Miracle-dz/UniFAR-FTUs huggingface dataset:
A practical and universal framework for generating publicly available medical notes of authentic quality via the power of crowds

Medical notes written by doctors in hospitals or clinics are information-rich. However, in many countries or cultures, few people have access to them for educational and research purposes, even once anonymized. This is because their contents, including patients’ disease information, are sensitive and require confidentiality. Therefore, publicly available pseudo-medical notes are needed. Authentic pseudo-medical notes must meet two requirements: (1) medical consistency, and (2) informal descriptions and specific sub-language; however, these are empirical knowledge, even for medical doctors, and are not clarified specifically. We combat this by harnessing the power of crowds. We propose a human-in-the-loop framework for generating publicly available professional medical notes utilizing human cognitive traits with a small dataset. The practical and universal framework has three steps. In Step 1, crowd workers imitated actual notes. In Step 2, crowds and algorithms collaboratively identified notes’ characteristics based on comparisons between actual and dummy notes. In Step 3, the texts generated in Step 1 that exhibited the characteristics from Step 2 were evaluated as authentic medical notes that met all requirements. We demonstrated this framework with a total of 1,662 crowds’ power. All data were preprocessed to protect patients’ privacy before the experiments. The crowds’ generated 9,756 notes were evaluated as the most realistic compared to dummy medical notes written by doctors. These crowd-generated medical notes, which are the largest publicly available dataset of Japanese medical notes, are published. This study was the first challenge for the crowds to solve the medical expert-level task.
Fluorapatite: a review of synthesis, properties and medical applications vs hydroxyapatite

Recently, using calcium phosphates and at the top of them, hydroxyapatite (HA) has been considered in medical and dental applications as an artificial biomaterial due to their chemical and structural similarity to the body's skeletal tissues such as bone and tooth. Because of reinforcement of hydroxyapatite's mechanical and biological properties by substitution of OHgroups by Fions to produce fluorapaptite (FA) has been proven, in this article synthesis methods, properties and medical applications of fluorapatite and its pros and cons in comparison with hydroxyapatite have been reviewed.
Exploring graphene-based materials’ genotoxicity: inputs of a screening method

Abstract Graphene-based materials (GBMs) are promising nanomaterials, and several innovations depend on their use. However, the assessment of their potential hazard must be carefully explored before entering any market. GBMs are indeed well-known to induce various biological impacts, including oxidative stress, which can potentially lead to DNA damage. Genotoxicity is a major endpoint for hazard assessment and has been explored for GBMs, but the available literature shows conflicting results. In this study, we assessed the genotoxicity of 13 various GBMs, one carbon black and one amorphous silica through a DNA damage response assay (using a human respiratory cell model, BEAS-2B). Concurrently, oxidative stress was assessed through a ROS production quantification (DCFH-DA assay using a murine macrophage model, RAW 264.7). We also performed a full physicochemical characterization of our samples to explore potential structure-activity relationships involving genotoxicity. We observed that surface oxidation appears linked to genotoxicity response and were able to distinguish several groups within our studied GBMs showing different genotoxicity results. Our findings highlight the necessity to individually consider each nanoform of GBMs since the tested samples showed various results and modes of action. We propose this study as a genotoxicity assessment using a high-throughput screening method and suggest few hypotheses concerning the genotoxicity mode of action of GBMs.
An Intelligent Control Strategy for a Highly Reliable Microgrid in Island Mode

An intelligent control strategy based on a membership cloud model in a high reliable off-grid microgrid with a reconfigurable inverter is proposed in this paper. The operating principle of the off-grid microgrid with the reconfigurable inverter is provided, which contains four operating modes. An open-circuit fault diagnosis for the inverter is presented first. The polarities of the midpoint voltages defined in the paper are used to recognize the faulty power switch. The reconfigurable inverter allows the power switches of different bridges to be reconfigured, when there are power switches faulty, to let the inverter operate in faulty state. The working principle of the reconfigurable inverter is given. The membership cloud model with two output channels is built to obtain the virtual impedance to suppress the circulating currents between inverters when the reconfigurable inverter is in faulty state. A pulse resetting method is presented. The general intelligent control strategy for the reconfigurable inverter is formed as the droop-virtual impedance-voltage-current-pulses resetting control. The validity of the intelligent control strategy of the system is verified by simulation.
Caloric adequacy of parenteral nutrition and its influence on the clinical outcome of hospitalised patients

Background: The adaptation of Parenteral Nutrition (PN) to actual energy requirements of hospitalised patients is essential, since excessive and insufficient nutritional intake have been associated with poor clinical outcomes. Aim: To evaluate the adaptation of prescribed PN to the estimated nutritional requirements using three predictive equations and the influence of excessive/insufficient nutrient intake on patient clinical outcomes (nutritional parameters, metabolic and infectious complications). Methods: Prospective, observational study in hospitalised patients nutritionally assessed. Data was collected the first and fifth/sixth day of PN with clinical (infection, length of hospital stay), biochemical (visceral proteins, cholesterol, glucose, triglycerides, lymphocytes, CRP) and anthropometric parameters (skin folds, height, weight). Theoretical requirements were calculated using Harris-Benedict (HB), Mifflin-St Jeor (MF) and 25 Kcal/Kg/day formulas. The HB formula was used to compare estimated and provided requirements. Results: A total of 94 patients (mean: 72 ± 13.7 years old) were included with initial mean weight and height of 69.2 Kg and 162.8 cm, respectively (mean BMI: 26.1 Kg/m2). No statistically significant differences were found between the actual (1620 Kcal/day) and estimated caloric mean calculated with HB (1643 Kcal/day) and MF (1628 Kcal/day). When comparing with the caloric estimation, 31.9% of patients were underfed, while 14.9% were overfed. Intergroup analysis demonstrated significant variations in albumin, prealbumin, glucose, cholesterol, triglycerides and MUAC, with a significant increase of hyperglycaemia (+37.86; p < 0.05) and hypertriglyceridemia (+63.10; p < 0.05), being higher in overfed patients. Conclusion: In our study, inadequate nutrient intake was associated with a higher degree of hyperglycaemia and hypertriglyceridemia, without positive impact on anthropometric parameters.
Multi-modal Hate Speech Detection using Machine Learning

With the continuous growth of internet users and media content, it is very hard to track down hateful speech in audio and video. Converting video or audio into text does not detect hate speech accurately as human sometimes uses hateful words as humorous or pleasant in sense and also uses different voice tones or show different action in the video. The state-of-the-art hate speech detection models were mostly developed on a single modality. In this research, a combined approach of multi-modal system has been proposed to detect hate speech from video contents by extracting feature images, feature values extracted from the audio, text and used machine learning and Natural language processing.
The effect of trade on market power — evidence from developing economies

ABSTRACT The rise of market power has sparked off increasing public debate over the past decade. Using a novel large-scale firm-level dataset, this paper examines the effect of trade liberalization on corporate market power in emerging and developing economies. We find that opening up to trade significantly reduces market power by promoting competition and improving resource allocation. The cumulative impact is estimated at 4 percent reduction in average markups over a 5-year horizon post each trade liberalization episode. We also draw on machine learning algorithms to account for nonlinearities in the relationship and show that trade reform is complementary to other structural reforms.
The Machine Method for Processing Chicken Feathers by Splitting them into Fibers and Rachis

Chicken feathers are obtained as a by-product in the production of poultry meat. Due to its stiffness, fragility and inelasticity, chicken feathers are not considered a valuable material and are mostly industrial waste, the volume of which is increasing. Therefore, chicken meat producers are trying to get rid of them in every possible way. In recent years, instead of getting rid of chicken feathers, research has been carried out towards using their wonderful properties from nature. Among these studies, a special place is occupied by the production of fibers from chicken feathers, which could be used in the textile industry. The presented work concerns the development of a mechanized method for the production of fiber from chicken feathers on an industrial scale. The proposed method is based on the separation of the fibrous part of the feather from the stem of the feather by mechanical cutting using an aerodynamic disperser. With this method, protein fibers with many valuable properties have been obtained, the weight of which is half that of chicken feathers. In addition to these fibers, called chicken feather fibers, a feather stalk is obtained as a by-product, which, due to its lightness and strength, is a suitable raw material for the production of composites. In this work, the properties of the products obtained by the above-mentioned method were studied, and it was determined that the materials obtained as a result of processing the produced chicken feathers are raw materials suitable for industrial use. The production parameters of the machine used to separate the fibers from the stem were also investigated and it was observed that the operating parameters of the machine did not adversely affect the natural properties of the fibers produced.
The Structure of T-DNA Insertions in Transgenic Tobacco Plants Producing Bovine Interferon-Gamma

Many of the most modern drugs are of a protein nature and are synthesized by transgenic producer organisms. Bacteria, yeast, or animal cell cultures are commonly used, but plants have a number of advantages—minimal biomass unit cost, animal safety (plants are not attacked by mammalian pathogens), the agricultural scale of production, and the ability to produce complex proteins. A disadvantage of plants may be an unstable level of transgene expression, which depends on the transgene structure and its insertion site. We analyzed the structure of T-DNA inserts in transgenic tobacco plants (Nicotiana tabacum L.) belonging to two lines obtained using the same genetic construct but demonstrating different biological activities of the recombinant protein (bovine interferon-gamma). We found that, in one case, T-DNA was integrated into genomic DNA in the region of centromeric repeats, and in the other, into a transcriptionally active region of the genome. It was also found that in one case, the insert has a clustered structure and consists of three copies. Thus, the structure of T-DNA inserts in both lines is not optimal (the optimal structure includes a single copy of the insert located in the active region of the genome). It is desirable to carry out such studies at the early stages of transgenic plants selection.
Dynamic Prototype Network Based on Sample Adaptation for Few-Shot Malware Detection

The continuous increase and spread of malware have caused immeasurable losses to social enterprises and even the country, especially unknown malware. Most existing methods use predefined class samples to train models, which cannot handle unknown malware detection. In this paper, we formalize unknown malware detection as a Few-Shot Learning problem. However, the existing model cannot dynamically adjust the model parameters according to the samples and does not deeply consider the influence of the correlation between samples, so it achieves sub-optimal performance. We propose a Dynamic Prototype Network based on Sample Adaptation for few-shot malware detection (DPNSA). Specifically, we use dynamic convolution to realize dynamic feature extraction based on sample adaptation. Secondly, we define the class feature (prototype) as the mean of the dynamic embedding of all malware samples of each class in the support set. Then, a dual-sample dynamic activation function is proposed, which uses the correlation of the dual-sample to reduce the impact of unrelated features between samples on the metric. Finally, we use the metric-based method to calculate the distance between the query sample and the prototype to realize malware detection. Experiments show that our method outperforms the existing few-shot malware detection models and achieves significant improvement.
Unsupervised Anomaly Detection for Surface Defects with Dual-Siamese Network

Unsupervised anomaly detection in real industrial scenarios is challenging since the small amount of defect-free images contain limited discriminative information, and anomaly defects are unpredictable. In this paper, a dual-siamese network is designed to simultaneously detect and locate anomalies in images. It first uses a pre-trained convolutional neural network (CNN)-based siamese architecture to embed discriminative features of normal samples and synthetic defective samples. A dense feature fusion (DFF) module is employed to obtain the dense feature representation of dual input. The following siamese network of perceptual defects is proposed to reconstruct and restore the dual-dense features of the previous stage. Compared to the existing methods that only employ a single residual map, the restoration of dense feature maps is proposed to locate the anomalies better. The experimental results on the MVTec AD dataset demonstrate that our method achieves state-of-the-art inspection accuracy and has potential for industrial application.
Short Term Effect of Physical Exercise on Selective Attention using Eeg and Stroop Task

This paper aims to investigate the short-term effect of physical exercise on Selective Attention for university students. Selective attention allows the participant to focus on essential details ignoring the other trivial information. Stroop task is a test widely used to study Selective Attention. EEG waves are very closely related to attention and can help investigate the effect of physical exercise on attention. We propose a methodology to investigate High-Intensity interval (HIIT) Exercise (Tabata routine) on Selective Attention on university students. We prove that Response time statistically after the Post-HIIT Stroop Task is significantly less than that of the pre-HIIT Stroop Task. We analyze the pre-HIIT Stroop test EEG data and post-HIIT Stroop test EEG data to get insights using EEG time-domain features: mean, variance, kurtosis, skewness, Shannon entropy, and total power. We observe that the post-HIIT variance for all channels and total power for the Fpz channel is higher, while pre-HIIT Shannon Entropy for all channels is higher for all 10 participants. Results indicate that exercise has a positive impact on stimulating the brain. Hence it impacts selective attention, as also indicated by the statistical analysis from the Stroop test results.
Carbon nanotube - inorganic hybrids: from synthesis to application

Esta Tesis describe la preparacion de varios hibridos formados por nanotubos de carbono y material inorganico para diferentes aplicaciones, que van desde la electronica hasta la biomedicina. El proposito de esta investigacion ha sido trabajar en la funcionalizacion de nanotubos de carbono mediante la decoracion externa y el llenado con nanotubos inorganicos para obtener hibridos con propiedades funcionales. Como paso previo a la funcionalizacion, los nanotubos de carbono se tienen que purificar para eliminar las impurezas no deseadas. En esta Tesis, hemos propuesto un metodo de purificacion para nanotubos de carbono multicapa consistente en el uso vapor de agua, que es un oxidante leve. Hemos investigado el efecto del tiempo de tratamiento con vapor de agua en el grado de purificacion y acortamiento de los nanotubos de carbono. Hemos visto que la purificacion con vapor de agua genera muestras de nanotubos de carbono de alta calidad y con puntas abiertas. Ademas, hemos apreciado que su longitud puede ser modulada facilmente. Una vez que se han purificado los nanotubos de carbono, hemos preparado diferentes tipos de hibridos mediante la incorporacion del material en sus paredes. Hemos procedido a la decoracion externa de los nanotubos de carbono con nanoparticulas de oxido de hierro superparamagneticas a traves de un metodo in situ. Se ha conseguido obtener un agente de contraste dual tanto para resonancia magnetica como para imagen nuclear a traves del etiquetado de las nanoparticulas con 99mTc. Ademas, se ha mostrado que el uso de nanotubos de carbono mas cortos mejoran las propiedades magneticas del hibrido, obteniendo asi valores de relajatividad mas elevados. Por otro lado, hemos incorporado de manera covalente clusters de metalacarboranos en las paredes de nanotubos de carbono monocapa, formando un hibrido de alto contenido de 10B, que sera apropiado para la terapia por captura neutronica de boro. Diferentes rutas sinteticas han sido investigadas. La dispersabilidad del hibrido resultante ha sido mas alta que en el caso de nanotubos de carbono monocapa oxidados y, por lo tanto, el hibrido es un buen candidato para aplicaciones biomedicas. Finalmente, hemos investigado el llenado de nanotubos de carbono multicapa con un solido van der Waals por capilaridad del material en su fase fundida. Hemos reportado por primera vez la formacion de nanotubos inorganicos monocapa dentro de los nanotubos de carbono. Asimismo, hemos investigado la transformacion dinamica de los nanomateriales encapsulados bajo la radiacion de un haz de electrones. Tambien hemos demostrado, usando la teoria del funcional de la densidad, que los nanotubos monocapa inorganicos son estables sin la presencia de los nanotubos de carbono. Los resultados presentados en el marco de esta Tesis expanden la capacidad de los hibridos formados por nanotubos de carbono y material inorganico.
terrainr: An R package for creating immersive virtual environments

1 Graduate Program in Environmental Science, State University of New York College of Environmental Science and Forestry, Syracuse, New York, USA 2 Department of Sustainable Resources Management, State University of New York College of Environmental Science and Forestry, Syracuse, New York, USA 3 Department of Landscape Architecture, State University of New York College of Environmental Science and Forestry, Syracuse, New York, USA DOI: 10.21105/joss.04060
Virtual force field algorithm for a behaviour-based autonomous robot in unknown environments

The present paper describes a real-time motion-planning approach which lies in the integration of three techniques: fuzzy logic (FL), virtual force field (VFF), and boundary following (BF). The FL algorithm is used for velocity control based on sonar readings. The path-planning algorithm is based on the VFF and BF methods. The proposed navigation system differs from previous works in terms of using different algorithms for planning robot motion. Other improvements concern functional and computational aspects of the design and integration of the modules. The robot shows robust performance in complex situations and local minimum scenarios. Simulation results show the effectiveness of the developed system in various environments with long walls, U-shaped, maze-like, and other types of clutter.
A 5.8 GHz RF Receiver Front-End With 77.6 dB Dynamic Range AGC for a DSRC Transceiver

This paper presents a 5.8 GHz highly sensitive, high-dynamic-range RF receiver front-end with Automatic-Gain Control (AGC) and a high image-rejection for a Dedicated Short-Range Communication (DSRC) application. It is formed by a transceiver common matching, a single to differential Low-Noise Amplifier (LNA), an active mixer with an Image Rejection Filter, and an AGC unit. The proposed AGC unit is composed of a power-detector over the intermediate-frequency signals of the downconverter mixer. The power detector produces a wide dynamic range response signal, which eases the controllability of the AGC unit. In addition, external components are minimized, and area occupation is optimized. The proposed RF-FE is fabricated and measured in a 130-nm RF CMOS process. Experimental results show an overall dynamic range of 77.6 dB while a high sensitivity performance to an input power level of −85 dBm is measured. An overall gain of 26.4 dB for the RF-FE is obtained. The input referred P1dB is measured to be around −28.3 dBm. The 2-stage RC poly-phase filter that is applied to reject the image signal results in a maximum image rejection ratio of 39 dB.