Search Results (273)

To investigate the slope instability caused by differential frost heaving mechanisms from the slope crest to the toe during frost heave processes, this study takes a typical silty clay slope in Xinjiang, China, as the research object. Through indoor triaxial consolidated undrained shear tests, eight sets of natural and frost-heaved specimens were prepared under confining pressure conditions ranging from 100 to 400 kPa. The geotechnical parameters of the soil in both natural and frost-heaved states were obtained, and a spatiotemporal thermo-hydro-mechanical coupled numerical model was established to reveal the dynamic evolution law of anchor rod axial forces and the frost heave response mechanism between the frame and slope soil. The analytical results indicate that (1) the frost heave process is influenced by slope boundaries, resulting in distinct spatial variations in the temperature field response across the slope surface—namely pronounced responses at the crest and toe but a weaker response in the mid-slope. (2) Under the coupled drive of the water potential gradient and gravitational potential gradient, the ice content in the toe area increases significantly, and the horizontal frost heave force exhibits exponential growth, reaching its peak value of 92 kPa at the toe in February. (3) During soil freezing, the reverse stress field generated by soil arching shows consistent temporal variation trends with the temperature field. Along the height of the soil arch, the intensity of the reverse frost heave force field displays a nonlinear distribution characteristic of initial strengthening followed by attenuation. (4) By analyzing the changes in anchor rod axial forces during frost heaving, it was found that axial forces during the frost heave period are approximately 1.3 times those under natural conditions, confirming the frost heave period as the most critical condition for frame anchor design. Furthermore, through comparative analysis with 12 months of on-site anchor rod axial force monitoring data, the reliability and accuracy of the numerical simulation model were validated. These research outcomes provide a theoretical basis for the design of frame anchor support systems in seasonally frozen regions. Full article

Multireference configuration interaction (MRCI), Davidson-corrected MRCI (MRCI+Q), coupled-cluster singles, doubles, and perturbative triples [CCSD(T)], and frozen-core full configuration interaction (fcFCI) calculations were carried out using large, correlation-consistent basis sets to investigate the excited states of the Sc atom and the spin–free and spin–orbit coupled potential energy profiles, energetics, spectroscopic constants, and electron populations of low-lying states of MH⁺ (M = Sc, Y, La). The core electron correlation effects, complete basis set effects, and spin–orbit coupling effects were also evaluated. The first four electronic states of all MH⁺ are 1²Δ, 1²Σ⁺, 1²Π, and 2²Σ⁺ with 1σ²1δ¹, 1σ²2σ¹, 1σ²1π¹, and 1σ²3σ¹ single-reference electron configurations, respectively. These states of MH⁺ can be represented by the M²⁺H^– ionic structure. The ground states of ScH⁺, YH⁺, and LaH⁺ are 1²Δ_3/2, 1²Σ⁺_1/2, and 1²Δ_3/2 with 55.45, 60.54, and 62.34 kcal/mol bond energies, respectively. The core electron correlation was found to be vital for gaining accurate predictions on the ground and excited state properties of MH⁺. The spin–orbit coupling effects are minor for ScH⁺ but become substantial moving to YH⁺ and LaH⁺. Overall, the results of this work are in good agreement with the limited set of experimental findings of MH⁺ available in the literature and will be of use for future investigations. Furthermore, the theoretical approaches, findings, and trends reported here are expected to aid studies of similar species. Full article

Background: Fat embolism and fat embolism syndrome are rare but well-known consequences of long bone fractures and orthopedic surgeries. These sources support the mechanical theory of their development. On the other hand, as an alternative pathway suggested by the biochemical theory, lipase activation and fat breakdown are also a possible background for lipid droplets appearing in the vasculature. According to Hulman’s theory, elevated C-reactive protein levels can facilitate calcium-dependent agglutination of very low-density proteins and chylomicrons forming fat globules. The level of this acute-phase protein can increase mainly in advanced-stage cancers but also has predictive or indicative value in treatment success. Methods: This study focused on strictly selected patients with different histological types and origins of cancer, as well as advanced cancer in approximately 90% of the deceased. After collecting the tissue samples, the frozen sections were stained with Oil Red O to detect fat emboli. Results: Less than 50% of the cases showed punctiform, non-clinically relevant pulmonary fat embolism, and fat embolism syndrome was identified in none of the cases. In one, non-advanced cancer case, punctiform kidney fat embolism was observed. Conclusions: The end-of-life anergic state of patients may influence the procedure. In the case of osseous metastases, since the intramedullary sinuses are affected, both the mechanical and the biochemical backgrounds may prevail and mediate fat embolism formation. Full article

Engineered cementitious composites (ECCs) exhibit superior mechanical properties (MPs) and excellent crack control capabilities, making them widely used in practical engineering applications. However, the MPs of ECCs in frozen states (FSs), particularly their flexural properties (FPs), still need to be better understood. MP tests were designed for frozen ECC samples to investigate the service performance of ECCs in an FS. The samples underwent 0 to 300 freeze–thaw cycles (FTs), followed by compressive and flexural tests at a constant freezing temperature of −18 °C. The compressive properties (CPs) and FPs of the samples and their influencing mechanisms were analyzed. Based on this analysis, a life prediction model (LPM) for freeze–thaw-damaged (FTD) ECCs was established using the entropy weight method and the GM(1,1) model to predict the durability changes of ECCs in FS. The results indicate that with an increasing number of FTs, the uniaxial compressive strength (CS), elastic modulus (E), initial crack strength, and ultimate strength of ECCs in the FS are higher than those in the thawed state (TS), with a notable increase in brittleness at ultimate failure. The overall stiffness of the specimens increased under high FTs. The established model effectively predicts the durability changes of ECCs in the FS. Full article

The global potato industry has changed dramatically over the past half century—the potato-planting area in Poland decreased from 2,819,200 hectares in 1961 to 188,580 hectares in 2023, representing a 1394.96% relative decrease; South Africa’s potato production increased from 332,000 tons in 1961 to 2.42 million tons in 2023, representing a 627.60% relative increase. This study provides a comprehensive comparison of the potato-processing industries in China and major global producers. The global potato-processing market was valued at USD 40.97 billion in 2023 and is projected to reach USD 60.08 billion by 2031, with significant variations in production and consumption patterns across countries. As the world’s largest potato producer, China processes approximately 15% of its total potato output, whereas India, the second-largest producer, processes only about 7%. In contrast, developed countries such as the United States, Canada, and leading European nations—including Germany, the Netherlands, France, and Belgium—demonstrate significantly higher levels of processing, underpinned by advanced technologies, automation, and efficient quality-control systems. In order to conduct an in-depth analysis of the competitiveness of China’s potato-processing industry, this paper employs the Diamond Model to carry out relevant research. Despite rapid progress, China’s potato-processing industry still lags behind these global leaders in key aspects such as automation, production efficiency, and product quality. Differences remain evident in major processed potato products, including French fries, potato chips, potato flakes, and starch, as well as in raw-material supply chains, environmental sustainability, and market competitiveness. However, China’s role in the global potato-processing industry is evolving. A major milestone was reached in 2022 when China became a net exporter of frozen French fries for the first time, signaling a shift in its position in the international market. This transformation highlights China’s emergence as a key player in global French fry exports and suggests a potential restructuring of the industry. While challenges remain, the growing acceptance of Chinese French fries in international markets reflects improving product quality. Future industry trends point toward increased automation, product innovation, circular economy practices, and greater international market integration. To enhance its competitiveness, China must further modernize its processing industry, adopt cutting-edge technologies, strengthen quality control, and expand its global footprint to secure a stronger position in the evolving international potato-processing landscape. Full article

The quality of raw meat is important for community health as its freshness is crucial to preventing foodborne illnesses. In the United States, the related illness cases were 9.4 million cases with 55,961 hospital admissions and 1351 deaths annually. This underscores the urgent need for improved meat quality monitoring. This study aims to develop an electronic nose (E-nose) that can differentiate between frozen–thawed and fresh pork meat samples, thereby enhancing food safety. We designed the E-nose using MQ series gas sensor array with temperature and humidity sensors, and an Arduino Uno microcontroller. Sensors were calibrated for accurate data collection. An adaptive support vector machine (ASVM) was used for data classification. We evaluated the model’s accuracy using a confusion matrix. The ASVM model exhibited robust performance, achieving an accuracy of 88%. Its performance was evaluated with recall, F1 scores, and precision. To further enhance the model’s performance, future studies are mandated to integrate additional gas sensors, increase sample sizes, advance data preprocessing techniques, and explore different machine learning algorithms or ensemble methods. Full article

In our research aimed at replacing precious transition metals like platinum with abundant base metals such as nickel for efficient triplet emitters, we synthesized and studied Ni(II) complexes [Ni(L^NHR)Cl]. These complexes containing the N^C^N cyclometalating dipyridyl-phenide ligand, equipped with pending H-bonding amine groups (NH(C₆H₅) (L^NHPh) and NH(C₆H₅CH₂), ClL^NHBn). Molecular structures determined from experimental X-ray diffractometry and density functional theory (DFT) calculations in the ground state showed marked deviation of the Cl⁻ coligand (ancillary ligand) from the ideal planar coordination, with τ₄ values of 0.35 and 0.33, respectively, along with hydrogen bonding interactions of the ligand NH function with the Cl⁻ coligand. The complexes exhibit long-wavelength absorption bands at approximately 425 nm in solution, with the experimental spectra being accurately reproduced through time-dependent density functional theory (TD-DFT) calculations. Vibrationally structured emission profiles and steady-state photoluminescence quantum yields of 30% for [Ni(L^NHPh)Cl] and 40% for [Ni(L^NHBn)Cl] (along with dual excited state lifetimes in the ns and in the ms range) were found in frozen 2-methyl-tetrahydrofuran (2MeTHF) glassy matrices at 77 K. Furthermore, within a poly(methyl methacrylate) matrix, the complexes showed emission bands centered at around 550 nm within a temperature range from 6 K to 300 K with lifetimes similar to 77 K. Based on TD-DFT potential scans along the metal–ligand (Ni–N) coordinate, we found that in a rigid environment that restricts the geometry to the Franck-Condon region, either the triplet T₅ or the singlet S₄ state could contribute to the photoluminescence. Full article

Crack segmentation is essential for structural health monitoring and infrastructure maintenance, playing a crucial role in early damage detection and safety risk reduction. Traditional methods, including digital image processing techniques have limitations in complex environments. Deep learning-based methods have shown potential, but still face challenges, such as poor generalization with limited samples, insufficient extraction of fine-grained features, feature loss during upsampling, and inadequate capture of crack edge details. This study proposes SECrackSeg, a high-accuracy crack segmentation network that integrates an improved UNet architecture, Segment Anything Model 2 (SAM2), MI-Upsampling, and an Edge-Aware Attention mechanism. The key innovations include: (1) using a SAM2 S-Adapter with a frozen backbone to enhance generalization in low-data scenarios; (2) employing a Multi-Scale Dilated Convolution (MSDC) module to promote multi-scale feature fusion; (3) introducing MI-Upsampling to reduce feature loss during upsampling; and (4) implementing an Edge-Aware Attention mechanism to improve crack edge segmentation precision. Additionally, a custom loss function incorporating weighted binary cross-entropy and weighted IoU loss is utilized to emphasize challenging pixels. This function also applies Multi-Granularity Supervision by optimizing segmentation outputs at three different resolution levels, ensuring better feature consistency and improved model robustness across varying image scales. Experimental results show that SECrackSeg achieves higher precision, recall, F1-score, and mIoU scores on the CFD, Crack500, and DeepCrack datasets compared to state-of-the-art models, demonstrating its excellent performance in fine-grained feature recognition, edge segmentation, and robustness. Full article

Structural analyses of protein filaments formed by self-assembly, such as actin, tubulin, or recombinase filaments, have suffered for decades from technical issues due to difficulties in crystallization, their large size, or the dynamic behavior inherent to their cellular function. The advent of cryo-electron microscopy has finally enabled us to obtain structures at different stages of the existence of these filaments. However, these structures correspond to frozen states, and the possibility of observations in solution is still lacking, especially for filaments characterized by a high plasticity, such as the RecA protein for homologous recombination. Here, we use a combination of SAXS measurements and integrative modeling to generate the solution structure of two known forms of the RecA nucleoprotein filament, previously characterized by electron microscopy and resolved by X-ray crystallography. The two forms differ in the cofactor bound to RecA–RecA interfaces, either ATP or ADP. Cooperative transition from one form to the other has been observed during single-molecule experiments by pulling on the filament but also in solution by modifying solvent conditions. We first compare the SAXS data against known structural information. While the crystal structure of the ATP form matches well with the SAXS data, we deduce from the SAXS profiles of the ADP-form values of the pitch (72.0 Å) and the number of monomers per turn (6.4) that differ with respect to the crystal structure (respectively, 82.7 Å and 6.0). We then monitor the transition between the two states driven by the addition of magnesium, and we show this transition occurs with 0.3 mM Mg ²⁺ ions with a high cooperativity. Full article

Frozen gait (FG) is an increasingly prevalent concern in individuals with Parkinson’s disease (PD) that limits mobility and increases the risk of falls. Traditional FG detection and monitoring methods using clinical observations and wearable sensors face limitations, such as inflexibility, lack of portability, inaccessibility to individuals, and the inability to provide continuous monitoring in real-life environments. To address these challenges, this experimental study presents the development of a software-defined radio (SDR)-based radio frequency (RF) sensing platform for continuous FG monitoring. Data were collected through multiple experiments involving various physical activities, including FG episodes. The acquired data were processed using advanced signal-processing (ASP) techniques to extract relevant wireless channel state information (WCSI) patterns. The physical activities were classified using machine learning and deep learning models developed on the dataset prepared from the SDR-based RF sensing system. The results demonstrated that the deep learning models outperformed the machine learning models. The bidirectional gated recurrent unit (BiGRU) achieved the highest accuracy of 99.7%. This indicates that the developed system has the potential for accurate, real-time monitoring of FG and other PD symptoms. The proposed RF sensing platform using SDR technology and artificial intelligence (AI) offers an intelligent and continuous monitoring solution, addressing the limitations of traditional methods. This system provides portable, continuous detection of FG events, potentially improving patient care, safety, and early intervention. Full article

Autonomous driving technology has advanced significantly. However, it is challenging to accurately generate captions for driving environment scenes, which involve dynamic elements such as vehicles, traffic signals, road conditions, weather, and the time of day. Capturing these elements accurately is important for improving situational awareness in autonomous systems. Driving environment scene captioning is an important part of generating driving scenarios and enhancing the interpretability of autonomous systems. However, traditional vision–language models struggle with domain adaptation since autonomous driving datasets with detailed captions of dashcam-recorded scenes are limited and they cannot effectively capture diverse driving environment factors. In this paper, we propose an enhanced method based on the bootstrapping language-image pre-training with frozen vision encoders and large language Model (BLIP-2) to optimize the domain adaptation by improving scene captioning in autonomous driving environments. It comprises two steps: (1) transforming structured dataset labels into descriptive captions in natural language using a large language model (LLM), and (2) optimizing Q-Former in a BLIP-2 module with low-rank adaptation (LoRA) to achieve efficient domain adaptation. The structured dataset labels are originally stored in JSON format, where driving environment scene factors are encoded as key-value pairs that represent attributes such as the object type, position, and state. Using the Large-Scale Diverse Driving Video Database (BDD-100K), our method significantly improves performance, achieving BLEU-4, CIDEr, and SPICE scores that were each approximately 1.5 times those for the baseline BLIP-2, respectively. Higher scores show the effectiveness of LoRA-based optimization and, hence, its suitability for autonomous driving applications. Our method effectively enhances accuracy, contextual relevance, and interpretability, contributing to improved scene understanding in autonomous driving systems. Full article

We present the first step toward the quantum computing (QC) formulation of the electron nuclear dynamics (END) method within the variational quantum simulator (VQS) scheme: END/QC/VQS. END is a time-dependent, variational, on-the-flight, and non-adiabatic method to simulate chemical reactions. END represents nuclei with frozen Gaussian wave packets and electrons with a single-determinantal state in the Thouless non-unitary representation. Within the hybrid quantum/classical VQS, END/QC/VQS currently evaluates the metric matrix M and gradient vector V of the symplectic END/QC equations on the QC software development kit QISKIT, and calculates basis function integrals and time evolution on a classical computer. To adapt END to QC, we substitute the Thouless non-unitary representation with Fukutome unitary representation. We derive the first END/QC/VQS version for pure electronic dynamics in multielectron chemical models consisting of two-electron units with fixed nuclei. Therein, Fukutome unitary matrices factorize into triads of one-qubit rotational matrices, which leads to a QC encoding of one electron per qubit. We design QC circuits to evaluate M and V in one-electron diatomic molecules. In log2-log2 plots, errors and deviations of those evaluations decrease linearly with the number of shots and with slopes = −1/2. We illustrate an END/QC/VQS simulation with the pure electronic dynamics of H₂⁺ We discuss the present results and future END/QC/QVS extensions. Full article

A new method for measuring internal pore water pressure (PWP) is introduced to determine the critical state line (CSL) in partially frozen sand, investigating the influence of temperature and strain rate on the critical state parameters. A series of consolidated undrained and drained triaxial tests, along with internal PWP measurements, were conducted on both dense and loose specimens under different temperatures and strain rates. Similarly to unfrozen sand, a unique CSL was established for the partially frozen sand at −3 °C in both stress ($q$-$p^{\prime }$) and void ratio $(e$-$p^{\prime })$ space. The results show that the critical state friction angle (${\phi }_{cs}^{\prime }$) is not affected by temperature (warmer than −5 °C) and strain rate, while the critical state cohesion ($c_{cs}^{\prime }$) varies with temperature, strain rate and failure mode. The $c_{cs}^{\prime }$ increases with decreasing temperature from 23 °C to −3 °C and to −10 °C, but decreases to zero when the strain rate was reduced from 1%/min to 0.1%/min. In $e$-$p^{\prime }$ space, the slope of CSL could be associated with the dilation of partially frozen sand, which increases with decreasing temperature and increasing strain rate, potentially due to the increased contact area between the pore ice and sand grains. Full article

This work presents an adaptive transfer learning approach for predicting the aging of lithium-ion batteries (LiBs) in electric vehicles using capacity fade as the metric for the battery state of health. The proposed approach includes a similarity-based and adaptive strategy in which selected data from an original dataset are transferred to a clean dataset based on the combined/weighted similarity contribution of feature and stress factor similarities and times series similarities. Transfer learning (TL) is then performed by pre-training a model with clean data, with frozen weights and biases to the hidden layer. At the same time, weights and biases toward the output node are recalculated with the target data. The error reduction lies between −0.4% and −8.3% for 20 computational experiments, attesting to the effectiveness and robustness of our adaptive TL approach. Considerations for data structure and representation learning are presented, as well as a workflow to enhance the application of transfer learning for predicting aging in LiBs. Full article

Human activity recognition by radar sensors plays an important role in healthcare and smart homes. However, labeling a large number of radar datasets is difficult and time-consuming, and it is difficult for models trained on insufficient labeled data to obtain exact classification results. In this paper, we propose a multiscale residual weighted classification network with large-scale, medium-scale, and small-scale residual networks. Firstly, an MRW image encoder is used to extract salient feature representations from all time-Doppler images through contrastive learning. This can extract the representative vector of each image and also obtain the pre-training parameters of the MRW image encoder. During the pre-training process, large-scale residual networks, medium-scale residual networks, and small-scale residual networks are used to extract global information, texture information, and semantic information, respectively. Moreover, the time–channel weighting mechanism can allocate weights to important time and channel dimensions to achieve more effective extraction of feature information. The model parameters obtained from pre-training are frozen, and the classifier is added to the backend. Finally, the classifier is fine-tuned using a small amount of labeled data. In addition, we constructed a new dataset with eight dangerous activities. The proposed MRW-CN model was trained on this dataset and achieved a classification accuracy of 96.9%. We demonstrated that our method achieves state-of-the-art performance. The ablation analysis also demonstrated the role of multi-scale convolutional kernels and time–channel weighting mechanisms in classification. Full article