Search Results (6,357)

Accurate prediction of thermodynamic properties of real gases is essential for the analysis and design of energy systems such as power plants, gas turbines, and energy conversion processes. Although many equations of state have been developed, they still have limitations in accurately predicting the behavior of real gases, especially near the critical point. Unlike previous studies that focused on refining parameters embedded in conventional equations of state, this study introduces a gas-specific, data-driven approach by adjusting the specific gas constant in the ideal gas law. The gas-specific fitting parameter R_corr is evaluated directly from experimentally measured saturated-vapor P-v-T data and has the same units as the specific gas constant; however, it does not represent a modified or redefined physical gas constant. The proposed method should be interpreted as an empirical engineering correlation rather than as a predictive thermodynamic equation of state. Despite its simple formulation, the ideal gas law incorporating the corrected specific gas constant showed agreement with experimental data comparable to that of the Peng–Robinson–Stryjek–Vera equation of state over most of the saturation range, except near the critical point. For saturated steam, the proposed correlation reduced the pressure deviation to within approximately ±2.6% over most of the saturated-vapor region. Although the present assessment is limited to the dataset used to develop the correlation, the proposed correlation provides engineering-level accuracy while retaining a compact analytical form, making it suitable for repeated saturated-vapor P-v-T evaluations. Full article

Despite the large volumes of marbleized limestone processing waste (MLPW) generated by the stone industry, its utilization in the production of high-strength masonry materials remains limited, and systematic comparative studies of white and gray Portland cements in hyperpressed MLPW-based bricks are scarce. In this study, four mixture formulations containing MLPW (70–85 wt.%), white Portland cement (CEM I 52.5, M500) or gray Portland cement (CEM I 42.5, M450), and water were prepared at a constant water-to-cement ratio of 0.25. Specimens were manufactured by hyperpressing under compaction pressures of 28 and 41 MPa and cured for 7 days in a sealed moist environment. The formulations compacted at 28 MPa achieved compressive strengths corresponding to brick strength grades M350 and M400. Increasing the pressure to 41 MPa yielded grades M400–M550 with white cement; under identical conditions with gray cement, grades M400 and M450 were attained. Among the tested formulations, those containing 75–80 wt.% MLPW (20–16 wt.% cement) showed the most favorable combination of compressive strength and density. The mechanical performance correlated well with the microstructural features observed by scanning electron microscopy (SEM) and EDS analysis. The average density of all specimens exceeded 2100 kg/m³, classifying the materials as heavyweight concrete. The novelty of this work lies in the comparative evaluation of white and gray Portland cements in hyperpressed MLPW bricks and the establishment of relationships between compaction pressure, binder type, overall mixture composition, and resulting strength grades. The results demonstrate a promising route for converting high volumes of industrial marble waste into high-strength masonry units. However, further investigation of durability-related parameters, including water absorption, frost resistance and flexural strength, is required before the practical application of the developed materials can be fully evaluated. Full article

Entropy estimation from short samples recurs in symmetric cryptography, where the reference distribution is uniform by design and no single estimator in the considered classical comparison set minimizes mean squared error (MSE) across the full range of ratios $n/k$. We introduce the adaptive sample-conditional entropy diagnostic (ASED), in which a compact network trained offline maps a frequency-of-frequencies descriptor of the sample to convex mixture weights over six classical estimators, at a cost of $O(n+k)$. We prove an oracle inequality bounding the excess risk of such a mixture by the $L_1$ error of its weights and fixed-alphabet consistency for every simplex-valued weighting rule, independently of the distribution used to train the weights. Under the uniform-null protocol, ASED attains an integrated MSE of $1.7\times {10}^{-3}$ for bytes, compared with $3.8\times {10}^{-2}$ for James–Stein shrinkage, a reduction that depends materially on the aggregation scheme: $95.4\%$ for summed or averaged MSE across sample sizes versus $24\%$ for the mean of per-size ratios. Both figures are reported together throughout, and neither is presented as the headline; the entire advantage is confined to the undersampled regime $n<k$, the two estimators being indistinguishable for $n\ge k$. A locked train/validate/test evaluation with an independently written implementation reproduces the reduction at $95.1\%$. Because a constant output achieves zero error by construction here, we add further checks: in a post hoc analysis, ASED is non-inferior to SHR in detection power at a $0.02$ margin, both pointwise and simultaneously, whereas a constant control has none, and the advantage persists on held-out configurations. The $0.02$ margin was selected after inspecting the power estimates, and at $n=8$ and $n=16$, it is smaller than the variation induced by tie handling at the empirical critical value, so the non-inferiority conclusion is confirmatory only for $n\ge 32$. At $n\le 16$, the two statistics have identical null distributions up to monotone relabeling, and no test of size $0.05$ exists, so no power difference is identifiable in either direction, and the comparison is withdrawn there. For strongly non-uniform sources, the ordering reverses, and NSB dominates, delimiting ASED as a uniformity diagnostic rather than a general-purpose entropy estimator. The design choices behind ASED, the feature map, architecture, and loss weighting were made while observing results on this same evaluation protocol, so no independent model-selection split separates development from the assessment reported here. That pipeline is accordingly designated exploratory, and the locked evaluation is confirmatory. The contribution is stated as sample-conditional convex aggregation of classical estimators rather than as the particular network realizing it: a five-feature variant and a gradient-boosted surrogate perform comparably, with no paired interval separating the three. Claims are restricted to short i.i.d. samples and marginal Shannon-entropy diagnostics under a uniform null; min-entropy, unpredictability, dependence, and randomness certification are out of scope. Full article

Accurate and robust detection of pear leaf diseases and pests is important for early warning in orchards, precision control, and intelligent disease and pest management. In natural environments, this detection task is still difficult because target scales vary greatly, lesion boundaries are often blurred, disease symptoms may look similar, small-target features are usually weak, and cluttered backgrounds can reduce detection performance. We develop AMET-YOLO as a task-specific extension of YOLOv11n. ADown is a downsampling operator borrowed from YOLOv9 that replaces selected stride-convolution layers and thereby preserves local details during scale reduction. The Memory-guided Sparse Expert Compensation Module (MSECM) is a feature-enhancement design which couples learnable memory retrieval with four dilation-specific experts and input-dependent Top-2 routing. In the neck, the Efficient Multi-scale Aggregation Fusion module (EMAFuse) substitutes four concatenation nodes and performs fixed-width channel alignment, element-wise aggregation, and lightweight depthwise–pointwise mixing. The Task-Aligned Detection Head (TAHead) is a modified decoupled head that retains the YOLOv11n prediction structure while it routes localization responses through a one-way gating path to modulate intermediate classification features. Experiments on the six-class PearLeaf-DP6 dataset show that AMET-YOLO achieves a precision of 88.5 ± 1.3%, a recall of 83.1 ± 0.9%, an mAP@50 of 88.7 ± 0.7%, and an mAP@50:95 of 51.8 ± 0.5%. Compared with the YOLOv11n baseline, AMET-YOLO improves these four metrics by 4.8, 3.9, 3.3, and 2.3 percentage points, respectively. Experiments on a second public tea leaf disease dataset further show that the proposed model stays effective when it is trained and evaluated independently on a different crop disease detection task. AMET-YOLO is a model with 6.32 M parameters and 10.2 GFLOPs, which constitutes a moderate rise in complexity relative to YOLOv11n while it remains far more compact than RT-DETR-ResNet50. These workstation-based results position AMET-YOLO as an accuracy-oriented image-based detector under the evaluated protocol, and real-time deployment on resource-limited orchard devices is left for future validation. Full article

This paper develops a Lorentz-covariant Double Coupled Field (DCF) framework in which a relativistic matter carrier is represented by eight real degrees of freedom organized as two quaternionic coordinates. Starting from coupled real fields, the construction establishes a real Cl(1,3) representation, an internal unit-quaternion symmetry Sp(1) ≅ SU(2), where Sp(1) denotes the group of unit quaternions, together with chirality and controlled Dirac and Schrödinger–Pauli limits. The work investigates the hypothesis that complex structures appearing in standard quantum formulations may represent compact descriptions of underlying coupled real degrees of freedom. The present results establish a consistent classical framework and its limiting correspondence with established relativistic quantum structures; they do not constitute a complete Poincaré-covariant quantum field theory, do not derive the spin–statistics connection, and do not claim experimentally distinguished predictions from the linear Dirac limit. Nonlinear DCF sectors, internal dynamics, and possible two-particle extensions are identified as directions for future investigation. The manuscript distinguishes three theoretical levels: the classical DCF ontology, a conditional full-Sp(1) extension, and a quantized two-particle extension; these levels are kept explicitly separate throughout the analysis. Full article

Endometriosis affects an estimated one in ten women of reproductive age and remains defined clinically by the presence of endometrial-like tissue outside the uterine cavity, yet its persistence depends on biological processes that extend well beyond estrogen exposure. This review argues that endometriosis is best understood as a set of molecularly heterogeneous, interconnected disease processes, hormonal, immune, vascular, epigenetic, and metabolic, that converge on a compact set of intracellular signaling nodes rather than as a single hormonal disorder. These convergence points are mapped onto candidate therapeutic targets spanning established, investigational, and preclinical stages of development, and onto biomarker strategies organized here by clinical application, diagnosis, disease stratification, prognosis, and treatment monitoring, rather than by sample source alone. Translational obstacles, including the limited predictive value of current animal models, the recurring disconnect between preclinical and clinical results, and the distinction between symptom control and disease modification, are addressed throughout rather than confined to a single closing section. Endometriosis management continues to rely on symptomatic hormonal suppression; treatment strategies grounded in the disease’s molecular heterogeneity, rather than a uniform hormonal model, appear to offer the most realistic path toward durable, individualized management. Full article

Earthquake and post-earthquake fire cascading hazards impose disproportionate risks on dense informal historic districts across South Asia. Conventional multi-hazard evaluation methodologies fail to accommodate non-engineered mixed-use constructions and ultra-compact unplanned urban morphologies prevalent in regional megacities, owing to two inherent limitations: decoupled quantification of seismic deterioration and structural fire performance, and fire spread parameters calibrated for regular Western urban grids rather than narrow, congested historic streetscapes. This work develops a localized probabilistic risk framework integrating seismic damage-induced fire degradation, and advances two dedicated methodological improvements to address the identified research voids. Seismic damage-dependent fire resistance reduction coefficients are embedded within cellular automaton iterations to dynamically modulate effective burnout durations of seismically compromised buildings; urban morphological correction factors are further incorporated to refine inter-building fire propagation probabilities tailored to compact informal settlements. Field inventories, nonlinear pushover finite element analysis, morphology-modified fire simulation and large-sample Monte Carlo stochastic sampling are integrated to execute full-process quantitative risk assessment, with model calibration and validation conducted against field survey data and the 2019 Chawkbazar chemical fire archive in Dhaka. The empirically validated framework (note: the validation primarily applies to the fire spread submodel; the coupled seismic–fire mechanism is supported by numerical simulation rather than empirical observation) delivers transferable quantitative benchmarks for multi-hazard governance of analogous South Asian historic agglomerations, and provides actionable technical evidence to inform targeted urban renewal schemes and the formulation of earthquake-fire coupled design specifications within Bangladesh’s national building regulatory codes. Full article

To address the trade-off between the rapid setting time and low mechanical strength of fluoroaluminate-based liquid accelerating agents, this study investigated the combined effects of synthetic C-S-H seeds and a polycarboxylate superplasticizer (PCE) on their performance. The underlying mechanisms were explored using isothermal calorimetry, X-ray diffraction (XRD), thermogravimetric analysis (TG), and scanning electron microscopy (SEM). The results indicated that the incorporation of C-S-H further accelerated the setting process and significantly improved the 1 d and 3 d compressive strengths of the mortar, with a 1 d strength increase exceeding 30% at a dosage of 1.2%. When combined with PCE, the 1 d strength was further enhanced to 31.5% above the control. Microstructural analysis revealed that C-S-H provided additional nucleation sites for ettringite (AFt), accelerating the early hydration of C₃A and increasing the volume of hydration products in the paste. Furthermore, the hydration of the C-S-H seeds themselves contributed to the formation of additional C-S-H gel. This helped alleviate the retarding effect of fluoride ions on C₃S hydration, promoted the formation of more hydration products, filled structural pores, and ultimately enhanced the compactness of the paste. The synergistic effect of PCE promoted more complete hydration of C₃S, further amplifying the benefits of C-S-H, and leading to a 30.9% increase in C-S-H gel content (reaching 46.1% at 3 days) compared to the control. Consequently, the shotcrete achieved higher mechanical properties and a denser internal structure. The findings demonstrate that a suspension containing 0.9% CSH-PCE significantly improves the performance of a fluoroaluminate-based liquid accelerator by enhancing the early hydration rate and mechanical properties. This provides a theoretical basis for the development of high-performance fluoroaluminate-based liquid accelerators. Full article

Contactless palmprint verification must generalize to unseen identities while remaining robust to variations in hand pose, scale, illumination, camera distance, and background. This study presents a cosine-consistent deep metric-learning framework combining landmark-first Hybrid region-of-interest (ROI) extraction, contrast-limited adaptive histogram equalization (CLAHE) enhancement, an ImageNet-initialized ResNet18 encoder, trainable generalized-mean (GeM) pooling, a compact projection head, batch-normalization neck (BNNeck), batch-hard triplet learning, and auxiliary identity supervision. The principal contribution lies not in introducing a new backbone or individual architectural component, but in the mathematically consistent integration of metric learning and verification with a rigorously controlled open-set evaluation protocol. The triplet objective operates on L2-normalized pre-batch-normalization (pre-BN) descriptors, whereas enrollment and cosine-based verification use L2-normalized post-batch-normalization (post-BN) descriptors. For unit-normalized vectors, squared Euclidean distance is shown to be a strictly decreasing affine function of cosine similarity, establishing exact ranking equivalence within each embedding space. A training-distribution lower bound relates the triplet margin and violation probability to the expected pre-BN score difference, without extending this result to post-BN test scores. Conditional sub-Gaussian false acceptance rate (FAR) and false rejection rate (FRR) bounds and Hoeffding-based plug-in threshold evaluations are also developed, with empirical score statistics explicitly distinguished from finite-sample risk certificates. Experiments on Tongji Session 1 and BMPD use one fixed participant-disjoint split, validation-only threshold selection, five model-training seeds, controlled one-factor ablations, and participant-level bootstrap confidence intervals for principal verification measures. The results provide the clearest empirical support for auxiliary identity supervision under the more variable BMPD acquisition conditions. The effect of ROI normalization is acquisition-dependent, while the effects of CLAHE, pooling, BNNeck, mining strategy, embedding normalization, and triplet margin are generally smaller or more variable across seeds and datasets. Normalized Euclidean and cosine matching produce identical verification results when applied to the same normalized post-BN descriptors. Full article

No. 5 and No. 8 coal seams in the Yichuan area, Ordos Basin, are primary targets for coalbed methane (CBM) development, yet their pore structure differences and controlling factors remain poorly understood. This study integrates CO₂ adsorption, low-temperature N₂ adsorption, and mercury intrusion porosimetry to characterize the pore structure across the full pore-size range, with fractal dimensions quantified by V-S, FHH, and J-function models. The results show that the No. 8 coal has greater CO₂-accessible specific surface area and micropore volume, with micropores (<2 nm) accounting for 58–81%. In contrast, the No. 5 coal exhibits higher mesopore (2–50 nm: 1–4%) and macropore (>50 nm: 31–48%) proportions, along with higher porosity and permeability, and better pore openness and connectivity. Fractal analysis indicates that the No. 5 coal has higher V-S and FHH fractal dimensions, suggesting greater micropore–mesopore structural complexity, while the No. 8 coal exhibits a higher J-function fractal dimension, reflecting stronger pore throat heterogeneity. Correlation analysis further indicates that micropore development is closely associated with coalification degree and vitrinite content; mesopore characteristics are associated with mineral matter and show a non-monotonic relationship with ash yield; and macropore–fracture systems are related to depositional setting and burial depth. The delta-plain-sourced No. 5 coal, with shallower burial and greater lithological heterogeneity, appears to retain better-developed seepage pathways, whereas the marine–continental transitional No. 8 coal, buried deeper and more compacted, is characterized by a dominance of adsorption space with limited connectivity. Accordingly, the pore structure of the No. 5 coal appears more favorable for gas seepage, while that of the No. 8 coal appears more favorable for gas adsorption. These pore-structure-based interpretations may provide a basis for differentiated CBM development strategies in the study area, although they should be regarded as preliminary pending validation with production data. Full article

Steam explosion processing of bamboo generates substantial quantities of granular residues that are often discarded, leading to resource waste and potential secondary pollution. In this study, a sustainable upcycling strategy was developed to convert these residues into mechanically reinforced cellulose-based composite films. Cellulose isolated from steam-exploded bamboo residues was separately converted into negatively charged TEMPO-oxidized cellulose nanofibrils (TOCNFs) and positively charged quaternized cellulose (QCell), with the latter prepared through modification using 2,3-epoxypropyltrimethylammonium chloride. The oppositely charged cellulose components were subsequently assembled into TOCNF/QCell composite films through electrostatic interactions. At an optimal TOCNF-to-QCell mass ratio of 3:1, the resulting composite film exhibited a tensile strength of 134 MPa, representing an improvement of 23.09% compared with that of the corresponding unmodified TOCNF₁/Cell₁ film. SEM observations revealed that the enhanced interfacial interactions facilitated the formation of a dense and compact network structure, thereby contributing to the improved mechanical performance. Although quaternization exerted little influence on the equilibrium moisture uptake of the films, it markedly reduced moisture sorption–desorption hysteresis, indicating enhanced structural reversibility during humidity cycling without substantially altering their overall hydrophilicity. FTIR and XPS analyses confirmed the successful chemical modification of cellulose, while thermogravimetric analysis demonstrated that the thermal stability of the cellulose-based films was largely preserved. Overall, these findings provide a sustainable and effective route for valorizing steam-exploded bamboo residues into high-performance cellulose-based materials. Full article

Unsupervised classification or clustering of complex time series generated from nonlinear dynamical systems remains challenging, particularly when signals exhibit noise and subtle structural differences. We develop a recurrence-based framework that captures local geometric patterns in recurrence plots by extracting triangular motifs, termed recurrence triangles (RTs), and mapping their relative frequencies to compact feature vectors for clustering. The approach is evaluated on four synthetic systems: the continuous-time Rössler and Lorenz systems and the discrete-time Logistic map and AR(2) model. RT-based features generally achieved higher clustering accuracy than classical recurrence quantification analysis (RQA), although performance depended on the dynamical system and signal length. RQA performed better than RT for AR(2) at longer signal lengths, while a statistical baseline, namely StatACF, also outperformed RT under several conditions, indicating that no single representation was uniformly optimal. We further applied the methods to gait recordings from younger and older adults. RT features achieved the highest observed clustering accuracy among the three representations (73.7%), followed by StatACF (68.4%) and RQA (52.6%). However, the differences among the methods were not statistically significant in the small cohort (n = 19). RT motif distributions also differed between age groups in a marker-dependent manner. These findings suggest that RT distributions capture fine-scale recurrence structure complementary to global recurrence statistics and warrant further validation in larger, independent cohorts. Full article

With the increasing exploration and development of deep (>4500 m) hydrocarbon resources, fault-controlled reservoirs have become important sweet-spot targets in deeply buried tight rocks. However, it remains challenging to decipher the coupled relationships among deformation, fluid flow, diagenesis, and reservoir evolution within fault damage zones. In this review, we synthesize recent advances, including contributions to this Special Issue, on deformation, diagenesis, and reservoir development within fault damage zones in sedimentary basins. Fault-zone architecture commonly comprises a narrow fault core and a wider damage zone, with fault cores supported by particles, matrix, or cement, and damage zones characterized by outward-decreasing deformation intensity and fracture network complexity. Although fracture intensity, porosity, and permeability commonly follow a power-law decrease with increasing distance from the fault core, these parameters are highly scattered because they are jointly controlled by complex structural and diagenetic overprinting. Non-Andersonian fault-tip propagation, fault interaction, linkage, and reactivation play key roles in the development of wide and heterogeneous fracture zones. During progressive burial, mechanical compaction and cementation generally reduce primary porosity, whereas dissolution may generate or enhance secondary pore systems. Hydrothermal dissolution can locally improve reservoir quality, whereas mineral precipitation commonly occludes pores and fractures and reduces permeability. Except for the high-energy microfacies, the coupling between fracturing and contemporaneous, burial-related, or supergene dissolution is critical for the formation and preservation of secondary reservoirs. Integrated faulting period analysis and U-Pb dating of carbonate cements within fractures provide an effective approach for reconstructing the timing of fracturing, fluid flow, and diagenetic modification. In sedimentary basins, heterogeneous fractured reservoirs are unevenly distributed along fault damage zones. Their formation is controlled by the complex coupling of lithology, structural deformation, fluid flow, and diagenesis. Therefore, the key scientific challenge is to quantitatively characterize the spatio-temporal evolution and coupling mechanisms of deformation, diagenesis and fluid flow control on reservoir properties within fault damage zones. Ultimately, this review provides a novel, time-resolved framework for predicting deep sweet-spot reservoirs, shifting the paradigm from purely structural description to quantitative structural–diagenetic modeling. Full article

Active biodegradable packaging materials capable of preserving fruit quality and reducing oxidative processes are promising alternatives to conventional petroleum-based plastics. In this study, alginate–gelatin composite films were developed using a dual crosslinking strategy combining microbial transglutaminase-mediated enzymatic crosslinking with Ca²⁺-induced ionic gelation. The films were then enriched with Pelargonium sidoides root extract as a natural antioxidant. The combined crosslinking approach resulted in a more compact protein–polysaccharide network, which was reflected in increased film density and reduced swelling. The highest density was observed for film 1.0F (ρ = 1.2004 ± 0.0151 g/cm³), while films containing Ca²⁺ showed lower swelling values, with SI₆₀ decreasing from 7655 ± 70 for 0F to 5853 ± 49 for 0Ca²⁺F. The addition of the extract promoted additional interactions within the polymer matrix and affected the swelling and barrier properties of the films. The WVTR values were 36.1 ± 0.9 g/m²·24 h for 1.0F and 49.8 ± 1.5 g/m²·24 h for 1.0Ca²⁺F. The incorporation of P. sidoides extract also significantly increased the antioxidant activity of the films, particularly at higher extract concentrations. Furthermore, films containing Ca²⁺ and the extract contributed to the preservation of anthocyanins in Flame Seedless grapes during 14 days of storage, with the anthocyanin content reaching 0.208% ± 0.019 for the 1.0Ca²⁺F treatment. Overall, the results indicate that dual-crosslinked alginate–gelatin films enriched with P. sidoides extract have favorable physicochemical and functional properties and may be suitable for the development of sustainable active packaging materials for anthocyanin-rich fruits. Full article

Plate heat exchangers are widely used in compact thermal systems, where heat-transfer enhancement must be balanced against hydraulic losses. This study develops a computational fluid dynamics (CFD) and regression-based surrogate framework for a novel plate heat exchanger (NPHE) under two working-fluid regimes. Three-dimensional finite-volume simulations were performed over Re = 500–5000 using air and water as hot-side fluids, while water was maintained on the cold side. The CFD results show that the proposed wavy geometry promotes flow redistribution and secondary-flow structures that enhance convective heat transfer. For the air-side case, an indicative literature-based comparison with a conventional 60° chevron plate heat exchanger suggests average increases of approximately 30% in Nusselt number and 56% in thermohydraulic performance index. A compact dimensionless surrogate was constructed using Reynolds and Prandtl numbers as inputs. Based on 20 CFD observations and leave-one-out cross-validation, the selected logarithmic Nusselt-number model reduced the mean absolute percentage error from 7.22% to 3.51% relative to a power-law baseline. The friction-factor surrogate achieved a MAPE of 3.51%, while the derived performance index exhibited a MAPE of 3.15%. The equations provide an interpretable interpolation for the investigated geometry, boundary conditions, Reynolds-number interval, and air–water cases without extrapolation. Full article