Sentiment Analysis Using Averaged Weighted Word Vector Features

People use the world wide web heavily to share their experience with entities such as products, services, or travel destinations. Texts that provide online feedback in the form of reviews and comments are essential to make consumer decisions. These comments create a valuable source that may be used to measure satisfaction related to products or services. Sentiment analysis is the task of identifying opinions expressed in such text fragments. In this work, we develop two methods that combine different types of word vectors to learn and estimate polarity of reviews. We develop average review vectors from word vectors and add weights to this review vectors using word frequencies in positive and negative sensitivity-tagged reviews. We applied the methods to several datasets from different domains that are used as standard benchmarks for sentiment analysis. We ensemble the techniques with each other and existing methods, and we make a comparison with the approaches in the literature. The results show that the performances of our approaches outperform the state-of-the-art success rates

One parameter dual lorentzian spherical motions and ruled surfaces

In this work, we first introduced one parameter dual Lorentzian spherical motions in three dimensional dual Lorentz space D^3_1 and spacelikeand timelike ruled surfaces in three dimensional Lorentz space IR^3_1 corresponding to dual curves on dual Lorentz unit sphere S^2_1. After that we have given the relations on the velocities and instantaneous rotation axis for one parameter Lorentzian spherical motions in dual Lorentz space D^3_1, with some examples on these timelike and spacelike ruled surfaces. Finally we have obtained the theorem related to the acceleration, acceleration centres and acceleration axis for these one parameter dual Lorentzian spherical motions. &nbsp

Experimental observation of speckle instability in nonlinear disordered media

Temporal fluctuations of the speckle pattern formed upon backscattering of a laser beam from an interface between gold and nonlinear polymer film have been observed as a function of optical power. The instability can be explained by coupling of laser light to surface plasmons and other guided modes, which experience multiple scattering while propagating in the film along the interface. The speckle pattern produced in this process is extremely sensitive to fluctuations of the scattering potential near the interface.Comment: 4 pages, 5 figure

Identification of phantom movements with an ensemble learning approach

Phantom limb pain after amputation is a debilitating condition that negatively affects activities of daily life and the quality of life of amputees. Most amputees are able to control the movement of the missing limb, which is called the phantom limb movement. Recognition of these movements is crucial for both technology-based amputee rehabilitation and prosthetic control. The aim of the current study is to classify and recognize the phantom movements in four different amputation levels of the upper and lower extremities. In the current study, we utilized ensemble learning algorithms for the recognition and classification of phantom movements of the different amputation levels of the upper and lower extremity. In this context, sEMG signals obtained from 38 amputees and 25 healthy individuals were collected and the dataset was created. Studies of processing sEMG signals in amputees are rather limited, and studies are generally on the classification of upper extremity and hand movements. Our study demonstrated that the ensemble learning-based models resulted in higher accuracy in the detection of phantom movements. The ensemble learning-based approaches outperformed the SVM, Decision tree, and kNN methods. The accuracy of the movement pattern recognition in healthy people was up to 96.33%, this was at most 79.16% in amputees. 2022 The Author(s)This study is supported by the Scientific and Technological Research Council of Turkey (TUBITAK) under grant no. EEEAG-117E579. The data that support the findings of this study are available on request from the principle investigator of the project EEEAG-117E579, Akhan Akbulut, PhD. The data are not publicly available due to the confidential information that could compromise the privacy of research participants. Open Access funding provided by the Qatar National Library.Scopus2-s2.0-8513934593

Is BMI Sufficient to Evaluate the Association between Obesity and Ovarian Reserves?

Body fat content and distribution might have an effect on ovarian reserves. Here, we studied the effects of body fat distribution on the antral follicle count (AFC) of women who consulted for infertility. In this two-center study, the ovarian reserves of patients who came to the hospital for infertility treatment was evaluated based on their AFC and early follicular phase follicle-stimulating hormone (FSH) levels. In addition, adiposity was evaluated using their body mass index (BMI) and waist-to-hip ratios (WHRs), the subcutaneous tissue thickness of the bicipital and tricipital regions, and the body adiposity index (BAI). Body fat distribution was evaluated using bioelectrical impedance analysis (BIA). We evaluated 58 patients in this study. While we failed to show a relationship between BMI and WHR based on the AFC, there was a significant relationship between body fat percentage and the AFC. The AFC in patients with < 35% body fat and ≥ 35% body fat was 11.54 ± 4.27 and 9.00 ± 3.95, respectively (p = 0.029). There was no significant relationship between the AFC and the WHR, BAI, and bicipital and tricipital subcutaneous tissue thickness. BMI may not reflect the adiposity of every patient. When evaluating the ovarian reserves of patients, we must consider other measures of obesity that reflect body fatness. Further large studies must be conducted to investigate the relationship between body fat and infertility

Do Bmı or Waıst-to-Hıp Ratıo Interfere wıth The Number of Oocytes Retrıeved ın IVF Cycles?

The effect of obesity on ovarian response to ovulation induction and on in vitro fertilization (IVF) outcome is controversial. This controversy might stem from the fact that almost all studies on the subject use body mass index (BMI) for obesity measurement. We aimed to determine which obesity measure predicts the possible effect of obesity on ovarian response in IVF patients. In this retrospective study, patients who presented for IVF and underwent an antagonist protocol were included. Their histories and cycle properties were recorded, as well as their BMI and waist-to-hip (W/H) ratios. A total of 35 patients were included. While normal BMI significantly lowered the gonadotropin dose, normal W/H ratio increased the antral follicle count (AFC). Both BMI and W/H ratio did not significantly affect either the number of oocytes retrieved or the metaphase II oocytes. Ovulation induction during IVF cycles can overcome the adverse effects of obesity on ovarian reserve. Large-sample-sized, well-designed studies must be performed to clarify the best obesity measurement method for infertility treatment and to determine the real effect of obesity on IVF success

Project Khepri: Mining Asteroid Bennu for Water

Deep space asteroid mining presents the opportunity for the collection of critical resources required to establish a cis-lunar infrastructure. In specific, the Project Khepri team has focused on the collection of water from asteroid Bennu. This water has the potential to provide a source of clean-energy propellant as well as an essential consumable for humans or agriculture on crewed trips to the Moon or Mars. This would avoid the high costs of launching from Earth - making it a highly desirable element for the future of cis-lunar infrastructure. The OSIRIS-REx mission provided a complete survey of asteroid Bennu and is set to return regolith samples to Earth in 2023. This makes asteroid Bennu a well-understood and low-risk target that is estimated to be around 6.26% water by mass. The Khepri Project comprises a team of international students, academics, and industry subject matter experts working on the technical design, business case, and political aspects of a mission to mine asteroid Bennu for water. The research output explores the multi-year mission that the Khepri team has proposed

Clinical features and major bleeding predictors for 161 fatal cases of COVID-19: A retrospective observational study

The aim of this study was to investigate the patient characteristics and laboratory parameters for COVID-19 non-survivors as well as to find risk factors for major bleeding complications. For this retrospective study, the data of patients who died with COVID-19 in our intensive care unit were collected in the period of March 20 - April 30, 2020. D-dimer, platelet count, C-reactive protein (CRP), troponin, and international normalized ratio (INR) levels were recorded on the 1st, 5th, and 10th days of hospitalization in order to investigate the possible correlation of laboratory parameter changes with in-hospital events. A total of 161 non-survivors patients with COVID-19 were included in the study.  The median age was 69.8±10.9 years, and 95 (59%) of the population were male. Lung-related complications were the most common in-hospital complications. Patients with COVID-19 had in-hospital complications such as major bleeding (39%), hemoptysis (14%), disseminated intravascular coagulation (13%), liver failure (21%), ARDS (85%), acute kidney injury (40%), and myocardial injury (70%). A multiple logistics regression analysis determined that age, hypertension, diabetes mellitus, use of acetylsalicylic acid (ASA) or low molecular weight heparin (LMWH), hemoglobin, D-dimer, INR, and acute kidney injury were independent predictors of major bleeding. Our results showed that a high proportion of COVID-19 non-survivors suffered from major bleeding complications

Effect of Maturity on Phenolics (Phenolic Acids and Flavonoids) Profile of Strawberry Cultivars and Mulberry Species from Pakistan

In this study, we investigated how the extent of ripeness affects the yield of extract, total phenolics, total flavonoids, individual flavonols and phenolic acids in strawberry and mulberry cultivars from Pakistan. In strawberry, the yield of extract (%), total phenolics (TPC) and total flavonoids (TFC) ranged from 8.5–53.3%, 491–1884 mg gallic acid equivalents (GAE)/100 g DW and 83–327 mg catechin equivalents (CE)/100 g DW, respectively. For the different species of mulberry the yield of extract (%), total phenolics and total flavonoids of 6.9–54.0%, 201–2287 mg GAE/100 g DW and 110–1021 mg CE/100 g DW, respectively, varied significantly as fruit maturity progressed. The amounts of individual flavonols and phenolic acid in selected berry fruits were analyzed by RP-HPLC. Among the flavonols, the content of myricetin was found to be high in Morus alba (88 mg/100 g DW), the amount of quercetin as high in Morus laevigata (145 mg/100 g DW) while kaempferol was highest in the Korona strawberry (98 mg/100 g DW) at fully ripened stage. Of the six phenolic acids detected, p-hydroxybenzoic and p-coumaric acid were the major compounds in the strawberry. M. laevigata and M. nigra contained p-coumaric acid and vanillic acid while M. macroura and M. alba contained p-hydroxy-benzoic acid and chlorogenic acid as the major phenolic acids. Overall, a trend to an increase in the percentage of extraction yield, TPC, TFC, flavonols and phenolic acids was observed as maturity progressed from un-ripened to fully-ripened stages

An Aggregate MapReduce Data Block Placement Strategy for Wireless IoT Edge Nodes in Smart Grid

Big data analytics has simplified processing complexity of large dataset in a distributed environment. Many state-of-the-art platforms i.e. smart grid has adopted the processing structure of big data and manages a large volume of data through MapReduce paradigm at distribution ends. Thus, whenever a wireless IoT edge node bundles a sensor dataset into storage media, MapReduce agent performs analytics and generates output into the grid repository. This practice has efficiently reduced the consumption of resources in such a giant network and strengthens other components of the smart grid to perform data analytics through aggregate programming. However, it consumes an operational latency of accessing large dataset from a central repository. As we know that, smart grid processes I/O operations of multi-homing networks, therefore, it accesses large datasets for processing MapReduce jobs at wireless IoT edge nodes. As a result, aggregate MapReduce at wireless IoT edge node produces a network congestion and operational latency problem. To overcome this issue, we propose Wireless IoT Edge-enabled Block Replica Strategy (WIEBRS), that stores in-place, partition-based and multi-homing block replica to respective edge nodes. This reduces the delay latency of accessing datasets for aggregate MapReduce and increases the performance of the job in the smart grid. The simulation results show that WIEBRS effective decreases operational latency with an increment of aggregate MapReduce job performance in the smart grid