Miracle-dz/UniFAR-FTUs huggingface dataset:
Control of spiral wave dynamics in active media

This thesis deals with the dynamics and control of spiral waves in active media. Regarding the control, we have to distinguish between external forcing and feedback–mediated control. For control with external forcing, we used traveling wave modulation. Under traveling wave modulation, synchronized drift of the spiral wave core was predicted within a certain frequency range. We were able to verify this prediction with results from experiments with the light–sensitive Belousov–Zhabotinsky–Reaction (BZR) as well as with numerical results from simulations of the Oregonator and the FitzHugh– Nagumo–model. Additionally, we have shown that this is not only true for rigidly rotating but also for meandering spiral waves. For feedback–mediated control we used different control methods. On the one hand we used the distance between the spiral wave tip and a chosen point to generate a control force, using proportional feedback control (PFC) and time delay autosynchronization (TDAS). With these methods we stabilized unstable periodic orbits (UPOs), specifically the unstable rigidly rotating movement of a spiral wave tip within the meandering regime. These methods can be used to stabilize unstable branches in the bifurcation tree and in so doing, identify the characteristics of unstable solutions. We also studied the robustness of both methods regarding the appearance of control loop latencies (CLL), since in almost every real control system control loop latencies can be found. On the other hand, we present a feedback–mediated control method with which the spiral wave core in a two–dimensional medium can be moved along or parallel to a one–dimensional detector. Aside from our experimental and numerical studies, we elaborated a theory. In this theory, the movement of the spiral wave core can be described by an iterated map. Additionally, we investigated instabilities induced by high control amplitudes or longer control loop latencies. In our studies of the different control methods, we always compared theoretical, numerical and experimental results and especially in our experimental studies of spatio–temporal control methods, we have a new experiential setup for the light– sensitive BZR.
Cyber Security measures in Protection and Control IEDs

The electric power grids and power systems are critical infrastructure parts of our modern society and are ascertained by high demands on reliability and stability. At the heart of these intelligent grids we find specialized IT systems, such as substation automation systems. These systems are following standards and trends, as of which one of them is in particular Ethernet and TCP/IP based communication protocols. Evolving technologies like Ethernet and industry-specific standards such as IEC 61850 are enablers for information exchange that support not only higher reliability, but also important ensures interoperability between systems from different vendors. The paper describes the trends, security measures and provide recommendations in order to get cyber security measures in Protection and Control Intelligent Electronic Devices (IEDs).
Novel Insights on Nitric Oxide Synthase and NO Signaling in Ascidian Metamorphosis

Nitric oxide (NO) is a pivotal signaling molecule involved in a wide range of physiological and pathological processes. We investigated NOS/NO localization patterns during the different stages of larval development in the ascidia Ciona robusta and evidenced a specific and temporally controlled pattern. NOS/NO expression starts in the most anterior sensory structures of the early larva and progressively moves towards the caudal portion as larval development and metamorphosis proceeds. We here highlight the pattern of NOS/NO expression in the central and peripheral nervous system of Ciona larvae which precisely follows the progression of neural signals of the central pattern generator necessary for the control of the movements of the larva towards the substrate. This highly dynamic localization profile perfectly matches with the central role played by NO from the first phase of settlement induction to the next control of swimming behavior, adhesion to substrate and progressive tissue resorption and reorganization of metamorphosis itself.
Congenital Anomalies of Kidney and Urinary Tract (CAKUT)-Literature Review

Kidneys as well as urinary tracts are the major excretory organs of the human body and removes waste products of protein metabolism.They also remove surplus water and salts from the blood.Kidneys as well as urinary tracts also important for survival as they are crucial for preserving the fluid-electrolyte balance of the body tissue fluids.The inaccuracies in the embryogenesis of the outflow tractsalong with thekidneys are vulnerable to the environmental endargements and disturb the development throughout gestation.Mutations in the patient with CAKUT are responsible for the chronic renal diseases in the 1st three decades of life.Fetus dose not survive in severe cases of CAKUT as a result of non formation of kidneys and outflow tract, but in less severe cases there are more chances to survive and may identified in adulthood.Though the critical cases of CAKUT are infrequent, marginal nephron number is considerablyfrequent disorder exerting its effect on renal functions with increasing age. Congenital anomalies of kidney and urinary tract have a wide range ofabnormalities inclusive offatalagenesis of both the kidneys andasymptomatic ectopic kidneys.Hence patient with the solitary kidney should follow-up regularly for better and close monitoring. The clinical presentation of CAKUT along with its long term outcomes and epidemiology is covered in this review.With a comphrehensive knowledge of the roots of CAKUT and marginal nephron numbers, identification of preventive treatment and establishment of clinical guidelines for the patients can be begun.Comphrehension of relevant endangerments and changes in the epidemiological trends especially modified maternal factors and delivery services is vital for prompt detection and prediction of prognosis in the course of pregnancy due to subsequent morbidity and mortality as a result of the presence of CAKUT.
Clustering algorithm based open-circuit failures diagnosis for modular multilevel converters

Based on the abnormal change of capacitor voltage caused by the fault of modular multilevel converter (MMC) sub-module (SM) switching device, a fast fault detection and diagnosis method is proposed in this paper. To enable effective consistency evaluation of numerous SM voltages, the density-based spatial clustering of applications with noise (DBSCAN) algorithm, which is an ideal analyzing method for the clustering analysis of big data, is applied to SM failure diagnosis in this paper. The DBSCAN is used to analyze the capacitor voltages of all sub-modules in the bridge arm, so as to realize the rapid detection and location of faulty SMs at the same time. The proposed method can be applied to any fault types of faults, including single fault, multiple faults in the same bridge arm, and multiple faults in different bridge arms. This method is not affected by the uncertainty of system parameters, and at the same time, has stronger applicability and faster diagnostic rate than the traditional methods. Experimental results demonstrate the validity of the proposed diagnosis strategy.
Extraction of Olive Crown Based on UAV Visible Images and the U2-Net Deep Learning Model

Olive trees, which are planted widely in China, are economically significant. Timely and accurate acquisition of olive tree crown information is vital in monitoring olive tree growth and accurately predicting its fruit yield. The advent of unmanned aerial vehicles (UAVs) and deep learning (DL) provides an opportunity for rapid monitoring parameters of the olive tree crown. In this study, we propose a method of automatically extracting olive crown information (crown number and area of olive tree), combining visible-light images captured by consumer UAV and a new deep learning model, U2-Net, with a deeply nested structure. Firstly, a data set of an olive tree crown (OTC) images was constructed, which was further processed by the ESRGAN model to enhance the image resolution and was augmented (geometric transformation and spectral transformation) to enlarge the data set to increase the generalization ability of the model. Secondly, four typical subareas (A–D) in the study area were selected to evaluate the performance of the U2-Net model in olive crown extraction in different scenarios, and the U2-Net model was compared with three current mainstream deep learning models (i.e., HRNet, U-Net, and DeepLabv3+) in remote sensing image segmentation effect. The results showed that the U2-Net model achieved high accuracy in the extraction of tree crown numbers in the four subareas with a mean of intersection over union (IoU), overall accuracy (OA), and F1-Score of 92.27%, 95.19%, and 95.95%, respectively. Compared with the other three models, the IoU, OA, and F1-Score of the U2-Net model increased by 14.03–23.97 percentage points, 7.57–12.85 percentage points, and 8.15–14.78 percentage points, respectively. In addition, the U2-Net model had a high consistency between the predicted and measured area of the olive crown, and compared with the other three deep learning models, it had a lower error rate with a root mean squared error (RMSE) of 4.78, magnitude of relative error (MRE) of 14.27%, and a coefficient of determination (R2) higher than 0.93 in all four subareas, suggesting that the U2-Net model extracted the best crown profile integrity and was most consistent with the actual situation. This study indicates that the method combining UVA RGB images with the U2-Net model can provide a highly accurate and robust extraction result for olive tree crowns and is helpful in the dynamic monitoring and management of orchard trees.
Cascade Control for a Rigid-Flexible Two-link Robot using Sliding Modes

Abstract The dynamical model of a two-link planar robot with one rigid-link, one flexible forearm and a payload attached to its tip, is represented in a cascade-like fashion using a convenient change of state coordinates which is found by observing the structure of the inertia matrix of the system model. The new system representation makes possible to devise a way to stabilize the overall system by designing a suitable trajectory for the angular positions of the joint shafts, in terms of the new state coordinates. These trajectories of the joints are used as virtual control signals that allow the controlling of the end effector position. Some simulation results are presented in which sliding mode control is employed to successfully perform regulation and trajectory tracking of the end effector.
Estimation of the canopy height model from multispectral satellite imagery with convolutional neural networks

The canopy height model (CHM) is a representation of the height of the top of vegetation from the surrounding ground level. It is crucial for the extraction of various forest characteristics, for instance, timber stock estimations and forest growth measurements. There are different ways of obtaining the vegetation height, such as through ground-based observations or the interpretation of remote sensing images. The severe downside of field measurement is its cost and acquisition difficulty. Therefore, utilizing remote sensing data is, in many cases, preferable. The enormous advances in computer vision during the previous decades have provided various methods of satellite imagery analysis. In this work, we developed the canopy height evaluation workflow using only RGB and NIR (near-infrared) bands of a very high spatial resolution (investigated on WorldView-2 satellite bands). Leveraging typical data from airplane-based LiDAR (Light Detection and Ranging), we trained a deep neural network to predict the vegetation height. The provided approach is less expensive than the commonly used drone measurements, and the predictions have a higher spatial resolution (less than 5 m) than the vast majority of studies using satellite data (usually more than 30 m). The experiments, which were conducted in Russian boreal forests, demonstrated a strong correlation between the prediction and LiDAR-derived measurements. Moreover, we tested the generated CHM as a supplementary feature in the species classification task. Among different input data combinations and training approaches, we achieved the mean absolute error equal to 2.4 m using U-Net with Inception-ResNet-v2 encoder, high-resolution RGB image, near-infrared band, and ArcticDEM. The obtained results show promising opportunities for advanced forestry analysis and management. We also developed the easy-to-use open-access solution for solving these tasks based on the approaches discussed in the study cloud-free composite orthophotomap provided by mapbox via tile-based map service.
Evaluation of Post-weaning Efficiency in Nellore-Angus Crossbred Steers through Model Predicted Residual Consumption

The objective of this work was to evaluate efficiency traits of Nellore-Angus crossbred steers (n = 349) on feed. Steers were fed a grain-based diet beginning at approximately 12 months of age for an average of 140 days. Contemporary groups were born in the fall or spring of 2003 through 2007 in full-sibling embryo transfer families or half-sibling families all sired by the same bulls. Individual intake was measured and weights were recorded to permit calculation of average daily gain. Residual feed intake (RFI) was estimated as the residual of models employing regressions on metabolic mid-test weight and ADG. An additional efficiency metric was also constructed and evaluated: model predicted residual consumption (MPRC). Mixed linear models were used to analyze daily dry matter intake, average daily gain (ADG), metabolic mid-test weight, RFI, and MPRC. Large positive associations of DMI with MPRC and RFI were identified along with low positive associations between metabolic mid-weight with ADG and MPRC. Genome wide association analysis revealed 5 regions associated with DMI, but none for the other traits analyzed. Residual feed intake values varied greatly between the contemporary group value and the overall value for the steers, showing the calculation’s dependency on the reference population. However, MPRC as based upon a standardized population, did not fluctuate. More selection phenotypes and strategies are needed for large-scale improvements in global beef cattle production sustainability. The stability of the MPRC metric could be beneficial for future feed efficiency research across multiple and diverse contemporary groups, and diverse production environments.
Instance-Aware Semantic Segmentation of Road Furniture in Mobile Laser Scanning Data

In this paper, we present an improved framework for the instance-aware semantic segmentation of road furniture in mobile laser scanning data. In our framework, we first detect road furniture from mobile laser scanning point clouds. Then we decompose the detected pieces of road furniture into poles and their attached components, and extract the instance information of the components with different features. Most importantly, we classify the components into different categories by combining a classifier and a probabilistic graphic model named DenseCRF, which is the major contribution of this paper. For the classification of the components using DenseCRF, the unary potentials and the pairwise potentials are first obtained. The unary potentials are obtained from the classifier which takes the instance information of components as the input. The pairwise potentials are calculated considering contextual relations between components. By utilising DenseCRF, the contextual consistency of components is preserved, and the performance is significantly improved compared to our previous work. We collect three datasets to test our framework, and compare the classification performances of six different classifiers with and without DenseCRF. The combination of random forest with DenseCRF outperforms the other methods and achieves high overall accuracies of 83.7%, 96.4% and 95.3% in these three datasets. Experimental results demonstrate that our framework reliably assigns both semantic information and instance information for mobile laser scanning point clouds of road furniture.
Emotions in learning, teaching and leadership: Asian perspectives

Chinese ancient classic Analects have recorded three stages of learning proposed by Confucius “They who know the truth are not equal to those who love it, and they who love it are not equal to those who delight in it”. It means that positive emotion in education is paramount, and enjoyment in education is inspiring. The first sentence in the foreword of this book, “Education is by and of, its nature an emotional practice” (p. xvii), shares the same meaning with this philosophical dictum. Emotions have cultural specifications. After the pioneering publishment Emotion in Education (Pekrun & Schutz, 2007) originating from Western background, it is instructive for Asian educational researchers and practitioners to read this volume originating from Asia, especially east Asian regions nowadays. This volume collects the latest researches on the emotion of students, teachers, and leaders in Asian perspectives and reflects the efforts to understand emotion in another community with special cultures and traditions. It presents a vivid picture of education with the embellishment and rendering of emotions in Asia. The title of this book suggested its structure of contents. The entire volume comprises three parts and covers the empirical articles and reviews about the emotion of students, teachers, and school leaders, respectively. What is worth noting is that editors have described each chapter compendiously in the introduction, which is convenient for readers who especially care about some specific topics of emotions in education.
Site-Specific Allometric Equations for Biomass Prediction of Moringa oleifera Lam. in the Northern Zone of Cameroon

Aims: Moringa oleifera is a species with multivariate uses. Despite its socio-economic importance in the northern part of Cameroon, it remains undervalued. Understanding its contribution to the fight against climate change requires precise quantification of its biomass. However, there is a lack of site-specific allometric equations for estimating this biomass. The development of site-specific allometric equations of M.oleifera is therefore imperative to facilitate this effort. Study Design: To achieve this objective, a sample of fourteen trees of M.oleifera was taken in from the two areas and divided into diameter classes between 5 and 13 cm. Place and Duration of Study: Sample: Agroecological zones of High guinean savannahs and Sudano-sahelian zone of Cameroon between April and July 2021. Methodology: The diameter at breast height of these trees and their height were measured. After tree cutting, biomass of compartments of leaves, branches, trunks and roots were determined after drying and weighing. Various allometric equations between biomasses and two parameters of the tree (the diameter and the height) were tested. The adjusted coefficient of determination (R2adj), the residual standard error (RSE) and the Akaike Information Criteria were used for choosing the best models. Results: The different analyses showed that diameter at breast height (DBH) is the variable that offers the best correlation for global above-ground (AGB) and below-ground (BGB) biomass in contrast to the other compartments. Thus, the best models for predicting total above-ground biomass of M. oleifera are -3.653-0.250lnD+5.119lnH (Sudano-Sahelian), -3.916 + 2.982lnD (High guinean Savannahs), -2.340 + 2.117lnD (Global) and finally for roots -6.426 + 2.182lnD. Conclusion: These mathematical models could help in estimating the biomass of Moringa oleifera in agroforestry systems under the ecological conditions of Cameroon.
Increasing community environmental awareness, participation in conservation, and livelihood enhancement through tourism

ABSTRACT In line with social exchange theory, this case study explores how a previously indifferent and unsympathetic local community participates in the conservation of Touran National Park through community-based tourism. An in-depth analysis of local community members, local authorities, and NGOs revealed that due to changes in society's socio-cultural structure, the community’s awareness of conservation has significantly increased and resulted in active participation in natural and cultural heritage. The findings provide an alternative to social exchange theory, suggesting that benefits should generally outweigh human social interaction and behaviour calculus costs. Furthermore, rural youth and women have become much more prominent despite many public participation dilemmas and rigid government structures. Finally, theoretical and practical implications for future research are discussed.
Synthesis and antimicrobial evaluation of novel tetracain thiosemicarbazide derivatives

Abstract In this study, new Tetracaine thiosemicarbazide derivatives were designed, synthesized, and evaluated for their antimicrobial activity. These compounds (2a-l) were characterized using spectroscopic (1H‐13C NMR, FT-IR and HR-MS) methods and tested for their antimicrobial activity against various Gram-positive and Gram-negative bacterial strains (Staphylococcus aureus ATCC 29213: Enterococcus faecium ATCC 29212; Escherichia coli ATCC 25922; Pseudomonas aeruginosa ATCC 27853), and fungus (Candida albicans ATCC 10231). The microbroth dilution method was utilized for the determination of the minimum inhibitory concentration (MIC) values of novel molecules. The antimicrobial activities of the molecules were found to have a wide range of MIC values with 4–256 μg/mL. Among the synthesized molecules, compound 2 h (2-[4-(butylamine)benzoyl]-N-(3,5-bistrifluoromethylphenyl)hydrazine-1-carbothioamide) showed the best MIC value for 4 (μg/mL) to S. aeurus isolate and 8 (μg/mL) to C. albicans, respectively. Compounds 2e (2-[4-(butylamine)benzoyl]-N-(2-fluorophenyl)hydrazine-1-carbothioamide) and 2f (2-[4-(butylamine)benzoyl]-N-(4-nitrophenyl)hydrazine-1-carbothioamide) showed moderate activity to C. albicans and S. aeurus with a 16 (μg/mL) MIC value. Graphical Abstract
Hybrid Multi-Vector Modulated Model Predictive Control Strategy for Voltage Source Inverters Based on a New Visualization Analysis Method

Conventional model predictive control (MPC) strategy for voltage source inverters has large current ripple since only one voltage vector is applied in each control cycle. Although the multi-vector modulated MPC strategy can reduce the current ripple, its lack of theoretical basis makes its optimality questionable. To solve this problem, a new visualization analysis method is proposed in this article to evaluate the effectiveness of the conventional multi-vector modulated MPC strategy, based on which a new hybrid multi-vector modulated MPC strategy is further presented to improve the current control performance. First, the principle of the proposed visualization analysis method is introduced, based on which it is verified that the conventional multi-vector modulated MPC has better control performance than the single-vector MPC. Second, based on the proposed visualization analysis method, it is also found that both the conventional double-vector and triple-vector modulated MPC strategies cannot achieve optimal control. Thus, a new hybrid multi-vector modulated MPC strategy is further proposed. By selecting an optimal voltage vector from 18 alternative voltage vector combinations, the current control performance is improved. Detailed comparative experimental results based on experimental platform and StarSim hardware-in-the-loop platform validate the effectiveness of the proposed hybrid multi-vector modulated MPC strategy.