Miracle-dz/UniFAR-FTUs huggingface dataset:
Graph Clustering Using Node Embeddings: An Empirical Study

A technique that has recently become popular to analyze graph data is node embedding learning. Many graph problems, such as node classification, link prediction and node clustering, can be solved using these embeddings. However, in the literature the efficiency of different embedding generation algorithms paired with different clustering algorithms is not extensively investigated. In this paper, we study the efficiency of well-known embedding generation algorithms in combination with clustering algorithms, to detect communities. We consider four embedding generation algorithms that use mechanisms such as convolution, attention, inductivity and shallowness; and three popular data clustering algorithms. Our experimental results reveal that the combination of GraphSAGE (that uses inductivity) with KMeans++ yields the best results. This can be due to high quality of embedding vectors generated by GraphSAGE and the regular shapes that the embeddings find in the vector space.
Quantitative Assessment of Hypovascular Component in Arterial Phase to Help the Discrimination of Combined Hepatocellular-Cholangiocarcinoma and Hepatocellular Carcinoma

Purpose To explore the imaging performance for discrimination of combined hepatocellular- cholangiocarcinoma (cHCC-CCA) and hepatocellular carcinoma (HCC). Methods In total, 35 patients with cHCC-CCA and a matched control group of HCC patients (n = 35) were included retrospectively. We quantitatively evaluated the hypovascular component in tumor and qualitatively assessed LI-RADS features and other aggressive features to develop model for cHCC-CCA diagnose. Subgroup analyses were performed by tumor size and LI-RADS category. Results cHCC-CCA frequently showed a larger proportion (≥50%) of hypovascular areas followed by HCC (P = 0.000). Among those patients with ≥50% hypovascular areas, 8 patients did not present rim enhancement in atrial phase. The LI-RADS major features were more commonly observed in HCC (82.9–45.7%,), than cHCC-CCA (P = 0.003–0.022). The targetoid appearances and non-smooth margin frequently appeared in cHCC-CCA (34.3–63.9%), compared with HCC (P = 0.000–0.023). We developed a radiologic model based on ≥50% hypovascular component and delayed enhancement, which presented AUC of 0.821, accuracy of 80%. We also obtained good performance by radiologic model in LR-M group and tumor size <50mm group (AUC: 0.841 and 0.866, respectively). Combined group which included CA 19–9 and ≥50% hypovascular component and delayed enhancement did not improve the distinction performance between cHCC-CCA and HCC, which presented good performance of identifying cHCC-CCA in the LR-4/5 subgroup and tumor size ≥50 mm subgroup (AUC: 0.717, 0.730, respectively). cHCC-CCA group presented heterogeneous dominant pathology involving 15 of HCC, 7 of intrahepatic cholangiocarcinoma (iCCA) or cholangiolocellular carcinoma (CLC), 13 of intermediate cells component. Macrotrabecular appearances were higher in cHCC-CCA than that in HCC. The proportion of Hepa-1 was significantly higher in true negative (TN) patients (29 [93.5%]) and false negative (FN) patients (10 [100%]) than in true positive (TP) patients (16 [64%]; P = 0.036). Conclusion Quantitative assessment of hypovascular component could help the discrimination of cHCC-CCA. Macrotrabecular appearances were more exhibited in cHCC-CCA than that in HCC.
FREE-MOLECULE LANGMUIR PROBE AND ITS USE IN FLOWFIELD STUDIES

Experiments were performed in a steady, low-density flow of argon plasma to determine the behavior of small cylindrical Langmuir probes in high-speed flows. In particular, the dependence of the ion current on the probe radius and on flow speed was investigated and correlated with the ratio of probe radius to Debye length and the ratio of flow speed to the ion acoustic speed. The probes were then employed to study the electron temperature and number density distributions in the stagnation region of a flat-nosed cylinder in supersonic flow. The Reynolds number Re^ based on model diameter and viscosity at stagnation temperature, was in the range 70 to 125, whereas the Langmuir probes were free molecular. The charged-particle number density distribution measurements provided a graphic demonstration of the merging of the bow shock and the boundary layer at Re^ ^ 100, and also furnished some evidence of the diffusive separation of the ions and atoms in the shock wave. Nozzle and free-jet flowfields were probed also. Nomenclature
Temperature behavior of sound velocity of fluorine-doped vitreous silica thin films studied by picosecond ultrasonics

Vitreous silica (v-SiO2) shows anomalous temperature dependence of velocity, including positive temperature coefficient of velocity (TCV) and velocity minimum around 70 K. The former characteristic allows its application in acoustic-resonator devices as a temperature compensating material. In this paper, we study the temperature dependence of velocity of fluorine-doped v-SiO2 (v-SiO2–xFx) thin films using picosecond ultrasonic spectroscopy. To correct the temperature increase caused by irradiation with light pulses, we calculated the steady temperature increase in the measuring volume with a finite volume method, considering the temperature dependence of thermal conductivity, and find that temperature in the measurement region remains high even when the back surface is cryogenically cooled. Using the corrected temperature, we determine TCV of v-SiO2–xFx thin films for 0<x<0.264, which increases as x increases and is smaller than reported bulk values by a factor of 0.5−0.7. The velocity minimum is absent f.
Investigation on Nurses’ Willingness to “Internet + Nursing Service” and Analysis of Influencing Factors

Objective To investigate the willingness of nurses in Yichang to participate in “Internet plus nursing services” and analyze the influencing factors in order to provide reference for the implementation of “Internet plus nursing services”. Methods Using the “Internet plus Nursing Service” questionnaire, a cross-sectional survey was conducted among 1447 nurses in Yichang by convenience sampling from July to September in 2022, and the related influencing factors were analyzed. The questionnaire was composed of two parts: the demographic characteristics and the questionnaire of nurses’ willingness to participate in “Internet plus nursing service”, including 4 dimensions: awareness, promoting factors, concern factors and training needs. Likert 5-point scoring method was used for scoring. Binary logistic regression was used to screen the variables, and the ROC curve and Nomogram risk prediction model were drawn. Results A total of 1233 valid questionnaires were collected. It shows that 76.07% of nurses in Yichang are willing to participate in “Internet plus nursing services”. Among them, education background, specialist nurses, awareness, promotion factors, concerns and training needs are the independent influencing factors (all P < 0.05). The area under the ROC curve (AUC) of the prediction model is 0.802, and the consistency index (c-index) of nomogram is 0.800. The average absolute error of internal calibration is 0.014, and the model has good accuracy and discrimination. Conclusion Nurses in Yichang have a high willingness to participate in “Internet plus nursing services”, a low awareness of the program and a high demand for relevant professional training. It is suggested that the government and hospitals should strengthen the publicity of “Internet plus nursing services”, improve relevant laws and regulations and strengthen the construction of specialist nurses’ team, so as to provide a good practice environment for nurses’ door-to-door service.
The Effect of Human Resource Competency and Government Internal Control System on The Quality of Local Government Financial Report with Information Technology as a Moderation Variable in Aceh Government

This study aims to examine the effect of human resource competency and government internal control system on the quality of local goverment financial report with information technology as a moderation variable in Aceh government. The population in this study is the Aceh Civil Service Unit which consists of 46 SKPA. The sampling technique uses non probability sampling with census method. The respondents who became the object of this research who would later answer the questionnaires for each SKPA were Budget Users (PA), Financial Administration Officers (PPK) and Spending Treasurers for each SKPA. The data used is primary data. Data collection techniques were carried out by distributing questionnaires to 138 respondents. Data analysis method used is Moderated Regression Analysis (MRA) with SPSS 25. The result of this research showed that collectively and partially the human resource competency and the government internal control system affect the quality of local government financial report in Aceh Government. Furthermore, information technology moderates the relationship between human resource competency and government internal control system on the quality of regional financial report in Aceh Government.
Trend-Free Block Designs: Theory.

Abstract : A common polynomial trend in one or more dimensions is assumed to exist over the plots in each block of a classical experimental design. An analysis of covariance model is assumed with trend components represented through use of orthogonal polynomials. The objective is to construct new designs through the assignment of treatments to plots within blocks in such a way that sums of squares for treatments and blocks are calculated as though there were no trend and sums of squares for trend components and error are calculated easily. Such designs are called trend-free and a necessary and sufficient condition for a trend-free design is developed. It is shown that these designs satisfy optimality criteria among the class of connected designs with the same incidence matrix. The analysis of variance for trend-free designs is developed. The paper concludes with two examples of trend-free designs. (Author)
Potential Health Benefits of Banana Phenolic Content during Ripening by Implementing Analytical and In Silico Techniques

Banana ranks as the fifth most cultivated agricultural crop globally, highlighting its crucial socio-economic role. The banana’s health-promoting benefits are correlated with its composition in bioactive compounds, such as phenolic compounds. Thus, the present study attempts to evaluate the potential health benefits of banana phenolic content by combing analytical and in silico techniques. Particularly, the total phenolic content and antioxidant/antiradical activity of banana samples during ripening were determined spectrophotometrically. In parallel, liquid chromatography-tandem mass spectrometry (LC-MS/MS) analysis was implemented to unravel the variations in the phenolic profile of banana samples during ripening. Chlorogenic acid emerged as a ripening marker of banana, while apigenin and naringenin were abundant in the unripe fruit. In a further step, the binding potential of the elucidated phytochemicals was examined by utilizing molecular target prediction tools. Human carbonic anhydrase II (hCA-II) and XII (hCA-XII) enzymes were identified as the most promising targets and the inhibitory affinity of phenolic compounds was predicted through molecular docking studies. This class of enzymes is linked to a variety of pathological conditions, such as edema, obesity, hypertension, cancer, etc. The results assessment indicated that all assigned phenolic compounds constitute great candidates with potential inhibitory activity against CA enzymes.
Characteristics of the Yamase Winds over Oceans around Japan Observed by the Scatterometer-Derived Ocean Surface Vector Winds

Yamase is the name for local northeasterly winds that blow from the Pacific Ocean to the east coastsof Hokkaido, and the Tohoku District from May to August. They are cool and moist winds accompanied by low-level clouds. In the present study, we investigate the Yamase winds over the oceans around Japan, using ocean surface vector winds observed by a satellite-scatterometer. In order to define the Yamase Phenomena, we employed an Empirical Orthogonal Function (EOF) analysis of the AMeDAS surface air temperatures over the Tohoku District from May to August. Daily mean temperature anomaly (ΔT), which is a deviation from the climatological daily mean temperature, is calculated for the 167 AMeDAS stations. The spatial coeflicient function of the EOF first mode (EOF1), shows synchronous temperature changes in the whole Tohoku District, while that of the EOF second mode (EOF2), shows a seesaw pattern of temperature variation, between the Pacific and Japan Sea sides. The EOF-decomposed temperature variation, and ΔT at the Hachinohe station (a Yamase index), has high correlations; for EOF1, the correlation is 0.78, and for EOF1+2, it becomes 0.89. The Yamase Phenomena represented by the Hachinohe negative ΔT, is thus associated with two different patterns of the temperature variations in the Tohoku District.The composite wind map made through the sampling of days, with the Hachinohe ΔT < −2°C, shows northeasterly ocean winds blowing toward the Sanriku coast, an anti-cyclonic circulation center in the eastern Okhotsk Sea, and northeasterly surface winds along the Kuril Islands. When the winds approach the Tohoku District in the Pacific Ocean, they separate two flows around 40°N off Miyako. The surface wind fields associated with the cold phenomena of EOF1 and EOF2 corresponding to the Hachinohe negative ΔT are different. The former indicates the northeasterly wind blowing toward the Sanriku coast, and the latter that the northeasterly winds from the sea south of the Kuril Islands, change their direction around 145°E and the easterly winds blow toward the Sanriku coast. The Kitakami Mountains influence significantly on the Yamase winds, over the coastal Pacific Ocean.
Cooling and trapping of molecules in highly excited rotational states

Collisional relaxation of highly rotating hydrogen molecules is investigated as a function of energy. Calculations demonstrate that inelastic collisions are dramatically suppressed for specific rotational levels of the molecule as the energy is lowered due to the closing of quasiresonant rotation-vibration channels. It is predicted that a 3 He buffer gas may be used to load these highly excited molecules into a trap without a significant loss of population. It is further predicted that evaporative cooling may be used to cool the ‘‘super rotors’’ to even lower temperatures.
Classification of Autism Spectrum Disorder Using rs-fMRI data and Graph Convolutional Networks

Autism spectrum disorder (ASD) affects large number of children and adults in the US, and worldwide. Early and quick diagnosis of ASD can improve the quality of life significantly both for patients and their families. Prior research provides strong evidence that structural and functional magnetic resonance imaging (MRI) data collected from individuals with ASD exhibit distinguishing characteristics that differ in local and global, spatial and temporal neural patterns of the brain – and therefore can be used for diagnostic purposes for various mental disorders. However, the data from MRI are high-dimensional and advanced methods are needed to make sense out of these datasets. In this paper, we present a novel model based on graph convolutional network (GCN) that can utilize resting state fMRI (rs-fMRI) data to classify ASD subjects from health controls (HC). In addition to using the graph from traditional correlation matrices, our proposed GCN model incorporates graphlet topological counting as one of the training features. Our results show that graphlets can preserve the topological information of the graphs obtained from fMRI data. Combined with our GCN, the graphlets retain enough topological information to differentiate between the ASD and HC. Our proposed model gives an average accuracy of 64.27% on the whole ABIDE-I data sets (1035 subjects) and highest site-specific accuracy of 75.9%, which is comparable to other state-of-the-art methods – while potentially open to being more interpretable.
The Effect of Common Variance and Structure Pattern on Random Data Eigenvalues: Implications for the Accuracy of Parallel Analysis

Selecting the correct number of factors to retain in a factor analysis is a crucial step in developing psychometric tools or developing theories. The present study assessed the accuracy of parallel analysis, a technique in which the observed eigenvalues are compared to eigenvalues from simulated data in which no real factors are present. Study 1 investigated the effect of the presence of one real factor on the size of subsequent noise eigenvalues. The size of real factors and the sample size were manipulated. Study 2 examined the effect that the pattern of structure coefficients and continuousness of the variables have on the size of real and noise eigenvalues. Study 3 compared the results of Studies 1 and 2 to actual psychometric data. These examples illustrate the importance of modeling the data more closely when parallel analysis is used to determine the number of real factors.
Image-Based Detection of Modifications in Assembled PCBs with Deep Convolutional Autoencoders

In this paper, we introduce a one-class learning approach for detecting modifications in assembled printed circuit boards (PCBs) based on photographs taken without tight control over perspective and illumination conditions. Anomaly detection and segmentation are essential for several applications, where collecting anomalous samples for supervised training is infeasible. Given the uncontrolled environment and the huge number of possible modifications, we address the problem as a case of anomaly detection, proposing an approach that is directed towards the characteristics of that scenario, while being well suited for other similar applications. We propose a loss function that can be used to train a deep convolutional autoencoder based only on images of the unmodified board—which allows overcoming the challenge of producing a representative set of samples containing anomalies for supervised learning. We also propose a function that explores higher-level features for comparing the input image and the reconstruction produced by the autoencoder, allowing the segmentation of structures and components that differ between them. Experiments performed on a dataset built to represent real-world situations (which we made publicly available) show that our approach outperforms other state-of-the-art approaches for anomaly segmentation in the considered scenario, while producing comparable results on a more general object anomaly detection task.
Neural networks for solving systems of linear equations and related problems

Various circuit architectures of simple neuron-like analog processors are considered for online solving of a system of linear equations with real constant and/or time-variable coefficients. The proposed circuit structures can be used, after slight modifications, in related problems, namely, inversion and pseudo-inversion of matrices and for solving linear and quadratic programming problems. Various ordinary differential equation formulation schemes (generally nonlinear) and corresponding circuit architectures are investigated to find which are best suited for VLSI implementations. Special emphasis is given to ill-conditioned problems. The properties and performance of the proposed circuit structures are investigated by extensive computer simulations. >
BTE-Sim: Fast Simulation Environment For Public Transportation

The public commute is essential to all urban centers and is an efficient and environment-friendly way to travel. Transit systems must become more accessible and user-friendly. Since public transit is majorly designed statically, with very few improvements coming over time, it can get stagnated, unable to update itself with changing population trends. To better understand transportation demands and make them more usable, efficient, and demographic-focused, we propose a fast, multi-layered transit simulation that primarily focuses on public transit simulation (BTE-Sim). BTE-Sim is designed based on the population demand, existing traffic conditions, and the road networks that exist in a region. The system is versatile, with the ability to run different configurations of the existing transit routes, or inculcate any new changes that may seem necessary, or even in extreme cases, new transit network design as well. In all situations, it can compare multiple transit networks and provide evaluation metrics for them. It provides detailed data on each transit vehicle, the trips it performs, its on-time performance and other necessary factors. Its highlighting feature is the considerably low computation time it requires to perform all these tasks and provide consistently reliable results.
HyperTwitter: A Hypergraph-based Approach to Identify Influential Twitter Users and Tweets

Social media platforms have become an easy method of communication for many users. Content posted on social media can influence those who are exposed to it, and users who posted that content are referred to as influencers. Identifying influencers has many applications in marketing, politics, and even health awareness. While research identifying influential users across multiple fields has been studied extensively, users’ influence varies in different topics. Recent studies in topic-specific influence have shown that identifying influencers on the topic-level is more effective. However, most of the existing influencer detection approaches focus only on influential user identification and do not consider that some content can be influential regardless of who published it. This paper investigates the problem of detecting topic-specific influential users and tweets in Twitter datasets. We introduce HyperTwitter, a framework that uses a Twitter sub-graph consisting of users, tweets, and interactions as input. HyperTwitter generates a hypergraph with hyperedges of two types: networks and topic edges, then measures the topic distribution for both users and tweets. With this distribution and the constructed hypergraph, we create a local, topic-based influence ranking for each user and tweet. We conduct extensive experiments with two Twitter datasets and show that the proposed framework outperforms existing baselines significantly.
Sequential circle criterion approach for robustly stabilizing individual generators with SVC

Absolute stability with the spatially defined linear time‐invariant (LTI) state‐space modelings is scrutinized by means of what we call the sequential Lyapunov approach, which possesses independent significance in stabilization when gain‐scheduling control laws are adopted. Then, this theoretical result is exploiting for stabilization of individual generators via SVC actions. More precisely, by remodeling the perturbed swing equations of synchronous generators in multimachine networks through spatially defined LTI state‐space expressions subjected to uncertainties and power disturbance, which are viewed as sector nonlinearities, we introduce frequency responses for coping with nonlinear power swing dynamics of individual generators. By sequentially relating the frequency responses to the circle criterion (substantially, the KYP theorem or the positive real lemma) claimed for LTI systems subject to sector disturbances, output feedback control laws for static VAR compensators are worked out to stabilize individual generators. The frequency‐domain approach is also useful in steady‐state specification besides stabilization in individual generators. Examples show efficacy of the suggested stabilization and steady‐state specification technique. Copyright © 2014 John Wiley & Sons, Ltd.