Miracle-dz/UniFAR-FTUs huggingface dataset:
Convention Concerning the Protection of the World Cultural and Natural Heritage

Noting that the cultural heritage and the natural heritage are increasingly threatened with destruction not only by the traditional causes of decay, but also by changing social and economic conditions which aggravate the situation with even more formidable phenomena of damage or destruction, Considering that deterioration or disappearance of any item of the cultural or natural heritage constitutes a harmful impoverishment of the heritage of all the nations of the world, Considering that protection of this heritage at the national level often remains incomplete because of the scale of the resources which it requires and of the insufficient economic, scientific, and technological resources of the country where the property to be protected is situated, Recalling that the Constitution of the Organization provides that it will maintain, increase, and diffuse knowledge, by assuring the conservation and protection of the world's heritage, and recommending to the nations concerned the necessary international conventions, Considering that the existing international conventions, recommendations and resolutions concerning cultural and natural property demonstrate the importance, for all the peoples of the world, of safeguarding this unique and irreplaceable property, to whatever people it may belong, Considering that parts of the cultural or natural heritage are of outstanding interest and therefore need to be preserved as part of the world heritage of mankind as a whole,
Network Traffic Prediction Based on Spatio-Temporal Graph Convolutional Network

Network traffic prediction has a good application prospect in network operation and maintenance. It is to predict the characteristics of future network traffic from the past traffic. In order to solve the problems of insufficient utilization of temporal features and spatial features in traditional network traffic prediction, a network traffic forecasting method based on temporal representation and spatial convolution is proposed. Concretely, we first utilize graph convolution network to explore the topological characteristics of network nodes and then employ LSTM to characterize the temporal features of the networks. Moreover, a representation vector of time is generated based on timestamp data to help the network to learn daily and weekly long-term temporal features. In this paper, the public dataset Abilene is used for testing. The results show on different scales show that the MSE, RMSE and MAPE value of our method reduced by 22.2%, 30.0% and 18.5%, respectively, compared with the original LSTM.
Nine-Level Multilevel Inverter with Low Switching Count and Voltage Stress for Solar Photovoltaic Application

MLIs, or multilevel inverters, are increasingly a crucial component for medium-voltage, high-power applications. Because to its high efficiency, low cost, and simplicity of control for an output with a greater number of levels, MLIs with fewer switches are more often used. In order to provide a 9-level output voltage, an improved multilevel inverter topology with two DC sources and fewer switches is shown in this work. Studies comparing the proposed MLI structure to recently suggested MLIs demonstrate its significance in terms of decreased switch count and lower voltage stresses across switches in these symmetrical designs. Seven switches, including one bidirectional switch, are employed in this configuration. Comparing the suggested topology to existing series and topologies reveals that it has fewer devices and an excellent efficiency of about 98.5%. Further generalized for n-level output voltages is the proposal topology. The suggested topology has been operationally and functionally simulated using PLECS and MATLAB (SIMULINK) software, and its experimental validation is also being carried out on the setup. Applications for the suggested topology include grid-connected systems, motor drives, and renewable energy systems.
Identification of plant diseases based on yolov5

Based on the increasingly severe reality of land desertification, it is of great practical significance to improve the efficiency of vegetation restoration, and technologies such as artificial intelligence, automation, and information communication will greatly improve the efficiency of vegetation restoration. In this study, a unified platform was constructed to test four activation functions (ReLU, SiLU, GELU, and Mish) under the same conditions, based on research on different neural network functions and their application in the YOLOv5 model. Through analysis of the experimental results, it was found that the YOLOv5 model with the SiLU activation function performed well in plant disease recognition tasks and showed outstanding performance in multi-target recognition.
A Multi-Disciplinary Arctic Research Facility: From the Koldewey - Rabot - Corbel - Stations to the AWI-IPEV Research Base on Spitsbergen

around the same time, but began regular operation in 1917. As the northernmost well equipped settlement of several hundred people, Ny-Ålesund soon became attractive for polar researchers, who tried to reach the North Pole. Most famous are Amundsen and Nobile, who used airships, which were stationed in Ny-Ålesund in 1926 ad 1928. Scientific activities after World War II began, when during the International Geophysical Year a French and a German station were set up east of Ny-Ålesund to observe the glaciers of the Kongsfjord, mainly the Kings Glacier (Kongsbreen) and Lovenbreen. Coal mining stopped in Ny-Ålesund in 1963, after deadly accidents in the mines. Slowly the settlement developed into the international research facility of today. A big satellite receiving station of ESRO (predecessor of today’s ESA) was the first big installation in 1966, and Norwegian installations followed, namely the establishment of a station by the Norwegian Polar Institute in 1968. With the end of the Cold War in the late 1980s, many nations became interested in Arctic research again. Until today institutions from 10 nations have established themselves in Ny-Ålesund, but only three of them are operative all year round. One of them, the German Koldewey Station, named after Carl Koldewey, captain of the first
A Reinforcement Learning-Based Approach to Improve the Trajectory Generalization Performance of DMP

Dynamic Motion Primitives (DMP) is a trajectory coding method based on imitation learning, which achieves a wide application owing to its robustness and generalization ability. However, the generalization of DMP in some special cases is not very satisfactory. For instance, when the target point changes, the similarity of the middle part of the trajectory is also greatly affected. Moreover, scaling the trajectory of the DMP is often followed by a reduction in endpoint accuracy. To solve the above problems, the following work is carried out in this paper: First, we reviewed the basic DMP framework and analyzed its worthy improvements. Second, the Pearson correlation coefficient is introduced to quantitatively assess the similarity of generalized trajectories. Next, a model-free, sampling-based reinforcement learning framework Path Integrals (PI2) is designed for the optimization of generalized trajectories, where the Gaussian weights of the DMP are iteratively updated probabilistically. Finally, the feasibility of the method in this paper is verified by simulation in the case of target variation and trajectory scaling.
Prescribed-Time Control for DC-DC Buck Converters

This paper proposes the prescribed-time control strategy for DC-DC buck converters. First, DC-DC buck converters are modeled as a nonlinear system in lower-triangular form. A state-feedback controller is then designed for such system by using the backstepping method and a prescribed-time adjustment function. It is proved that the designed controller can well stabilize the closed-loop system to ensure all the signals prescribed-time stability, and render the output voltage error converge to the origin in an arbitrarily prescribed time. Finally, the simulation results are provided to demonstrate the effectiveness of the designed scheme.
AUC Maximization for Flood Attack Detection on MQTT with Imbalanced Dataset

Several studies have conducted countermeasures against message queue telemetry transport (MQTT) attacks. However, existing research needs to examine further the imbalanced dataset that occurs in flood attacks, where these conditions can affect attack detection performance. Our research aim is to utilize the area under curve (AUC) maximization for flood attack detection on MQTT with imbalanced datasets. We leverage datasets from existing papers for flood attacks and legitimate network data. Then we utilize the AUC maximization methodology as follows: we perform parameter tuning of three machine learning methods: gradient boosting, naive Bayes, and k-nearest neighbor (KNN). Then we measure the performance of each model with the receiver operating curve (ROC) and AUC. We repeated these steps by performing parameter tuning and feature selection until the optimum AUC was found. Our test results show that the f1-score test can be used in the majority class but gives poor results in the minority class. At the same time, AUC shows that gradient boosting is the optimum detection method compared to naive Bayes and KNN, with AUCs of 0.96, 0.86, and 0.77, respectively. Finally, testing with feature selection shows that feature selection can increase AUC and decrease training and testing time by reducing features from 33 to two, revealing that the prominent features in a flood attack are tcp.len and tcp. time_delta.
Gene therapy using optimized LentiHBB T87Q vector in two patients with transfusion dependent β-thalassemia

Background: Gene therapy is gradually becoming recognized as a possibly curative therapeutic strategy for transfusion-dependent β-thalassemia (TDT). Gene therapy addresses the problem of donor scarcity through the application of autologous hematopoietic stem cells (HSCs), which also can reduce the risks that accompany allogeneic HSC transplantation. When using gene addition strategy, lentiviral vector is critical for the efficacy and safety of β-thalassemia gene therapy. In our preclinical studies, LentiHBB T87Q vector with optimized backbone was developed to efficiently restore − globin expression in HSCs-derived erythroblasts of TDT patients with minimal risk of tumorigenesis. Here, we presented the clinical trial results of gene therapy using LentiHBB T87Q vector in two TDT patients.
Quench dynamics of one dimensional multi-component quantum gases

OF THE DISSERTATION Quench Dynamics of One Dimensional Multi-component Quantum Gases by Huijie Guan Dissertation Director: Natan Andrei This thesis studies the quench dynamics of strongly correlated quantum systems described by one dimensional integrable Hamiltonians. We develop the Yudson approach for such systems in terms of contour integrals allowing the expansion of arbitrary states in terms of the Bethe Ansatz eigenstates which in turn provides the means to calculate the time evolution of such observables as densities or noise correlations from any initial states. As a motivation to the present work, I present the state of art ultracold atom techniques and fundamental questions related to the quench dynamics. Then various Bethe ansatz solutions are discussed. This is followed by an introduction to the Yudson approach where advantages and difficulties are listed. As applications of the Yudson approach, I studied the nonequilibrium dynamics of the Lieb-Liniger model,(bosonic) Gaudin-Yang model, quenched from a Mott state with a superfluid Hamiltonian. Integration contours are specified for various models and different interactions. It is shown that the Yudson approach incorporates all free states and bound states into the contour, separating them apart sheds light on the validity of the String hypothesis. The result is affirmative for Lieb-Linger model, but not for the Gaudin-Yang model. Our calculations for the density and correlation shows that for interacting system, if the pre-quench state has negligible overlap among the particles, the system retains this feature after the quench. Particularly, normalized noise function c(z,−z) at the origin shows different stages. Shortly after the quench, the sign are different for attractive and repulsive interaction. Then they both quickly approaches the value where possibility to find both particles equals zero. Then the value increases gradually for attractive models, while c(0, 0) remains small for repulsive systems.
Reflectance spectroscopy: Quantitative analysis techniques for remote sensing applications

Several methods for the analysis of remotely sensed reflectance data are compared, including empirical methods and scattering theories, both of which are important for solving remote sensing problems. The concept of the photon mean optical path length and the implications for use in modeling reflectance spectra are presented. It is shown that the mean optical path length in a particulate surface is in rough inverse proportion to the square root of the absorption coefficient. Thus, the stronger absorber a material is, the less photons will penetrate into the surface. The concept of apparent absorbance (-In reflectance) is presented, and it is shown that absorption bands, which are Gaussian in shape when plotted as absorption coefficient (true absorbance) versus photon energy, are also Gaussians in apparent absorbance. However, the Gaussians in apparent absorbance have a smaller intensity and a width which is a factor of √2 larger. An apparent continuum in a reflectance spectrum is modeled as a mathematical function used to isolate a particular absorption feature for analysis. It is shown that a continuum should be removed by dividing it into the reflectance spectrum or subtracting it from the apparent absorbance and that the fitting of Gaussians to absorption features should be done using apparent absorbance versus photon energy. Kubelka-Munk theory is only valid for materials with small total absorption and for bihemispherical reflectance, which are rarely encountered in geologic remote sensing. It is shown that the recently advocated bidirectional reflectance theories have the potential for use in deriving mineral abundance from a reflectance spectrum.
Research on Optimal Power Capture Strategy of Dual-port Direct-drive Wave Energy Converters

The single-port direct-drive wave energy converters (SPDDWECs), consisted of single linear electrical generator, has widely applied oceanic wave energy converters (WECs) system. In order to overcome the discontinuity of electrical energy transmission in SPDDWECs due to the intermittent nature of waves. This paper presents a dual-port direct-drive wave energy converters (DPDDWECs), consists of dual-translators connected by electromagnetic springs. The analysis shows that the performance of the DPDDWECs much depend on the motion relationship between the dual-translators and the optimal frequency point (OFP). For this reasons, the motion relationship is analyzed according to the dynamics. By controlling the electromagnetic spring coefficient, the DPDDWECs can also output power continuously when the vertical velocity of the wave remains zero. The OFP is selected according to the two-degree-of-freedom optimal power principle. Simulation results show that the OFP can capture more power compared with the spectral peak frequency point which used in most researches.
Selection of Optimal Rotor Geometry for PM-Assisted Synchronous Reluctance Motor used in EV Applications

Electric vehicular applications require electric motor design with high torque density, high efficiency and moderate power factor and moderate cost. PMSM is widely used and fulfill all the requirements for best candidate to be employed in EVs. Due to high requirement of PMs in PMSM, high cost and monopoly of PM material availability, researchers and EV manufacturing industries are finding the new alternative. In PM assisted Synchronous Reluctance Motor (PMASynRM), less number of PMs are required in flux barriers. This will mainly improve the PF of PMASynRM against SynRM and also add the PM torque component with reluctance torque component. Here two different number of rotor poles are selected. First by fixing the four rotor poles, number of flux barriers is varied from two to four and performance of motor is investigated through FEA. Then number of rotor poles increased from four to eight with flux barriers varied from two to four and performance of motor is investigated through FEA. The purpose of comparison of all six geometries of PMASynRM for all possible performance parameters is to develop the optimal design with best suitable combination of rotor poles and number of flux barriers. Although selection of geometry is also depend on manufacturability and performance requirements for EVs. The prototype for the PMASynRM is under developing stage to validate the investigation of performance parameters.
Distributed Optimal Consensus of Multiple Euler-Lagrange Systems Using only Relative Position Measurements Under a Directed Graph

In this paper, the distributed optimal consensus problem of multiple Euler-Lagrange systems under a directed graph using only relative position measurements is investigated. We adopt a model reference adaptive consensus strategy to assign each agent a reference to track, which transforms the consensus problem into a trajectory tracking problem. The proposed consensus algorithm only requires the relative position information and only the local gradient information is used. It is shown that the agents can achieve consensus to the optimal position. The numerical simulations are performed to show the effectiveness of the theoretical results.
Keyword Analysis of Artificial Intelligence Education Policy in South Korea

The purpose of this study is to analyze the direction of the Artificial Intelligence (AI) education policy announced by the South Korean government and how key tasks are reflected in detailed policies to find implications. The study first identified three major policy directions as found in the South Korean government’s publication on ‘Education Policy Directions and Core Tasks in the Era of AI’ and conducted a text mining analysis on government policy materials, press releases, and reports related to AI education from 2020 to 2022 by extracting keywords. According to our topic analysis results, various AI education contents were being developed both before and after the national curriculum revision. Moreover, our research indicates that the advancement and activation of AI education policies will take place to smoothly implement the curriculum in classrooms, and additional policies and challenges will emerge when the new curriculum comes into effect in 2025.
A YOLOv3-based Framework for Weed Detection in Agricultural Fields

Large-scale spraying of pesticides to remove weeds greatly pollutes the environment and poses a hidden risk to crop safety. The actual weeds will change constantly according to the influence of weather changes and other effects, which puts great demands on the real-time of the algorithm, therefore, there are still great shortcomings for the detection of weeds in farmland in practical applications. To meet this challenge, this paper uses the YOLOv3 network model as the core to achieve the target detection of weeds in agricultural fields with crops as the detection target. Through the practical application scenario of crop weed detection, we propose a YOLOv3-based weed detection framework with real-time as the basic and crop as the detection target, and train an improved YOLOv3 network model with higher recognition accuracy and faster recognition speed. The improved YOLOv3 network model is trained with higher recognition accuracy and faster recognition speed. The target detection effect of our target detection model is verified by comparing it with other well-performing target detection models. The experimental results show that the YOLOv3-based weed detection framework designed in this paper can meet the requirements in practical applications, and the detection speed is higher than other convolutional neural network target detection algorithms.
Estimation de l'évapotranspiration en région sahélienne : synthèse des connaissances et évaluation de modélisations (SISVAT, Ritchie) ; application à la zone d'HAPEX-Sahel (Niger)

En region sahelienne, l'estimation des divers composants evaporatifs (evaporation du sol, transpiration des plantes) demeure primordiale, que ce soit pour la connaissance du bilan hydrique au niveau de la surface, ou pour le phenomene de retroaction que ces flux entrainent sur le regime des pluies, via l'atmosphere. Dix ans apres l'experience HAPEX-Sahel (Niger, 1992), l'impact du changement climatique ou d'occupation des sols sur les flux evaporatifs reste ouvert dans la mesure ou les mesures sont limitees et aucun modele n'a ete teste sur l'ensemble des couverts vegetaux (champs de mil, zones de jachere, brousse tigree), ni sur un cycle hydrologique complet. Dans cet objectif, deux types de modelisation ont ete proposes et evalues. Dans un premier temps, le schema de surface SISVAT, a base physique, a ete teste sur les principaux couverts (mil et jachere) et la sensibilite de ses parametres (mesures et tabules) est discutee. Son couplage avec le modele atmospherique de mesoechelle MAR permettra bientot d'etudier la retroaction de la vegetation vers l'atmosphere. Un modele simple conceptuel, base sur le modele de Ritchie, complete cette etude. Il permet de simuler de facon realiste le bilan hydrologique d'une annee complete pour tous les types de couverts (mil, jachere et brousse tigree) et pour differentes pluviometries.
Automated Detection of Cystitis in Ultrasound Images Using Deep Learning Techniques

The proposed method aims to estimate the urinary Bladder Wall Thickness (BWT) from ultrasound (US) images to detect cystitis. Our method proposes a novel deep learning algorithm that segments the Bladder Wall from the ultrasound images of the urinary bladder, following which feature extraction and classification are performed to categorize the images as presence or absence of cystitis. The proposed study focused on a CYSNET CNN (Convolutional Neural Network) model for detecting cystitis in the urinary bladder and compares its accuracy with a transfer learning-based pre-trained model like ResNet50 and state-of-the-art Vision Transformer. Among the total population studied (N=250), 125 subjects with cystitis and 125 normal subjects, were included. The bladder wall thickness of cystitis was segmented using the U-Net semantic segmentation model. Eight features constituting contour and thickness were extracted from the segmented bladder wall. The best five features were selected using the Univariate feature selection method based on ANOVA F statistics as the scoring scale. The selected five features were classified into cystitis and normal using three different Machine Learning (ML) Classifiers such as AdaBoost, RepTree, and NaÏve Bayes. Three different CYSNET models with varying convolution layers were developed to detect cystitis in ultrasound images. The performance of the CYSNET models is compared with the ML classifiers, ResNet 50 model, and Vision Transformer. The CYSNET model 3 outperformed with the classification accuracy of 95% compared to the Adaboost network (90%), ResNet50 model (88.7%) and Vision Transformer (92.1%). Hence, the developed CYSNET model could be used as a computer-aided diagnostic tool for the detection of cystitis in ultrasound images.