Complex Structure Research Articles

The Integrator complex: an emerging complex structure involved in the regulation of gene expression by targeting RNA polymerase II.

The Integrator complex is a multisubunit complex that participates in the processing of small nuclear RNA molecules in eukaryotic cells by cleaving the 3' end. In protein-coding genes, Integrator is a key regulator of promoter-proximal pausing, release, and recruitment of RNA polymerase II. Research on Integrator has revealed its critical role in the regulation of gene expression and RNA processing. Dysregulation of the Integrator complex has been implicated in a variety of human diseases including cancer and developmental disorders. Therefore, understanding the structure and function of the Integrator complex is critical to uncovering the mechanisms of gene expression and developing potential therapeutic strategies for related diseases.

Unsupervised anomaly detection in shearers via autoencoder networks and multi-scale correlation matrix reconstruction

As the main equipment of coal mining production, the anomaly detection of shearer is important to ensure production efficiency and coal mine safety. One key challenge lies in the limited or even absence of labeled monitoring data for the equipment, coupled with the high costs associated with manual annotation. Another challenge stems from the complex structure of the mining machines, making it difficult to reflect the overall operational state through local anomaly detection. Consequently, the application of decoupled local anomaly detection for mining machines in practical production remains challenging. This paper presents an unsupervised learning-based method for detecting anomalies in shearer. The method includes a module for constructing a Multi-scale Correlation Matrix (MSCM) of mining machine operating conditions, as well as the CNN-ConvLSTM Autoencoder (C-CLA) network. The module for constructing an MSCM enhances the representation of interrelationships between various features of the equipment from different perspectives using multiple correlation analysis methods. The C-CLA network integrates convolutional and convolutional recurrent neural networks, with the convolutional structure extracting local spatial features and the ConvLSTM structure further capturing information from different time scales and feature scales, thereby enhancing the model’s perceptual capabilities towards changes in equipment status. Finally, shearer anomaly detection is achieved through the analysis of reconstructed residual matrices. The rationality and practicality of the proposed method have been validated on our dataset, and the model’s generalization capability has been verified through repeated experiments in similar scenarios. However, due to variations in the working environment of different mining faces and differences in equipment models, implementing detection on other mining faces often requires retraining the model with new data. Furthermore, we compared our method with other anomaly detection techniques, and our detection efficiency was superior by approximately 3%. This method effectively detects anomalies in the shearer.

Catalyzing the Enantioselective Tail‐to‐Head Terpene Cyclization Inside Optically Active Hexameric Resorcin[4]arene Capsules: A Surprising Odd‐Even Effect of the Catalyst

AbstractTerpenes, created in Nature via tail‐to‐head terpene (THT) cyclizations, have long fascinated chemists due to their complex structures and biological activities. Recreating such THT cyclizations in the laboratory using man‐made catalysts has been challenging. Especially difficult is performing the reaction in an enantioselective fashion, as the substrate is flexible and does not contain much functionality to form strong interactions with the chiral catalyst. This study explores the enantioselective THT cyclization inside hexameric capsules self‐assembled from optically active resorcin[4]arene derivatives. An unexpected odd‐even effect, where the enantioselectivity of the reaction depended on the length of the alkyl chain attached to the resorcin[4]arene capsule was uncovered. To our knowledge, this is the first example of an odd‐even effect in the catalyst itself, and also the first instance in which this effect is related to the enantioselectivity of the reaction.

A Generic Transformation Approach for Complex Laboratory Data Using the Fast Healthcare Interoperability Resources Mapping Language: Method Development and Implementation.

Reaching meaningful interoperability between proprietary health care systems is a ubiquitous task in medical informatics, where communication servers are traditionally used for referring and transforming data from the source to target systems. The Mirth Connect Server, an open-source communication server, offers, in addition to the exchange functionality, functions for simultaneous manipulation of data. The standard Fast Healthcare Interoperability Resources (FHIR) has recently become increasingly prevalent in national health care systems. FHIR specifies its own standardized mechanisms for transforming data structures using StructureMaps and the FHIR mapping language (FML). In this study, a generic approach is developed, which allows for the application of declarative mapping rules defined using FML in an exchangeable manner. A transformation engine is required to execute the mapping rules. FHIR natively defines resources to support the conversion of instance data, such as an FHIR StructureMap. This resource encodes all information required to transform data from a source system to a target system. In our approach, this information is defined in an implementation-independent manner using FML. Once the mapping has been defined, executable Mirth channels are automatically generated from the resources containing the mapping in JavaScript format. These channels can then be deployed to the Mirth Connect Server. The resulting tool is called FML2Mirth, a Java-based transformer that derives Mirth channels from detailed declarative mapping rules based on the underlying StructureMaps. Implementation of the translate functionality is provided by the integration of a terminology server, and to achieve conformity with existing profiles, validation via the FHIR validator is built in. The system was evaluated for its practical use by transforming Labordatenträger version 2 (LDTv.2) laboratory results into Medical Information Object (Medizinisches Informationsobjekt) laboratory reports in accordance with the National Association of Statutory Health Insurance Physicians' specifications and into the HL7 (Health Level Seven) Europe Laboratory Report. The system could generate complex structures, but LDTv.2 lacks some information to fully comply with the specification. The tool for the auto-generation of Mirth channels was successfully presented. Our tests reveal the feasibility of using the complex structures of the mapping language in combination with a terminology server to transform instance data. Although the Mirth Server and the FHIR are well established in medical informatics, the combination offers space for more research, especially with regard to FML. Simultaneously, it can be stated that the mapping language still has implementation-related shortcomings that can be compensated by Mirth Connect as a base technology.

Research on Smart City Road Network Capacity Optimization Configuration Based on Deep Learning Algorithms

At present, the urban traffic system is faced with the problems of congestion and low efficiency, and the traditional methods have certain limitations when dealing with the complex urban road network. This paper aims to explore a new method of capacity Optimization of Road Networks in a Smart City environment and proposes a method based on a Graph Neural network, named “Smart City Road optimization Graph Neural Network” (SCRO-GNN). SCRO-GNN first collects and preprocesses multi-source data, including road network data, traffic flow, accident records, environmental factors, etc. The key node and edge features, including the number of lanes at the intersection, the traffic flow of the section, and the adjacency matrix of the road network are defined to characterize the structure of the road network. Then, the graph neural network model is constructed and trained to predict the traffic flow of different sections and evaluate the road capacity by using the graph structure of the road network. This paper tests the performance of SCRO-GNN on real road networks in multiple cities. The results show that, compared with the traditional traffic flow prediction model, SCRO-GNN can significantly improve the prediction accuracy, especially when dealing with the highly complex urban road network structure. Based on these predictions, the proposed optimization strategy performed well in reducing traffic congestion and improving the efficiency of road use. The research in this paper not only demonstrates the potential of graph neural networks in smart city road network optimization, but also provides a new direction for future traffic system research. The successful implementation of the SCRO-GNN approach is expected to provide more efficient and intelligent solutions for urban traffic management and planning.

Prevalent harmonic interaction in the bat inferior colliculus.

Animal vocalizations and human speech are typically characterized by a complex spectrotemporal structure, composed of multiple harmonics, and patterned as temporally organized sequences. However, auditory research often employed simple artificial acoustic stimuli or their combinations. Here we addressed the question of whether the neuronal responses to natural echolocation call sequences can be predicted by manipulated sequences of incomplete constituents at the midbrain inferior colliculus (IC). We characterized the extracellular single-unit activity of IC neurons in the great roundleaf bat, Hipposideros armiger (both sexes), using natural call sequences, various manipulated sequences of incomplete vocalizations, and pure tones. We report that approximately two-thirds of IC neurons exhibited a harmonic interaction. Neurons with high harmonic interactions exhibited greater selectivity to natural call sequences, and the degree of harmonic interaction was robust to the natural amplitude variations between call harmonics. For 81% of the IC neurons, the responses to the natural echolocation call sequence could not be predicted by altered sequences of missing call components. Surprisingly, nearly 70% of the neurons that showed a harmonic interaction were characterized by a single excitatory response peak as revealed by pure tones. Our results suggest that prevalent harmonic processing has already emerged in the auditory midbrain inferior colliculus in the echolocating bat.Significance Statement Auditory cortex has long been suggested to be the main region for processing complex sound such as natural vocalizations. However, this cortex-centered view of complex stimuli processing was largely based on simple stimuli or their combinations, ignoring many crucial aspects of natural communication behaviors. Here, we took a neuroethological perspective to study the harmonic processing in the inferior colliculus, in the great roundleaf bat. We show that prevalent harmonic processing has already emerged in the auditory midbrain in the echolocating bat. Critically, harmonic interactions revealed by natural call sequences cannot be predicted by responses to either pure tones or altered sequences of missing call components. Our data suggest that auditory midbrain is involved in harmonic processing in the echolocating bat.

DLAP: A Deep Learning Augmented Large Language Model Prompting framework for software vulnerability detection

Software vulnerability detection is generally supported by automated static analysis tools, which have recently been reinforced by deep learning (DL) models. However, despite the superior performance of DL-based approaches over rule-based ones in research, applying DL approaches to software vulnerability detection in practice remains a challenge. This is due to the complex structure of source code, the black-box nature of DL, and the extensive domain knowledge required to understand and validate the black-box results for addressing tasks after detection. Conventional DL models are trained by specific projects and, hence, excel in identifying vulnerabilities in these projects but not in others. These models with poor performance in vulnerability detection would impact the downstream tasks such as location and repair. More importantly, these models do not provide explanations for developers to comprehend detection results. In contrast, Large Language Models (LLMs) with prompting techniques achieve stable performance across projects and provide explanations for results. However, using existing prompting techniques, the detection performance of LLMs is relatively low and cannot be used for real-world vulnerability detections. This paper contributes DLAP, a Deep Learning Augmented LLMs Prompting framework that combines the best of both DL models and LLMs to achieve exceptional vulnerability detection performance. Experimental evaluation results confirm that DLAP outperforms state-of-the-art prompting frameworks, including role-based prompts, auxiliary information prompts, chain-of-thought prompts, and in-context learning prompts, as well as fine-turning on multiple metrics.

Characteristics of the urban environmental regulation network and its impact on carbon emission efficiency in China

On the basis of 2007–2019 data covering 254 cities in China, we analyze the overall and individual characteristics of the environmental regulation network (ERN) using a modified gravity model and social network analysis (SNA) method and investigate the effect of the degree centrality of the EGN on carbon emission efficiency (CEE). The results suggest that, first, China’s municipal environmental regulations exhibit a significant and complex spatial correlation structure. However, the cities are not connected well to each other, resulting in a loose network structure. Second, cities in the Yangtze River Delta, such as Wuxi, Suzhou, and Shanghai, occupy the center of the network, and the degree centrality of approximately 20% of the cities exceeds the average level. Third, the degree centrality of the ERN increases CEE significantly, but its direction and intensity differ significantly according to geographical location, resource endowment and urban size. Fourth, degree centrality improves CEE by reducing the energy intensity of cities. Green innovation and foreign direct investment can strengthen the positive effect of degree centrality on CEE. Degree centrality enhances the CEE of a region but reduces the CEE of neighboring cities through the spatial spillover effect. Therefore, we should focus on promoting low-carbon and green lifestyles, formulate carbon reduction regulations tailored to local conditions, encourage enterprises to apply green technologies, and strengthen cooperation between cities.

Effect of tobacco–radish rotation for different years on bacterial wilt and rhizosphere microbial communities

Tobacco bacterial wilt is a major limiting factor for tobacco production and development, and it is more likely to occur under perennial single cropping of tobacco. In recent years, the rotation of tobacco-radish has gradually become popular. Therefore, we studied the effects of years of tobacco–radish rotation on tobacco bacterial wilt occurrence and rhizosphere microorganisms. The results indicated that both SY and TY could significantly reduce the risk of tobacco bacterial wilt occurrence, and SY had the lowest disease index. The rotation of radish plants significantly increased the soil pH but decreased the contents of alkali-hydrolysed nitrogen and organic matter in the soil. Alkali-hydrolysed nitrogen and pH are the key factors affecting the composition of the bacterial community. Furthermore, radish rotation changed the composition of the soil microbial community, increased the diversity of the bacterial community, and significantly altered the bacterial community structure. At the genus level, the abundance of Sphingomonas species negatively correlated with Ralstonia increased significantly, while the relative abundance of Rhodanobacter species positively correlated with Ralstonia decreased significantly. Disease index, pH and available phosphorus were the main factors affecting the variation in different bacterial genera. The network analysis results showed that Ralstonia was less connected in the network than in the CK group, and the SY treatment group had a more complex bacterial network structure. Overall, 2 years of tobacco and radish rotation improved the bacterial community structure of the rhizosphere soil and alleviated the harm caused by tobacco bacterial wilt, which is highly important for the stability and health of the rhizosphere soil ecosystem.

Personalized 3D Printing of Artificial Vertebrae: A Predictive Bone Density Modeling Approach for Robotic Cutting Applications

Robotic vertebral plate cutting poses significant challenges due to the complex bone structures of the lumbar spine, which consist of varying densities in cortical and cancellous regions. This study addresses these challenges by developing a predictive model for robotic vertebral plate cutting force and bone quality recognition through the fabrication of artificial vertebrae with controlled, consistent bone density. To address the variability in bone density between cortical and cancellous regions, CT data are utilized to predict target bone density, serving as a foundation for determining the optimal 3D printing process parameters. The proposed methodology integrates a Response Surface Methodology (RSM), Back Propagation (BP) neural network, and genetic algorithm (GA) to systematically evaluate the effects of key process parameters, such as the filling density, material flow rate, and layer thickness, on the printed vertebrae’s density. A one-factor experimental approach and RSM-based central composite design are applied to build an initial bone density prediction model, followed by Sobol’s sensitivity analysis to quantify the influence of each parameter. The GA-BP neural network model is then employed to rapidly and accurately identify optimal printing parameters for different bone layer densities. The resulting optimized models are used to fabricate personalized artificial lumbar vertebrae, which are subsequently validated through robotic cutting experiments. This research not only contributes to the advancement in personalized 3D printing technology but also provides a reliable framework for developing patient-specific surgical planning models in robot-assisted orthopedic surgery.

Highly stretchable carbon-based triboelectric nanogenerator textiles woven using coaxial fibres via direct ink writing

In recent years, triboelectric nanogenerators have seen great progress in the Internet of Things, wearable electronics, virtual reality and artificial intelligence technologies. However, current triboelectric nanogenerator devices generally face challenges of complex preparation processes and structures, hindering their application in large-area wearable textiles. In this study, we develop highly stretchable triboelectric fibres using direct ink writing as building blocks for further woven textiles. This consists of a conductive core and insulating sheath, namely composites of multi-wall carbon nanotube and fluorinated ethylene propylene particles, both doped in silicone rubber. The fibre enjoys outstanding electrical performance, as well as excellent mechanical stretchability. Particularly, a single-centimetre-long fibre, upon slight patting, can surprisingly produce an open-circuit voltage of 8 V, a short-circuit current of 110 nA and 2.5 nC short-circuit transferred charges, or equivalent lighting of four to five commercial light-emitting diodes alone, greatly outperforming current wearable devices. These fibres also can endure an extraordinary strain of up to ∼874 %. The fibres are then woven into wearable gloves and socks using traditional weaving techniques, which can realise motion pattern identification and recognition with classification accuracy as high as ∼96 % using convolutional neural networks. This creates application prospects in wearable electronics, providing a scalable and affordable method for weaving daily wearable textiles and favouring large-area, wearable, self-powered sensors and artificial intelligence technologies.

Brachial and Lumbosacral Plexopathies.

The brachial and lumbosacral plexuses are complex neural structures that transmit sensory, motor, and autonomic information between the spinal cord and the extremities. Plexus disorders can be particularly disabling because lesions in the plexus usually affect large groups of nerve fibers originating from several spinal levels. Electrodiagnostic studies are often required to confirm a plexus lesion and determine the extent of injury and prognosis. Magnetic resonance is the imaging modality of choice for detecting intrinsic nerve abnormalities; recently, high-resolution ultrasound has emerged as an alternative method for dynamic evaluation and visualization of internal nerve architecture. Once a plexopathy is confirmed, the list of possible etiologies is relatively limited and includes traumatic and nontraumatic causes. Treatment relies on symptom management and physical rehabilitation unless a treatable underlying condition is found. Surgical approaches, including nerve grafts or tendon transfers, may improve limb function when spontaneous recovery is suboptimal.

Some Remarks on Existence of a Complex Structure on the Compact Six Sphere

Some Remarks on Existence of a Complex Structure on the Compact Six Sphere

Investigation of printing turn angle effects on structural deformation and stress in selective laser melting

Additive manufacturing (AM) technology facilitates the creation of complex structures, where the printing path significantly impacts thermal distribution, subsequently influencing stress distribution and structural deformation. The primary challenge in path planning is to determine a printing turn angle that ensures uniform thermal distribution, thereby minimizing structural deformation while maintaining printing efficiency. To address this issue, we propose a composite function, which comprehensively characterizes the effects of the printing turn angle and the length of the printing path on the printing results. Combining a specific cubic porous structure, we calculate the maximum (Pmax) and minimum (Pmin) values of the composite function P, and compare the structural deformation and stress of the Pmax and Pmin paths with those of the typical Pzigzag path. Finite element method (FEM) simulation and experimental validation show that the Pmax path achieves significantly lower structural deformation and residual stress compared to the Pzigzag path and Pmin path.

Performance evaluation of image co-registration methods in photoacoustic mesoscopy of the vasculature

Objective:The formation of functional vasculature in solid tumours enables delivery of oxygen and nutrients, and is vital for effective treatment with chemotherapeutic agents. Longitudinal characterisation of vascular networks can be enabled using mesoscopic photoacoustic imaging, but requires accurate image co-registration to precisely assess local changes across disease development or in response to therapy. Co-registration in photoacoustic imaging is challenging due to the complex nature of the generated signal, including the sparsity of data, artefacts related to the illumination/detection geometry, scan-to-scan technical variability, and biological variability, such as transient changes in perfusion. To better inform the choice of co-registration algorithms, we compared five open-source methods, in physiological and pathological tissues, with the aim of aligning evolving vascular networks in tumours imaged over growth at different time-points.Approach:Co-registration techniques were applied to 3D vascular images acquired with photoacoustic mesoscopy from murine ears and breast cancer patient-derived xenografts, at a fixed time-point and longitudinally. Images were pre-processed and segmented using an unsupervised generative adversarial network. To compare co-registration quality in different settings, pairs of fixed and moving intensity images and/or segmentations were fed into five methods split into the following categories: affine intensity-based using 1)mutual information (MI) or 2)normalised cross-correlation (NCC) as optimisation metrics, affine shape-based using 3)NCC applied to distance-transformed segmentations or 4)iterative closest point algorithm, and deformable weakly supervised deep learning-based using 5)LocalNet co-registration. Percent-changes in Dice coefficients, surface distances, MI, structural similarity index measure and target registration errors were evaluated.Main results:Co-registration using MI or NCC provided similar alignment performance, better than shape-based methods. LocalNet provided accurate co-registration of substructures by optimising subfield deformation throughout the volumes, outperforming other methods, especially in the longitudinal breast cancer xenograft dataset by minimising target registration errors.Significance:We showed the feasibility of co-registering repeatedly or longitudinally imaged vascular networks in photoacoustic mesoscopy, taking a step towards longitudinal quantitative characterisation of these complex structures. These tools open new outlooks for monitoring tumour angiogenesis at the meso-scale and for quantifying treatment-induced co-localised alterations in the vasculature.

3D and 4D Technology for Pharmaceutical Drug Delivery: A Detailed Insight

3D Printing, sometimes referred to as additive manufacturing, has made the concept of personalized medicine a reality. The primary objective of 3D and 4D printing is to produce intricate, customized pharmaceuticals at a reasonable cost. With improvements in materials, resolution, and speed, 3D printing technology is quickly developing. It includes faster construction, cost efficiency through reduced waste, design flexibility for complex structures, and sustainability through optimized material usage. An extensive literature survey was done on 3D and 4D printing of pharmaceuticals using PubMed, Elsevier, ScienceDirect, and Springer. The results were then filtered based on the titles, abstracts, and accessibility of the complete texts. The search engine Google Scholar was accessed for literature data mining. From the data mining, it was found that from the year 2009 to 2024 the number of research publications surged more than 200 times on the current topic. Even though 3-D and 4-D printing technologies have advanced significantly in a short amount of time, the most often used ones are still stereolithography, nozzle-based deposition, inkjet, and selective laser sintering. Their use has been modified for the production of nanoparticles, polypills, tablets, and implants, etc. Pharma's aspirations for tailored medications are being revolutionized by 3D printing, but cost, flexibility, and bioequivalence still need to be investigated. The present review offers a thorough analysis of various 3D and 4D printing methods and emphasizes the major advantages and disadvantages and major key challenges of 3D and 4D printing related to pharmaceuticals. Compared to 3D Printing, 4D printing offers better quality, efficacy, and functionality.

New Catalytic Residues and Catalytic Mechanism of the RNase T1 Family.

The ribonuclease T1 family, including RNase Po1 secreted by Pleurotus ostreatus, exhibits antitumor activity. Here, we resolved the Po1/guanosine-3'-monophosphate complex (3'GMP) structure at 1.75 Å. Structure comparison and fragment molecular orbital (FMO) calculation between the apo form and the Po1/3'GMP complex identified Phe38, Phe40, and Glu42 as the key binding residues. Two types of the RNase/3'GMP complex in RNasePo1 and RNase T1 were homologous to Po1, and FMO calculations elucidated that the biprotonated histidine on the β3 sheet (His36) on the β3 sheet and deprotonated Glu54 on the β4 sheet were advantageous to RNase activity. Moreover, tyrosine (Tyr34) on the β3 sheet was elucidated as a crucial catalytic residues. Mutation of Tyr34 with phenylalanine decreased RNase activity and diminished antitumor efficacy compared to that in the wild type. This suggests the importance of RNase activity in antitumor mechanisms.

Aesthete Pattern Diversity in Chiton Clades (Mollusca: Polyplacophora): Balancing Sensory Structures and Strength in Valve Architecture.

Chitons possess the most elaborate system of shell pores found in any hard-shelled invertebrate. Although chitons possess some anteriorly located sense organs, they lack true cephalization, as their major sensory systems are not concentrated in a distinct head region. Instead, the aesthete system within their shells forms a dense sensory network that overcomes the barrier of their hard dorsal armour. The basic arrangement of neural structures embedded within a solid, opaque matrix, has confounded understanding of the overall network. In this study, we use synchrotron X-ray μCT to visualise the aesthete canal networks inside chiton valves. We selected representatives from all three major chiton clades: Lepidopleurida, the basal branching clade, and Callochitonida and Chitonida, which both havemore complex shell morphology, to compare internal structure. Lepidopleurida aesthete canals are oriented vertically and pass directly through the shell to connect with the body. By contrast, aesthetes canals in Callochitonida and Chitonida have complex internal structures with extended horizontal passages, coalescing at the shell diagonal that corresponds to the valve insertion slits. This represents a stepwise evolution of chiton shell form, where thicker and more complex valves require a diverting and rewiring of the entire sensorynetwork. Aspects of the aesthete system, such as the microscopic arrangement of surface pores, have long been used in chiton taxonomy for species diagnoses; insertion slits should also be understood as a secondary feature of the aesthete system. Chiton shell structures that are used for morphological systematics are driven by sensory adaptations.

Current Applications of Raman Spectroscopy in Intraoperative Neurosurgery.

Neurosurgery demands exceptional precision due to the brain's complex and delicate structures, necessitating precise targeting of pathological targets. Achieving optimal outcomes depends on the surgeon's ability to accurately differentiate between healthy and pathological tissues during operations. Raman spectroscopy (RS) has emerged as a promising innovation, offering real-time, in vivo non-invasive biochemical tissue characterization. This literature review evaluates the current research on RS applications in intraoperative neurosurgery, emphasizing its potential to enhance surgical precision and patient outcomes. Following PRISMA guidelines, a comprehensive systematic review was conducted using PubMed to extract relevant peer-reviewed articles. The inclusion criteria focused on original research discussing real-time RS applications with human tissue samples in or near the operating room, excluding retrospective studies, reviews, non-human research, and other non-relevant publications. Our findings demonstrate that RS significantly improves tumor margin delineation, with handheld devices achieving high sensitivity and specificity. Stimulated Raman Histology (SRH) provides rapid, high-resolution tissue images comparable to traditional histopathology but with reduced time to diagnosis. Additionally, RS shows promise in identifying tumor types and grades, aiding precise surgical decision-making. RS techniques have been particularly beneficial in enhancing the accuracy of glioma surgeries, where distinguishing between tumor and healthy tissue is critical. By providing real-time molecular data, RS aids neurosurgeons in maximizing the extent of resection (EOR) while minimizing damage to normal brain tissue, potentially improving patient outcomes and reducing recurrence rates. This review underscores the transformative potential of RS in neurosurgery, advocating for continued innovation and research to fully realize its benefits. Despite its substantial potential, further research is needed to validate RS's clinical utility and cost-effectiveness.

Bayesian inference for link travel time correlation of a bus route

Estimation of link travel time correlation of a bus route is essential to many bus operation applications. Link travel time on a bus route could exhibit complex correlation structures, such as long-range correlations, negative correlations, and time-varying correlations. This paper develops a Bayesian Gaussian model to estimate the link travel time correlation matrix of a bus route using smart-card-like data. Our method overcomes the small-sample-size problem in correlation matrix estimation by borrowing/integrating those incomplete observations from other bus routes. We first conduct a synthetic experiment and results show that the proposed method produces an accurate estimation for correlations with credible intervals. Next, we perform experiments on a real-world bus route with in-out-stop record data; results show that both local and long-range correlations exist on this bus route. Finally, we demonstrate an application of using the estimated covariance matrix to make probabilistic forecasting of link and trip travel time.