Search Results (3,966)

The results of a multiscale study of fault and fracture geometry, distribution, density, and intensity are reported for Mesozoic platform carbonates cropping out along the axial zones of the southern Apennines fold-and-thrust belt, Italy. By integrating field structural observations with digital outcrop analysis, the study focuses on Cretaceous limestone rocks exposed along natural creeks and artificial trails of the Castelsaraceno area, Raparo Mt., southern Italy. There, the limestone beds are bounded by mm- to cm-thick marly–clayey interbeds, forming a well-layered succession made up of a few m-thick bed packages bounded by several cm-thick clayish interlayers. The carbonate multilayer was first affected by thrust tectonics, with the formation of low-angle intra-carbonate thrust faults and fault bend-folding. Then, the multilayer was crosscut by extensional–transtensional high-angle faults, which displaced the previously formed contractional structural elements, and allowed carbonate exhumation from shallow crustal depths. At outcrop scales, thrust-related deformation was solved by low-angle joints and veins, rare high-angle stylolites, and low-angle sheared fractures displaying reverse kinematics. Quantitative analyses of fracture density (P20) and intensity (P21) conducted on selected portions of the thrust fault zones indicate that the low-angle joints and veins attain their highest values in the vicinity of the main slip surfaces, whereas they are almost absent in the surrounding carbonate host rocks. Plio-Quaternary transtensional deformation was solved by NW–SE- and NE–SW striking faults. The latter fault set, nicely exposed along the flanks of the Raganello Creek, was characterized by right-lateral components of slip. Incipient faults, with ca. 1 cm throw, are made up of vertically discontinuous slip surfaces, which crosscut single bed packages and abut against clayish interlayers. The slip surfaces form conjugate geometries, and are associated to high-angle fractures and veins striking NE–SW, dissecting the bed packages. The fault core is virtually absent, whereas the damage zones are very discontinuous along dip. The P20 values computed for the high-angle fractures and veins increase toward the slip surfaces, whereas the P21 values remain nearly constant. These data are interpreted as being due to fault nucleation processes associated with fracture nucleation within the limestone rocks. NE–SW striking small faults displaying throws between 10 and 60 cm are comprised of through-going main slip surfaces crosscutting multiple bed packages, and poorly developed, discontinuous fault cores flanked by m-thick damage zones. The damage zones include sub-parallel high-angle shear fractures, fractures and veins showing a positive correlation between P20 and P21, whose values increase in the vicinity of the main slip surfaces. Such a positive correlation is interpreted as due to fault growth by linkage and coalescence of pre-existing high-angle fractures, and formation of fault-related joints and veins at the extensional quadrants of single shear fractures. Similarly, large-scale NE–SW striking mature faults with throws on the order of tens of meters, made up of a m-thick fault core and 10 s of m-thick damage zones including sub-parallel fractures and veins, also show a positive P20 and P21 correlation. The main outputs of this work are synthesized into a conceptual model illustrating the transition from thrust-related deformation to extensional–transtensional faulting, documenting the evolution of fracture networks from incipient-to-small-to-mature faults. Full article

A novel modified generalized Riccati equation mapping neural network-based approach is the basic theme of this study by exploring the nonlinear dynamical characteristics of the the strain wave model’s soliton solutions, which govern wave propagation in micro structured solids. Strain waves are particularly intriguing, since they preserve their form and speed throughout transmission. The nonlinear dynamical behaviors of strain waves may be modeled by partial differential equations in micro structured materials. In the realm of micro structured solids, there exists a class of phenomena that are referred to as micro strain waves. These waves arise in solids possessing intricate internal architectures, including periodic lattices, precisely engineered metamaterials Understanding these waves is key to designing more complex materials and new acoustic technologies. The activation function and the weight function of the neural network are assigned to each input layer, hidden layer and output layer and the neural network itself is a multi-layer computational network. Using the structure of the neural network, every neuron in the first hidden layer is given solutions to the Riccati equation, and the new highly expressive trial functions are generated in a systematic way. In this way, a large variety of exact soliton solutions are obtained, such as bright, dark, kink, and combined solitons as well as periodic and hyperbolic wave profiles. The influence of the essential physical and mathematical parameters is explored systematically using three-dimensional, two-dimensional and contour visualizations, which illustrate how parameter variations lead to changes in the amplitude, shape and stability of the wave structures. The solutions presented reveal the dynamic properties of micro strain solitons which leads to new avenues of investigation in the study of related nonlinear phenomena in micro structured solids. In a broader context, our results highlight the great potential of analytical techniques using neural networks as a powerful and versatile toolset to study complex nonlinear wave models within the applied sciences from acoustics to photonics to smart materials engineering. Full article

Choriogenesis, the final stage of oogenesis in insects, is a highly coordinated developmental process responsible for the formation of the chorion (eggshell), a specialized multilayered extracellular matrix that protects the embryo and mediates essential physiological functions. Despite its fundamental importance for reproductive success and species survival, the mechanisms underlying chorion biogenesis remain incompletely understood across insect taxa. This review provides an updated synthesis and integrated view of choriogenesis, including cellular, molecular, biochemical, and structural perspectives. We examine the role of follicle cells in chorion formation, the regulatory mechanisms governing chorion gene expression, and the biochemical composition of the eggshell, including proteins, lipids, and carbohydrates. In addition, we compare the structural diversity of the chorion across insect taxa, highlighting both conserved multilayered organization and lineage-specific adaptations in surface morphology and internal architecture. Full article

High-throughput omics technologies have profoundly transformed our approach to studying biological systems, enabling system-level exploration of genomes, transcriptomes, proteomes, metabolomes, and beyond. Despite these advances, current omics approaches remain limited in capturing dynamic temporal changes, preserving spatial organization within tissues, and effectively integrating multi-layered datasets into coherent biological interpretations. The aim of this review is to provide a structured and critical overview of established, emerging, and conceptual omics sciences, and to propose a unifying framework that expands the boundaries of systems biology. This review organizes established and emerging omics into a structured framework, highlighting conceptual innovations such as adaptomics, resiliomics, chrono-adaptomics, and signalomics. We discuss technological advances, computational strategies, and potential applications for research and clinical practice. Full article

Background/Objectives: The “small-n-large-p” dilemma in mammography artificial intelligence (AI)—where the number of candidate imaging features far exceeds the number of labeled cases—commonly results in model overfitting, unstable feature selection, and poor generalization across clinical settings. This study aims to reassess the internal performance of the HiTopology-GOA-CSA (Grasshopper Optimization Algorithm–Crow Search Algorithm) feature-selection framework for mammography using a larger real Curated Breast Imaging Subset of Digital Database for Screening Mammography (CBIS-DDSM) cohort and a stricter leakage-aware evaluation strategy. Methods: In this retrospective computational study using public anonymized datasets, an expanded internal cohort of 98 CBIS-DDSM mass cases (49 benign, 49 malignant) was assembled from digital imaging and communications in medicine (DICOM) region of interest (ROI) series. A total of 1074 features were extracted per case, including 88 handcrafted radiomic descriptors and 986 EfficientNet-B5 deep features. HiTopology-GOA-CSA selected 102 features, corresponding to 91% feature reduction. Two internal evaluation modes were compared: Mode A, which matched the original pilot methodology by performing feature selection once on the full cohort before cross-validation, and Mode B, which used strict nested feature selection within training folds. Performance was assessed with 5-fold stratified cross-validation using a multilayer perceptron (MLP) classifier. Results: On the expanded cohort, Mode A achieved an area under the receiver operating characteristic curve (AUC) of 0.726 (95% CI: 0.594–0.858), sensitivity of 0.658, specificity of 0.651, and F1-score of 0.644. Under the stricter nested evaluation, Mode B achieved AUC of 0.683 (95% CI: 0.549–0.817), sensitivity of 0.598, specificity of 0.631, and F1-score of 0.595. Mean pairwise Jaccard similarity across nested folds was 0.604, indicating moderate feature stability. Benchmark comparisons showed that the proposed method was competitive but did not outperform standard baselines; LASSO logistic regression achieved the highest AUC of 0.739, while the proposed HiTopology-GOA-CSA + MLP achieved an AUC of 0.683. Real external validation on the locked VinDr-Mammo subset (n = 25) remained near-random (AUC of 0.500 [95% CI: 0.304–0.696]), with complete prediction collapse (sensitivity of 1.000, specificity of 0.000). Conclusions: The framework demonstrated feasibility for structured feature selection and stress testing in a small-cohort mammography AI setting; however, external validation revealed near-random discrimination and prediction collapse, indicating limited generalizability. These findings emphasize the need for benchmark comparisons, transparent uncertainty reporting, patient-level validation, and larger multicenter datasets before clinical translation. Full article

Fire safety in high-rise buildings represents a critical challenge in the United Arab Emirates (UAE), where intensive urbanization, extreme climatic conditions, and multilayered regulatory frameworks impose unique competency demands on architects and Fire Safety Engineering (FSE) consultants. Despite this, no empirically validated competency framework exists that simultaneously addresses both professional groups and is tailored to the specificities of the UAE context. This study aimed to construct and empirically validate such a framework. A three-phase sequential exploratory mixed-method design was employed. In the first phase, a systematic literature review yielded a preliminary set of 69 competency indicators organized within a Knowledge, Skills and Attitudes (KSA) structure. In the second phase, a three-round Delphi technique with an expert panel of 18 specialists validated the set to 62 final indicators. In the third phase, importance–performance analysis (IPA) was conducted on a sample of 250 professionals actively engaged in fire safety projects across four UAE. IPA identified 16 priority competency gaps, most pronounced in digital transformation (BIM, CFD, AI; gap = 1.23), proactive client advisory competencies (gap = 1.21), and regulatory navigation and Civil Defence coordination (gap = 1.00). A counterintuitive finding emerged whereby architects systematically rated competencies higher than FSE consultants across all dimensions (all p < 0.05). Psychometric validation confirmed excellent instrument reliability (Cronbach’s Alpha > 0.95) and a theoretically consistent three-factor KSA structure explaining 70.06% of variance. The developed framework of 62 empirically validated indicators represents the first competency model of its kind for architects and FSE consultants in the Gulf Cooperation Council (GCC) region. Its findings provide a direct empirical basis for curriculum reform, Continuing Professional Development (CPD) programmes, and professional licencing standards in the UAE and across the GCC region. The study makes three original contributions: the first empirically validated UAE-specific competency framework for these professional groups; a methodological combination of Delphi, IPA, EFA, Mann–Whitney, and Kruskal–Wallis not previously applied in fire safety competency research; and empirical confirmation that 74% of indicators required original development or adaptation, demonstrating the limitations of generic international competency models in the UAE context. Full article

Flexible polyurethane (PUR) foams and latex rubber foams are widely used in furniture and mattress cushioning, yet conventional standardized mechanical tests only partially capture comfort-relevant behavior, particularly in layered constructions where material interactions and sequencing can alter elastic response. This study aimed to compare the mechanical (elastic) properties of selected three-layer composites of approximately 60 mm thickness (composed of conventional PUR, high-resilience PUR, low-resilience PUR, and latex foam) and to preliminarily assess whether combining foam types improves support of such setup and whether changing layer order modifies elasticity and support. Indentation hardness testing of multilayer cushions was conducted by ISO 2439:2008 Method E. Six three-layer systems (Alpha–Zeta) were assembled in two groups. Group X showed nearly identical support factors (2.6–2.7), high recovery (64.3–66.2%), low hysteresis loss (24.3–24.5%), and overlapping force–indentation (IFD) curves, indicating minimal effect of layer order and dominance of the PUR layers. Group Y exhibited higher but more sequence-dependent support (3.1–3.7), markedly reduced, wider range recovery (30.0–45.9%), increased hysteresis (33.0–34.7%), and more dispersed IFD curves. Placing high-resilience foam at the top partially improve recovery, whereas locating low-resilience foam at the surface increase energy loss. The research contributes in part to the body of knowledge about the behavior of the tested materials according to standardized rules. These preliminary results can be compared with other research findings and used in the preparation of testing models for multilayer foam composites, thereby generating new knowledge to improve the design of future experiments, which will result in increased sitting and lying comfort. Full article

It is well-known that activation functions are crucial in determining spectral expressiveness, training dynamics, and reconstruction accuracy in implicit neural representations (INRs), which employ coordinate-based multilayer perceptrons to represent continuous signals. Despite showing excellent performance, sinusoidal activations, for example SIREN, are limited in their adaptability to diverse signal types due to their fixed harmonic structure. In this paper, we propose two novel periodic activation functions for INRs. (1) Harmonic generalizes sinusoidal activations by combining the fundamental frequency with learned second and third harmonics through per-neuron trainable amplitude coefficients, resulting in a richer spectral basis within the SIREN initialization framework. (2) PM-FINER (Phase-Modulated FINER) extends the variable-periodic FINER activation by embedding frequency modulation synthesis directly into the instantaneous phase, enabling data-driven phase distortion via a learnable modulation index and carrier ratio. We conducted comprehensive experiments spanning nine architectural configurations (including SIREN, WIRE, FINER, Gaussian, Harmonic, PM-FINER, and an additional direct comparison against the Subtractive Modulative Network (SMN)), using six natural images, three learning rate schedulers, and three random seeds, totaling 486 main training runs (534 runs total including an ${\omega }_0$ sensitivity sweep). Our evaluation combined peak signal-to-noise ratio (PSNR), structural similarity index (SSIM), and rigorous statistical analysis, such as paired t-tests, Wilcoxon signed-rank tests, Cohen’s d effect sizes, and Friedman rank tests. Under cosine annealing, Harmonic achieves a mean PSNR gain of 6.08 dB over SIREN and 2.57 dB over FINER (both $p<0.001$, Cohen’s $d>3.7$), while PM-FINER ranks statistically on par with Harmonic (mean difference $0.17$ dB, $p=0.36$), outperforming all of the other baselines. Compared with SMN, Harmonic outperforms it by $+7.94$ dB under cosine annealing (Bonferroni-adjusted $p<{10}^{-5}$, Cohen’s $d=12.3$), winning on all six images. Additionally, the Friedman ranking across the six images confirmed Harmonic (with mean rank $=1.33$) and PM-FINER (with mean rank $=1.67$), being the top two methods under cosine annealing. Our results establish interpretable multi-harmonic and phase-modulated activations as real alternatives to the existing INR activation functions. Full article

The rapid development of high-density cities has triggered severe ecological challenges, including habitat fragmentation, urban heat island (UHI) effects, and conflicting demands for public recreation. Traditional ecological networks (ENs) often focus only on “source” landscapes while neglecting degraded “sink” areas. This bias limits the ability of planners to resolve complex spatial conflicts. Therefore, the primary aim of this study is to develop a robust spatial planning framework that mitigates urban ecological conflicts and enhances regional resilience. To achieve this, we constructed a composite ecological network (CEN) for the high-density city of Guangzhou that harmonizes bird habitat conservation, thermal regulation, and cultural recreation. We combined the MaxEnt model, morphological spatial pattern analysis (MSPA), and circuit theory to identify functional “sources” and “sinks” across these three dimensions. Next, using complex network theory, we optimized the CEN and evaluated its structural robustness using low degree addition (LDA) and low betweenness addition (LBA) strategies. The results indicate the following: (1) The CEN effectively captured the complex mosaic landscape of the city. (2) Single-objective networks displayed distinct spatial differences—the recreational network formed a dispersed web of 242 corridors, while habitat and climate networks remained highly clustered. (3) The integrated CEN generated 1137 multi-layered corridors, creating a vital green skeleton to support species dispersal, mitigate UHI effects, and improve cultural access. (4) Optimization simulations verified that the LBA strategy provided the highest stability against targeted attacks by balancing network connectivity with local aggregation. Ultimately, this framework offers a highly adaptable planning tool for dense cities, providing precise spatial guidance to overcome ecological bottlenecks and harmonize urban growth with ecosystem resilience. Full article

6082 aluminum alloy is widely applied in marine engineering, rail transportation and other industries owing to its excellent comprehensive performance. Welding heat source characteristics exert a decisive influence on the microstructure and mechanical properties of welded joints and become a major constraint for the application of medium-thick aluminum alloy welded structures. In this work, comparative tests of TIG and MIG welding were carried out on 16 mm thick 6082 aluminum alloy plates. Combining thermal simulation, metallographic observation and mechanical property tests, the temperature field distribution, microstructure, microhardness, tensile properties and bending properties of the two kinds of joints were systematically studied. The results show that TIG welding possesses high heat input, forming a broad temperature field with steep thermal gradients. Its weld microstructure is coarse and accompanied by severe coarsening of Mg₂Si precipitates, and the joint presents a highly fluctuating M-shaped microhardness distribution. The average tensile strength of TIG welded joints is 194 MPa, and all specimens fracture in the heat-affected zone. By contrast, MIG welding with low heat input produces a uniform temperature field, as well as a fine and homogeneous weld microstructure with dispersed precipitates. Its microhardness distribution is stable, and the average tensile strength reaches 256 MPa, 32% higher than that of TIG joints. Both welding methods deliver favorable bending performance. The difference in heat input and cooling behavior changes the grain evolution and precipitate characteristics and further dominates the final mechanical performance of joints. MIG welding is more suitable for multi-layer, multi-pass welding of 16 mm thick 6082 aluminum alloy. This work clarifies the correlation between heat input, microstructure and mechanical properties, and the optimized process can effectively improve the microstructural uniformity of the weld joint and enhance its mechanical properties. Full article

Power systems continuously generate large-scale load time series data for forecasting, consumption analysis, and equipment health monitoring. However, real-world load measurements are often contaminated by anomalies caused by sensor faults, communication errors, and abnormal consumption behaviors, which may degrade data quality and affect operational decision-making. To address this issue, this paper proposes an integrated anomaly detection framework named PEL, which combines Prophet-based seasonal-trend decomposition, ensemble empirical mode decomposition (EEMD), and a multilayer long short-term memory (LSTM) network. Prophet is first employed to decompose the original series into trend, seasonal, holiday, and residual components. Sample entropy analysis and white noise tests are then adopted to evaluate whether the residual component still contains complex structured information requiring secondary decomposition. Next, EEMD is applied to the residual component to extract multi-scale intrinsic mode functions. Finally, all decomposed components are normalized and fed into a multilayer LSTM model for anomaly detection. Experiments on a real-world power load dataset demonstrate that the proposed PEL framework achieves an accuracy of 99.92%, a precision of 97.33%, a recall of 100%, an F1-score of 98.65%, and an AUC of 0.9996, outperforming or matching several baseline and hybrid models. Full article

Optical coherence tomography (OCT) is an optical imaging modality that provides high-resolution cross-sectional imaging of biological tissues noninvasively. In Fourier-domain OCT, axial resolution is governed by both the center wavelength and the spectral bandwidth of the light source; therefore, limited or discontinuous bandwidth degrades depth resolution and introduces sidelobes and artifacts in OCT images. To address these issues in OCT image reconstruction from gapped spectra, a sparse parameter estimation approach based on Sparse Iterative Covariance-based Estimation (SPICE) is proposed in this study. By utilizing a sparse parameter estimation framework to directly resolve depth-dependent components from discontinuous interferograms, SPICE enhances axial resolution while suppressing sidelobe artifacts inherent in standard interpolation. Experiments on multi-layered tape, oral epithelium, and finger skin show that SPICE visually suppresses gap-induced sidelobe artifacts and improves structural interpretability under representative gap conditions. Quantitative evaluations on multi-layer tape and biological tissues show that SPICE reduces axial FWHM by 30–45%, increases SSIM by 0.15–0.25, and achieves significantly lower computational cost than GAPES (p < 0.01). Full article

Triboelectric nanogenerators (TENG) show significant potential in pressure sensing by converting mechanical disturbances into electrical signals positively correlated with the magnitude of the applied force, yet their development as practical pressure sensors is severely hindered by the major drawback of only detecting transient mechanical inputs. Additionally, traditional dual-mode pressure sensors have typically required complex multilayer structures and time-consuming fabrication processes. Here, a simple dual-mode pressure sensor of novel structure integrated with TENG and anodic aluminum oxide (AAO) for both dynamic and static pressure detection is proposed. Nanoporous AAO is directly grown on an aluminum substrate to simplify the traditionally complex multi-layer structure of dual-mode pressure sensors. The AAO layer serves a dual functionality by acting as an active triboelectric layer that significantly enhances the triboelectric output performance while concurrently functioning as the capacitive dielectric layer. A polydimethylsiloxane (PDMS) film is employed as the elastic counterpart to pair with the AAO substrate. The influence of PDMS thickness on the charge accumulation and extraction of the TENG mode is investigated to optimize the device output. Under optimal configurations, the streamlined Al-AAO/PDMS sensor demonstrates good sensitivity and linearity (R² > 0.99) for both dynamic triboelectric voltage (1.05 V/kPa) and static capacitance (5.56 pF/kPa) over a wide sensing range of 1–73 kPa. This dual-mode sensor effectively overcomes the transient limitation of conventional single-mode TENGs and shows significant potential for future smart tactile applications. Full article

Dipeptidyl peptidase-4 (DPP-4) is a key therapeutic target for type 2 diabetes (T2D). Several synthetic anti-DPP-4 drugs are currently available for the treatment of T2D; however, the need for safe and effective therapies remains unmet due to the side effects associated with existing DPP-4 inhibitors. This study aimed to integrate structure-based and machine learning (ML)-based virtual high-throughput screening to identify natural DPP-4 inhibitors. Random forest, logistic regression, support vector machine (SVM), and multilayer perceptron (MLP) models were trained on DPP-4 IC₅₀ datasets. Among these, the SVM and MLP models achieved high predictive performance, with areas under the curve of 0.928 and 0.923, respectively. Screening of a natural compound database identified 107 compounds for further analysis. Subsequent structure-based screening, using sitagliptin as a positive control, identified sennidin B and doxorubicin hydrochloride as promising candidates with strong binding affinity for DPP-4. Molecular dynamics simulations (200 ns) and MM-PBSA calculations confirmed stable interactions with DPP-4. Further, sennidin B and doxorubicin hydrochloride inhibited DPP-4 activity in a concentration-dependent manner, with estimated IC₅₀ values of 39.39 and 19.78 μM, respectively. Sennidin B also reduced DPP-4 mRNA and protein expression levels in Caco-2 cells. Overall, sennidin B shows promise as a natural DPP-4 inhibitor and warrants further investigation as a potential antidiabetic agent. Full article

Low-frequency sound insulation remains a major challenge for conventional passive materials, as improved attenuation is usually achieved at the expense of increased thickness and mass. In this work, a smooth fixed third-order ring-curve fractal-maze acoustic metamaterial is proposed for compact low-frequency sound insulation, and a physics-guided long short-term memory–physics prediction surrogate network (LSTM–PPS-Net) tandem framework is developed for its full-spectrum inverse design. Different from conventional Hilbert-type, right-angled, or sharply folded labyrinthine structures, the proposed topology uses recursively arranged curved channels to extend the effective acoustic propagation path and enhance phase accumulation within a limited space. Based on this mechanism, four physically meaningful parameters, namely slit width d, characteristic radius R₃, wall thickness t_w, and inter-column spacing l_E, are selected to construct a low-dimensional design space. A COMSOL–MATLAB automated finite-element method (FEM) workflow is established to generate 1000 valid transmission-loss (TL) spectra over 100–1700 Hz with a 5 Hz interval. For forward prediction, PPS-Net is developed by integrating geometry encoding, frequency-conditioned spectral decoding, and peak-weighted learning. The proposed PPS-Net achieves the best prediction accuracy among the tested models, with a mean absolute error (MAE) of 0.75 dB, a root mean square error (RMSE) of 1.88 dB, and a coefficient of determination (R²) of 0.96, outperforming multi-layer perceptron (MLP), convolutional neural network (CNN) and Transformer models under the same dataset and training protocol. For inverse design, the LSTM encoder extracts frequency-ordered spectral features from the target TL curve, while the frozen PPS-Net decoder provides differentiable acoustic-response feedback, thereby addressing the non-unique mapping from acoustic response to structural parameters. Furthermore, a compactness-oriented optimization strategy is introduced to balance spectral consistency, peak alignment, bandwidth preservation, and occupied-area reduction. In two representative cases, the optimized designs reduce the occupied area by approximately 21% in both representative cases, while maintaining the target attenuation characteristics after FEM verification. These results demonstrate that the proposed framework provides an efficient and physically interpretable route for the full-spectrum inverse design and compact optimization of low-frequency acoustic metamaterials. Full article