Miracle-dz/UniFAR-FTUs huggingface dataset:
Uncovering placemaking needs with(in) a kindergarten community: a cross-disciplinary approach to participatory design

Introduction The design of early childhood education and care facilities faces the double challenge of creating a stimulating environment for young children and a supportive workplace for staff. The existing body of research suggests that placemaking strategies serve both requirements. A promising approach to meet placemaking needs is the participation of future occupants in the building design. Methods We pursued a participatory design study with the community of an Austrian kindergarten aiming to inform the future building renovation. We combined novel cultural fiction probes methods with conventional inquiry methods to gather information from children and teachers about their experience of the built environment. Using thematic and content analyzes we explored placemaking needs from different epistemic perspectives and converged findings through iterative exchange. Results Returns of children and teachers were interconnected and complementary. From a design-oriented perspective, children’s experience of place was relatable to spatial, temporo-spatial, and acoustic qualities as well as control needs. From a human-centered perspective, teachers’ experience of place was relatable to the needs of feeling embedded, protected, enacted, and socially connected. The converged findings revealed dynamic placemaking processes involving the elements of space, time, and control at different levels. Discussion Cross-disciplinary collaboration and research consolidation brought forth valuable insights on supportive structures for both children and teachers, facilitated timely knowledge transfer, and converted into design solutions that foster enacted placemaking. Albeit general transferability is limited, findings are interpretable within a solid framework of existing theories, concepts and evidence.
Overexpression of peanut (Arachis hypogaea L.) AhGRFi gene enhanced root growth inhibition under exogenous NAA treatment in Arabidopsis thaliana

The 14-3-3 protein is a kind of evolutionary ubiquitous protein family highly conserved in eukaryotes. Initially, 14-3-3 proteins were reported in mammalian nervous tissues, but in the last decade, their role in various metabolic pathways in plants established the importance of 14-3-3 proteins. In the present study, a total of 22 14-3-3 genes, also called general regulatory factors (GRF), were identified in the peanut (Arachis hypogaea) genome, out of which 12 belonged to the ε group, whereas 10 of them belonged to the non- ε-group. Tissue-specific expression of identified 14-3-3 genes were studied using transcriptome analysis. The peanut AhGRFi gene was cloned and transformed into Arabidopsis thaliana. The investigation of subcellular localization indicated that AhGRFi is localized in the cytoplasm. Overexpression of the AhGRFi gene in transgenic Arabidopsis showed that under exogenous 1-naphthaleneacetic acid (NAA) treatment, root growth inhibition in transgenic plants was enhanced. Further analysis indicated that the expression of auxin-responsive genes IAA3, IAA7, IAA17, and SAUR-AC1 was upregulated and GH3.2 and GH3.3 were downregulated in transgenic plants, but the expression of GH3.2, GH3.3, and SAUR-AC1 showed opposite trends of change under NAA treatment. These results suggest that AhGRFi may be involved in auxin signaling during seedling root development. An in-depth study of the molecular mechanism of this process remains to be further explored.
Study on classification of underwater targets based on modulation spectrum by wavelet transforms and cubic spline technique

One feature extraction method of modulation spectrum in the radiated noise of underwater targets was presented. Modulation curve is a linking route of maximum points of target waveshape. Maximum points of the target waveshape can be searched after applying the algorithm of wavelet transforms on the radiated noise. Then applying cubic spline technique, modulation curve are composed of these maximum points. Modulation spectrum is extracted from modulation curve. At last, the project is conducted by using fuzzy ART neural networks to the extracted character. The results show that it can efficiently classify underwater targets, at the same time it can show the period phenomenon of original wave.
Lightweight Huffman Coding for Efficient GPU Compression

Lossy compression is often deployed in scientific applications to reduce data footprint and improve data transfers and I/O performance. Especially for applications requiring on-the-flight compression, it is essential to minimize compression's runtime. In this paper, we design a scheme to improve the performance of cuSZ, a GPU-based lossy compressor. We observe that Huffman coding - used by cuSZ to compress metadata generated during compression - incurs a performance overhead that can be significant, especially for smaller datasets. Our work seeks to reduce the Huffman coding runtime with minimal-to-no impact on cuSZ's compression efficiency. Our contributions are as follows. First, we examine a variety of probability distributions to determine which distributions closely model the input to cuSZ's Huffman coding stage. From these distributions, we create a dictionary of pre-computed codebooks such that during compression, a codebook is selected from the dictionary instead of computing a custom codebook. Second, we explore three codebook selection criteria to be applied at runtime. Finally, we evaluate our scheme on real-world datasets and in the context of two important application use cases, HDF5 and MPI, using an NVIDIA A100 GPU. Our evaluation shows that our method can reduce the Huffman coding penalty by a factor of 78--92×, translating to a total speedup of up to 5× over baseline cuSZ. Smaller HDF5 chunk sizes enjoy over an 8× speedup in compression and MPI messages on the scale of tens of MB have a 1.4--30.5× speedup in communication time.
Highly Isolated UWB MIMO Antenna with Modified Ground

A compact ultra-wideband (UWB) multiple input multiple output (MIMO) antenna with high isolation is proposed for UWB transmission requirements in wireless communication systems. The UWB MIMO antenna consists of two circular antenna elements and defective ground structures (DGS). Bandwidth of the antenna is expanded by loading a rectangular strip on circular radiating patches. High isolation is achieved by truncating a funnel-shaped slot in the center of the ground and introducing a fork-shaped branch in the center of the slot. The proposed antenna size is 46 × 30 × 1.6 mm3. The measured results show that the proposed UWB MIMO antenna satisfies S11 < -10 dB from 1.52 to 11.2 GHz with isolation better than 20 dB. The envelope correlation coefficient (ECC) is below 0.5 and can meet the requirements of UWB wireless communication in practical engineering applications.
Formal Specification and Model Checking of Raft Leader Election in Maude*

Raft is widely known as one of the distributed consensus protocols and is used to build highly available and strongly consistent services. Using Maude, we formally specify the leader election in Raft and conduct model checking to check whether the protocol enjoys the Election Safety Property that at most one leader can be elected in each logical time, which is one of the properties that Raft is expected to guarantee. Our model checking experiments show that the protocol enjoys the property under the condition that we limit the logical time and the number of servers. We also aim to promote understanding of the leader election in Raft.
Performance comparison of bio-inspired and learning-based clustering analysis with machine learning techniques for classification of EEG signals

A comprehensive analysis of an automated system for epileptic seizure detection is explained in this work. When a seizure occurs, it is quite difficult to differentiate the non-stationary patterns from the discharges occurring in a rhythmic manner. The proposed approach deals with it efficiently by clustering it initially for the sake of feature extraction by using six different techniques categorized under two different methods, e.g., bio-inspired clustering and learning-based clustering. Learning-based clustering includes K-means clusters and Fuzzy C-means (FCM) clusters, while bio-inspired clusters include Cuckoo search clusters, Dragonfly clusters, Firefly clusters, and Modified Firefly clusters. Clustered values were then classified with 10 suitable classifiers, and after the performance comparison analysis of the EEG time series, the results proved that this methodology flow achieved a good performance index and a high classification accuracy. A comparatively higher classification accuracy of 99.48% was achieved when Cuckoo search clusters were utilized with linear support vector machines (SVM) for epilepsy detection. A high classification accuracy of 98.96% was obtained when K-means clusters were classified with a naive Bayesian classifier (NBC) and Linear SVM, and similar results were obtained when FCM clusters were classified with Decision Trees yielding the same values. The comparatively lowest classification accuracy, at 75.5%, was obtained when Dragonfly clusters were classified with the K-nearest neighbor (KNN) classifier, and the second lowest classification accuracy of 75.75% was obtained when Firefly clusters were classified with NBC.
Examining temporal features of BOLD-based cerebrovascular reactivity in clinical populations

Background Conventional cerebrovascular reactivity (CVR) estimation has demonstrated that many brain diseases and/or conditions are associated with altered CVR. Despite the clinical potential of CVR, characterization of temporal features of a CVR challenge remains uncommon. This work is motivated by the need to develop CVR parameters that characterize individual temporal features of a CVR challenge. Methods Data were collected from 54 adults and recruited based on these criteria: (1) Alzheimer’s disease diagnosis or subcortical Vascular Cognitive Impairment, (2) sleep apnea, and (3) subjective cognitive impairment concerns. We investigated signal changes in blood oxygenation level dependent (BOLD) contrast images with respect to hypercapnic and normocapnic CVR transition periods during a gas manipulation paradigm. We developed a model-free, non-parametric CVR metric after considering a range of responses through simulations to characterize BOLD signal changes that occur when transitioning from normocapnia to hypercapnia. The non-parametric CVR measure was used to examine regional differences across the insula, hippocampus, thalamus, and centrum semiovale. We also examined the BOLD signal transition from hypercapnia back to normocapnia. Results We found a linear association between isolated temporal features of successive CO2 challenges. Our study concluded that the transition rate from hypercapnia to normocapnia was significantly associated with the second CVR response across all regions of interest (p < 0.001), and this association was highest in the hippocampus (R2 = 0.57, p < 0.0125). Conclusion This study demonstrates that it is feasible to examine individual responses associated with normocapnic and hypercapnic transition periods of a BOLD-based CVR experiment. Studying these features can provide insight on between-subject differences in CVR.
Predictors for early cardiac death after discharge from successfully treated acute myocardial infarction

Background The use of a cardioverter defibrillator for the primary prevention of sudden cardiac death is not recommended within 40 days after acute myocardial infarction (AMI). We investigated the predictors for early cardiac death among patients who were admitted for AMI and successfully discharged. Methods Consecutive patients with AMI were enrolled in a multicenter prospective registry. Among 10,719 patients with AMI, 554 patients with in-hospital death and 62 patients with early non-cardiac death were excluded. Early cardiac death was defined as a cardiac death within 90 days after index AMI. Results Early cardiac death after discharge occurred in 168/10,103 (1.7%) patients. A defibrillator was not implanted in all patients with early cardiac death. Killip class ≥3, chronic kidney disease stage ≥4, severe anemia, cardiopulmonary support usage, no dual antiplatelet therapy at discharge, and left ventricular ejection fraction (LVEF) ≤35% were independent predictors for early cardiac death. The incidence of early cardiac death according to the number of factors added to LVEF criteria in each patient was 3.03% for 0 factor, 8.11% for 1 factor, and 9.16% for ≥2 factors. Each model that sequentially added the factors in the presence of LVEF criteria showed a significant gradual increase in predictive accuracy and an improvement in reclassification capability. A model with all factors showed C-index 0.742 [95% CI 0.702–0.781], p < 0.001; IDI 0.024 [95% CI 0.015–0.033], p < 0.001; and NRI 0.644 [95% CI 0.492–0.795], p < 0.001. Conclusion We identified six predictors for early cardiac death after discharge from AMI. These predictors would help to discriminate high-risk patients over current LVEF criteria and to provide an individualized therapeutic approach in the subacute stage of AMI.
Family history of substance use disorder and parental impulsivity are differentially associated with neural responses during risky decision-making

Background Risky decision-making is associated with the development of substance use behaviors during adolescence. Although prior work has investigated risky decision-making in adolescents at familial high risk for developing substance use disorders (SUDs), little research has controlled for the presence of co-morbid externalizing disorders (EDs). Additionally, few studies have investigated the role of parental impulsivity in offspring neurobiology associated with risky decision-making. Methods One-hundred twenty-five children (28 healthy controls, 47 psychiatric controls with EDs without a familial history of SUD, and 50 high-risk children with co-morbid EDs with a familial history of SUD) participated in the Balloon Analog Risk Task while undergoing functional magnetic resonance imaging. Impulsivity for parents and children was measured using the UPPS-P Impulsive Behavior Scale. Results We found that individuals in the psychiatric control group showed greater activation, as chances of balloon explosion increased, while making choices, relative to the healthy control and high-risk groups in the rostral anterior cingulate cortex (rACC) and lateral orbitofrontal cortex (lOFC). We also found a positive association between greater activation and parental impulsivity in these regions. However, within rACC, this relationship was moderated by group, such that there was a positive relationship between activation and parental impulsivity in the HC group, but an inverse relationship in the HR group. Conclusions These findings suggest that there are key differences in the neurobiology underlying risky decision-making in individuals with EDs with and without a familial history of SUD. The current findings build on existing models of neurobiological factors influencing addiction risk by integrating parental factors. This work paves the way for more precise risk models in which to test preventive interventions.
Association between immune cells in peripheral blood and psychiatric symptoms

Background There are bidirectional associations between immunological dysfunction and psychiatric symptoms. However, the associations between the levels of immune cells in the peripheral blood and psychiatric symptoms remain unclear. The present study aimed to evaluate levels of immune cells in peripheral blood in people with positive psychiatric symptoms. Methods This retrospective study analyzed data from routine blood tests and psychopathology and sleep quality assessments. Data were compared between a group of 45 patients with de novo psychological symptoms and 225 matched controls. Results Patients with psychiatric symptoms had higher white blood cell and neutrophil counts compared with controls. However, in a subgroup analysis, neutrophil counts were significantly higher than in controls only in patients with multiple psychiatric symptoms. Furthermore, monocyte counts were significantly higher in patients with multiple psychiatric symptoms than in controls. Further, sleep quality was lower in patients with psychiatric symptoms than in controls. Conclusion White blood cell and neutrophil counts in the peripheral blood of patients with psychiatric symptoms were significantly higher and sleep quality was significantly lower than in controls. Participants with multiple psychiatric symptoms showed more significant differences in peripheral blood immune cell counts than other subgroups. These results provided evidence for the relationship between psychiatric symptoms, immunity, and sleep.
A Deep Learning-based Pest Identification Model for the Rice Leaf Roller

Using deep learning for rice pest detection can provide important information that can effectively prevent rice pests that can cause significant damage to rice, which is important for rice yield. In this paper, we propose the lightweight network MobileNet-v2 as feature extractor for SSD to do rice pest detection, which can meet the requirements of embedded memory not large applications to some extent. In this paper, four more feature extraction layers are added at the end of the Mobilenetv2-SSD network, all of which adopt the inverted residual network structure to improve the network depth while the convolutional layer performs feature extraction from two-dimensional arrays of different sizes to ensure the contextual information of the extracted features. The batch normalization algorithm (BN) is added to the network structure to improve the stability and robustness of the network feature extraction. The rice pest rice leaf curl borer is selected for experiments in the public dataset IP102, and the experimental results of MobileNetv2-SSD algorithm can reach 98.15% accuracy.
Three dimensional attitude and shape control of spacecraft with articulated appendages and reaction wheels

Three dimensional attitude and shape control problems are studied for a class of spacecraft with articulated appendages and reaction wheels. We provide a new unified formulation which includes many of the attitude control problems that have been studied previously. The development is based on a nonlinear, drift-free, control model that characterizes the attitude and shape change dynamics, assuming zero angular momentum of the system. Controllability results are presented for the general case, and specialized results are identified for interesting multibody spacecraft configurations. These results are made explicit by providing computable formulas for the Lie brackets in terms of the spacecraft geometry, inertial properties, and shape. Emphasis is given to the interplay between the shape change dynamics and the attitude change dynamics.
Overview of deterministic sampling filtering algorithms for nonlinear system

Deterministic sampling filters,including Unscented Kalman filter(UKF),central difference Kalman filter(CDKF) and cubature Kalman filter(CKF),are a class of nonlinear suboptimal Gaussian filtering algorithms based on deterministic and analytical sampling approximation,which have advantages of high precision and simple implementation,and have been received wide attention from scholars.The basic principle of deterministic sampling filter is described,and its research situation is summarized in detail,including various improved methods and applications in different areas.Then the problems of deterministic sampling filter at present are analyzed and presented.Finally,its development tendency and research orientation are prospected.
Electron and proton acceleration efficiency by merger shocks in galaxy clusters

Radio relics in galaxy clusters are associated with powerful shocks that (re) accelerate relativistic electrons. It is widely believed that the acceleration proceeds via diffusive shock acceleration. In the framework of thermal leakage, the ratio of the energy in relativistic electrons to the energy in relativistic protons should be smaller than K-e/p similar to 10(-2). The relativistic protons interact with the thermal gas to produce gamma-rays in hadronic interactions. Combining observations of radio relics with upper limits from gamma-ray observatories can constrain the ratio K-e/p. In this work, we selected 10 galaxy clusters that contain double radio relics, and derive new upper limits from the stacking of gamma-ray observations by Fermi. We modelled the propagation of shocks using a semi-analytical model, where we assumed a simple geometry for shocks and that cosmic ray protons are trapped in the intracluster medium. Our analysis shows that diffusive shock acceleration has difficulties in matching simultaneously the observed radio emission and the constraints imposed by Fermi, unless the magnetic field in relics is unrealistically large (>> 10 mu G). In all investigated cases (also including realistic variations of our basic model and the effect of re-acceleration), the mean emission of the sample is of the order of the stacking limit by Fermi, or larger. These findings put tension on the commonly adopted model for the powering of radio relics, and imply that the relative acceleration efficiency of electrons and protons is at odds with predictions of diffusive shock acceleration, requiring K-e/p >= 10 - 10(-2).
Small-bias sets from extended norm-trace codes

As demonstrated by Naor and Naor [9] among others [1, 2], the construction of small-bias probability spaces, or small-bias sets, is connected to that of error-correcting codes. Small-bias sets are probability spaces that in some sense approximate larger ones. Error-correcting codes have provided explicit constructions of such spaces. For instance, the concatenation of a Reed-Solomon code with a Hadamard code provides a now standard construction. Recently, Ben-Aroya and Ta-Shma used Hermitian codes to construct small-bias sets [3]. In this paper, we consider small-bias sets constructed from the extended norm-trace function eld Fqr (x;y)=Fqr dened by TrFqr=Fq (y) = x u where q is a power of a prime, r 2, and uj q r 1 q 1 ; here, TrFqr=Fq denotes the trace with respect to the extension Fqr=Fq. The Hermitian function eld y q + y = x q+1 , its quotient y q + y = x u where ujq + 1, and the norm-trace function eld given by TrFqr=Fq (y) = NFqr=Fq (x) are special cases of the extended norm-trace function eld. We detail the resulting small-bias sets.
Directed mutation operator in the research of solving GA deceptive problem

For improving the efficiency of Solving the GA(genetic algorithms)deceptive problem,on the base of elaborating GA deceptive problem and presenting research situation,it absorbs the idea of the relative mechanism of biology evolution,presents a kind of directed mutation operator,and gives comparison and experiment results with uniformity mutation operator and normal mutation operator.Theory analysis and numerical simulation show that the effectiveness of the operator in dealing with deceptive problems of consecutive functions is good.
Map-matching algorithm research based on computational geometry

Since the existing map-matching algorithm has less study on real-time and robustness of the algorithm,which utilization of available information is not enough in improving accuracy of map-matching.Therefore a map-matching algorithm based on computational geometry is proposed according to the continuity of the vehicle movement.This algorithm can complete the selection of matching candidate roads and the definition of matching road quickly.Through running testing the correct matching rate of the new algorithm is proven to be no less than 95% and the matching time for the individual positioning point is less than 0.04ms. Thus,the algorithm is better in robustness,practicability and real-time.