Miracle-dz/UniFAR-FTUs huggingface dataset:
An Adaptive Community-Based Influence Maximization Algorithm in Social Networks

Influence maximization (IM) is a problem of selecting the most influential vertices with a limited budget under a given propagation model. A significant challenge faced by many existing algorithms pertains to their inability to reconcile the competing goals of solution quality and computational efficiency, rendering them unsuitable in large-scale social networks. In this paper, we propose an adaptive community-based influence maximization algorithm, named AComA, to solve the IM problem with a balance of effectiveness and efficiency. First, we introduce a community detection method to divide a large-scale network into several communities. An adaptive indicator is then defined to identify vertices with high propagation values in divided community networks. Based on community detection and the adaptive influence indicator, the number of candidate vertices is reduced, which significantly reduces the search space of the problem. Second, to select the final seed set from these candidate vertices, a genetic algorithm (GA) is introduced. The crossover and mutation operations are modified explicitly to adapt to the IM problem. By extracting information from the local neighborhood and the global community structure, AComA achieves a more accurate measurement of the influence spread for each vertex. The method proposed in this paper is tested on several real-world datasets. The experimental results show that AComA is promising.
VLSI-Inspired Design Automation for Scalable Power Electronics Layout Optimization

Application of very large-scale integration circuit partitioning techniques toward those of power electronic systems reduces interconnect parasitics while improving overall power density. To that end, an EDA tool is currently being developed that utilizes VLSI partitioning and floorplanning approaches to synthesize power module layouts and arrange and interconnect them into functional converter systems from an annotated input netlist description. When coupled with a tool like PowerSynth, synthesized module layouts are quickly evaluated and optimized to find tradeoffs in electrical and thermal performance metrics while improving the quality of the results. Hierarchically arranging and interconnecting synthesized modules within this framework is then shown to reduce overall footprint and interconnects for a three-level active neutral point clamped inverter.
Influence of the physical pressure of an ultrasound probe on shear-wave elastography measurements of the gastrocnemius muscle in a paediatric population: a non-interventional cohort study

Purpose The aim of our study is to evaluate the impact of ultrasound probe mechanical pressure on the stiffness of the gastrocnemius muscle in a healthy paediatric population. As far as we know, there has been no previous qualitative in vivo study on the impact of probe pressure on muscle shear-wave elastography results with objective evaluation of compression in the paediatric population. Material and methods In this cohort study, a group of 22 children (mean age 8.99 years, SD 2.74, 11 males) underwent elastography of the gastrocnemius muscle of the dominant leg. A custom-made, 3-dimensional printed probe cover was used to measure the mechanical pressure of the probe on tissues. Results The obtained results were related to the age, sex, BMI, and calf circumference of the subjects. We observed a significant difference in the stiffness parameter at a pressure of 1 N, with a further increase if force was increased (p < 0.001). A significant, very weak positive correlation of age and stiffness was observed (p < 0.001, r2 = 0.022). There was no significant correlation of stiffness, BMI, and calf circumference. Conclusions The use of compression during muscle elastography in children causes a significant bias in results, regardless of age, sex, BMI, or calf size.
Dialogue-Clues: Dual-channel Dialogue Clues Embedding Context Perception Network for Emotion Recognition in Conversations

In recent years, emotion recognition in conversations has gained widespread attention due to its extensive applications. Many recent studies have focused on perceiving conversational context from the perspective of capturing dialogue clues. However, these studies often use the entire conversation sequence to represent the dialogue clues, which may result in insufficient representation of the speaker's emotional dynamics in multi-turn conversation. To address these issues, we propose a novel dual-channel dialogue clues embedding context perception network (Dialogue-Clues) that integrates dialogue clues information into global conversation context modeling. We also introduce a dual-channel dialogue clues perception architecture that captures and reinforces both static and dynamic dialogue clues in conversations, and bidirectionally reinforces both types of dialogue clues information. To better represent dynamic dialogue clues, we construct a novel speaker-interaction isomorphic graph structure for the dual-channel dialogue clues perception architecture. Through extensive comparisons with ten existing methods on four public datasets, we confirm the effectiveness of the proposed method. The results demonstrate that integrating Dialogue-Clues information can improve the ability of context modeling.
A study on intermittent duffing array detection and multi-intermittent duffing array feature fusion

The conventional Duffing chaotic detection system uses the system jump excited by the same frequency signal to detect. The actual target signal frequency will occur frequency offset. The conventional Duffing chaotic detection system based on fixed frequency detection is difficult to meet the practical application requirements. Variable scale intermittent Duffing chaotic detection system can realize target signal detection with frequency offset. According to the driving frequency, the signal to be measured is transformed by variable scale, and the target detection is realized by matching the detection system. The intermittent frequency difference of intermittent Duffing chaotic detection system is an important parameter for the application of intermittent chaos. The optimization of interval frequency difference range determines the detection performance of intermittent Duffing chaotic detection system. In order to improve the detection performance of intermittent chaotic system, this paper focuses on the relationship between the amplitude of the output signal and the normalized intermittent frequency difference under specific SNR and the relationship between the detection performance of the system and the normalized intermittent frequency difference under different SNR. Through the evaluation and comparison analysis, the amplitude of the system output signal decreases with the increase of the intermittent chaotic frequency difference. The amplitude of the output signal of the system generally decreases with the decrease of SNR. The intermittent Duffing chaotic system exhibits sensitivity to weak line spectrum signals in a certain range of intermittent frequency difference. The same frequency point of the stable output of the multiple intermittent Duffing array is the weak line spectrum information of the target to be measured. Through the feature fusion processing of the multi-intermittent Duffing array detection results, the target feature information of interference elimination after feature fusion is obtained. In order to analyze the detection performance of the multi-intermittent Duffing array system, by comparing with the short-time Fourier method, the intermittent Duffing array detection system has better frequency resolution and lower feature detection threshold.
Distributed Prescribed-Time Containment Tracking for Heterogeneous Linear Systems

The problem of distributed prescribed-time containment tracking in heterogeneous multi-agent systems is investigated in the study. Herein, prescribed-time refers to the ability to set the stabilization time offline and not affected by any initial parameters. Using the Pontryagin maximum principle, the continuous-time tracking control problem is transformed into a multi-step motion planning problem. Utilizing this framework, a distributed control protocol based on relative sampling measurement information is developed. With the sampling time series, the designed protocol solves the prescribed-time containment tracking problem in heterogeneous linear multi-agent systems. Finally, the effectiveness of the theoretical approach is validated.
Curcuma longa extract reduces serum inflammatory markers and postprandial hyperglycemia in healthy but borderline participants with overweight and glycemia in the normal/prediabetes range: a randomized, double-blind, and placebo-controlled trial

The spice turmeric, which has the Latin name Curcuma longa (C. longa), has various physiological effects. This study evaluated the effects of a hot water mixture with supercritical carbon dioxide C. longa extracts, CLE, and the potential active components of C. longa, turmeronols A and B and bisacurone on inflammation and glucose metabolism. First, we investigated the effect of CLE and the potential active components of C. longa on lipopolysaccharide-induced inflammation in RAW264.7 macrophages. We found a significant decrease in the production of interleukin (IL)-1β, IL-6, tumor necrosis factor (TNF)-α, and nitric oxide with CLE, turmeronol A, and bisacurone, Significant inhibition of each of these substances was also observed, except for TNF-α with turmeronol B. The second part of our work was a 12-week randomized, double-blind, placebo-controlled study in healthy but borderline adults aged 40 to 69 years with overweight and normal/prediabetes glycemia. We compared blood inflammatory and glycometabolic markers in the CLE (n = 55) and placebo groups (n = 55). We found significantly lower serum high-sensitivity C-reactive protein and hemoglobin A1c levels in the CLE group. This group also showed significant improvements in postprandial hyperglycemia and insulin sensitivity indices. Our findings indicate that CLE may reduce low-grade inflammation and thus improve insulin sensitivity and postprandial hyperglycemia. Clinical trial registration: https://center6.umin. ac.jp/cgi-open-bin/ctr/ctr_view. cgi? recptno=R000051492, UMIN-CTR, UMIN000045106.
Interest and Perseverance Are Not Enough to Be Physically Active: The Importance of Self-Efficacy toward Healthy Eating and Healthy Weight to Move More in Adolescents

(1) Background: Insufficient physical activity in adolescents remains an important issue for health promotion. Given the current relevance of understanding the adoption and maintenance of moderate and vigorous physical activity (MVPA), the aim of this study was to analyze, in a sample of adolescents, the role of grit personality as an antecedent of healthy eating and healthy weight (HEW) self-efficacy and its implications for the practice of MVPA. (2) Methods: Participants were 987 adolescents (597 girls, 390 boys) aged between 15 and 19 years from Mexico and Spain. The Spanish versions of the grit personality scale, the healthy eating and weight self-efficacy scale and the global physical activity questionnaire were used to measure the variables of interest. (3) Results: Mediated regression analysis showed that grit personality was not directly related to MVPA practice. However, the results indicate the significant relationship between grit personality and HEW self-efficacy, as well as the positive and significant relationship of this self-efficacy on MVPA practice. HEW self-efficacy totally mediated the relationship between grit personality and MVPA in both boys and girls. (4) Conclusions: These results suggest that having a grit personality (i.e., having interest and perseverance) is not enough for adolescents to be physically active, but that perceiving oneself as effective in having a healthy diet and healthy weight may be the key for adolescents to move more. At the intervention level, we suggest targeting an enhancement of young people’s competence to eat healthily and regulate their weight as a strategy to enhance the performance of more MVPA, with a possible transfer between healthy behaviors (spill over).
Sensorless Field-Oriented Control of High-Speed Low-Inductance Motors for Small Unmanned Quadcopters

This paper presents an application of sensorless field-oriented control (FOC) for high-speed, low-inductance motors used in small, unmanned quadcopters. The motors in such applications are usually high pole count BLDC motors with an outrunner rotor. The electronic speed controllers (ESC) generally adopt a sensorless six-step commutation algorithm based on the back-EMF zero crossing. While simple and computationally inexpensive, this type of algorithm has numerous issues such as low efficiency, increased torque ripple, issues at low speed and more. This in turn leads to reduced flight time, noisy operation and increased losses due to heating of the motor. The goal of this work is to implement a sensorless field-oriented control algorithm and study its performance.
General decay anti-synchronization of coupled delayed memristive neural networks

In this paper, the network models of coupled delayed memristive neural networks (CDMNNs) without and with delayed coupling are studied respectively. Firstly, according to the definitions of ψ-type stability and ψ -type function, the notion of general decay anti-synchronization (GDAS) is introduced. Secondly, by constructing Lyapunov functional, designing controller and using inequality techniques, the corresponding sufficient conditions that reaching GDAS for CDMNNs with and without delayed coupling are deduced. Thirdly, the validity of the results is proved by proper numerical simulation.
Results of Nucleos(t)ide Analog Treatment Discontinuation in Hepatitis B e-Antigen-Negative Chronic Hepatitis B: NUCSTOP Study

Background/Aims: This study aims to investigate the effects of nucleos(t)ide analogs (NAs) discontinuation in eligible patients in accordance with the Asian Pacific Association for the Study of the Liver hepatitis B guideline and the factors affecting clinical and virological relapses. Materials and Methods: In this prospectively designed study, hepatitis B e antigen (HBeAg)-negative chronic hepatitis B patients who were followed up between 2012 and 2019 were evaluated and 57 patients were included. All participants enrolled the study were HBeAg-negative status at NA initiation. Results: The median age of the patients was 49 (29-72) years and 24 (42%) were females. The median treatment duration was 96 (36-276) months and patients were followed for a median duration of 27 months. Sixteen patients had a previous history of NA switch, and thirteen of these patients had a history of lamivudine resistance. Thirty-eight of 57 patients (66%) developed an elevated hepatitis B virus deoxyribonucleic acid level of >2000 IU/mL at least once, defined as virological relapse and 23 (60%) of them, experienced clinical relapse. Thirty-one of 57 patients were re-treated during the follow-up, and hepatitis B surface antigen (HBsAg) loss occurred among 4 (7%) patients. All patients who experienced HBsAg loss had a history of lamivudine resistance (P = .002). Conclusion: Despite receiving NAs suppression therapy for a long time, HBsAg loss occurs rarely. Although it was not life-threatening, most patients experienced relapses and treatment should be restarted. In our study, whether it is a coincidence that all patients with HBsAg loss are patients in whom NAs are used sequentially due to lamivudine resistance is an issue that needs to be further investigated.
Cascaded Weak Reflector Coaxial Cable Structure for Point and Distributed Large-Strain Sensing

In this article, we present a truly distributed sensing modality based on a cascaded weak reflector coaxial cable structure (CWR-CCS) for large strain measurements. Compared to an optical fiber, a coaxial cable is much more robust and has a larger strain capability to survive harsh conditions, which may enable important applications in structural health monitoring (SHM). By drilling serial shallow holes into a commercial flexible coaxial cable perturbing the local impedance along its axial direction, CWRs along the coaxial cable are introduced due to impedance mismatch, forming the CWR-CCS. Gating a certain number of sequential reflectors in the time-domain reflection signal of the CWR-CCS followed by Fourier transform, a localized multibeam interferometer (LMI) is revealed, which serves as a point sensor. By monitoring the spectral shift of the multibeam interferometer, localized disturbances can be determined. By sliding the time gate along the entire coaxial cable, distributed sensing is realized. Large strain measurements (up to 18 000 $\mu \varepsilon $ ) are conducted to verify the utility of the proposed CWR-CCS as a sensing modality. The proposed distributed sensing configuration has the advantages of ease of fabrication, robustness, large strain survivability, and ease of signal interrogation.
Online Continual Learning Data Quality Framework for Entity Resolution

If companies have access to more data than ever, integrating such data into a single platform has become more challenging. One of the main challenges faced for data integration is duplicated data. To address data duplication, records referring to the same entity should be identified and merged into a single record according to a process called Entity Resolution (ER). If the used ER models rely on data to learn and make predictions, they assume that the provided data will be similar to the initial training data, which is not usually the case. Indeed, data is constantly changing, especially in Big Data environments where a high Variability characterizes data. Therefore, Continual Learning should be performed by retraining the model with new data, a process that could also be done in a real-time mode with a live stream of data,known as Online Continual Learning (OCL). Despite its high importance and impact on the model’s performance, OCL is less considered in ER approaches. Thus, this paper aims to address model decay issues by suggesting an Online Continual learning framework for Entity resolution approaches. The proposed framework is based on record linkage and consists of data preprocessing, data integration and indexing, data comparison, labeling, model retraining, and a performance evaluation. The framework also addresses catastrophic forgetting, a commonly faced issue by OCL approaches. In addition, the obtained results show that our approach has not only allowed avoiding a model decay but also has increased the model’s Recall by 6.4% and the F-score by 3.7% during the serving. Moreover, the suggested approach was implemented using a Large-Scale Dataset, ensuring its appropriateness to Big Data.
Classical model of two electrons interacting in a saddle-shaped electric potential, and a constant perpendicular magnetic field

Electron collisions are fundamental phenomena in modern physics with diverse applications ranging from collision ionization to quantum computing and high-energy plasma studies. This paper focuses on the intricate dynamics of electron collisions within a saddle-shaped electric potential and a constant magnetic field, mimicking the conditions found in beam splitters (and particle accelerators). While existing studies often employ a quantum framework to model these interactions and actually perform the experiment, we adopt a classical approach to provide a more intuitive understanding of the phenomena involved. The transition from quantum to classical representation eliminates probabilistic elements, allowing for precise and deterministic simulations. We utilized a self-programmed python script based on mathematical expressions for the situation, which are also self-derived in the paper. We have managed to replicate 2/4 results simply by altering the starting positions of the electrons and have added further functionality within the python simulations for further research and development i.e., a softening factor controlling the relative screening effect of the material of the constriction, and a time delay feature to be able to offset the starting time of one of the particles relative to the other. Our hope with this paper is for people to be able to take our results, and the model used to derive them, and use it for the purposes of further research and exploration.
Development and validation of a new diagnostic prediction model of ENHO and NOX4 for early diagnosis of systemic sclerosis

Objective Systemic sclerosis (SSc) is a chronic autoimmune disease characterized by fibrosis. The challenge of early diagnosis, along with the lack of effective treatments for fibrosis, contribute to poor therapeutic outcomes and high mortality of SSc. Therefore, there is an urgent need to identify suitable biomarkers for early diagnosis of SSc. Methods Three skin gene expression datasets of SSc patients and healthy controls were downloaded from Gene Expression Omnibus (GEO) database (GSE130955, GSE58095, and GSE181549). GSE130955 (48 early diffuse cutaneous SSc and 33 controls) were utilized to screen differentially expressed genes (DEGs) between SSc and normal skin samples. Least absolute shrinkage and selection operator (LASSO) regression and support vector machine recursive feature elimination (SVM-RFE) were performed to identify diagnostic genes and construct a diagnostic prediction model. The results were further validated in GSE58095 (61 SSc and 36 controls) and GSE181549 (113 SSc and 44 controls) datasets. Receiver operating characteristic (ROC) curves were applied for assessing the level of diagnostic ability. Reverse transcription-quantitative polymerase chain reaction (RT-qPCR) was used to verify the diagnostic genes in skin tissues of out cohort (10 SSc and 5 controls). Immune infiltration analysis were performed using CIBERSORT algorithm. Results A total of 200 DEGs were identified between SSc and normal skin samples. Functional enrichment analysis revealed that these DEGs may be involved in the pathogenesis of SSc, such as extracellular matrix remodeling, cell-cell interactions, and metabolism. Subsequently, two critical genes (ENHO and NOX4) were identified by LASSO and SVM-RFE. ENHO was found down-regulated while NOX4 was up-regulated in skin of SSc patients and their expression levels were validated by above three datasets and our cohort. Notably, these differential expressions were more pronounced in patients with diffuse cutaneous SSc than in those with limited cutaneous SSc. Next, we developed a novel diagnostic model for SSc using ENHO and NOX4, which demonstrated strong predictive power in above three cohorts and in our own cohort. Furthermore, immune infiltration analysis revealed dysregulated levels of various immune cell subtypes within early SSc skin specimens, and a negative correlation was observed between the levels of ENHO and Macrophages M1 and M2, while a positive correlation was observed between the levels of NOX4 and Macrophages M1 and M2. Conclusion This study identified ENHO and NOX4 as novel biomarkers that can be serve as a diagnostic prediction model for early detection of SSc and play a potential role in the pathogenesis of the disease.