Miracle-dz/UniFAR-FTUs huggingface dataset:
Investigation of velocity and temperature measurement sensitivities in cross-correlation filtered Rayleigh scattering (CCFRS)

The cross-correlation filtered Rayleigh scattering method is presented for planar measurements of time-averaged velocity, temperature, density, and pressure using laser scanning, Rayleigh scattering, and cross-correlation-based signal processing. Of particular note, the methods and analyses presented show that velocity and temperature measurements are obtainable at acceptable uncertainties for many aerodynamic applications with or without particles present. In the current work, velocity and temperature measurement uncertainties for this method are analysed by Monte Carlo simulations and evaluation of the Cramér–Rao lower bounds for the signals generated in this approach with aerodynamic applications in mind. A key aspect considered is the relative strength of Rayleigh (molecular) scattering and Mie (particle) scattering contributions to the simulated flow signal. Compared to particle-based velocity estimation, the effects due to convolution between the molecular filter and the broadband Rayleigh scattering spectrum are found to increase the estimator variance of the velocity. For given flow conditions, the velocity error associated with the signal processing routine with noise-free signals is found to be less than , and bias error associated with discrete sampling in the laser frequency domain is within . For these same conditions, temperature measurement simulations indicate bias error bounds of 0. 014% with random uncertainty bounds of less than ±1.4% for a signal with no Mie scattering contribution, and bias error bounds of less than ±0.14% with random uncertainty bounds of less than 3.2% for flows contaminated with Mie scattering sources at a signal-to-noise ratio of 25 dB or greater. This technique is also being further developed to measure density and pressure using the principles of laser Rayleigh scattering in flows containing particles of unknown sizes and concentrations.
Freezing limits for Calogero–Moser–Sutherland particle models

One‐dimensional interacting particle models of Calogero–Moser–Sutherland type with N particles can be regarded as diffusion processes on suitable subsets of RN$\mathbb {R}^N$ like Weyl chambers and alcoves with second‐order differential operators as generators of the transition semigroups, where these operators are singular on the boundaries of the state spaces. The most relevant examples are multivariate Bessel processes and Heckman–Opdam processes in a compact and noncompact setting where in all cases, these processes are related to special functions associated with root systems. More precisely, the transition probabilities can be described with the aid of multivariate Bessel functions, Jack and Heckman–Opdam Jacobi polynomials, and Heckman–Opdam hypergeometric functions, respectively. These models, in particular, form dynamic eigenvalue evolutions of the classical random matrix models like β‐Hermite, β‐ Laguerre, and β‐Jacobi, that is, MANOVA, ensembles. In particular, Dyson's Brownian motions and multivariate Jacobi processes are included. In all cases, the processes depend on so‐called coupling parameters. We review several freezing limit theorems for these diffusions where, for fixed N, one or several of the coupling parameters tend to ∞. In many cases, the limits will be N‐dimensional normal distributions and, in the process case, Gauss processes. However, in some cases, normal distributions on half spaces and distributions related to some other ensembles appear as limits. In all cases, the limits are connected with the zeros of the classical one‐dimensional orthogonal polynomials of order N.
Effect of Atmospheric Temperature on Epoxy Coating Reinforced with Carbon Nanotubes for De-Icing on Road Systems

Traffic accidents caused by road icing are a serious global problem, and conventional de-icing methods like spraying chemicals have several limitations, including excessive manpower management, road damage, and environmental pollution. In this study, the carbon nanotubes reinforced de-icing coating for the road system with a self-heating function was developed as part of the development of a new system to prevent accidents caused by road icing. The electrical characteristics of the fabricated coating were analyzed, and the carbon nanotube coating heating performance experiment was conducted to measure the temperature increments by applying a voltage to the coating at a sub-zero temperature using an environmental chamber. In addition, the coating was installed on the road pavement and the applicability was investigated through a heating test in winter. As a result of the experiment, the coating made with the higher carbon nanotube concentration presented higher heating owing to its higher electrical conductivity. In addition, the coating showed sufficient heating performance, although the maximum temperature by Joule heating decreased for the entire coating at sub-zero temperatures. Finally, field tests demonstrated the potential of electrically conductive coatings for de-icing applications.
Scene modeling from motion-free radar sensing

Radar can offer remarkable advantages for robotic safeguarding, mapping and navigation because it is not as vulnerable to vacuum, dust, fog, rain, snow and dark. These conditions challenge the performance of sonar, laser and stereo in automating construction, mining, agriculture and planetary-exploration. Sonar cannot operate in vacuum conditions, low visibility and scanning mechanisms limit laser's reliability, and stereo is dependent on scene texture and illumination. Another radar advantage is electronic steering. However, radar has shortcomings such as a large footprint, sidelobes, specularity effects and limited range resolution, all of which result in poor perception models. Some of these problems worsen with the introduction of motion-free scanning, which is important for high reliability, size reduction, and higher frame rate. This thesis presents a short-range radar interpreter that accounts for properties such as sidelobes, varying antenna-radiation patterns and noise distribution. The interpreter uses a three-dimensional evidence-grid representation to accumulate the radar data. A radar interpretation model, which accounts for the antenna radiation properties and the signal and noise distributions, populates the grid with each radar observation. The combination of data from disparate vantage points mitigates the ambiguities introduced by the sidelobes, attenuates the noise, and emphasizes the evidence of objects in the scene. To exhibit the capabilities of the interpretation methodology, the thesis presents the results of simulations and experimental activities. A radar simulator provides data for exploring the different aspects of the interpretation methodology. Two experimental tasks, landscape modeling from multiple viewpoints and vehicle-mounted radar interpretation, show the applicability of the technique for robotic perception tasks.
An Augmented Modulated Deep Learning Based Intelligent Predictive Model for Brain Tumor Detection Using GAN Ensemble

Brain tumor detection in the initial stage is becoming an intricate task for clinicians worldwide. The diagnosis of brain tumor patients is rigorous in the later stages, which is a serious concern. Although there are related pragmatic clinical tools and multiple models based on machine learning (ML) for the effective diagnosis of patients, these models still provide less accuracy and take immense time for patient screening during the diagnosis process. Hence, there is still a need to develop a more precise model for more accurate screening of patients to detect brain tumors in the beginning stages and aid clinicians in diagnosis, making the brain tumor assessment more reliable. In this research, a performance analysis of the impact of different generative adversarial networks (GAN) on the early detection of brain tumors is presented. Based on it, a novel hybrid enhanced predictive convolution neural network (CNN) model using a hybrid GAN ensemble is proposed. Brain tumor image data is augmented using a GAN ensemble, which is fed for classification using a hybrid modulated CNN technique. The outcome is generated through a soft voting approach where the final prediction is based on the GAN, which computes the highest value for different performance metrics. This analysis demonstrated that evaluation with a progressive-growing generative adversarial network (PGGAN) architecture produced the best result. In the analysis, PGGAN outperformed others, computing the accuracy, precision, recall, F1-score, and negative predictive value (NPV) to be 98.85, 98.45%, 97.2%, 98.11%, and 98.09%, respectively. Additionally, a very low latency of 3.4 s is determined with PGGAN. The PGGAN model enhanced the overall performance of the identification of brain cell tissues in real time. Therefore, it may be inferred to suggest that brain tumor detection in patients using PGGAN augmentation with the proposed modulated CNN technique generates the optimum performance using the soft voting approach.
Due-date assignment scheduling involving job-dependent learning effects and convex resource allocation

ABSTRACT This article addresses single machine resource allocation scheduling problems with learning effects, where learning effects mean job-dependent position-based learning effects. For the common due-date assignment (CON) and slack due-date assignment (SLK) methods, a bi-cost analysis of the scheduling cost and the total weighted resource consumption cost is provided. The objective is to determine the optimal job sequence and the resource allocation simultaneously, such that the scheduling cost (the total weighted resource consumption cost) is minimized subject to the total weighted resource consumption cost (the scheduling cost) being limited. Solution procedures are provided for the problems under consideration.
A Segmentation-based Token Identification for Recognition of Audio Mathematical Expression

—In human-computer interaction, humans can interact with the computer with the help of text, audio, images, speech, etc. Interacting with the computer using speech, speech recognitions in particularly audio segmentation is a challenging task due to accent or way of pronouncing style. To input mathematical symbols, words, functions, and expressions with the help of a keyboard are tedious and time-consuming. Input this with the help of audio, speeds up the input process. In this paper, an SBTI (audio Segmentation Based Token Identification) algorithm is proposed for the recognition of words in an audio mathematical expression. 6 types of audio mathematical expressions are considered for recognition. The proposed algorithm segments the audio file into chunks and from each chunk temporal and spectral characteristics of audio signals are selected to extract the features. The model is trained using a neural network. The proposed algorithm shows a classification accuracy of 100% for the algebraic, quadratic, area, and differentiation expression, 99% for trigonometric expression, and 92% for summation expression.
Migratory fishes from rivers to reservoirs: seasonal and longitudinal perspectives

Migratory fishes have high ecological, social, and commercial value, and are strongly affected by river regulation. This study aimed to understand how migratory fishes use the longitudinal gradient, in an upstream to downstream direction of two free-flowing rivers and two reservoirs in a cascade within the Upper Grande River Basin, Brazil. The numeric abundance, biomass, richness, presence of fingerlings and juveniles, and the macroscopic gonadal maturation stage of migratory fishes were ascertained and evaluated bimonthly for two years. Recruitment in migratory fishes seems to rely completely on the free-flowing rivers upstream and their floodplains. Transition areas do not seem to have a significant role in recruitment. Therefore, we highlight the importance of maintaining the free flow of rivers and the integrity of their floodplains.
PENGARUH PERCEIVED ORGANIZATION SUPPORT, INFORMATION AND COMMUNICATION TECHNOLOGIES MODEL TERHADAP JOB SATISFACTION DENGAN MEDIASI WORK-FAMILY BALANCE SELAMA PANDEMI COVID-19

Dalam proses penyesuaian aktivitas pembelajaran mahasiswa mengharuskan Universitas untuk menggunakan teknologi dalam proses pembelajaran secara sinkronus selama Covid-19. Keadaan ini mempengaruhi job satisfaction tenaga kependidikan di Universitas XYZ. Penelitian ini mengajukan model penelitian mengenai pengaruh perceived organization support, ICT’s demands, dan ICT’s resources terhadap job satisfaction yang dimediasi oleh work-family balance selama pandemi Covid-19 di Universitas XYZ. Penelitian ini mengumpulkan data dengan menyebarkan kuisioner secara daring melalui Microsoft Forms yang diberikan kepada 210 karyawan Universitas XYZ. Hasil yang didapatkan bahwa adanya pengaruh positif perceived organization support dan pengaruh negatif ICT’s demands terhadap work-family balance. Terdapat juga pengaruh positif work-family balance terhadap job satisfaction. Sedangkan untuk variabel mediasi menunjukkan bahwa work-family balance mampu memediasi perceived organization support dan ICT’s demands terhadap job satisfaction. Variabel ICT’s resources tidak signifikan terhadap work-family balance. Serta, tidak ada pengaruh ICT’s resources terhadap job satisfaction dengan mediasi work-family balance.
Exploring Nanocarriers as Treatment Modalities for Skin Cancer

Cancer is a progressive disease of multi-factorial origin that has risen worldwide, probably due to changes in lifestyle, food intake, and environmental changes as some of the reasons. Skin cancer can be classified into melanomas from melanocytes and nonmelanoma skin cancer (NMSC) from the epidermally-derived cell. Together it constitutes about 95% of skin cancer. Basal cell carcinoma (BCC) and cutaneous squamous cell carcinoma (CSCC) are creditworthy of 99% of NMSC due to the limited accessibility of conventional formulations in skin cancer cells of having multiple obstacles in treatment reply to this therapeutic regime. Despite this, it often encounters erratic bioavailability and absorption to the target. Nanoparticles developed through nanotechnology platforms could be the better topical skin cancer therapy option. To improve the topical delivery, the nano-sized delivery system is appropriate as it fuses with the cutaneous layer and fluidized membrane; thus, the deeper penetration of therapeutics could be possible to reach the target spot. This review briefly outlooks the various nanoparticle preparations, i.e., liposomes, niosomes, ethosomes, transferosomes, transethosomes, nanoemulsions, and nanoparticles technologies tested into skin cancer and impede their progress tend to concentrate in the skin layers. Nanocarriers have proved that they can considerably boost medication bioavailability, lowering the frequency of dosage and reducing the toxicity associated with high doses of the medication.
Perturbation modifications by pre-magnetisation of inertial confinement fusion implosions

Pre-magnetisation of inertial confinement fusion implosions on the National Ignition Facility has the potential to raise current high-performing targets into the ignition regime [Perkins et al. “The potential of imposed magnetic fields for enhancing ignition probability and fusion energy yield in indirect-drive inertial confinement fusion,” Phys. Plasmas 24, 062708 (2017)]. A key concern with this method is that the application of a magnetic field inherently increases asymmetry. This paper uses 3-D extended-magnetohydrodynamics Gorgon simulations to investigate how thermal conduction suppression, the Lorentz force, and α-particle magnetisation affect three hot-spot perturbation scenarios: a cold fuel spike, a time-dependent radiation drive asymmetry, and a multi-mode perturbation. For moderate magnetisations (B0 = 5 T), the single spike penetrates deeper into the hot-spot, as thermal ablative stabilisation is reduced. However, at higher magnetisations (B0 = 50 T), magnetic tension acts to stabilise the spike. While magnetisation of α-particle orbits increases the peak hot-spot temperature, no impact on the perturbation penetration depth is observed. The P4-dominated radiation drive asymmetry demonstrates the anisotropic nature of the thermal ablative stabilisation modifications, with perturbations perpendicular to the magnetic field penetrating deeper and perturbations parallel to the field being preferentially stabilised by increased heat-flows. Moderate magnetisations also increase the prevalence of high modes, while magnetic tension reduces vorticity at the hot-spot edge for larger magnetisations. For a simulated high-foot experiment, the yield doubles through the application of a 50 T magnetic field-an amplification which is expected to be larger for higher-performing configurations.Pre-magnetisation of inertial confinement fusion implosions on the National Ignition Facility has the potential to raise current high-performing targets into the ignition regime [Perkins et al. “The potential of imposed magnetic fields for enhancing ignition probability and fusion energy yield in indirect-drive inertial confinement fusion,” Phys. Plasmas 24, 062708 (2017)]. A key concern with this method is that the application of a magnetic field inherently increases asymmetry. This paper uses 3-D extended-magnetohydrodynamics Gorgon simulations to investigate how thermal conduction suppression, the Lorentz force, and α-particle magnetisation affect three hot-spot perturbation scenarios: a cold fuel spike, a time-dependent radiation drive asymmetry, and a multi-mode perturbation. For moderate magnetisations (B0 = 5 T), the single spike penetrates deeper into the hot-spot, as thermal ablative stab.
Real-Time Joint Regulations of Frequency and Voltage for TSO-DSO Coordination: A Deep Reinforcement Learning-Based Approach

The increasing scale of distributed energy resources (DERs) in the active distribution network (ADN) offers valuable opportunities for distribution system operators (DSOs) to assist transmission system operators (TSOs) in regulating operational issues and reducing overall operational costs. The paper proposes a multi-agent deep reinforcement learning (MADRL)-based TSO-DSO coordination framework for jointly regulating the frequency and voltage measured at the grid supply point (GSP) of the transmission network (TN) with a sufficient utilization of control flexibility of converter-based DERs. To facilitate MADRL, a simple grid partitioning method is employed for the balanced partitioning of ADNs with the connectivity constraint imposed on each sub-region. On this basis, a MADRL algorithm is designed by combining the QMIX and twin delayed deep deterministic policy (TD3) algorithm for an effective optimization of the decentralized regulation scheme. The proposed QMIX-TD3 is equipped with the graph convolutional network (GCN) for its temporal-spatial learning ability to tackle the challenges of the complicated transmission and distribution (T/D) system dynamics. In addition, a policy smooth regularization (PSR) loss is proposed to damp action oscillations and enhance sample efficiency. Experiments on the integrated T/D system demonstrate that the proposed framework can effectively mitigate the impact of system disturbances and therefore benefit the system operation.
Actitudes diferenciadas hacia las TIC en la docencia universitaria antes y después de la pandemia

En este trabajo se analizan comparativamente actitudes de docentes hacia el uso de tecnologías de la información y comunicación (TIC) en dos momentos temporales: antes de la pandemia y una vez iniciada la misma. Se realizó un estudio de métodos mixtos del que participaron 350 profesores universitarios de tres universidades argentinas. Se administró un cuestionario previamente validado, junto a reactivo de respuesta abierta. La información cuantitativa se analizó mediante el software SPSS y la plataforma R para análisis estadístico mientras que los datos cualitativos obtenidos en respuesta a la pregunta abierta se analizaron mediante el software IRaMuTeQ. Los resultados evidencian cambios en las actitudes de los docentes de las tres instituciones, en el sentido de una predisposición más favorable hacia el uso de tecnologías, junto con mayor conocimiento y una valoración más positiva de las mismas. A partir de ello se infiere que la necesidad imperiosa de usar las TIC casi como único recurso para sostener el vínculo pedagógico durante el periodo de aislamiento sanitario condujo a un aumento en el conocimiento y dominio de las mismas (dimensión cognitiva) y, en consecuencia, a un uso aumentado (dimensión comportamental) que derivó en una valoración más positiva de las mismas (dimensión afectiva).
CFD study of droplet formation in a cross-Junction microfluidic device: investigating the impact of outflow channel design and viscosity ratio

This study employed computational fluid dynamics (CFD) to investigate various regimes of droplet formation in a square microfluidic cross-junction device. The first part of the study focused on investigating the impact of viscosity ratios ranging from 0.1 to 5 on droplet characteristics under different flow rate ratios. The transition regime between the dripping and jetting exhibited particularly interesting behavior, with droplet volume being proportional to viscosity ratio. However, when conditions were far from the transition regime, the volume of droplets did not vary significantly with respect to viscosity ratios greater than one. The results of the study indicate the feasibility of reducing droplet volume to the appropriate level while minimizing the impact on the continuous phase's flow rate and the risk of transitioning to jetting. Moreover, in the final part of the study, we investigated the potential of shifting the transition boundary between the squeezing and jetting regimes. Our findings indicate that modifying the outflow channel's design can increase the frequency of droplet generation without altering the operating conditions for both the continuous and dispersed phases.