Miracle-dz/UniFAR-FTUs huggingface dataset:
Endoprothèses coronaires et diabète

L'atherosclerose coronaire du patient diabetique est le plus souvent diffuse, severe, pluritronculaire et/ou atteignant le tronc commun coronaire gauche (TCG). Le fait d'etre diabetique est associe a un pronostic pejoratif apres revascularisation coronarienne, qu'elle soit chirurgicale ou percutanee. Le diabete est un facteur predictif independant de restenose apres angioplastie coronaire. Les endoprotheses coronaires actives ou drug eluting stents (DES) sont constituees d'une plateforme metallique recouverte d'un polymere qui delivre une drogue cytotoxique inhibant la proliferation neo-intimale, source de restenose. Les DES reduisent le taux de restenose par rapport aux endoprotheses coronaires metalliques ou bare metal stent (BMS), y compris chez le diabetique. La chirurgie de pontages coronaires (PC) reste la methode de reference de revascularisation des diabetiques pluritronculaires. La revascularisation coronaire percutanee (ICP) est indiquee chez le diabetique devant des lesions focales mono- ou bitronculaires, avec si possible, implantation d'un DES plutot que d'un BMS. En cas d'ICP avec implantation d'un DES, une double antiagregation plaquettaire doit etre prolongee pour une duree minimale d'un an, avec poursuite d'un seul antiagregant a vie ensuite.
Head and neck lymphedema: what is the physical therapy approach? A literature review

Introduction: Head and neck lymphedema is considered a chronic and complex complication with potential to cause physical, functional, emotional and social impairment. Objective: To identify the approaches to physical therapy used to treat head and neck cancer-related lymphedema. Method: A bibliographic search was conducted in February and March 2012 in books and electronic databases, LILACS, MEDLINE, SCIELO, Cochrane, PEDro, and BDTD using the following keywords: lymphedema, treatment, head and neck cancer, and physical therapy connected by the Boolean operator AND without a specific time frame. Results and discussion: Early diagnosis and assessment is key to properly managing and effectively treating lymphedema. Diagnosis is reached through clinical history and physical assessment by measuring the distance between two anatomical landmarks, circumference measures, and lymphedema rating scales. Complex decongestive therapy, which includes manual lymph drainage, compressive bandaging, kinesiotherapy and skin care, is the technique most frequently used and currently considered to be the gold standard. Conclusions: No consensus is reported in the literature in regard to a standard procedure to assess and treat head and neck cancer-related lymphedema. Assessments and treatments described in the literature are mainly restricted to the limbs; therefore, further studies are needed to support effective clinical actions in the physical therapy approach to this condition.
An analysis of English language learning needs and problems of underprivilged children in a slum area in Bangkok metropolis

For underprivileged children living in a slum area, education is one of the primary tools contributing to a better life when they grow up. The present study aimed at investigating the needs and problems in English language learning of disadvantaged children in the Klong Toey slum, the largest slum in the Bangkok Metropolis. Data were collected by means of questionnaires and interviews with 100 children, volunteer teachers, and community members. The study findings showed that children from the slum area realize the significance of English and that they are interested in learning English through games, songs, videos, and TV programs. They also enjoy learning English through technology. However, the teachers state that these children need more motivation in the classroom, which could be increased through interesting activities and a fun atmosphere that supports their communicative skill development. Furthermore, community members, despite their understanding of the significance of the English language, admit that their living conditions form a major hindrance to giving more support to these children’s learning. Language policy planners and educators can make use of these findings to revise existing English curricula to 12 | PASAA Vol. 46 July-December 2013 better suit context-specific needs and problems of similar children.
Il ruolo dell'endoscopia nei tumori neuroendocrini gastroenteropancreatici

Gastroenteropancreatic (GEP) neuroendocrine tumors (NET) are rare neoplasia arisen from neuroendocrine cells present in the gut mucosa and pancreas. Digestive endoscopy and endoscopic ultrasonography play a relevant role in NET diagnosis, stadiation and surveillance. Moreover, in selected patients, surgical endoscopy allows the tratment of these cancers at an early stage.
Versión paralela del test de aprendizaje verbal España-Complutense (TAVEC)

Introduccion. La disponibilidad de formas paralelas de instrumentos de evaluacion neuropsicologica es escasa. El uso repetido del material y el consiguiente efecto de la practica dificultan la interpretacion de los cambios observados en evaluaciones sucesivas. La memoria es una de las funciones mas afectadas por este efecto. Objetivo. Obtener una version paralela de uno de los instrumentos disponibles en espanol para la evaluacion del aprendizaje y la memoria verbal, el test de aprendizaje verbal Espana-Complutense (TAVEC). Sujetos y metodos. Se realizo un estudio normativo con una muestra de 110 sujetos para la obtencion de los items de la forma paralela, siguiendo los criterios utilizados en la version original. La muestra para el estudio de la version paralela estuvo formada por 70 sujetos neurologicamente sanos, de 18-89 anos. Se aplicaron ambas versiones en un intervalo de 15-20 dias. Resultados. Los analisis multivariados mostraron que no se producia efecto de la forma, del orden de administracion ni de la sesion. Las correspondientes interacciones tampoco fueron significativas. Estos resultados se observaron tanto para la muestra total como para el grupo de jovenes (18-29 anos), edad intermedia (30-59 anos) y envejecimiento (60-89 anos). Los analisis correlacionales mostraron la validez y consistencia interna de la forma alternativa. Conclusiones. Los resultados muestran la equivalencia entre la version original del TAVEC y la version elaborada en esta investigacion. Es, por tanto, una version recomendable para su uso en el estudio de la evolucion de los deficits de aprendizaje y memoria.
ePoSt: Practical and Client-Friendly Proof of Storage-Time

Data availability is the major concern of outsourced data storage services. While reliability is promoted by many storage providers, it is infeasible for users to verify these claims. Proofs of Storage-Time are proposed to address this issue: they allow a prover to convince a verifier that the prover indeed stores the outsourced data continuously during the whole storage period. These protocols, however, either fail to guarantee the actual duration of data possession, or require the client to perform a computationally expensive storing process for each file, marking them far from being practical. We present $\mathsf {ePoSt}$ , the first secure, stateless and efficient Proof of Storage-Time protocol with public verifiability. Not only does it ensure continuous data availability, but it also minimizes the cost of the client, which is crucial for real-world deployment. Specifically, processing a 1GB file for outsourcing, in anticipation for 3 proof/verify interactions, each attesting a period of 1 year at 25 minutes intervals requires only 66.34 minutes. In contrast, state-of-the-art solution requires 51 hours of processing to prepare a file for outsourcing. Proof size and verification remains at a reasonable cost: a proof is of size 48.82KB, and can be verified in 36.05 ms. Furthermore, our solution enjoys public verifiability and remains stateless. The former allows the data owner to outsource the verification process, and the latter allows unlimited number of proofs and verification to be conducted after the file has been stored. These properties make $\mathsf {ePoSt}$ particularly suitable for new business models such as decentralised storage networks (e.g., Filecoin).
Equivalent Angular Resolution for Distributed Radar Sensors

To further improve the spatial object separation capabilities of radar systems in automotive applications, distributed multistatic setups are increasingly being investigated. They range from fully coherent cooperative networks to non-coherent ones using varying numbers of sensor nodes. Instead of increasing the size of the aperture by increasing the number of antennas and receiving channels, objects can also be distinguished by exploiting the high range resolutions by means of trilateration. In this paper, we examine how a non-coherent distributed system can be compared to a conventional monostatic system in terms of an equivalent angular resolution and provide experimental measurement results.
Tracking Control for Motion Constrained Robotic System via Dynamic Event-Sampled Intelligent Learning Method

This paper introduces a new event-sampled intelligent learning method to solve tracking control for motion constrained robotic system. In many applications, motion constraints on joint movements of a robot are needed for its safe operation or to accommodate limitations arising from its mechanical structures. To handle the control design problem, a state-transformation method is employed to guarantee motion constraints of the robot and obtain a corresponding unconstrained tracking error system. Next, the constrained control problem is transformed into a general optimal tracking control problem for the unconstrained error system. Finally, we implement neural networks based learning structures to obtain the optimal solution. An important highlight of our proposed algorithm is that we have integrated a dynamic event-sampled approach to the learning-based controller design, thus reducing the system state sampling times. Using the proposed event-sampled learning control method, the stability of the closed-loop system and the convergence of the weights of neural networks are both established. Lastly, the effectiveness of the proposed intelligent control method is verified via simulation.
Classification of Rice Leaves Diseases by Deep CNN-Transfer Learning Approach for Improved Rice Agriculture

Rice leaves may suffer serious impacts such as low production or yield of the respective products if necessary precautions are not taken. Therefore, to ensure the healthy and normal growth of the rice plants, early diagnosis of any disease and application of the necessary therapy to the damaged plants are paramount. Because manual disease diagnosis requires a lot of time and effort, an effective automated method is required for early disease diagnosis. As a result, this study presents a deep learning-based solution to the aforementioned issue for the automated detection of three plant diseases: leaf smut, bacterial leaf blight, and brown spot that frequently affect rice plants. The transfer learning with VGGNet convolutional neural network (CNN), which was pre-trained on a sizable Imagenet dataset, was used in this study to effectively classify the illnesses of rice leaves. A number of cutting-edge classifiers, including Support Vector Machine (SVM), k-nearest neighbour (kNN), Convolutional Neural Network (CNN), Random Forest, and Decision Tree, are used to compare the performance of the proposed framework. The results demonstrate that the proposed CNN-transfer learning framework outperforms other classifiers with a mean accuracy of 97.22% in the 5-fold cross-validation.
Towards Artificial Neural Network Based Intrusion Detection with Enhanced Hyperparameter Tuning

Due to the development of complex communication paradigms and the rise in the number of inter-connected digital devices, intrusion detection system (IDS) has become one basic and important security mechanism to identify cyber intrusions and protect computer networks. Currently, various deep learning algorithms have been studied in intrusion detection to achieve a high detection rate, whereas the detection performance may be still dependent on specific datasets. To maintain the detection performance, parameter optimization is believed as an effective solution. Motivated by this observation, in this work, we propose a concise but effective hyperparameter tuning process to enhance the artificial neural network (ANN) based IDS. In the evaluation, we consider three ANN variants and four datasets. The experimental results indicate that our approach can outperform similar studies and typical learning algorithms.
A New Non-Isolated Active Quasi Z-Source Multilevel Inverter With High Gain Boost

This paper proposes a three-level diode clamped active impedance source inverter (AIS-TLI) based on the quasi Z-source inverter (qZSI). Apart from having minimal components and the inherent benefits of three-level qZSIs, such as single-stage buck-boost capability, shoot-through (ST) immunity, and continuity of input current, the proposed topology has a higher boost capability and excellent efficiency. In addition, the inverter bridge has a higher modulation index, which improves the quality of the output waveform and necessitates less inductance. The proposed topology provides common ground between input and output terminals, which effectively eliminates leakage current in PV-powered single-phase systems. This paper describes the different operating modes principle, dynamic analysis, steady-state analysis, and parameter selection instructions for the proposed in-depth. Furthermore, the suggested inverter’s benefits and limitations are compared to the traditional (q)ZSIs and some other AIS-TLIs. Finally, modeling and experimental results are used to confirm the effectiveness of the suggested topology.
Characterization of Dielectric Properties of 3D printing Materials for Solid Biomedical Phantoms

In this paper, the relative permittivity $\varepsilon_{\mathrm{r}}^{\prime}$ and conductivity $\sigma$ of commercially available 3D printing filaments are presented. The dielectric properties of common filaments, as well as filaments enriched with carbon, metal, and stone, are measured using a commercial dielectric probe, 4MHz to 20GHz. The dielectric properties of the samples are compared to several biological tissues. Using these materials, 3D printing can be used to increase the level of detail in biomedical phantoms for various applications, for example by adding rigid or solid parts with realistic dielectric properties. This will lead to a more realistic representation and thus better results.
Prediction of Local PM2.5 Concentrations Based on Time-Series Feature Learning Using Multivariate LSTM

Air pollution causes various health problems and diseases. Long-term PM2.5 monitoring and prediction of its occurrence and sources are necessary not only in global areas based on public monitoring stations but also in local areas using cost-effective sensor systems. For this study, we developed a sensor system to achieve simplified and high-frequency PM2.5 measurements. We attempted to learn and to predict local PM2.5 concentrations from observed data using long short-term memory (LSTM) as a dominant time-series feature learning network. For improving learning and prediction accuracy evaluated according to the root mean square error (RMSE), sensor calibration is performed using a higher sensor. Moreover, we strove to reduce RMSE by optimizing its five major parameters. Experimentally obtained results demonstrate that the prediction accuracy is improved gradually after calibration and parameter optimization. As an ablation experiment, five meteorological factors are imported externally to verify the factors which contribute to reducing RMSE. Results verify the strong effects of local pressure and temperature for training and relative humidity and temperature for testing as validation.
A Design of Low Profile Broadband Omnidirectional Vertical Polarization Slot Antenna

In this paper, a vertically polarized slot antenna with an omnidirectional radiation pattern is proposed. The antenna is based on the hollow cylinder structure. The omnidirectional vertical polarization is realized by two horizontal slots along the side wall of closed hollow cylinder structure. The simulation results show that the slot antenna impedance bandwidth is of 48% (0.94GHz to 1.54GHz), at the same time, the gain variation in the horizontal plane is less than 2.5dB over the bandwidth (0.95-1.25GHz). At the same time, the height of this antenna is only 0.063 λmax, the designed antenna has much potential for its flush-mount capability.
Mechanism of Cancer Cachexia: Progress on Disentangling a Complex Problem

Introduction Weight Joss and associated symptoms and complications are a frequent and distressing problem for patients with faradvanced cancer and for their loved ones. Initially, weight Joss associated with cancer (cancer cachexia) was understood as primarily a problem of insufficient calorie intake in the presence of a consurning tumour. Subsequently, lirnited success with nutritional therapy in patients with advanced cancer ( l) led to the disco very of the paraneoplastic anorexiacachexia syndrome (ACS), which is characterised by systernic dysregulation of the host metabolism promoted by proinflarnmatory cytokines or tumour-derived factors. This syndrome is believed to be one of the major causes for cancer-
PPsky: Privacy-Preserving Skyline Queries with Secret Sharing in eHealthcare

Applying skyline queries to medical data can considerably benefit medical analysis in eHealthcare. However, as medical data often involves sensitive personal data, privacy concerns have become a significant impediment to the development of eHealthcare. Although several privacy-preserving skyline query schemes in eHealthcare have been put forth, they need a trusted platform to generate and assign secret keys for the multi-source scenario. In addition, those schemes incur non-trivial computational costs on resource-limited entities. To address these limitations, we propose a novel privacy-preserving skyline query scheme, named PPsky, based on arithmetic secret sharing, in which a series of secure protocols are designed to handle the basic operations in skyline queries. Security analysis illustrates that our PPsky scheme is privacy-preserving, and the evaluation results also validate the efficiency of our PPsky scheme.
Cost Efficient Service Mesh Controller Placement for Edge Native Computing

Cloud native computing featured by microservice has been regarded as a compelling trend in cloud application development. Edge computing, as an alternative or complemen-tary to cloud computing, is potential to expand the microservice to edge computing, simplifying the development and deployment of edge applications. Despite that, there is still a challenge on how to manage the microservices efficiently in the open and heterogeneous distributed environment. To this end, service mesh provides a potential solution in efficient microservices management. However, as a traditional cloud-oriented architecture, it can not be applied into edge computing directly since the centralized controller policy. To address this problem, in this paper, we propose an edge service mesh architecture with distributively deployed controllers for edge native computing. We further inves-tigate the problem on how to deploy these distributive controllers in a cost efficient manner with the consideration of control cost and the synchronization cost. The problem is formulated into a non-linear optimization form and then linearized into an integer linear programming (ILP) problem. To tackle the computation complexity, we then come up with a customized k-means based algorithm (i.e., ck-means) in polynomial computation complexity. The experimental results verify the efficiency of our ck-means algorithm in comparison with the traditional k-means algorithm.
Study of the Contribution of Negative Work Assistance at the Ankle with a Multi-Articular Soft Exosuit During Loaded Walking

Of the recent exoskeletons that successfully reduce the metabolic cost of walking, some deliver mostly positive joint work assistance, while others deliver a combination of positive and negative joint work assistance. There is no consensus about the contribution of negative joint work assistance on the metabolic cost of walking. The aim of the present study is to investigate the contribution of negative work assistance at the ankle with a soft exosuit. We tested eight participants who walked with a soft exosuit that uses an off-board actuator to assist with both plantarflexion and hip flexion through a multiarticular suit architecture. In four active conditions, we provided different rates of negative work assistance. All active conditions significantly reduced metabolic rate by 11 to 15% compared to wearing the exosuit powered off. On average there was a trend toward higher reduction in metabolic rate with higher rates of negative work assistance, but this was not significant.
Anti-Helicobacter pylori and protective effects of aqueous Fumaria vaillantii L. extract in pylorus-ligated, indomethacin- and toxic-induced ulcers in rats.

This paper evaluated anti-ulcer, anti-secretory and anti- helicobacter pylori effects of folkloric medicinal plant, Fumaria vaillantii L, in rats. Different toxic agents; ethanol (80%), NaOH (0.2 mol/L), NaCl (25%) and indomethacin (30 mg/kg, body weight) were used to produce acute gastric ulceration in rats. Antisecretory studies were undertaken by using pylorus-ligation, Shay rat technique. F. vaillantii L aqueous extract (Aq-FV) was used in three doses (100, 200 and 300 mg/kg bodyweight). Aq-FV was further tested for anti- H. pylori activity. Extract significantly attenuated gastric mucosal damage induced by toxic agents and indomethacin in rats. Aq-FV restored partially mucus secretion in indomethacin model. In pylorus-ligated rats, Aq-FV significantly reduced the basal gastric acid secretion, acidity and ulceration. Aq-FV possesses significant and more potent anti-ulcer and cytoprotective activities against experimentally-induced gastric ulcers in comparison to Omeprazole. Extract showed significant but less potent anti- H. pylori activity in comparison to Clarithromycin. Evaluation agreed with the folkloric use of F. vaillantii L as anti-ulcer and anti-secretory tool.
Deep learning algorithm applied to bacteria recognition

Pathogenic bacteria are harmful microorganisms capable of causing diseases. To fight or eliminate those microorganisms, antibiotics with antimicrobial action have been developed wich can be synthetic or semi synthetic. Through time, bacteria has developed mechanisms to fight this antimicrobial action, generating the antibiotic resistance. This issue is a serious global problem that affects the health area. Because of this, a workflow based on the KDD methodology. The proposed approach use data obtained through MALDI-TOF mass spectrometry techniques without preprocessing, in conjunction with deep learning, to implement a multiclass classification neural network of bacteria at the species level, with the purpose of obtaining a fast and reliable recognition. Different tests were implemented to this neural network, obtaining promising precision results with the aproach, reaching accuracy 99.15% the highest and 98.09% the lowest. Implementing evaluation metrics such as confusion matrix and classification reports to measure the recognition of species at the individual level, finding cases in wich the precision was 100% the highest and 94% the lowest.