The signatures of the new particles h2h_2 and ZμτZ_{\mu\tau} at e-p colliders in the U(1)Lμ−LτU(1)_{L_\mu-L_\tau} model

Considering the superior performances of the future e-p colliders, LHeC and FCC-eh, we discuss the feasibility of detecting the extra neutral scalar $h_{2}$ and the light gauge boson $Z^{}_{\mu\tau}$, which are predicted by the ${U(1)}_{L^{}_{\mu} - L^{}_{\tau}}$ model. Taking into account the experimental constraints on the relevant free parameters, we consider all possible production channels of $h_{2}$ and $Z^{}_{\mu\tau}$ at e-p colliders and further investigate their observability through the optimal channels in the case of the beam polarization P($e^{-}$)= -0.8. We find that the signal significance above 5$\sigma$ of $h_{2}$ as well as $Z^{}_{\mu\tau}$ detecting can be achieved via $e^{-}p\to{e^{-}jh_{2}(\to{Z_{\mu\tau}Z_{\mu\tau}})}\to~e^{-}j+/\!\!\!\!{E}^{}_{T}$ process and a 5$\sigma$ sensitivity of $Z^{}_{\mu\tau}$ detecting can be gained via $e^{-}p\to{e^{-}jh_{1}(\to{Z^{}_{\mu\tau}Z^{}_{\mu\tau}})\to}~e^{-}j+/\!\!\!\!{E}^{}_{T}$ process at e-p colliders with appropriate parameter values and a designed integrated luminosity. However, the signals of $h_{2}$ decays into pair of SM particles are difficult to be detected.Comment: 22 pages, 9 figures, references added and typos are correcte

Orchestrating cancer cell migration: Quantatitive analysis of protrusion, adhesion and contraction dynamics regulated by epidermal growth factor and collagen

Cell migration plays an important role in cancer metastasis. Traditional diagnostic methods often involve obtaining tissue biopsies and examining the morphology of the cells and the molecular composition of the microenvironment in static microscopy images. A link between dynamic cellular processes and static microenvironmental inputs must be made. This connection is often made qualitatively with a lack of quantitative information. Therefore, the aims of this work are to investigate how subcelluar dynamics of cell migration such as protrusion and adhesion are quantitatively modulated under different environmental inputs such as epidermal growth factor (EGF) and collagen. There are two major subcellular processes of migration, protrusion and adhesion. Protrusion is a dynamic movement of the cell edge and adhesion is mediated through macromolecular complexes called focal adhesions (FA). EGF concentration is an input that regulates FA and protrusion dynamics, whereas cell speed is an output that integrates information determined by inputs such as EGF. Several FA signatures and protrusion waves are associated with fast migration, but not necessarily with EGF. This suggests that other factors like contractility or extracellular matrix (ECM) might alter protrusion and FA for fast migration. Because fast migrating cells are usually invasive and cause metastasis, I designed a high-throughput method to identify the fast cells for determining what differences in cell properties such as protein expression level lead to the cell-to-cell variability. As mentioned above, contractility and ECM adhesivity are other inputs that affect migration. Although their effects on migration may be similar, upstream responses may vary. For example, both increasing adhesivity and decreasing contractility decreased migration speed, but their impact on protrusion and adhesion were distinct. Adhesivity affects migration not only on uniform substrates, but also under contact guidance. Both increasing adhesivity and the number of lines a cell contacted resulted in decreased directionality with more protrusion waves, which suggest that adhesivity and line spacing drive the effciency of contact guidance through the presence of protrusion waves. In summary, quantification of protrusion and FA properties might provide signatures that relate short timescale dynamics to long timescale migrational properties, making them ideal measurements for cancer diagnosis

Automatic pavement texture recognition using lightweight few-shot learning

Texture is a crucial characteristic of roads, closely related to their performance. The recognition of pavement texture is of great significance for road maintenance professionals to detect potential safety hazards and carry out necessary countermeasures. Although deep learning models have been applied for recognition, the scarcity of data has always been a limitation. To address this issue, this paper proposes a few-shot learning model based on the Siamese network for pavement texture recognition with a limited dataset. The model achieved 89.8% accuracy in a four-way five-shot task classifying the pavement textures of dense asphalt concrete, micro surface, open-graded friction course and stone matrix asphalt. To align with engineering practice, global average pooling (GAP) and one-dimensional convolution are implemented, creating lightweight models that save storage and training time. Comparative experiments show that the lightweight model with GAP implemented on dense layers and one-dimensional convolution on convolutional layers reduced storage volume by 94% and training time by 99%, despite a 2.9% decrease in classification accuracy. Moreover, the model with only GAP implemented on dense layers achieved the highest accuracy at 93.5%, while reducing storage volume and training time by 83% and 6%, respectively

The State-of-the-Art Review on Applications of Intrusive Sensing, Image Processing Techniques, and Machine Learning Methods in Pavement Monitoring and Analysis

In modern transportation, pavement is one of the most important civil infrastructures for the movement of vehicles and pedestrians. Pavement service quality and service life are of great importance for civil engineers as they directly affect the regular service for the users. Therefore, monitoring the health status of pavement before irreversible damage occurs is essential for timely maintenance, which in turn ensures public transportation safety. Many pavement damages can be detected and analyzed by monitoring the structure dynamic responses and evaluating road surface conditions. Advanced technologies can be employed for the collection and analysis of such data, including various intrusive sensing techniques, image processing techniques, and machine learning methods. This review summarizes the state-of-the-art of these three technologies in pavement engineering in recent years and suggests possible developments for future pavement monitoring and analysis based on these approaches

Introduction to ‘Artificial intelligence in failure analysis of transportation infrastructure and materials'

Transportation infrastructures, including roads, bridges, tunnels, stations, airports and subways, play fundamental roles in modern society. Engineering failures of transportation infrastructures may result in significant damage to the public. The traditional methods are to monitor, store and analyse the information during the infrastructure and material design, testing, construction, numerical simulations, evaluation, operation, maintenance and preservation, using mechanistic-based, material-based and statistics-based approaches. In recent decades, artificial intelligence (AI) has drawn the attention of many researchers and has been used as a powerful tool to understand and analyse the engineering failures in transportation infrastructure and materials. AI has the advantages of conveniently characterizing infrastructure materials in multi-scale, extracting failure information from images and cloud points, evaluating performance from the signals of sensors, predicting the long-term performance of infrastructure based on big data and optimizing infrastructure maintenance strategies, etc. In the future, AI techniques will be more effective and promising for data collection, transmission, fusion, mining and analysis, which will help engineers quickly detect, analyse and finally prevent the engineering failures of transportation infrastructure and materials. This theme issue presents the latest developments of AI in failure analysis of transportation infrastructure and materials