Bump feature detection of the road surface based on the Bi-LSTM

The road network is the basic facility for transportation systems in the city. Every day, a large number of vehicles move on the road and exert different pressure on the ground, which leads to various problems for the road surface, such as the bump features of the road surface (BFRS). However, traditional methods, such as detecting BFRS manually or with professional equipment, require a lot of professional management and devices. Based on the mobile sensor and the bidirectional long short-term memory (Bi-LSTM), a detection method for BFRS is proposed. The BFRS detection method proposed in this article solves the problem that other BFRS detection methods cannot detect large area road surface efficiently and provides an algorithm idea for efficient detection of large area road surface BFRS. The mobile phone with multi-sensors is carried on vehicles, and the BFRS information is logged during the movements. The orientation of the mobile is computed according to the gyroscope. The actual posture of the acceleration sensor is adjusted with the reference coordinate system, whose z-axis is vertical to the ground. This article uses the adjusted acceleration data as the training dataset and labels it according to time stamps and videos recorded by the driving recorder. Finally, the Bi-LSTM is constructed and trained, followed by the BFRS detection. The results show that it can detect BFRS in different regions. The detection accuracy of the campus section and the extended experiment was 92.85 and 87.99%, respectively

Mapping knowledge landscapes and emerging trends of Marburg virus: A text-mining study

Background: Marburg virus (MARV), a close relative of Ebola virus, could induce hemorrhagic fevers in humans with high mortality rate. In recent years, increasing attention has been paid to this highly lethal virus due to sporadic outbreaks observed in various African nations. This bibliometric analysis endeavors to elucidate the trends, dynamics, and focal points of knowledge that have delineated the landscape of research concerning MARV. Methods: Relevant literature on MARV from 1968 to 2023 was extracted from the Web of Science Core Collection database. Following this, the data underwent bibliometric analysis and visualization procedures utilizing online analysis platform, CiteSpace 6.2R6, and VOSviewer 1.6.20. Three different types of bibliometric indicators including quantitative indicator, qualitative indicators, and structural indicators were used to gauge a researcher's productivity, assess the quality of their work, and analyze publication relationships, respectively. Results: MARV is mainly prevalent in Africa. And approximately 643 confirmed cases have been described in the literature to date, and mortality observed was 81.2 % in overall patients. A total of 1014 papers comprising 869 articles and 145 reviews were included. The annual publications showed an increasing growth pattern from 1968 to 2023 (R2 = 0.8838). The United States stands at the forefront of this discipline, having dedicated substantial financial and human resources to scientific inquiry. However, co-authorship analysis showed the international research collaboration needs to be further strengthened. Based on reference and keywords analysis, contemporary MARV research encompasses pivotal areas: primarily, prioritizing the creation of prophylactic vaccines to impede viral spread, and secondarily, exploring targeted antiviral strategies, including small-molecule antivirals or MARV-specific monoclonal antibodies. Additionally, a comprehensive grasp of viral transmission, transcription, and replication mechanisms remains a central focus in ongoing investigations. And future MARV studies are expected to focus on evaluating clinical trial safety and efficacy, developing inhibitors to contain viral spread, exploring vaccine immunogenicity, virus-host association studies, and elucidating the role of neutralizing antibodies in MARV treatment. Conclusion: The present study offered comprehensive insights into the contemporary status and trajectories of MARV over the past decades. This enables researchers to discern novel collaborative prospects, institutional partnerships, emerging topics, and research forefronts within this domain

Point Cloud Segmentation from iPhone-Based LiDAR Sensors Using the Tensor Feature

With widely used LiDAR sensors included in consumer electronic devices, it is increasingly convenient to acquire point cloud data, but it is also difficult to segment the point cloud data obtained from these unprofessional LiDAR devices, due to their low accuracy and high noise. To address the issue, a point cloud segmentation method using the tensor feature is proposed. The normal vectors of the point cloud are computed based on initial tensor encoding, which are further encoded into the tensor of each point. Using the tensor from a nearby point, the tensor of the center point is aggregated in all dimensions from its neighborhood. Then, the tensor feature in the point is decomposed and different dimensional shape features are detected, and the point cloud dataset is segmented based on the clustering of the tensor feature. Using the point cloud dataset acquired from the iPhone-based LiDAR sensor, experiments were conducted, and results show that both normal vectors and tensors are computed, then the dataset is successfully segmented

Urban Road Surface Condition Sensing from Crowd-Sourced Trajectories Based on the Detecting and Clustering Framework

Roads play a crucial role in urban transportation by facilitating the movement of materials within a city. The condition of road surfaces, such as damage and road facilities, directly affects traffic flow and influences decisions related to urban transportation maintenance and planning. To gather this information, we propose the Detecting and Clustering Framework for sensing road surface conditions based on crowd-sourced trajectories, utilizing various sensors (GPS, orientation sensors, and accelerometers) found in smartphones. Initially, smartphones are placed randomly during users’ travels on the road to record the road surface conditions. Then, spatial transformations are applied to the accelerometer data based on attitude readings, and heading angles are computed to store movement information. Next, the feature encoding process operates on spatially adjusted accelerations using the wavelet scattering transformation. The resulting encoding results are then input into the designed LSTM neural network to extract bump features of the road surface (BFRSs). Finally, the BFRSs are represented and integrated using the proposed two-stage clustering method, considering distances and directions. Additionally, this procedure is also applied to crowd-sourced trajectories, and the road surface condition is computed and visualized on a map. Moreover, this method can provide valuable insights for urban road maintenance and planning, with significant practical applications

Quantitative analysis of cervical vertebral maturation in Chinese adolescents based on three-dimensional morphology of cervical vertebrae

Objective To investigate associations between three-dimensional(3D) morphology of cervical vertebrae and skeletal maturation by cone-beam computed tomography(CBCT) and establish corresponding regression models for quantitatively evaluating cervical vertebral maturation(CVM). Methods The analyzed sample consisted of 358 CBCT images (175 male, 183 female), of which 277 images were randomly selected as the model development group and 81 as the performance test group. Twenty-one 3D morphological parameters were defined and measured, incorporating all parts of the cervical vertebrae, including the cervical vertebral bodies, transverse processes, spinous processes, pedicles, lamina, and articular processes. The cervical vertebral maturation index (CVMI) was determined by experienced orthodontists as reference standard. Spearman’s rank correlation coefficient and multivariable stepwise regression analysis were used to identify the associations and build regression models. The performance test group was employed to examine each model’s reliability. Paired-samples Wilcoxon signed-rank test compared the CVMI of the model prediction with the reference standard. Results Three-dimensional morphological changes in various parts of the cervical vertebrae correlated with CVMI (P<0.05). Six 3D morphometric parameters were each recognized for male and female models, three of which were identical. The adjusted R2 was 0.899 for males and 0.902 for females, with corresponding accuracies of 85.0% and 85.4%, respectively. These models showed no difference as compared with the reference standard (P>0.05). Conclusion New associations were found between 3D morphology of cervical vertebrae and skeletal maturation. The 3D-driven morphometric CVM assessment method and corresponding regression models exhibited good credibility and high consistency with experts

Holistic bursting cells store long-term memory in auditory cortex

The sensory neocortex has been suggested to be a substrate for long-term memory storage, yet which exact single cells could be specific candidates underlying such long-term memory storage remained neither known nor visible for over a century. Here, using a combination of day-by-day two-photon Ca2+ imaging and targeted single-cell loose-patch recording in an auditory associative learning paradigm with composite sounds in male mice, we reveal sparsely distributed neurons in layer 2/3 of auditory cortex emerged step-wise from quiescence into bursting mode, which then invariably expressed holistic information of the learned composite sounds, referred to as holistic bursting (HB) cells. Notably, it was not shuffled populations but the same sparse HB cells that embodied the behavioral relevance of the learned composite sounds, pinpointing HB cells as physiologically-defined single-cell candidates of an engram underlying long-term memory storage in auditory cortex