Miracle-dz/UniFAR-FTUs huggingface dataset:
Potential Years of Lost Life of the Lip, Oral Cavity and Pharynx Neoplasms in Slovak Population - 2010-2015

Objective: To analyse Potential Years of Life Lost (PYLL) rates trends of lip, oral cavity and pharynx neoplasms in the Slovak population by age and gender. Material and Methods: The study analyses PYLL rates (age-standardized) of lip, oral cavity and pharynx neoplasms (C00-C14) per 100,000 in the period of six years (2010-2015). The study sample was divided into two age sub-categories (all ages: 0-69y. and working group: 20-69y.) National mortality data (C00-C14) (3,138 mortality causes) were analysed from the Statistical Office of the Slovak Republic. Results: The highest PYLL rate was found in sub-category 20-69 years in males (378 per 100,000) in 2012 and in females (64 per 100,000) in 2013. The highest PYLL rate was observed in sub-category all age groups in males (296 per 100,000) in 2012 and in females (50 per 100,000) in 2013. The PYLL rates of lip, oral cavity and pharynx neoplasms had in the period 2011-2015 upward trend in both sexes, however, in the 2014 was found the opposite trend. In 2015 the highest PYLL rates (non-standardized) in both sexes was found in age sub-category 55-59y. Conclusion: Our study should contribute to the development of oral cancer intervention programs.
Channel Equalization With Expectation Propagation at Smoothing Level

In this paper we propose a novel turbo equalizer based on the expectation propagation (EP) algorithm. Optimal equalization is computationally unfeasible when high-order modulations and/or large memory channels are used. In these scenarios, low-cost and suboptimal equalizers, such as those based on the linear minimum mean square error (LMMSE), are commonly used. The LMMSE-based equalizer can be efficiently implemented with a Kalman smoother (KS), i.e., a forward and backward Kalman filtering whose predictions are merged in a posterior smoothing step. Recently, it was shown that applying EP at the forward and backward stages of a KS equalizer could significantly improve its performance. In this paper, we investigate applying EP at the smoothing level instead. Also, we propose some further modifications to better exploit the information coming from the channel decoder in turbo equalization schemes. Overall, we remarkably reduce the computational complexity while highly improving the performance in terms of bit error rate.
High-sensitivity determination of Cd isotopes in low-Cd geological samples by double spike MC-ICP-MS

With new advancements in MC-ICP-MS technology, small Cd isotope fractionations in geological processes have attracted more interest in recent years. However, challenges remain in obtaining high-precision Cd isotope data for geological samples with low Cd concentrations and complex matrices. By an improved Cd purification and cleaning resin scheme for low-Cd samples using AGMP-1M resin with >90% Cd recovery, the ratios of trace elements Sn, Mo, Zr, etc. to Cd in the samples, which could potentially jeopardize the accuracy and precision of Cd isotope analysis under a high-sensitivity setup, were less than 0.001 after purification, and the total procedure blank is ≤0.1 ng. Hence, a sample containing 20–30 ng Cd can be employed for purification and subsequent isotope measurement. Cadmium isotope fractionation (expressed in δ114/110Cd relative to NIST 3108) during column separation and instrument mass bias was corrected for by a 111Cd–113Cd double spike. NIST 3108 was determined at a concentration of 5–25 ng mL−1 on a Neptune Plus (112Cd signal intensity: ∼640 V ppm−1) and 25–50 ng mL−1 on a Nu plasma III (112Cd signal intensity:∼280 V ppm−1) MC-ICP-MS equipped with an Aridus II/III desolvator for more than one year, with a total external analytical precision of 0.034‰ (2SD, n = 181). NIST SRM 2711a, NOD-P-1 and GSS-1 were measured at 5, 10, 20, 25 and 50 ng mL−1 levels to be 0.532 ± 0.038‰, 0.133 ± 0.038‰ and 0.098 ± 0.027‰, respectively, indicating that good accuracy and precision of Cd isotope analysis can be achieved with an injecting sample size as low as 5 ng Cd. Our method can be reliably applied to various geological reference materials (GRMs). The δ114/110Cd values of GRMs with relatively high and low Cd concentrations are consistent with previous studies: NOD-A-1 (0.124 ± 0.067‰), GSD-11 (−0.274 ± 0.037‰) and BCR-2 (−0.030 ± 0.063‰). SGR-1b (0.069 ± 0.049‰), GSH-1 (−0.337 ± 0.077‰) and others are reported for the first time in this paper. The δ114/110Cd in basalt and stream sediments is relatively homogeneous and overlaps with that of the bulk silicate earth (BSE). The δ114/110Cd in shale and soils varies greatly from −0.694 to 0.532‰. Human hair and other animal organs are enriched in light isotopes relative to BSE, whereas terrestrial plants are enriched in heavy isotopes.
How Ethical Leadership Supports Employee Performance: The Role of Psychological Capital and Employee Engagement

This research investigated the relationship between ethical leadership and employee performance using data from the employees in Vietnamese service firms. We examined four dimensions of psychological capital (efficacy, hope, resilience, and optimism), and employee engagement as mediators of the ethical leadership to two dimensions of employee performance (task performance and contextual performance) relationship. Results from 563 respondents of 73 service firms revealed that ethical leadership was positively and significantly related to task performance and contextual performance and that this relationship was mediated by four dimensions of psychological capital and employee engagement, and work experience and education level, controlling for task performance and contextual performance. We discuss implication of our findings for theory and practice.
NEAR-FIELD AND FAR-FIELD MICROSCOPIC AND SPECTROSCOPIC CHARACTERIZATIONS OF COUPLED PLASMONIC, EXCITONIC AND POLYMERIC MATERIALS

The properties of localized surface plasmons (LSP) have been broadly utilized for chemical sensing, surface enhanced Raman spectroscopy, biomedical imaging and photothermal treatments. By exploiting well-established plasmonic effects, the spectroscopic investigation of intriguing quantum phenomena, such as excitonic interband and intersubband (ISB) transitions in semiconductor heterostructures, was examined and extended in both farand near-field optical measurements. For far-field characterization, we used colloidal plasmonic Au nanorods (AuNRs) to increase the quantum efficiency of InGaAs/GaAs single quantum well. By analyzing the temperature-dependent
Decision-making for small industrial Internet of Things using decision fusion

The industrial Internet of Things (IIoT) is a new field of Internet of Things (IoT) that has gained more popularity recently in industrial units and makes it possible to access information anywhere and anytime. In other words, geographic coordinates cannot prevent obtaining equipment and its data. Today, it is possible to manage and control equipment simply without spending time in an operational area and just by using the IIoT. This system collects data from manufacturing and production units by using wireless sensor networks or other networks for classification of fault detection. These data are then used after analysis to allow operational decisions to be made in shorter amounts of time. In fact, the IIoT increases the efficiency and accuracy of the ? connection, collection, analysis, and operation? cycle. The information collected through different sensors in the IIoT is unreliable and uncertain due to the sensitivity of the sensors to noise, failure, and loss of information during transmission. One of the most important techniques offered to deal with this uncertainty in information is the decision fusion method. Among the decision fusion techniques, the Dempster--Shafer and improved Dempster--Shafer theory, which is also known as Yager theory, are efficient and effective ways to manage the uncertainty and have been used in many types of research. This paper offers an architecture for decision fusion in a small IIoT using Dempster--Shafer and Yager theories. In this architecture, data collected from the desired environment are fed to classifiers for classification. In this architecture, artificial neural networks and a dendrogram-based support vector machine are used as classifiers. To increase the accuracy of classifier results, the Dempster--Shafer and Yager theories are used to combine these results. To prove the performance, the proposed method was applied for detection of faults in an induction motor and human activity detection in an environment. This proposed method improved the accuracy of the system and decreased its uncertainty significantly according to obtained results from these two example use cases.
Dual‐band CPW fed MIMO antenna with polarization diversity and improved gain

A dual‐band MIMO slot antenna with polarization diversity and improved gain is proposed in this article. The antenna is composed of two C‐type back‐to‐back connected slot resonators and offers resonances at 3.5 and 5.2 GHz. This antenna element is further used to design a MIMO antenna. By introducing one U‐shaped slot between two antenna elements, isolation between the ports of this MIMO antenna is improved further. Finally, an artificial magnetic conductor (AMC) is placed below the MIMO antenna to enhance its gain. Gain enhancement of 1.5 and 2.2 dB is achieved at lower and upper band, respectively. S‐parameters, radiation patterns, gain, envelope correlation coefficient, and channel capacity loss are investigated to conclude about its performances in MIMO applications. Dual band dual polarization (circular and linear), improved isolation, polarization diversity (right‐hand circular polarization and left‐hand circular polarization), gain enhancement all are presented in a simple design represents the novelty of the proposed MIMO antenna.
STAPHYLOKINASE: A BOON IN MEDICAL SCIENCES – REVIEW

Every year most of the deaths were leaded by cardio and cerebrovascular disorders around the globe. The use of first or second generation thrombolytics may rise several side effects like bleeding in biological system at the time of treatment. New generation thrombolytics plays an important role in the treatment of thromboembolic disorders which leads to the death. An ideal fibrin specific plasminogen activator, staphylokinase converts a precursor, plasminogen to active enzyme, plasmin to dissolve the blood clot during thrombolytic therapy and it also capable to destroy the normal components of hemostatic system which leads to life threatening consequence and also death. Out of four different thrombolytics which are available in market like alteplase, reteplase, streptokinase and tenecteplase, streptokinase was widely used drug because of its low cost. But, to overcome the hurdles like bleeding and reocculsion efforts have been made to construct the more potent staphylokinase devoid of risk. In this review, progressive development on staphylokinase has been discussed. Key words: Cerebrovascular disorders, thrombolytics, fibrin specific, streptokinase, staphylokinase, reocclusion.
Utilization of Tutopatch® bovine pericardium xenograft for nasal dorsum augmentation in primary and secondary rhinoplasties

Objectives: The aim of this study was to present the utilization of Tutopatch® bovine pericardium xenograft for nasal dorsum augmentation and/or camouflage in primary and secondary rhinoplasties. Patients and Methods: Between January 2017 and July 2019, the Tutopatch® bovine pericardium was used for nasal dorsum or glabellar augmentation in eight patients (3 males, 5 females: mean age 35 years; range, 25 to 43 years) and used for camouflage in six patients (1 male, 5 females; mean age: 30 years; range, 27 to 38 years) having rhinoplasty operations. Medical data of the patients were retrospectively analyzed. Preand postoperative results were compared. Degree of satisfaction was self-reported by the patients. Results: The mean follow-up was 16 (range, 6 to 24) months. Only one major complication due to the overcorrection of the nasal dorsum with a small hump was observed in a secondary rhinoplasty case necessitating further revision. Other types of complications such as erythema, undercorrection, malposition, seroma, hematoma, or graft extrusion were not observed. All patients, except for the case necessitating further revision were satisfied with the outcomes. Conclusion: Utilization of Tutopatch® bovine pericardium xenograft in wrapping the diced cartilage for nasal dorsum augmentation or as the camouflage material over the nasal dorsum and nasal tip skeleton in either primary or secondary rhinoplasty operations can be a wise option without any adverse allergic reaction or donor site morbidity.
Reinforcement learning in process control

Using reinforcement learning as controllers in the process industries was explored as an alternate path of doing control compared to the regular controllers. Methods such as value-based and policy-based methods were used as controllers for three different cases of tank level regulation. The controllers were compared to a traditional P-controller for evaluation of the controller performance. The reinforcement learning controllers showed promising results as they managed to control the liquid level between the predetermined constraints. However, the P-controllers proved a better performance with smaller input changes compared to the reinforcement learning controllers which had large input changes that resulted in oscillatory liquid level. This thesis shows that the creation of reinforcement learning controllers is complicated and time-consuming and a well-tuned controller would most likely perform better. However, with more research and standardized approaches, there is a huge potential of including this field into the process industries due to its ability to handle nonlinearity and long term evaluations.
Adult children caregivers of parents with Alzheimer’s Dementia

ii DEDICATION.iii LIST OF ABREVIATIONS AND SYMBOLS.iv ACKNOWLEDGMENTS.v LIST OF TABLES.viii LIST OF FIGURES.ix Chapter One INTRODUCTION.1 Chapter Two LITERATURE REVIEW.3 Dementia.3 Alzheimer’s Caregiver Burden.4 Family Dynamics and Coping.6 Caregiver Health Habits.7 Theoretical Consideration.8 Present Study.11 Chapter Three – METHODS.12 Participants. 12 Materials. 16 Procedure.19 Data Analysis.20
Sub-50-mV Nanoelectromechanical Switch Without Body Bias

This brief presents a fabricated and characterized 3-terminal (3T) nanoelectromechanical switch (NEMS) featuring for the first time a sub-50-mV pull-in voltage operation without body biasing. The proposed NEMS demonstrates a low hysteresis (<20 mV), low turn-on delay (15 ns), and record low subthreshold slope of 2 mV/decade due to the small air gap of only 100 nm between the gate and the source beam realized using a simple and low-cost fabrication process. NEMS is a propitious candidate toward ideal switch exhibiting a zero leakage with ON-state conductance of 0.1 A. ${V}/{ {\mu } {\text {m}}}$ and a sub-50-mV swing providing an ultralow active power consumption.
Co‐Ethnic and Neighborhood Ties and Financial Social Capital Formation Among the Urban Poor in Kenya

Objective: Lacking access to formal institutions, the poor in developing countries often use informal savings groups to financially prepare for unexpected events. They often base these groups on social ties to reduce risks, which occur when group members do not make payments. This study examines whether Kenya's urban poor rely on social ties, such as co-ethnicity and co-residency, when forming informal savings groups. Methods: This study uses list experiments on a sample of informal settlement residents in and around Nairobi. Results: Approximately 28.7 percent and 17.5 percent of respondents would consider someone outside their ethnic group or informal settlement as a member of an informal savings group, respectively. Most respondents were reluctant to accept members without social ties, with greater reluctance against those outside the settlement. Conclusions: Kenya's urban poor rely primarily on co-residents for financial security, which elevates risk as they experience shocks simultaneously and cannot help one another. Objective. Lacking access to formal institutions, the poor in developing countries often use informal savings groups to ﬁnancially prepare for unexpected events. They often base these groups on social ties to reduce risks, which occur when group members do not make payments. This study examines whether Kenya’s urban poor rely on social ties, such as co-ethnicity and co-residency, when forming informal savings groups. Methods. This study uses list experiments on a sample of informal settlement residents in and around Nairobi. Results. Approximately 28.7 percent and 17.5 percent of respondents would consider someone outside their ethnic group or informal settlement as a member of an informal savings group, respectively. Most respondents were reluctant to accept members without social ties, with greater reluctance against those outside the settlement. Conclusions. Kenya’s urban poor rely primarily on co-residents for ﬁnancial security, which elevates risk as they experience shocks simultaneously and cannot help one another.
Neural Machine Translation for Low-resource English-Bangla

Neural machine translation has recently been able to gain state-of-the-art translation quality for many language pairs. However, neural machine translation has been less tested for English-Bangla language pair, two linguistically distant and widely spoken languages. In this paper, we apply neural machine translation to the task of English-Bangla translation in both directions and compare it against a standard phrase-based statistical machine translation system. We obtain up to +0.30 and +4.95 BLEU improvement over phrase-based statistical machine translation for English-to-Bangla and Bangla-to-English respectively. Due to low-resource and morphological richness of Bangla, English-Bangla translation task produces a large number of rare words. We apply subword segmentation with byte pair encoding to handle this rare words issue. We obtain up to +0.69 and +0.30 BLEU improvement over baseline neural machine translation for English-to-Bangla and Bangla-to-English respectively. We further investigate our system output for several challenging linguistic properties like subject-verb agreement, noun inflection, long distance reordering and rare words translation. We observe that neural machine translation with and without subword segmentation significantly outperform the phrase-based statistical machine translation system, thus establishing itself as the state-of-the-art technology for low-resource English-Bangla machine translation.
Risk map of the novel coronavirus (2019-nCoV) in China: proportionate control is needed

Background China is running a national level antivirus campaign against the novel coronavirus (2019-nCoV). Strict control measures are being enforced in either the populated areas and remote regions. While the virus is closed to be under control, tremendous economic loss has been caused. Methods and findings We assessed the pandemic risk of 2019-nCoV for all cities/regions in China using the random forest algorithm, taking into account the effect of five factors: the accumulative and increased numbers of confirmed cases, total population, population density, and GDP. We defined four levels of the risk, corresponding to the four response levels to public health emergencies in China. The classification system has good consistency among cities in China, as the error rate of the confusion matrix is 1.58%. Conclusions The pandemic risk of 2019-nCoV is dramatically different among the 442 cities/regions. We recommend to adopt proportionate control policy according to the risk level to reduce unnecessary economic loss.
A variational Bayesian neural network for structural health monitoring and cost-informed decision-making in miter gates

Many physics-based and surrogate models used in structural health monitoring are affected by different sources of uncertainty such as model approximations and simplified assumptions. Optimal structural health monitoring and prognostics are only possible with uncertainty quantification that leads to an informed course of action. In this article, a Bayesian neural network using variational inference is applied to learn a damage feature from a high-fidelity finite element model. Bayesian neural networks can learn from small and noisy data sets and are more robust to overfitting than artificial neural networks, which make it very suitable for applications such as structural health monitoring. Also, uncertainty estimates obtained from a trained Bayesian neural network model are used to build a cost-informed decision-making process. To demonstrate the applicability of Bayesian neural networks, an example of this approach applied to miter gates is presented. In this example, a degradation model based on real inspection data is used to simulate the damage evolution.
Automated joint skull-stripping and segmentation with Multi-Task U-Net in large mouse brain MRI databases

Skull-stripping and region segmentation are fundamental steps in preclinical magnetic resonance imaging (MRI) studies, and these common procedures are usually performed manually. We present Multi-task U-Net (MU-Net), a convolutional neural network designed to accomplish both tasks simultaneously. MU-Net achieved higher segmentation accuracy than state-of-the-art multi-atlas segmentation methods with an inference time of 0.35 seconds and no pre-processing requirements. We evaluated the performance of our network in the presence of skip connections and recently proposed framing connections, finding the simplest network to be the most effective. We tested MU-Net with an unusually large dataset combining several independent studies consisting of 1,782 mouse brain MRI volumes of both healthy and Huntington animals, and measured average Dice scores of 0.906 (striati), 0.937 (cortex), and 0.978 (brain mask). These high evaluation scores demonstrate that MU-Net is a powerful tool for segmentation and skull-stripping, decreasing inter and intra-rater variability of manual segmentation. The MU-Net code and the trained model are publicly available at https://github.com/Hierakonpolis/MU-Net.
Assessment of neuroprotective role of PPAR-gamma agonist in spinal cord injury as possible therapeutic agents

It has been reported that peroxisome proliferator-activated receptor (PPAR)-gamma agonist, pioglitazone, has several beneficial roles in many pathological states of nervous tissues. Then in the present study, we aimed to examine the neuroprotective actions of pioglitazone (PPAR-gamma agonist) on motor function, histopathological changes and oxidative damage during spinal cord injury (SCI) in rats. Twenty-four male Wistar rats were randomly divided into three groups as follows; sham, control injury and pioglitazone-treated injured groups. SCI was performed according to the Ping-Weight Drop (contusion) model in rat. The animals received pioglitazone (3 mg/kg) intraperitoneally at times of 15 min after injury and then each 12 hours for seven days. At day seven after SCI, the malondialdehyde and glutathione levels were assessed using biochemical techniques. Histopathological alterations in injured spinal cord and motor function recovery were also assessed after six weeks. Induction of SCI in control group significantly increased the malondialdehyde levels (56%, P=0.002) and decreased the content of glutathione (39±4 nMol/mL) compared to control group (49±6 nMol/mL). Pioglitazone in treated injured rats significantly decreased the malondialdehyde levels (37%, P=0.018) but not glutathione levels (42±1 nMol/mL) compared to sham group. In addition, pioglitazone noticeably improved the histopathological changes of injured spinal cord but not motor function. Our findings revealed that pioglitazone decreases histopathological changes and oxidative damage of injured spinal cord. However, it is suggested that pioglitazone must be applied at higher doses for improving motor function during SCI.