bluuebunny/crossref_metadata_2025_split huggingface dataset:
Real-time detection of isoprene marker gas based on micro-integrated chromatography system with GOQDs-modified μGC column and metal oxide gas detector
Authors: Han Baoqing , Zhang Xinyu , Wang Yan , Wang Wenjuan , Wang Benben , Li Shuai , Wang Hairong , Yan Yuefei , Han Jiusheng , Wang Chuanliu , Wang Congsi 
Published: August 2023

Abstract
Isoprene is a typical physiological marker that can be used to screen for chronic liver disease. This work developed a portable micro-integrated chromatography analysis system based on micro-electromechanical system technology, nanomaterials technology and embedded microcontroller technology. The system integrated components such as graphene oxide quantum dots modified semi-packed microcolumn, In2O3 nanoflower (NF) gas-sensitive detector and 3D printed miniature solenoid valve group. The effectiveness of the separation effect of the micro-integrated system was verified by gas mixture test; the laws of the influence of carrier gas pressure and column temperature on the chromatographic separation performance, respectively, were investigated, and the working conditions (column temperature 90 °C and carrier gas pressure 7.5 kPa) for system testing were determined. The percentages of relative standard deviation of the peak areas and retention times obtained for the separated gases were in the range of 0.95%–6.06%, indicating the good reproducibility of the system. Meanwhile, the microintegrated system could detect isoprene down to 50 ppb at small injection volume (1 ml). The system response increased with increasing isoprene concentration and was linearly correlated with isoprene concentration (R
2 = 0.986), indicating that the system was expected to be used for trace detection of isoprene, a marker gas for liver disease, in the future.
Topological phase transition between non-high symmetry critical phases and curvature function renormalization group
Authors: R Kumar Ranjith , Kartik Y R , Sarkar Sujit 
Published: August 2023

Abstract
The interplay between topology and criticality has been a recent interest of study in condensed matter physics. A unique topological transition between certain critical phases has been observed as a consequence of the edge modes living at criticalities. In this work, we generalize this phenomenon by investigating possible transitions between critical phases which are non-high symmetry (non-HS) in nature. We find the triviality and non-triviality of these critical phases in terms of the decay length of the edge modes and also characterize them using the winding numbers. The distinct non-HS critical phases are separated by multicritical points with linear dispersion at which the winding number exhibits the quantized jump, indicating a change in the topology (number of edge modes) at the critical phases. Moreover, we reframe the scaling theory based on the curvature function, i.e. curvature function renormalization group method to efficiently address the non-HS criticalities and multicriticalities. Using this we identify the conventional topological transition between gapped phases through non-HS critical points, and also the unique topological transition between critical phases through multicritical points. The renormalization group flow, critical exponents and correlation function of Wannier states enable the characterization of non-HS criticalities along with multicriticalities.
Time-dependent probability density function for partial resetting dynamics
Authors: Di Bello Costantino , Chechkin Aleksei V , Hartmann Alexander K , Palmowski Zbigniew , Metzler Ralf 
Published: August 2023

Abstract
Stochastic resetting is a rapidly developing topic in the field of stochastic processes and their applications. It denotes the occasional reset of a diffusing particle to its starting point and effects, inter alia, optimal first-passage times to a target. Recently the concept of partial resetting, in which the particle is reset to a given fraction of the current value of the process, has been established and the associated search behaviour analysed. Here we go one step further and we develop a general technique to determine the time-dependent probability density function (PDF) for Markov processes with partial resetting. We obtain an exact representation of the PDF in the case of general symmetric Lévy flights with stable index

0
<
α
⩽
2

. For Cauchy and Brownian motions (i.e.

α
=
1
,
2

), this PDF can be expressed in terms of elementary functions in position space. We also determine the stationary PDF. Our numerical analysis of the PDF demonstrates intricate crossover behaviours as function of time.
Numerical investigation of the freezing property of impinging water droplets on inclined cold superhydrophobic surfaces
Authors: Hou Jianqiang , Gong Jianying , Wu Xin 
Published: August 2023

Abstract
We numerically study the freezing process of impinging water droplets on inclined cold superhydrophobic surfaces, concerning the dynamic and heat transfer properties of impinging water droplets. Increasing the surface inclination angle and decreasing the contact angle hysteresis (CAH) is found to promote the rebound of impinging water droplets at a higher cold superhydrophobic temperature (such as −10 °C). The influence of the cold surface temperature on the receding side of impinging water droplets is stronger than that on the front side, and so is the CAH. The frozen region inside impinging water droplets first appears on the receding side, and this becomes more obvious as the surface inclination angle increases. The relatively intensive heat transfer process appears early in the impinging process of water droplets on an inclined cold superhydrophobic surface. Increasing the surface inclination angle could weaken the heat transfer process of impinging water droplets at a higher cold surface temperature (such as −10 °C), but it is not remarkable at a lower cold surface temperature (such as −30 °C). By increasing the supercooling degree, impinging Weber number, and CAH, the freezing process of impinging water droplets is enhanced.
Classical and quantum correlations between two qubits interacting with a field in thermal spin states
Authors: Algarni M , Abdel-Khalek S , Berrada K 
Published: August 2023

Abstract
In this manuscript, we introduce a quantum physical model consisting of two-atom system that interact with a quantized field initially prepared in the thermal spin states (TSSs). We study the effect of the main parameters of the model on the dynamical behavior atom–atom coherence, atoms-field entanglement, atom–atom entanglement and classical correlation. We show how the quantumness measures can be influenced by the spin number and thermal noise in the absence and presence of time-dependent coupling effect. We obtain that, despite the destructive influence of thermal noise, a considerable amount of coherence, entanglement and classical correlation still remain during the time evolution according the values of spin number. The results also show that the TSSs can offer the advantage of generating the maximal amount of coherence, entanglement and classical correlation during the dynamics.
Auditory neural correlates and neuroergonomics of driving assistance in a simulated virtual environment
Authors: Baqapuri Halim I , Roecher Erik , Zweerings Jana , Wolter Stefan , Schmidt Eike A , Gur Ruben C , Mathiak Klaus 
Published: August 2023

Abstract

Objective. Driver assistance systems play an increasingly important role in modern vehicles. In the current level of technology, the driver must continuously supervise the driving and intervene whenever necessary when using driving assistance systems. The driver’s attentiveness plays an important role in this human–machine interaction. Our aim was to design a simplistic technical framework for studying neural correlates of driving situations in a functional magnetic resonance imaging (fMRI) setting. In this work we assessed the feasibility of our proposed platform. Methods. We proposed a virtual environment (VE) simulation of driver assistance as a framework to investigate brain states related to partially automated driving. We focused on the processing of auditory signals during different driving scenarios as they have been shown to be advantageous as warning stimuli in driving situations. This provided the necessary groundwork to study brain auditory attentional networks under varying environmental demands in an fMRI setting. To this end, we conducted a study with 20 healthy participants to assess the feasibility of the VE simulation. Results. We demonstrated that the proposed VE can elicit driving related brain activation patterns. Relevant driving events evoked, in particular, responses in the bilateral auditory, sensory-motor, visual and insular cortices, which are related to perceptual and behavioral processes during driving assistance. Conceivably, attentional mechanisms increased somatosensory integration and reduced interoception, which are relevant for requesting interactions during partially automated driving. Significance. In modern vehicles, driver assistance technologies are playing an increasingly prevalent role. It is important to study the interaction between these systems and drivers’ attentional responses to aid in future optimizations of the assistance systems. The proposed VE provides a foundational first step in this endeavor. Such simulated VEs provide a safe setting for experimentation with driving behaviors in a semi-naturalistic environment.
Modulating individual axons and axonal populations in the peripheral nerve using transverse intrafascicular multichannel electrodes
Authors: Xie Yuyang , Qin Peijun , Guo Tianruo , Al Abed Amr , Lovell Nigel H , Tsai David 
Published: August 2023

Abstract

Objective. A transverse intrafascicular multichannel electrode (TIME) may offer advantages over more conventional cuff electrodes including higher spatial selectivity and reduced stimulation charge requirements. However, the performance of TIME, especially in the context of non-conventional stimulation waveforms, remains relatively unexplored. As part of our overarching goal of investigating stimulation efficacy of TIME, we developed a computational toolkit that automates the creation and usage of in silico nerve models with TIME setup, which solves nerve responses using cable equations and computes extracellular potentials using finite element method. Approach. We began by implementing a flexible and scalable Python/MATLAB-based toolkit for automatically creating models of nerve stimulation in the hybrid NEURON/COMSOL ecosystems. We then developed a sciatic nerve model containing 14 fascicles with 1,170 myelinated (A-type, 30%) and unmyelinated (C-type, 70%) fibers to study fiber responses over a variety of TIME arrangements (monopolar and hexapolar) and stimulation waveforms (kilohertz stimulation and cathodic ramp modulation). Main results. Our toolkit obviates the conventional need to re-create the same nerve in two disparate modeling environments and automates bi-directional transfer of results. Our population-based simulations suggested that kilohertz stimuli provide selective activation of targeted C fibers near the stimulating electrodes but also tended to activate non-targeted A fibers further away. However, C fiber selectivity can be enhanced by hexapolar TIME arrangements that confined the spatial extent of electrical stimuli. Improved upon prior findings, we devised a high-frequency waveform that incorporates cathodic DC ramp to completely remove undesirable onset responses. Conclusion. Our toolkit allows agile, iterative design cycles involving the nerve and TIME, while minimizing the potential operator errors during complex simulation. The nerve model created by our toolkit allowed us to study and optimize the design of next-generation intrafascicular implants for improved spatial and fiber-type selectivity.
Automated sleep classification with chronic neural implants in freely behaving canines
Authors: Mivalt Filip , Sladky Vladimir , Worrell Samuel , Gregg Nicholas M , Balzekas Irena , Kim Inyong , Chang Su-youne , Montonye Daniel R , Duque-Lopez Andrea , Krakorova Martina , Pridalova Tereza , Lepkova Kamila , Brinkmann Benjamin H , Miller Kai J , Van Gompel Jamie J , Denison Timothy , Kaufmann Timothy J , Messina Steven A , St Louis Erik K , Kremen Vaclav , Worrell Gregory A 
Published: August 2023

Abstract

Objective. Long-term intracranial electroencephalography (iEEG) in freely behaving animals provides valuable electrophysiological information and when correlated with animal behavior is useful for investigating brain function. Approach. Here we develop and validate an automated iEEG-based sleep–wake classifier for canines using expert sleep labels derived from simultaneous video, accelerometry, scalp electroencephalography (EEG) and iEEG monitoring. The video, scalp EEG, and accelerometry recordings were manually scored by a board-certified sleep expert into sleep–wake state categories: awake, rapid-eye-movement (REM) sleep, and three non-REM sleep categories (NREM1, 2, 3). The expert labels were used to train, validate, and test a fully automated iEEG sleep–wake classifier in freely behaving canines. Main results. The iEEG-based classifier achieved an overall classification accuracy of 0.878 ± 0.055 and a Cohen’s Kappa score of 0.786 ± 0.090. Subsequently, we used the automated iEEG-based classifier to investigate sleep over multiple weeks in freely behaving canines. The results show that the dogs spend a significant amount of the day sleeping, but the characteristics of daytime nap sleep differ from night-time sleep in three key characteristics: during the day, there are fewer NREM sleep cycles (10.81 ± 2.34 cycles per day vs. 22.39 ± 3.88 cycles per night; p < 0.001), shorter NREM cycle durations (13.83 ± 8.50 min per day vs. 15.09 ± 8.55 min per night; p < 0.001), and dogs spend a greater proportion of sleep time in NREM sleep and less time in REM sleep compared to night-time sleep (NREM 0.88 ± 0.09, REM 0.12 ± 0.09 per day vs. NREM 0.80 ± 0.08, REM 0.20 ± 0.08 per night; p < 0.001). Significance. These results support the feasibility and accuracy of automated iEEG sleep–wake classifiers for canine behavior investigations.
Intelligent Path Planning of Mobile Robot Based on Genetic Algorithm
Authors: Wang JiaQi 
Published: August 2023

Abstract
In this study, in order to deal with the path planning problems with genetic algorithm (GA) and improve its time efficiency, a new method combined with K-Nearest Neighbor (KNN) is proposed. GA is a well-known approach to figure out a path between two designated points while avoiding obstacles in the way. In traditional GA process, a set of solutions from the starting node to the destination are randomly generated in the beginning, which are called initial population. Although not every solution is feasible and the feasible ones may not be ideal, optimal path is likely to be obtained during the iterations. A new method is designed to reduce the number of iterations by constructing a dataset using traditional GA and selecting elite solutions as initial population when facing a new map. The selection criteria is the similarity between the distribution of obstacles and previous ones. Relevant experiments are organized to test the performance of the proposed method by comparing it with the traditional GA. According to the simulation results, the proposed method with elite initial population tends to converge more quickly.
Data Generation Using Simulation Technology to Improve Perception Mechanism of Autonomous Vehicles
Authors: Cao Minh , Ramezani Ramin 
Published: August 2023

Abstract
Recent advancements in computer graphics technology allow more realistic rendering of car driving environments. They have enabled self-driving car simulators such as DeepGTA-V and CARLA (Car Learning to Act) to generate large amounts of synthetic data that can complement the existing real-world dataset in training autonomous car perception. Furthermore, since self-driving car simulators allow full control of the environments, they can generate dangerous driving scenarios that the real-world dataset lacks such as bad weather and accident scenarios. In this paper, we will demonstrate the effectiveness of combining data gathered from the real-world with data generated in simulated world to train perception system on object detection and localization task. We will also propose a multi-level deep learning perception framework that aims to emulate a human learning experience in which series of tasks from simple to more difficult ones are learnt in a certain domain. The autonomous car perceptron can learn from easy-to-drive scenarios to more challenging ones customized by simulation software.
Machine Learning and Deep Learning Methods on Breast Cancer Metastases Detection
Authors: Zheng Wentao , Liu Kan , Che Fuqi , Zheng Yichen 
Published: August 2023

Abstract
There is a lot of room for research in how to detect breast cancer metastases in whole slide images more quickly and precisely. This work contrasts several machine learning and deep learning models on the breast cancer metastases detection problem and experiments the impacts of feature extraction, dimension reduction and the sizes of the datasets on these models. This work also establishes a breast cancer metastases detection pipeline based on the most accurate model in the experiments. Among the models, Dense Model using features trained from DenseNet201 with weights trained by ImageNet is of the best performance. This work develops a computer aided method to examine the cancerous regions in the whole slide images of breast cancer more quickly and precisely than an experienced doctor does.
Image Data Visualization Using T-SNE for Urban Pavement Disease Recognition
Authors: Huang Siyu 
Published: August 2023

Abstract
Urban pavement disease recognition is for the most part, a mission performed manually. Recently, video analysis task has been one of the most important applications in various fields. Aims to renovate on the automated vision-based disease recognition and to experiment new methods of road disease detection, this paper analyzes the images took from a city and performed image data visualization of road issues. From image features extracted from histogram of oriented gradient, we perform principal components analysis to reduce the dimensionality of features. Results are presented by visualizing datapoints using t-distributed stochastic neighbor embedding. The experiment shows that the image data visualization using t-SNE is suitable for the growing field of urban road disease recognition.
Lane Detection Methods Survey for Automatic Driving
Authors: Yang Mingyue 
Published: August 2023

Abstract
In realizing automatic driving, environment perception is an important task, and the research of lane detection algorithms occupies the leading position in environment perception. After many years of research on lane detection by researchers, many effective lane detection methods are now available. This article will research and analyze mainstream lane detection methods, including traditional methods and learning-based methods. Later, through an in-depth analysis of these methods, the advantages and disadvantages of each method are evaluated, and the direction of lane detection is discussed. It is hoped to help choose suitable lane detection methods in different scenes.
Causal Fusion for Recommender System
Authors: Sun Liwen , Zhang Chi , Su Zilin , Zhang Feiran , Zhou Yuen 
Published: August 2023

Abstract
Recommender system suffers from huge selection bias from users, which makes utilizing causal inference to solve this problem becomes a necessary problem. However, traditional rating model only utilizing biased dataset and unbiased estimator cannot remarkably be improved. Thus, we fused the unbiased dataset into training model and combine it with jointly learning method to better improve the original baselines. Extensive experiments verify that the proposed doubly robust joint leaning method after fusing unbiased, missing rating data can significantly outperform the state-of-the-art methods and can dramatically reduce the error from rating model with the increasing number of unbiased data.
Cooperative Spectrum Sensing for IoT System
Authors: Guo Mengxi 
Published: August 2023

Abstract
This paper it mainly writes how to make better use of spectrum and propose different algorithms to reduce excess spectrum waste in the case of spectrum shortage. Using Randomly distribute PU and SU’s location in certain area, with the help of graphs mark the PU and SU’s, we calculate the SNR considering channel fading, using formula to find the path loss. To perform energy detection, we generated an observation period of H1 signals and run simulations and evaluated it based on the graph of probability of detection (Pd) vs probability of false alarm (Pf). We also made some improvement on the algorithm to deal with sudden impulsive change in the environment variables. We subsequently used OR rule to decide presence of PUs. To get good performance and affordable work, we divided PU sensors into groups to detect energy of different PUs. When grouping according to SNR between PU and SU pairs, we achieved stable performance and less burden.
Computer Vision and Deep Learning for Precise Agriculture: A Case Study of Lemon Leaf Image Classification
Authors: Yuan Yang 
Published: August 2023

Abstract
Crop protection, an crucial field of precise agriculture, requires attention and improvement, as it secures sustainability and safety of crop and food production. There are various threats to crops in which pest is one of the severest. Computer vision technologies based on deep learning have shown great advantages on image classification as they enable real-time pest recognition on devices with cameras, such as drones. Thus, it is promising for pest monitoring and control and many DL models have been developed. Furthermore, early and accurate diagnosis is need as it minimizes pest damage. However, traditional models are limited on speed because the massive parameters require huge computing resource. In this work, we investigate the capability of lightweight model based on DL for the task of leaf disease classification on uncontrolled environment and compare it with traditional DL model. Lightweight models, in general, are designed to reduce computation on convolution layers with acceptable accuracy lose. We use an open database named LeLePhid, which contains lemon leave images, healthy or affected by aphid. The damage caused by aphid is general as the pest makes obvious changes to leaf outlooks. We focus on two typical DL models: the traditional, DenseNet and the lightweight, MobileNet, and discuss the balance between speed and accuracy, in order to support real-time analytics. Finally, we discuss the challenges and opportunities in practice.
Short-term Load Forecasting with Deep Learning Techniques
Authors: Dai Shijie 
Published: August 2023

AbstractShort-term load forecasting is essential to the modern power system, which is the basis to guarantee the equilibrium of power supply and demand. It not only ensures the secure and stable operation of power grid, but also provides an important basis for power load dispatch, which can effectively reduce the economic loss caused by energy surplus or energy shortage in a short term. In the previous research work, traditional linear regression models, typical machine learning models and other models have been explored, but there are still problems of low accuracy or single influence factors. In this paper, a short-term forecasting model based on Temporal Convolutional Network (TCN) is proposed. Historical power load data, weather conditions, date information and their combinations are separately used to forecast the power load in different prediction models. Compared with other models, the evaluation metric of TCN model shows that its prediction accuracy is higher, and it is more suitable for short-term load forecasting.