Miracle-dz/UniFAR-FTUs huggingface dataset:
Fast digit-parallel conversion of signed digit into conventional representations

A new method for conversion from signed digit (SD) to conventional signed-magnitude (SM), radix complement, or diminished radix complement representations is presented. For conversion of an n-digit input, sign detection of n SD numbers called ‘partial’ SD numbers is performed first. Then, the digits of the conventional representation can be generated in parallel. Conversion from radix-2 SD representation to SM representation is presented, as a means of illustrating the method. The proposed method is faster and has a more regular circuit design than the conventional one.
Psychometric Evaluation of the Hope-Action Inventory in Individuals with Substance Use Issues

Abstract This study evaluated the psychometric properties of the Hope-Action Inventory (HAI) scores with a problematic substance use population (N = 783). The hierarchical seven-factor structure of the HAI fit the data well. Further, the HAI scores had satisfactory internal consistency reliability and good convergent evidence for validity.
Elaphomycescastilloi (Elaphomycetaceae, Ascomycota) and Entolomasecotioides (Entolomataceae, Basidiomycota), two new sequestrate fungi from tropical montane cloud forest from south Mexico

﻿Abstract Two new species of sequestrate fungi are described from south Mexico based on morphological and molecular evidences. Here we describe Elaphomycescastilloi characterized by the yellowish mycelial mat, dull blue gleba and ascospores of 9.7–11.5 µm; Entolomasecotioides is characterized by the secotioid basidiomata, sulcate, pale cream pileus, and basidiospores of 7–13 × 5–9 µm. Both species grow in montane cloud forest under Quercus sp. in the state of Chiapas, Mexico. Descriptions, photographs, and multilocus phylogeny for both species are presented.
SemanticFlow: Semantic Segmentation of Sequential LiDAR Point Clouds From Sparse Frame Annotations

Sequential point clouds acquired by light detection and ranging (LiDAR) technology provide accurate spatial information for environmental sensing. However, semantic segmentation of point cloud sequences relies on many manual point-wise annotations, which are error-prone and expensive. Existing mainstream weakly supervised methods tackle this by reducing the percentage of labeled points, but they are mostly designed for static indoor scenes and are hard to apply practically. From the viewpoint of realistic annotation procedures and the nature of point cloud sequences, this article proposes a novel semantic segmentation method, SemanticFlow, for LiDAR point cloud sequences using sparse frames with annotations. The proposed method achieves competitive performance compared with fully supervised methods. Specifically, we designed a bidirectional cross-frame pseudolabel propagation module that uses scene flow to learn the correlation and propagate pseudolabels across neighboring frames. In addition, a label refinement mechanism is proposed to select reliable pseudolabels for learning. Extensive experiments on SemanticKITTI, SemanticPOSS, and Synthia 4-D datasets demonstrate that our sparse frame annotation method is compatible with some fully supervised counterparts.
Using Classifier with Gated Recurrent Unit-Sigmoid Perceptron, Order to Get the Right Bird Species Detection

The method for classifying a picture using the CNN algorithm and various activation functions is the subject of this research study. From a human perspective, there are several sorts of situations found in birds, such as various sizes, altitudes, colours, and forms. There is a more substantial variety in photos to recognize the species of bird than there is in the accepted audio categorization of birds. Additionally, people have a really tough time identifying birds from photos. The system is suggested to support the concepts of identifying a portion of the birds and extracting the CNN characteristics using several convolutional layers in order to discriminate and categorise the birds. These characteristics are then gathered and presented to the decoder at that time for classification. Various bird photos and activation functions are used to test the CNN method. The nodes of the picture are checked to the different testing data sets, such as cifar-10 and Berkeley Birds 200, with order to determine their correctness. Python is used to create the suggested system in either Omnibus 3 or Dragonfly 3 programs. According to the experiment results and analyses, CNN with activated Re LUSigmoid outperforms CNN with the other activation functions in terms of accuracy.
Estimating shift at deep brain targets in deep brain stimulation: a comparison between a machine learning approach and a biomechanical model

The success of deep brain stimulation (DBS) is dependent on the accurate placement of electrodes in the operating room (OR). However, due to intraoperative brain shift, the accuracy of pre-operative scans and pre-surgical planning are often degraded. To compensate for brain shift, we created a finite element bio-mechanical brain model that updates preoperative images by assimilating intraoperative sparse data from the brain surface or deep brain targets. Additionally, we constructed an artificial neural network (ANN) that leveraged a large number of ventricle nodal displacements to estimate brain shift. The machine learning method showed potential in incorporating ventricle sparse data to accurately compute shift at the brain surface. Thus, in this paper, we propose using this machine learning model to estimate brain atrophy at deep brain targets such as the anterior commissure (AC) and the posterior commissure (PC). The ANN consists of an input layer with nine hand-engineered features, such as the distance between the deep brain target and the ventricle node, two hidden layers and an output layer. This model was trained using eight patient cases and tested on two patient cases.
Accuracy Enhancement and Feature Extraction for GNSS Daily Time Series Using Adaptive CEEMD-Multi-PCA-Based Filter

Global navigation satellite system (GNSS) positions include various useful signals and some unmodeled errors. In order to enhance the accuracy and extract the features of the GNSS daily time sequence, an improved method of complete ensemble empirical mode decomposition (CEEMD) and multi-PCA (MPCA) based on correlation coefficients and block spatial filtering was proposed. The results showed that the mean standard deviations of the raw residual time sequence were 1.09, 1.20 and 4.79 mm, while those of the newly proposed method were 0.15, 0.20 and 2.86 mm in north, east and up directions, respectively. The proposed method outperforms wavelet decomposition (WD)-PCA and empirical mode decomposition (EMD)-PCA in effectively eliminating low- and high-frequency noise, and is suitable for denoising nonlinear and nonstationary GNSS position sequences. Furthermore, feature extraction of the denoised GNSS daily time series was based on CEEMD, which is superior to WD and EMD. Results of noise analysis suggested that the noise components in the original and denoised GNSS time sequence are complex. The advantages of the proposed method are the following: (i) it fully exploits the merits of CEEMD and WD, where CEEMD is first used to obtain the limited intrinsic modal functions (IMFs) and then to extract seasonal and trend features; (ii) it has good adaptive processing ability via WD for noise-dominant IMFs; and (iii) it fully considers the correlation between the different components of each station and the non-uniform behavior of common mode error on a spatial scale.
Zn Supplementation Mitigates Drought Effects on Cotton by Improving Photosynthetic Performance and Antioxidant Defense Mechanisms

Drought is recognized as a paramount threat to sustainable agricultural productivity. This threat has grown more severe in the age of global climate change. As a result, finding a long-term solution to increase plants’ tolerance to drought stress has been a key research focus. Applications of chemicals such as zinc (Zn) may provide a simpler, less time-consuming, and effective technique for boosting the plant’s resilience to drought. The present study gathers persuasive evidence on the potential roles of zinc sulphate (ZnSO4·7H2O; 1.0 g Kg−1 soil) and zinc oxide (ZnO; 1.0 g Kg−1 soil) in promoting tolerance of cotton plants exposed to drought at the first square stage, by exploring various physiological, morphological, and biochemical features. Soil supplementation of ZnSO4 or ZnO to cotton plants improved their shoot biomass, root dry weight, leaf area, photosynthetic performance, and water-use efficiency under drought stress. Zn application further reduced the drought-induced accumulations of H2O2 and malondialdehyde, and electrolyte leakage in stressed plants. Antioxidant assays revealed that Zn supplements, particularly ZnSO4, reduced reactive oxygen species (ROS) accumulation by increasing the activities of a range of ROS quenchers, such as catalase, ascorbate peroxidase, glutathione S-transferase, and guaiacol peroxidase, to protect the plants against ROS-induced oxidative damage during drought stress. Increased leaf relative water contents along with increased water-soluble protein contents may indicate the role of Zn in improving the plant’s water status under water-deficient conditions. The results of the current study also suggested that, in general, ZnSO4 supplementation more effectively increased cotton drought tolerance than ZnO supplementation, thereby suggesting ZnSO4 as a potential chemical to curtail drought-induced detrimental effects in water-limited soil conditions.
Nucleolar Protein 56 Deficiency in Zebrafish Leads to Developmental Abnormalities and Anemia via p53 and JAK2-STAT3 Signaling

Simple Summary Ribosomopathies are a series of disorders caused by ribosomal dysfunction, usually causing tissue-specific defects such as anemia. Defects in several nucleolar proteins have been observed in human ribosomopathies. It remains to be determined whether any other ribosome proteins are involved in regulating erythropoiesis. We generated a nucleolar protein 56 (nop56)−/− zebrafish model using the CRISPR/Cas9 system and investigated its function. A nop56 deficiency induced severe morphological abnormalities and anemia. The erythroid lineage in definitive hematopoiesis and the maturation of erythroid cells were impaired in the nop56 mutants. Additionally, the p53 signaling pathway and the JAK2-STAT3 signaling pathway were abnormally activated in the mutants. The knockdown of p53 signaling using morpholino partially rescued the malformation, and the inhibition of JAK2 partially rescued the anemic phenotype. This study suggests that nop56 is a potential target for investigation in erythropoietic disorders, particularly those that may be associated with JAK-STAT activation. Abstract Ribosomes are the vital molecular machine for protein translation in a cell. Defects in several nucleolar proteins have been observed in human ribosomopathies. In zebrafish, a deficiency in these ribosomal proteins often results in an anemic phenotype. It remains to be determined whether any other ribosome proteins are involved in regulating erythropoiesis. Here, we generated a nucleolar protein 56 (nop56)−/− zebrafish model and investigated its function. A nop56 deficiency induced severe morphological abnormalities and anemia. WISH analysis showed that the specification of the erythroid lineage in definitive hematopoiesis and the maturation of erythroid cells were impaired in the nop56 mutants. Additionally, transcriptome analysis revealed that the p53 signaling pathway was abnormally activated, and the injection of a p53 morpholino partially rescued the malformation, but not the anemia. Moreover, qPCR analysis showed that the JAK2-STAT3 signaling pathway was activated in the mutants, and the inhibition of JAK2 partially rescued the anemic phenotype. This study suggests that nop56 is a potential target for investigation in erythropoietic disorders, particularly those that may be associated with JAK-STAT activation.
Microwave Irradiation vs. Structural, Physicochemical, and Biological Features of Porous Environmentally Active Silver–Silica Nanocomposites

Heavy metals and other organic pollutants burden the environment, and their removal or neutralization is still inadequate. The great potential for development in this area includes porous, spherical silica nanostructures with a well-developed active surface and open porosity. In this context, we modified the surface of silica spheres using a microwave field (variable power and exposure time) to increase the metal uptake potential and build stable bioactive Ag2O/Ag2CO3 heterojunctions. The results showed that the power of the microwave field (P = 150 or 700 W) had a more negligible effect on carrier modification than time (t = 60 or 150 s). The surface-activated and silver-loaded silica carrier features like morphology, structure, and chemical composition correlate with microbial and antioxidant enzyme activity. We demonstrated that the increased sphericity of silver nanoparticles enormously increased toxicity against E. coli, B. cereus, and S. epidermidis. Furthermore, such structures negatively affected the antioxidant defense system of E. coli, B. cereus, and S. epidermidis through the induction of oxidative stress, leading to cell death. The most robust effects were found for nanocomposites in which the carrier was treated for an extended period in a microwave field.
Or gate Bayesian networks for text classification: A discrimin ative alternative approach to multinomial naive Bayes

We propose a simple Bayesian network-based text classifier, which may be considered as a discriminative counterpart of the generative multinomial naive Bayes classifier. The method relies on the use of a fixed network topology with the arcs going form term nodes to class nodes, and also on a network parametrization based on noisy or gates. Comparative experiments of the proposed method with naive Bayes and Rocchio algorithms are carried out using three standard document collections.
LUSTC: A Novel Approach for Predicting Link States on Dynamic Attributed Networks

Predicting link states on dynamic attributed networks is one of the most fundamental problems for network analysis. To accurately predict the formations and disappearances of links, we need to utilize three types of information, that is, structural information, node content information, and temporal information, simultaneously. To this end, we propose a novel approach called LUSTC for predicting links and unlinks with structural information, temporal information, and node content information on dynamic attributed networks. For each snapshot, LUSTC first randomly chooses some sets of active nodes whose degree changes between adjacent snapshots and collects higher-order structural information using a random walk method based on the active nodes. Then, LUSTC generates two global matrices containing the temporal structural information and temporal node content information, respectively, as well as a sequence of auxiliary matrices for reconstructing the structural information and node content information of snapshots. These generated matrices are optimized using a nonnegative matrix factorization (NMF) method based on the structural information and node content information of snapshots. Finally, LUSTC estimates the similarity matrix for future snapshots to predict the links that are more likely to be formed or broken. The experimental results on various dynamic attributed networks demonstrate the effectiveness of LUSTC on the link prediction and unlink prediction tasks.
Couplage des équations de Navier-Stokes et de la chaleur : le modèle et son approximation par éléments finis

On etudie un modele de fluide visqueux incompressible ou les equations de Navier-Stokes sont couplees avec celle de la chaleur. On demontre des resultats d'existence et d'unicite locale pour le probleme continu, puis on effectue l'analyse numerique de son approximation dans des espaces d'elements finis usuels.
Network of Things-Based Smart Towns

IoT will be capable to openly provide entry to selected data groups to enable the building of diverse digitized programs while also clearly and fluidly integrating a large range of different and unsuitable end devices. It is a highly challenging task to develop a common design for IoT due to the large variety of devices, connection layer technologies, and applications that could be incorporated in such a system. Urban Iot applications, while still a sizable segment, are the focus of this investigation. The target application domain of these algorithms sets them apart. Urban IoTs are actually created to support the idea of the "Urban Development," which aims to use the most modern networking technology to allow additional offerings for both the municipal government and the citizens. Thus, this article provides a full survey of technology options, rules and regulations, and building design for simply an urban IoT. This Padova initiative, that serves as a convincing example of an IoT offshore rollout conducted out in cooperation with the municipal administration inside the Italian province of Padova, will be covered in detail along with the methodological techniques and finest standards employed there.
Machine Learning Approach on Efficient Routing Efficient Techniques in Wireless Sensor Network

Network lifetime optimization is a tough and important problem in Wireless Sensor Networks (WSNs). The mainstream current works outline a variety of methods for increasing network lifetime, such as reducing energy consumption, lowering latency, load balancing, clustering, effective data aggregation, and lowering data transmission delays. The use of Machine Learning (ML) in WSNs has recently received much interest. Because WSN s have limited resources, figuring out methods for increasing usage of resources and accomplish power-effective load balancing is a crucial problem. Traditional routing procedures seek to do this by lowering energy ingestion and extending system lifetime with WSN-optimized routing strategies. However, there are frequently issues like limited flexibility, a reliance on precise mathematical models, and a single consideration element. The key benefits of fast environmental adaptation and ability to combine various parameters can be utilized in WSN s to improve efficiency of routing protocols in terms of power consumption. This study reviews current methods and ML-based routing rules for WSN s. Additionally, suggest future prospects for ML methods to improve WSN routing.
Impact of an Improved Longwave Radiative Transfer Model on the NCAR Community Climate Model

The effect of introducing a new longwave (LW) radiation parameterization, rapid radiative transfer model (RRTM), on the energy budget and thermodynamic properties of the National Center for Atmospheric Research (NCAR) Community Climate Model (CCM3) is described. RRTM is a rapid and accurate, correlated-k, radiative transfer model (Mlawer et al. 1997) developed for the Atmospheric Radiation Measurement (ARM) Program to address the ARM objective of improving radiation models in global climate models. Among the important features of RRTM are its connection to radiation measurements through comparison to the extensively validated line-by-line radiative transfer model (LBLRTM) (Clough et al. 1992; Clough and Iacono 1995), and its use of the improved and validated CKD water vapor continuum model (Clough et al. 1989). RRTM is accurate to within 1 W m of LBLRTM, which itself is validated against ARM measurements.
Modular Coupler With Integrated Planar Transformer for Wireless EV Charging

A dynamic inductive power transfer (IPT) system is a safe and convenient solution for providing power to electric vehicles (EVs) while they are driving along a roadway. This technology can help reduce the size and cost of the bulky EV batteries without limiting the EV's travelling range. In recent years, practical aspects of dynamic IPT systems have been investigated to advance various IPT technologies from laboratories into roadways. This article suggests an IPT roadway layout, focusing on a modular IPT system configuration with an integrated semiloosely coupled planar transformer inside the in-road coupler to mitigate the installation and maintenance concerns of IPT roadways. The proposed configuration keeps the current in the couplers constant and load-independent while providing galvanic isolation between the coupler and the converter, and can be used as a replacement for the conventional LCL and transformer configuration. In order to reduce the number of components used in each module, each in-road coupler is connected to a common H-bridge through a single dc switch to enable individual couplers to be energized independently. Mathematical analysis and simulation results are provided, and validated using a scaled 7.7-kW prototype IPT system employing the proposed configuration.
Human Interaction-Free Object Localization in a Scene

Object detection methods use NMS (Non-Maximum Suppression) to remove multiple detections for a particular object for its localization. To perform this task, NMS requires a confidence threshold and an IoU (Intersection-over-Union) threshold which need to be supplied by an user. Thresholds are fixed and different for different object detection methods, e.g., R-CNN, Faster R-CNN, YOLO, etc. In this paper, we propose a method that uses a suitable regression model to find the threshold values which is adaptive in nature, eliminating the need for human interaction for localization of objects in the scene. The order of the model is determined through bias-variance trade-off and its goodness-of-fit is justified by R2 (R-squared) score and χ2 (Chi-squared) test. Results are impressive and attractive.
Translation, validity, and reliability of the European Portuguese version of the Touch Experiences and Attitudes Questionnaire

Background Positive touch experiences have proved to be extremely important throughout our lifespan, with cascading effects on our social life. However, few questionnaires are available to measure attitudes and experiences of touch in the Portuguese population. This study aimed to translate and validate the European Portuguese version of the Touch Experiences and Attitudes Questionnaire (TEAQ), as a reliable and valid instrument to measure different aspects of affective touch experiences and attitudes. Methods Therefore, an online sample of 384 (299 females and 85 males) participants, aged between 18 and 75 years (M = 24.59; SD = 9.56) was collected. Multidimensional Rasch model and confirmatory factor analysis were carried out, and also reliability and convergent and discriminant validity were determined. In addition, we examined sex differences in attitudes and experiences of touch. Results Results showed good fit indexes for the 52-item six-factor model structure (friends and family touch, current intimate touch, childhood touch, attitudes to self-care, attitudes to intimate touch, and attitudes to unfamiliar touch). This instrument also showed good reliability and acceptable convergent and discriminant validity. Significant sex differences were found, with female participants reporting more positive touch experiences (including childhood touch, friends and family touch, and current intimate touch) and a more favourable attitude to self-care, with males showing a more positive attitude towards unfamiliar touch. Regarding attitudes towards the intimate touch, scores for both groups were comparable. Conclusion Overall, the European Portuguese version of the TEAQ presented good psychometric properties and appears to be a reliable and valid self-report measure, being a useful and beneficial instrument in research and clinical settings.