Miracle-dz/UniFAR-FTUs huggingface dataset:
Grammatophyllum speciosum Ethanolic Extract Promotes Wound Healing in Human Primary Fibroblast Cells

Grammatophyllum speciosum is a plant in Orchidaceae family which contains a variety of phytochemical compounds that might be beneficial for medicinal use. This study aimed to evaluate the activity of pseudobulb of G. speciosum extract (GSE) in wound healing processes in human primary fibroblast cells along with in vitro antioxidant activity and total phenolic content of GSE. Scratch wound healing assay indicated that GSE was capable of increasing migration rate after 6 and 9 hours of treatment. Besides, the extract was able to scavenge DPPH, ABTS, and superoxide anion radicals indicating the antioxidative property of GSE. This study suggested a novel role of the of pseudobulb extract of G. speciosum as a wound healing enhancer. The results from this study might be beneficial for the development of further novel active compounds for skin wound healing.
A Novel Controller for Grid-Interfacing Solar Arrays Through Five-Level Diode-Clamped Converters

This paper presents an approach to control a grid-connected, five-level, diode-clamped converter (DCC) with solar arrays directly connected to each capacitor of the dc-link, On one hand, the control of such converters is challenging as the voltage imbalance among the capacitor may affect the power controller. On the other hand, when dealing with solar arrays, the dc voltage is a key point considering that the efficiency of the array highly depends on it. This article presents a solution where both aims are fulfilled: the desired capacitor voltages, set by the maximum power point tracking (MPPT) algorithm, are reached at the same time that these values are taken into account in the power controller. This is an unusual approach for five-level DCC that avoids the use of DC-DC stages, therefore it makes the system more affordable and avoids extra losses. Thanks to the capacitor voltage controller, proper power extraction from each solar array is achieved. The theoretical presentation of this approach is shown along with simulation results that validates its effectiveness.
A Low Cost Smart Pollution Measurement System Using REST API and ESP32

The ongoing trends in technology enable the communication between devices through wired or wireless medium. The Internet of Things (IoT) and Machine to Machine (M2M) communication are such trends in technology which make the networked devices to communicate and performs the action without human interventions. Moreover, one problem is debilitating to air quality and its measurement in Indian cities. The major contributors to air pollution are the smog, sulfur dioxide, nitrogen oxides, CO, PM10, PM2.5. These gases are found as the most important in terms of human health Problems. It can cause lung cancer, asthma, breathing problems. In this proposed system, MQ-9 is used for measuring CO, LPG, CH4 value, DHT22 is used for measuring temperature in degree celsius and humidity in percentage, dust sensor is used for measuring dust particles MQ-135 is used for measuring benzene, ammonia, sulfur dioxide in the Environment. This measured data is put on to the server by using REST protocol (REpresentational State Transfer protocol)and ESP32. All sensors readings are displayed on the webpage and mobile application.
An Ontology Model for Clinical Pathway Audit

Clinical pathway is implemented to systematically record every intervention given and the development of patient. Hence, it is expected to improve the quality of services and lower the expense cost of the hospital. Ontology has been used to build a clinical pathway in order to gain semantic richness and reasoning ability. In this paper, we propose a generic clinical pathway ontology, which is specifically designed to support clinical pathway audit. It is done to measures a clinical outcome or a process against well-defined standards set on the principles of evidence-based medicine, which aims to improve the care provided. The model consists of five aspects: person, clinical pathway, record, organizational structure, and clinical category. The evaluation of the model is done using SPARQL and SQWRL to demonstrate the consistency of clinical pathway's knowledge and to demonstrate the readiness of the model to reason audit related knowledge. The evaluation shows that the model can provide information and knowledge to support audit program, including clinical pathway events/tasks, conformity of clinical pathway, clinical pathway-related services management, and patient record details.
Trajectory Modelling to Assess Trends in Long-Term Readmission Rate among Abdominal Aortic Aneurysm Patients

Introduction The aim of the study was to use trajectory analysis to categorise high-impact users based on their long-term readmission rate and identify their predictors following AAA (abdominal aortic aneurysm) repair. Methods. In this retrospective cohort study, group-based trajectory modelling (GBTM) was performed on the patient cohort (2006-2009) identified through national administrative data from all NHS English hospitals. Proc Traj software was used in SAS program to conduct GBTM, which classified patient population into groups based on their annual readmission rates during a 5-year period following primary AAA repair. Based on the trends of readmission rates, patients were classified into low- and high-impact users. The high-impact group had a higher annual readmission rate throughout 5-year follow-up. Short-term high-impact users had initial high readmission rate followed by rapid decline, whereas chronic high-impact users continued to have high readmission rate. Results Based on the trends in readmission rates, GBTM classified elective AAA repair (n=16,973) patients into 2 groups: low impact (82.0%) and high impact (18.0%). High-impact users were significantly associated with female sex (P=0.001) undergoing other vascular procedures (P=0.003), poor socioeconomic status index (P < 0.001), older age (P < 0.001), and higher comorbidity score (P < 0.001). The AUC for c-statistics was 0.84. Patients with ruptured AAA repair (n=4144) had 3 groups: low impact (82.7%), short-term high impact (7.2%), and chronic high impact (10.1%). Chronic high impact users were significantly associated with renal failure (P < 0.001), heart failure (P = 0.01), peripheral vascular disease (P < 0.001), female sex (P = 0.02), open repair (P < 0.001), and undergoing other related procedures (P=0.05). The AUC for c-statistics was 0.71. Conclusion Patients with persistent high readmission rates exist among AAA population; however, their readmissions and mortality are not related to AAA repair. They may benefit from optimization of their medical management of comorbidities perioperatively and during their follow-up.
An Evaluation of Asynchronous Software Events on Modern Hardware

Runtimes and applications that rely heavily on asynchronous event notifications suffer when such notifications must traverse several layers of processing in software. Many of these layers necessarily exist in order to support a general-purpose, portable kernel architecture, but they introduce considerable overheads for demanding, high-performance parallel runtimes and applications. Other overheads can arise from a mismatched event programming or system call interface. Whatever the case, the average latency and variance in latency of commonly used software mechanisms for event notifications is abysmal compared to the capabilities of the hardware, which can exhibit orders of magnitude lower latency. We leverage the flexibility and freedom of the previously proposed Hybrid Runtime (HRT) model to explore the construction of low-latency, asynchronous software events uninhibited by interfaces and execution models commonly imposed by general-purpose OSes. We propose several mechanisms in a system we call Nemo which employs kernel mode-only features to accelerate event notifications by up to 4,000 times and we provide a detailed evaluation of our implementation using extensive microbenchmarks. We carry out our evaluation both on a modern x64 server and the Intel Xeon Phi. Finally, we propose a small addition to existing interrupt controllers (APICs) that could push the limit of asynchronous events closer to the latency of the hardware cache coherence network.
Time and Frequency Analysis of Heart Rate Variability at Different Sleep Stages of Primary Insomnia

Insomnia is the inability to sleep that causes interruption in sleep. ECG signals which measured during sleep carry valuable information about heart activity. In the current study, ECG signals of 10 primary insomnia patients and 10 healthy controls during four sleep stages namely, N1, N2, N3 and REM were analyzed. Time and frequency domain of Heart Rate Variability (HRV) were computed and statistically analyzed to evaluate existence of statistical significant differences between the two groups at different sleep stages. A high value of LF index was observed during N3 sleep stage in the primary insomnia patient can be regarded as predominant of sympathetic nervous system.
Investigating the relationship between boards independence and firm performance with moderating role of ownership structure

The purpose of this research is to answer the question of whether the concentration of ownership can modify the relationship between board independence and corporate performance? Methodology:For this purpose, 108 active companies were studied during the years 1389- 1393.Results:The results of this research indicate that the independence of the board of directors on the performance of the company (ROA) has no significant effect on the 95% confidence level. Therefore, ownership concentration does not modify the relationship between board autonomy and corporate performance (ROA). On the other hand, the independence of the board of directors (QTOBIN) has a negative and significant effect on the 95% confidence level. As a result of a decrease in ownership concentration will increase the effectiveness of the Board of Directors' equity on the company's performance (QTOBIN). Also, according to the survey, the type of company has no effect on the relationship between the independence of the board of directors and the company's performance. As a result, a decrease in ownership concentration will increase the effectiveness of the Board of Directors' equity on the company's performance (QTOBIN). Also, according to the survey, the type of company has no effect on the relationship between the independence of the board of directors and the company's performance.Conclusion:Finally,in companies with high independence of the board, attention should be paid to the role of supervisory role of major shareholders, and the results and rules can be found in public and private companies. The same thing exploited. It is also recommended that the Stock Exchange and Exxon Societies of Iran create disclosure requirements for accepted companies and provide this disclosure to investors quickly.
A conceptual framework for clinicians working with artificial intelligence and health‐assistive Smart Homes

The Smart Home designed to extend older adults independence is emerging as a clinical solution to the growing ageing population. Nurses will and should play a key role in the development and application of Smart Home technology. Accordingly, conceptual frameworks are needed for nurse scientists who are collaborating with multidisciplinary research teams in developing an intelligent Smart Home that assists with managing older adults' health. We present a conceptual framework that is grounded in critical realism and pragmatism, informing a unique mixed methodological approach to generating, analyzing, and contextualizing sensor data for clinician-based machine learning. This framework can guide nurse scientists in knowledge construction as they participate in multidisciplinary health-assistive Smart Home and artificial intelligence research. In this paper, we review philosophical underpinnings and explicate how this framework can guide nurse scientists collaborating with engineers to develop intelligent health-assistive Smart Homes. It is critical that clinical nursing knowledge is integrated into Smart Home and artificial intelligence features. A conceptual framework and practical method will provide needed structure for knowledge construction by nurse scientists.
Automatic Generation of Head Nods using Utterance Texts

We propose a model to generate head nods accompanying an utterance from natural language. To the best of our knowledge, previous models generated simple nods from the final words at the end of an utterance, i.e., using bag of words. We focused on various text analyzed using various types of language information such as dialog act, part of speech, a large-scale Japanese thesaurus, and word position in a sentence. We also generated detailed parameters of speaker's nodding presence, frequency, and depth, which was the first attempt to do so. First, we compiled a Japanese corpus of 24 dialogues including utterance and nod information. Next, using the corpus, we constructed our generation model that estimates nodding presence, frequency, and depth, during a phrase by using such various types of language information as well as bag of words. The results indicate that our model outperformed simple automatic nod-generating models using only bag of words and chance level. The results also indicate that dialog act, part of speech, the large-scale Japanese thesaurus, and word position are useful for generating nods. We also evaluated, through subjective evaluation, if our nod-generation model is useful with conversational agents. The results show that the nodding generated with our model improves user impressions of naturalness, humanness, likability, and reliability toward a conversational agent.
Methods and Tools for Focusing and Prioritizing the Testing Effort

Software testing is essential for any software development process, representing an extremely expensive activity. Despite its importance recent studies showed that developers rarely test their application and most programming sessions end without any test execution. Indeed, new methods and tools able to better allocating the developers effort are needed to increment the system reliability and to reduce the testing costs. In this work we focus on three activities able to optimize testing activities, specifically, bug prediction, test case prioritization, and energy leaks detection. Indeed, despite the effort devoted in the last decades by the research community led to interesting results, we highlight some aspects that might be improved and propose empirical investigations and novel approaches. Finally, we provide a set of open issues that should be addressed by the research community in the future.
MIRA: Proactive Music Video Caching Using ConvNet-Based Classification and Multivariate Popularity Prediction

Music belongs to one of the most popular content categories overall, and it is nowadays mainly consumed using online streaming services. With YouTube being the largest source of traffic in most networks about half of all YouTube requests address music videos. To cope with the continuously growing demand for content and thus increasing network traffic, YouTube operates its own CDN, a globally distributed network of caches. This allows serving content from locations close to the users, which circumvents potential network bottlenecks and increases the user-perceived QoE due to reduced latency. Recently, proactive caching and prefetching has shown superior performance results compared with traditional reactive caching schemes such as LRU and LFU. Due to the substantial footprint of music videos on today's Internet, we propose a novel proactive caching strategy specifically for music videos. This strategy incorporates two key observations: i) Music genre and mood popularity varies over the course of the day and ii) A video's past views are predictive for its future popularity development. For the classification task, we use a Convolutional Neural Network while investigating several predictive models for the popularity estimation. The proposed caching system can increase the cache hit rate up to 4.5% which is substantial for caching systems.
An Initial Attempt of Combining Visual Selective Attention with Deep Reinforcement Learning

Visual attention serves as a means of feature selection mechanism in the perceptual system. Motivated by Broadbent's leaky filter model of selective attention, we evaluate how such mechanism could be implemented and affect the learning process of deep reinforcement learning. We visualize and analyze the feature maps of DQN on a toy problem Catch, and propose an approach to combine visual selective attention with deep reinforcement learning. We experiment with optical flow-based attention and A2C on Atari games. Experiment results show that visual selective attention could lead to improvements in terms of sample efficiency on tested games. An intriguing relation between attention and batch normalization is also discovered.
Surgical Stabilization Improves 30-day Mortality in Patients With Traumatic Flail Chest: A Comparative Case Series at a Major Trauma Center

Objectives: To evaluate the outcomes including early mortality after surgical rib fixation using a locking plate system as part of a newly introduced chest trauma pathway. Design: Prospective cohort study with retrospective case-controlled matching with a minimum of 1-year follow-up. Setting: Regional Level 1 trauma center. Patients/Participants: Consecutive patients undergoing surgical rib fixation were prospectively recruited over a 3-year period (56 patients) and matched to similar patients managed nonoperatively (89 patients) using our local trauma registry. Matching was based on injury severity scores and patient demographics. Intervention: Surgical rib fixation with locking plates. Main Outcome Measurements: The primary outcome measure was 30-day mortality with secondary outcomes of hospital length of stay, intensive care unit length of stay, and patient-reported outcome measures including quality of life (EuroQol—EQ-5D-5L). Official causes of death were obtained from the coroner including postmortem results. Results: There was a significant reduction in 30-day mortality in the fixed patients with 1.8% (1/56) compared to 12.4% (11/89) of the nonfixed patients (P = 0.03). This difference remained significant after further exclusion of other nonsurvivable injuries (P = 0.046). Although hospital length of stay was significantly longer in the fixed group, there was no difference on multivariable analysis. Other secondary outcomes were comparable between the groups with no significant differences in any patient-reported measures. Conclusions: Mortality was significantly lower in patients who underwent rib stabilization. Quality of life and other patient-reported measures were similar, demonstrating no evidence of detrimental longer-term effects of rib stabilization. Level of Evidence: Therapeutic Level III. See Instructions for Authors for a complete description of levels of evidence.
Greediness Characterization of Genetic Algorithms

This research investigates the effect of selection, replacement, and generational gap on the selective pressure, indicated by the greediness of the genetic algorithms. An a priori ordering of the greediness of genetic algorithms is constructed based on the nature of selection strategy that favors fitter candidate solutions. For the implementers of genetic algorithms, this ordering represents the design space of genetic algorithms involving selection, replacement, and generation gap on which design decision should be taken. These relative orderings of the greediness were examined by experimental runs on timetabling benchmark problems. The results suggest that for a fixed computational budget, a high greediness can be achieved by biased selection with a large generational gap and elitism.
Communicating Directional Intent in Robot Navigation using Projection Indicators

Smooth and efficient robot navigation among humans is a crucial requirement for successful integration of robots in human society. Towards this end, an indispensable characteristic of robot action is legibility while communicating its intention. However, unlike humans, present robots cannot convey its intention through human-like non-verbal communication. This paper explores the use of projection indicators for communicating directional intent of a robot across three different ‘crossing scenarios' as a means of overcoming the shortcomings of the robot's non-verbal communication abilities. The results of the study show statistically significant improvement in perceived feelings of the measured attributes when using the auxiliary communication method. The studied method also improves cooperation from the participants. Nevertheless, the improvement in perceived feeling does not necessarily replicate in terms of smoothness across all the scenarios.
Role of p16 deletion and BAP1 loss in the diagnosis of malignant mesothelioma.

Malignant mesothelioma (MM) is a rare but highly aggressive neoplasm. It has a poor prognosis and a median survival time of 20 months after diagnosis (1). Tumor development is associated with exposure to several known carcinogens such as asbestos fiber, rhesus virus 40, and radiation, of which asbestos exposure is the most important risk factor (2). The early clinical symptoms of MM lack specificity and are often characterized by large amounts of pleural effusion, which make clinical and imaging diagnoses difficult. Mesothelioma often goes undiagnosed until the late stages of the disease, at which time no marked effects can be achieved, regardless of whether the patient and medical team select surgery or radiotherapy and chemotherapy.
Content Popularity-Based Selective Replication for Read Redirection in SSDs

Despite high degrees of parallelism in terms of the number of chips and channels on state-of-the-art SSDs, resource contention continues to be a big impediment to boosting their performance for both read and write requests. This is particularly significant in the delays due to queueing for service from individual NAND-flash chips that can take dozens/hundreds of microseconds to perform the read/write operations. Owing to the no-write-in-place policy that is employed in flash chips, writes are inherently suited to be redirected to chips with lower load, in case their original destination chip is overloaded. However, to date, there has been no work to redirect read requests, since they cannot be serviced by other chips, which do not have the data. While blindly replicating all the data everywhere seems very promising from a read redirection perspective, doing so results in high space overheads, high write/replication overheads and lower endurance. This paper presents a novel approach to selective replication, wherein the popularity of data is used to figure out the "what", "how much", "where" and "when" questions for replication. Leveraging value locality/popularity, that is often observed in practice, popular data is replicated across multiple chips to provide more opportunities for dynamic read redirection to less loaded flash chips. Using extensive workload traces running over weeks from real systems, we show that our Read Redirected SSD (RR-SSD) can provide up to 45% improvement in read performance, with average improvement of 23.9%, and up to 40% improvement when considering both read and write requests, with 16% improvement on average.