Miracle-dz/UniFAR-FTUs huggingface dataset:
Treatment strategies for advanced hepatocellular carcinoma: Sorafenib vs hepatic arterial infusion chemotherapy

Sorafenib is used worldwide as a first-line standard systemic agent for advanced hepatocellular carcinoma (HCC) on the basis of the results of two large-scale Phase III trials. Conversely, hepatic arterial infusion chemotherapy (HAIC) is one of the most recommended treatments in Japan. Although there have been no randomized controlled trials comparing sorafenib with HAIC, several retrospective analyses have shown no significant differences in survival between the two therapies. Outcomes are favorable for HCC patients exhibiting macroscopic vascular invasion when treated with HAIC rather than sorafenib, whereas in HCC patients exhibiting extrahepatic spread or resistance to transcatheter arterial chemoembolization, good outcomes are achieved by treatment with sorafenib rather than HAIC. Additionally, sorafenib is generally used to treat patients with Child-Pugh A, while HAIC is indicated for those with either Child-Pugh A or B. Based on these findings, we reviewed treatment strategies for advanced HCC. We propose that sorafenib might be used as a first-line treatment for advanced HCC patients without macroscopic vascular invasion or Child-Pugh A, while HAIC is recommended for those with macroscopic vascular invasion or Child-Pugh A or B. Additional research is required to determine the best second-line treatment for HAIC non-responders with Child-Pugh B through future clinical trials.
A User-Friendly Low-Cost Mobile App Based Home Appliance Control And Circuit Breaker

Along with the constant improvement of different electronic devices, the safety of technicians has also become a matter of great concern, as the lives of technicians are at risk while they work by switching off the circuit breakers, because even after the circuit breaker has been turned off, someone can unknowingly turn it on while the technician is still working. There must be a system for ensuring security for the technicians. Also, people do not like having to walk to switches all the time to turn on/off appliances such as fan/light/air conditioner. It results in wasted energy because of unnecessarily keeping appliance on. To solve these problems, we came up with a system with password controlled circuit breakers and wireless control of home appliances using an Android app. It replaces the traditional circuit breaker on/off system with a password controlled system, where nobody can turn on the circuit breaker without the password. Remote control of the home appliances helps the user save electricity. It also enhances the quality and comfort of life. The system also includes a home security mechanism against illegal intrusion using a password controlled door lock system and a mechanism for detecting dangerous gas leakages. The system is built using an Arduino mega microcontroller, a Bluetooth module, a matrix 4x4 keyboard, a methane gas sensor and an Android mobile application. The whole system can be made compact in an integrated system.
Event Based Sentiment Analysis of Twitter Data

Everyday large volumes of data are produced. Millions of users share and dissipate most up-to-date information on twitter. Traditional text mining suffers severely from short and noisy nature of tweets. Event detection from twitter data has many new challenges when compared to event detection from traditional media. Noisy nature and limited length are the challenges imposed by twitter data. Event detection performance on twitter is negatively affected by nature of tweets. This paper proposes SegAnalysis framework to tackle these challenges. It performs tweet segmentation, event detection and sentiment analysis. Tweet segmentation is performed in a batch mode using POS (part of speech) tagger on recent online tweets fetched by the user. Segmentation of a tweet preserves the named entities and its stickiness score is calculated. Naïve Bayes classification and online clustering detect events. These events improve situational awareness and decision support. Sentiment analysis categorizes tweets as positive, negative and neutral depending on sentiment score of a tweet. SegAnalysis framework can be extended to deal with events belonging to multiple clusters.
Gender differences in sociodemographic and behavioural factors associated with BMI in an adult population in rural Burkina Faso – an AWI-Gen sub-study

ABSTRACT Background: The global health transition is linked with an increased burden of non-communicable diseases with cardiovascular diseases leading the epidemic. In sub-Saharan Africa (SSA), the prevalence of obesity has increased during the past decades and there is a need to investigate the associated driving factors. In Burkina Faso obesity remains low, especially in rural areas. In this study we recruited middle-aged adults, as part of a larger study on genetic and environmental contributions to cardiometabolic disease among Africans. Objectives: To investigate the distribution of BMI and prevalence of obesity in a cross-sectional population-based study and to determine the sociodemographic and behavioural correlates with BMI. Methods: Participants (N = 2,076) were recruited from the Nanoro Health and Demographic Surveillance System area and were aged 40–60 years. We applied hierarchical modelling to identify factors associated with BMI and structural equation modelling to identify mediated effects of sociodemographic and behavioural variables on BMI. Results: Data are presented on 2,076 participants (49.9% female). Men had significantly higher BMI than women with medians of 21.1 (19.2 – 23.4) vs 19.8 (18.1 – 21.6) (p < 0.001), and there were significantly more underweight women compared to men (31.0% vs 17.4%) (p < 0.001). More men were overweight and obese than women (11.9% vs 5.2% and 2.2% vs 1.4%). Socioeconomic status was the major contributor to increased BMI for men, and education was the main contributor in women. Tobacco smoking and chewing, and problematic alcohol consumption were associated with a decrease in BMI in men and women. Conclusion: Overweight and obesity are relatively low among adults in rural Burkina Faso, and men had a higher median BMI than women. Behavioural factors, including tobacco use and alcohol consumption, contributed to a decrease in BMI, whereas socioeconomic status and education (which were both generally low in this community) contributed to an increase in BMI.
Multimodal Optical Medical Imaging Concepts Based on Optical Coherence Tomography

Optical medical imaging techniques in general exhibit outstanding resolution and molecule-specific contrast. They come however with a limited penetration in depth and small field of view. Multimodal concepts help to combine complementary strengths of different imaging technologies. The present article reviews the advantages of optical multimodal imaging concepts using optical coherence tomography (OCT) as core technology. In particular we first discuss polarization sensitive OCT, Doppler OCT and OCT angiography, OCT elastography and spectroscopic OCT as intramodal concepts. To highlight intermodal imaging concepts, we then chose the combination of OCT with photoacoustics, and with non-linear optical microscopy. The selected multimodal concepts and their particular complementary strengths and applications are discussed in detail. The article concludes with notes on standardization of OCT imaging and multimodal extensions.
Cascading Collapse of a Large-Scale Mixed Source Microgrid Caused by Fast-Acting Inverter-Based Distributed Energy Resources

Power electronic converter-interfaced distributed energy resources (DERs) are being increasingly deployed for achieving high energy efficiency, power quality, and flexibility of power system operation and controls. They facilitate access to a wide array of energy sources, including renewables, fuel cells, microturbines, variable speed engine-generator sets, etc. However, recent tests carried out at the Consortium for Electric Reliability Technology Solutions (CERTS) Microgrid have indicated that their deployment in the mixed source microgrid can cause a cascading collapse during extreme events. To investigate the problem, simulation models of two types of DERs are developed in PSCAD/EMTDC software and validated with the experimental test results. Furthermore, the validated models are used to study a cascading collapse problem in a large-scale mixed source microgrid on the benchmark IEEE 33-bus test system. In this paper, three alternative techniques are evaluated to prevent the cascading collapse in the large-scale microgrid caused by fast-acting inverter-based DERs.
Quantifying Human Autonomy Recovery During Ankle Robot-Assisted Reversal of Foot Drop After Stroke

In this paper, we present a measure to quantify the profile of human autonomy recovery during ankle robot-assisted locomotor training to reverse foot drop after hemiparetic stroke. Underlying this kinetic-based measure is the estimation of the human ankle torque contribution during dynamic (step-by-step) patient-robot interaction, by measuring the device peak torques that meet desired design criteria across gait cycles. As with our previous clinical trials with the ankle robot, we employ an adaptive control approach for a six-week treadmill intervention in chronic stroke patients with well-defined ankle deficits. Here, we provide the first evidence of ankle robot-mediated changes in human autonomy index (HAl) and assess the worthiness of the HAI to predict changes in key volitional ankle neuromotor and whole-body functional gait outcomes. We also propose ways to incorporate the HAl into practice, including a model to customize the duration of robotic therapy for each patient based on minimally acceptable improvements in outcome measures.
Finding Optimal Bayesian Networks with Local Structure

The idea of using decision trees as local models in Bayesian networks is revisited. A class of dyadic decision trees—proposed previously only for continuous conditioning variables—is augmented by incorporating categorical variables with arbitrary context-specific recursive splitting of their state spaces. It is shown that the resulting model class admits computationally feasible maximization of a Bayes score in a range of moderate-size problem instances. In particular, it enables global optimization of the Bayesian network structure, including the local structure, using state-of-the-art exact algorithms. The paper also introduces a related model class that extends ordinary conditional probability tables to continuous variables by employing an adaptive discretization approach. The two model classes are compared empirically by learning Bayesian networks from benchmark realworld and synthetic data sets. The relative strengths of the model classes are discussed.
A Pin Display Delivering Distance Information in Electronic Travel Aids

An electronic travel aid is an assistive device for people with visual impairments which comprised of a sensor and feedback display to extend sensing range beyond the range of a conventional white cane and present augmented feedback. While various electronic travel aids have been developed, the conventional white cane still remains as the most frequently used mobility tool. In this paper we introduce a new, small and lightweight haptic pin display driven by a single actuator based on a sliding plate or camshaft mechanism. The filtered information of obstacles is obtained via a LIDAR sensor and presented through the proposed pin display. Five different rotational frequencies of the motor shaft correspond to five distance levels. Together with an ultrasonic sensor for detecting head level obstacles and a piezo speaker for alarming obstacles at the head level, a prototype of an electronic travel aid was implemented. Further, ten subjects participated in the distance level identification study by utilizing the developed electronic travel aid. Result shows about 85 % accuracy rate in the identification of five distance levels.
A journey from the endothelium to the tumor tissue: distinct behavior between PEO-PCL micelles and polymersomes nanocarriers

Abstract Polymeric nanocarriers must overcome several biological barriers to reach the vicinity of solid tumors and deliver their encapsulated drug. This study assessed the in vitro and in vivo passage through the blood vessel wall to tumors of two well-characterized polymeric nanocarriers: poly(ethyleneglycol-b-ε-caprolactone) micelles and polymersomes charged with a fluorescent membrane dye (DiO: 3,3'-dioctadecyloxacarbo-cyanine perchlorate). The internalization and translocation from endothelial (human primary endothelial cells HUVEC) to cancer cells (human tumor cell line HCT-116) was studied in conventional 2D monolayers, 3D tumor spheroids, or in an endothelium model based on transwell assay. Micelles induced a faster DiO internalization compared to polymersomes but the latter crossed the endothelial monolayer more easily. Both translocation rates were enhanced by the addition of a pro-inflammatory factor or in the presence of tumor cells. These results were confirmed by early in vivo experiments. Overall, this study pointed out the room for the improvement of polymeric nanocarriers design to avoid drug losses when crossing the blood vessel walls.
Abstractive summarization by neural attention model with document content memory

In this paper, we propose a generative approach for abstractive summarization, which creates summaries based on a language model. The main goal of our paper is to generate a long sequence of words with coherent sentences by reflecting the key concepts of the original document and the characteristics of summaries. To achieve this goal, we propose an attention mechanism that uses Document Content Memory for learning the language model effectively. To evaluate its effectiveness, the proposed methods are compared with other language models and an extractive summarization method. The results demonstrated that the proposed methods could be competitive with other approaches.
A Variable Perturbation Logic based Adaptive MPPT Controller design for PV System: a Real-time Implementation

This paper presents a design of an adaptive maximum power point tracker for a photovoltaic (PV) system. For online modeling of PV system, B-Spline curve approximation technique is used for online modeling PV system. A variable perturbation based adaptive algorithm is proposed for MPPT to find the global maxima effectively even in partially shaded condition. The PWM is generated by a new perturbation logic. The proposed algorithm is implemented and validated in the real-time experimental setup. From the results, it is proved that the proposed algorithm is very simple to implement and cost effective. The proposed tracker effectively tracks the maximum power point.
Entity Competition Network for Video Classification

Feature aggregation focuses on the problem of how to fuse the extracted frame-level features into video-level features, which is crucial to the video classification task. The common aggregation networks directly deploy a pooling strategy over time to generate video-level feature. Such approaches treat inputs equally, therefore the spatio-temporal information cannot be fully utilized for varied inputs. To tackle this issue, we introduce a learnable non-linear network called Entity Competition Network (ECN) which harnesses the relationships among entities from adjacent frames. By doing this, we can reassess the importance of each entity and distinguish the entities related to target event from redundant visual information, thus better video-level features can be generated. Our approach achieves the state-of-the-art results on the action recognition datasets of HMDB51 and UCF101. Improvement is also obtained with RGB modality on the untrimmed video classification dataset ActivityNet 200.
Wearable UHF RFID Sensor-Tag Based on Customized 3D-Printed Antenna Substrates

The combination of radiofrequency identification (RFID) technology with 3D-printed structures is very appealing to design new RFID-based smart but cost-effective devices. In this paper, two different 3D-printed devices in the RFID framework are designed, realized, and tested. In particular, since permittivity and loss tangent of the printed substrate depend on the volume fraction of air in the host material, the first proposed device is purposely a T-resonator-based system suitable for the dielectric characterization of 3D-printed substrates in UHF RFID band, realized in 3D-printing technology itself. Based on the obtained results, the second device is a compact 3D-printed wearable antenna feeding a UHF RFID sensor tag designed to passively sense and retrieve the human body temperature. Both devices have been realized by combining 3D-printed plastic elements and conductive parts and then tested so to demonstrate the aptness of 3D-printing technology in designing RFID devices.
Capturing Meaning: Toward an Abstract Wikipedia

Semantic Web languages allow to express ontologies and knowledge bases in a way meant to be particularly amenable to the Web. Ontologies formalize the shared understanding of a domain. But the most expressive and widespread languages that we know of are human natural languages, and the largest knowledge base we have is the wealth of text written in human languages. This paper looks for a path to bridge the gap between knowledge representation languages such as OWL and human natural languages such as English. We propose a project to simultaneously expose that gap, allow to collaborate on closing it, make progress widely visible, and is highly attractive and valuable in its own right: a Wikipedia written in an abstract language to be rendered into any natural language on request. This would make current Wikipedia editors about 100x more productive, and increase the content of Wikipedia by 10x. For billions of users this will unlock knowledge they currently do not have access to.
Deep Learning Integrated with Multiscale Pixel and Object Features for Hyperspectral Image Classification

The spectral resolution and spatial resolution of hyperspectral images are continuously improving, providing rich information for interpreting remote sensing image. How to improve the image classification accuracy has become the focus of many studies. Recently, Deep learning is capable to extract discriminating high-level abstract features for image classification task, and some interesting results have been acquired in image processing. However, when deep learning is applied to the classification of hyperspectral remote sensing images, the spectral-based classification method is short of spatial and scale information; the image patch-based classification method ignores the rich spectral information provided by hyperspectral images. In this study, a multi-scale feature fusion hyperspectral image classification method based on deep learning was proposed. Firstly, multiscale features were obtained by multi-scale segmentation. Then multiscale features were input into the convolution neural network to extract high-level features. Finally, the high-level features were used for classification. Experimental results show that the classification results of the fusion multi-scale features are better than the single-scale features and regional feature classification results.
An Investigation Into the Effect of PM Arrangements on PMa-SynRM Performance

The paper aims to provide a guideline for rotor design of permanent magnet (PM) assisted synchronous reluctance machine (PMa-SynRM). Unlike interior PM machines, the PMa-SynRM is essentially an SynRM with dominant reluctance torque. The PMa-SynRM is often designed by directly embedding magnets into an SynRM rotor to improve the performance. However, how the PMs can be arranged in the rotor to enhance motor performance has not been fully discussed. This paper investigates the influence of several key factors concerning the PM arrangements, including number of PM layers, inserted positions, and PM sizes. The torque, flux linkages, power factor, and motor performance are comprehensively evaluated. Based on the analysis, a brief guideline is suggested for PMa-SynRM rotor design. Finite element analysis is used for evaluation and experiments are conducted to validate the analysis.
Design of a Wearable Mechatronic Device to Measure the Wrist Rigidity in Parkinson's Disease Patients

The aim of this work is to present a novel wearable mechatronic device (called PDMeter) designed to objectively assess the wrist rigidity in Parkinson's Disease (PD) patients. The system is low-weight, long-term wearable and portable in order to i) perform clinical assessments during Activities of Daily Living (ADLs) in unstructured environments, and ii) to provide several rigidity measurements per day. In this scenario, we defined two different working modalities: i) measurement mode, in which the system measures the wrist rigidity, and ii) backdrivable mode, in which it does not measure, but it has to be transparent for the user during ADLs. In this paper we present the overall mechatronic design of the PDMeter, including the kinematic structure, the actuation system, the sensory system (both force and position) and the control electronics. The overall structure is optimized in terms of dimension and weight: the design of the electronic system allow to integrate in a single compact PCB both the control system and the wireless communication with an external device (laptop or smartphone); the mechanical structure, characterized by one active degree of freedom and five passive ones, is entirely made of aluminium alloy (A16082), and the whole system with the electronics embedded has an overall mass of about 0.46 kg. Future efforts will focus on the implementation and testing of the most suitable algorithms to assess the wrist rigidity, and their validation in clinical trials.
Quantitative Aspects of PDTB-Style Discourse Relations across Languages

Abstract Frequency distribution of words, syntax and semantics in many languages abides by certain laws. However, because of the shortage of discourse corpora, few studies have examined whether the frequency of discourse relations follows some distributional patterns. Although there is some research based on the Rhetorical Structure Theory discourse treebank (RST-DT), each of these studies is limited to a single language. Otherwise to the RST-DT, the Penn Discourse Treebank (PDTB), adopting another annotation system, has had an enormous influence on the study of discourse structure and discourse annotation. Discourse corpora in other languages, such as Chinese, Hindi, Turkish, Czech and Arabic have been annotated following PDTB style. With the data from these discourse treebanks, we find that the rank-frequency of discourse relations follow the same pattern and that these languages share significant similarities in using semantic relations to organize the discourse. It is evidenced in our research that humans assume the relationship between two consecutive sentences is a causal connection or expansion link for fewer connectives used, but the relation of contrast is the most marked by connectives. This research will be of significance for understanding the homogeneity of discourse structure across languages.