Miracle-dz/UniFAR-FTUs huggingface dataset:
Obtención de un polímero biodegradable a partir de la mezcla de alcohol polivinílico y amilosa extraída del almidón de Colocasia esculenta (Vituca) proveniente del distrito de yambrasbamba.

The research was aimed at obtaining a biodegradable polymer from a mixture ofpolyvinyl alcohol in different concentrations of3: 1, 2: 2 and 1: 3 and amylose starch extracted from Colocasia esculenta "Vituca"; l from the town of Yambrasbamba, Amazonas Pero, biodegradable polymer films were obtained, which were subjected to evaluations by 30 days; physical, chemical and biological properties were observed, taking into account different means of degrada ti en and 1% yeast (B 1 ), wastewater (B2), organic soil (B3 ), and environmental conditions; these assessments were moisture, pH, titratab1e acidity, microbiological analysis, strength analysis, Y oung's modulus, the percentage of degradation and degradation time. Of different treatments to give a translucent biodegradable polymer of 50 cm2 (5cm x·10cm) ofthe mixture ofpolyvinyl alcohol and amylose concentration of 1: 1, which showed a maximum resistance of 12446.67 Pa, a Y oung's modulus maximum of 1 41 71. 1 6 Pa , to environmental conditions and mini mal degradation time of 2 days in a medium degradation with 1% yeast.
Sparse Composite Quantile Regression with Ultra-high Dimensional Heterogeneous Data

: Although quantile regressions are widely employed for heterogeneous data, simultaneously selecting covariates that globally aﬀect the response and estimating the coeﬃcients is very challenging. We introduce a novel sparse composite quantile regression screening method for the analysis of ultrahigh-dimensional heterogeneous data. The proposed method enjoys the sure screening property, provides a consistent selection path, and yields consistent estimates of the coeﬃcients simultaneously across a continuous range of quantile levels. An extended Bayesian information criterion is employed to select the “best” candidate from the path. Ex-tensive simulation studies demonstrate the eﬀectiveness of the proposed method, and an application to a gene expression data set is provided.
Critical insights into modern hyperspectral image applications through deep learning

Hyperspectral imaging has shown tremendous growth over the past three decades. Hyperspectral imaging was evolved through remote sensing. Along, with the technological enhancements hyperspectral imaging has outgrown, conquering over other various application areas. In addition to it, data enriched data cubes with abundant spectral and spatial information works as perk for capturing, analyzing, reviewing, and interpreting results from data. This review concentrates on emerging application areas of hyperspectral imaging. Emerging application areas are selected in ways where there is a vast scope for future enhancements by exploiting cutting edge technology, that is, deep learning. Applications of hyperspectral imaging techniques in some selected areas (remote sensing, document forgery, history and archaeology conservation, surveillance and security, machine vision for fruit quality inspection, medical imaging) are focused. The review pivots around the publicly available datasets and features used domain wise. This review can act as a baseline for deep learning and machine vision experts, historical geographers, and scholars by providing them a view of how hyperspectral imaging is implemented in multiple domains along with future research prospects.
Rainfall on the Greenland Ice Sheet: Present‐Day Climatology From a High‐Resolution Non‐Hydrostatic Polar Regional Climate Model

Greenland ice sheet rainfall is expected to increase under a warming climate. Yet, there have been no active long‐term in‐situ rainfall records on the ice sheet due to observational difficulties. Here, we utilize the state‐of‐the‐art 5 km polar non‐hydrostatic regional climate model NHM‐SMAP to evaluate the ice sheet’s rainfall over 40 years (1980–2019). The largest trends include a fourfold increase in annual rainfall for the northwestern ice sheet; 3.1 Gt year−1 or 12 mm m−2 year−1. September ice‐sheet‐wide rainfall amount and intensity increase by 7.5 Gt month−1 and 20.8 mm h−1 year−1. In the last two decades, the increasing September maximum hourly rainfall rate exceeded 50 mm h−1 six times. The increased surface water delivery has numerous implications, including for snow metamorphism and ice flow dynamics.
Structures and properties of Mg0.95Mn0.01TM0.04O (TM = Co, Ni, and Cu) nanoparticles synthesized by sol–gel auto combustion technique

The room temperature structural, optical and dielectric properties of Mg0.95Mn0. 05O and Mg0.95Mn0. 01TM0.04O (TM = Co, Ni, and Cu) nanoparticles are reported. All transition metal nanocrystalline samples were successfully prepared by sol–gel auto combustion method. X-ray powder diffraction patterns at room temperature confirmed the formation of single-phase cubic structure with an Fm3̄m space group for all prepared samples. Slight variation in the lattice parameter of TM doped Mg0.95Mn0. 05O has been observed. Using Rietveld refinement of XRD data, the space group and lattice parameters are determined. Scanning electron microscopy (SEM) measurements were performed to understand the morphology and grain size of the Mg0.95Mn0. 01TM0.04O (TM = Co, Ni, and Cu) nanocrystals. The estimated band gaps as calculated by using UV-Vis spectroscopy are found to be 3.59, 3.61, 5.63 and 3.55 eV for Mg0.95Mn0. 05O and Mg0.95Mn0. 01TM0.04O (TM = Co, Ni, and Cu) nanocrystals, respectively. Both dielectric constant and dielectric loss is found to decrease due to TM (transition metal) doping. The ac conductivity is found to increase with increase in frequency. Electric modulus spectra reflect the contributions from grain effects: the large resolved semicircle arc caused by the grain effect. The results obtained in this study were discussed comparatively with those cited in the literature.
Body adornment and interaction aesthetics: a new frontier for assistive wearables

The paper addresses the challenge of balancing the tension between a problem-solving attitude in the design of assistive devices, with an ethical, aesthetic and cultural approach to design for people living with a temporary or permanent impairment. The topic is developed presenting two design cases. The first case addresses a permanent disability. It is a suite of smart jewels tailored for hearing impairment, which sense environmental sounds (e.g. doorbell, someone calling) and notify the wearer of their occurrence through different modalities (light patterns, vibrations, shape changes). The second case addresses a temporary impairment. It is an orthodontic facemask for the correction of malocclusions in children, that has customised aesthetics and ergonomics and is associated with a digital game. The cases illustrate the experience-centred participatory design adopted to mitigate the stigma associated with current wearable assistive devices and promote a cultural shift to transform assistive wearables into beautiful, playful, gender-appropriate accessories.
Application of Nonlinear Krylov Solvers for Conjugate Heat Transfer Simulations of Electrical Battery Packs

Krylov-based methods are an attractive alternative to traditional fixed-point iterative schemes, being much more robust and accurate when solving elliptic equations (e.g., the energy equation in the solid domain). This study assesses the performance of a Krylov-based accelerator, when used for conjugate heat transfer (CHT) simulations of an electrical battery pack. The nonlinear nature of CHT simulations (due to spatial and temporal changes in boundary conditions) necessitates the use of the non-inear form of the Krylov-based accelerator (termed NKA), which utilizes the generalized minimized residual (GMRES) method, and works by accelerating an existing fixed-point iteration scheme. NKA is used while performing steady-state CHT simulations of an air-cooled lithium-ion battery pack, specifically to help accelerate the solution of the solid domain energy equation. The effect of using either isotropic or anisotropic thermal conductivity within the cylindrical lithium-ion battery cells is also evaluated. Results obtained using the NKA accelerator are compared, in terms of accuracy and speed, with those obtained from a traditional nonlinear fixed-point iterative scheme based on successive over-relaxation (SOR). The NKA accelerator is found to perform quite well for the problem at hand, providing results with the specified accuracy, while also being between 5 and 20 times faster than SOR (while solving the solid energy equation). The robust nature of NKA also leads to better global heat balance within the battery pack at all times during the simulation. These observations hold for both the isotropic and anisotropic thermal conductivity conditions. Overall, computational cost reductions of 30–40% are observed when using NKA for the battery pack simulations. Although the performance of NKA is demonstrated for a battery cooling application, NKA performs quite well in other applications also.
Digitization in the Era of Coronavirus – A Review

In the modern world, the development of technology has a significant impact on various areas of human functioning. However, the appearance of the COVID-19 coronavirus pandemic and the need to implement numerous restrictions resulted in an even greater increase in the importance of new technologies, especially in such sectors as education, administration, or finance. In Poland, the introduction of the remote work system encountered many difficulties related to the unpreparedness of previous structures to implement such solutions. An example of a country where they operate efficiently, responding to the problems that Poland faces today, is Estonia, in which, beginning from 2004, the state administration sector has been gradually replaced by a digital system based on Blockchain technology. At present, in Poland, appropriate steps are already taken by the Ministry of Digitization to digitize public administration in the form of a plan of four streams. Given the protracted pandemic, the possibilities for immediate solutions and considering the advantages and disadvantages of digitizing the state seem worth attention.
Polarization-resolved second-harmonic generation imaging through a multimode fiber

Multimode fiber-based endoscopes have recently emerged as a tool for minimally invasive endoscopy in tissue, at depths well beyond the reach of multiphoton imaging. Here, we demonstrate label-free second-harmonic generation (SHG) microscopy through such a fiber endoscope. We simultaneously fully control the excitation polarization state and the spatial distribution of the light at the fiber tip, and we use this to implement polarization-resolved SHG imaging, which allows imaging and identification of structural proteins such as collagen and myosin. We image mouse tail tendon and heart tissue, employing the endoscope at depths up to 1 mm, demonstrating that we can differentiate these structural proteins. This method has the potential for enabling instant and in situ diagnosis of tumors and fibrotic conditions in sensitive tissue with minimal damage.
Amateur radio sensing technique using a combination of energy detection and waveform classification

A critical problem in spectrum sensing is to create a detection algorithm and test statistics. The existing approaches employ the energy level of each channel of interest. However, this feature cannot accurately characterize the actual application of public amateur radio. The transmitted signal is not continuous and may consist only of a carrier frequency without information. This paper proposes a novel energy detection and waveform feature classification (EDWC) algorithm to detect speech signals in public frequency bands based on energy detection and supervised machine learning. The energy level, descriptive statistics, and spectral measurements of radio channels are treated as feature vectors and classifiers to determine whether the signal is speech or noise. The algorithm is validated using actual frequency modulation (FM) broadcasting and public amateur signals. The proposed EDWC algorithm's performance is evaluated in terms of training duration, classification time, and receiver operating characteristic. The simulation and experimental outcomes show that the EDWC can distinguish and classify waveform characteristics for spectrum sensing purposes, particularly for the public amateur use case. The novel technical results can detect and classify public radio frequency signals as voice signals for speech communication or just noise, which is essential and can be applied in security aspects.
Robust Fabric Defects Inspection System Using Deep Learning Architecture

Clothing is one of the fundamental requirements for living. The fabric business is a steadily developing industry because the interest in dress will never diminish. To support the development of the clothing industry, the clothing industry needs to take rigid measures to keep up the quality of the pieces of fabric they produce. The industry needs a worker to screen the quality of the fabric using a manual fabric review framework. The goal of this article is to plan a profound deep learning algorithm to recognize the fabric types using computer vision. This article focuses on identification of fabric defects using convolutional neural network with the use of appropriate pooling layer, softmax layer, and rectified linear activation layer to acquire an undeniable degree of precision. The photographs of garments with various fabric defects like fabric broken pick defect, fabric with pattern, soiled fabric, fabric weft yarn defect, and plain fabric are considered for evaluation of the architecture. The performance of the architecture is measured with various performance measures like sensitivity, specificity, and accuracy. The algorithm produces the highest accuracy of 97.5 and 100 % for the training and testing samples, respectively, for soiled fabric type.
An Investigation of Technological Pedagogical and Content Knowledge (TPACK) Competencies of Pre-Service Visual Arts Teachers

This study aimed to examine TPACK (Technology, Pedagogy and Content Knowledge) competencies of pre-service visual arts teachers. Based on the descriptive and comparative survey model, TPACK competencies of pre-service visual arts teachers were compared on variables of gender, year of study and academic achievement. The participants of the study were 253 pre-service teachers studying in department of visual arts in Education Faculties of Atatürk, Karadeniz Teknik, Mersin and Necmettin Erbakan Universities. TPACK Competencies Scale was used to collect research data. The findings showed that TPACK competencies of pre-service visual arts teachers were low in terms of technology knowledge, but high in content knowledge. In addition, their TPACK competencies vary based on gender, year of study and academic achievement.
Peer Regulation in a Peer-to-Peer Business Model

Peer-to-peer (P2P) businesses in the hospitality and tourism industry pose a regulatory challenge that has disrupted traditional regulatory schemes. This article proposes peer regulation as a form of regulation that complements and supplements command-and-control regulation and platform self-regulation in a P2P business model. Using the polycentric coregulation framework and impression management theory as a theoretical basis, this study systematically explores peer regulation at intrapeer (i.e., self-monitoring and prosocial behaviors), interpeer (i.e., trust-enforcing mechanism and belongingness-enhancing mechanism), and platform (i.e., peer-centric platform self-regulation and de-individualization) levels. The article also discusses critical peer regulation issues such as P2P evaluation and reputation systems in a multifarious regulatory environment, P2P employment, and leveraging platform self-regulation and jurisdictional regulation. This article offers a theoretical account of multilevel peer regulation as a form of P2P regulation and provides future research directions on the topic.
Genetic diversity and relationships among Hypericum L. species by ISSR Markers: A high value medicinal plant from Northern of Iran

Hypericum L. species are generally known locally in Iran with the names “Hofariqun” which Ebn Sina (or Bo Ali Sina) called it. Plants of the genus Hypericum have traditionally been used as medicinal plants in various parts of the world. Hypericum perforatum L. is the source to one of the most manufactured and used herbal preparations in recent years, especially as a mild antidepressant. Therefore, due to the importance of these plant species, we performed a molecular data for this species. For this study, we used 175 randomly collected plants from 17 species in 9 provinces. Amplification of genomic DNA using 10 primers produced 141 bands, of which 127 were polymorphic (95.78%). The obtained high average PIC and MI values revealed high capacity of ISSR primers to detect polymorphic loci among Hypericum species. The genetic similarities of 17 collections were estimated from 0.617 to 0.911. According to Inter-Simple sequence repeats (ISSR) markers analysis, H. androsaemum and H. hirtellum had the lowest similarity and the species of H. perforaturm and H. triquetrifolium had the highest similarity. The aims of present study are: 1) can ISSR markers identify Hypericum species, 2) what is the genetic structure of these taxa in Iran, and 3) to investigate the species inter-relationship? The present study revealed that ISSR markers can identify the species.
Real-Time Weld Gap Monitoring and Quality Control Algorithm during Weaving Flux-Cored Arc Welding Using Deep Learning

In the flux-cored arc welding process, which is most widely used in shipbuilding, a constantexternal weld bead shape is an important factor in determining proper weld quality; however, thesize of the weld gap is generally not constant, owing to errors generated during the shell formingprocess; moreover, a constant external bead shape for the welding joint is difficult to obtain whenthe weld gap changes. Therefore, this paper presents a method for monitoring the weld gap andcontrolling the weld deposition rate based on a deep neural network (DNN) for the automationof the hull block welding process. Welding experiments were performed with a welding robotsynchronized with the welding machine, and the welding quality was classified according to theexperimental results. Welding current and voltage signals, as the robot passed through the weldseam, were measured using a trigger device and analyzed in the time domain and frequency domain,respectively. From the analyzed data, 24 feature variables were extracted and used as input for theproposed DNN model. Consequently, the offline and online performance verification results for newexperimental data using the proposed DNN model were 93% and 85%, respectively
Investigation of photovoltaic properties of nanostructure indoline dye-sensitised solar cells using changes in assembling materials

Purpose The purpose of this paper is to study assembling parameters in dye-sensitised solar cells (DSSCs) performance. For this end, 3a,7a-dihydroxy-5s-cholanic acid (cheno) are selected as anti-aggregation agent and two solutions, namely, tetrabutyl ammonium iodide and (PMII)IL used as electrolyte. Design/methodology/approach A series of organic dyes were selected using N-substituents carbazole as electron donor group and acrylic acid and cyanoacrylic acid as electron acceptor groups. Absorption properties of purified dyes were studied in solution and on photoelectrode substrate. DSSCs were prepared in the presence of anti-aggregation agent and different electrolyte to determine the photovoltaic performance of each dyes. Findings The results showed that all organic dyes form J-aggregation on the photoanode substrate in the absence of anti-aggregation agent and the amounts of aggregation were reduced in the presence of anti-aggregation agent. DSSCs were fabricated in the presence of anti-aggregation agent. The photovoltaic properties were improved using tetrabutyl ammonium iodide as electrolyte. The maximum power conversion efficiency was achieved for D12 in the presence of cheno and tetrabutyl ammonium iodide as anti-aggregation agent and electrolyte, respectively. Social implications Organic dye attracts more and more attention due to low cost, facile route synthesis and less hazardous. Originality/value The effect of anti-aggregation agent and electrolyte on DSSCs performance was investigated for the first time.
Leveraging machine learning methods to estimate heterogeneous effects: father absence in China as an example

Abstract Individuals differ in their personal and environmental characteristics, and the same treatment or condition may affect individuals in different ways or magnitudes. Heterogeneity in effects thus has important implications for academic research and policymaking. However, it is difficult to uncover and estimate heterogenous effects using conventional parametric models without making assumptions based on limited information, and results can be difficult to interpret when involving a large number of moderators. To address these limitations, this paper introduces three supervised machine learning methods for estimating heterogeneous treatment effects with experimental and observational data: causal forest, Bayesian Additive Regression Trees (BART), and an ensemble approach called X-learner. These methods are first applied to simulated datasets and then implemented using empirical education survey data from China to estimate heterogeneous effects of father absence on student cognitive ability across a series of individual and family characteristics.
Assessment of Tire Features for Modeling Vehicle Stability in Case of Vertical Road Excitation

Two trends could be observed in the evolution of road transport. First, with the traffic becoming increasingly intensive, the motor road infrastructure is developed; more advanced, greater quality, and more durable materials are used; and pavement laying and repair techniques are improved continuously. The continued growth in the number of vehicles on the road is accompanied by the ongoing improvement of the vehicle design with the view towards greater vehicle controllability as the key traffic safety factor. The change has covered a series of vehicle systems. The tire structure and materials used are subject to continuous improvements in order to provide the maximum possible grip with the road pavement. New solutions in the improvement of the suspension and driving systems are explored. Nonetheless, inevitable controversies have been encountered, primarily, in the efforts to combine riding comfort and vehicle controllability. Practice shows that these systems perform to a satisfactory degree only on good quality roads, as they have been designed specifically for the latter. This could be the cause of the more complicated car control and accidents on the lower-quality roads. Road ruts and local unevenness that impair car stability and traffic safety are not avoided even on the trunk roads. In this work, we investigated the conditions for directional stability, the influence of road and vehicle parameters on the directional stability of the vehicle, and developed recommendations for the road and vehicle control systems to combine to ensure traffic safety. We have developed a refined dynamic model of vehicle stability that evaluates the influence of tire tread and suspensions. The obtained results allow a more accurate assessment of the impact of the road roughness and vehicle suspension and body movements on vehicle stability and the development of recommendations for the safe movement down the road of known characteristics.