Miracle-dz/UniFAR-FTUs huggingface dataset:
Genome-wide identification and co-expression network analysis of Aux/IAA gene family in Salvia miltiorrhiza

The auxin/indole-3-acetic acid (Aux/IAA) gene family serves as a principal group of genes responsible for modulating plant growth and development through the auxin signaling pathway. Despite the significance of this gene family, the identification and characterization of members within the well-known Chinese medicinal herb Salvia miltiorrhiza (S. miltiorrhiza) have not been thoroughly investigated. In this study, we employed bioinformatics methods to identify 23 Aux/IAA genes within the genome of S. miltiorrhiza. These genes were classified into typical IAA and atypical IAA based on their domain structure. Our analysis of the promoter regions revealed that the expression of these genes is regulated not only by auxins, but also by other hormones and environmental factors. Furthermore, we found that the expression patterns of these genes varied across various tissues of S. miltiorrhiza. While our initial hypothesis suggested that the primary function of these genes was the interaction between SmIAA and ARF, gene co-expression network analysis revealed that they are also influenced by various other transcription factors, such as WRKY and ERF. The findings establish a sturdy basis for future investigations into the function of the Aux/IAA gene family and exhibit promising prospects for enhancing the genetics of this medicinal flora and its associated species.
Clinical profile of Brazilian patients aged over 50 years at the diagnosis of celiac disease

INTRODUCTION Celiac disease (CD) is a multisystemic complex immune-mediated disorder (IMD), which is triggered and maintained by gluten in genetically susceptible individuals. Despite the availability of autoantibodies as CD biomarkers and upper endoscopy facilities with duodenal biopsies, most patients with this disorder remain undiagnosed1. CD is traditionally diagnosed in children and adolescents. However, some authors reported higher detection in the elderly population1,2 and that about 25% of celiac patients were first diagnosed in the seventh decade in countries such as Canada, the United States, and Northern Europe2. The heterogeneous mode of clinical presentation, with digestive and extra-digestive manifestations, might be responsible for the delay in the diagnosis, besides the poor awareness by primary care providers or specialists without a high index of suspicion2-4. The physician should consider the diagnosis, order the correct tests, interpret them, and know when to refer the patient to a gastroenterologist expert in CD. As the CD prevalence in adults occurs in the third and fourth decades of life, patients aged above 50 years can be misdiagnosed with great delay and repercussions in their quality of life (QoL)1,3,4. This study aimed to evaluate the clinical profile of Brazilian patients aged over 50 years at the diagnosis of CD.
Artificial Intelligence for sEMG-Based Muscular Movement Recognition for Hand Prosthesis

The muscular activities gathered by real-time myoelectric interfaces of surface electromyography (sEMG) can be used to develop myoelectric prosthetic hands for physically disabled people. However, the acquired myoelectric signals must be accurately classified in real time to properly control the operation of the external devices. In this study, we propose methods for detecting and classifying muscular activities using sEMG signals. These methods include outlier removal, data manipulation, data preprocessing, dimensionality reduction, and classification. We use the Ninapro database 1 (DB1) containing sEMG signals from 27 intact subjects while performing 53 hand movements repeatedly. We apply the Principle Component Analysis (PCA), Independent Component Analysis (ICA), and t-distributed Stochastic Neighbor Embedding (t-SNE) feature extraction methods for dimensionality reduction. Five machine learning (ML) algorithms and deep learning artificial neural networks (ANN) are applied for the classification of muscular movements. It is observed that for the recognition of 53 muscular movements of 27 subjects with preprocessed raw data, ANN obtains the highest accuracy of 93.92% for inter-subject and 97.73% for intra-subject movement recognition. Among the ML algorithms, K-Nearest Neighbors (KNN) performs the best with both t-SNE features and the preprocessed raw data in least computational time. With the preprocessed raw data, KNN obtains 93.174% and 97.458% for inter-subject and intra-subject movement classification, respectively while with the t-SNE features, KNN obtains 89.844% accuracy for inter-subject and 95.04% accuracy for intra-subject in reduced computational time.
Dynamics and Energy Balance of the Hadley Circulation and the Tropical Precipitation Zones. Part II: Sensitivity to Meridional SST Distribution

Abstract A series of GCM experiments is performed in order to examine the dynamics of the time-averaged distribution of precipitation and Hadley circulation in low-latitude areas. As an extension to Part I of this study, the sensitivity to the latitudinal distribution of the globally prescribed sea surface temperature (SST) is examined. When the peak of SST is not at the equator but nearby, a single precipitation peak appears on the opposite side of the equator. When the latitude of the SST peak becomes high enough (about 15°), an abrupt jump of the precipitation peak is observed. This behavior is completely different if the evaporation from the ocean is estimated independent of the surface wind speed. Factors controlling the latitude of the precipitation zone are discussed. It is found that one essential component is the convergent flow in the planetary boundary layer, which is driven by the SST gradient. Another important factor is the net moist static energy input into the atmosphere, which is the diff.
Theoretical and experimental analysis of the directionality of the electromagnetic scattering by magnetodielectric small spherical particles

Magnetodielectric small spheres present unusual electromagnetic scattering features, theoretically predicted a few decades ago by Kerker et al. (1). However, achieving such behavior has remained elusive, due to the non-magnetic character of natural optical materials or the difficulty in obtaining low-loss highly permeable magnetic materials in the gigahertz regime. Here we present unambiguous experimental evidence that a single low-loss dielectric subwavelength sphere of moderate refractive index (n≈4 like some semiconductors (Si, Ge) at near-infrared) and radius a<λ radiates fields identical to those from equal amplitude crossed electric and magnetic dipoles, and indistinguishable from those of ideal magnetodielectric spheres. The measured far-field scattering radiation patterns (see Fig. 1(a)) and degree of linear polarization (3-9 GHz/33-100mm range) show that, by appropriately tuning the a/λ ratio, zero-backward ('Huygens' source) or almost zero-forward ('Huygens' reflector) radiated power can be obtained (2). Also, the near-field scattering distributions and their correlation with those measured in far-field, are numerically calculated and analyzed (see Fig. 1(b)). These Kerker scattering conditions (1) only depend on a/λ. Our results open new technological challenges from nano and micro-photonics to science and engineering of antennas, metamaterials and electromagnetic devices.
PTC-Net: Point-Wise Transformer With Sparse Convolution Network for Place Recognition

In the point-cloud-based place recognition area, the existing hybrid architectures combining both convolutional networks and transformers have shown promising performance. They mainly apply the voxel-wise transformer after the sparse convolution (SPConv). However, they can induce information loss by the sparse voxelization and further result in loss propagation to the transformer, significantly degrading the performance of the network, especially in outdoor scenes with complex geometric structures and multiple small objects. To address this issue, we propose a novel Point-wise Transformer with sparse Convolution (PTC). Specifically, SPConv is applied to the sparsely voxelized point cloud to extract local features, which are then converted to the point-based representation via a feature transformation unit (FTU). As such, our PTC can apply a transformer model based on the point-wise representation rather than on the voxel-wise one. To enhance the ability to capture long-range features and reduce the computational complexity of the transformer, we propose a two-step transformer, each with different grouping strategies. Meanwhile, in both steps, the attention matrix is computed with much fewer points by grouping a single point cloud into different attention domains. The experiments show that the PTC-Net can achieve state-of-the-art (SOTA) performance, with an improvement of 3.6% on average recall@1. Furthermore, to demonstrate the effectiveness of the PTC, we introduce an extremely light-weight version, PTC-Net-L, with only one PTC layer and half initial channel dimensions, also achieving SOTA performance in terms of the average recall rate and running time with only 0.08 M parameters.
Ground Validation for the Tropical Rainfall Measuring Mission (TRMM)

Abstract An overview of the Tropical Rainfall Measuring Mission (TRMM) Ground Validation (GV) Program is presented. This ground validation (GV) program is based at NASA Goddard Space Flight Center in Greenbelt, Maryland, and is responsible for processing several TRMM science products for validating space-based rain estimates from the TRMM satellite. These products include gauge rain rates, and radar-estimated rain intensities, type, and accumulations, from four primary validation sites (Kwajalein Atoll, Republic of the Marshall Islands; Melbourne, Florida; Houston, Texas; and Darwin, Australia). Site descriptions of rain gauge networks and operational weather radar configurations are presented together with the unique processing methodologies employed within the Ground Validation System (GVS) software packages. Rainfall intensity estimates are derived using the Window Probability Matching Method (WPMM) and then integrated over specified time scales. Error statistics from both dependent and independent val.
Evaluation of low-cost particulate matter sensor in indoor and outdoor micro-environments

Air quality in many Indian cities has become worse due to urbanisation. Setting up monitoring stations across the cities enables policymakers to ensure clean air and a sustainable environment. Unfortunately, this equipment is very expensive and is sparsely located in the cities. This makes precise and economic decision-making a challenge. Low-cost sensors are being looked at to supplement the existing monitoring techniques and address some challenges associated with them. This study evaluates the performance of the low-cost particulate matter sensor in indoor and outdoor environments. It was found that the sensors underestimated when compared to the reference instruments, but they captured the variations and trends in the concentration changes. A calibration technique which considers both the meteorological conditions and service life may be considered to improve the performance of the sensors.
A blockchain‐based privacy‐preserving advertising attribution architecture: Requirements, design, and a prototype implementation

In the era of digital marketing, advertisements have become an indispensable part. One of the central challenges is advertising attribution which explains the amount of contribution every publisher has with the conversions. However, through observation, we have found that current advertising platforms attribution, advertisers attribution, or third‐party platforms attribution all have the problems of trust, data leakage, and data forgery. To fill the gap, our work's main contribution is combining blockchain with advertising attribution to propose an architecture for improving the privacy‐preserving degree and amount. In the proposed architecture, publishers, and advertisers can store real‐time data on a blockchain. The attribution results are credible because blockchain is decentralized, tamper‐proof, and traceable. We combine privacy set intersection and zero‐knowledge proof technology to increase the privacy of flowing data. In addition, we describe a preliminary prototype in which publishers, advertisers, and advertising platforms can get the corresponding attribution details. To show its effectiveness, we analyze it from different perspectives, including communication cost, attribution accuracy, and time cost. The results show that our communication cost has significantly reduced compared to the recent studies.
Pressure effects on Jahn-Teller distortion in perovskites: The roles of local and bulk compressibilities

The interplay between the Jahn-Teller (JT) effect and octahedron tilting in transition-metal perovskites is investigated as a function of pressure. Our focus is on its effects on the exchange and electron-phonon interactions, both having a strong influence on materials properties. We demonstrate that the JT distortion in Cu${}^{2+}$ and Mn${}^{3+}$ is reduced upon compression and is eventually suppressed at pressures above 20 GPa. X-ray diffraction and x-ray absorption measurements in ${A}_{2}{\text{CuCl}}_{4}$ layer perovskites ($A$: Rb, C${}_{n}$H${}_{2n+1}$NH${}_{3}$; $n=1$\char21{}3) show that, although pressure slightly reduces the long Cu-Cl distance in comparison to the Cu-Cu distance in the layer, the JT distortion is stable in the 0\char21{}20 GPa range. The difference between lattice (${\ensuremath{\beta}}_{0}^{C}=0.14$ GPa${}^{\ensuremath{-}1}$) and local CuCl${}_{6}$ (${\ensuremath{\beta}}_{0}=0.016$ GPa${}^{\ensuremath{-}1}$) compressibilities, together with the high stability of the JT distortion, lead to ${\mathrm{CuCl}}_{6}$ tilts upon compression. The evolution of the elongated CuCl${}_{6}$ octahedron in ${A}_{2}$CuCl${}_{4}$, as well as MnF${}_{6}$ in CsMnF${}_{4}$ and MnO${}_{6}$ in LaMnO${}_{3}$ and DyMnO${}_{3}$, toward a nearly regular octahedron takes place above 20 GPa, in agreement with experimental results and a model analysis based on the JT energy derived from optical absorption spectroscopy: ${E}_{\mathrm{JT}}=0.25$\char21{}0.45 eV/Cu${}^{2+}$, ${E}_{\mathrm{JT}}=0.45$ eV/Mn${}^{3+}$ (CsMnF${}_{4}$), and ${E}_{\mathrm{JT}}=0.25$ eV/Mn${}^{3+}$ (LaMnO${}_{3}$). The proposed model clarifies controversial results about pressure-induced JT quenching in Cu${}^{2+}$ and Mn${}^{3+}$ systems, providing an efficient complementary means to predict pressure behavior in perovskites containing JT transition-metal ions.
Inventing Young Offenders: The Legal and Medical Categorization of Juvenile Delinquency in Hashemite Iraq, 1921–1958

Abstract:This article traces the emergence of juvenile delinquency as a legal and medical category in Hashemite Iraq (1921–58). We argue that as children and youth became increasingly visible through actions and inactions that highlighted the weaknesses of political and social structures, state institutions adopted international frameworks and vernaculars concerning the participation of youth in labor. Medicine played a key role in defining the "juvenile" and determining young people's physical abilities and, by extension, their productive potential. Focusing on children and youth who were for the most part unaffiliated with organized political groups, the article demonstrates that Iraq's correctional facilities, including reformatories, were integral to the Hashemite state's attempt at crafting and regulating citizens. Juvenile delinquency was also a uniquely gendered and classed category. Gender in particular became a distinguishing marker that in effect deprived young women and girls of the juvenile status that allowed mostly lower-class young men and boys to access reformatories rather than adult prisons. While defiant young men and boys who were considered as potentially redeemable gained access to judicial and social institutions that aimed to rehabilitate the juvenile delinquent, the crimes of young women and girls were often sexualized and assessed according to interpretations of maturity determined by the onset of puberty rather than legal age.
SHOULDER AND ELBOW JOINT ANGLE ESTIMATION FOR UPPER LIMB REHABILITATION TASKS USING LOW-COST INERTIAL AND OPTICAL SENSORS

This paper proposes a new method to improve accuracy and real-time performance of inertial joint angle estimation for upper limb rehabilitation applications by modeling body acceleration and adding low-cost markerless optical position sensors. A method based on a combination of the 3D rigid body kinematic equations and Denavit-Hartenberg (DH) convention is used to model body acceleration. Using this model, body acceleration measurements of the accelerometer are utilized to increase linearization order and compensate for body acceleration perturbations. To correct for the sensor-to-segment misalignment of the inertial sensors, position measurements of a low-cost markerless position sensor are used. Joint angles are estimated by Extended Kalman Filter (EKF) and compared with Unscented Kalman Filter (UKF) in terms of performance. Simulations are performed to quantify the existing error and potential improvements achievable by the proposed method. Experiments on a human test subject performing an upper limb rehabilitation task is used to validate the simulation results in realistic conditions.
Sur quelques idées fausses ayant des conséquences en identification

Identification is more an art form than an exact science, and it is based on intuition just as much as on a large variety of techniques. Tradition is a major factor, and customs that may only have history as a justification should be questioned. Examples are the confidence bestowed on classical mathematical formulas even when they turn out not to be suited for computer implementation, the quasi-exclusive role played by the minimization of quadratic cost functions, the use of finite-difference approximations for the computation of gradients, the almost exclusive resort to local, non-guaranteed techniques for the identification and simulation of nonlinear models. For all of these topics, alternative approaches are suggested.
River Surface Velocimetry Based on Virtual River Dataset and Modulated-RAFT

In recent years, there has been a surge of interest in image-based velocimetry methods for river surface velocity (RSV) estimation due to their efficiency and accuracy, including large-scale particle image velocimetry (LSPIV), space-time image velocimetry (STIV) and optical flow velocimetry (OFV). Among these methods, OFV-based methods have received significant attention owing to their high field resolution and low tracer requirements. Deep optical flow estimation (DOFE), which is a powerful approach for accurate and efficient estimation of optical flow, has also been employed in OFV-based methods. However, the immeasurability of optical flow often results in the usage of irrelevant datasets for training, leading to limited generalization due to domain drift. Moreover, the high similarity of river surfaces can lead to ambiguous correlation volumes extracted by DOFE models, resulting in mismatches. To address the domain shift and mismatch challenges, we proposed a method to generate optical flow datasets and these datasets are used as the training set for the DOFE model MRAFT, which incorporates correlation volume modulation. Experiment results demonstrate that our method effectively mitigates underlying domain shift and mismatch issues, enabling accurate and robust RSV estimation under velocity ranges of 0-6.0 m/s. Our work facilitates the application of DOFE on RSV estimation and provides optical flow datasets for fine-tuning to other related researches.
Typical reconstruction performance for distributed compressed sensing based on ℓ2,1-norm regularized least square and Bayesian optimal reconstruction: influences of noise

A signal model called joint sparse model 2 (JSM-2) or the multiple measurement vector problem, in which all sparse signals share their support, is important for dealing with practical signal processing problems. In this paper, we investigate the typical reconstruction performance of noisy measurement JSM-2 problems for ℓ2,1-norm regularized least square reconstruction and the Bayesian optimal reconstruction scheme in terms of mean square error. Employing the replica method, we show that these schemes, which exploit the knowledge of the sharing of the signal support, can recover the signals more precisely as the number of channels increases. In addition, we compare the reconstruction performance of two different ensembles of observation matrices: one is composed of independent and identically distributed random Gaussian entries and the other is designed so that row vectors are orthogonal to one another. As reported for the single-channel case in earlier studies, our analysis indicates that the latter ensemble offers better performance than the former ones for the noisy JSM-2 problem. The results of numerical experiments with a computationally feasible approximation algorithm we developed for this study agree with the theoretical estimation.
Phytochemical profiling, antimicrobial, antibiofilm, insecticidal, and anti-leishmanial properties of aqueous extract from Juglans regia L. root bark: In vitro and in silico approaches

ABSTRACT The Juglans regia root aqueous extract (JRRAE) phytochemical variability and the investigation of their antimicrobial, antibiofilm, insecticidal, and anti-leishmanial properties were studied. As a result, the JRRAE chemical profile revealed the presence of biomolecules using UV, HPLC, and FT-IR analysis. The investigations showed marked antibacterial activities, highlighted inhibited biofilm formation, induced lethality Leishmania amazonensis promastigotes (50%), and possess an insecticidal activity at 100 µg/ml. A molecular docking study was performed against S. aureus gyrase, S. aureus tyrosyl-tRNA synthetase, human peroxiredoxin 5, cyclooxygenase-2, and trypanothione reductase and confirmed that the activities are mainly due to coumarin, epicatechin, juglone, epicatechin, and epicatechin presence, respectively. The stability of these complexes is essentially related to the strong hydrogen bonds formed between the most active compounds and the binding site amino acids. The identified compounds were found to pass the absorption, distribution, metabolism, and excretion property as well as five Lipinski’s rules with good drug-likeness and pharmacokinetic behavior.
Application of AI-enabled Simulation in Power Package Development

The design of power packages with both high efficiency and high-power density performance while maintaining the highest possible reliability is a challenge. During development for power package, design of experiment (DOE) by simulation can effectively shorten the design cycle, reduce costs, and effectively optimize the packaging structure. However, the simulation analysis results are highly dependent on the individual researcher and are only part of overview of factor. Especially, as the package size continuously decrease for high-density designs, there are lots of challenge areas, like thin die pick-up process, material optimization to enhance process and reliability performance. Maching learning (ML) within the field of Artificial Intelligence (AI) simulation can be very effective tools to help address these challenges.In this paper, application of ML algorithms and enabled simulation for die pick-up process and reliability testing related to power device are discussed. For thin die pick-up process, Bayesian optimization was used to optimize the parameters to achieve the lowest die stress under a given condition. An automatic molding compound selection framework is proposed to generate the optimal material properties of epoxy molding compound (EMC) properties for solder joint reliability. Through these cases, it has been demonstrated that the AI-enabled simulation can be utilized to significantly improve parameter optimization and material selection for robust power device development.