Search Results (258)

Various factors influence the formation and adjustment of network freight prices, including transportation costs, cargo characteristics, and policies and regulations. The interaction of these factors increases the difficulty of accurately predicting network freight prices through regressions or other machine learning models, especially when the amount and quality of training data are limited. This paper introduces large language models (LLMs) to predict network freight prices using their inherent prior knowledge. Different data sorting methods and serialization strategies are employed to construct the corpora of LLMs, which are then tested on multiple base models. A few-shot sample dataset is constructed to test the performance of models under insufficient information. The Chain of Thought (CoT) is employed to construct a corpus that demonstrates the reasoning process in freight price prediction. Cross entropy loss with LoRA fine-tuning and cosine annealing learning rate adjustment, and Mean Absolute Error (MAE) loss with full fine-tuning and OneCycle learning rate adjustment to train the models, respectively, are used. The experimental results demonstrate that LLMs are better than or competitive with the best comparison model. Tests on a few-shot dataset demonstrate that LLMs outperform most comparison models in performance. This method provides a new reference for predicting network freight prices. Full article

Data analysis and machine learning have become essential cross-disciplinary skills for engineering students and professionals. Traditionally, these topics are taught through lectures or online courses using pre-existing datasets, which limits the opportunity to engage with the full cycle of data analysis and machine learning, including data collection, preparation, and contextualization of the application field. To address this, we designed and implemented a learning activity that involves students in every step of the learning process. This activity includes multiple stages where students conduct experiments to record their own electroencephalographic (EEG) signals and use these signals to learn data analysis and machine learning techniques. The purpose is to actively involve students, making them active participants in their learning process. This activity was implemented in six courses across four engineering careers during the 2023 and 2024 academic years. To validate its effectiveness, we measured improvements in grades and self-reported motivation using the MUSIC model inventory. The results indicate a positive development of competencies and high levels of motivation and appreciation among students for the concepts of data analysis and machine learning. Full article

In this cross-disciplinary investigation, we uncover a suite of previously unexamined factors and their intricate interplay that hold causal relationships with the distribution of Trachurus japonicus in the northern reaches of the South China Sea, thereby extending the existing research paradigms. Leveraging advanced machine learning algorithms and causal inference, our robust experimental design uncovered nine key global and regional factors affecting the distribution of T. japonicus density. A robust experimental design identified nine key factors significantly influencing this density: mean sea-level pressure (msl-0, msl-4), surface pressure (sp-0, sp-4), Summit ozone concentration (Ozone_sum), F10.7 solar flux index (F10.7_index), nitrate concentration at 20 m depth (N3M20), sonar-detected effective vertical range beneath the surface (Height), and survey month (Month). Crucially, stable causal relationships were identified among Ozone_sum, F10.7_index, Height, and N3M20. Variations in Ozone_sum likely impact surface UV radiation levels, influencing plankton dynamics (a primary food source) and potentially larval/juvenile fish survival. The F10.7_index, reflecting solar activity, may affect geomagnetic fields, potentially influencing the migration and orientation behavior of T. japonicus. N3M20 directly modulates primary productivity by limiting phytoplankton growth, thereby shaping the availability and distribution of prey organisms throughout the food web. Height defines the vertical habitat range acoustically detectable, intrinsically linking directly to the vertical distribution and availability of the fish stock itself. Surface pressures (msl-0/sp-0) and their lagged effects (msl-4/sp-4) significantly influence sea surface temperature profiles, ocean currents, and stratification, all critical determinants of suitable habitats and prey aggregation. The strong influence of Month predominantly reflects seasonal changes in water temperature, reproductive cycles, and associated shifts in nutrient supply and plankton blooms. Rigorous robustness checks (Data Subset and Random Common Cause Refutation) confirmed the reliability and consistency of these causal findings. This elucidation of the distinct biological and physical pathways linking these diverse factors leading to T. japonicus density provides a significantly improved foundation for predicting distribution patterns globally and offers concrete scientific insights for sustainable fishery management strategies. Full article

Corrosion can accelerate the deterioration of the mechanical properties of steel structures. However, few studies have systematically evaluated its impact on seismic performance, particularly with respect to seismic economic losses. In this paper, the seismic fragility and loss assessment of a multi-story steel frame with viscous dampers (SFVD) building are investigated through experimental and numerical analysis. Based on corrosion and tensile test results, OpenSees software 3.3.0 was used to model the SFVD, and the effect of corrosion on the seismic fragility was evaluated via incremental dynamic analysis (IDA). Then, the economic losses of the SFVD during different seismic intensities were assessed at various corrosion times based on fragility analysis. The results show that as the corrosion time increases, the mass and cross-section loss rate of steel increase, causing a decrease in mechanical property indices, and theprobability of exceedance of the SFVD in the limit state increases gradually with increasing corrosion time, with an especially significant impact on the collapse prevention (CP) state. Furthermore, the economic loss assessment based on fragility curves indicates that the economic loss increases with corrosion time. Thus, the aim of this paper is to provide guidance for the seismic design and risk management of steel frame buildings in coastal regions throughout their life cycle. Full article

Advances in Industry 4.0 and the emergence of Industry 5.0 are driving the development of intelligent, sustainable manufacturing systems, where embedded sensing and real-time health diagnostics play a critical role. However, implementing robust predictive maintenance in production environments remains challenging due to the variability in machine operations and the lack of access to internal control data. This paper introduces a lightweight, embedded-compatible framework for health status signature extraction based on empirical mode decomposition (EMD), leveraging only data from a single triaxial accelerometer. The core of the proposed method is a cycle-synchronized segmentation strategy that uses accelerometer-derived velocity profiles and cross-correlation to align signals with machining cycles, eliminating the need for controller or encoder access. This ensures process-aware decomposition that preserves the operational context across diverse and dynamic machining conditions to address the inadequate segmentation of unstable process data that often fails to capture the full scope of the process, resulting in misinterpretation. The performance is evaluated on a challenging real-world manufacturing benchmark where the extracted intrinsic mode functions (IMFs) are analyzed in the frequency domain, including quantitative evaluation. As results show, the proposed method shows its effectiveness in detecting subtle degradations, following a low computational footprint, and its suitability for deployment in embedded predictive maintenance systems on brownfield or controller-limited machinery. Full article

Palladium (Pd) and titanium (Ti) exhibit opposite dielectric responses upon hydrogenation, with stronger effects observed in the near-infrared (NIR) region. Leveraging this contrast, we investigated Ti/Pd bilayer thin films as a platform for NIR hydrogen sensing—particularly at telecommunication-relevant wavelengths, where such devices have remained largely unexplored. Ti/Pd bilayers coated with Teflon AF (TAF) and fabricated via sequential electron-beam and thermal evaporation were characterized using optical transmission measurements under repeated hydrogenation cycles. The Ti (5 nm)/Pd (x = 2.5 nm)/TAF (30 nm) architecture showed a 2.7-fold enhancement in the hydrogen-induced optical contrast at 1550 nm compared to Pd/TAF reference films, attributed to the hydrogen ion exchange between the Ti and Pd layers. The optimized structure, with a Pd thickness of x = 1.9 nm, exhibited hysteresis-free sensing behavior, a rapid response time (${\mathrm{t}}_{90}$ < 0.35 s at 4% ${\mathrm{H}}_2$), and a detection limit below 10 ppm. It also demonstrated excellent selectivity with negligible cross-sensitivity to $\mathrm{C}{\mathrm{O}}_2$, $\mathrm{C}{\mathrm{H}}_4$, and CO, as well as high durability, showing less than 6% signal degradation over 135 hydrogenation cycles. These findings establish a scalable, room-temperature NIR hydrogen sensing platform with strong potential for deployment in automotive, environmental, and industrial applications. Full article

With the widespread application of lithium-ion batteries (LIBs), safety performance has become a critical factor limiting the commercialization of large-scale, high-capacity LIBs. The main reason for the safety problem is that the electrolytes of LIBs are extremely flammable. Adding flame retardants to conventional electrolytes is an effective method to improve battery safety. In this paper, trimethyl phosphate (TMP) and trimethyl phosphite (TMPi) were used as research objects, and the flame-retardant test and differential scanning calorimetry (DSC) of the electrolytes configured by them were first carried out. The self-extinguishing time of the electrolyte with 5% TMP and TMPi is significantly reduced, achieving a flame-retardant effect. Secondly, the electrochemical performance of LiFePO₄|Li half-cells after adding different volume ratios of TMP and TMPi was studied. Compared with TMPi5, the peak potential difference between the oxidation peak and the reduction peak of the LiFePO₄|Li half-cell with TMP5 added is reduced, the battery polarization is reduced, the discharge specific capacity after 300 cycles is large, the capacity retention rate is as high as 99.6%, the discharge specific capacity is larger at different current rates, and the electrode resistance is smaller. TMPi5 causes the discharge-specific capacity to attenuate, which is more obvious at high current rates. LiFePO₄|Li half-cells with 5% volume ratio of flame retardant have the best electrochemical performance. Finally, the influence mechanism of the phosphorus valence state on battery safety and electrochemical performance was compared and studied. After 300 cycles, the surface of the LiFePO₄ electrode with 5% TMP added had a smoother and more uniform CEI film and higher phosphorus (P) and fluorine (F) content, which was beneficial to the improvement of electrochemical performance. The cross-section of the LiFePO₄ electrode showed slight collapse and cracks, which slowed down the attenuation of battery capacity. Full article

Canopy gaps are vital microhabitats for forest carbon cycling and species regeneration, whose accurate extraction is crucial for ecological modeling and smart forestry. However, traditional monitoring methods have notable limitations: ground-based measurements are inefficient; remote-sensing interpretation is susceptible to terrain and spectral interference; and traditional algorithms exhibit an insufficient feature representation capability. Aiming at overcoming the bottleneck issues of canopy gap identification in mountainous forest regions, we constructed a multi-task deep learning model (ES-Net) integrating an edge–semantic collaborative perception mechanism. First, a refined sample library containing multi-scale interference features was constructed, which included 2808 annotated UAV images. Based on this, a dual-branch feature interaction architecture was designed. A cross-layer attention mechanism was embedded in the semantic segmentation module (SSM) to enhance the discriminative ability for heterogeneous features. Meanwhile, an edge detection module (EDM) was built to strengthen geometric constraints. Results from selected areas in Yunnan Province (China) demonstrate that ES-Net outperforms U-Net, boosting the Intersection over Union (IoU) by 0.86% (95.41% vs. 94.55%), improving the edge coverage rate by 3.14% (85.32% vs. 82.18%), and reducing the Hausdorff Distance by 38.6% (28.26 pixels vs. 46.02 pixels). Ablation studies further verify that the synergy between SSM and EDM yields a 13.0% IoU gain over the baseline, highlighting the effectiveness of joint semantic–edge optimization. This study provides a terrain-adaptive intelligent interpretation method for forest disturbance monitoring and holds significant practical value for advancing smart forestry construction and ecosystem sustainable management. Full article

With engineering plastics increasingly replacing traditional materials in various drive and control gear systems across numerous industrial sectors, material selection for any gearwheel critically impacts its mechanical and thermal properties. This paper investigates the engagement of steel and Polyvinylidene Fluoride (PVDF) gear pairs tested under several load conditions to determine polymer gears’ characteristic service life and failure modes. Furthermore, recognizing that the application of polymer gears is limited by insufficient data on their temperature-dependent mechanical properties, this study establishes a correlation between the tribological contact, meshing temperatures, and wear coefficients of PVDF gears. The results demonstrate that the flank surface wear of the PVDF gears is directly proportional to the temperature and load level of the tested gears. Several distinct load-induced failure modes have been detected and categorized into three groups: abrasive wear resulting from the hardness disparity between the engaging surfaces, thermal failure caused by heat accumulation at higher load levels, and tooth fracture occurring due to stiffness changes induced by the compromised tooth cross-section after numerous operating cycles at a specific wear rate. Full article

The complex climate in permafrost regions poses severe challenges to infrastructure, and freeze-thaw cycles accelerate the deformation and damage of road embankments. Conventional thermosyphon technology, though effective in lowering permafrost temperatures, has a limited range of effect, making it hard to meet the demand for large-scale temperature regulation. This paper proposes a V-shaped transverse thermosyphon design with bidirectional heat conduction. It connects at the embankment centerline and transversely penetrates the entire cross-section to expand the temperature regulation range. Using a hydro-thermal coupling model, the temperature regulation effects of vertical, inclined, and V-shaped thermosyphons were calculated. Results show that the V-shaped design outperforms the other two in temperature control across different embankment areas. Transverse temperature analysis indicates uniform cooling around the embankment center, while depth temperature analysis reveals more stable temperature control with lower and less fluctuating temperatures at greater depths. Long-term temperature analysis demonstrates superior annual temperature regulation, providing consistent cooling. This research offers a scientific basis for embankment temperature regulation design in permafrost regions and is crucial for ensuring long-term embankment stability and safety. Full article

Islanding detection remains a critical challenge for grid-connected distributed generation systems, as passive techniques suffer from inherent non-detection zones (NDZ), and active methods often degrade power quality. This paper introduces a hybrid detection strategy based on monitoring inherent grid harmonics via a Digital Lock-In Amplifier. By comparing real-time 5th and 7th harmonic amplitudes against their three-cycle-delayed values, the passive stage adaptively identifies potential islanding without fixed thresholds. Upon detecting significant relative variation, a brief injection of a non-characteristic 10th harmonic (limited to under 3% distortion for three line cycles) serves as active verification, ensuring robust discrimination between islanding and normal disturbances. Case studies demonstrate detection within 140 ms—faster than typical reclosing delays and well below the 2 s limit of IEEE std. 1547—while preserving current zero-crossings and enabling grid impedance estimation. The method’s resilience to grid disturbances and stiffness is validated through PSIM simulations and laboratory experiments, meeting IEEE 1547 and UL 1741 requirements. Comparative analysis shows superior accuracy and minimal power-quality impact relative to existing passive, active, and intelligent approaches. Full article

The widespread implementation of next-generation Li metal anodes is limited, in part, due to the formation of dendritic and/or mossy electrodeposits during cycling. These morphologies can lead to battery failure due to the formation of short circuits and significant volumetric expansion at the anode. One strategy to control the electrodeposition of Li metal is to use lithiophilic materials at the anode. Here, we evaluate the impact of Ag and Au on the early stages of Li metal electrodeposition and cycling. The alloying substrates decrease the voltage for Li reduction and improve Li wetting/adhesion. We probe volumetric expansion directly through dilatometry measurements and find that the degree of volumetric expansion is less when lithium is cycled on an alloying substrate compared to a non-alloying substrate (Cu). Dilatometry experiments reveal that Au has the least amount of volumetric expansion and coin cell cycling experiments indicate that Ag yields more stable cycling compared to Au or Cu. The evaluation of in situ cross-sectional images of cycled coin cells shows that Ag has the lowest volumetric expansion in a coin cell format. Full article

Cyclic hydraulic loading frequently affects embankment dams during reservoir regulation, tidal fluctuations, and intense rainfall. It potentially worsens fine particle migration during internal erosion and increases dam failure risks. This study is the first to systematically explore the influence of cyclic hydraulic loading on the critical hydraulic gradient (i_cr) of gap-graded soils, providing new insights into suffusion behavior. Transparent soil experiments, which enable direct observation of soil structural evolution, are combined with coupled DEM–Darcy simulations that offer microscopic mechanical insights, marking the first integrated use of these two approaches to investigate suffusion behavior. To quantify fine particle migration, we propose a novel modified grayscale threshold segmentation (MGTS) method for analyzing cross-sectional images captured during transparent soil experiments. The results from both methods show consistency in fine particle migration, clogging formation, and failure, with differences in permeability and i_cr remaining within acceptable limits. Fine particle content significantly influences the post-cyclic i_cr of internally unstable soils. For soils with lower fine particle content (15%), i_cr increases after cyclic hydraulic loading and rises with the mean hydraulic gradient during cycling. Conversely, soils with higher fine particle content (20%) exhibit a decrease in post-cyclic i_cr. This behavior is explained by changes in the average contact force between fine particles (F_ff) observed in DEM simulations. Full article

At least three betacoronaviruses have spilled over from bats to humans and caused severe diseases, highlighting the threat of zoonotic transmission. Thus, it is important to enhance surveillance capabilities by developing tools capable of detecting a broad spectrum of bat-borne betacoronaviruses. Three monoclonal antibodies (mAbs) targeting the nucleocapsid (N) protein were generated using recombinant N proteins from severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) and Middle East Respiratory Syndrome Coronavirus (MERS-CoV). The cross-reactivities of these mAbs were evaluated against a panel of betacoronaviruses. Sandwich ELISAs (sELISAs) were subsequently developed to detect bat-borne betacoronaviruses that have high zoonotic potential. Among the mAbs, 7A7 demonstrated the broadest cross-reactivity, recognizing betacoronaviruses from the Sarbecovirus, Merbecovirus and Hibecovirus subgenera. The first sELISA, based on mAbs 7A7 and 6G10, successfully detected N protein in all clinical swab samples from COVID-19 patients with cycle threshold (Ct) values < 25, achieving 75% positivity overall (12/16). Using this as a reference, a second sELISA was established by pairing mAb 7A7 with mAb 8E2, which binds to multiple merbecoviruses. This assay detected the N protein of two merbecoviruses, namely the human MERS-CoV and bat-borne HKU5-CoV, at high sensitivity and has a limit of detection (LOD) that is comparable to the first sELISA used successfully to detect COVID-19 infection. These broadly reactive mAbs could be further developed into rapid antigen detection kits for surveillance in high-risk populations with close contact with wild bats to facilitate the early detection of potential zoonotic spillover events. Full article

Objective: Transvaginal ultrasonography plays a crucial role in contemporary fertility management, offering insights into uterine and endometrial blood flow. Three-dimensional ultrasonography utilizing power Doppler angiography (3D-CPA) allows precise measurement of endometrial volume and vascular parameters, such as the vascularization index (VI), blood flow index (FI), and vascularization flow index (VFI); variables that indirectly assess endometrial receptivity and integrity. Doppler technology allows for the capture of changes in the uterus induced by hormonal-related fluctuations during the menstrual cycle, revealing a significant correlation between endometrial receptivity and vascularity. Age-related changes in endometrial function are implicated in declining fertility, with limited research exploring this aspect. The aim of this study was to investigate the impact of aging on various ultrasound parameters of the uterus, including endometrial vascularity. Methods: A retrospective cross-sectional study of women who attended the Feto-Maternal Centre from January 2022 to December 2023. Each woman whose menstrual cycle was regular underwent 3D ultrasound with power Doppler as part of the routine assessment of the pelvis. Parameters assessed include the VI, FI, and VFI as well as uterine volume, endometrial volume, and endometrial thickness. The women were grouped based on age, and the variables measured in the follicular and luteal phases were compared between the age groups using SPSS version 30 September 2024. Results: A total of 907 women (427 follicular and 480 luteal phase) were studied: 297 (131 follicular and 166 luteal) were 20–29 years old; 471 (240 follicular and 231 luteal) were aged 30–39; and 139 (56 follicular and 83 luteal) were aged 40–49. Uterine volume, endometrial volume, and thickness increased significantly and steadily with age. VI, VFI, and FI decreased significantly with age in the follicular phase, but in the luteal phase there was no statistically significant difference in any of these indices with age. Conclusions: Uterine volume, endometrial thickness, and endometrial volume increased with age, but the vascular indices decreased with age in the follicular but not in the luteal phase. These age-related changes in endometrial vascularity may partly explain the decrease in age-related fertility. Further research is needed to comprehensively explore the complexities of uterine aging and its implications for female fertility. Full article