Miracle-dz/UniFAR-FTUs huggingface dataset:
3D registration of point clouds from time-of-flight cameras

A novel approach for aligning two 3D point clouds acquired by time-of-flight (TOF) cameras is presented. Given that the density of 3D data of these devices is not high, the alignment will be carried out using descriptors based on a direct encoding of the environment of a set of previously selected points-of-interest. This will allow the design of a similarity measure that behaves correctly when both the density of the point clouds and the overlap between them are low. Furthermore, this combination of descriptor and similarity measure facilitates the alignment of point clouds from devices with different accuracy and resolution (such as a laser scanner and a TOF camera).
Influence of Narcotrend-Assisted Anesthesia In-Depth Monitor on Cognitive Impairment of Elderly Patients under General Anesthesia

Objective This research is designed to probe into the influence of Narcotrend- (NT-) assisted anesthesia in-depth monitor on cognitive impairment of elderly patients under general anesthesia (GA). Methods One hundred and forty-four elderly patients with GA in our hospital from October 2020 to April 2021 were randomized into two groups, namely, NT group (supervised anesthesia under NT monitoring) and group C (anesthesia according to doctors' experience). The heart rate (HR), mean arterial pressure (MAP), and central venous pressure (CVP) were recorded before surgery (T0), at the beginning of surgery (T1), at the end of surgery (T2), and 1 day after surgery (T3). Serum of patients was obtained at these four time points for measurements of C-reactive protein (CRP), interleukin-6 (IL-6), and cortisol (Cor) levels using the enzyme-linked immunosorbent assay (ELISA). The alterations in cognitive function pre- and post-anesthesia were assessed using the mini-mental state examination (MMSE), and adverse events (AEs) during anesthesia recovery, postoperative recovery, and dosage of anesthetics were recorded. Results At T1 and T2, MAP was higher and CVP was lower in NT group, versus group C. NT group presented higher CRP, IL-6, and Cor than group C at T1-T3. MMSE scores were higher in TN group than in group C at 12, 24, and 48 h after surgery. The incidence rates of postoperative cognitive dysfunction (POCD) and total AEs in group C were noticeably higher than those in NT group. Compared with group C, the time of anesthesia recovery, extubation, and postanesthesia care unit (PACU) residence in NT group reduced remarkably. Conclusions NT has little effect on the physical condition of elderly patients under GA, and can reduce the dosage of narcotic drugs and promote the recovery of patients from anesthesia, which has high clinical value.
HOW TO FIND YOUNG MASSIVE CLUSTER PROGENITORS

We propose that bound, young massive stellar clusters form from dense clouds that have escape speeds greater than the sound speed in photo-ionized gas. In these clumps, radiative feedback in the form of gas ionization is bottled up, enabling star formation to proceed to sufficiently high efficiency so that the resulting star cluster remains bound even after gas removal. We estimate the observable properties of the massive proto-clusters (MPCs) for existing Galactic plane surveys and suggest how they may be sought in recent and upcoming extragalactic observations. These surveys will potentially provide a significant sample of MPC candidates that will allow us to better understand extreme star-formation and massive cluster formation in the Local Universe.
Deep Learning Based Detector YOLOv5 for Identifying Insect Pests

Insect pests are a major element influencing agricultural production. According to the Food and Agriculture Organization (FAO), an estimated 20–40% of pest damage occurs each year, which reduces global production and becomes a major challenge to crop production. These insect pests cause sooty mold disease by sucking the sap from the crop’s organs, especially leaves, fruits, stems, and roots. To control these pests, pesticides are frequently used because they are fast-acting and scalable. Due to environmental pollution and health awareness, less use of pesticides is recommended. One of the salient approaches could be to reduce the wide use of pesticides by spraying on demand. To perform spot spraying, the location of the pest must first be determined. Therefore, the growing population and increasing food demand emphasize the development of novel methods and systems for agricultural production to address environmental concerns and ensure efficiency and sustainability. To accurately identify these insect pests at an early stage, insect pest detection and classification have recently become in high demand. Thus, this study aims to develop an object recognition system for the detection of crops damaging insect pests and their classification. The current work proposes an automatic system in the form of a smartphone IP- camera to detect insect pests from digital images/videos to reduce farmers’ reliance on pesticides. The proposed approach is based on YOLO object detection architectures including YOLOv5 (n, s, m, l, and x), YOLOv3, YOLO-Lite, and YOLOR. For this purpose, we collected 7046 images in the wild under different illumination and background conditions to train the underlying object detection approaches. We trained and test the object recognition system with different parameters from scratch. The eight models are compared and analyzed. The experimental results show that the average precision (AP@0.5) of the eight models including YOLO-Lite, YOLOv3, YOLOR, and YOLOv5 with five different scales (n, s, m, l, and x) reach 51.7%, 97.6%, 96.80%, 83.85%, 94.61%, 97.18%, 97.04%, and 98.3% respectively. The larger the model, the higher the average accuracy of the detection validation results. We observed that the YOLOv5x model is fully functional and can correctly identify the twenty-three species of insect pests at 40.5 milliseconds (ms). The developed model YOLOv5x performs the state-of-the-art model with an average precision value of (mAP@0.5) 98.3%, (mAP@0.5:0.95) value of 79.8%, precision of 94.5% and a recall of 97.8%, and F1-score with 96% on our IP-23 dataset. The results show that the system works efficiently and was able to correctly detect and identify insect pests, which can be employed for realistic application while farming.
An Improved Popular Items Extraction for Covering Reduction Collaborative Filtering

: Recommender Systems are systems that aid users in finding relevant items, products, or services, usually in an online setting. Collaborative Filtering is the most popular approach for building recommender system due to its superior performance. There are several collaborative filtering methods developed, however, all of them have an inherent problem of data sparsity. Covering Reduction Collaborative Filtering (CRCF) is a new collaborative filtering method developed to solve the problem. CRCF has a key feature called popular items extraction algorithm which produces a list of items with the most ratings, however, the algorithm fails in a denser dataset because it allows any item to be in the list. Likewise, the algorithm does not consider the rating values of items while considering the popular items. These make it to produce less accurate recommendation. This research extends CRCF by developing a new popular item extraction algorithm that removes items with low modal ratings and similarly utilizes the rating values in considering the popular items. This newly developed method is incorporated in CRCF and the new method is called Improved Popular Items Extraction for Covering Reduction Collaborative Filtering (ICRCF). Experiment was conducted on Movielens-1M and Movielens-10M datasets using precision, recall and f1-score as performance metrics. The result of the experiment shows that the new method, ICRCF provides a better recommendation than the base method CRCF in all the performance metrics. Furthermore, the new method is able to perform well both at higher and lower levels of sparsity
IMPLEMENTAÇÃO DA INTELIGÊNCIA ARTIFICIAL NO PROCESSO DE PREVISÃO DE DEMANDA

A partir de mudanças no cenário atual empresas necessitam cada vez mais se envolver no processo de transformação digital com objetivo de se manterem competitivas no mercado e criar valor aos seus clientes. Neste cenário a implementação de tecnologias digitais em diferentes áreas vem sendo o foco no mercado, uma delas é a Inteligência Artificial (IA). Destaca a importância de pesquisas ligadas a aplicação de IA na gestão de operações em diferentes processos, em especial, previsão de demanda. Desta forma, o presente estudo tem objetivo principal analisar como a inteligência artificial (IA) está sendo aplicada no processo de gestão de demanda identificando os recursos necessários para formação da capacidade de um Modelo de IA no processo de previsão de demanda e os resultados obtidos. A metodologia foi qualitativa , através do estudo de caso realizado em empresa do setor alimenticio. A contribuição do estudo consiste na identificação dos recursos que são importantes na formação da capacidade para a implementação de um Modelo de IA.
Reentrant cluster glass behavior in La2CoMnO6 nanoparticles

Magnetic study on La2CoMnO6 nanoparticles revealed multiple magnetic transitions at 218 K, 135 K and below 38 K and the nature of the low temperature transition was unclear [J Appl Phys 111, 024102 2012]. Presence of mixed valance states of Co and Mn has been confirmed from the XPS measurement and its presence along with antisite disorder affects in reducing the saturation magnetization of the nanoparticles. The zero field cooled and field cooled bifurcation in dc magnetization, relaxation in zero field cooled magnetization and large enhancement in coercive field below the glassy temperature has been discussed. Frequency dependence of ac susceptibility using power law has revealed cluster glass behavior. Further, the dc field superimposed on ac susceptibility and absence of memory effect in ac susceptibility has suggested the existence of non interacting clusters comprising of competing interactions below 38 K. Competing magnetic interactions due to the presence of mixed valances and antisite disorder found to establish a reentered cluster glassy state in the nanoparticles.
Prediction of the Compressive Strength of Waste-Based Concretes Using Artificial Neural Network

In the 21st century, numerous numerical calculation techniques have been discovered and used in several fields of science and technology. The purpose of this study was to use an artificial neural network (ANN) to forecast the compressive strength of waste-based concretes. The specimens studied include different kinds of mineral additions: metakaolin, silica fume, fly ash, limestone filler, marble waste, recycled aggregates, and ground granulated blast furnace slag. This method is based on the experimental results available for 1303 different mixtures gathered from 22 bibliographic sources for the ANN learning process. Based on a multilayer feedforward neural network model, the data were arranged and prepared to train and test the model. The model consists of 18 inputs following the type of cement, water content, water to binder ratio, replacement ratio, the quantity of superplasticizer, etc. The ANN model was built and applied with MATLAB software using the neural network module. According to the results forecast by the proposed neural network model, the ANN shows a strong capacity for predicting the compressive strength of concrete and is particularly precise with satisfactory accuracy (R² = 0.9888, MAPE = 2.87%).
CONSERVATIVE CASCADE OF KINETIC ENERGY IN COMPRESSIBLE TURBULENCE

The physical nature of compressible turbulence is of fundamental importance in a variety of astrophysical settings. We investigate the question: “At what scales does the mechanism of pressure-dilatation operate? ” and present the first direct evidence that mean kinetic energy cascades conservatively beyond a transitional “conversion” scale range despite not being an invariant of the dynamics. We use high-resolution 10243 subsonic and transonic simulations. The key quantity we measure is the pressure-dilatation cospectrum, EPD(k), where we show that it decays at a rate faster than k−1 in wavenumber in at least the subsonic and transonic regimes. This is sufficient to imply that mean pressure-dilatation acts primarily at large scales and that kinetic and internal energy budgets statistically decouple beyond a transitional scale range. However, we observe that small-scale dynamics remains highly compressible locally in space and that the statistical decoupling in the energy budgets is unrelated to the existence of a subsonic scale range. Our results suggest that an extension of Kolmogorov's inertial-range theory to compressible turbulence is possible.
Variation of the Photoionization Cross Section Depending on the Choice of Impurities in the Case of GaAs and CdTe

In this present work, a theoretical study of the variation of the photoionization cross-section, the impact of the vibrational wave functions of the crystal lattice, and the choice of the acceptor nature of some impurities in the case of GaAs and CdTe on this last are carried out. In this work, we added the contribution of overlapping vibrational wave functions to the photoionization of cross section in a polar semiconductor for a charge carrier bound to an impurity. We adopted the Born Oppenheimer approximation to describe the initial state of impurity through a test function and the Lee-Low-Pines function for the final state. Chemical shift, charge carrier-phonon interaction, and central cell correction are taken into account. We used the variational method by minimizing the average value of the energy in order to calculate the binding energy. The behavior of the photoionization cross-section spectra obtained with various frequently used impurities is compared with the experimental and theoretical data. It is observed that the photoionization cross sections increase up to their peaks starting from a quasi-zero value and then decrease as the photon frequency increases. The peaks of the photoionization cross section are almost identical but sometimes shifted. This shows that the vibrational contribution of the crystal lattice is important in the process of calculating the photoionization cross-section. The spectra obtained indicate that the photoionization cross-section is sensitive to the choice and the nature of the impurity.
Synthesis of Au:TiO2 Nanoparticles via Laser Ablation in Liquid Deposited on Porous-Si for Improved Spectral Responsivity

In this study, Au:TiO 2 nanoparticles (NPs) are prepared by using the laser ablation method in liquid at different laser energies (600, 800, and1000 mJ). After that, Au: TiO 2 NPs were deposited on porous-Si(PS). Porous silicon (PS) is synthesized by using the photoelectrochemical etching (PECE) of n-type crystalline Si (c-Si) wafers of (100) orientation. The intensity of the etching current density was (4, 12, and 20 mA/cm 2 ), with 16% (HF), and the etching time was 15 minutes. The X-ray diffraction (XRD) techniques, scanning electron microscopy (SEM), UV–visible spectrophotometry, and electrical properties are used to characterize the obtained particles. From the photo-detector measurements, the spectral responsivity curves three inclusive regions; the first peak was due to the absorption of UV light by Au: TiO 2 NPs. The second peak was corresponding to the visible light absorption with the PS layer and the third peak was due to the absorption edge of the Si substrate. The higher responsivity of Au: TiO 2 NPs/PS photo-detector was found to be 2.56A/W for specimens prepared at laser energy 800mJ.
Eating Disorders in Hospitalized School-Aged Children and Adolescents during the COVID-19 Pandemic: A Cross-Sectional Study of Discharge Records in Developmental Ages in Italy

Eating disorders (EDs) are characterized by behavioral and cognitive aspects that result in a significant impairment of an individual’s well-being. COVID-19 pandemic consequences negatively impacted healthcare services and people’s mental health. Particularly, in developmental ages, difficulties in coping with the situation could have had an impact on eating behaviors. Therefore, the aim of this study was to assess EDs’ hospitalization trend before, during and after the pandemic peak to evaluate whether it has been influenced. A retrospective cross-sectional study was conducted on the hospital discharge forms of patients from 5 to 19 years old in Piedmont, which is a region in northern Italy. Overall, hospitalization, age, and gender-specific rates due to EDs that occurred in 2020 and 2021 were compared to those that occurred in 2018–2019. Since 2020, there has been a 55% reduction in overall hospitalizations, while the total proportion of EDs admissions has doubled from 2020 to 2021 (from 13.9‰ to 22.2‰). Significant hospitalization rate increases were observed both in 15–19 and in 10–14 females’ age groups in 2021. Non-significant increases were observed in all males’ age groups. The increase in hospitalizations for EDs should be further investigated, as it might be the tip of an iceberg not yet acknowledged.
ON A TIME-NONLOCAL BOUNDARY VALUE PROBLEM FOR TIME-FRACTIONAL PARTIAL DIFFERENTIAL EQUATION

: A boundary value problem with a nonlocal m -point condition in time for a space-degenerate partial diﬀerential equation involving the bi-ordinal Hilfer fractional derivative is the main subject of the present investigation. We aim to prove a unique solvability of this problem based on certain properties of the Legendre polynomials and the two-parameter Mittag-Leﬄer function. An explicit solution to the considered problem is found in the form of the Fourier-Legendre series.
Remarks on a quasi-linear model of the Navier-Stokes Equations

Dinaburg and Sinai recently proposed a quasi-linear model of the Navier-Stokes equations. Their model assumes that nonlocal interactions in Fourier space are dominant, contrary to the Kolmogorov turbulence phenomenology where local interactions prevail. Their equation corresponds to the linear evolution of small scales on a background field with uniform gradient, but the latter is defined as the linear superposition of all the small scale gradients at the origin. This is not self-consistent.
CfA Plasma Talks

Notes from a series of 13 one hour (or more) lectures on Plasma Physics given to Ramesh Narayan' research group at the Harvard-Smithsonian Center for Astrophysics, between January and July 2012. Lectures 1 to 5 cover various key Plasma Physics themes. Lectures 6 to 12 mainly go over the Review Paper on "Multidimensional electron beam-plasma instabilities in the relativistic regime" [\emph{Physics of Plasmas} \textbf{17}, 120501 (2010)]. Lectures 13 talks about the so-called Biermann battery and its ability to generate magnetic fields from scratch.
Equations of motion in Double Field Theory: from classical particles to quantum cosmology

The equation of motion for a point particle in the background field of double field theory is considered. We find that the motion is described by a geodesic flow in the doubled geometry. Inspired by analysis on the particle motion, we propose a modified model of quantum string cosmology, which includes two scale factors. The report is based on Phys. Rev. D84 (2011) 124049 [arXiv:1108.5795].
A Variable-Order Fuzzy Logic Controller Design Method for an Unmanned Underwater Vehicle Based on NSGA-II

UUV depth control requires the controlled system to have good transient response and robustness under the premise of ensuring real-time performance. The flexibility of fractional-order control provides an idea to solve this problem. This paper proposes a controller design method for UUV depth control (VD-SIFLC) based on fractional calculus, fuzzy control, dynamic parameters and a fast non-dominated sorting genetic algorithm (NSGA-II). First, the overall structure of the controller, the UUV model and the model of external disturbances are presented. Then, the design methods of control input, order selector, membership function and scale factor selector are given, respectively. Then, the necessary conditions, such as optimization objectives and optimization parameters in the optimization algorithm, are analyzed. Finally, the effectiveness of the proposed control scheme is verified by comparative experiments with the SIFLC. Simulation results show that the controlled system with the VD-FIFLC could achieve better robustness and dynamic and steady-state performance. Moreover, according to the actual task requirements, the appropriate parameters can be selected by the user from the pareto solution set, which is suitable to be used in the actual applications.
Identification de l'évolution de l'usure d'un outil de tournage basée sur l'analyse des efforts de coupe et des vibrations

L’influence de l’usure des outils coupants sur la qualite de l’etat de surface demeure un probleme principal dans un procede d'usinage. La surveillance des processus d'usinage joue alors un role tres important pour eviter l'apparition de certains phenomenes non desires, tels que l'usure excessive et la rupture de l'outil. Dans ce travail, nous proposons une methode de surveillance basee conjointement sur l'analyse temporelle et l'analyse frequentielle des signaux mesures en tournage pour identifier l’usure de l’outil. Les objectifs principaux de ce travail sont de montrer, d’une part, la sensibilite d'indicateurs scalaires a la variation de l'usure et d’autre part de determiner plusieurs indicateurs de l’usure a l’image des frequences caracteristiques, du niveau vibratoire et des changements brusques qui permettent de deceler l’endommagement de l’outil de coupe. Cette derniere etude frequentielle est basee sur une analyse multiresolution en ondelettes. Les resultats obtenus et presentes dans cet article, montrent l'efficacite de ces approches en tournage. Mots-cles : Usure d’outil- Effort de coupe - Signatures vibratoires - Indicateurs scalaires - Analyse frequentielle- Analyse multiresolution en ondelettes.