Miracle-dz/UniFAR-FTUs huggingface dataset:
Data-Driven Local Control Design for Active Distribution Grids Using Off-Line Optimal Power Flow and Machine Learning Techniques

The optimal control of distribution networks often requires monitoring and communication infrastructure, either centralized or distributed. However, most of the current distribution systems lack this kind of infrastructure and rely on sub-optimal, fit-and-forget, local controls to ensure the security of the network. In this paper, we propose a data-driven algorithm that uses historical data, advanced optimization techniques, and machine learning methods to design local controls that emulate the optimal behavior without the use of any communication. We demonstrate the performance of the optimized local control on a three-phase, unbalanced, low-voltage, distribution network. The results show that our data-driven methodology clearly outperforms standard industry local control and successfully imitates an optimal-power-flow-based control.
Internet of Things Background: An Empirical Study on the Payment Intention of Central Bank Digital Currency Design

This article proposes and verifies the consumer acceptance intention model under the background of central bank digital currency payment. It modifies the theory of technology acceptance (TAM), rational action theory (TRA), and extended valence framework (EVF) through the construction of perceived privacy, perceived security, perceived system quality, and perceived benefits. The data collected from 325 questionnaires on consumers’ attitudes toward the central bank’s digital currency were used to apply the structural equation model to evaluate the prediction model. The empirical results indicate that perceived privacy, perceived security, and perceived system quality are important predictors of perceived trust, and perceived trust is an important predictor of perceived interest and intention. Perceived privacy, perceived security, and perceived system quality are key factors influencing consumers’ intentions.
Femtosecond Laser-Inscripted Direct Ultrafast Fabrication of a DNA Distributor Using Microfluidics

A femtosecond laser can be used for single or multiple writing processes to create sub 10-μm lines or holes directly without the use of masks. In this study, we characterized the depth and width of micro-channels created by femtosecond laser micro-scribing in polydimethylsiloxane (PDMS) under various energy doses (1%, 5%, 10%, 15% and 20%) and laser beam passes (5, 10 and 15). Based on a microfluidic simulation in a bio-application, a DNA distributor was designed and fabricated based on an energy dose of 5% and a laser beam pass of 5. The simulated depth and width of the micro-channels was 3.58 and 5.27 μm, respectively. The depth and width of the micro-channels were linearly proportional to the energy dose and the number of laser beam passes. In a DNA distribution experiment, a brighter fluorescent intensity for YOYO-1 Iodide with DNA was observed in the middle channels with longer DNA. In addition, the velocity was the lowest as estimated in the computational simulation. The polymer processability of the femtosecond laser and the bio-applicability of the DNA distributor were successfully confirmed. Therefore, a promising technique for the maskless fabrication of sub 10-μm bio-microfluidic channels was demonstrated.
Dynamic Prediction of the Incident Duration Using Adaptive Feature Set

Non-recurring incidents such as accidents and vehicle breakdowns are the leading causes of severe traffic congestions in large cities. Consequently, anticipating the duration of such events in advance can be highly useful in mitigating the resultant congestion. However, availability of partial information or ever-changing ground conditions makes the task of forecasting the duration particularly challenging. In this paper, we propose an adaptive ensemble model that can provide reasonable forecasts even when a limited amount of information is available and further improves the prediction accuracy as more information becomes available during the course of the incidents. Furthermore, we consider the scenarios where the historical incident reports may not always contain accurate information about the duration of the incidents. To mitigate this issue, we first quantify the effective duration of the incidents by looking for the change points in traffic state and then utilize this information to predict the duration of the incidents. We compare the prediction performance of different traditional regression methods, and the experimental results show that the Treebagger outperforms other methods. For the incidents with duration in the range of 36 – 200 min, the mean absolute percentage error (MAPE) in predicting the duration is in the range of 25% – 55%. Moreover, for longer duration incidents (greater than 65 min), prediction improves significantly with time. For example, the MAPE value varies over time from 76% to 50% for incidents having a duration greater than 200 min. Finally, the overall MAPE value averaged over all incidents improves by 50% with elapsed time for prediction of reported as well as effective duration.
A Localization Based Routing Protocol for Dynamic Underwater Sensor Networks

Owing to the rapid development of underwater applications, the research on underwater wireless sensor networks (UWSNs) is becoming more and more significant. Low bandwidth, high latency, limited energy and node float mobility are basic challenges for underwater sensor networks. Most of the theories for terrestrial sensor networks can not be applied to underwater sensor networks. In this paper, we focus on the localization and routing issues for dynamic UWSNs. Location based routing protocols can suit the dynamic situation of UWSNs well. However, most research assumes that the nodes know their locations, which may be not appropriate for some applications. This paper combines these two aspects together to design a localization based routing protocol for dynamic UWSNs. The simulation results show our method can effectively route data from the source nodes to the sink nodes in a dynamic environment.
DIComP: Lightweight Data-Driven Inference of Binary Compiler Provenance with High Accuracy

Binary analysis is pervasively utilized to assess software security and test vulnerabilities without accessing source codes. The analysis validity is heavily influenced by the inferring ability of information related to the code compilation. Among the compilation information, compiler type and optimization level, as the key factors determining how binaries look like, are still difficult to be inferred efficiently with existing tools. In this paper, we conduct a thorough empirical study on the binary's appearance under various compilation settings and propose a lightweight binary analysis tool based on the simplest machine learning method, called DIComP to infer the compiler and optimization level via most relevant features according to the observation. Our comprehensive evaluations demonstrate that DIComP can fully recognize the compiler provenance, and it is effective in inferring the optimization levels with up to 90% accuracy. Also, it is efficient to infer thousands of binaries at a millisecond level with our lightweight machine learning model (1MB).
An Experimental Validation of the Polynomial Curvature Model: Identification and Optimal Control of a Soft Underwater Tentacle

The control possibilities for soft robots have long been hindered by the lack of accurate yet computationally treatable dynamic models of soft structures. Polynomial curvature models propose a solution to this quest for continuum slender structures. Nevertheless, the results produced with this class of models have been so far essentially theoretical. With the present work, we aim to provide a much-needed experimental validation to these recent theories. To this end, we focus on soft tentacles immersed in water. First, we propose an extension of the affine curvature model to underwater structures, considering the drag forces arising from the fluid-solid interaction. Then, we extensively test the model's capability to describe the system behavior across several shapes and working conditions. Finally, we validate model-based control policies, proposing and solving an optimal control problem for directional underwater swimming. Using the model we show an average increase of more than 3.5 times the swimming speed of a sinusoidal baseline controller, with some tentacles showing an improvement in excess of 5.5 times the baseline.
Hemocompatibility Evaluation of Thai Bombyx mori Silk Fibroin and Its Improvement with Low Molecular Weight Heparin Immobilization

Bombyx mori silk fibroin (SF), from Nangnoi Srisaket 1 Thai strain, has shown potential for various biomedical applications such as wound dressing, a vascular patch, bone substitutes, and controlled release systems. The hemocompatibility of this SF is one of the important characteristics that have impacts on such applications. In this study, the hemocompatibility of Thai SF was investigated and its improvement by low molecular weight heparin (LMWH) immobilization was demonstrated. Endothelial cell proliferation on the SF and LMWH immobilized SF (Hep/SF) samples with or without fibroblast growth factor-2 (FGF-2) was also evaluated. According to hemocompatibility evaluation, Thai SF did not accelerate clotting time, excess stimulate complement and leukocyte activation, and was considered a non-hemolysis material compared to the negative control PTFE sheet. Platelet adhesion of SF film was comparable to that of the PTFE sheet. For hemocompatibility enhancement, LMWH was immobilized successfully and could improve the surface hydrophilicity of SF films. The Hep/SF films demonstrated prolonged clotting time and slightly lower complement and leukocyte activation. However, the Hep/SF films could not suppress platelet adhesion. The Hep/SF films demonstrated endothelial cell proliferation enhancement, particularly with FGF-2 addition. This study provides fundamental information for the further development of Thai SF as a hemocompatible biomaterial.
THEM6: A Novel Molecular Biomarker Predicts Tumor Microenvironment, Molecular Subtype, and Prognosis in Bladder Cancer

Background Thioesterase superfamily member 6 (THEM6) has been implicated in the development and progression of various cancers. However, prior research emphasized on its regulatory role merely, we aim to investigate the effect of THEM6 gene on the immunological role and its relationship with molecular subtype in bladder cancer (BLCA). Methods Through pan-cancer analysis, we explored the THEM6 expression pattern and immunological role using The Cancer Genome Atlas (TCGA) database. In addition, we performed a correlation of THEM6 and its immunological functions, including immunomodulators, immune checkpoints, cancer immunity cycles, T cell inflamed score, and tumor-infiltrating immune cells in the BLCA tumor microenvironment (TME) based on TCGA and BLCA microarray cohort from Xiangya Hospital. We also assessed the accuracy of THEM6 in predicting the molecular subtype and its response to different interventions in BLCA. Finally, we computed and validated a prediction model established by THEM6-related different expressed immune-related genes that might help in BLCA prognosis. Results THEM6 led to immunosuppression in BLCA TME. Furthermore, there was a downregulation in the immunological functions. Besides, THEM6 could effectively distinguish BLCA molecular subtypes, and THEM6 low expression implied basal subtype that was more effective to several interventions, such as immune checkpoint blockade (ICB) therapies, neoadjuvant chemotherapy, and radiotherapy. While THEM6 high expression indicated luminal subtype, hyperprogression and better response to targeted therapies, such as blocking THEM6 and several immune-inhibited oncogenic pathways. Conclusions THEM6 may be with potential immune-modulating properties and may become a potential new immunotherapy target for BLCA. THEM6 could accurately predict the molecular subtype of BLCA, which was helpful for guiding the treatment. Simultaneously, the prediction model may exhibit an excellent predictive value in patients with BLCA.
A Polypyrrole/Nanoclay Hybrid Film for Ultra-Sensitive Cardiac Troponin T Electrochemical Immunosensor

An electrochemical immunosensor based on a nanohybrid film of carboxylated polypyrrole and amine nanoclay was developed for label-free detection of the human cardiac troponin T (cTnT). The nanohybrid film was formed in situ on the surface of the glassy carbon electrode, followed by the covalent immobilization of anti-troponin T antibodies by glutaraldehyde. Morphological and chemical characterizations of the nanohybrid film were performed by scanning electron microscopy and Fourier-transform infrared spectroscopy. Under the optimized conditions, a calibration curve for cTnT in spiked serum was obtained by square wave voltammetry, and a low limit of detection and quantification was achieved (0.35 and 1.05 pg mL−1, respectively). This was the first time that this type of nanohybrid film was used in the development of an immunosensor for cTnT that proved to be a simple and efficient strategy for the manufacture of a label-free electrochemical device that could be applied in the diagnosis of acute myocardial infarction.
TRPM7 Modulates Human Pancreatic Stellate Cell Activation

Pancreatic diseases, such as pancreatitis or pancreatic ductal adenocarcinoma, are characterized by the presence of activated pancreatic stellate cells (PSCs). These cells represent key actors in the tumor stroma, as they actively participate in disease development and progression: reprograming these PSCs into a quiescent phenotype has even been proposed as a promising strategy for restoring the hallmarks of a healthy pancreas. Since TRPM7 channels have been shown to regulate hepatic stellate cells proliferation and survival, we aimed to study the role of these magnesium channels in PSC activation and proliferation. PS-1 cells (isolated from a healthy pancreas) were used as a model of healthy PSCs: quiescence or activation were induced using all-trans retinoic acid or conditioned media of pancreatic cancer cells, respectively. The role of TRPM7 was studied by RNA silencing or by pharmacological inhibition. TRPM7 expression was found to be correlated with the activation status of PS-1 cells. TRPM7 expression was able to regulate proliferation through modulation of cell cycle regulators and most importantly p53, via the PI3K/Akt pathway, in a magnesium-dependent manner. Finally, the analysis of TCGA database showed the overexpression of TRPM7 in cancer-associated fibroblasts. Taken together, we provide strong evidences that TRPM7 can be considered as a marker of activated PSCs.
Toward Weak Signal Analysis in Hyperspectral Data: An Efficient Unmixing Perspective

Many unmixing methods hold the assumption that endmembers correspond to major land covers, but not true for some unmixing tasks where observed minor object signals corresponding to some special types of endmembers are relatively weak. When there exist weak signals that have low intensity potentially caused by subtle mixing abundance fractions regarding the endmembers of minor objects, the traditional unmixing techniques may fail. This article pioneers weak signal scenarios in hyperspectral unmixing using an efficient method called HyperWeak. In particular, HyperWeak involves a sparse nonnegative matrix factorization (NMF) model that contains two main parts, where the unsupervised part estimates the endmember and abundance matrices, and the supervised part ensures the minimal degradation of prior knowledge. To enhance the robustness of the HyperWeak model, this article considers a reweighted sparsity constraint to boost the sparseness of the abundance matrix. For effectively solving optimization problems, a Nesterov’s optimal gradient method (OGM) is used in this article. Experiments conducted on synthetic and real hyperspectral images indicate that HyperWeak can improve the unmixing performances of hyperspectral data in weak signal situations.
An Integrated Step-Up Inverter Without Transformer and Leakage Current for Grid-Connected Photovoltaic System

In this paper, an integrated step-up inverter without transformer is investigated for photovoltaic (PV) power generation. The proposed topology can be derived by combining a traditional boost converter with a single-phase full bridge dc–ac converter. The main features of the integrated inverter are: First, the leakage current caused by the solar cell array-to-ground parasitic capacitance can be theoretically reduced to zero due to the characteristics of the converter configuration, which can improve the efficiency and the reliability of the PV generation system; second, the output ac voltage of the proposed inverter can be higher than the input dc voltage, which is capable of connecting low voltage PV panels to the grid; third, only five active switches are used in the presented inverter, and those switching devices can be synchronously driven by various sinusoidal pulsewidth modulation methods based on the carrier; therefore, the proposed inverter is compact and with curtailed cost. The working principle and analysis of the proposed integrated inverter are elaborated. Finally, simulation and experimental results are obtained in a lab prototype, which agree well with the theoretical analysis.
Partial Label Learning via GANs With Multiclass SVMs and Information Maximization

Partial label learning (PLL), an important branch of weakly supervised learning, addresses the problem that each instance is associated with a set of candidate labels and only one is correct. In this paper, a novel adversarial model PL-MIGAN is proposed to simultaneously mitigate two fundamental issues of generative adversarial networks (GANs) in PLL: label disambiguation performance of discriminator and instance synthesis quality of generator. First of all, multi-class support vector machines (SVMs) applied in discriminator to disambiguate the candidate labels and identify fake instances. This strategy not only improves the disadvantage that traditional supervised loss is unable to perform disambiguation but also reduces the influence of cumulative error caused by noise label propagation. Furthermore, a partial contrastive loss is constructed to extend the self-supervised contrastive approach to PLL, allowing us to effectively leverage ambiguous labels information. Finally, the generator jointly employ mutual information (MI) and partial contrastive loss to estimate the latent distribution of each class label. In addition, in order to reduce the impact of ambiguous information, an iteratively optimization procedure is designed to update the label confidence matrix as conditional information guides the generation of instance classes. As adversarial learning proceeds, both the discriminator and the generator alternately and iteratively boost their performance. Simulation results reveal the overwhelming performance of PL-MIGAN.
Maintenance of Physical Activity Behavior by Individuals with Prediabetes and Diabetes during the COVID-19 Pandemic after Completing an Exercise Intervention in Brazil

This cross-sectional study evaluated the perception of individuals with prediabetes/diabetes about their living conditions during the COVID-19 pandemic to identify the facilitators, barriers, and reasons to remain physically active at home and adhere to recommended exercise. It included individuals with prediabetes/diabetes who had completed an exercise intervention, which started on-site and moved to a remote home-based regime due to the COVID-19 pandemic and were advised to keep exercising at home. The outcomes were assessed by a bespoke questionnaire that was developed by the research team, the Brazilian Portuguese adapted version of the Exercise Adherence Rating scale, and the Motives for Physical Activity Measure-Revised scale. Of 15 participants (8 female, 58 ± 11 years), most reported positive perceptions about their living conditions and few difficulties maintaining some physical activity at home. However, only 53.8% of them adhered to the recommended exercise. Time flexibility, no need for commuting, and a sense of autonomy were the main facilitators of home exercise, while a lack of adequate space was the main barrier. The descending order of median scores that were obtained in each reason for physical activity was fitness, enjoyment, competence, social, and appearance. Individuals with prediabetes/diabetes maintained some physical activity during the pandemic, mainly motivated by health concerns.
Association of hepatitis C virus genotype 2 spread with historic slave trade and commerce routes in Western Africa

Abstract The hepatitis C virus genotype 2 (HCV2) is endemic in Western and Central Africa. The HCV2 evolutionary origins remain uncertain due to the paucity of available genomes from African settings. In this study, we investigated the molecular epidemiology of HCV infections in rural Guinea, Western Africa, during 2004 and 2014. Broadly reactive nested reverse transcription polymerase chain reaction (RT-PCR)-based screening of sera from 1,571 asymptomatic adults resulted in the detection of 25 (1.5 per cent; 95 per cent confidence interval 0.9–2.3) positive samples, with a median viral load of 2.54E + 05 IU/ml (interquartile range 6.72E + 05). HCV-infected persons had a median age of 47 years, and 62.5 per cent were male and 37.5 per cent were female. The full polyprotein-encoding genes were retrieved by a combination of high throughput and Sanger sequencing from 17 samples showing sufficiently high viral loads. Phylogenetic analysis and sequence distances ≥13 per cent averaged over the polyprotein genes compared to other HCV2 subtypes revealed nine previously unknown HCV2 subtypes. The time to the most recent common ancestor of the Guinean HCV2 strains inferred in a Bayesian framework was 493 years (95 per cent Highest posterior density (HPD) 453–532). Most of the Guinean strains clustered poorly by location on both the level of sampling sites within Guinea and the level of countries in the phylogenetic reconstructions. Ancestral state reconstruction provided decisive support (Bayes factor > 100) for an origin of HCV2 in Western Africa. Phylogeographic reconstructions in a Bayesian framework pointed to a radial diffusion of HCV2 from Western African regions encompassing today’s countries like Ghana, Guinea Bissau, or Burkina Faso, to Central and Northern African regions that took place from the 16th century onwards. The spread of HCV2 coincided in time and space with the main historic slave trade and commerce routes, supported by Bayesian tip-association significance testing (P = 0.01). Our study confirms the evolutionary origins of HCV2 in Western Africa and provides a potential link between historic human movements and HCV2 dispersion.
Information Design for Supporting Collaborative Communities

Abstract A growing number of people are organizing around common needs that affect their daily life meeting in new ways, seeking solutions and using digital technology for re-shaping their environments. This paper introduces the definition of Collaborative Communities and focus on the digital tools that they use. From the Information Design scope, it’s relevant to observe and support their actions, supplying them with better tools for designing their environments. A first step is to identify which actions they pursue and the characteristics of the involved tools. Fourteen participative-map cases have been analyzed for both their visual and technical features as well as their outcomes. A data cross-matching was conducted between their objectives, foci and a typology of collaborative actions on digital platforms. It identifies a technological discourse regarding the use of platforms and tools, while a political discourse can be seen in the use of either open source or private tools.