Search Results (15,266)

The Jiaodong Peninsula constitutes a world-class gold province in eastern China, containing more than 5000 t of identified gold resources. The Jiaojia gold deposit is one of the largest deposits within this gold province, and mineralization is primarily distributed along the northern segment of the Jiaojia Fault. The structural characteristics and mineralization processes of the northern segment have been extensively documented. In contrast, the ore-forming mechanisms of the southern Jiaojia Fault remain poorly constrained, hindering further exploration targeting. We chose several gold deposits and one drill core along the Jiaojia Fault, then present whole-rock major and trace elements data to evaluate magmatic affinities and their ore-forming potential. The results show that the lithological differences in plutonic and stratigraphic units suggest that variations in petrogenesis may have exerted a fundamental control on mineralization styles. Almost all samples are characterized by enrichment in light rare earth elements, relative enrichment in Europium, and pronounced depletion in heavy rare earth elements. Alteration characteristics indicate the northern segment is dominated by advanced argillic alteration, whereas phyllic alteration is more prevalent in the southern segment. The rare earth elements discrimination plot clearly suggests differentiation from the northern and southern fault segments. Consequently, we propose that the northern segment records synorogenic arc magmatism, while the southern segment experienced both synorogenic and a subsequent intraplate extensional transitional stage. Full article

Rocky shores are characterized by rough, multi-scale bathymetric variations that result in enhanced wave energy dissipation by bottom friction compared to sandy beaches. Realistic SWAN simulations of surface gravity waves across the rocky shores of Monterey (CA, USA) are conducted, and model results are compared to 20 inner-shelf observational sites spanning 34–5 m water depth. The wave field was highly variable during the study, including alternately low energy waves dominated by southern swell and higher energy local waves aligned with strong north-westerly winds. Including a modified bottom friction parameterization is required for the model to reproduce bulk wave statistics with high skill across the entire inner shelf. The SWAN simulation with the default bottom friction parameterization overestimates significant wave height relative to observations because the friction factor $f_e$ parameterization has a maximum value of 0.3. Additional simulations included two empirical formulations relating $f_e$ to the normalized wave excursion $A_b/k_N$ in the large roughness regime $A_b/k_N<1$. Both simulations incorporate a higher $f_e$ that is required to model strong bottom friction dissipation over rocky seabeds. The higher friction factors, with 80% falling within the range 0.43 to 5.38, are associated with variability in the normalized orbital excursion within $0.1<A_b/k_N<1$. This range corresponds to a large bottom roughness length scale, $k_N=0.5$ m, characteristic of rocky shore environments. Full article

Background/Objectives: This article is a narrative review that examines the development of attachment from intrauterine life to the first thousand days of a child’s life, integrating psychoanalytic, neuroscientific, genetic, and cross-cultural perspectives. Biological, relational, neurological, and cultural factors interact and shape individual differences in socio-emotional functioning. This paper aims to propose a reinterpretation of early attachment, describing it as both a clinical and relational phenomenon and an adaptive process inscribed in human evolutionary history, according to the Four-Domain Integrative Framework described herein. Methods: The review examined three main areas of evidence: early attachment characteristics, cross-cultural caregiving variations, and genetic and epigenetic mechanisms underlying environmental sensitivity. Results: The review first identified seven characteristics of early attachment (proximity seeking, emotional attunement, intrauterine experiences, maternal holding, security patterns, brain plasticity, and maternal stress) which represent developmental mechanisms that generate individual differences in trust, self-regulation, resilience, and psychopathological vulnerability. Second, cross-cultural variations in six distinct caregiving contexts were examined, demonstrating that secure attachment emerges through culturally specific pathways, differentially influencing motor development, sleep patterns, hypothalamic–pituitary–adrenal axis maturation, and social skills. Finally, the differential susceptibility model was provided through the analysis of five genetic and epigenetic systems (oxytocin receptor gene, serotonin transporter gene, dopamine receptor gene, glucocorticoid receptor methylation, and fetal programming) that modulate environmental sensitivity. Conclusions: Biological, relational, neurological, and cultural factors interact and shape individual differences in socio-emotional functioning. Full article

Despite recent advances in automatic speech recognition (ASR), performance remains limited for Korean-accented English due to the limited availability of accent-specific speech data, including pronunciation and prosodic variations. To address this limitation, we propose a synthetic data generation framework for improving Whisper-based ASR performance. Synthetic speech is generated by converting English text into Hangul-based phonetic transcriptions using an intermediate IPA representation to reflect the phonological characteristics of Korean-accented English. The ASR model is fine-tuned using Low-Rank Adaptation with a mixture of synthetic and authentic speech data. Experimental results demonstrate relative reductions of up to 16.40% in the character error rate, 14.93% in the word error rate, and 14.81% in the phoneme error rate compared to the pretrained baseline. Full article

This study is based on time-series land use data of Xinjiang from 2000 to 2022. Using grid tools, bivariate autocorrelation models and other methods, we systematically analyzed the spatiotemporal variation characteristics of land use and ecosystem service value. The results show the following: Firstly, from 2000 to 2022, Xinjiang’s LUCC exhibits differentiated evolution characteristics: cropland, forestland, and built-up land expanded continuously, while the areas of grassland and unused land showed a steady reduction trend, and the area of water bodies showed a fluctuating growth pattern. Secondly, according to the calculation of carbon emissions from LUCC in Xinjiang from 2000 to 2022, the carbon emissions from LUCC have increased significantly, from 27.79 million tons in 2000 to 226.43 million tons in 2022, with built-up land being the main source of carbon emissions, but the continuous reduction in grassland area has led to the weakening of carbon sequestration capacity. Thirdly, from 2000 to 2022, Xinjiang’s ESV shows a fluctuating upward trend, increasing from 1880.528 billion yuan in 2000 to 1894.198 billion yuan in 2022, with grassland and water area being the core contributors to ESV, accounting for over 80% of the total contribution. Fourthly, in terms of spatial distribution, there is an overall negative correlation between the intensity of carbon emissions from LUCC and the intensity of ESV, mainly aggregated as “low–low” and “low–high”, with “high–low” aggregation primarily distributed in the desert areas of the Tarim Basin and Junggar Basin and “low–high” aggregation concentrated in the marginal mountainous areas and oasis regions of Xinjiang. The findings provide a solid scientific basis for the optimization of land use structure, the achievement of carbon emission reduction targets, and the protection of ecosystems in Xinjiang and similar arid regions worldwide. Full article

Background/Objectives: Malignant transformation of hepatocellular adenoma (HCA) represents a clinically significant yet incompletely understood process. Although the pathological and clinical characteristics of HCA have been extensively described, its spatial molecular heterogeneity and spatially organized molecular variation at the tissue level remain insufficiently characterized. This study aimed to establish a spatially integrated multi-omics workflow and to delineate spatially organized molecular variation across histologically defined regions from adenoma to carcinoma. Methods: A sequential dual-layer matrix-assisted laser desorption/ionization mass spectrometry imaging (MALDI-MSI) workflow was developed to acquire small-molecule metabolomic and N-glycan spatial data from the same formalin-fixed paraffin-embedded (FFPE) tissue section. Four rare HCA specimens containing focal carcinoma transformation were included in this study. Pixel-level clustering, region-based co-localization analysis, and diffusion pseudotime modeling were applied to characterize spatial metabolic and N-glycan patterns across normal liver tissue (NL), hepatocellular adenoma (HCA), and carcinoma-transformed regions within adenoma (HCA-HCC). Results: Small-molecule MSI revealed spatial metabolic stratification within HCA, with variation observed in nucleotide-related, lipid-related, sulfur-related, and sugar nucleotide–associated metabolites. Pseudotime analysis revealed a spatial ordering of samples across NL, HCA, and HCA-HCC regions, showing differences in antioxidant-associated metabolites, lipid-related features, and bile acid-related metabolites across regions. N-glycan MSI identified independent glycosylation niches, with increasing structural complexity and enrichment of highly branched glycans in carcinoma-transformed regions. Integration of metabolomic and glycomic data suggested spatially associated patterns between metabolite features and glycan structures across regions. Conclusions: This study provides spatially resolved evidence of spatially organized patterns of molecular variation across histologically defined regions of HCA. The identified metabolic and N-glycan gradients provide insights into spatial molecular organization during malignant transformation of hepatocellular adenoma. Full article

In recent years, China’s rapid economic development has driven the improvement of infrastructure, with mass concrete widely applied in engineering for its unique structural functions. However, mass concrete is prone to temperature stress and thermal cracks due to its low thermal conductivity, huge volume, complex construction conditions, and frequent environmental changes, which pose potential structural safety risks. The hydration heat of mass concrete can also cause structural deformation, so targeted measures must be taken based on actual engineering conditions to minimize cracks. Real-time temperature monitoring during pouring is of crucial significance to ensure the quality and safety of mass concrete in practical projects. Taking the Phase I Project of Qingdao Metro Line 9 as the research object, this paper explores the temperature variation characteristics of mass concrete during pouring and forming on-site. It analyzes the temperature changes in mass concrete based on field temperature-monitoring data and laboratory test results, plots temperature measurement curves, and identifies the temperature variation trend of mass concrete caused by hydration heat. A numerical model is established via ANSYS to study the effects of ventilation temperature and velocity by simulation. Results show that the temperature of mass concrete pouring blocks rises rapidly to a peak and then decreases to room temperature, which is analyzed from the perspectives of hydration heat reaction mechanism and heat transfer. Laboratory test data are highly consistent with field data, verifying the temperature variation characteristics of concrete pouring. The numerical simulation of heat transfer-influencing factors reveals that the optimal ventilation velocity is 4 m/s for sufficient air circulation in the foundation pit; when the ventilation temperature is below 25 °C, the surface temperature of concrete decreases significantly with an obvious cooling effect. Full article

This study examines the confined compression behavior of soils with varying clay content under controlled boundary conditions. A carefully designed experimental setup was utilized, maintaining constant parameters including the soil thickness-to-plate diameter ratio (H/D), initial bulk density (ρ), and plate diameter (D). This controlled framework enabled the isolated investigation of the effects of clay content on soil compression behavior. A systematic range of soil textures, characterized by increasing clay content, was tested to observe trends and establish relationships between clay content and confined compression response. The evaluation involved the calculation of key parameters relevant to terrain–vehicle systems, such as the load-bearing capacity factor (k) and vertical soil pressure (p). By analyzing the variation in these parameters in relation to clay content, the study aims to clarify how clay proportion and associated soil characteristics, such as plasticity and cohesion, affect load-bearing capacity under confined conditions. Furthermore, the influence of moisture content on the load-bearing capacity factor was investigated within the same boundary conditions, providing additional insight into the interaction between moisture, clay content, and soil strength. The findings of this research will enhance the understanding of soil mechanical behavior under confined compression, with particular relevance to terrain–vehicle interactions and the optimization of off-road mobility. Full article

The self-directed channel (SDC) class of memristors employs a multilayer architecture that is designed to enable robust Ag ion conduction, long cycling lifetime, and thermal stability. While several layers contribute to mechanical and chemical reliability, two layers primarily govern the electrical behavior: the amorphous Ge–chalcogenide active layer that is adjacent to the bottom electrode and the overlying metal–chalcogenide source layer. In this work, we investigate how the variation in the chalcogen species in these two layers influences switching characteristics in the pre-write regime, both in the pristine state and after a write/erase cycle, as well as the conduction behavior at room temperature. The devices were fabricated using Ge-rich chalcogenides containing O, S, Se, or Te, combined with SnS, SnSe, or Ag₂Se metal–chalcogenide layers. The DC current-voltage measurements were analyzed using the standard linearization approaches to examine whether the transport behavior in the pre-write regime exhibits characteristics that are associated with Ohmic, Schottky, Poole–Frenkel, or space charge limited conduction. These measurements specifically probe the pre-write region of the I-V curve, where early ionic redistribution and structural rearrangement precede the abrupt formation of the conductive channels responsible for the resistive switching. The results show that the chalcogen composition strongly affects the threshold voltage, the resistance window, and the onset of field-enhanced transport, reflecting the differences in ionic distribution and channel formation dynamics. The results indicate that transport evolves with a bias and a compliance current, transitioning between regimes that are influenced by the interface injection and bulk-limited conduction, depending on the material stack. These findings clarify the role of chalcogen chemistry in governing the SDC switching behavior and provide guidance for the material selection in application-specific device design. Full article

Edge intelligence enables significant latency reduction and enhances the timeliness of model-based fault diagnosis. However, existing deep learning-driven bearing fault diagnosis models are ill-suited for deployment on edge devices, primarily due to three critical limitations: (1) Lightweight models typically exhibit inadequate anti-noise performance, failing to meet the reliability requirements of real-world engineering scenarios. (2) Models with superior anti-noise capabilities often demand high-performance hardware for operation, thereby restricting their deployment on resource-constrained edge devices. (3) These models adopt a fixed input length, which makes it difficult to guarantee diagnostic accuracy across diverse application scenarios—attributed to variations in sampling frequencies, bearing parameters, and other relevant factors. To address these challenges, this paper proposes a lightweight anti-noise diagnostic framework (LADF) for edge-intelligent bearing fault diagnosis in complex engineering environments. The LADF comprises three core modules: a dynamic input module (DIM), a lightweight network module (LNM), and a denoising branch. Specifically, the DIM is designed based on the envelope spectrum, leveraging its inherent demodulation characteristics to dynamically adapt to input signals across diverse scenarios. Group convolution and layer normalization are employed to construct the LNM, ensuring robust diagnostic performance while achieving efficient computation. The denoising branch constrains the feature extractor via a loss function, enabling it to learn generalized fault features under varying noise environments and thereby enhancing the anti-noise capability of the framework. Finally, the proposed LADF is validated through test rig experiments on two datasets of train axle box bearings. Comparative analysis with state-of-the-art models demonstrates that the LADF achieves superior diagnostic stability and anti-noise performance while maintaining a more lightweight architecture, making it well-suited for edge deployment in railway bearing fault diagnosis. Full article

In view of the complex seabed response and pipeline force characteristics induced by wave loading and long-term cross-shore profile evolution on shoreward submarine pipelines, this study investigates the coupled effects of profile evolution, burial depth, and pipeline angle on the surrounding seabed and resulting wave-induced forces. Physical model experiments were conducted in a wave flume under irregular wave conditions. A controlled variable design was adopted, dividing the experiments into five main groups and 17 subgroups based on the pipeline angle, initial burial depth, and seabed topography at different evolution stages. Pore pressure around the pipeline and wave height were measured synchronously, and seabed topography was scanned using a laser system. The results show that increasing the initial burial depth reduces both pore pressure and forces on the pipeline. Under cross-shore profile evolution, pore pressure and forces in sedimentation zones are lower and decrease further with continued evolution, whereas the opposite trend is observed in erosion zones. Changes in pipeline angle induce an asymmetric pore pressure distribution around the pipeline, with the resultant force first decreasing and then increasing. The direction of the resultant force shows greater rotation amplitude in sedimentation zones while, in erosion zones, the direction remains more concentrated. In sedimentation zones, pore pressure decreases and force changes are relatively gradual; in erosion zones, pore pressure increases and force changes are more pronounced. Overall, the variations in force direction and magnitude exhibit distinct characteristics depending on the zone type. These findings provide a scientific basis for the rational design of shoreward pipelines, enabling stability and safety optimization through integration with cross-shore profile evolution patterns, reducing engineering risks, and enhancing the economic viability and reliability of nearshore pipeline projects. Full article

Tactile sensing is an important perceptual modality that enables robots to understand human contact behaviors. Estimating the fingertip contact angle based on tactile pressure distribution provides a simplified representation of the finger’s contact configuration and supports tactile-based perception in human–robot interaction. However, the relationship between tactile pressure distributions and fingertip contact configuration remains insufficiently understood. In this study, a simplified contact mechanics model was employed to investigate the relationship between tactile pressure characteristics and fingertip contact conditions. Theoretical analysis indicates that both the contact area and the contour dimensions of the pressure distribution are influenced by the contact angle and contact force, with varying sensitivities in different directions to these factors. Based on this theory, simplified finite element modeling of the fingertip and multi-subject experiments were conducted. The deformation behavior of the contact region under different contact angles and contact forces was analyzed. The experimental results were generally consistent with the theoretical analysis. Furthermore, contour descriptors were extracted from the tactile pressure distribution to establish a relationship model for estimating the fingertip contact angle, and the model’s accuracy was analyzed. The experimental results indicate that the extracted contour features exhibit systematic variations with contact angle, and the proposed method achieves a mean absolute error (MAE) of 2.73° and a root mean square error (RMSE) of 7.25°. These results demonstrate that tactile pressure contours provide an effective and computationally efficient cue for estimating fingertip contact configuration. This approach may help robots understand human behavior and has potential applications in human–robot interaction and robotic grasping. Full article

Numerous studies have demonstrated the potential of Synthetic Aperture Radar (SAR) in monitoring glacier velocity. However, owing to the complex dynamics of glaciers and the variability of their surface features, velocity fields derived from even short-interval SAR image pairs often exhibit missing parts. This study proposes a missing glacier velocity recovery method based on a spatiotemporal hybrid neural network to solve the above problem. Considering the spatiotemporal characteristics of glacier velocity fields, a hybrid network combining an Artificial Neural Network (ANN) and a Denoising Autoencoder (DAE) is developed. The ANN is first employed to capture spatial correlations associated with missing values, after which it is integrated with the DAE to model temporal variations using a time-aware loss function. An iterative weighting strategy adaptively balances spatial and temporal features during training. The method is applied to SAR–derived velocity fields of Petermann Glacier. Experimental results show that the method significantly improves the performance of glacier velocity recovery compared to traditional methods. Additionally, the study compares and analyzes the velocity of Petermann Glacier in different seasons, and the findings indicate that the glacier exhibits more pronounced seasonal differences in the accumulation zone. Full article

Supercritical heat transfer in regenerative cooling channels is strongly influenced by thermophysical property variations near the pseudo-critical temperature, yet their direct implications for cooling performance have not been fully addressed. This study investigates how incorporating supercritical property considerations into surrogate fuel formulation affects heat transfer behavior in a regenerative cooling channel. RP-3 surrogate fuels were constructed using a genetic algorithm by matching both temperature-independent properties and temperature-dependent properties under supercritical conditions. Unlike previous approaches employing distillation curves as a secondary objective, the present formulation adopted supercritical density distribution and pseudo-critical temperature (T_pc) as optimization targets. The formulated surrogate fuels were evaluated in a regenerative cooling channel model surrounding a combustor, and their flow and heat transfer characteristics were compared with those of literature-based surrogate fuels. The results show that differences in T_pc and density variation trends significantly influence buoyancy-induced asymmetric flow structures and the onset of heat transfer deterioration. Surrogate fuels with lower T_pc exhibit earlier density reduction and earlier development of asymmetric flow, whereas fuels with higher T_pc demonstrate relatively mitigated wall temperature rise. The results of the present study suggest that surrogate fuel formulation based on supercritical thermophysical properties can have a significant influence on the predicted heat transfer behavior in regenerative cooling channels under the operating conditions considered. Full article

Active non-line-of-sight (NLOS) human detection aims to infer the presence of hidden individuals by analyzing indirectly reflected photons between a relay surface and occluded targets. In this study, a single-photon avalanche diode (SPAD) and time-correlated single-photon counting (TCSPC)-based acquisition system were used to measure time–photon waveforms in controlled NLOS environments designed to represent post-disaster rubble scenarios. Although the effective temporal resolution of the system is limited by the detector timing jitter and laser pulse width, the recorded transient signals retain distinguishable intensity and temporal delay patterns associated with the primary and secondary reflections. To construct a representative dataset, measurements were collected under varying subject poses, orientations, and surrounding object configurations. The recorded signals were processed using a unified preprocessing pipeline that included normalization, histogram shaping, and signal windowing. Three machine learning models, namely, Convolutional Neural Network, Gated Recurrent Unit, and Random Forest, were trained and evaluated for human presence classification. All models achieved full sensitivity in detecting human presence; however, notable differences emerged in the classification of human-absent scenarios. Among the tested approaches, random forest achieved the highest overall accuracy and specificity, demonstrating stronger robustness to statistical variations in time–photon histograms under limited photon conditions. These results suggest that tree-based classifiers capture amplitude distribution patterns and temporal dispersion characteristics more effectively than deep neural architectures under the present acquisition constraints. Overall, the findings indicate that low-cost SPAD-based NLOS sensing systems can provide reliable human detection in indirect-observation scenarios. Full article