A survey of sustainable development of intelligent transportation system based on urban travel demand

This paper provides a comprehensive exploration of urban travel demand forecasting and its implications for intelligent transportation systems, emphasizing the crucial role of intelligent transportation systems in promoting sustainable urban development. With the increasing challenges posed by traffic congestion, environmental pollution, and diverse travel needs, accurate prediction of urban travel demand becomes essential for optimizing transportation systems, fostering sustainable travel methods, and creating opportunities for business development. However, achieving this goal involves overcoming challenges such as data collection and processing, privacy protection, and information security. To address these challenges, the paper proposes a set of strategic measures, including advancing intelligent transportation technology, integrating intelligent transportation systems with urban planning, enforcing policy guidance and market supervision, promoting sustainable travel methods, and adopting intelligent transportation technology and green energy solutions. Additionally, the study highlights the role of intelligent transportation systems in mitigating traffic congestion and environmental impact through intelligent road condition monitoring, prediction, and traffic optimization. Looking ahead, the paper foresees an increasingly pivotal role for intelligent transportation systems in the future, leveraging advancements in deep learning and information technology to more accurately collect and analyze urban travel-related data for better predictive modeling. By combining data analysis, public transportation promotion, shared travel modes, intelligent transportation technology, and green energy adoption, cities can build more efficient, environmentally friendly transportation systems, enhancing residents’ travel experiences while reducing congestion and pollution to promote sustainable urban development. Furthermore, the study anticipates that intelligent transportation systems will be intricately integrated with urban public services and management, facilitating efficient and coordinated urban functions. Ultimately, the paper envisions intelligent transportation systems playing a vital role in supporting urban traffic management and enhancing the overall well-being of urban construction and residents’ lives. In conclusion, this research not only enhances our understanding of urban travel demand forecasting and the evolving landscape of intelligent transportation systems but also provides valuable insights for future research and practical applications in related fields. The study encourages greater attention and investment from scholars and practitioners in the research and practice of intelligent transportation systems to collectively advance the progress of urban transportation and sustainable development

DGSD: Dynamical Graph Self-Distillation for EEG-Based Auditory Spatial Attention Detection

Auditory Attention Detection (AAD) aims to detect target speaker from brain signals in a multi-speaker environment. Although EEG-based AAD methods have shown promising results in recent years, current approaches primarily rely on traditional convolutional neural network designed for processing Euclidean data like images. This makes it challenging to handle EEG signals, which possess non-Euclidean characteristics. In order to address this problem, this paper proposes a dynamical graph self-distillation (DGSD) approach for AAD, which does not require speech stimuli as input. Specifically, to effectively represent the non-Euclidean properties of EEG signals, dynamical graph convolutional networks are applied to represent the graph structure of EEG signals, which can also extract crucial features related to auditory spatial attention in EEG signals. In addition, to further improve AAD detection performance, self-distillation, consisting of feature distillation and hierarchical distillation strategies at each layer, is integrated. These strategies leverage features and classification results from the deepest network layers to guide the learning of shallow layers. Our experiments are conducted on two publicly available datasets, KUL and DTU. Under a 1-second time window, we achieve results of 90.0\% and 79.6\% accuracy on KUL and DTU, respectively. We compare our DGSD method with competitive baselines, and the experimental results indicate that the detection performance of our proposed DGSD method is not only superior to the best reproducible baseline but also significantly reduces the number of trainable parameters by approximately 100 times

Simultaneous detection of cerebral blood perfusion and cerebral edema using swept‐source optical coherence tomography

The progression of ischemic cerebral edema (CE) is closely related to the level of cerebral blood perfusion (CBP) and affects each other. Simultaneous detection of CBP and CE is helpful in understanding the mechanisms of ischemic CE development. In this article, a wide field of view swept‐source optical coherence tomography system was used to detect CE status and CBP levels simultaneously in middle cerebral artery occlusion rats. Images reflecting these two physiological states can be reconstructed with only one C‐scan. We quantify these two physiological states into four parameters, which contain two vascular parameters (vascular displacement distance and vascular perfusion density) and two edema parameters (optical attenuation coefficient and edema area). The association between the two vascular parameters and the two edema parameters was analyzed. The results show that there is a strong linear relationship between blood flow parameters and edema parameters. This work provides a new option for CE in vivo detection, and is very likely to play an important role in the development of relevant drugs or in selection of treatment options.In this article, a wide field of view swept‐source optical coherence tomography system was used to detect cerebral edema status and cerebral blood perfusion levels simultaneously in middle cerebral artery occlusion rats. Images reflecting these two physiological states can be reconstructed with only one C‐scan. We quantify these two physiological states into four parameters, which contain two vascular parameters and two edema parameters. The association between the two vascular parameters and the two edema parameters was analyzed. The results show that there is a strong linear relationship between blood flow parameters and edema parameters.Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/153673/1/jbio201960087_am.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/153673/2/jbio201960087.pd

Integration of metabolomics and transcriptomics provides insights into enhanced osteogenesis in Ano5Cys360Tyr knock-in mouse model

IntroductionGnathodiaphyseal dysplasia (GDD; OMIM#166260) is a rare autosomal dominant disorder characterized by diaphyseal sclerosis of tubular bones and cemento-osseous lesions in mandibles. GDD is caused by point mutations in the ANO5 gene. However, the mechanisms underlying GDD have not been disclosed. We previously generated the first knock-in mouse model for GDD expressing a human mutation (p.Cys360Tyr) in ANO5 and homozygous Ano5 knock-in (Ano5KI/KI) mice exhibited representative traits of human GDD especially including enhanced osteogenesis.MethodsMetabolomics and transcriptomics analyses were conducted for wildtype (Ano5+/+) and Ano5KI/KI mature mouse calvarial osteoblasts (mCOBs) grown in osteogenic cultures for 14 days to identify differential intracellular metabolites and genes involved in GDD. Subsequently, related differential genes were validated by qRT-PCR. Cell proliferation was confirmed by CCK8 assay and calcium content in mineral nodules was detected using SEM-EDS.ResultsMetabolomics identified 42 differential metabolites that are primarily involved in amino acid and pyrimidine metabolism, and endocrine and other factor-regulated calcium reabsorption. Concomitantly, transcriptomic analysis revealed 407 differentially expressed genes in Ano5KI/KI osteoblasts compared with wildtype. Gene ontology and pathway analysis indicated that Ano5Cys360Tyr mutation considerably promoted cell cycle progression and perturbed calcium signaling pathway, which were confirmed by validated experiments. qRT-PCR and CCK-8 assays manifested that proliferation of Ano5KI/KI mCOBs was enhanced and the expression of cell cycle regulating genes (Mki67, Ccnb1, and Ccna2) was increased. In addition, SEM-EDS demonstrated that Ano5KI/KI mCOBs developed higher calcium contents in mineral nodules than Ano5+/+ mCOBs, while some calcium-related genes (Cacna1, Slc8a1, and Cyp27b1) were significantly up-regulated. Furthermore, osteocalcin which has been proved to be an osteoblast-derived metabolic hormone was upregulated in Ano5KI/KI osteoblast cultures.DiscussionOur data demonstrated that the Ano5Cys360Tyr mutation could affect the metabolism of osteoblasts, leading to unwonted calcium homeostasis and cellular proliferation that can contribute to the underlying pathogenesis of GDD disorders

The organum vasculosum of the lamina terminalis and subfornical organ: regulation of thirst

Thirst and water intake are regulated by the organum vasculosum of the lamina terminalis (OVLT) and subfornical organ (SFO), located around the anteroventral third ventricle, which plays a critical role in sensing dynamic changes in sodium and water balance in body fluids. Meanwhile, neural circuits involved in thirst regulation and intracellular mechanisms underlying the osmosensitive function of OVLT and SFO are reviewed. Having specific Nax channels in the glial cells and other channels (such as TRPV1 and TRPV4), the OVLT and SFO detect the increased Na+ concentration or hyperosmolality to orchestrate osmotic stimuli to the insular and cingulate cortex to evoke thirst. Meanwhile, the osmotic stimuli are relayed to the supraoptic nucleus (SON) and paraventricular nucleus of the hypothalamus (PVN) via direct neural projections or the median preoptic nucleus (MnPO) to promote the secretion of vasopressin which plays a vital role in the regulation of body fluid homeostasis. Importantly, the vital role of OVLT in sleep-arousal regulation is discussed, where vasopressin is proposed as the mediator in the regulation when OVLT senses osmotic stimuli

A large dumbbell glossopharyngeal schwannoma involving the vagus nerve: a case report and review of the literature

<p>Abstract</p> <p>Introduction</p> <p>Schwannoma arising from the glossopharyngeal nerve is a rare intracranial tumor. Fewer than 40 cases have been reported. Accurate pre-operative diagnosis and optimal treatment are still difficult.</p> <p>Case presentation</p> <p>We present one case of schwannoma originating from the ninth cranial nerve with palsies of the trigeminal nerve, facial-acoustic nerve complex, and vagus nerve in addition to ninth nerve dysfunction. Magnetic resonance imaging showed tumors located in the cerebellopontine angle with extracranial extension via the jugular foramen, with evident enhancement on post-contrast scan. Surgical management single-staged with the help of gamma knife radiosurgery achieved total removal.</p> <p>Conclusion</p> <p>Glossopharyngeal schwannoma is devoid of clinical symptoms and neurological signs. High resolution magnetic resonance imaging may play a key role as an accurate diagnostic tool. A favorable option of approach and appropriate planning of surgical strategy should be the goal of operation for this benign tumor.</p

Whole‐brain microcirculation detection after ischemic stroke based on swept‐source optical coherence tomography

The occurrence and development of ischemic stroke are closely related to cerebral blood flow. Real‐time monitoring of cerebral perfusion level is very useful for understanding the mechanisms of the disease. A wide field of view (FOV) is conducive to capturing lesions and observing the progression of the disease. In this paper, we attempt to monitor the whole‐brain microcirculation in middle cerebral artery occlusion (MCAO) rats over time using a wide FOV swept‐source OCT (SS‐OCT) system. A constrained image registration algorithm is used to remove motion artifacts that are prone to occur in a wide FOV angiography. During ischemia, cerebral perfusion levels in the left and right hemispheres, as well as in the whole brain were quantified and compared. Changes in the shape and location of blood vessels were also recorded. The results showed that the trend in cerebral perfusion levels of both hemispheres was highly consistent during MCAO, and the position of the blood vessels varied over time. This work will provide new insights of ischemic stroke and is helpful to assess the effectiveness of potential treatment strategies.En face maximum intensity projections (MIP) of the whole‐brain vascular networks obtained by wide field of view (FOV) swept‐source optical coherence tomography (SS‐OCT) system.Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/151895/1/jbio201900122_am.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/151895/2/jbio201900122.pd

A High-Performance Mid-infrared Optical Switch Enabled by Bulk Dirac Fermions in Cd3As2

Pulsed lasers operating in the 2-5 {\mu}m band are important for a wide range of applications in sensing, spectroscopy, imaging and communications. Despite recent advances with mid-infrared gain media, the lack of a capable pulse generation mechanism, i.e. a passive optical switch, remains a significant technological challenge. Here we show that mid-infrared optical response of Dirac states in crystalline Cd3As2, a three-dimensional topological Dirac semimetal (TDS), constitutes an ideal ultrafast optical switching mechanism for the 2-5 {\mu}m range. Significantly, fundamental aspects of the photocarrier processes, such as relaxation time scales, are found to be flexibly controlled through element doping, a feature crucial for the development of convenient mid-infrared ultrafast sources. Although various exotic physical phenomena have been uncovered in three-dimensional TDS systems, our findings show for the first time that this emerging class of quantum materials can be harnessed to fill a long known gap in the field of photonics.Comment: 17 pages, 3 figure

Construction of cotton leaf nitrogen content estimation model based on the PROSPECT model

Leaf nitrogen content (LNC) is an important index to measure the nitrogen deficiency in cotton. The rapid and accurate monitoring of LNC is of great significance for understanding the growth status of cotton and guiding precise fertilization in the field. At present, the hyperspectral technology monitoring of LNC is very mature, but it is interfered with by external factors such as shadow and soil, and data acquisition is still dependent on manpower. Therefore, on the basis of clarifying the correlation and quantitative relationship between physiological parameters and cotton LNC, the 400-2500 nm spectral curve was simulated based on PROSPECT-5 model. Combined with the measured spectra, the sensitive bands of leaf nitrogen content were screened, and four machine learning algorithms based on the reflectance of the sensitive bands were compared to construct a model for the estimation of LNC in cotton and determine the optimal model. The results show the following: (1) The parameter with the best correlation with nitrogen content was Cab, and the linear relationship was y=0.3942x+12.521, R2=0.81, RMSE=12.87 g/kg. (2) The shuffled frog leaping algorithm (SFLA) and the successive projections algorithm (SPA) were used to screen the relevant bands sensitive to LNC. SFLA selected nine characteristic bands, mainly distributed between 700 and 750 nm. SPA screened seven characteristic bands, mainly distributed between 670 and 760 nm. The characteristic bands of both screening methods were distributed near the red edge. (3) Based on the sensitive bands, the four machine learning algorithms were compared. Among them, the band modeling of SFLA screening under the random forest (RF) algorithm was the best (modeling set R2=0.973, RMSE=1.001 g/kg, rRMSE=3.41%, validation set R2=0.803, RMSE=3.191 g/kg, rRMSE=10.85%). In summary, this study proposes an optimal estimation model of cotton leaf nitrogen content based on the radiative transfer model, which provides a theoretical basis for the dynamic, accurate, and non-destructive monitoring of cotton leaf nitrogen content