary performance bottleneck appears to have shifted from architectural complexity to the preservation of spatial information during the encoding stage and the learning of semantic discriminative representations. This review can serve as a valuable reference for both newcomers seeking to understand current trends and researchers exploring future directions for infrared weak target detection. Full article

The guarana seed peel (GSP), an agroindustrial waste, was characterized for its chemical composition and evaluated for its antimicrobial activity as a potential source of antibacterial compounds for wastewater disinfection using UPLC-QTOF-MS analysis. UPLC-QTOF-MS analysis of GSP, its raw extract (GSPE), and its purified extract (GSPP) identified 36 compounds, including catechin and its oligomers: a B-type procyanidin dimer, pavetannin B6 (trimer), and cinnamtannin A2 (tetramer). Principal component analysis (PCA) separated GSPE from GSPP along PC2, and the purification step concentrated flavonoids—mainly catechin and its oligomers—in GSPP. Antimicrobial activity was assessed against the human pathogens Escherichia coli and Salmonella typhimurium, causing gastroenteritis and invasive disease, and Staphylococcus aureus, causing skin and invasive infections. In real wastewater, GSPE (500 mg L⁻¹) reduced total coliforms by up to 81 ± 7% and E. coli by up to 74 ± 7% within 180 min. Confocal microscopy with the LIVE/DEAD^® BacLight™ kit (Thermo Fhisher, Eugene, OR, USA) confirmed loss of membrane integrity in GSPE-treated Escherichia coli, and disc diffusion with GSPP produced inhibition zones of 9–11 mm against Staphylococcus aureus, Escherichia coli and Salmonella typhimurium at 5 mg per disc. These results show that guarana seed peel—currently discarded—is a viable source of catechin-rich antibacterial extracts for wastewater disinfection. Full article

Monocular 3D Human Pose Estimation (3D HPE) typically adopts a two-stage approach: estimating 2D joint positions from images and then lifting them to 3D coordinates, effectively reducing dataset bias inherent in direct methods. However, current lifting techniques face two key challenges: many Transformer-based methods rely on attention-based or staged spatial–temporal modeling, which can limit efficient long-range frame-joint reasoning, while diffusion models support probabilistic modeling of pose uncertainty but remain sensitive to joint-coordinate noise. We propose DiMMPose, a diffusion-based framework enhanced by Mamba’s state-space model for robust and efficient 3D pose estimation. Its denoising process consists of two coordinated modules. The Spatiotemporal Mamba Block (STMB) serves as the core feature extraction module, employing internal Pose Mamba components with bidirectional state propagation and linear complexity to efficiently model long-range frame-joint dependencies. STMB further refines these features through Spatiotemporal Scan and Merge, which traverses the same skeleton tokens in complementary frame-joint orders and fuses the resulting representations. The Multi-Prompt Mamba Denoiser (MPMD) combines structured prompts encoded by LongCLIP with learnable prompt representations to provide anatomical and motion-related guidance during denoising. DiMMPose achieves an average MPJPE of 28.9 mm on Human3.6M under the DET setting, with action-specific errors of 21.2 mm for Walking and 22.0 mm for WalkTogether. It improves over FinePOSE by 3.0 mm, reduces inference latency by 57.1%, and achieves 23.0 mm MPJPE on MPI-INF-3DHP (N = 243). Full article

Fractured shale oil reservoirs possess ultra-tight matrix pores and suffer unsatisfactory oil recovery under conventional exploitation, while systematic comparisons among CO₂ flooding, CO₂/CH₄ mixed gas flooding, and multi-component thermal fluid (MTF) flooding remain insufficient for guiding field-parameter design. This work establishes a matrix–fracture coupled dual-porosity compositional model and adopts a single-variable method to quantitatively clarify how injection composition, reservoir permeability, injection pressure, and temperature govern sweep efficiency and the oil recovery factor, as well as the synergistic EOR mechanisms of different displacement fluids. The results show that CH₄ acts as a weak active component with limited crude-oil-swelling and displacement capacities; MTF yields the maximum recovery via thermal viscosity reduction, molecular diffusion, and crude oil swelling, whereas pure CO₂ is reported to have comprehensive cost advantages according to field-scale practical experience (no quantitative techno-economic calculation is carried out in this work). An injection miscibility threshold of 20–30 MPa is identified, with declining incremental oil yield beyond this range. According to published engineering observations, excessively high injection temperatures may trigger liquid-phase permeability impairment, which is not captured in the present model. The oil recovery factor positively correlates with permeability within 0.02–0.1 mD, and volumetric fracturing is indispensable for ultra-low-permeability matrices to expand seepage pathways. This study delivers quantitative theoretical references for displacing-agent screening and injection–production parameter optimization in fractured shale oil reservoirs. Full article

Effective flood planning requires spatially complete hazard information, yet regulatory flood products can contain unresolved hazard classifications and provide limited uncertainty information. In addition, existing models for flood mapping often rely on random sampling that neglects spatial dependence among neighboring areas, struggle to model zero-inflated, bounded area shares (i.e., shares with many zero values and a bounded 0–1 range), and offer limited distribution-level explainability. To address these limitations, this study developed a calibrated hurdle conditional diffusion framework to estimate the Federal Emergency Management Agency (FEMA) Special Flood Hazard Area (SFHA) share and the probability that coverage exceeds 10% for Louisiana census block groups. Within this framework, a hurdle component separates zero from positive coverage, a conditional diffusion model estimates bounded positive-share distributions, spatial blocking evaluates geographic transfer, and probabilistic calibration supports exceedance probabilities and prediction intervals. Terrain, land-cover, wetland, hydrographic, climate, and soil predictors were evaluated across 3776 block groups with resolved FEMA information. The conditional regression component achieved a mean absolute error of 0.172 for the primary continuous SFHA-share prediction and strong parish-level agreement (Pearson r = 0.834). For the secondary screening task of identifying census block groups with ≥10% SFHA share, the calibrated hurdle diffusion model yielded an area under the receiver operating characteristic curve of 0.852. Statewide predictions were generated for all 4294 census block groups, including 518 unresolved units with a mean predicted SFHA share of 33.3%. Distributional Reliability Explanation Attribution (DREA) identified flooded soils, elevation, topographic wetness, wetlands, and water proximity as reliable predictors of distributional displacement, uncertainty, exceedance probability, and probabilistic skill. Overall, the framework supports leakage-safe, uncertainty-aware screening while complementing authoritative FEMA flood maps. Full article

In this study, the removal of vancomycin and rifampicin from aqueous media by three types of granular activated carbon was investigated under a wide range of operational conditions. There is growing concern about antimicrobial resistance to vancomycin, a last-line antibiotic for serious Gram-positive infections, and rifampicin, a key component of first-line combination therapy for tuberculosis. However, there has been little research into adsorption of these antibiotics from aquatic environments. Effect of temperature (5–45 °C), pH (3–11), contact time (up to 336 h) and the adsorbate initial concentration (5–100 µg.mL⁻¹) were evaluated on removal efficiency and uptake of the antibiotics by activated carbon from water. The results showed that all adsorption processes in this study were mainly controlled by physisorption, as they exhibited the enthalpy values between 5.97 kJ.mol⁻¹ to 21.19 kJ.mol⁻¹ (i.e., lower than 40 kJ.mol⁻¹). Temperature had a limited impact on the adsorption process. On the other hand, the contact time, initial adsorbate concentration and solution pH had a substantial effect in removing and retaining the studied antibiotics from water. The attributes of the antibiotics and the pore characteristics of the adsorbents determined the effectiveness of adsorption. Intra-particle diffusion assessments identified that the rate of the studied antibiotics’ removal from water is not controlled only by intra-particle diffusion and follows the sequential transport mechanisms, containing three steps for vancomycin (bulk transfer, passage through the boundary layer and diffusion into micropores) and two steps for rifampicin (passing through the boundary layer and diffusion into mesopores). Full article