Miracle-dz/UniFAR-FTUs huggingface dataset:
Variable Pitch Active Disturbance Rejection Control of Wind Turbines Based on BP Neural Network PID

When wind speeds are above the rated speed of variable speed variable pitch wind turbines, pitch angles are changed to keep output powers and rotor speeds at their rated values. For wind turbines with nonlinear and complex structure, conventional PID variable pitch controller is difficult to achieve precise control. In this paper, a variable pitch controller combining back-propagation(BP) neural network with PID (BP-PID) is proposed. By real-time detecting the deviation of the rotor speeds, the BP neural network with self-learning and weighting coefficient correction capability is used to adjust the PID parameters online and further to achieve the optimal combination of the PID parameters. Considering various uncertain disturbances and parameter changes on the mechanical components of the wind turbines, an active disturbance rejection pitch controller of the wind turbines is designed based on BP-PID algorithm. Combined with a tracking differentiator, an extended state observer (ESO) is employed to observe the state and disturbance of the system. In addition, in order to compensate the BP-PID variable pitch controller, nonlinear state error feedback control laws are designed by configuring nonlinear structures according to the state deviation between the extended state observer and the tracking differentiator. The simulation results show that the variable pitch active disturbance rejection control (ADRC) based on BP-PID can effectively estimate the system states and disturbances. And the proposed controller has good dynamic performance and strong robustness.
Comparative study of circularly polarized patch antenna using multiple substrates

This paper presents a micro-strip antenna for 5G, WLAN applications. The antenna proposed here is simple in structure, low cost and light in weight. The overall dimension of the antenna is $50 \times 50$ mm. Three different types of substrates are used here to discover the working of antenna in depth. The antenna hence has single wide band of 4.44GHz, 4.92GHz and 4.8GHz respectively in all the three substrates namely FR-4(lossy), Rogers RO4450F(lossy) and Quartz fused(lossy). The antenna possess circular polarization. Overall the antenna sustains good performance and can be used in areas of wireless communication.
Research on Cross-border Electricity Price Spread Model of Nordic Power Market

In this paper, the cross-border electricity price difference data in the Nordic power market is modeled with the standard generalized autoregressive conditional heteroscedasticity model, GJR model and Markov-switching GARCH model. These models are estimated with normal, skewed normal and skewed student-t-t distributions. Studies show that cross-border electricity price difference data is not only a "spike" higher than the electricity price data, data jumping is more obvious, but also "volatility cluster" and strongly regime-dependent. The example analysis shows that the MS-GARCH model estimated with skewed normal distribution has the better predictive performance than other models, according to evaluation index like mean squared error MSE and the mean absolute error MAE for one- and multi-step ahead forecasts. Accurate cross-border electricity price difference forecast results can enable power companies and consumers to avoid risks when auctioning in the financial transmission right market.
Feature Selection: Multi-source and Multi-view Data Limitations, Capabilities and Potentials

Feature Selection (FS) is a crucial step in high-dimensional and big data analytics. It mitigates the ‘curse of dimensionality’ by removing redundant and irrelevant features. Most FS algorithms use a single source of data and struggle with heterogeneous data, yet multi-source (MS) and multi-view (MV) data are rich and valuable knowledge sources. This paper reviews numerous, emerging FS techniques for both these data types. The major contribution of this paper is to underscore uses and limitations of these heterogeneous methods concurrently, by summarising their capabilities and potentials to inform key areas of future research, especially in numerous applications.
Full-field Simulation of ±800kV Valve Hall in Flexible DC System

The limitation of corona inception field strength is a key issue in the design of flexible DC system. In this paper, a full-field simulation of converter valve hall based on Udongde ±800kV Flexible HVDC Project is present. Two major methods are employed to reduce computational complexity. One is the application of component decomposition method based on superposition principle instead of traditional method, and the other is the reasonable simplification when building simulation model. Analysis of simulation results shows that the maximum electric field strength at the given valve hall design is within the electric field limit, which meets the design requirements.
UAV-aided Data Collection in Self Organized IoT Network for Social Augmented Reality

To realize a seamlessly connected living in smart city is an attractive topic nowadays. With the help of massive machine type communication (mMTC) technology and drone-based networking strategy, we can create ubiquitous computing framework which is both agile and green. In this paper, we propose a cooperative data collecting mechanism in unmanned aerial vehicle (UAV) covered internet of things (IoT) community, aiming at delay-sensitive context information update and system energy efficiency (EE) optimization. To better collect small and fragmentized IoT data scattering around, we first propose a self-organizing algorithm to compress them into a hotspot area and derive the theoretical collecting efficiency (CE) therein. Then, a system EE maximizing power control algorithm is proposed, exploiting cooperative communication theory. When dealing with this highly non-convex programming problem, we look carefully into its Hessian matrix and prove that under two physically meaningful conditions, the modified problem can be turned into a strictly concave one and tractably solved. Finally, a social augmented reality (AR) use case is proposed and tested with our mechanism. Simulation results validate our analysis and demonstrate nearly 20% improvement in EE performance comparing with benchmark solutions.
Artificial Intelligence based Semantic Text Similarity for RAP Lyrics

Data mining is the primary method of gathering large volumes of knowledge. To make use of such data to implementation requires the use of effective machine learning strategies. Semantic Textual Similarity is one of the primary machine learning strategies. At its core, semantic textual similarity is the identification of words with similar context. Initial work in STS involved text reuse, word search among others. The proposed research work uses a specific method of determining textual similarity using Google’s Word2Vec framework and the Continuous-bag-of-words algorithm for identifying word similarity in rap records. A large data set consisting of over 50,000 rap records is used. The key aspect of proposed methodology is to determine the words with similar context and cluster them into different word clusters also called bags. To achieve the desired result, the dataset is first processed to obtain the features. Once the features are selected, a model is generated by passing the data onto the Word2Vec framework. The research work on semantic textual similarity was repeated across three different runs, with the data set size changing in every run. At the end of each the accuracy of similarity obtained by the model was determined. The current research work has achieved average accuracy as 85%.
A Survey On Road Crack Detection Techniques

Driverless or Autonomous cars are slowly catching the automobile market. Due to improper road management in India it becomes must to have a feature in the car which can detect and identify the cracked road and avoid the bad route and also updating the road condition. Roads are the simplest route to connect source to reach their destination. Due to natural causes like heavy rainfall, poor drainage, waterlogging and other factors such as the movement of heavily loaded vehicles are the frequent causes of road surface degradation. Crack and its types can be detected by a technique called as surface crack detection. In order to detect different cracks various methods and classifiers used are summarized in this paper. Traditionally road inspection is done manually which is time-consuming and costly. Instead of this automatic detection of cracks in the roads is preferred which can later be utilized as a feature in autonomous car. The focus of this paper is on different classifiers, for which various recent papers were studied and analyzed. Due to high accuracy and better performance CNN is highly preferred technique.
Computational Intelligence Based Model Detection of Disease using Chest Radiographs

There are many diseases associated with lungs or thoracic cavity and the diagnosis of these diseases at once becomes difficult for any medical profusion. The most common way of screening done when the thoracic cavity comes into the picture is Chest Radiography. However, diagnosing multiple diseases from a single scan becomes difficult. This paper proposes an intelligent machine learning-based model which tries to detect 14 chest diseases out from a single radiograph with greater accuracy. This paper makes use of advanced deep learning techniques like neural networks, masking algorithms, etc. to assure higher performances.
Vitamin D Insufficiency is Prevalent in Severe COVID-19

Background: COVID-19 is a major pandemic that has killed more than 196,000 people. The COVID-19 disease course is strikingly divergent. Approximately 80-85% of patients experience mild or no symptoms, while the remainder develop severe disease. The mechanisms underlying these divergent outcomes are unclear. Emerging health disparities data regarding African American and homeless populations suggest that vitamin D insufficiency (VDI) may be an underlying driver of COVID-19 severity. To better define the VDI-COVID-19 link, we determined the prevalence of VDI among our COVID-19 intensive care unit (ICU) patients. Methods: In an Institutional Review Board approved study performed at a single, tertiary care academic medical center, the medical records of COVID-19 patients were retrospectively reviewed. Subjects were included for whom serum 25-hydroxycholecalcifoerol (25OHD) levels were determined. COVID-19-relevant data were compiled and analyzed. We determined the frequency of VDI among COVID-19 patients to evaluate the likelihood of a VDI-COVID-19 relationship. Results: Twenty COVID-19 patients with serum 25OHD levels were identified; 65.0% required ICU admission.The VDI prevalence in ICU patients was 84.6%, vs. 57.1% in floor patients. Strikingly, 100% of ICU patients less than 75 years old had VDI. Coagulopathy was present in 62.5% of ICU COVID-19 patients, and 92.3% were lymphocytopenic. Conclusions: VDI is highly prevalent in severe COVID-19 patients. VDI and severe COVID-19 share numerous associations including hypertension, obesity, male sex, advanced age, concentration in northern climates, coagulopathy, and immune dysfunction. Thus, we suggest that prospective, randomized controlled studies of VDI in COVID-19 patients are warranted.
PENGARUH PEMANFAATAN BAKTERI PENGHASIL FITASE (Pantoea agglomerans) DALAM RANSUM TERHADAP KUALITAS KARKAS AYAM BROILER

Broiler chicken was one of poultry livestock that can be relyed as meat producting. One important factor in its maintenance was its welt. The main composition of broiler chicken welt was 80% made from seeds. The nutrient contained in seed was difficult to be digested in chiken’s digestive system because it bonded by phytate compound. One method used to reduce the phytate contained in ration was done by using Pantoea agglomerans bacteria as the phytase enzyme producer. It was hoped that by the existence of this enzyme, the nutrient contained in welt can be absorbed by the tisue, thus good growth can be obtained and better broiler’s carcass can be obtained as well. Objective of this research was to know the effect of phytase producer, Pantoea agglomerans bacteria, in welt due to broiler’s carcass and to know the Pantoea agglomerans bacteria utilization as the most optimal Phytase producer due to broiler’s carcass quality. This research was conducted for 6 weeks, from September 7th 2006 to October 18th 2006 in Poultry’s Cage of Livestock Production Division in Jatikuwung of Gondangrejo Sub district of Karanganyar Regency. Subject of this research was 75 male Hubbard strain broiler chicken DOC. The research design used was one way Complete Random Design (CRD) with three treatments (P0, P1, and P2) each treatment was repeated five times and each repetition consisted of five chickens. Treatment to be tested was P0 (welt 100% + without bacteria), P1 (welt 100% + Pantea agglomerans bacteria 102,5), P2 (welt 100% + Pantea agglomerans bacteria 105). Control welt (P0) to be used consisted of yellow corn mix, soybean meal, fish flour, wheat pollard, coconut oil, limestone and salt. Parameters to be observed were: lump weight, carcass percentage and abdominal fat parentage. Result of variance analysis showed that Pantoea agglomerans bacteria utilization until 105 CFU level /ml has no significant effect due to lump weight, carcass percentage and abdominal fat parentage. The weight average were 1586.60 – 1632.30 gram/each, the carcass percentage 0.285-0.304%, back meat 0.227-0.236%, thigh meat 0.347-0.352%, wing meat 0.123-0.127% and abdominal fat was 0.010-0.013%. Conclusion of this research was the Pantoea agglomerans bacteria utilization in welt until 105 CFU/ml level has not increases the carcass and abdominal fat yet for broiler chicken. Key word: broiler , carcass quality, Pantoea agglomerans
Broader Implications of Modeling and Simulation (M&S) Tools in Pharmacotherapeutic Decisions: A Cautionary Optimism

While not an entirely new concept, emergence of precision medicine has been catalyzed by the National Institutes of Health (NIH)’s “Precision Medicine Initiative” (Collins and Varmus, 2015). This initiative has goals to understand how one’s genetics, environment, and lifestyle can help determine the best approach to prevent or treat disease. The “precision” concept was initially interpreted rather narrowly to the clinical implementation of pharmacogenomics, but the clinical community recognized the importance of broader application to all stages of clinical care: prevention, diagnosis, and treatment (Peck, 2018; Verstegen and Ito, 2019). Pharmacotherapy is an important component of treatment phase of the clinical care which aims to provide appropriate medicine and dose to each patient, which we now call “precision dosing. ” Clinical pharmacologists and clinical pharmacists have long been applying such concept to drug dosing within the framework of “Therapeutic Drug Monitoring (TDM)” for narrow therapeutic index drugs. Drugs are generally approved based on “average” improvement of disease outcomes in a population. There are a myriad of factors that contribute to variability in drug exposure and response including genetic polymorphisms in drug metabolism (e.g., CYP2D6, CYP2C19) and pharmacodynamic targets (e.g., HER+ve versus HER-ve), demographic differences (e.g., age, weight), drug-drug interactions, hepatic/renal dysfunction, and patient behaviors (e.g. adherence). The response here extends from drug-related efficacy to safety. There has been a major shift in the drug development paradigm in the past few decades where the scientific focus shifted from “confirming” efficacy to “learning” drug exposure-response relationships (Sheiner, 1997). The most important enabler of this shift was the development of a pharmacostastical modeling methodology, nonlinear mixed effects modeling (NLME), that can analyze sparsely sampled exposure and response data (Sheiner and Beal, 1980; Sheiner and Beal, 1981; Sheiner and Beal, 1983). The original intent was to use the methodology to study patient populations by utilizing routinely collected TDM data. Simplistically, a model is a set of mathematical expressions that characterize drug absorption, distribution, metabolism, and elimination. Any response, safety or efficacy, may be assumed to be a function of the drug exposure. Such models can be expanded to include characteristics related to the patient (e.g., age, weight, adherence, maturation, and/or polymorphisms of metabolic enzymes,
The Serum MicroRNA Signatures for Pancreatic Cancer Detection and Operability Evaluation

Pancreatic cancer (PC) has high morbidity and mortality. It is the fourth leading cause of cancer death. Its diagnosis and treatment are difficult. Liquid biopsy makes early diagnosis of pancreatic cancer possible. We analyzed the expression profiles of 2,555 serum miRNAs in 100 pancreatic cancer patients and 150 healthy controls. With advanced feature selection methods, we identified 13 pancreatic cancer signature miRNAs that can classify the pancreatic cancer patients and healthy controls. For pancreatic cancer treatment, operation is still the first choice. But many pancreatic cancer patients are already inoperable. Therefore, we compared the 79 inoperable and 21 operable patients and identified 432 miRNAs that can predict whether a pancreatic cancer patient was operable. The functional analysis of the 13 pancreatic cancer signatures and the 432 operability miRNAs revealed the molecular mechanisms of pancreatic cancer and shield light on the diagnosis and therapy of pancreatic cancer in clinical practice.
Pharmacogenomic Testing to Guide Personalized Cancer Medicine Decisions in Private Oncology Practice: A Case Study

Innovative tumor profiling methodologies are utilized to elucidate the pharmacogenomic landscape of tumor cells in order to support the molecularly guided delivery of therapeutics. Indeed, improved clinical outcomes are achieved in oncology practice by providing the physicians with expert-guided, standardized, and easily interpretable knowledge, translated from molecular profiling analysis to support clinical decision-making. However, there is still limited utilization of the technology especially in small private oncology practices. In this work, we analyzed how molecularly guided interventions in 17 consented cancer patients led to an overall improvement of disease response rates in a private oncology center. The precision medicine strategy was based on the OncoDEEP™ profiling solutions and focused on finding clinically actionable relationships between tumor biomarkers and drug responses. The obtained data support the notion that (a) following the pharmacogenomic-derived recommendations favorably impacted cancer therapy progression, and (b) the earlier profiling followed by the delivery of molecularly targeted therapy led to more durable and improved pharmacological response rates. Moreover, we report the example of a patient with metastatic gastric adenocarcinoma who, based on the molecular profiling data, received an off-label therapy that resulted in a complete response and a current cancer-free maintenance status. Overall, our data provide a paradigm on how molecular tumor profiling can improve decision-making in the routine private oncology practice.
Which side of the tree is more basal?

When a tree is rooted, it has a base. Everything near that base is basal. A clade branching off near the base is a basal clade, isn’t it? Does this make sense? No, it doesn’t. As editors of Systematic Entomology we regularly receive discussions of ‘basal’ clades or taxa. We find them in a great proportion of submitted papers and even in a widely used textbook (Forey et al., 1992: 134; eliminated from the second edition). Google found 100 hits for ‘the most basal clade’ and seventy-five for ‘the most basal taxon’, including a few from Systematic Entomology (search on 14 January 2004). Although we try regularly to convince the authors individually that this doesn’t make sense, we have decided to raise this issue in an editorial. Why one clade cannot be most basal Every branching in a (phylogenetic) tree is rotatable (see Fig. 1). Of course, the tree has a base, and there is a most basal branching and a next most basal branching, but there is no such thing as themost basal clade. Because branchings are rotatable, there are always two most basal clades (if the most basal branching is completely resolved, Fig. 1) or even more most basal clades (if the most basal branching is not completely resolved, e.g. in Polyneoptera in Fig. 1). Both branches originating from a node (i.e. the two sister groups) are of equal age and have undergone equivalent evolutionary change. Whether a group has branched off early (basal)
Propeller-Like All-Fused Perylene Diimide Based Electron Acceptors With Chalcogen Linkage for Efficient Polymer Solar Cells

Perylene diimide (PDI) is a widely explored chromophore for constructing non-fullerene acceptors (NFAs) for polymer solar cells (PSCs). The advantage of using PDI derivatives lies in the readily availability of PDI unit which largely reduces the synthesis cost and improves material stability. Indeed, the recent development of high performance NFAs shed light on the feasibility of the commercialization, but the complex synthesis and poor stability of the top performing NFAs cast a shadow on this bright future. Our previous work has demonstrated a propeller-like structure with three PDIs lined to a benzene center core with a C-C bond which prevented the PDIs to aggregate into undesired large crystals. In this work, we designed and synthesized three new propeller-like PDI derivatives with extra chalcogen linkages between the PDIs and the center core to form all-fused rigid structures. These molecules showed more suitable absorption range than that of their unfused counterparts when blend with donor polymer PTB7-Th. Comparing between the molecules with extra oxygen, sulfur or selenium linkages, the sulfur-based BTT-PDI outperformed the others due to its higher photon absorption and charge transport abilities. This work demonstrated the great potential of PDI derivatives for PSC applications and explored the influences of linkage type on the fused PDI derivatives, which provided a useful tuning knob for molecular design of PDI-based NFAs in the future.
Skimmin Improves Insulin Resistance via Regulating the Metabolism of Glucose: In Vitro and In Vivo Models

Skimmin is the major pharmacologically active component present in Hydrangea paniculata, in the traditional Chinese medicine as an anti-inflammatory agent, and its anti-inflammation and anti-diabetic effect has had been studied in previous studies. The metabolism of glucose plays an important role in the pathophysiology of diabetes. Therefore, it was identified as an important target for improving diabetic. Herein, we found that skimmin relieved the palmitic acid and high-fat and high sugar-induced insulin resistance. Furthermore, skimmin enhanced the glucose uptake via inhibiting reactive oxygen species (ROS) and reducing the level of inflammatory correlation factor. Meanwhile, skimmin reduced the glucose output by promoting PI3K/Akt signaling pathway and down-regulating the expression of glycogen synthase kinase-3β (GSK3β) and glucose-6-phosphatase (G6Pase). In conclusion, skimmin can improve the insulin resistance by increasing glucose uptake and decreasing glucose output in vitro and in vivo.