Miracle-dz/UniFAR-FTUs huggingface dataset:
Power Quality Improvement by Optimum Gain controlled SAPF

Now-a-days active power filters (APFs) became popular for harmonics mitigation and compensation of reactive power generated by non-linear power electronics load devices. This paper illustrates modeling and simulation of three-phase and three-wire shunt active power filters (SAPF). The active power filters (APF) model consist a three-leg voltage source inverter (VSI) having a common DC-link capacitor. For switching pulse generation of VSI hysteresis band pulse width modulation (PWM) based current controller is used. Reference current generation is important part of controlling of APFs, since any inaccuracy in current references generation results incorrect compensation. Instantaneous reactive power (IRPT) theory is used for current references estimation for various manually selected gains of PI-controller and their results are analyzed to get the optimum gains for minimum harmonics & Total Harmonics Distortion (THD). This technique is implemented in MATLAB/Simulink environment & harmonics determining parameter, Total Harmonics Distortion (THD) is calculated and compared for different manually selected values of gains of PI-controller.
Polarimetric SAR Calibration and Processing Tool V1.0: A Tool for Polarimetric Calibration and Processing of SAR Data

Polarimetric SAR (PolSAR) has been widely used as an active imaging remote sensing technique to characterize manmade and natural features. The PolSAR data needs to be corrected from the polarimetric distortions to minimize the ambiguities for the accurate information retrieval of scatterers. The present work is focused on the minimization of system-induced and atmosphere-induced errors from PolSAR data. A new software Polarimetric SAR Calibration & Processing Tool v1.0 (PSCP v1.0) has been introduced for processing of fully polarimetric and hybrid polarimetric SAR data. The PSCP v1.0, a software tool will be available for the scientific community to calibrate and process the fully polarimetric and the hybrid polarimetric space borne and airborne SAR data of current and future missions of ISRO, India and also for other space agencies.
Optimal-tuned Parameters for ℒ1 Adaptive Control

Disturbance and noise exerted to the systems are two challenging problems in both classical and modern control approaches due to their unknown nature. While there are different theories to tackle disturbance and noise problems, ℒ1adaptive control theory is a promising approach to guarantee the robust performance of the system. In this paper, ℒ1control theory with output feedback of the system is presented. Then, an inverted pendulum system is considered to implement the ℒ1controller in the presence of the disturbance applied to the actuator of the pendulum. Finally, control parameters of the system are optimized with respect to the output and control effort of the system. Results of the simulation of the optimal-tuned ℒ1controller for the inverted pendulum system is presented and discussed.
SenGen: A Two-Phase Dynamic Simulation and Toolbox of an Indoor Mobile Wireless Sensor Network for Sensor Monitoring and Dataset Generation

A Mobile Wireless Sensor Network (MWSN) is a network of mobile sensor nodes that are spatially separated in an open or closed space, which work altogether to sense various system environmental and physical parameters. The state-of-the art is full of approaches for modelling WSNs and MWSNs using different simulation tools and programming languages. Such models require the system expert interference to change the simulated model itself whenever any change is required. Without this interference or having knowledge of the simulated system, these models tend to generate fixed-case sensor data and lack the dynamicity and the ability for further user-specific changes at run-time. In this paper, a two-phase dynamic simulation toolbox -so-called 'SenGen'- is presented and tested, where a full simulation of an indoor MWSN system is established using Simulink. Then a Graphical User Interface (GUI) is created with MATLAB, to overall perform as a dynamic toolbox for sensor data generation in MWSNs.
Identifying Influential Spreaders Based on Adaptive Weighted Link Model

Identifying influential spreaders in complex networks is crucial in understanding, controlling and accelerating spreading processes for diseases, information, innovations, behaviors, and so on. We proposed a semi-local-information-based algorithm named the adaptive weighted link model (AWLM), which classifies the links in the subgraph made up of the second-order neighbors of nodes and gives them different weights adaptively. The adaptive weighted link model is completely depends on the semi-local topological structure and thus can be calculated not only faster but also under the case where the global topology is not known, especially when the network is sparse, the time complexity is approximate linear. Empirical analyses of the Susceptible-Infected-Recovered (SIR) spreading dynamics on ten real networks show that the adaptive weighted link model always perform the best in comparison with well-known state-of-the-art methods.
A Comparative study of Lung Cancer Detection and Classification approaches in CT images

Lung disease is a genuine medical problem. In India there are roughly 70,275 individual cases in every year are determined to have lung malignancy. However, early identification and treatment can increase the survival rate. Usually Computed Tomography scan imaging is used in the medical field because of its high clarity and low noise. Only CT scans cannot give proper interpretation to radiologist and the medical practitioner, therefore the Computer Aided Diagnosis system will be extremely useful for radiologists to detect the cancer precisely. Many Computer aided system using image processing and Machine learning has been designed. In this survey various segmentation, feature extraction and classification techniques are considered such as Artificial Neural Network, Convolutional Neural Network, SVM, Gray level co-occurrence matrix, Discrete wavelet transform and many more. We observed that the SVM classifier achieved 96% accuracy, ANN achieved 99% accuracy, CNN achieved 94% accuracy and DNN achieved 97% accuracy.
Holistic Consideration and Optimization of Signal Processing on a Resolver-based Rotor Position Sensor Systems in Electric Drive Trains

Rotor position sensor systems based on resolvers are state-of-the-art in electric drive train applications, e.g. for permanent magnet synchronous motors, due to their robustness and accuracy. However, the effort for signal processing in terms of angle determination has a great complexity. Electric powertrains in automotive applications make high demands on the quality of control, which requires a precise detection of the current motor shaft position and speed. Different types of influences, like sensor signal cables, interfaces, sensor signal quantization, conditioning (e.g. filtering) and processing, cause error accumulation that interacts with the general mechanical-based disturbing factors, e.g. displacements, shock, vibrations and temperature influences. This work introduces a holistic view of the sensor signal measurement chain in terms of analysis of signal processing and error characteristics, exemplary shown on a state-of-the-art resolver system of an electric powertrain architecture. The second emphasis of this work investigates the mechanisms of signal processing and the signal processing - based effects that provoke additional errors to the measured rotor position information. The methods developed in the scope of this work are based on both, simulations of the entire electric drive train sensor system and verifications under realistic conditions by use of a highly accurate reference system on a specifically developed position sensor system test bench. The developed methods support measures of optimization to reduce the angular rotor position error of the entire sensor system based on an integration of computational and experimental investigations. As a result, causes and consequences of signal processing characteristics are pointed, which provides fundamental knowledge about the different contributing factors in terms of rotor position distortion. In this way, an approach is introduced that enables improvement and optimization of the resolver-based rotor position sensor system and thus the overall quality of control of electric traction motors even in the early design phase of electric drivetrain systems.
Localization of an indoor mobile robot using decentralized data fusion

An important problem in navigation of a mobile robot is localization. The robot can estimate its location in a predefined environment by combining the information obtained from its sensors. There are various methods to combine the information obtained from sensors and the models used to estimate the position and velocity of the robot. In this paper, a decentralized data fusion algorithm is used, where the sensors are used in several independent estimation loops. Then, the estimates of these loops are exchanged to improve the accuracy of the position estimates. The sensors used in this study consist of a laser scanner, Kinect depth sensor, encoder and IMU, which are divided into two local loops. The algorithm was implemented in a real environment on a robot built at the Mechatronics Laboratory of the Department of Mechanical Engineering, Yazd University. It is observed that the decentralized estimates of the two loops track the real trajectory of the robot satisfactorily. It is observed that the precision of the decentralized approach is somewhat less than the accuracy of the centralized data fusion, since different kinematic models are used in the two loops and also due to asynchrony of the sensors data. However, the decentralized approach entails advantages such as robustness of estimation, possibility of error crosschecking and sensor failure detection.
Distributed optimal control for traffic networks with fog computing

This paper presents a distributed optimization strategy for large-scale traffic network based on fog computing. Different from the traditional cloud-based centralized optimization strategy, the fog-based distributed optimization strategy distributes its computing tasks to individual sub-processors, thus significantly reducing computation time. A traffic model is built and a series of communication rules between subsystems are set to ensure that the entire transportation network can be globally optimized while the subsystem is achieving its local optimization. Finally, this paper numerically simulates the operation of the traffic network by mixed-Integer programming, also, compares the advantages and disadvantages of the two optimization strategies.
Facile fabrication of Mn2+-doped ZnO photocatalysts by electrospinning

In this study, a high-efficiency photocatalyst was synthesized by Mn2+-doped ZnO nanofibres (NFs) fabricated by facile electrospinning and a following annealing process, in which Mn2+ successes incorporate to ZnO NFs lattice without changing any morphology and crystalline structure of ZnO. The photodegradation properties of ZnO loading with different concentrations of Mn2+ (5, 10, 15 and 50 at%) were investigated. The 50% MnO–ZnO composite owns excellent active photocatalytic performance (quantum efficiency up to 7.57%) compared to pure ZnO (0.16%) under visible light and can be considered as an efficient visible light photocatalyst material. We systematically analysed its catalytic mechanism and found that the enhancement belongs to the Mn doping effect and the phase junction between MnO and ZnO. The dominant mechanism of Mn doping leads to the presence of impurity levels in the band gap of ZnO, narrowing the optical band gap of ZnO. In addition, doped Mn2+ ions can be used as electron traps that inhibit the recombination process and promote electron–hole pair separation. In summary, this paper provides a convenient method for fabricating highly efficient visible light photocatalysts using controlled annealing.
Insufficient implementation of the IAP/APA guidelines on aetiology in acute pancreatitis: Is there a need for implementation managers in pancreatology?

Identifying the aetiology of acute pancreatitis (AP) on admission is crucially important in selecting the best and, in some cases, the most specific therapy. For example, early endoscopic retrograde cholangiopancreatography is the best intervention in biliary AP with cholangitis, lipid-lowering therapy in hypertriglyceridaemia-induced AP, pancreatic stent placement in pancreatic duct obstruction-evoked AP or steroid therapy in autoimmune pancreatitis. Unfortunately, the cause of AP remains unclear in almost a quarter of all cases, a situation which could be due to either an insufficient diagnostic work-up or other unknown aetiological factors. In addition, the importance of clear information on aetiology is underlined by the fact that the cause of about 40% of fatal AP cases is idiopathic. It should also be emphasised that knowing the aetiology is not only crucial for the index AP, but also essential in preventing recurrent or chronic pancreatitis. Therefore, it is not surprising that the current IAP/APA guidelines suggest endoscopic ultrasound (EUS) or magnetic resonance cholangiopancreatography (MRCP) after a negative work-up for biliary AP. Moreover, a genetic test is recommended after a second episode of idiopathic pancreatitis in order to diagnose hereditary pancreatitis or to understand the genetic risks of AP. In this current issue of the United European Gastroenterology Journal, Hallensleben et al. investigate the diagnostic work-up and outcomes of ‘presumed’ idiopathic AP after the first attack of AP. Out of 191 first episodes of idiopathic AP patients, 176 underwent additional diagnosis, and the aetiological factor was identified in 64/176 (36%) patients. The underlying cause was mostly an occult biliary stone or neoplasm, which established an aetiological diagnosis, leading to a lower recurrence rate in these patients (15% vs. 43%). They concluded that additional diagnostic evaluations and stronger adherence to the current guidelines could identify the aetiology in one third of cases. The study was conducted brilliantly. However, the article did not share information on the level of diagnostic awareness (i.e. how many idiopathic cases were due to an insufficient onadmission diagnostic work-up) or adherence to the last part of recommendation A3 of the guidelines, which advises genetic testing after the second episode of idiopathic AP.
Regional Variation in Kemp’s Ridley Sea Turtle Diet Composition and Its Potential Relationship With Somatic Growth

Reptile growth is influenced by many ecological processes that can cumulatively give rise to divergent somatic growth rates within spatially structured populations. As somatic growth variation can strongly influence a species’ population dynamics, identifying proximate drivers can be critical to the conservation and management of protected species. Kemp’s ridley sea turtles (Lepidochelys kempii) exhibit spatial variation in both diet composition and growth, but whether components of this variation are linked has not been evaluated. Through an integration of skeletochronological and stable isotope analyses of stranded turtle humerus bones we characterized regional variation in Kemp’s ridley diet composition and potential relationships with somatic growth rates. Turtles were divided among five regions within the United States Gulf of Mexico (GoM) and Atlantic Coast based on location of stranding, and humerus bones were sampled for stable carbon (δ13C) and nitrogen (δ15N) isotope ratios. These data were combined with region-specific prey stable isotope data sourced from the primary literature into Bayesian stable isotope mixing models (MixSIAR) to estimate the proportional contribution of five prey groups (crustaceans, bivalves, gastropods, fish, and macroalgae/seagrass) to Kemp’s ridley diets. Our analysis revealed strong regional differences in mixing model-derived diet composition estimates that closely tracked published records of Kemp’s ridley diet. Invertebrates generally comprised the largest proportion (43.5–97.7%) of turtle diets. However, we also observed high proportional contributions of fish (42.6–43.1%) to western GoM turtle diets and macroalgae/seagrass (42.4–47.8%), or isotopically similar prey resources (e.g., tunicates), to eastern GoM turtle diets. Growth rates were poorly correlated with δ15N values—a proxy for trophic level—and diet composition estimates, suggesting that diet composition alone may not explain the regional differences in somatic growth observed in this species. This study highlights the value of complementary skeletal and isotopic analyses to understanding regional diet variation in sea turtles as well as the importance of continued collection of isotopic data for both sea turtles and their prey. These results also help fill critical knowledge gaps pertaining to the relationship between sea turtle foraging ecology and somatic growth dynamics, a topic of high importance to sea turtle conservation and management.
Vitamin B and Vitamin C Affect DNA Methylation and Amino Acid Metabolism in Mycobacterium bovis BCG

Vitamins are essential nutrients and key cofactors of enzymes that regulate cellular metabolism, and also activate the immune system. Recent studies have shown that vitamin B1 (VB1) and vitamin C (Vc) can inhibit Mycobacterium tuberculosis growth, but the precise mechanism is still not well understood. In the present study, we have used RNA-sequencing (RNA-seq), liquid chromatography coupled to mass spectrometry (LC-MS) and single-molecule real-time (SMRT) sequencing to analyze the transcriptional, metabolic and methylation profiles of Mycobacterium bovis BCG when treated with VB1 and Vc. Our results show that, after vitamin treatment, variant metabolites were mainly clustered in pathways related to amino acid metabolism. Treatment with both vitamins significantly up-regulated the gene encoding cysteine synthase A. Additionally, only BCG that was treated with VC showed m4c modifications. Genes harboring this methylation were up-regulated, suggesting that m4c methylation can promote gene transcription to some extent. Overall, this study contributes to the understanding of the effects of VB1 and VC, and suggests that these vitamins constitute potential anti-tuberculosis drugs.
Traditional Chinese Medicine and Gut Microbiome: Their Respective and Concert Effects on Healthcare

Advances in systems biology, particularly based on the omics approaches, have resulted in a paradigm shift in both traditional Chinese medicine (TCM) and the gut microbiome research. In line with this paradigm shift, the importance of TCM and gut microbiome in healthcare, as well as their interplay, has become clearer. Firstly, we briefly summarize the current status of three topics in this review: microbiome, TCM, and relationship of TCM and microbiome. Second, we focused on TCM's therapeutic effects and gut microbiome's mediation roles, including the relationships among diet, gut microbiome, and health care. Third, we have summarized some databases and tools to help understand the impact of TCM and gut microbiome on diagnosis and treatment at the molecular level. Finally, we introduce the effects of gut microbiome on TCM and host health, with two case studies, one on the metabolic effect of gut microbiome on TCM, and another on cancer treatment. In summary, we have reviewed the current status of the two components of healthcare: TCM and gut microbiome, as well as their concert effects. It is quite clear that as the holobiont, the maintenance of the health status of human would depend heavily on TCM, gut microbiome, and their combined effects.
Dissecting the Epigenetic Changes Induced by Non-Antipsychotic Mood Stabilizers on Schizophrenia and Affective Disorders: A Systematic Review

Background Epimutations secondary to gene-environment interactions have a key role in the pathophysiology of major psychiatric disorders. In vivo and in vitro evidence suggest that mood stabilizers can potentially reverse epigenetic deregulations found in patients with schizophrenia or mood disorders through mechanisms that are not yet fully understood. However, their activity on epigenetic processes has made them a research target for therapeutic approaches. Methods We conducted a comprehensive literature search of PubMed and EMBASE for studies investigating the specific epigenetic changes induced by non-antipsychotic mood stabilizers (valproate, lithium, lamotrigine, and carbamazepine) in animal models, human cell lines, or patients with schizophrenia, bipolar disorder, or major depressive disorder. Each paper was reviewed for the nature of research, the species and tissue examined, sample size, mood stabilizer, targeted gene, epigenetic changes found, and associated psychiatric disorder. Every article was appraised for quality using a modified published process and those who met a quality score of moderate or high were included. Results A total of 2,429 records were identified; 1,956 records remained after duplicates were removed and were screened via title, abstract and keywords; 129 records were selected for full-text screening and a remaining of 38 articles were included in the qualitative synthesis. Valproate and lithium were found to induce broader epigenetic changes through different mechanisms, mainly DNA demethylation and histones acetylation. There was less literature and hence smaller effects attributable to lamotrigine and carbamazepine could be associated overall with the small number of studies on these agents. Findings were congruent across sample types. Conclusions An advanced understanding of the specific epigenetic changes induced by classic mood stabilizers in patients with major psychiatric disorders will facilitate personalized interventions. Further related drug discovery should target the induction of selective chromatin remodeling and gene-specific expression effects.
Beyond Family: Modeling Non-hereditary Heart Diseases With Human Pluripotent Stem Cell-Derived Cardiomyocytes

Non-genetic cardiac pathologies develop as an aftermath of extracellular stress-conditions. Nevertheless, the response to pathological stimuli depends deeply on intracellular factors such as physiological state and complex genetic backgrounds. Without a thorough characterization of their in vitro phenotype, modeling of maladaptive hypertrophy, ischemia and reperfusion injury or diabetes in human pluripotent stem cell-derived cardiomyocytes (hPSC-CMs) has been more challenging than hereditary diseases with defined molecular causes. In past years, greater insights into hPSC-CM in vitro physiology and advancements in technological solutions and culture protocols have generated cell types displaying stress-responsive phenotypes reminiscent of in vivo pathological events, unlocking their application as a reductionist model of human cardiomyocytes, if not the adult human myocardium. Here, we provide an overview of the available literature of pathology models for cardiac non-genetic conditions employing healthy (or asymptomatic) hPSC-CMs. In terms of numbers of published articles, these models are significantly lagging behind monogenic diseases, which misrepresents the incidence of heart disease causes in the human population.