Miracle-dz/UniFAR-FTUs huggingface dataset:
Influence of Clay/Sulfonated Polyether-ether Ketone as Polymer Electrolyte Membrane for Microbial Fuel Cell

A Microbial fuel cell (MFC) consist of anode and cathode separated by polymer electrolyte membrane. Recently, the most membrane used for MFC application is Nafion. Excessively membrane of Nafion is high ionic conductivity but is high cost membrane. To solve this problem, many researches have been done developing alternative of polymer substitute Nafion is composite membrane based on clay or active clay dispersed in sulfonated polyether-ether ketone (sPEEK). The ionic conductivity and water uptake were determined at room temperature. The results showed that ionic conductivity, addition of clay increases in compare to blank membrane and for water uptake, it is slightly increase. The ionic conductivity of sPEEK-clay and sPEEK-active clay electrolyte membrane are approximately 0.018 – 0.036 S/cm. Accordingly, this composite membrane is potential for MFC application.
Factors Related to Resilience, Anxiety/Depression, and Quality of Life in Patients with Colorectal Cancer Undergoing Chemotherapy in Japan

Objective: This study clarifies the relationship between resilience and anxiety and depression, quality of life (QOL), and other related factors that influence colorectal cancer patients undergoing chemotherapy. Methods: This cross-sectional study focused on outpatients with colorectal cancer undergoing chemotherapy. A questionnaire including the Connor-Davidson Resilience Scale, Hospital Anxiety and Depression Scale, the MOS 12-item Short-Form Health Survey, and items based on a literature review was administered between August 2019 and August 2020. SPSS version 26 was used for data analysis. Correlation analysis and t-test were applied. In addition, Amos version 26 was used and structural equation modeling was applied to create a causal model. Results: A total of 121 participants (94.5%) were included in the study. The goodness of fit (GFI) of the causal model was GFI = 0.94, adjusted GFI = 0.906, comparative fit index = 0.997, and root mean square error of approximation = 0.011. Resilience had a negative effect on anxiety and depression and a positive effect on QOL. Depression had a negative effect on QOL. Conclusions: The results of this study indicate that resilience is a predictor of anxiety, depression, and QOL in colorectal cancer patients undergoing chemotherapy. Therefore, I believe that it is important to enhance resilience in order to maintain and improve patients’ mental health and QOL. In addition, since resilience was affected by cancer metastasis and the presence of metastasis was a predictor of resilience, it is a challenge to explore interventions to enhance resilience, especially in patients with metastasis.
Assessment of Pulmonary Arterial Hemodynamic and Vascular Changes by Pulmonary Pulse Transit Time in Patients with Human Immunodeficiency Virus Infection

Introduction: Pulmonary arterial hypertension and human immunodeficiency virus (HIV) infection is a well-known association. Pulmonary pulse transit time (pPTT) is a recent echocardiographic marker that might be used for evaluation of pulmonary arterial stiffness (PAS) in patients with HIV infection. We aimed to investigate whether pPTT elevated in patients with HIV infection compared to healthy controls and its association with echocardiographic indices of right ventricular functions. Materials and Methods: Fifty HIV (+) patients from infectious disease outpatient clinics and fifty age- and sex-matched HIV (−) healthy volunteers were enrolled in this study. pPTT was measured from pulmonary vein flow velocity as the time interval between the R-wave in the electrocardiography and corresponding peak late systolic was then calculated as the mean from two separate pw-Doppler measurements. Results: pPTT, tricuspid annular peak systolic excursion (TAPSE) and right ventricle fractional area change (FAC) were significantly lower in patients with HIV than control patients (177.1 ± 34.9 vs. 215.7 ± 35.7 msn, P < 0.001; 2.33 ± 0.28 vs. 2.19 ± 0.22, P = 0.039; 45 [4.25] vs. 41.1 [4.0], P = 0.032, respectively). pPTT was positively correlated with FAC, TAPSE and cluster of differentiation 4 count (r = 0.210; P = 0.036, r = 0.256; P = 0.041, r = 0.304; P = 0.044, respectively). Conclusion: Our study showed that pPTT, TAPSE, and right ventricle FAC levels were lower in patients with HIV infection. pPTT is an important predictor in patients with HIV expected to develop pulmonary vascular pathology.
A SIMPLE APPROACH TO DEFINE CURRICULA FOR TRAINING NEURAL NETWORKS

In practice, sequence of mini-batches generated by uniform sampling of examples from the entire data is used for training neural networks. Curriculum learning is a training strategy that sorts the training examples by their difﬁculty and gradually exposes them to the learner. In this work, we propose two novel curriculum learning algorithms and empirically show their improvements in performance with convolutional and fully-connected neural networks on multiple real image datasets. Our dynamic curriculum learning algorithm tries to reduce the distance between the network weight and an optimal weight at any training step by greed-ily sampling examples with gradients that are directed towards the optimal weight. The curriculum ordering determined by our dynamic algorithm achieves a training speedup of ∼ 45% in our experiments. We also introduce a new task-speciﬁc curriculum learning strategy that uses statistical measures such as standard deviation and entropy values to score the difﬁculty of data points in natural image datasets. We show that this new approach yields a mean training speedup of ∼ 43% in the experiments we perform. Further, we also use our algorithms to learn why curriculum learning works. Based on our study, we argue that curriculum learning removes noisy examples from the initial phases of training
Congenital and acquired tooth loss-a causative factors among children

Tooth loss can rob people of much more than the ability to chew and properly digest food. It has serious social, psychological and emotional consequences, impacting quality of life, self image and self esteem.The aim of this study is to investigate early loss of permanent teeth and its causative factors below 18 yrs old children. A retrospective study was conducted using the patient records of children visiting Saveetha dental college.The children between 6-18 years with early permanent tooth loss were selected for this study.Data including age,tooth loss and its reason was collected.The data collected were entered into SPSS version 20.0 and subjected to statistical analysis.Level of significance was set at 5% (p<0.05). Descriptive statistics and chi square test were applied . The results showed overall prevalence of permanent tooth loss,15.09%, dental caries was the main causative factor (31.85%) in 16-18 year old children showing statistical significant difference (p<0.05) and about 56.8% male predominance.It can be concluded that dental caries remains a real public health problem in developing countries. Decision makers need to focus on strategies on prevention of dental caries.
A Translation Technique for Parallelizing Sequential Code using a Single Level Model

Running the code sequentially can be slower, and then the execution time will increase in case of the code has compute-intensive parts. Unfortunately, the sequential code does not employ the device's resources in ideal shape, because it executes one instruction at a time, which means it can perform only a single thread. To overcome the massive time taking issue while large executions, using a paralleling computing approach is a vital solution. A parallel computing code reduces the execution time by executing multiple tasks at the same time. Most researchers and programmers face some difficulties to run their sequential code as parallel due to a lack of knowledge about parallel programming models and the dependency analysis on their codes. Therefore, auto parallelization tools can be helpful to solve this issue. In this study, we have introduced a novel automatic serial to parallel code translation technique that takes serial code written in C++ as an input and generates its parallel code automatically. To validate the objectives of the current study, we compare the results of our proposed method with existing methods. Consequently, the proposed AP4OpenACC tool outperformed the other existing method mentioned in comparative analysis.
Vector Runahead

The memory wall places a significant limit on performance for many modern workloads. These applications feature complex chains of dependent, indirect memory accesses, which cannot be picked up by even the most advanced microar-chitectural prefetchers. The result is that current out-of-order superscalar processors spend the majority of their time stalled. While it is possible to build special-purpose architectures to exploit the fundamental memory-level parallelism, a microarchi-tectural technique to automatically improve their performance in conventional processors has remained elusive.Runahead execution is a tempting proposition for hiding latency in program execution. However, to achieve high memory-level parallelism, a standard runahead execution skips ahead of cache misses. In modern workloads, this means it only prefetches the first cache-missing load in each dependent chain. We argue that this is not a fundamental limitation. If runahead were instead to stall on cache misses to generate dependent chain loads, then it could regain performance if it could stall on many at once. With this insight, we present Vector Runahead, a technique that prefetches entire load chains and speculatively reorders scalar operations from multiple loop iterations into vector format to bring in many independent loads at once. Vectorization of the runahead instruction stream increases the effective fetch/decode bandwidth with reduced resource requirements, to achieve high degrees of memory-level parallelism at a much faster rate. Across a variety of memory-latency-bound indirect workloads, Vector Runahead achieves a 1.79× performance speedup on a large out-of-order superscalar system, significantly improving on state-of-the-art techniques.
AllogeneicMesenchymal Precursor Cells (MPC) in Diabetic Nephropathy: A Randomized, Placebo-controlled, Dose Escalation Study

Article history: Received 13 August 2016 Received in revised form 9 September 2016 Accepted 12 September 2016 Available online 17 September 2016 Background: Diabetic nephropathy is the most common cause of end stage renal failure. We assessed the safety, tolerability, and explored therapeutic effects of adult allogeneic bone-marrow derived mesenchymal precursor cells (MPC) in patients with moderate to severe diabetic nephropathy. Methods:Multicenter, randomized, double-blind, dose-escalating, sequential, placebo-controlled trial assessing a single intravenous (IV) infusion of allogeneic MPC (United States adopted name: rexlemestrocel-L) 150 × 10 (n = 10), 300 × 10 (n = 10) or placebo (n = 10) in adults with diabetic nephropathy with an estimated glomerular filtration rate (eGFR) 20–50 ml/min/1.73 m. Thirty patients at three Australian centers were enrolled between July 2013 and June 2014 and randomized 2:1, in two sequential dose cohorts, to receive rexlemestrocel-L or placebo. Study durationwas 60weeks. Primary endpoint was safety and tolerability. Primary exploratory efficacy endpointwas change frombaseline in eGFR and directlymeasuredGFRby Tc-DTPA plasma clearance (mGFR) at 12 weeks post-infusion. The trial was registered on ClinicalTrials.gov (NCT01843387). Findings: All patients completed the study and were included in analyses applied to the intention to treat population. There were no acute adverse events (AEs) associated with infusion and no treatment-related AEs or serious AEs were deemed treatment-related by investigators. No patients developed persistent donor specific antiHLA antibodies. Relative to placebo, a single IV rexlemestrocel-L infusion showed trends of stabilizing or improving eGFR andmGFR atweek 12. The adjusted least squaresmean (LSM±SE) differences fromplacebo in changes from baseline at 12 weeks in the rexlemestrocel-L groups were 4.4 ± 2.16 and 1.6 ± 2.15 ml/min/1.73 m for eGFR and 4.1 ± 2.75 and 3.9 ± 2.75 for mGFR for the 150 × 10 and 300 × 10 cell groups, respectively. Interpretation: This study demonstrates the safety of rexlemestrocel-L in diabetic nephropathy with suggestive effects on renal function to be confirmed in larger, appropriately powered trials. © 2016 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).
Effects of probiotic BaSC06 on intestinal digestion and absorption function, antioxidant capacity and macrophage polarization and microbiota composition in fattening pigs

Background: The previous study in our team found that supplementation of probiotic Bacillus amyloliquefaciens SC06 (BaSC06) as an alternative to antibiotics (Kitasamycin) can effectively reduce the diarrhea rate and promote the growth performance of weaned piglets. We speculate that BaSC06 may become a better substitute for antibiotics for fattening pigs. Hence, the objective of the present study is to evaluate the effects of BaSC06 substitute antibiotics on growth performance, digestive functions, antioxidant capacity, macrophage polarization and intestinal microbiota of fattening pigs. Methods: A total of 117 fattening pigs with similar weight and genetic basis were selected and divided into three groups: Anti group, fed with basal diet supplemented with 40 g/t Kitasamycin; Anti + Ba group, fed with 1 × 108 CFU/kg BaSC06 and 20 g/t Kitasamycin; Ba group, fed with 1 × 108 CFU/kg BaSC06 replaced Kitasamycin. Each treatment had three replications of 13 pigs per pen. Results: It indicated that BaSC06 replacement significantly improved ADFI and ADG in fattening pigs. It also improved intestinal digestion and absorption function with the increased activities of intestinal digestive enzymes and higher expression of glucose transporters SGLT1 and small peptide transporters PEPT1, maintained intestinal integrity significantly. Besides, supplementation with BaSC06 enhanced intestinal and body antioxidant capacity by activating Nrf2/ Keap1 antioxidant signaling pathway. In addition, BaSC06 as a substitute could alleviate intestinal inflammation by inhibiting the production of pro-inflammatory cytokines and chemokine and simultaneously increase the expression of M1 and M2 macrophage marker proteins in the intestinal mucosa. The transcription factor STAT3 regulated M2 polarization, which maintained immune homeostasis and reduced inflammation. What’s more, BaSC06 could improve the composition of the microbiota as well by increasing the proportion of Firmicutes, and reducing the proportion of Bacteroidetes and Proteobacteria. Conclusions: Taken together, our findings indicated that diet supplementation with BaSC06 instead of antibiotics increased the growth performance of piglets by improving the digestion and absorption of nutrients, intestinal microbiota composition and mucosal structure, antioxidative capacity and immune functions (including increasing M1and M2 polarization of macrophage).
A Lyapunov Analysis of Accelerated Methods in Optimization

Accelerated optimization methods, such as Nesterov’s accelerated gradient method, play a signiﬁcant role in optimization. Several accelerated methods are provably optimal under standard oracle models. Such optimality results are obtained using a technique known as estimate sequences which yields upper bounds on convergence properties. The technique of estimate sequences has long been considered diﬃcult to understand and deploy, leading many researchers to generate alternative, more intuitive methods and analyses. We show there is an equivalence between the technique of estimate sequences and a family of Lyapunov functions in both continuous and discrete time. This connection allows us to develop a uniﬁed analysis of many existing accelerated algorithms, introduce new algorithms, and strengthen the connection between accelerated algorithms and continuous-time dynamical systems.
Driver Safety System by Drowsiness Detection using MATLAB

— Life is urgent and it is brimming with hazard. Subsequently, security precautionary measure ought to consistently be taken before any sort of disaster writing happens. Street mishap is one of the significant reasons forever frailty in now days. Each and every second is significant or critical to fare thee well while driving. Single minute inconsiderateness can cause lifetime lament. The vast majority of the investigations state that the greater part of the street mishaps happens because of lack of regard and latency of the driver. Drowsiness Detection System has been created, utilizing a machine vision based ideas. The framework utilizes a little camera that focuses legitimately towards the driver's face and screens the driver's eyes so as to distinguish exhaustion or drowsiness. For a situation if weariness is identified, an admonition signs or caution signal is given to make the driver aware of wake up and leave the tired state. As a matter of first importance, the framework recognizes the face and afterward the eyes, and afterward decides if the eyes are open or shut.
Impact Of COVID-19 Lockdown On Surface Ozone Build-up at an Urban Site In Western India Based On Photochemical Box Modelling

Elevated ozone (O3) near the earth’s surface causes adverse impacts on human health and vegetation, besides impacting air chemistry and climate Intense lockdown to contain the spread of Coronavirus disease 2019 (COVID-19) offered a rare opportunity to delineate the anthropogenic impact on urban O3 build-up In this regard, we incorporated observations of chemical species and environmental conditions into a photochemical box model (NCAR Master Mechanism) to study the O3 changes at a semi-arid urban site in western India (Ahmedabad;23°N, 72 6°E) In contrast with primary pollutants, daytime O3 build-up is observed to be enhanced during the lockdown by ~39% Model, driven by lower nitrogen oxides (NOx) during the lockdown, also simulated enhanced O3 (by ~41%) showing the role of nonlinear dependence of O3 on NOx Further, a sensitivity simulation unravelled an important role of the meteorological changes in the O3 enhancement (by ~16%) during the lockdown The results highlight that the lockdown impacts can be modulated profoundly by the complex chemistry plus meteorological changes, offsetting the benefits of lower precursor levels in the context of O3 pollution [ABSTRACT FROM AUTHOR] Copyright of Current Science (00113891) is the property of Indian Academy of Sciences and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission However, users may print, download, or email articles for individual use This abstract may be abridged No warranty is given about the accuracy of the copy Users should refer to the original published version of the material for the full abstract (Copyright applies to all Abstracts )
O C ] 2 0 N ov 2 01 3 Control Contraction Metrics and Universal Stabilizability

In this paper we introduce the concept of universal stabilizability: the condition that every solution of a nonlinear system can be globally stabilized. We give sufficient conditions in terms of the existence of a control contraction metric, which can be found by solving a pointwise linear matrix inequality. Extensions to approximate optimal control are straightforward. The conditions we give are necessary and sufficient for linear systems and certain classes of nonlinear systems, and have interesting connections to the theory of control Lyapunov functions.
QaN I have Your Attention? Exploring Attention in Question-Answering Model Architectures

In this project, we build non-pre-trained models for the question-answering task on the Stanford Question Answering (SQuAD) 2.0 dataset, exploring on the effect of attention on the result. We explore the performance of deep learning model architectures that utilize attention: BiDAF (context-query attention), Dynamic Co-Attention (second-level attention) and QANet (self-attention). We explored the baseline BiDAF model, and improved it through character embeddings and co-attention, as well as re-implemented QANet. We ensembled results, and obtained highest performance of F1/EM of 67.96/64.41 for single model dev, F1/EM of 71.02/68.39 for ensemble dev, and F1/EM of 68.39/65.83 for ensemble test. We performed analysis on the single model and ensembles to better understand the model mechanisms and performance.
PEST DETECTION BASED LEAF DISEASE PREDICTION USING DEEP LEARNING TECHNIQUES

Agricultural productivity is something on which economy highly depends. This is the one of the reasons that disease detection in plants plays an important role in agriculture field, as having disease in plants are quite natural. If proper care is not taken in this area then it causes serious effects on plants and due to which respective product quality, quantity or productivity is affected. Plant diseases are extremely significant, as that can adversely affect both quality and quantity of crops in agriculture production. Plant disease diagnosis is very essential in earlier stage in order to cure and control them. Plant diseases can increase the cost of agricultural production and may extend to total economic disaster of a producer if not cured appropriately at early stages. Inspired by the deep learning breakthrough in image-based plant disease recognition, this work proposes deep learning models for image-based automatic diagnosis of plant disease severity. Deep learning algorithm in image can offer an alternative solution in plant monitoring and such an approach may anyway be controlled by a professional to offer his services with lower cost. It includes image segmentation which includes active contour method and image classification approach which includes neural network algorithm to predict various types of diseases. Back Propagation Neural Network (BPNN) is a multilayer neural network consisting of an input layer, minimum one hidden layer and an output layer. As the name of the algorithm implies, the errors (hence, learning) propagate backwards from the output layer to the input layer. Therefore, the process of back propagation is used to estimate weights which minimize the
Design Optimization of Lithium Niobate Modulators using a Genetic Algorithm

This work presents the optimization of some design parameters of a lithium niobate modulator using a genetic algorithm to iteratively refine candidate set of parameters. Each of these candidate solutions is evaluated by applying the Finite Element Method. Numerical results show that the genetic algorithm is able to optimize design parameters of the lithium niobate modulator. Test cases include optimization of single and multiple parameters of the modulator. The set of characteristics include the half wave voltage, the electrode characteristic impedance, and the resulting bandwidth of the device.