Miracle-dz/UniFAR-FTUs huggingface dataset:
TWO META-HEURISTIC ALGORITHMS FOR THE DUAL-RESOURCE CONSTRAINED FLEXIBLE JOB-SHOP SCHEDULING PROBLEM

Systems where both machines and workers are treated as constraints are termed dual- resource constrained (DRC) systems. In the last few decades, DRC scheduling has attracted much attention from researchers. This paper addresses the dual-resource constrained flexible job-shop scheduling problem (DRCFJSP) to minimize makespan. This problem is NP-hard and mainly includes three sub-problems: (1) assigning each operation to a machine out of a set of compatible machines, (2) determining a worker among a set of skilled workers for operating each operation on the selected machine, and (3) sequencing the operations on the machines considering workers in order to optimize the performance measure. This paper presents two meta-heuristic algorithms, namely simulated annealing (SA) and vibration damping optimization (VDO), to solve the DRCFJSP. The proposed algorithms make use of various neighborhood structures to search in the solution space. The Taguchi experimental design method as an optimization technique is employed to tune different parameters and operators of the presented algorithms. Numerical experiments with randomly generated test problems are used to evaluate performance of the developed algorithms. A lower bound is used to obtain the minimum value of makespan for the test problems. The computational study confirms the proper quality of results of the proposed algorithms.
Machine learning techniques in Internet of Things

The Internet of Things (IoT) and Machine Learning (ML) are two very hot technologies these days. IoT requires a lot of data processing, and ML is a useful means of processing data. Therefore, the combination of IoT and ML has become a very promising research direction. This paper is a investigation of the combination of IoT and ML. It first introduces the development history of IoT and ML, then introduces some achievements that have emerged in the field of ML and IoT combination. After that, the paper refers some ML technologies which will play important roles in IoT. In this process, this paper also proposes a scheme to improve the accuracy of YOLO algorithm by identifying picture groups. Finally, the paper discusses the existing problems and future development directions of the combination of IoT and ML and provides some references and suggestions for scholars who study the combination of ML and IoT technology.
Intercalation of p-Aminopyridine and p-Ethylenediamine Molecules into Orthorhombic In1.2Ga0.8S3 Single Crystals

A single crystalline layered semiconductor In1.2Ga0. 8S3 phase was grown, and by intercalating p-aminopyridine (NH2-C5H4N or p-AP) molecules into this crystal, a new intercalation compound, In1.2Ga0. 8S3·0.5(NH2-C5H4N), was synthesized. Further, by substituting p-AP molecules with p-ethylenediamine (NH2-CH2-CH2-NH2 or p-EDA) in this intercalation compound, another new intercalated compound—In1.2Ga0. 8S3·0.5(NH2-CH2-CH2-NH2) was synthesized. It was found that the single crystallinity of the initial In1.2Ga0. 8S3 samples was retained after their intercalation despite a strong deterioration in quality. The thermal peculiarities of both the intercalation and deintercalation of the title crystal were determined. Furthermore, the unit cell parameters of the intercalation compounds were determined from X-ray diffraction data (XRD). It was found that increasing the c parameter corresponded to the dimension of the intercalated molecule. In addition to the intercalation phases’ experimental characterization, the lattice dynamical properties and the electronic and bonding features of the stoichiometric GaInS3 were calculated using the Density Functional Theory within the Generalized Gradient Approximations (DFT-GGA). Nine Raman-active modes were observed and identified for this compound. The electronic gap was found to be an indirect one and the topological analysis of the electron density revealed that the interlayer bonding is rather weak, thus enabling the intercalation of organic molecules.
Identification of parameters in normal error component logit-mixture (NECLM) models

Although the basic structure of logit-mixture models is well understood, important identification and normalization issues often get overlooked. This paper addresses issues related to the identification of parameters in logit-mixture models containing normally distributed error components associated with alternatives or nests of alternatives (normal error component logit mixture, or NECLM, models). NECLM models include special cases such as unrestricted, fixed covariance matrices; alternative-specific variances; nesting and cross-nesting structures; and some applications to panel data. A general framework is presented for determining which parameters are identified as well as what normalization to impose when specifying NECLM models. It is generally necessary to specify and estimate NECLM models at the levels, or structural, form. This precludes working with utility differences, which would otherwise greatly simplify the identification and normalization process. Our results show that identification is not always intuitive; for example, normalization issues present in logit-mixture models are not present in analogous probit models. To identify and properly normalize the NECLM, we introduce the 'equality condition', an addition to the standard order and rank conditions. The identifying conditions are worked through for a number of special cases, and our findings are demonstrated with empirical examples using both synthetic and real data. Copyright © 2007 John Wiley & Sons, Ltd.
Segmentation of fingerprint images based on variance and gradient factor

The task of fingerprint segmentation is to isolate the fingerprint area from the image background. In this paper, a new algorithm of fingerprint segmentation based on block variance and gradient factor is proposed. A new composite feature for each block is used in this method. At first, the fingerprint image is partitioned into blocks, and then the product of variance and gradient factor is calculated as the new feature. Then the thresholds are computed adaptively. After segmentation, the isolated image blocks are wiped to get the final segmentation result. The experimental results show that this algorithm is effective and programmed easily.
The HEAO A-1 all-sky survey of fast X-ray transients

Ten fast X-ray transient sources were discovered in the first complete pass of the sky by the HEAO A-1 Sky Survey Experiment, including 7 relatively faint sources and 3 bright ones. The survey technique and each of the transients are described. Four of the transients are identified with active coronal sources; the remaining ones are unidentified. The statistical properties of the total sample of 10 sources are discussed, including event rates, distribution on the sky, and the number-flux distribution. 57 references.
Blocking probabilities in large circuit-switched networks

This paper is concerned with blocking and loss probabilities in circuit-switched networks. We show that when the capacity of links and the offered traffic are increased together, a limiting regime emerges in which loss probabilities are as if links block independently, with blocking probabilities given by the solution of a simple convex programming problem. We then show that an approximate procedure, based on solving Erlang&s formula under the assumption of independent blocking, produces a unique solution when routes are fixed, and that under the limiting regime the estimates of loss probabilities obtained from the procedure converge to the correct values.
In vivo anticancer activity of benzoxazine and aminomethyl compounds derived from eugenol

Background Indonesia is the world’s primary producer of clove. In order to find new utilization for clove and new biologically active compounds, eugenol, the main constituent of clove, has been converted to its derivatives. Objective This study aims to examine in vivo anticancer activity of benzoxazine and aminomethyl compounds derived from eugenol. Methods Fibrosarcoma was induced by injection of benzo(a)pyrene solution. The test compounds were given per oral at 20, 40, and 80 mg/Kg body weight, once a day for 30 days. Results As a result, all the tested compounds showed activity in reducing the cancer incidence rate. All the tested compounds were also found to reduce tumor weight. Benzoxazine derivatives gave slightly better activity compared to aminomethyl derivatives. The strongest activity was exhibited by 6-allyl-3-(furan-2- ylmethyl)-8-methoxy-3,4-dihydro-2H-benzo(e)(1,3)oxazine. Conclusions All four benzoxazine and aminomethyl compounds derived from eugenol that were tested exhibited anticancer activity in mice fibrosarcoma.
Dynamics of the December 2020 Ash‐Poor Plume Formed by Lava‐Water Interaction at the Summit of Kīlauea Volcano, Hawaiʻi

On 20 December 2020, after more than 2 years of quiescence at Kīlauea Volcano, Hawaiʻi, renewed volcanic activity in the summit crater caused boiling of the water lake over a period of ∼90 min. The resulting water‐rich, electrified plume rose to 11–13 km above sea level, which is among the highest plumes on record for Kīlauea. Although conventional models would infer a high mass flux from explosive magma‐water interaction, the plume was not associated with an infrasound signal indicative of “explosive” activity, nor did it produce a measurable ash‐fall deposit. We use multisensor data to characterize lava‐water interaction and plume generation during this opening phase of the 2020–21 eruption. Satellite, weather radar, and eyewitness observations revealed that the plume was rich in water vapor and hydrometeors but transported less ash than expected from its maximum height. Volcanic lightning flashes detected by ground‐based cameras were confined to freezing altitudes of the upper cloud, suggesting that the ice formation drove the electrification of this plume. The low acoustic energy from lava‐water interaction points to a weakly explosive style of hydrovolcanism. Heat transfer calculations show that the lava to water heat flux was sufficient to boil the lake within 90 min. Limited mixing of lava and water inhibited major steam explosions and fine fragmentation. Results from one‐dimensional plume modeling suggest that the models may underpredict plume height due to overestimation of crosswind air‐entrainment. Our findings shed light on an unusual style of volcanism in which weakly explosive lava‐water interaction generated an outsized plume.
Subjective evaluation of image quality measures for white noise and Gaussian blur-distorted images

Abstract Image quality assessment has diverse applications. A number of image quality measures (IQMs) are proposed, but none is proved to be true representative of human perception of image quality. We have subjectively investigated spectral distance-based and human visual system (HVS)-based IQMs for their effectiveness in representing the human perception for images corrupted with white noise and Gaussian blur. Two sets of 160 images with various degrees of white noise and Gaussian blur are subjectively evaluated by 50 human subjects, resulting in 16 000 human judgments. On the basis of evaluations, image-independent human perception values are calculated. The perception values are plotted against spectral distance-based and HVS-based IQMs. The performance of quality measures is determined by graphical observations and polynomial curve fitting, resulting in best performance by HVS absolute norm and block spectral phase-magnitude error for white noise and Gaussian blur distortions, respectively. It is also observed that the performances of various quality measures differ for different noise distortions, suggesting the use of different quality measures for different noise types rather than a single universal IQM.
Design of Optimal Robust Steady-state Tracking Controller under Coprime Factor Perturbations

The robust steady-state tracking problem subjected to coprime factor perturbations and unknown external bounded disturbance is addressed.An exact formula for computing the worst-possible steady-state absolute value of the control error is proposed by using Youla parameterization.With this formula,it is shown that the design of optimal robust steady-state tracking controller is reduced to 1 optimal robust controller design ensuring robust stability.
Novel Dual Endothelin Inhibitors in the Management of Resistant Hypertension

Resistant hypertension (RH) is defined as the failure to achieve blood pressure control despite using triple combination therapy with a renin-angiotensin system inhibitor (RAS-i), a calcium antagonist, and a diuretic. The endothelin (ET) system is implicated in the regulation of vascular tone, primarily through vasoconstriction, intervenes in cardiac contractility with inotropic effects, and contributes to water and sodium renal reabsorption. ET inhibitors, currently approved for the treatment of pulmonary hypertension, seem to be also useful for essential hypertension and RH as well. Studies into the development of new dual ET inhibitors, which inhibit both type A and B ET (ETA and ETB) receptors, present initial results of managing RH. Aprocitentan (ACT-132577) is a novel, orally active and well tolerated dual ET receptor antagonist, which has been examined in several experimental studies and clinical trials with promising results for RH control. The recent publication of the large PRECISION study in The Lancet journal provides further reassurance regarding the efficacy and safety of aprocitentan for RH, with the aim of overcoming unmet needs in the management of this difficult group of patients.
Josephson Effect in the Quantum Hall Regime

We consider a SNS junction, where the normal metal is taken to be in the quantum Hall regime. The induced supercurrent is carried by the edge states which are always extended. The coherence length along the direction of the edge state is determined by the drift velocity due to the surface electric field, and is much larger than the magnetic length. In a point contact junction, the critical supercurrent is independent of the positions of the leads, and depends only on the junction perimeter. We estimate the magnitude of this effect and comment on its experimental realizability.
Social justice in English language learning in Ecuador: beliefs of indigenous higher education teachers and indigenous and mestizo student teachers

ABSTRACT The aim of this study was to explore indigenous teachers’ and indigenous and mestizo student teachers’ beliefs about social justice and English language learning in the Ecuadorian context. Based on data collected through interviews and one focus group session, it was found that many indigenous believe English might be a convenient tool for personal and professional development as well as intercultural communication. On the other hand, even though some laws have been passed and awareness has been raised in Ecuador regarding the need for social justice pertaining to indigenous people, the participants indicated that this community is still an underprivileged group.
Gimbal Axes Control with PID Controllers

Gimbal is a system in missiles that allows the seeker to lock onto the target and follow it and increases the angle of view with its mobility in two axes. In this study, the control of the axes of a two-axis gimbal system used in the missile was carried out. PID controller tuned with Particle Swarm Optimization (PSO) is used in the control algorithm. In the optimization, a smoother controller is aimed by using the multi-objective objective function, which also includes the controller output with the position error. At the same time, the bandwidth of the system is also included as a constraint. The Butterworth Polynomial Method (BPM), which can adjust the coefficient according to the bandwidth criterion, was used for comparison purposes. As a result of the experimental studies show that the PID tuned with PSO can control the system with a lower positional error by responding faster to external factors than the PID designed with BPM.
A regularization approach to state estimation using observers

State estimation is an inverse problem, since causes are determined for observed effects. Inverse problems are generally ill-posed. Essentially, their solution is not unique and/or unstable with respect to perturbation in the data. They are therefore difficult to solve. To cope with the nonuniqueness and stability problems, regularization methods have been developed in the mathematical literature on inverse problems. In this work linear state estimation, which has been traditionally solved by optimal filters or observers, is reconsidered from the viewpoint of the theory of inverse problems.
Brillouin and Raman imaging for plant cell wall mechanics

Plant cell walls are highly complex structures, which are adaptable to various environmental stresses. While many biochemical pathways have been discovered, cellular stress indicators are widely unknown. Here we demonstrate a label-free optical setup, that can map both mechanical properties and chemical composition in biological samples. We measured changes in mechanical stiffness and chemical response of rose leaf cells due to inoculation of the fungal disease Diplocarpon rosae. With this we are able to image fungal growth in a dynamical and high-resolution manner.
High thermal conductivity epoxy-silver composites based on self-constructed nanostructured metallic networks

We demonstrate epoxy-silver nanoparticle composites with high thermal conductivity κ enabled by self-constructed nanostructured networks (SCNN) forming during the curing process at relatively low temperatures (150 °C). The networks formation mechanism involves agglomeration of the polyvinylpyrrolidone (PVP) coated nanoparticles, PVP removal, and sintering of the nanoparticles at suppressed temperatures induced by their small diameters (20–80 nm). Sintering and the SCNN formation are supported by differential scanning calorimetry and electron microscopy investigations. The formation of SCNN with high aspect ratio structures leads to enhancements in the measured thermal conductivity κ of the composite by more than two orders of magnitude versus the pure epoxy. However, κ enhancements are modest if microparticles (1.8–4.2 μm) are employed instead of PVP coated nanoparticles. The κ trends are qualitatively explained using a percolating threshold thermal conductivity model for the microcomposites. For the nano.
Operation and maintenance management and emergency response of data protection system in market supervision

Based on the trend research of market supervision scheduling automation, this study analyzes the function of establishing market supervision scheduling automation operation and maintenance supervision system, expounds the application value of market supervision scheduling automation operation and maintenance supervision system, puts forward the key points of market supervision scheduling automation operation and maintenance supervision system design, provides the key points of market supervision application scheduling automation operation and maintenance supervision system application, It is hoped that through the evaluation of dispatching automation operation and maintenance supervision system, the design and application of dispatching automation operation and maintenance supervision system will be strengthened, and a dispatching automation operation and maintenance supervision system suitable for the actual development of market supervision will be established, which will be helpful for the popularization and application of dispatching automation operation and maintenance supervision system in market supervision.
A Comparison of Data Augmentation Methods on Ultrasound Tongue Images for Articulatory- to-Acoustic Mapping towards Silent Speech Interfaces

—Silent Speech Interfaces (SSI), being a subfield of speech technology, break the limitations of automatic speech recognition when acoustic signals cannot be produced or clearly captured. SSI focuses on the articulation process of speech production in order to map articulatory data into acoustics. Ultrasound tongue imaging (UTI), a non-invasive, clinically safe technique to view the shape, position, and movements of the tongue, has recently become popular in the process of collecting articulatory data of the tongue movement. Despite advancements in the field of SSI, the majority of related research has been conducted using limited datasets due to challenges in acquiring additional information, which results in overfitting. It has already been shown that data augmentation can be helpful for solving the overfitting problem and improving the generalization ability of deep neural networks. In this paper, we discuss the preliminary implementation and comparison of data augmentation methods on Azerbaijani ultrasound and speech recordings that has been recorded by our team. These strategies include consecutive and intermittent time masking, sinusoidal noise injection, and random scaling. We explore the generation of new data samples using the provided methods on the dataset. We use mean-squared error validation loss as an evaluation metric to measure the performance of all the above data augmentation methods.
Solitonic monopole solution in metric-affine gauge theory carrying Weyl charges

Examples in which spacetime might become non-Riemannian above Planck energies occur in string theory or in the very early universe in the inflationary model. The simplest such geometry is metric-affine geometry, in which nonmetricity appears as a field strength, side by side with curvature and torsion. In this paper we obtain a solitonic solution of the metric-affine gauge theory of gravity, which is endowed with shear and dilational Weyl charges.
System Derived Spatial-Temporal CNN for High-Density fNIRS BCI

An intuitive and generalisable approach to spatial-temporal feature extraction for high-density (HD) functional Near-Infrared Spectroscopy (fNIRS) brain-computer interface (BCI) is proposed, demonstrated here using Frequency-Domain (FD) fNIRS for motor-task classification. Enabled by the HD probe design, layered topographical maps of Oxy/deOxy Haemoglobin changes are used to train a 3D convolutional neural network (CNN), enabling simultaneous extraction of spatial and temporal features. The proposed spatial-temporal CNN is shown to effectively exploit the spatial relationships in HD fNIRS measurements to improve the classification of the functional haemodynamic response, achieving an average F1 score of 0.69 across seven subjects in a mixed subjects training scheme, and improving subject-independent classification as compared to a standard temporal CNN.
Evaluation of the Tropical Rainfall Measuring Mission 3B43 product over an inland area in Brazil and the effects of satellite boost on rainfall estimates

Abstract The main objective of this study was to evaluate the monthly mean areal rainfall estimated using the Tropical Rainfall Measuring Mission (TRMM) 3B43 version 7 product over an inland area in Central Brazil. Furthermore, we investigated the effect of TRMM orbit boost (in August 2001) over the 3B43 estimates. The TRMM 3B43 estimates were compared to reference rainfall data, collected at 67 rain gauge stations irregularly distributed in the study area. The results showed a good agreement between the TRMM 3B43 areal monthly mean rainfall estimations and reference data ( r > 0.97 ). The error analysis showed that the TRMM 3B43 product tends to overestimate the areal monthly mean rainfall at approximately 1.24%. The root-mean-square error (RMSE) for the entire period was 19.66 mm month − 1 (15.75%). A Monte Carlo simulation and Wilcoxon statistical test showed that the RMSE increased significantly ( p -value < 0.01 ) after the boost, rising from 15.20 to 23.06 mm month − 1 . However, the increase in the RMSE does not preclude the use of the TRMM 3B43 product for estimating the monthly mean areal rainfall over the Upper Paraná watershed. The impacts of boost on TRMM 3B43 estimates were observed only for rainfall rates higher than 250 mm month − 1 .