Miracle-dz/UniFAR-FTUs huggingface dataset:
International Journal of Emerging Electric Power Systems Comparison of Available Silicone Rubber Coatings for High Voltage Applications

This paper presents the results of an investigation on evaluating room-temperature-vulcanized (RTV) silicone rubber coatings for high-voltage insulators of substations and overhead transmission lines. It is based on tests conducted for three different available RTVs that are currently used by utilities. The study compared three commercial coatings: RTV-A and RTV-B filled with ATH (aluminum tri-hydrate) as the primary filler, and RTV-C filled with silica. The relative comparison between them is based on their electrical and chemical characteristics. The evaluation employed standard and research-based evaluation methods, including the salt fog test, the inclined plane test, thermo-gravimetric analyses (TGA), thermal conductivity measurement, soaking in water, the mechanical test, and SEM photographs. Results of the inclined plane test showed that the silica-filled coating is not resistive against tracking and erosion compared with ATH-filled coatings. It was observed that filler size is one of the main reasons for better performance of RTV-A compared with RTV-B, whereas both have ATH as the primary filler. The higher thermal conductivity of RTV-A resulted in a very good performance in the salt fog test and the IPT.
An Integrated Computational/Experimental Approach to UCAV Stability & Control Estimation: Overview of NATO RTO AVT-161

A comprehensive research program designed to investigate the ability of computational methods to predict stability and control characteristics of realistic flight vehicles has been undertaken. The integrated approach to simulating static and dynamic stability characteristics for a generic UCAV and the X-31 configuration was performed by NATO RTO Task Group AVT-161. The UCAV, named SACCON (Stability and Control Configuration), and the X-31 are the subject of an intensive computational and experimental study. The stability characteristics of the vehicles are being evaluated via a highly integrated approach, where CFD and experimental results are being used in a parallel and collaborative fashion. The results show that computational methods have made great strides in predicting static and dynamic stability charactersitics, but several key issues need to be resolved before efficient, affordable, and reliable predictions are available.
Bacterial Community and Diversity from the Watermelon Cultivated Soils through Next Generation Sequencing Approach

Knowledge and better understanding of functions of the microbial community are pivotal for crop management. This study was conducted to study bacterial structures including Acidovorax species community structures and diversity from the watermelon cultivated soils in different regions of South Korea. In this study, soil samples were collected from watermelon cultivation areas from various places of South Korea and microbiome analysis was performed to analyze bacterial communities including Acidovorax species community. Next generation sequencing (NGS) was performed by extracting genomic DNA from 92 soil samples from 8 different provinces using a fast genomic DNA extraction kit. NGS data analysis results revealed that, total, 39,367 operational taxonomic unit (OTU), were obtained. NGS data results revealed that, most dominant phylum in all the soil samples was Proteobacteria (37.3%). In addition, most abundant genus was Acidobacterium (1.8%) in all the samples. In order to analyze species diversity among the collected soil samples, OTUs, community diversity, and Shannon index were measured. Shannon (9.297) and inverse Simpson (0.996) were found to have the highest diversity scores in the greenhouse soil sample of Gyeonggi-do province (GG4). Results from NGS sequencing suggest that, most of the soil samples consists of similar trend of bacterial community and diversity. Environmental factors play a key role in shaping the bacterial community and diversity. In order to address this statement, further correlation analysis between soil physical and chemical parameters with dominant bacterial community will be carried out to observe their interactions.
H-WPS: Hybrid Wireless Positioning System Using an Enhanced Wi-Fi FTM/RSSI/MEMS Sensors Integration Approach

Indoor wireless localization toward the next generation Wi-Fi access point has attracted considerable attention due to the presentation of the state-of-art Wi-Fi fine time measurement (FTM) protocol. In order to improve the autonomy, accuracy, and universality of wireless positioning based on the Internet of Things (IoT) terminals, this article proposes a hybrid wireless positioning system which contains the integration of Wi-Fi FTM, crowdsourced received signal strength indicator (RSSI) fingerprinting and micro-electro-mechanical-system (MEMS) sensors (H-WPS). A light-weight pedestrian aimed inertial navigation system (PINS) is proposed, which contains multilevel constraints and a global optimization model in order to eliminate the cumulative error caused by INS update. A deep-learning-based Wi-Fi fingerprinting database generation framework is developed for crowdsourced trajectories evaluation and selection. In addition, three different multisource integration models are applied to fuse the information of PINS, Wi-Fi FTM and RSSI fingerprinting, and calibrate the Wi-Fi ranging bias in real time, which is further enhanced by a novel misclosure check and the multilayer perceptron contained signal quality evaluation strategy. The comprehensive experiments demonstrate that the proposed H-WPS achieves much more precise and universal indoor positioning performance compared with the single location source, and meter-level localization precision can be realized in the Wi-Fi FTM-covered indoor scenes.
Cellular Robotic Architecture

An emerging need for interactive architecture is currently making buildings mutable, flexible in use, and adaptable to changes in climate by introducing robotic systems. However, the feasibility of the seamless integration of building construction details and kinetic robotics has become a critical issue for developing robotic architecture. The objective of this work is to develop a robotic architecture with an emphasis on the integration of cellular robotics with a distributed kinetic building surface. The kinetic building surface integrates an actuating system, a localization and remote control system, which become part of the kinetic building system. This paper presents a systematic framework by reviewing theories and related work of robotic architecture and automated control. An architectural design scheme is proposed to simulate a scenario of application in a physical space. The functionality of the electrical and control system and the integration of the effects of actual construction were examined by a prototype of a kinetic surface. Our prototype presents a feasible construction method, and a prominent energy-saving effect. The potential strength and restrictions of the cellular robotic approach to architectural applications are discussed. The applicability of the prototype system and issues about controlling the behavior of spatial robots are demonstrated in this paper.
A Constellation of Rare Gynecological Malignancies and Familial Adenomatous Polyposis Gastrointestinal Adenocarcinoma: A Case Report

Ovarian microcystic stromal tumors (MST) are a rare subtype of sex-cord stromal tumors. We are presenting a case of a MST arising in a patient with familial adenomatous polyposis (FAP) and concurrent colonic adenocarcinoma. During the patient’s workup of an ampullary adenoma associated with her FAP, she was found to have an enlarged uterus with a thickened endometrium and an incidental pelvic mass on the fundus of the uterus. Subsequent imaging identified heterogenous bulky ovaries. This patient underwent surgical resection including a total abdominal hysterectomy, bilateral salpingo-oophorectomy, omentectomy, bilateral pelvic sentinel lymph node biopsy during her planned total proctocolectomy and transduodenal ampullectomy. Extensive histologic and immunohistochemical investigations were completed and the final pathology report revealed a unique compilation of International Federation of Gynecology and Obstetrics Stage II, grade 1 endometrioid endometrial adenocarcinoma, bilateral ovarian MST, a sperate pedunculated mass favoring a diagnosis of uterine tumor resembling ovarian sex cord tumor (UTROSCT), 2 distinct adenocarcinomas of the colon (T2N0 and T1N0) and a tubular adenoma of the ampulla. The pathology showed the endometroid adenocarcinoma was β-catenin negative while the MST and UTROSCT both showed nuclear positivity with β-catenin. To our knowledge this is the first reported case of a UTROSCT with concurrent endometrial adenocarcinoma presenting with bilateral ovarian MST’s and adenomatous polyposis coli gene positive FAP colon adenocarcinoma.
Response of water fluxes and biomass production to climate change in permanent grassland soil ecosystems

Abstract. Effects of climate change on the ecosystem productivity and water fluxes have been studied in various types of experiments. However, it is still largely unknown whether and how the experimental approach itself affects the results of such studies. We employed two contrasting experimental approaches, using high-precision weighable monolithic lysimeters, over a period of 4 years to identify and compare the responses of water fluxes and aboveground biomass to climate change in permanent grassland. The first, manipulative, approach is based on controlled increases of atmospheric CO2 concentration and surface temperature. The second, observational, approach uses data from a space-for-time substitution along a gradient of climatic conditions. The Budyko framework was used to identify if the soil ecosystem is energy limited or water limited. Elevated temperature reduced the amount of non-rainfall water, particularly during the growing season in both approaches. In energy-limited grassland ecosystems, elevated temperature increased the actual evapotranspiration and decreased aboveground biomass. As a consequence, elevated temperature led to decreasing seepage rates in energy-limited systems. Under water-limited conditions in dry periods, elevated temperature aggravated water stress and, thus, resulted in reduced actual evapotranspiration. The already small seepage rates of the drier soils remained almost unaffected under these conditions compared to soils under wetter conditions. Elevated atmospheric CO2 reduced both actual evapotranspiration and aboveground biomass in the manipulative experiment and, therefore, led to a clear increase and change in seasonality of seepage. As expected, the aboveground biomass productivity and ecosystem efficiency indicators of the water-limited ecosystems were negatively correlated with an increase in aridity, while the trend was unclear for the energy-limited ecosystems. In both experimental approaches, the responses of soil water fluxes and biomass production mainly depend on the ecosystems' status with respect to energy or water limitation. To thoroughly understand the ecosystem response to climate change and be able to identify tipping points, experiments need to embrace sufficiently extreme boundary conditions and explore responses to individual and multiple drivers, such as temperature, CO2 concentration, and precipitation, including non-rainfall water. In this regard, manipulative and observational climate change experiments complement one another and, thus, should be combined in the investigation of climate change effects on grassland.
Load Index Forecasting Method Based on Nonlinear Dimensionality Reduction of Correlated Factors and Generalized Regression Neural Network Fitting

The power load characteristic index is an industry index that describes the characteristics of the load and the law of load change, and is an important reference basis for the decision-making of the power grid dispatching and planning department. However, the medium and long-term load characteristic index data points are sparse, and the forecasting method that only analyzes the data trend has great limitations. Therefore, it is necessary to consider the external influence factors of the load in the forecasting model to improve the effectiveness and accuracy of the forecast. First, the weight-improved gray correlation analysis method is used in the article to evaluate the degree of influence of external factors such as weather, economy, and society on the load characteristic indicators. The factors with low correlation are removed, and then t-SNE is used. Reduce the dimensions of multiple influencing factors to reduce data redundancy. Then build multiple linear and nonlinear regression models of mid and long term load indicators through generalized regression neural network, and determination of optimal super parameters by one dimensional optimization to achieve mid and long term load characteristic indicators prediction. Finally, the feasibility of the method is verified by using relevant data such as load in a certain area of southwest.
Evaluating the Quality of Life of 231 Children With Primary Nephrotic Syndrome and Assessing Parental Awareness of the Disease

Objective: This study aims to investigate the quality of life of children with primary nephrotic syndrome (PNS), assess their parents' disease awareness, and provide a basis for the comprehensive management of children with PNS. Methods: A total of 231 children with PNS who were hospitalized in the Department of Pediatric Renal Rheumatology and Immunology in the ShengJing Hospital of the China Medical University from March 2019 to October 2020 were selected as the study subjects. The subjects and their parents were surveyed via a disease education and communication WeChat group and online questionnaire to investigate the children's quality of life, the needs of the parents, and their knowledge related to the disease. Results: In 93.51% of cases, the child's quality of life was affected, with mild to moderate effects being the most frequent (90.47%). The lowest overall quality of life scores were recorded for children who had been diagnosed 1–3 year prior to inclusion in the study, and the scores plateaued thereafter. On the physical functioning scale, the longer the illness, the greater the physical impact, with children typically experiencing pain and fatigue. The children generally scored low on the emotional functioning scale, exhibiting sleep disturbances for up to 5 years and worrying about accidents. The children's average score on the social functioning scale was high, with males achieving significantly higher scores (69.61 ± 25.42) than females (62.30 ± 27.51), and more than one-third of the children experiencing problems getting along with other teenagers and making friends. The primary problems expressed by parents were anxiety (59%), sadness (44%), fear (43%), and depression (40%), and several parents indicated that they struggled with issues of self-blame. Conclusion: PNS impacts the physical and psychological wellbeing of children suffering from the condition, significantly reduces their quality of life, and negatively impacts the psychological wellbeing of their parents. Therefore, children with PNS and their families need integrated management by doctors, nurses, dieticians, psychotherapists, educational institutions, and social stakeholders to improve their quality of life.
Validation of the Turkish version of the Pain Sensitivity Questionnaire in patients with chronic pain

Objective The Pain Sensitivity Questionnaire (PSQ) is a clinically beneficial instrument that has been proven to be correlated with various experimental pain sensitivity assessments in healthy people and in patients with chronic pain. In this study, we aimed to translate the PSQ into Turkish (PSQ-T) and validate it for the measurement of pain sensitivity among Turkish people. Methods Seventy-three patients with chronic back pain who were planning to undergo an interventional procedure completed the Brief Pain Inventory-Short Form (BPI-SF), Beck Depression Inventory (BDI), Beck Anxiety Inventory, Pain Catastrophizing Scale, and PSQ prior to their procedure. Subcutaneous infiltration of lidocaine was used as a standardized experimental pain stimulus. Pain was evaluated using a visual analog scale (VAS 1: infiltration in the hand, and VAS 2: infiltration in the procedure area) Results Scores on the PSQ-T were significantly correlated with those on the BPI-SF. A significant positive relationship was observed between VAS 1 and VAS 2 values and the PSQ-T score, BPI pain score, and BPI interference score. Conclusions The PSQ-T can be used as a valid and reliable tool for the assessment of pain sensitivity in the Turkish population.
An Agriculture Application Research on Cloud Computing

Today’s agriculture is embedded with advance services like GPS, sensors etc, that enable to communicate to each other, analyse the data and also exchange data among them. IT provides services in the form of cloud to agriculture. Agriculture-Cloud and IT offers expertise service to farmers regarding cultivation of crops, pricing, fertilizers, disease detail method of cure to be used etc. Scientists working at Agriculture research stations can add their discoveries, suggestions regarding modern techniques for cultivation, usage of fertilizers, can obtain cultivation history of the region etc. In this paper, our study was based on is to design and implement a simple Cloud based application on Agriculture System which is based on Agri-Cloud that enhance agriculture production and also enhance the availability of data related to research projects in field and also in lab. The impact of doing it would cut the cost, time, and make the communication system much faster and easier. We performed the simulation and experiments by using PHP server, mySQL database.
Dynamic Time Warping Under Product Quantization, With Applications to Time-Series Data Similarity Search

The similarity search on sensor data generated by a myriad of sensing devices is a frequently encountered problem in the era of the Internet of Things (IoT). This sensor data generally appear in the form of time series, a temporally ordered sequence of real numbers obtained regularly in time. It has been widely accepted that the dynamic time warping (DTW) currently is the most prevalent similarity measure in the time-series mining community, mainly due to its flexibility and broad applicability. However, calculating DTW between two time series has quadratic time complexity, leading to unsatisfactory efficiency when performing the similarity search over the large time-series data set. The main contribution of this article is to propose a method called product quantization (PQ)-based DTW (PQDTW) for fast time-series approximate similarity search under DTW. The PQ, a well-known approximate nearest neighbor search approach, is used in PQDTW. Nevertheless, the conventional PQ is developed with the Euclidean distance and is not designed for DTW. To solve this problem, the DTW barycenter averaging (DBA) technique is utilized to adapt the PQ for DTW before using it. We employ PQDTW along with the filter-and-refine framework to efficiently and accurately perform the time-series similarity search. Our method can reasonably reduce many DTW computations in the filtering phase; thus, the query process is accelerated. We compare PQDTW with related popular algorithms using public time-series data sets. Experimental results verify that the proposal achieves the best tradeoff between query efficiency and retrieval accuracy compared to the competitors.
Neural net implementation of steam properties on FPGA

Real time applications like model predictive control, monitoring and data reconciliation of power plants and industrial processes employ nonlinear mathematical models and require thermodynamic properties and their derivatives of working fluids. Applications like super heater temperature control based on energy balance and real time data reconciliation, require an efficient and a compact method for simultaneous estimation of thermodynamic properties, and their partial derivatives suitable for implementation in field-programmable gate array (FPGA). However, the complex mathematical formulations of these properties prohibit direct implementations in FPGAs. Single artificial neural network (ANN) architecture is used to replace the entire code in higher level languages, running into a few thousand lines. FPGA implementation of a compact neural network for the entire range of thermodynamic properties is presented. Large arguments in sigmoid function are factored into a product of integer and a fractional part which is represented using series approximation with five terms only and the integers are represented in look up table (LUT). This ensures optimum storage and computational burden for the above applications. The ANN is implemented in IEEE 754 floating point with synthesis in Xilinx ISE design suite using Verilog HDL. The results are presented for a typical pressure versus saturation temperature.
Case Study of Deep Learning Model of Temperature-Induced Deflection of a Cable-Stayed Bridge Driven by Data Knowledge

A cable-stayed bridge is a typical symmetrical structure, and symmetry affects the deformation characteristics of such bridges. The main girder of a cable-stayed bridge will produce obvious deflection under the inducement of temperature. The regression model of temperature-induced deflection is hoped to provide a comparison value for bridge evaluation. Based on the temperature and deflection data obtained by the health monitoring system of a bridge, establishing the correlation model between temperature and temperature-induced deflection is meaningful. It is difficult to complete a high-quality model only by the girder temperature. The temperature features based on prior knowledge from the mechanical mechanism are used as the input information in this paper. At the same time, to strengthen the nonlinear ability of the model, this paper selects an independent recurrent neural network (IndRNN) for modeling. The deep learning neural network is compared with machine learning neural networks to prove the advancement of deep learning. When only the average temperature of the main girder is input, the calculation accuracy is not high regardless of whether the deep learning network or the machine learning network is used. When the temperature information extracted by the prior knowledge is input, the average error of IndRNN model is only 2.53%, less than those of BPNN model and traditional RNN. Combining knowledge with deep learning is undoubtedly the best modeling scheme. The deep learning model can provide a comparison value of bridge deformation for bridge management.