Miracle-dz/UniFAR-FTUs huggingface dataset:
From Pigments to Pixels: A Comparison of Human and AI Painting

From entertainment to medicine and engineering, artificial intelligence (AI) is now being used in a wide range of fields, yet the extent to which AI can be effectively applied to the creative arts remains to be seen. In this research, a neural algorithm of artistic style was used to generate six AI paintings and these were compared with six paintings on the same theme by an amateur painter. Two sets of paintings were compared by 380 participants, 70 percent of whom had previous painting experience. Results indicate that color and line are the key elements of aesthetic appreciation. Additionally, the style transfer had a marked effect on the viewer when there was a close correspondence between the painting and the style transfer but not when there was little correspondence, indicating that AI is of limited effectiveness in modifying an existing style. Although the use of neural networks simulating human learning has come a long way in narrowing the gap between paintings produced by AI and those produced in the traditional fashion, there remains a fundamental difference in terms of aesthetic appreciation since paintings generated by AI are based on technology, while those produced by humans are based on emotion.
Comprehensive Characterization of Platelet-Enriched MicroRNAs as Biomarkers of Platelet Activation

Dysregulation of platelet function is causally connected to thrombus formation and cardiovascular diseases. Therefore, assessing platelet reactivity is crucial. However, current platelet function tests come with pitfalls, limiting clinical use. Plasma miRNA signatures have been suggested as novel biomarkers for predicting/diagnosing cardiovascular diseases and monitoring antiplatelet therapy. Here, we provide results from a comprehensive study on the feasibility of using circulatory platelet miRNAs as surrogate markers of platelet activation. We performed small RNA-Seq on different blood cell types to confirm known and identify novel platelet-enriched miRNAs and validated a panel of 16 miRNAs using RT-qPCR. To identify the main carrier of these blood-based platelet miRNAs, we enriched and analyzed distinct microvesicle populations. Platelets were stimulated with GPVI and P2Y12 agonists in vitro to monitor the release of the selected miRNAs following activation. Finally, the miRNA panel was also measured in plasma from mice undergoing the Folts intervention (recurrent thrombus formation in the carotid artery). Applying an unbiased bioinformatics-supported workflow to our NGS data, we were able to confirm a panel of previously established miRNA biomarker candidates and identify three new candidates (i.e., miR-199a-3p, miR-151a-5p, and miR-148b-3p). Basal levels of platelet-derived miRNAs in plasma were mainly complexed with proteins, not extracellular vesicles. We show that changes in miRNA levels due to platelet activation are detectable using RT-qPCR. In addition, we highlight limitations of studying the in vitro release of miRNAs from platelets. In vivo thrombosis resulted in significant elevations of platelet-derived miRNA levels in mice. In conclusion, we provide in-depth evidence that activated platelets release miRNAs, resulting in measurable changes in circulatory miRNA levels, rendering them promising biomarker candidates.
Recognition of Red-Bed Landslides over Eastern Sichuan through Remote Sensing and Field Investigations

Red-bed soft rock, characterized by the gentle slope, low intensity, and weak weathering resistance, is widely distributed over the Eastern Sichuan Province, China, threatening many lives of people, economy development, and urbanization progress. In this study, Multi-Temporal Synthetic Aperture Radar interferometry (MTInSAR) techniques and detailed field investigations were carried out to detect the potential red-bed landslides. The strategies of small baselines, phase optimization, and atmospheric delay removal were proved to be effective in improving the deformation results. Finally, dozens of slow-moving slopes were found and attributed to frequent human activities, and the applicability of various monitoring tools and the comparisons of their results were discussed. This research is aimed at proving the applicability of remote sensing measures in the monitoring of landslides, increasing the efficiency of this method, and helping the hazard prevention in Southwest China.
A Novel Torque Matching Strategy for Dual Motor-Based All-Wheel-Driving Electric Vehicles

: The market for electric vehicles is growing rapidly. Among them, the demand for a dual motor type 4 WD (Four -Wheel Driving) system is increasing. In this paper, we present the Torque Matching Strategy (TMS) method to select the optimal torque distribution ratio for dual motors. The TMS controller operates to set the optimal efﬁciency point by linearizing the drive efﬁciency combination of the two motors. Driving simulation and testing were performed through ﬁve drive cycles in the driver model interworking environment implemented in MATLAB and Carsim. The optimal distribution ratio was derived according to the front and rear gear ratios under the load condition, and the driving was veriﬁed by comparing it with the TMS control method. The efﬁciency was numerically veriﬁed by comparing the power loss of the driving motor. It reduced up to 34% in Urban Dynamometer Driving Schedule and up to 56.3% in Highway fuel efﬁciency test. The effectiveness of the TMS control method was demonstrated through the distribution rate trend based on the operation cycle and power loss.
Effects of Non-Polar Dietary and Endogenous Lipids on Gut Microbiota Alterations: The Role of Lipidomics

Advances in sequencing technologies over the past 15 years have led to a substantially greater appreciation of the importance of the gut microbiome to the health of the host. Recent outcomes indicate that aspects of nutrition, especially lipids (exogenous or endogenous), can influence the gut microbiota composition and consequently, play an important role in the metabolic health of the host. Thus, there is an increasing interest in applying holistic analytical approaches, such as lipidomics, metabolomics, (meta)transcriptomics, (meta)genomics, and (meta)proteomics, to thoroughly study the gut microbiota and any possible interplay with nutritional or endogenous components. This review firstly summarizes the general background regarding the interactions between important non-polar dietary (i.e., sterols, fat-soluble vitamins, and carotenoids) or amphoteric endogenous (i.e., eicosanoids, endocannabinoids-eCBs, and specialized pro-resolving mediators-SPMs) lipids and gut microbiota. In the second stage, through the evaluation of a vast number of dietary clinical interventions, a comprehensive effort is made to highlight the role of the above lipid categories on gut microbiota and vice versa. In addition, the present status of lipidomics in current clinical interventions as well as their strengths and limitations are also presented. Indisputably, dietary lipids and most phytochemicals, such as sterols and carotenoids, can play an important role on the development of medical foods or nutraceuticals, as they exert prebiotic-like effects. On the other hand, endogenous lipids can be considered either prognostic indicators of symbiosis or dysbiosis or even play a role as specialized mediators through dietary interventions, which seem to be regulated by gut microbiota.
Identification of Quality Factors in the Study Environment of Architecture Students in Supporting Online Learning in the Pandemic Period

—The psychological condition of a person while studying is influenced by the conditions of the physical environment. The implementation of online learning due to the COVID-19 pandemic has an impact on the implementation of learning because not all homes prepare a special room for childrens learning. This study identifies a description of Architecture students perceptions of the quality of their learning environment during the pandemic. This quantitative descriptive study was conducted using a questionnaire survey to 148 respondents. The data sample is Architecture students at levels 3 and 4 with different learning experiences before and during the pandemic. This study describes the tendency of a pretty good student perception (67.16%) about the quality of the learning environment on the factor of space elements. Perception Space components that are not fixed, namely neatness and smells are prominent elements (87.95%). In addition, there are differences in perceptions of environmental quality aspects in supporting learning between urban respondents on the flexibility aspect (7.11%), districts on the sound stimulant aspect (5.5%) and women on the flexibility aspect (13.15%) and men on the aspects of natural elements (9.45%).
Adaptive Control for Trajectory Tracking of an Unmanned Aerial Vehicle

In this paper, an unmanned aerial vehicle (UAV) with fixed-wing in normal condition flight, and fixed height, is considered and along with this process, kinematics model of UAV, assumed to have parametric uncertainty. In this situation the target of designing of proper controller family, based on switching logic, is to control the speed and roll angle of fixed-wing unmanned aerial vehicle in order to track desired path with minimum error. The desired path will be generated by trajectory maker block. The results of simulation on a fixed-wing UAV are presented to show the efficiency of the method.
Generating High Coherence Monophonic Music Using Monte-Carlo Tree Search

Music generation task is commonly considered as a note-by-note prediction problem. Moreover, prediction models generating one musical note at a time may ignore the overall coherence because the music phrase is incomplete and unable to demonstrate musicality. To address these issues, in this study, we propose a feasible monophonic music generation framework that can simulate subsequent trends for each predicted musical note. The framework generates a musical note mainly in three steps: 1) a sequence prediction model is used to predict the most potential candidates, 2) the subsequent trends for each candidate are modeled and evaluated, and 3) the best candidate is selected as the final result. We use the Monte-Carlo tree search algorithm because of its great capability of discovering near-optimal results. We establish a method of training a value network that can assess musical coherence to evaluate the simulated sequences. Further, we used a smoothed polynomial upper confidence trees algorithm to improve the accuracy and efficiency of the search process. An accurate dataset labeled by us, which contains 36 transcribed samples from real-world pop songs, was used to validate our framework. Compared with the note-by-note sequence prediction model, our framework exhibits a better sense of musicality. Our framework can be applied to generate symbolic monophonic music, particularly the main melody track in pop music.
“Don’t bully! We are all different”: Pedagogical potential of early critical literacy with bullying-themes

In spite of the increased attention that has been given to early critical literacy practices, there remains a limited amount of scholarship dedicated to exploring how critical literacy may be implemented in early childhood classrooms to prevent bullying. As such, the purpose of this article is to examine the pedagogical potential of early critical literacy with bullying-themes, for educating young children on anti-bullying. Using a qualitative study approach, this study was conducted in a classroom of twenty 5-year-old children in a metropolitan city in South Korea. The central focus of this study was dedicated to examining how children’s understanding of bullying toward their peers of different racial/ethnic backgrounds and special needs change through a series of critical literacy activities, including reading, discussion, and follow-up role-playing and writing activities. Findings suggest that critical literacy activities on bullying have the potential to aid in creating anti-bullying environments in which young children critically examine bullying issues from multiple perspectives and actively generate their own understanding of bullying.
Optomechanical system design under road vibration conditions

Optomechanical systems used in vehicles or moving platforms are exposed to vibration loads from road effects. This study investigated the dynamic behavior of an optomechanical system exposed to random vibrations. Random vibration analysis and laboratory tests of the system were performed with the MIL-STD-810 test method standard. The relative displacements of two lenses in the optomechanical system were obtained under vibration loads by using the finite element (FE) method. The displacements were examined by tests. The obtained relative displacements of the lenses were used as input to the optical analysis and the laser spot position was obtained. The obtained spot positions were evaluated comparatively with the tests. The optical analysis results were consistent with the tests. Using the verified FE and optical models, structural modifications were made in the basic design. Five different design changes were examined. The natural frequencies and the mass of the entire system and the relative displacements of the lenses were obtained for comparative evaluation. Finally, the optimum design minimally affected by vibration loads was achieved.
Memristors Enabled Computing Correlation Parameter In-Memory System: A Potential Alternative to Von Neumann Architecture

The von Neumann bottleneck has significantly increased the energy consumption of processing units and memory systems, especially in data-intensive computations such as the correlation parameter, which is being used in medical research, financial market analysis, biometrics, etc. Recently, memristor-enabled in-memory processing has gained tremendous research attraction to extenuate the von Neumann bottleneck as it processes operands at the location of storage, which obviates the need to transfer data between memory and the processing units. Hence, in this article, an innovative memristor crossbar-based architecture computing correlation parameter in-memory (CoCoPIM) has been proposed to accelerate correlation coefficient computations. Three different applications such as computing correlation between electrocardiogram (ECG) signals, faces, and H1N1 models were implemented based on the architecture. To evaluate the architecture, Neurosim was modified to support data mapping and computation steps, whereas Micro Architectural and System Simulator (MARSS) and multicore power, area, and timing (McPAT) were used to evaluate the von Neumann machine. In these applications, it was found that CoCoPIM was <inline-formula> <tex-math notation="LaTeX">$41.04\times $ </tex-math></inline-formula>, <inline-formula> <tex-math notation="LaTeX">$66.5\times $ </tex-math></inline-formula>, <inline-formula> <tex-math notation="LaTeX">$67\times $ </tex-math></inline-formula>, and <inline-formula> <tex-math notation="LaTeX">$33.2\times $ </tex-math></inline-formula> times energy-efficient against a four-core out-of-order processor in performing the respective tasks. It also achieved a speedup of <inline-formula> <tex-math notation="LaTeX">$143.5\times $ </tex-math></inline-formula>, <inline-formula> <tex-math notation="LaTeX">$52.5\times $ </tex-math></inline-formula>, <inline-formula> <tex-math notation="LaTeX">$52.5\times $ </tex-math></inline-formula>, and <inline-formula> <tex-math notation="LaTeX">$597\times $ </tex-math></inline-formula> times against the same von Neumann machine (multicore processor) for the respective tasks.
CreeSo – software for creep simulation of complex alloys

Summary The MDC-model (Microstructural Dislocation Creep – model) is an advanced approach incorporating numerous creep mechanisms in complex alloys. It leads to a differential equation system that cannot be solved in a closed algebraic form. Also, some parameters of these equations cannot be measured directly. The application CreeSo (from ‘Creep Software’) helps finding numerical solutions of this model and it can also help determining missing parameters by comparing simulated with experimental creep curves. CreeSo is not limited to the MDC-model but open to any model formulated in its script language. The application is written in C++ and JavaScript and it will be available as a desktop version for MS-Windows and as an online version for web browsers.
Forecasting Number of Calls to the Call Center Using Machine Learning

Abstract – This paper presents a forecast of a number of call arrivals in the call center per hour using supervised machine learning. For the forecast, the WEKA machine learning software tool was used. The results of the forecast are verified using several methods, which shows very good results. Finally, the results of the forecast are presented graphically using Excel diagrams. Keywords – Machine learning, Forecasting, WEKA
Security of the control of the transportation systems

Abstract – Traffic congestion and resulting pollution affect the quality of life in cities, notably in countries with dominant old diesel engines. One solution is Adaptive Traffic Signal Control using vehicle type and emission measurements. Therefore, a fuzzy controller using magnetic sensor for classifying vehicles and sensor for measuring emissions is proposed. Keywords – security of control, transportation systems, reliability.
Transformações na indústria fonográfica no Brasil: mudanças no modelo de negócio pelo uso da internet Transformations in the music industry in Brazil: changes in the business model by the use of internet

The aim of this study was to describe the business model of content creators and distributors of the music industry due the use of the Internet, understood as innovation. The analysis of the business model was based on that proposed by Osterwalder (2004). The method used was a multiple case study with four content creators (artists) and two content distributors (one major-Sony-and one indie). Results showed changes in the product (digital rather than physical support), distribution - made by online retailers, disintermediation occurring, enabling artists to connect directly to their customers, and increased revenue for artists and indie distributors, who have benefited from the dissemination over the Internet, while major distributors did not realize this increase, perhaps due to piracy .
Impact of Self- Help Groups Bank Linkage Programme in India

Self-Help Group means a group of 10-20 members which may or may not be registered representing financial intermediation, but the financial intermediation is not the only primary objective of the groups. The motive is to combine the access to low-cost financial services with a process of self management and development. NGOs or Government agencies usually form and support these SHGs. The members of these groups save the amount which is used for loans. Inspite of considerable expansion in the branch network, a large share of India’s population continues to remain outside the formal banking system. Due to widespread rural bank branch network, the SHG-Bank Linkage Programme is very suitable to the Indian context. Although various alternative ways are being experimented in order to meet the objectives of financial inclusion. The SHG-Bank Linkage Programme is considered as most successful, promising and widely accepted model in India. Self Help Group- Bank Linkage Programme- a pilot project started by NABARD is widely accepted model as one of the largest and successful one in the world. The present study is analytical and based upon secondary data which has been collected from different published reports, journals and existing available literature. The objective of this study is to evaluate the progress and impact of self help group bank linkage programme.
Exercise Intensity and Pacing Pattern During a Cross-Country Olympic Mountain Bike Race

Objective: To examine the power profiles and pacing patterns in relation to critical power (CP) and maximal aerobic power (MAP) output during a cross-country Olympic (XCO) mountain bike race. Methods: Five male and two female national competitive XCO cyclists completed a UCI Cat. 1 XCO race. The races were 19 km and 23 km and contained five (female) and six (male) laps, respectively. Power output (PO) during the race was measured with the cyclists’ personal power meters. On two laboratory tests using their own bikes and power meters, CP and work capacity above CP (W') were calculated using three time trials of 12, 7, and 3 min, while MAP was established based on a 3-step submaximal test and the maximal oxygen uptake from the 7-min time trial. Results: Mean PO over the race duration (96 ± 7 min) corresponded to 76 ± 9% of CP and 63 ± 4% of MAP. 40 ± 8% of race time was spent with PO > CP, and the mean duration and magnitude of the bouts >CP was ~8 s and ~120% of CP. From the first to last lap, time >CP and accumulated W' per lap decreased with 9 ± 6% and 45 ± 17%, respectively. For single >CP bouts, mean magnitude and mean W' expended decreased by 25 ± 8% and 38 ± 15% from the first to the last lap, respectively. Number and duration of bouts did not change significantly between laps. Conclusion: The highly variable pacing pattern in XCO implies the need for rapid changes in metabolic power output, as a result of numerous separate short-lived >CP actions which decrease in magnitude in later laps, but with little lap-to-lap variation in number and duration.
Dynamic state estimation for power grids with unconventional measurements

State estimation problem for power systems has long been a fundamental issue that demands a variety of methodologies dependent on the system settings. With recent introduction of advanced devices of phasor measurement units (PMUs) and dedicated communication networks, the infrastructure of power grids has been greatly improved. Coupled with the infrastructure improvements are three emerging issues for the state estimation problems, namely, the coexistence of both traditional and PMU measurements, the incomplete information resulting from delayed, missing and quantized measurements due to communication constraints, and the cyber-attacks on the communication channels. Three challenging problems are faced when dealing with the three issues in the state estimation program of power grids: 1) how to include the PMU measurements in the state estimator design, 2) how to account for the phenomena of incomplete information occurring in the measurements and design effective state estimators resilient to such phenomena, and 3) how to identify the system vulnerability in state estimation scheme and protect the estimation system against cyber-attacks. In this thesis, with the aim to solve the above problems, we develop several state estimation algorithms which tackle the issues of mixed measurements and incomplete information, and examine the cyber-security of the dynamic state estimation scheme. • To improve the estimation performance of power grids including PMU measurements, a hybrid extended Kalman filter and particle swarm optimization algorithm is developed, which has the advantages of being scalable to the numbers of the installed PMUs and being compatible with existing dynamic state estimation software as well. • Two kinds of network-induced phenomena, which leads to incomplete information of measurements, are considered. Specifically, the phenomenon of missing measurements is assumed to occur randomly and the missing probability is governed by a random variable, and the quantized nonlinear measurement model of power systems is presented where the quantization is assumed to be of logarithmic type. Then, the impact of the incomplete information on the overall estimation performance is taken into account when designing the estimator. Specifically, a modified extended Kalman