Miracle-dz/UniFAR-FTUs huggingface dataset:
Shared Subway Shuttle Bus Route Planning Based on Transport Data Analytics

The development requirements of shared buses are extremely urgent to alleviate urban traffic congestions by improving road resource utilization and to provide a neotype transportation mode with good user experiences. The key to shared bus implementation lies in accurately predicting travel requirements and planning dynamic routes. However, the sparseness and the high volatility of shared bus data bring a great resistance to accurate prediction of travel requirements. Based on the consideration of user experiences, optimization objectives of shared bus route planning are significantly different from traditional public transportation and shared bus route planning is far more challenging than online car-hailing services due to the relatively high number of passengers. In this paper, we put forward a two-stage approach (SubBus), which is composed of travel requirement prediction and dynamic routes planning, based on various crowdsourced shared bus data to generate dynamic routes for shared buses in the “last mile” scene. First, we analyze the resident travel behaviors to obtain five predictive features, such as flow, time, week, location, and bus, and utilize them to predict travel requirements accurately based on a machine learning model. Second, we design a dynamic programming algorithm to generate dynamic, optimal routes with fixed destinations for multiple operating buses utilizing prediction results based on operating characteristics of shared buses. Extensive experiments are performed on real crowdsourced shared subway shuttle bus data and demonstrate that SubBus outperforms other methods on dynamic route planning for the “last mile” scene. Note to Practitioners—This paper is inspired by the problem of shared subway shuttle bus dynamic route planning for the “last mile” scene, and it is also applicable to other scenes, including commuting scenes, urban transportation hub scenes, and destination scenes of the tourist market. Shared bus operation routes at such scenes are usually aimed at trips with fixed destinations. Existing approaches to planning routes are generally designed for traditional transportation, such as traditional buses and taxis. In this paper, we propose a novel two-stage dynamic route planning approach (SubBus) based on the operation characteristics of shared subway shuttle buses. We perform a resident travel behavior analysis to improve the accuracy of travel requirement prediction. After that, we combine the prediction results and station properties to gain shared bus optimal routes. We then display how to apply SubBus to optimize shared bus operation status based on crowdsourced shared subway shuttle bus data generated by Panda Bus Company. We keep a continuous collaboration with the company to optimize the approach details and experimental effects, which demonstrate that our approach can generate effective routes for shared subway shuttle buses to optimize operation status on the “last mile” issue.
Arteriolar injuries in clinical obstetric and gynecologic syndromes

Narrowed, uterine arterioles in the placental bed are the histologic hallmark of many, clinical obstetric syndromes including preeclampsia, IUGR, preterm labor, midtrimester loss, and, placental abruption. Early reports (1945-1980) illustrate, but do not explain, accompanying “halos” of hyalinised cells around these narrowed arterioles that are consistent features of these lesions (Figure 1a). Similar, narrowed arterioles are common in many gynecologic syndromes where there are “halos” of regenerating, injured nerves around narrowed uterine arterioles in presentations of chronic pelvic pain, dysmenorrhea, endometriosis , dyspareunia, vulvodynia, irritative bladder, and, bowel syndromes (Figure 1b). The neurovascular injury is one, and the same, in both obstetric and gynecologic, clinical syndromes. It results from injuries to pelvic vasomotor nerves (the “neural” injury) that release neural cytokines and growth factors causing transmural hyperplasia and narrowing of denervated arterioles, that become susceptible to successive, hyaline and fibrinoid changes (the “vascular” injury). Their common origins have been difficult to recognise because during pregnancy the injured arterioles extend to the placental bed, but, the injured nerves do not, leaving one, or more, layers of hyalinised cells around the injured arteriole in the placental bed biopsy (Figures 1a-b). Injuries to pelvic nerves result from “difficult” first labors, physical efforts during defecation, complications of gynecologic surgical procedures, as well as hypertension. Injuries to arteriolar vasomotor nerves induce “new” and “novel” receptors including purinergic, P2X3 and VEGF receptors in arteriolar smooth muscle that may respond to “pain” and “stretch” , or, activate other autonomic reflexes. Recent evidence suggests that the unexpectedly diverse, and varying, consequences of these neurovascular injuries may contribute to the pathophysiology of different, obstetric and gynaecologic syndromes. *Correspondence to: MJ Quinn, MD, LLM, Visiting Professor, Medical University of Wenzhou, 1, Nanbaixiang Road, Ouhai, Wenzhou, Zhejiang, CHINA, PR 325000, E-mail: mjquinn001@btinternet.com
Real-Time Continuous Action Recognition Using Pose Contexts With Depth Sensors

We present an efficient approach for real-time continuous human action recognition with depth sensors. Instead of using the powerful but quite complex deep neural networks, our approach uses a light-weight discriminative frame-level descriptor, which is called the context pose (CP). The objective is to make our approach realistic in mobile depth sensor applications. CP integrates the context of the motion within a depth video. CP can increase the discriminative power of the frames and enable more frames to represent actions. CP is incorporated with a new part-based random decision forest (PRDF) method. The PRDF is designed to automatically select the optimal combination of body parts to represent and distinguish each action. We evaluate our approach on three classical single-action benchmark datasets. The experiments show that our approach has 200 frames/s and wins superior performances to the existing frame-level descriptors and classifiers in terms of accuracy.
Numerical Dosimetry of Electromagnetic Pulse Exposures Using FDTD Method

A novel frequency-dependent finite-difference time-domain (FDTD) method, previously proposed by the author, is applied to numerical dosimetry of exposures by an electromagnetic (EM) pulse incident to biological bodies, which are characterized by four Cole–Cole relaxation terms. The proposed method applies the fast inverse Laplace transform to determine the time-domain impulse responses of the complex permittivity function and utilizes Prony’s method to determine the infinite impulse response representation in the <inline-formula> <tex-math notation="LaTeX">$z$ </tex-math></inline-formula>-domain. The FDTD formulation for the update of the electric field is then directly derived by using the <inline-formula> <tex-math notation="LaTeX">$z$ </tex-math></inline-formula>-transformation. The validity of the method is demonstrated by the 3-D analyses of a homogeneous dielectric sphere and a multilayer sphere illuminated by an EM pulse. It is demonstrated that the numerical results of induced electric field distributions inside the spheres are in good agreement with those derived from Mie’s theory over a broad frequency range, proving the validity of the proposed method. Finally, numerical dosimetry of a heterogeneous human head excited by an EM pulse is performed and specific energy absorption due to the pulse illumination is calculated by using the proposed method.
Estimation of Terrestrial Global Gross Primary Production (GPP) with Satellite Data-Driven Models and Eddy Covariance Flux Data

We estimate global terrestrial gross primary production (GPP) based on models that use satellite data within a simplified light-use efficiency framework that does not rely upon other meteorological inputs. Satellite-based geometry-adjusted reflectances are from the MODerate-resolution Imaging Spectroradiometer (MODIS) and provide information about vegetation structure and chlorophyll content at both high temporal (daily to monthly) and spatial (∼1 km) resolution. We use satellite-derived solar-induced fluorescence (SIF) to identify regions of high productivity crops and also evaluate the use of downscaled SIF to estimate GPP. We calibrate a set of our satellite-based models with GPP estimates from a subset of distributed eddy covariance flux towers (FLUXNET 2015). The results of the trained models are evaluated using an independent subset of FLUXNET 2015 GPP data. We show that variations in light-use efficiency (LUE) with incident PAR are important and can be easily incorporated into the models. Unlike many LUE-based models, our satellite-based GPP estimates do not use an explicit parameterization of LUE that reduces its value from the potential maximum under limiting conditions such as temperature and water stress. Even without the parameterized downward regulation, our simplified models are shown to perform as well as or better than state-of-the-art satellite data-driven products that incorporate such parameterizations. A significant fraction of both spatial and temporal variability in GPP across plant functional types can be accounted for using our satellite-based models. Our results provide an annual GPP value of ∼140 Pg C year - 1 for 2007 that is within the range of a compilation of observation-based, model, and hybrid results, but is higher than some previous satellite observation-based estimates.
An optimal 
χ ‐bound for ( 
P
6 , diamond)‐free graphs

In this paper we show that every ( P 6 , diamond)‐free graph G satisfies χ ( G ) ≤ ω ( G ) + 3 , where χ ( G ) and ω ( G ) are the chromatic number and clique number of G , respectively. Our bound is attained by the complement of the famous 27‐vertex Schläfli graph. Our result unifies previously known results on the existence of linear χ ‐binding functions for several graph classes. Our proof is based on a reduction via the Strong Perfect Graph Theorem to imperfect ( P 6 , diamond)‐free graphs, a careful analysis of the structure of those graphs, and a computer search that relies on a well‐known characterization of 3‐colourable ( P 6 , K 3 ) ‐free graphs.
A Dual-Mode Split-Ring Resonator to Eliminate Relative Humidity Impact

In this letter, a compact resonant-based dual-mode microwave sensor is proposed, aimed to eliminate the erroneous effect of relative humidity (RH) in microwave chemical sensing within the uncontrolled environment. A single split-ring resonator (SRR) is employed as the sensor core and the first resonance (<inline-formula> <tex-math notation="LaTeX">$f_{1}\sim 0.552$ </tex-math></inline-formula> GHz) and the second resonance (<inline-formula> <tex-math notation="LaTeX">$f_{2}\sim 1.03$ </tex-math></inline-formula> GHz) of the resonator are utilized to calibrate the potential measurement error arising from ambient RH. Simulations of the sensor’s design and sensitivity using the finite-element method were performed and presented, which are further confirmed by measurements for resonant profile variation considering RH change of 5%–70%. Using the proposed technique, SRR size reduction of 50% is achieved. In addition, common chemical materials of methanol, ethanol, 2-Isopropanol (IPA), and heptane are successfully detected, achieving clear distinction from the impact of external humidity.
Automatic Vision-Guided Micromanipulation for Versatile Deployment and Portable Setup

In this paper, an automatic vision-guided micromanipulation approach to facilitate versatile deployment and portable setup is proposed. This paper is motivated by the importance of micromanipulation and the limitations in existing automation technology in micromanipulation. Despite significant advancements in micromanipulation techniques, there remain bottlenecks in integrating and adopting automation for this application. An underlying reason for the gaps is the difficulty in deploying and setting up such systems. To address this, we identified two important design requirements, namely, portability and versatility of the micromanipulation platform. A self-contained vision-guided approach requiring no complicated preparation or setup is proposed. This is achieved through an uncalibrated self-initializing workflow algorithm also capable of assisted targeting. The feasibility of the solution is demonstrated on a low-cost portable microscope camera and compact actuated microstages. Results suggest subpixel accuracy in localizing the tool tip during initialization steps. The self-focus mechanism could recover intentional blurring of the tip by autonomously manipulating it 95.3% closer to the focal plane. The average error in visual servo is less than a pixel with our depth compensation mechanism showing better maintaining of similarity score in tracking. Cell detection rate in a 1637-frame video stream is 97.7% with subpixels localization uncertainty. Our work addresses the gaps in existing automation technology in the application of robotic vision-guided micromanipulation and potentially contributes to the way cell manipulation is performed. Note to Practitioners—This paper introduces an automatic method for micromanipulation using visual information from microscopy. We design an automatic workflow, which consists of: 1) self-initialization; 2) vision-guided manipulation; and 3) assisted targeting, and demonstrate versatile deployment of the micromanipulator on a portable microscope camera setup. Unlike existing systems, our proposed method does not require any tedious calibration or expensive setup making it mobile and low cost. This overcomes the constraints of traditional practices that confine automated cell manipulation to a laboratory setting. By extending the application beyond the laboratory environment, automated micromanipulation technology can be made more ubiquitous and expands readily to facilitate field study.
A Paper-Based Screen Printed HF RFID Reader Antenna System

Screen printing is a stencil process where conductive inks are patterned onto substrates through a fine mesh of threads. Nowadays, screen printing can be used to print radio frequency identification (RFID) antenna structures onto flexible and ultra-low-cost substrates such as paper. In this paper, we present a high frequency RFID (HF RFID) reader antenna system, operating at 13.56 MHz, using screen printed Ag particle ink as conductor and using HP photo paper as substrate. The proposed antenna system comprises four loop antenna elements, matched to <inline-formula> <tex-math notation="LaTeX">$50~ \Omega $ </tex-math></inline-formula>, and one I<sup>2</sup>C addressed SP4T multiplexer circuitry, controlled through an exterior embedded system. The geometries, designs and characterizations of the antenna system are described in the manuscript in details. Measurement results show that the antenna system has low power reflections and a suitable <inline-formula> <tex-math notation="LaTeX">${Q}$ </tex-math></inline-formula> factor. It has a maximum 11.1 cm RFID tag read range at an antenna system input power of 33 dBm. 2-D RFID tag positioning can be enabled by utilizing the RFID tag interrogation zones formed by the four loop antenna elements. In addition, a parametrical study is carried out to investigate the effect of loop antenna element dc resistance on the antenna element performance. It can be concluded that the proposed method can be used to create low-cost and large-area HF RFID reader antenna systems.
Dark Model Adaptation: Semantic Image Segmentation from Daytime to Nighttime

This work addresses the problem of semantic image segmentation of nighttime scenes. Although considerable progress has been made in semantic image segmentation, it is mainly related to daytime scenarios. This paper proposes a novel method to progressive adapt the semantic models trained on daytime scenes, along with large-scale annotations therein, to nighttime scenes via the bridge of twilight time — the time between dawn and sunrise, or between sunset and dusk. The goal of the method is to alleviate the cost of human annotation for nighttime images by transferring knowledge from standard daytime conditions. In addition to the method, a new dataset of road scenes is compiled; it consists of 35,000 images ranging from daytime to twilight time and to nighttime. Also, a subset of the nighttime images are densely annotated for method evaluation. Our experiments show that our method is effective for knowledge transfer from daytime scenes to nighttime scenes, without human annotation.
An IT Service Development Model Based on a Service Ecosystem: Case Studies of Cloud Service Companies

In recent years, information technology (IT) companies as IT providers have developed not only IT systems but also related services for users. These providers aim to standardize IT services to reduce development costs and improve quality. However, IT service requirements differ for each user; therefore, the providers cannot completely meet the users' requirements. Furthermore, the business environment of users is changing rapidly, and accordingly, the IT services should change as well. Therefore, IT providers cannot manage a continuous IT service development process very well. To address this challenge, we propose a new IT service development model based on a service ecosystem. This model suggests building a service ecosystem and defining the requirement phase before IT service development. Furthermore, it recommends that IT providers develop IT services and create value with partners in the service ecosystem. Consequently, there are diverse IT services in the service ecosystem. Compared with traditional IT development models, the IT providers can manage their services more effectively with the implementation of the proposed model.
Analysis of Progression Toward Alzheimer’s Disease Based on Evolutionary Weighted Random Support Vector Machine Cluster

Alzheimer’s disease (AD) could be described into following four stages: healthy control (HC), early mild cognitive impairment (EMCI), late MCI (LMCI) and AD dementia. The discriminations between different stages of AD are considerably important issues for future pre-dementia treatment. However, it is still challenging to identify LMCI from EMCI because of the subtle changes in imaging which are not noticeable. In addition, there were relatively few studies to make inferences about the brain dynamic changes in the cognitive progression from EMCI to LMCI to AD. Inspired by the above problems, we proposed an advanced approach of evolutionary weighted random support vector machine cluster (EWRSVMC). Where the predictions of numerous weighted SVM classifiers are aggregated for improving the generalization performance. We validated our method in multiple binary classifications using Alzheimer’s Disease Neuroimaging Initiative dataset. As a result, the encouraging accuracy of 90% for EMCI/LMCI and 88.89% for LMCI/AD were achieved respectively, demonstrating the excellent discriminating ability. Furthermore, disease-related brain regions underlying the AD progression could be found out on the basis of the amount of discriminative information. The findings of this study provide considerable insight into the neurophysiological mechanisms in AD development.
Artificial intelligence for traffic signal control based solely on video images

ABSTRACT Learning-based traffic control algorithms have recently been explored as an alternative to existing traffic control logics. The reinforcement learning (RL) algorithm is being spotlighted in the field of adaptive traffic signal control. However, no report has described the implementation of an RL-based algorithm in an actual intersection. Most previous RL studies adopted conventional traffic parameters, such as delays and queue lengths to represent a traffic state, which cannot be exactly measured on-site in real time. Furthermore, the traffic parameters cannot fully account for the complexity of an actual traffic state. The present study suggests a novel artificial intelligence that uses only video images of an intersection to represent its traffic state rather than using handcrafted features. In simulation experiments using a real intersection, consecutive aerial video frames fully addressed the traffic state of an independent four-legged intersection, and an image-based RL model outperformed both the actual operation of fixed signals and a fully actuated operation.
Hypertension, dietary salt and cognitive impairment

Dementia is growing at an alarming rate worldwide. Although Alzheimer disease is the leading cause, over 50% of individuals diagnosed with Alzheimer disease have vascular lesions at autopsy. There has been an increasing appreciation of the pathogenic role of vascular risk factors in cognitive impairment caused by neurodegeneration. Midlife hypertension is a leading risk factor for late-life dementia. Hypertension alters cerebrovascular structure, impairs the major factors regulating the cerebral microcirculation, and promotes Alzheimer pathology. Experimental studies have identified brain perivascular macrophages as the major free radical source mediating neurovascular dysfunction of hypertension. Recent evidence indicates that high dietary salt may also induce cognitive impairment. Contrary to previous belief, the effect is not necessarily associated with hypertension and is mediated by a deficit in endothelial nitric oxide. Collectively, the evidence suggests a remarkable cellular diversity of the impact of vascular risk factors on the cerebral vasculature and cognition. Whereas long-term longitudinal epidemiological studies are needed to resolve the temporal relationships between vascular risk factors and cognitive dysfunction, single-cell molecular studies of the vasculature in animal models will provide a fuller mechanistic understanding. This knowledge is critical for developing new preventive, diagnostic, and therapeutic approaches for these devastating diseases of the mind.
Image Processing Based Method For Identification Of Fish Freshness Using Skin Tissue

The quality of the fish is mainly altered by different cooling methods, exporting, handling etc. In this paper a nondestructive framework is proposed for identification of fish freshness using image processing techniques. In this paper skin tissue is selected as focal tissue for basic analysis and identification of freshness of fish from fish images. Statistical features are extracted in the HSV color space which gives degradation pattern for fish freshness which is used to design the framework for identification of fish freshness. The experiment result indicates monotonic degradation pattern. Experiments were carried on fish images and results are encouraging. The maximum classification accuracy of contributed methodology is 96.66%.