Search Results (9,855)

Generating high-precision normalized digital surface models (nDSMs) from a single remote sensing image remains a challenging and ill-posed problem due to the absence of reliable geometric constraints. In this work, we show that monocular depth provides structurally stable cues of local geometry but lacks the global scale and vertical reference required for absolute height recovery. This intrinsic mismatch limits direct depth-to-height regression, particularly when transferring across heterogeneous terrains, land-cover compositions, and imaging conditions. Building on this idea, we propose the Relative Depth–Absolute Height Prediction Network (RDAH-Net), a framework that exploits relative depth as a geometry-aware prior while learning terrain-dependent height mappings from image appearance to absolute height. As the backbone, we employ a lightweight MobileNetV2 enhanced with a Convolutional Block Attention Module (CBAM), and further incorporate a cross-modal bidirectional attention fusion scheme with positional encoding to achieve a deep and effective fusion of image appearance and depth prior cues. Finally, a PixelShuffle-based upsampling strategy is used to sharpen prediction details and mitigate typical upsampling artifacts. Extensive experiments across diverse regions demonstrate that RDAH-Net achieves robust and generalizable height estimation, providing a practical alternative for large-scale mapping and rapid update scenarios. Full article

The safety and stability of underground coal mining are largely determined by the structural features of coal seams and surrounding rocks. Geological heterogeneities such as faults, fracture zones, and lithological variations strongly influence the distribution of rock pressure and the occurrence of geodynamic hazards. This highlights the need for reliable geophysical methods capable of identifying such zones under mining conditions. Electrical prospecting represents a promising diagnostic approach, as it is highly sensitive to changes in the physical properties of rocks. Unlike conventional geological mapping, it enables the detection of hidden structures and weakened zones often invisible to direct observation. Advances in instrumentation and data processing have further expanded the applicability of electrical methods in complex environments. This study introduces a methodology of electrical prospecting observations for the diagnosis of coal seams. The analysis focuses on conductivity anomalies that reflect tectonic disturbances, fracture systems, and lithological heterogeneities. Field investigations demonstrated the sensitivity of the method to local environmental variations. Comparison with geological records confirmed the validity of the approach: the identified anomalous zones correlated well with documented tectonic features. The methodology showed a stable performance and revealed potential for integration into mine monitoring systems. It allows the identification of areas associated with elevated rock pressure and possible geodynamic activity, thereby contributing to safer underground operations. In the longer term, electrical prospecting may be applied to other coal deposits, including those with a high gas content and complex structure. The development of automated interpretation tools and machine learning algorithms could further increase processing efficiency and improve predictive reliability. Overall, the results confirm that electrical prospecting in mining environments can become an effective instrument for enhancing safety and building more accurate geological–geophysical models of coal seams. Full article

Mitogen-activated protein kinase kinase kinase 1 (MAP3K1) possesses dual enzymatic functions, i.e., kinase and E3 ubiquitin ligase activities, orchestrating proliferation, survival, apoptosis, DNA damage response, and immune modulation. Recent genomic and mechanistic studies have revealed MAP3K1’s paradoxical, context-dependent roles as both an oncogene and a tumor suppressor. We discuss MAP3K1’s multidomain architecture, featuring an N-terminal RING and PHD domain (E3 ligase activity), a TOG domain (microtubule dynamics), and a C-terminal kinase domain, enabling the integration of c-jun N-terminal kinase (JNK), p38 mitogen-activated protein kinase (p38 MAPK), extracellular signal-regulated kinase (ERK), and nuclear factor kappa B (NF-κB) signaling pathways. MAP3K1 functions as a molecular switch balancing survival and apoptosis, with caspase-3 cleavage at Asp878 activating pro-apoptotic JNK/p38 signaling. Genomic analyses across >35 cancer types reveal MAP3K1 alterations at frequencies of <1–14%, highest in breast and endometrial cancers. These alterations show tissue specificity: loss-of-function mutations predominate in hormone receptor-positive breast cancer with a favorable prognosis, whereas gain-of-function mutations in melanoma activate oncogenic ERK signaling. MAP3K1 mutations predict response to mitogen-activated protein kinase kinase (MEK) and phosphoinositide 3-kinase (PI3K) inhibitors, with mutant cancers showing higher MEK inhibitor response than wild-type tumors. Despite substantial progress, critical gaps remain regarding MAP3K1’s E3 ligase substrates, context-dependent activity determinants, and therapeutic strategies. Addressing these through inhibitor development, biomarker validation, and mechanistic studies will accelerate potential clinical translation of MAP3K1 biology. Full article

This study develops a unified multiscale–machine learning framework to interpret and predict thermo-mechanical wear regime transitions in MWCNT- and nanoclay-reinforced bio-based epoxy composites. A physics-informed master wear formulation integrating real contact mechanics, geometry-dependent shear transfer, interfacial adhesion energetics, and fracture-controlled matrix detachment was combined with interpretable machine learning analytics on a unified tribological dataset. In the CNT system, increasing loading from 0.1 to 0.4 wt.% enhanced interfacial adhesion energy density from 0.00813 to 0.01906 J/m², resulting in a monotonic reduction in the wear rate from 0.00918 to 0.00613 mm³/N·m (~33% reduction). In contrast, nanoclay exhibited an optimum behavior, with a minimum wear at 0.25 wt.% (0.000093 mm³/N·m; 7.9% reduction vs. neat clay baseline), followed by deterioration at a higher loading due to dispersion loss. The unified probabilistic regime classification of low-wear conditions (k < 0.007 mm³/N·m) achieved an ROC − AUC = 0.9256 and balanced accuracy = 94.3%, with thermo-mechanical severity identified as the dominant regime-switching driver. Reinforcement identity significantly modulated regime stability, confirming distinct shear transfer (Carbon Nano Tubes(CNT)) and confinement/tribofilm (clay) mechanisms within a common mathematical framework. By enabling the durability-oriented design of bio-based tribological systems and extending component service life through predictive stability mapping, this work contributes to resource-efficient materials engineering and reduced lifecycle waste, supporting Sustainable Development Goals SDG 9 (Industry, Innovation and Infrastructure), SDG 12 (Responsible Consumption and Production), and SDG 13 (Climate Action). Full article

Hollow auxetic structures enable lightweight mechanical design by reducing mass while preserving architected deformation. However, hollow auxetic studies focus on LPBF metals. This study presents a manufacturing-constrained design and validation framework for a hollow hybrid re-entrant chiral lattice produced by stereolithography. The unit cell was parameterised by chiral angle, re-entrant strut length, and hollow internal diameter, with drainage features integrated into the CAD model to preserve hollow channels during printing and post-processing. A minimum internal diameter study defined the printable design window. Within these limits, a central composite design coupled with finite element analysis mapped the response surface and identified an optimised geometry of θ = 15°, L = 3.5 mm, and d = 1.68 mm, with a predicted unit-cell negative Poisson’s ratio of about −1.17. Compression testing confirmed that the printed unit cell and 3 × 3 × 3 lattice retained the intended rotation-dominated auxetic deformation mode. At the selected comparison strain, the unit cell showed a negative Poisson’s ratio of −0.68 and the 3 × 3 × 3 lattice showed −0.29. Relative to the solid lattice, the hollow lattice reduced density by 42.4% with only a 3.0% reduction in stiffness, increasing specific stiffness by 68.9% and specific peak strength by 5.2%, but reducing specific energy absorption by 25.6% due to earlier localisation and junction driven fracture. These results provide practical design guidance for manufacturable hollow SLA auxetic lattices, especially for lightweight and stiffness-limited applications where low mass and high specific stiffness are more important than energy absorption. Full article

We propose a fully nonparametric empirical autoregressive copula framework for univariate time series, designed to capture nonlinear and asymmetric serial dependence while exactly preserving the empirical marginal distribution. The method decouples marginal behavior from temporal dependence by (i) constructing a shape-preserving empirical marginal via monotone interpolation and mapping observations to the unit interval, and (ii) estimating the lag–lead dependence through a nonparametric conditional AR(1) copula density on ${(0,1)}^2$. To ensure stable estimation near the boundaries, we employ reflection-based kernel methods that mitigate edge effects and yield well-behaved conditional densities on the unit support. Forecasts are obtained from the implied conditional predictive density: we compute point forecasts either as conditional modes (maximum a posteriori) on the copula scale or as conditional means, and then back-transform exactly using the empirical quantile function, guaranteeing marginal fidelity and support-respecting predictions. Empirically, we evaluate the approach on three CBOE volatility indices (VIX, VXD, and RVX) and benchmark it against linear ARMA models, copula-based parametric competitors, and state-space/heteroskedasticity baselines (Local level, TVP–AR, and ARMA–GARCH). The results highlight that modeling the full conditional transition density nonparametrically can deliver competitive—often best or near-best—forecast accuracy across horizons, particularly in the presence of pronounced volatility regimes and asymmetric adjustments. Full article

To address the limited in situ stress data and poor nonlinear fitting of existing methods, a Particle Swarm Optimization (PSO)–XGBoost inversion approach is proposed. XGBoost effectively models complex relationships between finite element results and measured stresses, leveraging its strong nonlinear mapping and suitability for small samples. PSO globally optimizes XGBoost hyperparameters, utilizing its fast convergence and global search capability. Combined with 5-fold cross-validation, this avoids empirical tuning errors and enhances generalization. The model uses finite-element-based stress-response values as inputs and calculates in situ stress data derived from hydraulic fracturing interpretations as targets. Engineering applications show that the PSO-XGBoost model outperforms common methods, achieving superior prediction accuracy and generalization with fast convergence. This offers a high-precision inversion approach for small-sample conditions, supporting engineering design and safety assessment. Full article

Background/Objectives: Salidroside, a bioactive phenylethanol glycoside primarily derived from Rhodiola rosea, and its major in vivo metabolite tyrosol exhibit diverse pharmacological activities. However, their direct molecular targets remain poorly defined. Methods: In the present study, an integrated strategy combining transcriptomic profiling, Connectivity Map (CMap) analysis, and multi-level experimental validation was employed. Transcriptomic signatures derived from A549 cells treated with salidroside or tyrosol were queried against the CMap database. Molecular docking, surface plasmon resonance (SPR), and cellular thermal shift assays (CETSA) were performed to predict and validate binding interactions. Functional validation was performed in SH-SY5Y cells. The phosphorylation level of extracellular signal-regulated kinase (ERK), a downstream signaling event of dopamine D2 receptor (DRD2), was detected after salidroside and tyrosol treatment. DRD2 antagonist sulpiride pre-intervention and small interfering RNA (siRNA)-mediated DRD2 knockdown were conducted to verify the receptor dependence of the compounds’ effects. Results: CMap analysis revealed that the transcriptomic signatures of salidroside and tyrosol showed significant similarity to known DRD2 modulators. Molecular docking predicted potential binding interactions between the two compounds and DRD2, which was confirmed by SPR and CETSA to be direct physical binding. Functional studies showed that both compounds rapidly induced DRD2 downstream ERK phosphorylation in SH-SY5Y cells; this effect was abrogated by sulpiride or DRD2 knockdown, indicating DRD2-dependent signaling activation. Conclusions: These findings identify DRD2 as a direct molecular target of salidroside and tyrosol and provide mechanistic insight into their dopaminergic regulatory effects. This study highlights the utility of CMap-guided target discovery combined with rigorous experimental validation for elucidating the molecular mechanisms of natural products. Full article

Computed tomography (CT) enables non-destructive insight into internal log structure, yet fully automated interpretation of CT images remains limited by inconsistent annotations, boundary ambiguity, and insufficient spatial context in 2D slice-based analysis. These challenges restrict the industrial deployment of deep learning for wood quality assessment. This study applies artificial intelligence (AI) and deep learning to the automated analysis of computed tomography (CT) scans of wood logs for detecting internal qualitative features and segmenting bark. Using convolutional neural networks (CNNs), trained models accurately distinguish healthy and damaged regions and segment bark, including discontinuous parts. We introduce a novel pseudo-spatial representation by merging consecutive slices into red–green–blue (RGB) format, which improves prediction accuracy and model robustness across logs. To enhance interpretability, Gradient-weighted Class Activation Mapping (Grad-CAM) highlights regions contributing most to defect detection, particularly knots. Comprehensive evaluation using Sørensen–Dice similarity coefficients and confusion matrices confirms the effectiveness of the proposed approach under industrial conditions. These findings demonstrate that AI-driven CT image analysis can address key limitations of current log-grading workflows and enable more reliable, objective, and scalable quality assessment for timber-dependent economies. Full article

Artificial Intelligence (AI)-based crack detection in buildings uses computer vision and deep learning to automatically identify structural cracks from inspection images. In recent years, many studies have explored this topic, but the overall development of the field, its methodological practices, and the remaining challenges are still not fully clear. Unlike most previous reviews that focus mainly on technical methods, this study combines a large-scale scientometric mapping of the research field with a focused technical analysis of recent AI-based crack detection methods specifically applied to building structures. This study therefore provides a dual-layer review covering research published between 2015 and 2025. A total of 146 Scopus-indexed publications were analysed using Visualization of Similarities viewer (VOSviewer) to examine publication growth, thematic evolution, collaboration patterns, and citation structures. In addition, a focused technical review of 36 highly relevant studies was carried out to analyse task formulations, model families, datasets, evaluation protocols, and methodological practices. The results show a rapid increase in research activity after 2020, largely driven by advances in deep-learning and Unmanned Aerial Vehicle (UAV)-based inspections. At the same time, collaboration networks remain uneven, and citation influence is concentrated in a limited number of research communities. The technical review further shows that most studies focus on detection-level tasks, particularly You Only Look Once (YOLO)-based models, while predictive diagnostics, automated inspection reporting, and decision-oriented Structural Health Monitoring (SHM) are still rarely addressed. Current datasets and evaluation protocols also remain mostly perception-oriented, which makes it difficult to assess robustness, generalisability and long-term predictive capability. Full article

Bothriochloa ischaemum is a key forage species with strong grazing tolerance and high nutritional value, making precise quantification of spike and seed traits essential for germplasm evaluation and yield prediction. However, the compact architecture and minute seed size in natural field conditions render manual counting inefficient and labor-intensive. To address this limitation, this study presents a non-destructive and automated quantification framework integrating advanced object detection and regression analysis for accurate in situ estimation of spikes and seed numbers. To further address the challenges of dense spike detection caused by occlusion and small object sizes, this study developed a modified model named YOLOv12-DAN by integrating DySample dynamic upsampling, ASFF feature fusion, and NWD loss, which achieved a mean average precision (mAP) of 91.6%. Meanwhile, for the detection of dense kernels on compact spikes, an improved YOLOv12 architecture incorporating an Explicit Visual Center (EVC) module was proposed to enhance multi-scale feature representation. The optimized model attained a bounding box precision of 96.5%, a recall rate of 86.4%, an mAP50 of 94.3%, and an mAP50-95 of 73.9%. Furthermore, a univariate linear regression model based on 132 spike samples verified the reliable consistency between the predicted and actual seed counts, with a mean absolute error (MAE) of 6.30, a mean absolute percentage error (MAPE) of 9.35, and an R-squared (R²) value of 0.808. Finally, the model was deployed through a lightweight end-to-end web application, enabling real-time field operation and promoting its applicability in breeding programs and agronomic decision-making. This study provides a robust technical pathway for automated phenotyping and precision forage improvement. Full article

Excessive macrophage activation is thought to be the primary cause of the cytokine storm that results in severe coronavirus disease 2019 (COVID-19) complications. The underlying mechanisms remain elusive, and more research is needed to find disease-critical genes and develop effective therapies. In this study, we used publicly accessible microarray datasets of cytokine storm in cultured human monocyte-derived macrophages challenged with cytokines, and employed bioinformatics, such as weighted gene co-expression network analysis (WGCNA) and differential expression analysis, to dissect gene expression profiles and identify putative disease-related molecules. Initially, three co-expression modules and related key genes were discovered, which highly correlated to macrophages challenged with cytokines. Then, a preliminary gene expression signature consisting of 203 upregulated and 24 downregulated genes was identified. Next, protein–protein interaction analysis and hub gene identification were used to identify 11 crucial hub genes, namely tripartite motif-containing 21 (TRIM21), interferon regulatory factor 1 (IRF1), guanylate binding protein 1 (GBP1), transporter associated with antigen processing 1 (TAP1), nuclear myosin I (NMI), interleukin 15 receptor subunit alpha (IL15RA), apolipoprotein L1 (APOL1), intercellular adhesion molecule 1 (ICAM-1), protein tyrosine phosphatase non-receptor type 1 (PTPN1), E74-like ETS transcription factor 4 (ELF4) and guanylate binding protein 2 (GBP2). Then, the LINCS L1000 characteristic direction signatures search engine (L1000CDS2) was employed for drug repurposing studies. Dasatinib was predicted to be the leading therapeutic compound to perturb the gene signature of cytokine storm in human macrophages. Connectivity Map results suggested that dasatinib may normalize ICAM-1 expression. In addition, the results of molecular docking studies and molecular dynamics simulation revealed that dasatinib may spontaneously interact with ICAM-1 via several key residues and form a relatively stable protein–ligand complex. Overall, this work, based on an analysis of co-expression correlation networks, gene expression signatures and pivotal genes in human macrophages challenged with cytokines, combined with drug repurposing studies, demonstrated that dasatinib may interact with ICAM-1 and could be a potential candidate for cytokine storm. However, due to the limitations of computational approaches, further experimental validation is necessary. Full article

Addressing the challenge of coordinated dispatch between wind/solar and thermal power in new energy grids, this research proposes a thermal power unit output prediction method based on a Transformer-BiLSTM hybrid deep learning model. First, a simulated annealing algorithm optimizes the output configuration of solar thermal power plants to mitigate fluctuations in wind and solar combined generation. An ant colony-greedy algorithm is then integrated to determine the optimal dispatch data for thermal power units, constructing a high-quality training dataset under physical constraints. In the model design, a bidirectional long short-term memory network captures short-term temporal features, while the Transformer’s multi-head self-attention mechanism models long-term dependencies. The model innovatively incorporates the learnable positional encoding to enhance temporal awareness. Experimental results demonstrate accurate predictions, with the power constraint mechanism effectively correcting over-limit forecasts. This ensures 98.7% of predictions during low-load periods comply with unit technical specifications. Compared to existing methods, this model avoids data limitations and manual feature engineering bottlenecks through the end-to-end wind–solar–thermal mapping, providing a high-precision solution for dispatch decisions in renewable-dominated grids. Full article

Underwater cultural heritage represents a fragile and largely unexplored component of historical landscapes, particularly in dynamic fluvial and coastal environments. Despite increasing international attention to its protection, the spatial identification of submerged heritage remains methodologically challenging. This study proposes a geo-historical approach that integrates historical cartography and Geographic Information Systems (GIS) to identify areas of high archaeological potential in underwater contexts. Focusing on the Douro River in Porto (Portugal), a UNESCO World Heritage city with a long maritime and fluvial history, the research analyses a set of key historical maps from the eighteenth and nineteenth centuries, complemented by documentary and archaeological sources. These cartographic materials were georeferenced and critically assessed in QGIS, enabling the digitisation of features associated with land–water interaction, navigation hazards, port infrastructures, and military defences. The resulting spatial dataset was used to generate an interpretative map and a kernel density model highlighting potential underwater heritage hotspots along the riverbed and riverbanks. The findings identify several priority zones, including the river mouth, historic quays, former shipbuilding areas, and sectors linked to nineteenth-century defensive structures. While the study does not include in situ verification, it demonstrates the value of historical maps as predictive tools for guiding targeted underwater surveys and proposes a transferable, cost-effective framework for heritage prospection and management in historically active fluvial–estuarine settings. Full article

Traditional Post-Occupancy Evaluation (POE) is static and incompatible with dynamic systems like Digital Twins, creating a digital gap in managing health-oriented urban environments, especially in Urban Underground Spaces (UUS). This paper bridges this gap with a deep learning framework that automates the continuous prediction of human physiological arousal. We created a novel multimodal dataset from in situ experiments, synchronizing first-person video, environmental data, and Galvanic Skin Response (GSR) as a real-time physiological arousal proxy. Our dual-branch spatial–temporal model fuses these data streams to predict GSR with high accuracy (Pearson’s r = 0.72), effectively mapping objective environmental inputs to continuous human physiological dynamics. This framework provides an automated, human-centric analysis engine for urban planning, design validation, and real-time building management. It establishes a foundational ‘human dynamics layer’ for urban Digital Twins, evolving them into predictive tools for simulating human-environment interactions and embedding physiological perception into intelligent urban systems. Full article