Search Results (27)

Jumping translocations (JTs) are rare chromosomal abnormalities that play a crucial role in the pathogenesis of various cancer types. These rearrangements, especially those involving chromosome 1q, are frequently associated with tumor progression, therapeutic resistance, and poor prognosis. One gene of particular interest, human Jumping Translocation Breakpoint (JTB), has been identified at the site of translocation breakpoints and exhibits complex, context-dependent roles in cancer biology. JTB protein functions as a pivotal regulator in mitosis, chromosomal segregation, apoptosis, and cellular metabolism. It is functionally linked with the chromosomal passenger complex (CPC) and is implicated in processes such as epithelial–mesenchymal transition (EMT), immune evasion, and therapy resistance, especially in breast and prostate cancers. Advances in genomic, transcriptomic, and proteomic research have highlighted the significant potential of JTB as a diagnostic biomarker and a target for therapeutic interventions. This review underscores the dual role of JTB as both a tumor suppressor and oncogene, depending on the cellular context, and advocates for its continued investigation at the genomic, transcriptomic, and proteomic levels. Understanding JTB’s multifaceted contributions to tumor biology may pave the way for novel biomarkers and targeted treatments in cancer management. Full article

This study investigates the mechanism behind the cavitation-induced noise in an automotive heater core and proposes a structural solution to eliminate it. Abnormal noise during cold-start conditions in a compact passenger vehicle was traced to cavitation in the heater core of the heating, ventilation, and air conditioning (HVAC) system. Controlled bench tests, in-vehicle measurements, and computational fluid dynamics (CFD) simulations were conducted to analyze flow behavior and identify the precise location and conditions for cavitation onset. Results showed that high flow rates and low coolant pressure generated vapor bubbles near the junction of the upper tank and outlet pipe, producing distinctive impulsive noise and vibration signals. Flow visualization using a transparent pipe and accelerometer data confirmed cavitation collapse at this location. CFD analysis indicated that the original geometry created a high-velocity, low-pressure region conducive to cavitation. A redesigned outlet with a tapered transition and larger diameter significantly improved flow conditions, raising the cavitation index and eliminating cavitation events. Experimental validation confirmed the effectiveness of the modified design. These findings contribute to improving the acoustic performance and reliability of automotive HVAC systems and offer broader insights into cavitation mitigation in fluid systems. Full article

The vertical curve is designed to smooth sudden gradient changes in the longitudinal profile, enhancing train operational safety and passenger comfort. However, dynamic detection in these segments has consistently encountered issues with long-wavelength vertical irregularities exceeding tolerance limits. To investigate the root causes of this phenomenon and develop a targeted solution, a comprehensive vehicle-track dynamics simulation model was first constructed, based on the design principles for intercity railway vertical curves. The inertial reference method was then applied to process the acceleration and relative displacement data between the detection beam and the track, yielding virtual irregularities. These were compared with excitation irregularities to identify key factors affecting detection accuracy in vertical curve segments. Through further analysis of abnormal exceedances in detection data, the reference cancellation method was proposed. By employing smoothing filters and orthogonal least squares fitting, this method effectively removes track alignment components from the acceleration integration results. Detection errors under various conditions were then compared between the two methods to evaluate the feasibility and effectiveness of the reference cancellation approach. Results indicate that regions with increased longitudinal profile detection errors are primarily located at and near gradient transition points. The vertical curve radius was found to be the primary factor influencing the accuracy of long-wavelength irregularity detection. The proposed reference cancellation method effectively reduces detection errors in areas near gradient transition points to levels comparable to other track sections. Compared to the inertial reference method, the reference cancellation method reduces the maximum detection error by up to 71.77% and the root mean square error by up to 86.61%, effectively mitigating the abnormal exceedances associated with vertical curves. Full article

In the task of human behavior detection, video classification based on deep learning has become a prevalent technique. The existing models are limited due to an inadequate understanding of behavior characteristics, which restricts their ability to achieve more accurate recognition results. To address this issue, this paper proposes a new model, which is an improvement upon the existing PPTSM model. Specifically, our model employs a multi-scale dilated attention mechanism, which enables the model to integrate multi-scale semantic information and capture characteristic information of abnormal human behavior more effectively. Additionally, to enhance the characteristic information of human behavior, we propose a gradient flow feature information fusion module that integrates high-level semantic features with low-level detail features, enabling the network to extract more comprehensive features. Experiments conducted on an elevator passenger dataset containing four abnormal behaviors (door picking, jumping, kicking, and door blocking) show that the top-1 Acc of our model is improved by 10% compared to the PPTSM model, reaching 95%. Moreover, experiments with four publicly available datasets(UCF24, UCF101, HMDB51, and the Something-Something-v1 dataset) demonstrate that our method achieves results superior to PPTSM by 6.8%, 6.1%, 21.2%, and 3.96%, respectively. Full article

In current transportation hubs, passengers travelling with wheeled luggage or suitcases is a common phenomenon. Due to the fact that most luggage occupies a certain space in dense passenger crowds with high mass inertia, its abnormal motion, such as drifting, can frequently trigger unavoidable local disturbances and turbulence in the surrounding pedestrian flows, further increasing congestion risk. Meanwhile, there still is a lack of quantitative disturbance propagation analysis, since most state-of-the-art achievements rely on either scenario-based experiments or the spatial characteristics of crowd distribution assessed qualitatively. Therefore, this study considers the luggage-laden passenger as a deformable particle. The resulting disturbance on surrounding non-luggage-carrying passengers is analyzed and quantified into a nonlinear pressure term. Subsequently, the disturbance propagation model of passenger-owned luggage is developed by adapting the classical Aw–Rascle traffic flow model with a pressure term. Simulation experiments of disturbances caused by luggage drifting and retrograding were conducted in Pathfinder 2022 Software. Experimental results showed that the disturbing force of a left-sided crowd can reach a peak of 238 N with a passenger density of 3.0 $\mathrm{p}/{\mathrm{m}}^2$, and the maximum force difference between the left- and right-sided disturbing force can reach 153 N, as confirmed by a case study in an L-shaped corridor of a transportation hub. Furthermore, it is recommended that the proposed model can be applied in crowd flow analysis and intelligent decision-making for passenger management in transportation hubs. Full article

The road passenger transportation enterprise is a complex system, requiring a clear understanding of their active safety situation (ASS), trends, and influencing factors. This facilitates transportation authorities to promptly receive signals and take effective measures. Through exploratory factor analysis and confirmatory factor analysis, we delved into potential factors for evaluating ASS and extracted an ASS index. To predict obtaining a higher ASS information rate, we compared multiple time series models, including GRU (gated recurrent unit), LSTM (long short-term memory), ARIMA, Prophet, Conv_LSTM, and TCN (temporal convolutional network). This paper proposed the WDA-DBN (water drop algorithm-Deep Belief Network) model and employed DEEPSHAP to identify factors with higher ASS information content. TCN and GRU performed well in the prediction. Compared to the other models, WDA-DBN exhibited the best performance in terms of MSE and MAE. Overall, deep learning models outperform econometric models in terms of information processing. The total time spent processing alarms positively influences ASS, while variables such as fatigue driving occurrences, abnormal driving occurrences, and nighttime driving alarm occurrences have a negative impact on ASS. Full article

AIS ship-type code categorizes ships into broad classes, such as fishing, passenger, and cargo, yet struggles with finer distinctions among cargo ships, such as bulk carriers and containers. Different ship types significantly impact acceleration, steering performance, and stopping distance, thus making precise identification of unfamiliar ship types crucial for maritime monitoring. This study introduces an original classification study based on AIS data for cargo ships, presenting a classifier tailored for bulk carriers, containers, general cargo, and vehicle carriers. The model’s efficacy was tested within the Changhua Wind Farm Channel using eight classification algorithms across tree-structure-based, proximity-based, and regression-based categories and employing standard metrics (Accuracy, Precision, Recall, F1-score) to assess the performance. The results show that tree-structure-based algorithms, particularly XGBoost and Random Forest, demonstrated superior performance. This study also implemented a feature selection strategy with five methods, revealing that a model trained with only four features (three ship-geometric features and one trajectory behavior feature) can achieve high accuracy. Conclusively, the classifier effectively overcame the challenges of limited AIS data labels, achieving a classification accuracy of 97% for ships in the Changhua Wind Farm Channel. These results are pivotal in identifying abnormal ship behavior, highlighting the classifier’s potential for maritime monitoring applications. Full article

Airlines provide one of the most popular and important transportation services for passengers. While the importance of the airline industry is rising, flight cancellations are also increasing due to abnormal weather factors, such as rainfall and wind speed. Although previous studies on cancellations due to weather factors considered both aircraft and weather factors concurrently, the complex network studies only treated the aircraft factor with a single-layer network. Therefore, the aim of this study was to apply a multilayer complex network (MCN) method that incorporated three different factors, namely, aircraft, rainfall, and wind speed, to investigate aircraft cancellations at 14 airports in the Republic of Korea. The results showed that rainfall had a greater impact on aircraft cancellations compared with wind speed. To find out the most important node in the cancellation, we applied centrality analysis based on information entropy. According to the centrality analysis, Jeju Airport was identified as the most influential node since it has a high demand for aircraft. Also, we showed that characteristics and factors of aircraft cancellation should be appropriately defined by links in the MCN. Furthermore, we verified the applicability of the MCN method in the fields of aviation and meteorology. It is expected that the suggested methodology in this study can help to understand aircraft cancellation due to weather factors. Full article

In the context of the network operation of urban rail transit systems, disruptions caused by signal interruptions influence not only the operation of the service at a single station but also the level of service of the whole network. Moreover, it is even possible to induce the cascading failure of the urban rail transit network. Therefore, it is essential to maintain the real-time dynamic monitoring of abnormal stations in urban rail transit systems for security reasons. Based on the large amounts of automated fare collection (AFC) data, a real-time calculation method to estimate the influence intensity of the passenger flow is presented, the spatiotemporal distribution of the influence characteristics is analyzed, and the propagation law of disruptions in the urban rail transit network is explored. First, the fluctuation threshold of passenger flow in a normal situation for all stations was calculated. Accordingly, abnormal stations influenced by the disruption were identified. Then, an evaluation method for calculating the influence intensity of the passenger flow was proposed. Finally, a real-world case study based on the Beijing rail transit system was conducted. All abnormal stations were identified dynamically and displayed in real time, and the distribution and propagation law of abnormal stations were constructed by spatiotemporal diagrams. The influence intensity of passenger flow was analyzed in detail from the perspective of the whole network and representative stations. The results revealed that transfer stations were more vulnerable to the effects of disruption, and the duration for which these stations were affected was longer than that of ordinary stations. Moreover, short-distance travelers were less affected by the disruption than long-distance travelers. The method proposed in this paper can provide a theoretical basis for rail management departments to grasp the characteristics of passenger flow in real time, formulate disposal measures dynamically, and provide more accurate information services for passengers. Full article

Hydrogen is an effective route for achieving zero carbon dioxide emissions, with a contained cost compared to electric powertrains. When considering the conversion of spark ignition (SI) engines to H₂ fueling, relatively minor changes are required in terms of added components. This study looks at the possibility of converting a small-size passenger car powered by a turbocharged SI unit. The initial evaluation of range and peak power showed that overall, the concept is feasible and directly comparable to the electric version of the vehicle in terms of powertrain performance. Injection phasing effects and cylinder imbalance were found to be potential issues. Therefore, the present work applied an 0D/1D simulation for investigating the effects of hydrogen fueling with respect to the likelihood of backfire. A range of engine speeds and load settings were scrutinized for evaluating the possibility of achieving the minimal risk of abnormal combustion due to pre-ignition. Ensuring the correct flow was predicted to be essential, especially at high loads and engine speeds. Fuel delivery phasing with respect to valve intake and closing events was also found to be a major factor that influenced not only backfire occurrence but conversion efficiency as well. Interactions with the electronic control unit were also evaluated, and additional requirements compared to standard conversion kits for LPG or CNG fueling were identified. Full article

Abnormal passenger behavior in rail transit has become a top priority, as it affects operational safety. Passenger travel time is the main basis for identifying abnormal behavior while considering the flexibility of travel time. Currently, the main method is to use absolute threshold discrimination based on the distribution of travel time. However, there is a problem of missing abnormal passenger behavior due to the large difference in travel time between the Origin-Destinations (ODs). Therefore, this paper proposes a method of setting corresponding thresholds for each OD. By analyzing the percentile curves of the overall and individual OD pairs, it was found that the turning point of the curve had a significant feature, and the difference between the two sides of the curve was obvious. This paper proposes a bilateral fitting method, and the results show that this method can calculate the relative threshold for different OD pairs. The significant advantages of this method are its low cost and wide coverage. Full article

Uterine leiomyomas (ULs), frequent benign tumours of the female reproductive tract, are associated with a range of symptoms and significant morbidity. Despite extensive research, there is no consensus on essential points of UL initiation and development. The main reason for this is a pronounced inter- and intratumoral heterogeneity resulting from diverse and complicated mechanisms underlying UL pathobiology. In this review, we comprehensively analyse risk and protective factors for UL development, UL cellular composition, hormonal and paracrine signalling, epigenetic regulation and genetic abnormalities. We conclude the need to carefully update the concept of UL genesis in light of the current data. Staying within the framework of the existing hypotheses, we introduce a possible timeline for UL development and the associated key events—from potential prerequisites to the beginning of UL formation and the onset of driver and passenger changes. Full article

Air pollution in the urban environment is a topical subject. Aero-suspended particles can cause respiratory diseases in humans, ranging from inflammation to asthma and cancer. One of the components that is most prevalent in particulate matter (PM) in urban areas is the set of tire microparticles (1–20 μm) and nanoparticles (<1 μm) that are formed due to the friction of wheels with asphalt and are increased in slow-moving areas that involve a lot of braking actions. In this work, we studied the effect that microparticles generated from passenger tires (PTWP, passenger tire wear particles) have in vitro on murine macrophages cells RAW 264.7 at two concentrations of 25 and 100 μg/mL, for 24 and 48 h. In addition to the chemical characterization of the material and morphological characterization of the treated cells by transmission electron microscopy, gene expression analysis with RT-PCR and active protein analysis with Western blotting were performed. Growth curves were obtained, and the genotoxic effect was evaluated with a comet assay. The results indicate that initially, an induction of the apoptotic process is observable, but this is subsequently reversed by Bcl2. No genotoxic damage is present, but mild cellular abnormalities were observed in the treated cells. Full article

Abnormal-trajectory detection can be used to detect fraudulent behavior by taxi drivers when carrying passengers. Existing methods usually detect abnormal trajectories based on the characteristics of “few and different”, which require large data sets and, therefore, may identify “few and near” trajectories chosen by drivers according to their driving experience as abnormal situations. This study proposed an abnormal-trajectory detection method based on a variable grid to address this problem. First, the urban road network was divided into three regions: high-, medium-, and low-density road network regions using a kernel density analysis method. Second, grids with different sizes were set for different types of road network regions; trajectory tuples were obtained based on the grid division results, and the abnormality rate of the trajectory was calculated. Finally, a trajectory-abnormality probability function was developed to calculate the deviation of each trajectory from the benchmark trajectory to detect abnormal trajectories. Experimental results on a real taxi trajectory dataset demonstrated that the proposed method achieved a higher accuracy in detecting abnormal trajectories than similar methods. Full article

Anomalous driving behavior detection is becoming more popular since it is vital in ensuring the safety of drivers and passengers in vehicles. Road accidents happen for various reasons, including health, mental stress, and fatigue. It is critical to monitor abnormal driving behaviors in real time to improve driving safety, raise driver awareness of their driving patterns, and minimize future road accidents. Many symptoms appear to show this condition in the driver, such as facial expressions or abnormal actions. The abnormal activity was among the most common causes of road accidents, accounting for nearly 20% of all accidents, according to international data on accident causes. To avoid serious consequences, abnormal driving behaviors must be identified and avoided. As it is difficult to monitor anyone continuously, automated detection of this condition is more effective and quicker. To increase drivers’ recognition of their driving behaviors and prevent potential accidents, a precise monitoring approach that detects abnormal driving behaviors and identifies abnormal driving behaviors is required. The most common activities performed by the driver while driving is drinking, eating, smoking, and calling. These types of driver activities are considered in this work, along with normal driving. This study proposed deep learning-based detection models for recognizing abnormal driver actions. This system is trained and tested using a newly created dataset, including five classes. The main classes include Driver-smoking, Driver-eating, Driver-drinking, Driver-calling, and Driver-normal. For the analysis of results, pre-trained and fine-tuned CNN models are considered. The proposed CNN-based model and pre-trained models ResNet101, VGG-16, VGG-19, and Inception-v3 are used. The results are compared by using the performance measures. The results are obtained 89%, 93%, 93%, 94% for pre-trained models and 95% by using the proposed CNN-based model. Our analysis and results revealed that our proposed CNN base model performed well and could effectively classify the driver’s abnormal behavior. Full article