Miracle-dz/UniFAR-FTUs huggingface dataset:
THE EFFECT OF RESIDENTS’ PLACE ATTACHMENT ON THEIR ATTITUDE TOWARDS DEVELOPMENT OF RELIGIOUS TOURISM: THE MODERATING EFFECT OF PERSONAL BENEFIT

Residents’ attitude towards tourism development in religious tourism destinations is especially relevant for tourism planning. Nevertheless, there are few studies that analyse how residents’ attitude towards development of religious tourism is formed. This paper analyses the effect of residents’ place attachment on their attitudes towards development of religious tourism, considering perceived impacts of tourism as mediator and the personal benefit derived from tourism as moderating effect. On the basis of a sample of 410 residents of Montecristi, a religious tourism destination of Ecuador, and using Partial Least Squares Structural Equation Modeling (PLS-SEM), the results show that place attachment directly influences the attitude towards development of religious tourism, but mainly through the perceived impacts of tourism. Moreover, this paper finds that the influence of the perceived impacts on the support for the development of religious tourism is higher in residents with a lower personal benefit than in residents with a higher personal benefit. These findings enable a series of recommendations to be made to the agents concerned regarding the development of religious tourism in sacred destinations.
Electronic Nose for Tea Identification Detection based on Machine Learning K-Nearest Neighbors Method and Raspberry Pi 4

This study aims to identify the types of tea to determine their authenticity and quality using the Machine Learning K-Nearest Neighbors (KNN) method and Raspberry Pi 4. The developed system uses tea aroma data collected through an Electronic Nose (E-Nose) connected to Raspberry Pi 4, which utilizes eight gas sensors (MQ 2, MQ 3, MQ 4, MQ 6, MQ 7, MQ 8, MQ 9, MQ 135) to measure the gas levels formed by various volatile compounds that evaporate in different types of tea with varying compositions, namely 100% green tea, 100% jasmine tea, 100% black tea, 80% green tea and 20% jasmine tea, and 75% green tea and 25% jasmine tea. The data is processed using the Machine Learning KNN method to classify tea types based on the detected aroma patterns. The evaluation of the analysis takes into account metrics such as accuracy, precision, recall (sensitivity), true negative rate (specificity), F-1 score, confusion matrix, and Principal Component Analysis (PCA). The results of this study indicate that the Machine Learning KNN method using Raspberry Pi 4 can accurately identify the types of tea. The evaluation of the analysis shows satisfactory model performance, with high levels of accuracy, precision, recall, specificity, and F1 scores. The confusion matrix provides a clear picture of the model’s ability to classify tea types, while the PCA plot provides an intuitive understanding of the data structure, making it easier for researchers and stakeholders to interpret and analyze the complexity of the data with ease. The results of this study show that the E-Nose system with the KNN method is capable of differentiating between green tea, black tea, and a combination of green tea and black tea, with an accuracy value of up to 93%.
Statistical Modeling and Reliability Analysis for Degradation Processes Indexed by Two Scales

Degradation is an important phenomenon for industrial products, which manifests as the gradually deterioration of some performance characteristics. The degradation process is often relevant to both time and usage, and indexing the degradation process merely by the time or usage cannot characterize the process accurately. Considering a stochastic usage process, this study proposes a degradation process model indexed by two scales, i.e., the time and the usage, where the degradation along the two scales are modeled as correlated nonlinear Wiener processes. We develop two simulation-based algorithms for reliability evaluation and study the model inference problems for the proposed model. The estimation procedure and the reliability assessment algorithms are validated by simulations. The performance of the proposed model is justified with an application to a real degradation dataset of outdoor coating materials, which shows that indexing the degradation process by two scales can considerably improve the degradation modeling performance.
Automatic Cardiac MRI Segmentation Using Deep Learning

In the biomedical realm, automatic medical image segmentation is regarded as a challenging research topic. Among the different automatic methods, U-shaped models have significantly advanced a wide range of medical image segmentation. When training deep neural networks like U-net, gradient degradation is one of the problems we need to prevent from occurring. In addition, giving more focus to the important objects in the image while disregarding unneeded areas is one of the desired properties in medical image segmentation. In order to prevent gradient degradation and focus on the important objects in the image, a novel U-net-based architecture is proposed for cardiac MRI segmentation. The model uses the merit of U-Net, residual U-Net (ResU-Net), and Attention U-Net (AttU-Net) for better performance and prediction results. U-Net, ResU-Net, and AttU-Net are used as benchmark models. For the model to efficiently train the data and to improve its accuracy, a data preprocessing mechanism is applied. The data preprocessing step includes contrast enhancement, data augmentation, and data normalization. For the contrast enhancement method, we tested three different contrast enhancement methods. These methods are non-linear contrast enhancement (gamma correction), multi-scale Retinex, and adaptive histogram equalization. After testing the three contrast enhancement methods on the data, the best one was selected for use in the deep learning algorithms. The proposed and benchmark models are tested on data from Anzhen Hospital and ACDC public data set. The result shows that the proposed model achieved a better prediction result on both data sets than the benchmarks. This is due to the benefits of using ResU-Net and AttU - Net, which enable it to concentrate on the small class, reintroduce features, and prevent gradient degradation, resulting in its advantage.
Surface Defect Detection of Steel Based on Improved YOLOv4

An important measure of the nation's industrial level is the development of the steel sector. And the surface quality of steel is related to the development of high technology such as automotive, energy and avionics. To address the issues of traditional steel surface flaw detection's low accuracy and slow speed, a steel surface defect detection method based on large convolution kernel module combined with channel segmentation and re-parameterizing mechanism is designed. A large convolutional kernel is introduced into the YOLOV4 backbone network to increase the effective receptive field of the feature layer and improve the feature extraction capability of the network. At the same time, a channel segmentation technique is introduced to enable the network to learn differentially as much as possible to improve the generalisation capability of the network. To increase the detection accuracy of the approach for a variety of complicated flaws, a tiny convolutional kernel parallel to the large convolutional kernel is employed to re-parameterize the network structure during model training. According to the testing findings, the enhanced algorithm's average accuracy mean mAP is 91.15%, which is 3.55% better than the baseline YOLOV4 algorithm, and its single picture detection speed FPS is 6.48 f/s, which is sufficient for the identification of steel surface defects.
Dynamics Modeling and Controller Design for a Novel Indoor Blimp

Due to its safety and endurance, the indoor blimp robot is an ideal choice for cruise monitoring and inspection. In order to adapt to the complex indoor scene, a helium-filled blimp is studied in this paper. The dynamic model of the aerial robot is established by using Newton Euler equation, and the main characteristics are then analyzed based on the derived model. Furthermore, the linear quadratic regulator controller is designed to control the airship motion in the vertical plane. Based on the model, a simulation platform is constructed for further analysis and controller verification. Finally, simulation results are presented to show the correctness of the dynamic model.
Adaptive Cooperative Control of the Multi-Agent System

This paper deals with the Model Reference Adaptive Controller (MRAC)-based leader-followers consensus problem of a multi-agent system (MAS) characterized by linearized state-space models. The transmission graph of the agents is undirected among the followers and directed towards the leader agents. Feedback to the leader agent is given from the neighboring agent. The distributed control law is derived based on the relative output errors between the neighboring agents. Closed-loop reference model-based MRAC algorithm is proposed and gives the necessary/sufficient criteria for the existence of the leader-following consensus protocol. The stability analysis of the system with the suggested control framework has been investigated by applying the Lyapunov method. Lastly, a simulation case study is provided to illustrate the proficiency of the theoretical finding.
Robust Output Regulation for a Flexible Wing System with Output Disturbances

Robust output regulation has been addressed by two observer based tracking error feedback controls for disturbances and references from an exosystem. However, output disturbances and references can not be considered together, since it is impossible to only use tracking errors to distinguish the output disturbances and references. In this paper, the robust output regulation of a flexible wing system is addressed in the presence of arbitrary output disturbances. Based on two tracking errors only, two new exosystems are defined to make the state feedback control have known coefficients, and further estimated by two new exosystems. Two observer based robust controls are then obtained so that the exponential convergence of the tracking errors can be guaranteed. A simulation example is provided to show the robust output regulation problem.
Journal of Cerebral Blood Flow and Metabolism Extracellular Antioxidants and Amino Acids in the Cortex of the Rat: Monitoring by Microdialysis of Early Ischemic Changes

Extracellular concentrations of ascorbic acid, glutathione, cysteine, uric acid, tyrosine, and tryptophan were monitored using intracerebral microdialysis in the left frontoparietal cortex of spontaneous hypertensive rats before, and for 3 h after, either focal ischemia [left middle cerebral artery occlusion (MCAO)] or sham oper­ ation. The size of the ischemic area and the position of the microdialysis probe were checked using the enzyme his­ totopochemical acid phosphatase reaction. The probe was always located in the cortex inside the stained area. Ascorbic acid levels rose immediately after MCAO and remained at about 12-fold for 3 h. There was a transient Microdialysis (Tossman and V ngerstedt, 1986; Benveniste and Huttemeier, 1990) provides a mini­ mally invasive method for continuously monitoring changes in concentrations of low-molecular-weight substances in the extracellular (EC) fluid of various organs. It has been used to study cerebral ischemia in the rat, measurements being made mainly in the striatum, one of the areas most susceptible to isch­ emic damage. Changes have been found in this re­ gion at the onset of cerebral ischemia in the EC concentrations of various parameters, 96 release of glutathione during 1-1.5 h. Uric acid concen­ trations were also increased but the differences did not reach significance. The levels of the amino acids tyrosine and tryptophan increased steadily after MCAO. The in­ creases in cysteine were variable but significant. In some experiments, the pH of the dialysate was measured on­ line. The parameters ascorbic acid, glutathione, cysteine, and pH are suitable for the early detection of cortical ischemic events by microdialysis. amino acids (Hillered et aI. , 1989), and dopamine (Globus et aI. , 1988). In the cortex, EC concentra­ tions of ascorbic acid (Hillered et aI. , 1990a) and of energy-related metabolites (Nilsson et aI. , 1990) have been determined in relation to trauma; how­ ever, to our knowledge, no measurements have ever been made in the rat in this region after isch­ emia. This is surprising because secondary cortical ischemic changes are frequent in patients after se­ vere head injury, and decisive for the clinical course. In spite of improvements in intensive care, these lesions are still the main cause of death after brain injury (Graham et aI. , 1989). The role of free radical mechanisms in cerebral ischemia has been widely discussed. In a previous study, we measured the total contents (nmollg of fresh tissue) of the antioxidants glutathione (GSH), ascorbic acid …
Journal of Cerebral Blood Flow and Metabolism Cerebrovascular Anatomy and Blood Flow Measurements in the Rabbit

The arterial supply and venous drainage of the rabbit's brain were characterized by intravascular injection of casting material and intra-arterial administration of markers (crystal violet or dissolved hydrogen gas). The inter­ nal carotid artery supplies the homolateral cerebral cortex and subcortical structures except for the thalamus and the posterior portion of the nucleus caudatus; it also supplies the homolateral retina and optic nerve. No noncere­ bral structures are supplied by this artery. The dorsal sagittal sinus drains the dorsal and lateral parts of the frontal and parietal areas of the cerebral cortex, with no detectable extracerebral contamination. Electromagnetic measurement of flow in the internal carotid artery (ICBF), volumetric or H2-clearance mea­ surement of flow in the dorsal sagittal sinus (SSBF), and H2-clearance determi­ nation in cerebral cortex yield comparable results on the cerebrovascular re­ sponse to hyper-and hypocapnia. ICBF and SSBF are reliable and valid esti­ mates of average blood flow through the homolateral cerebral hemisphere and the cerebral cortex, respectively. The study of cerebral blood flow (CBF) in the common laboratory animals through measurement of arterial inflow or venous outflow has been con­ founded by the peculiarities of the cerebral vascu­ lature. In the dog, the arterial supply is via external and internal carotid arteries, as well as vertebral arteries, with considerable amount of anastomosis among these (Schmidt, 1950; Sagawa and Guyton, 1961). The cat, on the other hand, lacks a significant internal carotid artery, and the external carotid supplies the brain by way of a rete mirabile, in addi­ tion to supplying extracerebral tissue such as bone, 55 either preclude a practical measurement of cerebral arterial inflow or necessitate extensive surgical in­ tervention (Sagawa and Guyton, 1961). A similar difficulty in isolating the cerebral arterial inflow exists for all other laboratory species with the ex­ gave a description of the cerebral arterial supply in the rabbit and measured the flow in the internal carotid artery of this animal using a volumetric device (Hiirthle stromuhr). Later, Schmidt and Hendrix (1938) again measured, by means of the Rein ther­ mostromuhr, the blood flow in the internal carotid artery of the rabbit as an index of CBF. Since then no attempts have been made to adapt to this vessel some of the more precise modern flow-measuring instruments.
Development of an objective ozone forecast model for the South Coast Air Basin

The South Coast Air Quality Management District (SCAQMD) provides daily forecasts of the expected maximum one hour average ozone concentrations at 42 locations in the South Coast Air Basin (SCAB). The forecast, released for dissemination at approximately 11:00 am PDT, and valid for the following afternoon, relies upon a set of implicit relationships between observed ozone air quality and a combination of synoptic and mesoscale meteorological variables. A variety of empirical models and forecast methodologies have been employed to predict the spatial and temporal distribution of ozone in the SCAB. Model development was based upon the assumption of a time consistent daily emission profile. However, with the continued implementation of emission control measures, the set of implicit relationships that have linked air quality and meteorology have been slowly altered. To remedy the observed erosion in forecast capability, an objective ozone forecast model was developed that is designed for mid-to-late 1980's ozone air quality prediction. The model is presented in this paper.
GoCLIP: Graph one-class CLassification for Intellectual Property Circuit Identification

Circuit Intellectual Property (IP) Identification is crucial to prevent malicious misuse of IP cores/blocks in Integrated Circuits (ICs). The main objective in IP identification is to identify a known set of IP cores/blocks in the circuit netlists extracted from manufactured IC products. Conventional methods are usually based on similarity matching to a library of IPs. Such methods are tedious and their performance can be compromised by small changes to the recovered circuit netlists caused by imperfections in the recovery process. In this paper, we propose GoCLIP, a machine learning based graph one-class classification method for IP identification. We reformulate circuit IP identification into a graph one-class classification problem and employ Graph Neural Network (GNN) for graph structure learning. Our results show that our machine learning models produce highly differentiable graph features for different circuits with promising results in IP identification of mean Average Precision (mAP) >95%.
Research on Data-Driven Fault Diagnosis of Aero-Engine Transmission Systems

In this brief paper, a data-based method on the fault diagnosis in aero-engine transmission systems is developed. Firstly, during the operation of splines, we acquire the acceleration vibration signal. We process the signal into time-frequency images using the short-time Fourier transform. Then, the improved convolutional neural network with channel attention is trained using time-frequency images of multiple fault signals. Finally, we implement fault prediction for spline wear and misalignment faults in aero-engine transmission systems. The result shows that the fault diagnosis using the proposed method has a high level of accuracy.
Retinogeniculate Visual Pathway Reconstruction Using Reinforcement Learning

Reconstructing the retinogeniculate visual pathway with the pituitary tumor compression is a difficult task. The tumor not only compresses the retinogeniculate visual pathway to produce a large angular deviation, but also interferes with the diffusion magnetic resonance image signal. In order to solve the above problems, the anatomical prior knowledge and fiber direction distribution are used in this study. We first manually drawn regions of interest as anatomical priors. The regions of interest are located in the eyeball and optic tract. Then, we purpose a mathematical model that combine the anatomical prior knowledge and fiber direction distribution. The mathematical model is an Markov decision process, which can be solved by reinforcement learning. In the comparison experiments, the purposed method shows high performance on retinogeniculate visual pathway reconstruction. Furthermore, only our method is able to tracking retinogeniculate visual pathway with the pituitary tumor.
Cooperation or collaboration? On a human-inspired impedance strategy in a human-robot co-manipulation task

—We investigate whether a robot should behave as a collaborator or as a cooperator of a human partner in a co-manipulation task. In a previous study, we addressed the same question for a human-human dyad and found that collaboration is preferable to make fewer errors at the expense of increased arm stiffness for the humans, who behave as if they were both leaders. In this study, the human is coupled with a Franka robot and they jointly engage in the same co-manipulation task in different conditions. In the cooperation conditions, the robot is either a leader or a follower, exhibiting ﬁxed impedance strategies. In the collaborative conditions, the robot exhibits either reciprocal or mirrored adaptive impedance strategies that vary according to an EMG-based online estimation of the human arm stiffness. Our results show that, for the co-manipulation task, a robot collaborator seems more preferable than a robot cooperator, leader or follower, and that the reciprocal strategy for impedance seems to be the most indicated.
. Performance analysis of IEEE 802.11n solutions combining MIMO architectures with iterative decoding and sub-optimal ML detection via MMSE and zero forcing GIS solutions. IEEE Wireless Communications and Networking Conference

— This paper investigates the point-to-point PHY layer performance of two newly proposed IEEE 802.11n candidate solutions by suggesting extensions to 802.11a. The new solutions incorporate a MIMO architecture for the wireless links between the network terminals. The FEC block in the PHY layer chain is also enhanced with a Turbo encoder to provide interleaving gains for the larger sized PSDUs (1-4095 bytes). Due to the ambitious data rate targets identified in 802.11n, where ‘on-air’ data rates of up to 320Mbit/s are quoted, a large MIMO architecture comprising 6 Transmit and 6 Receive antenna elements is adopted. ML detection using such large configurations is often prohibitively complex due to an unrealistic enumerated symbol list. In this paper we propose Zero-Forcing and MMSE-GIS solutions to ease complexity at the receiver. PER results for these new schemes are presented and Link throughputs are compared to demonstrate the viability of the chosen solutions and their benefits over the existing 802.11a standard.
Distributed Adaptive Fault-Tolerant Formation of Linear Multi-Agent Systems

In this article, distributed fault-tolerant formation control strategy of linear multi-agent systems (MASs) is studied. An adaptive formation protocol is investigated to eliminate the influence of approximation error by solving several sets of matrix inequalities to obtain the adaptive estimation of fault tolerance factor. It is shown that the formation of the MASs can be reached and the fault can be tolerant. Finally, the designed formation adaptive fault-tolerant control system is verified by simulations.
Audio Visual Cross-modal Matching based on Relational Similarity Learning

Audio visual cross-modal matching aims to integrate facial and vocal information to improve identity recognition. Current methods focus on learning a shared representation across all modalities, but ignoring the impact of information inequality between different modalities. To fully investigate the intrinsic relationship between audio and visual, we propose a novel relational similarity learning network (RLSNet) for audio visual cross-modal matching by directly learning natural pairwise similarities, instead of explicitly learning a common subspace. In addition, considering the diversity of sample learning difficulty, we incorporate a re-weighting strategy in our metric learning approach to assign greater weight to challenging samples. Extensive experiments on audio visual cross-modal matching demonstrate the effectiveness of the proposed approach compared with the state-of-the-art methods.