Arsenic (As), a well-known ‘traditional’ environmental contaminant, and carbamazepine (CBZ), an emerging contaminant of a pharmaceutical category, are both frequently detected in the environment and have been shown to exhibit toxicity at exposure concentrations present in the environment. This study aimed to assess the single and combined exposure effects of these pollutants on the adult common vole (Microtus arvalis L.), a model mammalian organism. This study assessed As and CBZ accumulation, their biotransformation processes, and antioxidant enzyme defence responses after sole and combined exposure. The animals were fed a diet containing either a low (1.25 mg/kg) or high level (166 mg/kg) of As. Moreover, half of the animals were exposed to CBZ via drinking water infused with 10 ng/L of CBZ, and the second half had the use of drinking water devoid of CBZ. The results showed enhanced total As and As species contents in the organs of the As + CBZ exposure group compared to the As exposure group. High As exposure in the As + CBZ group did not cause an enhanced uptake of CBZ in the liver compared to the CBZ exposure group. There was a potential accumulation of CBZ in the liver of the CBZ exposed groups (CBZ and As + CBZ), raising concerns about potential toxic effects in mammals from long-term exposure. Glutathione peroxidase (GPx) activity, reflecting the antioxidant enzyme defence responses against single and co-exposure of the two pollutants, showed that the CBZ group exhibited comparable activity to the control group, while the As group had down-regulation and the As + CBZ group had up-regulation. These findings suggest that the CBZ group experienced minimal oxidative stress conditions, similar to the control group. The As group showed a rapid adaptation response to curtail or offset potential oxidative stress tissue damage conditions, compared to the slow adaptation/response in the As + CBZ group. The findings of this experiment indicate that the possible interactions of various environmental pollutants could alter the potential effects of the individual pollutants after a sole exposure. These findings indicate the necessity of investigating these interactions for better understanding of the potential risk of these pollutants in real environmental conditions.
Reactive powder concrete (RPC) is widely used in ultra-high-rise buildings, hydropower stations, bridges, and other important infrastructures. To study the dynamic response and damage characteristics of RPC columns and frames considering coupled fire and explosions, an analytical model of RPC columns and frames with coupled fire and explosions was established by using ABAQUS (2021) finite element software. The dynamic response and damage degree of RPC columns under coupled fire and explosions were investigated to reveal the influence laws of parameters such as cross-section size, axial compression ratio, reinforcement rate, and fire duration on the dynamic response of RPC columns at high temperatures. The dynamic response of the frame structure was analyzed when the explosion load was applied to the bottom corner columns, side columns, and top beams, respectively. The results show that the fire severely weakened the blast resistance of RPC columns; the maximum mid-span deformation and residual deformation of RPC columns decreased with the increase in cross-section size and longitudinal bar reinforcement ratio and increased with the increase in fire duration and axial compression ratio. When the explosion load was applied to the corner columns of the bottom floor of the frame, the bottom corner columns were almost completely destroyed, and there was a significant risk of the structure collapsing. Based on the results of the data analysis, a method to enhance the explosion resistance of RC frame structures using RPC materials at high temperatures is proposed.
This study aims to examine the spatiotemporal variations of extreme rainfall events in Seoul, South Korea, utilizing subhourly rainfall data collected from an extensive observation network. Employing the Mann–Kendall trend detection test and Moran’s I index, this study analyzed temporal trends and spatial distributions in the extreme rainfall time series at 37 weather stations. The findings revealed that the annual maximum precipitation (AMP) series for various durations in Seoul generally exhibited a decreasing trend, with a marked decline observed for durations of 300 and 360 min. Furthermore, the study identified notable changes in the spatial patterns of AMP events, particularly for subhourly AMP events. The results indicate an intensification in the spatial concentration of AMP distribution. Consequently, despite the overall reduction in AMP, the likelihood of urban flooding may increase. This suggests that existing hydraulic design criteria, based on past concepts that temporal and spatial variability are constant, may no longer adequately represent extreme rainfall events. Therefore, revising these criteria to account for the evolving nature of extreme rainfall patterns is imperative.
The method proposed in this paper provides a new research idea for biomimetic three-dimensional grid structure material design. The wings of a dragonfly exhibit a complex grid structure, comprising approximately 1–2% of its total weight, yet demonstrating exceptional mechanical efficiency. In order to investigate the feasibility of applying the design optimization method simulating this structure to the material structure design, we adopted a multi-step method to realize the formation of multi-scale grid structures and folds. Initially, the main vein of the front wing was simulated using a branching structure generation technique. Subsequently, a Voronoi grid was overlaid to generate the complete bionic grid structure. Finally, the fold structure of the wing was simulated using origami principles to create a three-dimensional grid structure. This method can obtain the rigid–flexible coupling 3D grid structure by simulating the 3D fold structure design of the dragonfly wing. The results show that the proposed method can obtain structural materials with excellent structural properties by simulating the structural characteristics of dragonfly wings.
This research focuses on the dynamic analysis of an oscillating conveyor mechanism using numerical methods to solve nonlinear differential equations that govern its motion. The system under study is modeled by a second-order differential equation of the form R(t)dω1dt+Q(t)ω12(t)=W(t), where R(t), Q(t), and W(t) are time-dependent functions representing system parameters such as resistance, damping, and external driving forces. To solve these equations, we employed a numerical approach based on Euler’s method, which discretizes the time domain into small steps h and approximates the derivatives of angular velocity and angular displacement. The angular velocity ωk+1 and angular displacement φk+1 are updated iteratively using the formulas ωk+1=ωk+h(WkRk−QkRkωk2) and φk+1=φk+hωk, respectively. Initial conditions, with ω0=0 and φ0=0, were specified, and the system was simulated over a specified time range divided into N time steps. In the simulation, key parameters such as A(t), B(t), D(t), E(t), F(t), H(t), N(t), M(t), Q(t), R(t), and W(t) were evaluated at each time step based on the system’s geometry and the angular displacements. Due to the complexity of the system, analytical solutions were impractical, so the Runge–Kutta method was employed for higher accuracy in the integration process. The results from the numerical simulations were validated by comparing them with theoretical expectations, and the system’s dynamic behavior was visualized using time-series and 3D plots. The simulation demonstrated that the system’s stability and accuracy were highly dependent on the time step h, with smaller values providing more precise results at the cost of increased computational time. The research confirms the applicability of numerical methods in solving complex nonlinear differential equations for dynamic systems and provides insights into the system’s behavior under various operating conditions.
Robotic auscultation has the potential to solve problems associated with gender issues by allowing examinations that eliminate the need for physical contact between doctor and patient. Aiming toward a robotic auscultation device capable of safely acquiring chest and back auscultatory sounds simultaneously, this study aimed to develop a unique actuator-less hugging mechanism with a multi-acoustic sensor array that can be transformed to wrap around the chest and back to fit the patient’s body shape. The mechanism consists of a twin-articulated arm with multi-layer gear coupling and a cam mechanism for power transmissions. The hugging motion is generated by pushing the cam mechanism by the patient. The force applied to the cam mechanism acts as the driving force for the twin-articulated arm. The trajectory of the arm changes depending on the distance that the cam mechanism is pressed, and it was designed to fit typical body types (obese, standard, and slender). Our results demonstrated that the proposed mechanism was able to be transformed for each body type, and its positional error was less than 15 mm in all body types. This means that the proposed mechanism is capable of safely acquiring chest and back auscultatory sounds whilst simultaneously fitting to various body shapes.
The inertial navigation system (INS) is a robust and reliable navigation strategy to provide position, attitude and velocity information of a carrier with signal acquired from inertial sensors without external assistant. However, the lack of external correction information leads to the accumulation of navigation errors, thereby limiting the reliability and applicable range of INS. In a typical INS, the accuracy and robustness of INS are mainly hindered by sensor’s measuring accuracy, installation misalignments and navigation algorithm effectiveness. To address the limitations of navigation accuracy degradation caused by sensor measurement errors, a calibration and compensation method of the gyro bias was proposed to improve the navigation accuracy. Through analyzing the influence of individual navigation errors, we found that the bias noise of gyroscope is the dominant factor in degrading the navigation accuracy. Aiming to improve the performance of navigation, a rotational modulation method is employed to eliminate the influence of gyro bias drift. Specifically, the rotational modulation could average the gyro bias to zero through the periodic rotational mechanism. Furthermore, the rotational turntable output angle can be used to correct navigation-resolved attitude results, which has a highly precise angle and can be used to calibrate the gyro drift. By compensating for gyro bias in a navigation algorithm, the performance of the navigation results is improved by a matter of one order from 7 km to less than 1 km over a period of 6 h. Several individual navigation experiments were also conducted, and the results prove the effectiveness of our method. The theoretical and experimental results show that the proposed error analysis and the compensation method are feasible and can been applied to the practical navigation system.
Maritime operations play a critical role in global trade but face persistent safety challenges due to human error, environmental factors, and operational complexities. This review explores the transformative potential of Large Language Models (LLMs) in enhancing maritime safety through improved communication, decision-making, and compliance. Specific applications include multilingual communication for international crews, automated reporting, interactive training, and real-time risk assessment. While LLMs offer innovative solutions, challenges such as data privacy, integration, and ethical considerations must be addressed. This review concludes with actionable recommendations and insights for leveraging LLMs to build safer and more resilient maritime systems.
Urogenital infections impact millions of individuals globally each year, with vulvovaginal candidiasis (VVC) being one of the most prevalent conditions affecting women. Candida albicans is the primary pathogen responsible for VVC. The utilization of probiotics as an alternative therapeutic approach to antibiotics in managing such infections has gained increasing attention. This study aimed to evaluate the potential of THY-F51, a lactic acid bacterium isolated from kiwi, as a probiotic to support vaginal health through its antifungal, anti-biofilm, and anti-inflammatory properties against C. albicans. The identification of THY-F51 was confirmed through 16S rRNA gene sequencing. A series of evaluations were performed to determine its antifungal efficacy against C. albicans, biofilm-inhibitory activity, antioxidant properties, and effects on inflammatory cytokines. Cytotoxicity assays and assessments of bacterial survival under vaginal pH conditions (pH 3.8–4.5) were also conducted. The results demonstrated that THY-F51, identified as Leuconostoc citreum, exhibited potent antifungal activity against C. albicans, with an MIC of 1.25 mg/mL and an MFC of 2.5 mg/mL. Furthermore, THY-F51 displayed a strong inhibition of C. albicans biofilm formation, as well as notable antioxidant activity in the supernatant. Additionally, THY-F51 demonstrated high survival rates under vaginal pH conditions, an absence of cytotoxic effects, and a significant reduction in C. albicans adhesion to HeLa cells. Moreover, THY-F51 effectively suppressed C. albicans-induced inflammatory cytokines, including TNF-α, IL-1β, IL-6, and IL-8. These findings suggest that THY-F51, isolated from kiwi, holds substantial promise as a safe and effective probiotic for reducing vaginal inflammation and promoting vaginal health.
Shield performance prediction plays a critical role in construction decision-making. However, current models suffer from significant performance degradation in long-distance prediction. To address this gap, we propose a novel Long-Distance Shield Performance Prediction model (LSPP), which leverages the long-term prediction capabilities of Informer. The LSPP model incorporates conventional monitoring data, tunnelling parameters, and stratigraphic spatial information and is optimized using a ProbSparse self-attention mechanism and dynamic decoding techniques. A series of experiments demonstrate that LSPP significantly outperforms traditional models, such as LSTM and GRUs, particularly in long-distance predictions and under conditions of stratigraphic changes. Notably, the model achieves an R2 of 0.82 when predicting penetration after six rings, making it highly accurate and stable for engineering decision-making.
This investigation focuses on the dynamical effects caused by varying the central-cut width within the Box Assembly with Removable Component (BARC) system. The central-cut widths included in this study are a 0.5″ cut, a 0.25″ cut, a thin 0.1″ cut, and a structure that did not have a cut at all. Finite element analysis was conducted to determine the mode shapes and natural frequencies of each of the BARC structures. Structural dynamics experiments were run to examine the effects of the central-cut width on the dynamical responses and nonlinear characteristics of the BARC system. Free vibration testing with an impact hammer was carried out to excite the system and extract the dominant frequencies and directions of the significant responses. A pseudorandom vibration test that allows for the qualitative determination of any nonlinear behavior within the system was performed. This type of behavior can include nonlinear softening, nonlinear hardening, and the most common, nonlinear damping due to the presence of several bolted-joint connections and the possible activation of geometric and inertia nonlinearities. To quantitatively investigate the impacts of the central-cut width on the dynamics of the system, swept sinusoidal testing was conducted. It is determined that almost all systems with central cuts demonstrate the presence of nonlinear softening, but at times, nonlinear hardening trends are seen, particularly in the 0.1″ cut and no-cut systems when testing harmonically. Each of the central-cut systems displays nonlinear damping, with the amount of damping generally increasing as the central cut decreases in size. The effect of the central cut of the BARC system on the mode-switching ability of the system is negligible; however, mode switching takes place when comparing the central-cut configurations to the no-cut one. These results show the significance of accurately measuring the central-cut width and how geometric uncertainty may change the dynamical responses and nonlinear properties of the system.
Automated tasks, mainly in the biomedical field, help to develop new technics to provide faster solutions for monitoring patients’ health status. For instance, they help to measure different types of human bio-signal, perform fast data analysis, and enable overall patient status monitoring. Eulerian Video Magnification (EVM) can reveal small-scale and hidden changes in real life such as color and motion changes that are used to detect actual pulse. However, due to patient movement during the measurement, the EVM process will result in the wrong estimation of the pulse. In this research, we provide a working prototype for effective artefact elimination using a face movement compensated EVM (MC-EVM) which aims to track the human face as the main Region Of Interest (ROI) and then use EVM to estimate the pulse. Our primary contribution lays on the development and training of two face detection models using TensorFlow Lite: the Single-Shot MultiBox Detector (SSD) and the EfficientDet-Lite0 models that are used based on the computational capabilities of the device in use. By employing one of these models, we can crop the face accurately from the video, which is then processed using EVM to estimate the pulse. MC-EVM showed very promising results and ensured robust pulse measurement by effectively mitigating the impact of patient movement. The results were compared and validated against ground-truth data that were made available online and against pre-existing solutions from the state-of-the-art.
In complex and dynamic environments, traditional motion detection techniques that rely on visual feature extraction face significant challenges when detecting and tracking small-sized moving objects. These difficulties primarily stem from the limited feature information inherent in small objects and the substantial interference caused by irrelevant information in complex backgrounds. Inspired by the intricate mechanisms for detecting small moving objects in insect brains, some bio-inspired systems have been designed to identify small moving objects in dynamic natural backgrounds. While these insect-inspired systems can effectively utilize motion information for object detection, they still suffer from limitations in suppressing complex background interference and accurately segmenting small objects, leading to a high rate of false positives from the complex background in their detection results. To overcome this limitation, inspired by insect visual neural structures, we propose a novel dual-channel visual network. The network first utilizes a motion detection channel to extract the target’s motion position information and track its trajectory. Simultaneously, a contrast detection channel extracts the target’s local contrast information. Then, based on the target’s motion trajectory, we determine the temporal variation trajectory of the target’s contrast. Finally, by comparing the temporal fluctuation characteristics of the contrast between the target and background false positives, the network can effectively distinguish between the target and background, thereby suppressing false positives. The experimental results show that the visual network performs excellently in terms of detection rate and precision, with an average detection rate of 0.81 and an average precision as high as 0.0968, which are significantly better than those of other comparative methods. This indicates that it has a significant advantage in suppressing false alarms and identifying small targets in complex dynamic environments.
Jiao-Liao Mandarin, a distinguished dialect in China, reflects the linguistic features and cultural heritage of the Jiao-Liao region. However, the labor-intensive and costly nature of manual transcription limits the scale of transcribed corpora, posing challenges for speech recognition. We present JLMS25, a transcribed corpus for Jiao-Liao Mandarin, alongside a novel multi-dialect knowledge transfer (MDKT) framework for low-resource speech recognition. By leveraging phonetic and linguistic knowledge from neighboring dialects, the MDKT framework improves recognition in resource-constrained settings. It comprises an acoustic feature extractor, a dialect feature extractor, and two modules—WFAdapter (weight decomposition adapter) and AttAdapter (attention-based adapter)—to enhance adaptability and mitigate overfitting. The training involves a three-phase strategy: multi-dialect AID-ASR multi-task learning in phase one, freezing the dialect feature extractor in phase two, and fine-tuning only the adapters in phase three. Experiments on the Jiao-Liao Mandarin subset of the KeSpeech dataset and JLMS25 dataset show that MDKT outperforms full-parameter fine-tuning, reducing Character Error Rate (CER) by 5.4% and 7.7% and Word Error Rate (WER) by 6.1% and 10.8%, respectively.
Few-shot font generation seeks to create high-quality fonts using minimal reference style images, addressing traditional font design’s labor-intensive and time-consuming nature, particularly for languages with large character sets like Chinese and Korean. Existing methods often require multi-stage training or predefined components, which can be time-consuming and limit generalizability. This paper introduces Patch-Font, a novel single-stage method that overcomes the limitations of prior approaches, such as multi-stage training or reliance on predefined components, by integrating a patch-based attention mechanism and a multitask encoder. Patch-Font jointly captures global style elements (e.g., overall font family characteristics) and local style details (e.g., serifs, stroke shapes), ensuring high fidelity to the target style while maintaining computational efficiency. Our approach incorporates triplet margin loss with hard positive/negative mining to disentangle style from content and a style fidelity loss to enhance local style consistency. Experiments on Korean (printed and handwritten) and Chinese fonts demonstrate that Patch-Font outperforms state-of-the-art methods in style accuracy, perceptual quality, and generation speed while generalizing robustly to unseen characters and font styles. By simplifying the font creation process and delivering high-quality results, Patch-Font represents a significant step forward in making font design more accessible and scalable for diverse languages.
Landslides present a significant global hazard, resulting in substantial socioeconomic losses and casualties each year. Traditional monitoring approaches, such as geodetic, geotechnical, and geophysical methods, have limitations in providing early warning capabilities due to their inability to detect precursory subsurface deformations. In contrast, the acoustic emission (AE) technique emerges as a promising alternative, capable of capturing the elastic wave signals generated by stress-induced deformation and micro-damage within soil and rock masses during the early stages of slope instability. This paper provides a comprehensive review of the fundamental principles, instrumentation, and field applications of the AE method for landslide monitoring and early warning. Comparative analyses demonstrate that AE outperforms conventional techniques, with laboratory studies establishing clear linear relationships between cumulative AE event rates and slope displacement velocities. These relationships have enabled the classification of stability conditions into “essentially stable”, “marginally stable”, “unstable”, and “rapidly deforming” categories with high accuracy. Field implementations using embedded waveguides have successfully monitored active landslides, with AE event rates linearly correlating with real-time displacement measurements. Furthermore, the integration of AE with other techniques, such as synthetic aperture radar (SAR) and pore pressure monitoring, has enhanced the comprehensive characterization of subsurface failure mechanisms. Despite the challenges posed by high attenuation in geological materials, ongoing advancements in sensor technologies, data acquisition systems, and signal processing techniques are addressing these limitations, paving the way for the widespread adoption of AE-based early warning systems. This review highlights the significant potential of the AE technique in revolutionizing landslide monitoring and forecasting capabilities to mitigate the devastating impacts of these natural disasters.
This paper proposes a steady-state thermal model for the passive cooling of photovoltaic (PV) modules integrated into a vertical building façade by means of a solar chimney, including an empirical correlation for turbulent free convection from a vertical isothermal plate. The proposed analytical model estimates the air velocities at the inlet and at the outlet of the ventilation channel of such a cooling system and the average temperature of the façade-integrated PV modules. A configuration composed of a maximum of six vertically installed PV modules and one solar chimney is considered. The air velocities at the inlet and at the outlet of the ventilation channel obtained for the case of installing PV modules on the building façade are compared with those calculated for the case where the PV modules are integrated into the roof with a slope of 37°. By comparing each of the solutions with one PV module to the corresponding one with six PV modules, it was found that the increase in the air velocity due to the effects of the solar irradiance and the height difference between the two openings of the ventilation channel ranges between 41.05% in the case of “Roof” and 141.14% in the case of “Façade”. In addition, it was obtained that an increase in the solar chimney height of 1 m leads to a decrease in the average PV section temperature by 1.95–7.21% and 0.65–2.92% in the cases of “Roof” and “Façade”, respectively. Finally, the obtained results confirmed that the use of solar chimneys for passive cooling of façade-integrated PV modules is technically justified.
Oyster mushrooms (Pleurotus species) have gained significant attention in food science and nutrition due to their exceptional nutritional and functional properties. This review examines various oyster mushroom species’ comprehensive physicochemical characteristics, functional attributes, and antioxidative properties. Through analysis of current literature, we establish that oyster mushrooms contain substantial amounts of protein (15–30%), carbohydrates (43–60%), dietary fibre, and essential micronutrients while maintaining low-fat content (~1.2%). The mushrooms demonstrate significant antioxidant activity through compounds such as ergothioneine, phenolics, and flavonoids, with different species showing varying levels of bioactive compounds. Scientific studies reveal that processing methods, particularly different drying techniques, significantly impact their nutritional composition and functional properties. Notable findings include the identification of specific compounds in coloured varieties, such as pink chromoprotein in Pleurotus djamor and various melanin types, which contribute to both aesthetic and functional properties.
In response to the typical fault issues encountered during the operation of marine diesel engines, a fault diagnosis method based on a convolutional neural network (CNN), a temporal convolutional network (TCN), and the attention mechanism (ATTENTION) is proposed, referred to as CNN-TCN–ATTENTION. This method successfully addresses the issue of insufficient feature extraction in previous fault diagnosis algorithms. The CNN is employed to capture the local features of diesel engine faults; the TCN is employed to explore the correlations and temporal dependencies in sequential data, further obtaining global features; and the attention mechanism is introduced to assign different weights to the features, ultimately achieving intelligent fault diagnosis for marine diesel engines. The results of the experiments demonstrate that the CNN-TCN–ATTENTION-based model achieves an accuracy of 100%, showing superior performance compared to the individual CNN, TCN, and CNN-TCN methods. Compared with commonly used algorithms such as Transformer, long short-term memory (LSTM), Gated Recurrent Unit (GRU), and Deep Belief Network (DBN), the proposed method demonstrates significantly higher accuracy. Furthermore, the model maintains an accuracy of over 90% in noise environments such as random noise, Gaussian noise, and salt-and-pepper noise, demonstrating strong diagnostic performance, generalization capability, and noise robustness. This provides a theoretical basis for its practical application in the fault diagnosis of marine diesel engines.
This study aimed (1) to explore the association between body composition [fat-to-muscle ratio (FMR), waist circumference (WC), and body mass index (BMI)] with motor competence (MC) and physical fitness (PFI) in 7-year-old children; (2) to ascertain whether FMR modifies the associations between MC and PF. A total of 164 children (7 yr) were included. Anthropometric (fat and muscle mass were calculated with TANITA), motor competence (CAMSA test) and physical fitness (1/2 mile run, handgrip strength, 10 × 5, standing long jump, and sit and reach tests) variables were collected. FMR, WC and BMI correlated negatively with MC (all between r ≤ −0.333 and −0.183 and p < 0.05) and PFI (all between r ≤ −0.410 and −0.246 and p < 0.05) in all children. However, the associations were stronger for FMR. In linear regressions analyses, only FMR was independently and negatively associated with MC and PFI, explaining 12.6% and 20.9% of its variance, respectively. Positive correlations between PFI and MC existed. Also, the association was not modified by FMR levels, since in both groups (above and below FMR median), the associations were maintained (p < 0.0001). However, the t-test showed that children with higher FMRs have lower levels of MC (p = 0.005) and PFI (p < 0.0001). FMR may be the best body composition parameter related to MC and PFI. Furthermore, the association between MC and PFI is not modulated by FMR but children with higher FMRs show lower levels of MC and PFI.
