nils-herrmann/clean_scirepeval huggingface dataset:
Neuropilin 1 Promotes Epithelial to Mesenchymal Transition by Stimulating Nuclear Factor Kappa B and Is Associated with Poor Prognosis in Human Oral Squamous Cell Carcinoma
Venue: PloS one
Year: 2014

The epithelial to mesenchymal transition (EMT) is a key process in carcinogenesis, invasion, and metastasis of oral squamous cell carcinoma (OSCC) In our previous studies, we found that neuropilin 1 (NRP1) is overexpressed in tongue squamous cell carcinoma and that this overexpression is associated with cell migration and invasion. Nuclear factor kappa B (NF kB) plays an essential role both in the induction and the maintenance of EMT and tumor metastasis. Therefore, we hypothesized that NRP1 induces EMT, and that NRP1 induced migration and invasion may be an important mechanism for promoting invasion and metastasis of OSCC through NF kB activation.  The variations in gene and protein expression and the changes in the biological behavior of OSCC cell lines transfected with a vector encoding NRP1, or the corresponding vector control, were evaluated. NRP1 overexpression promoted EMT and was associated with enhanced invasive and metastatic properties. Furthermore, the induction of EMT promoted the acquisition of some cancer stem cell (CSC) like characteristics in OSCC cells. We addressed whether selective inhibition of NF kB suppresses the NRP1 mediated EMT by treating cells with pyrrolidinedithiocarbamate ammonium (PDTC) an inhibitor of NF kB. Immunohistochemical analysis of NRP1 in OSCC tissue samples further supported a key mediator role for NRP1 in tumor progression, lymph node metastasis, and indicated that NRP1 is a predictor for poor prognosis in OSCC patients.  Our results indicate that NRP1 may regulate the EMT process in OSCC cell lines through NF kB activation, and that higher NRP1 expression levels are associated with lymph node metastasis and poor prognosis in OSCC patients. Further investigation of the role of NRP1 in tumorigenesis may help identify novel targets for the prevention and therapy of oral cancers.
Smart bacterial foraging algorithm based controller for speed control of switched reluctance motor drives
Year: 2014

In this paper, a innovative methodology for Switched Reluctance Motor (SRM) drive control using Smart Bacterial Foraging Algorithm (SBFA) is presented. This method mimics the chemotactic behavior of the E. Coli bacteria for optimization. The proposed algorithm uses individual and social intelligences, so that it can search responses among local optimums of the problem adaptively. This method is used to tune the coefficients of a conventional Proportion Integration (PI) speed controller for SRM drives with consideration of torque ripple reduction. This matter is done by applying the proposed algorithm to a multi objective function including both speed error and torque ripple. This drive is implemented using a DSP based (TMS320F2812) for an 8/6, 4 kW SRM. The simulation and experimental results confirm the improved performance of adjusted PI controller using SBFA in comparison with adjusted PI controller using standard BFA. Excellent dynamic performance, reduced torque ripple and current oscillation can be achieved when the coefficients of PI controller are optimized by using SBFA.
Measurement of thyroglobulin by liquid chromatography tandem mass spectrometry in serum and plasma in the presence of antithyroglobulin autoantibodies.
Venue: Clinical chemistry
Year: 2013

Measurement of serum thyroglobulin (Tg) is used to monitor patients after treatment for differentiated thyroid carcinoma (TC) Difficulty in using Tg as a biomarker of the recurrence of TC in many patients stems from the presence of endogenous anti Tg autoantibodies (Tg AAbs) which can interfere with immunoassays (IAs) and cause false negative results.  We enriched Tg from serum samples using rabbit polyclonal anti Tg antiserum and protein precipitation. Unrelated proteins were partially depleted in the process. Enriched proteins were then denatured, reduced, and digested with trypsin after the addition of a winged internal standard peptide. A Tg specific tryptic peptide was purified by immunoaffinity extraction and analyzed by 2 dimensional LC MS/MS. Instrument cycle time was 6.5 min per sample.  The lower limit of quantification was 0.5 ng/mL (0.76 fmol/mL dimer) Total imprecision of triplicate measurements in serum samples over 5 days was <10% Comparison with a commercial IA using serum samples free of Tg AAb (n 73) showed Deming regression, IA 1.00 LC MS/MS 2.35, r 0.982, standard error of the estimate (S(y|x) 9.52. In a set of Tg AAb positive samples that tested negative for Tg using IA (n 71) concentrations determined by LC MS/MS were =0.5 ng/mL in 23% of samples (median 1.2, range 0.7 11 ng/mL)  The introduced method has acceptable performance characteristics for use in clinical diagnostic applications. The most substantial disagreement between methods was observed in Tg AAb positive samples with concentrations <2 ng/mL (determined with LC MS/MS) The affinity assisted enrichment strategy used for Tg in this method should be applicable to other biomarkers that have endogenous autoantibodies.
Thematic analysis of the international journal of educational Technology in Higher Education (ETHE) between 2004 and 2017
Venue: International Journal of Educational Technology in Higher Education
Year: 2018

Universitat Oberta de Catalunya, Barcelona, Spain Full list of author information is available at the end of the article  It gives us great pleasure to be able to use this editorial to present a thematic analysis of the articles published in the International Journal of Educational Technology in Higher Education (ETHE) since it began back in 2004. The study uses the articles published in the 14 years of the journal's history (2004 2017) to show what research was carried out in the field of educational technology in higher education in this period. This qualitative analysis could be considered to be complementary to the bibliometric analysis between 2004 and 2013 (Ramiro, Ramiro Sanchez, Alba Ruiz, 2014) Although we cannot say that the study reflects all the areas of research covered during this period given that, as with any other journal of its kind, the articles published have to have passed a peer review process and fit with the topic of the special issues proposed by the editors, but the results do show that the areas of research and the subjects covered in the articles published correspond to the research carried out in the field as a whole over the same period (Hsu, Hung, Ching, 2013) As in previous studies with other journals in the field Edutec e (Marin, Zawacki Richter, Perez Garcias, Salinas, 2017) IRRODL (Zawacki Richter, Alturki, Aldraiweesh, 2017) or Distance Education (Zawacki Richter Naidu, 2016) the key themes covered in the publications over this period and the semantic relationships among these themes were explored with the text mining tool LeximancerTM.
ICU and ventilator mortality among critically ill adults with COVID 19
Venue: medRxiv
Year: 2020

We report preliminary data from a cohort of adults admitted to COVID designated intensive care units from March 6 through April 17, 2020 across an academic healthcare system. Among 217 critically ill patients, mortality for those who required mechanical ventilation was 29.7% (49/165) with 8.5% (14/165) of patients still on the ventilator at the time of this report. Overall mortality to date in this critically ill cohort is 25.8% (56/217) and 40.1% (87/217) patients have survived to hospital discharge. Despite multiple reports of mortality rates exceeding 50% among critically ill adults with COVID 19, particularly among those requiring mechanical ventilation, our early experience indicates that many patients survive their critical illness.
Risk factors for severity and mortality in adult COVID 19 inpatients in Wuhan
Venue: Journal of Allergy and Clinical Immunology
Year: 2020

In December 2019, COVID 19 outbreak occurred in Wuhan. Data on the clinical characteristics and outcomes of patients with severe COVID 19 are limited.  The severity on admission, complications, treatment, and outcomes of COVID 19 patients were evaluated.  Patients with COVID 19 admitted to Tongji Hospital from January 26, 2020 to February 5, 2020 were retrospectively enrolled and followed up until March 3, 2020. Potential risk factors for severe COVID 19 were analyzed by a multivariable binary logistic model. Cox proportional hazard regression model was used for survival analysis in severe patients.  We identified 269 (49.1% of 548 patients as severe cases on admission. Elder age, underlying hypertension, high cytokine levels (IL 2R, IL 6, IL 10, and TNF a) and high LDH level were significantly associated with severe COVID 19 on admission. The prevalence of asthma in COVID 19 patients was 0.9% markedly lower than that in the adult population of Wuhan. The estimated mortality was 1.1% in nonsevere patients and 32.5% in severe cases during the average 32 days of follow up period. Survival analysis revealed that male, elder age, leukocytosis, high LDH level, cardiac injury, hyperglycemia, and high dose corticosteroid use were associated with death in patients with severe COVID 19.  Patients with elder age, hypertension, and high LDH level need careful observation and early intervention to prevent the potential development of severe COVID 19. Severe male patients with heart injury, hyperglycemia, and high dose corticosteroid use may have high risk of death.
Measurements of wind turbulence parameters by a conically scanning coherent Doppler lidar in the atmospheric boundary layer
Year: 2017

The method and results of lidar studies of spatiotemporal variability of wind turbulence in the atmospheric boundary layer are reported. The measurements were conducted by a Stream Line pulsed coherent Doppler lidar with the use of conical scanning by a probing beam around the vertical axis. Lidar data are used to estimate the kinetic energy of turbulence, turbulent energy dissipation rate, integral scale of turbulence, and momentum fluxes. The dissipation rate was determined from the azimuth structure function of radial velocity within the inertial subrange of turbulence. When estimating 10 the kinetic energy of turbulence from lidar data, we took into account the averaging of radial velocity over the sensing volume. The integral scale of turbulence was determined on the assumption that the structure of random irregularities of the wind field is described by the von Karman model. The domain of applicability of the used method and the accuracy of estimation of turbulence parameters were determined. Turbulence parameters estimated from Stream Line lidar measurement data and from data of a sonic anemometer were compared.
A Surveillance System Using CNN for Face Recognition with Object, Human and Face Detection
Year: 2016

Recently, surveillance system plays an important role in solving several crimes by replacing human to watch monitors. Not only many functions are complicatedly integrated to a system but also a system is evolved to capture statistical data to extract useful information. But integrating many functions should be considered to make it have reduced processing time because a system has limited processing ability. If a system considers moving objects, it could reduce processing time because surveillance system normally has static background that is useless information. People and face detection are performed in detected objects. Detected faces are recognized using CNN(Convolutional Neural Network) The processing time of the proposed system is reduced and true rate of face recognition is 72.7% under varying distance from 2m to 5m.
Utilization of ghee residue for the preparation of chocolate burfi
Year: 2012

Ghee residue is one of the important by products of the dairy industry, which is often wasted. It has high nutritive value. In this study burfi was preparedby incorporation, with ghee residue in the proportion of 5% 10% 15% and 20% which were represented as T1, T2, T3 and T4 respectively and assessing its sensory and chemical properties. the experiment was replicated 4 times. Sensory evaluation of the prepared burfi was carried out using the nine point hedonic scale. Ash and moisture content was determined by (AOAC, 1980) method, fat content of the product was determined by soxlet method, protein was determined by micro kjeldal method and carbohydrate was determined by difference method. The data was obtained during investigation were statistically analysed by using analysis of variance (ANOVA) and critical difference (CD) techniques. Significant difference in taste and flavor, color and appearance, body and texture and overall accpetability, energy, fat, protein, carbohydrate, moisture and non significant differences in color, appearance and ash were observed amongst the various treatment combinations.
An Ensemble Learning Strategy for Eligibility Criteria Text Classification for Clinical Trial Recruitment: Algorithm Development and Validation
Venue: JMIR medical informatics
Year: 2020

Eligibility criteria are the main strategy for screening appropriate participants for clinical trials. Automatic analysis of clinical trial eligibility criteria by digital screening, leveraging natural language processing techniques, can improve recruitment efficiency and reduce the costs involved in promoting clinical research.  We aimed to create a natural language processing model to automatically classify clinical trial eligibility criteria.  We proposed a classifier for short text eligibility criteria based on ensemble learning, where a set of pretrained models was integrated. The pretrained models included state of the art deep learning methods for training and classification, including Bidirectional Encoder Representations from Transformers (BERT) XLNet, and A Robustly Optimized BERT Pretraining Approach (RoBERTa) The classification results by the integrated models were combined as new features for training a Light Gradient Boosting Machine (LightGBM) model for eligibility criteria classification.  Our proposed method obtained an accuracy of 0.846, a precision of 0.803, and a recall of 0.817 on a standard data set from a shared task of an international conference. The macro F1 value was 0.807, outperforming the state of the art baseline methods on the shared task.  We designed a model for screening short text classification criteria for clinical trials based on multimodel ensemble learning. Through experiments, we concluded that performance was improved significantly with a model ensemble compared to a single model. The introduction of focal loss could reduce the impact of class imbalance to achieve better performance.
Art of Business Transformation
Year: 2013

The paper analyzes the end to end process of Telecom Business Transformation (which can be applied to other Industries as well) Today majority of the Telcos around the globe are engaged in a business transformation in some form or the other. A business transformation is required to keep pace with the internal and/or the external factors within the Telco. An internal factor maybe defined as an initiative which is driven from within the organization, example "Sustenance like keeping pace with the subscriber growth" While on the other hand, an external factor may also drive an organization to initiate a business transformation like the readiness for Mobile Number Portability. Business Transformations are critical to the success of an organization in near as well as long term. The same is required to provide the necessary fillip to an organization and the competitive advantage in the market place. Therefore, getting a business transformation "first time right" is crucial and the paper analyzes all aspects that essentially contribute to the success of a transformation program: Definition of Business Case, Resource deployment, Planning Budgeting, Partner Product Selection, Stakeholder Management, Performance Management, Change Management and overall Program Management. The paper aims at assimilating the knowledge garnered during the implementation of large scale telecom business transformations within and outside the country. And present the key challenges faced by various Telcos in an anecdote manner and introspects the proactive resolution that would have helped in the transformation journey.
METODE MYER BRIGGS TYPE INDICATOR (MBTI) UNTUK TES KEPRIBADIAN SEBAGAI MEDIA PENGEMBANGAN DIRI
Year: 2020

The MBTI (Myer Briggs Type Indicator) is the most accurate and easy to use inventory personality test. The Personality Test Expert System using the MBTI method is designed to be a guide for information about professions that are in accordance with students' talents and interests so that they can help in determining the appropriate choice of higher education majors. The MBTI Test Expert System application was successfully implemented using the Forward Chaining Method with the PHP programming language. The test results show the application runs in accordance with predetermined specifications. The percentage of eligibility for testing by experts is 92.5% which means good.
Near Optimality of Finite Memory Feedback Policies in Partially Observed Markov Decision Processes
Venue: ArXiv
Year: 2020

In the theory of Partially Observed Markov Decision Processes (POMDPs) existence of optimal policies have in general been established via converting the original partially observed stochastic control problem to a fully observed one on the belief space, leading to a belief MDP. However, computing an optimal policy for this fully observed model, and so for the original POMDP, using classical dynamic or linear programming methods is challenging even if the original system has finite state and action spaces, since the state space of the fully observed belief MDP model is always uncountable. Furthermore, there exist very few rigorous approximation results, as regularity conditions needed often require a tedious study involving the spaces of probability measures leading to properties such as Feller continuity. In this paper, we rigorously establish near optimality of finite window control policies in POMDPs under mild non linear filter stability conditions and the assumption that the measurement and action sets are finite (and the state space is real vector valued) We also establish a rate of convergence result which relates the finite window memory size and the approximation error bound, where the rate of convergence is exponential under explicit and testable geometric filter stability conditions. While there exist many experimental results and few rigorous asymptotic convergence results, an explicit rate of convergence result is new in the literature, to our knowledge.
Cross Media Retrieval based on Pseudo Label Learning and Semantic Consistency Algorithm
Year: 2018

To retrieve heterogeneous multimodal data with the same semantics, many algorithms for retrieval over multimodal data have been suggested. The organization and analysis of heterogeneous data have become the focus of intensive research. Here, a new and efficient algorithm for cross media retrieval is proposed based on pseudo label learning and semantic consistency (PLSC) In this algorithm, an adaptive learning projection matrix optimization method is proposed, and in the process of learning the projection matrices, the method fully considers the semantic information of the labeled and unlabeled samples. Thus, the PLSC algorithm can utilize more useful information than other methods and can learn the more efficient projection matrices. Firstly, the class centers of labeled text are computed. We use median feature vectors as the class center vectors. Next, unlabeled images are projected onto the text space and are assigned pseudo labels by comparing with the class center vectors of the text data. Finally, a new training dataset, which includes labeled and unlabeled data, is generated for training the projection matrix. Using the projection matrix to project image or text data onto the same feature space, the data can be compared with each other for similarity, and the distance between data points can be calculated using the Euclidean metric. Validation experiments suggest that the PLSC outperforms other state of the art algorithms.
Design of LDPC coding schemes for exploitation of bit error rate diversity across dies in NAND flash memory
Venue: 2013 International Conference on Computing, Networking and Communications (ICNC)
Year: 2013

LDPC codes have been proposed for Flash memories due to their capability to correct higher number of bit errors. Several mechanisms can lead to bit errors in Flash memories. An extensive list of these mechanisms as enlisted in  includes program disturb from tunneling, quantum level noise effects, erratic tunneling, SILC related data retention and read disturb, and detrapping induced retention. Each of these effects can be modeled as a random event with its own statistical behavior. Due to the process uncertainties in the lithography, the above mechanisms vary from die to die leading to variability in the raw bit error rate (BER) for different dies of the Flash memory. This has an immense impact on design of error correction coding (ECC) schemes which combat the BER to achieve a specified uncorrectable bit error rate (UBER) target. If conventional ECC techniques are used, they have to be designed to handle the worst die BER, which is a big overkill. We propose a new LDPC coding scheme which exploits the BER diversity to its advantage and enables a system design for the average BER of the dies. The method is based on spreading the LDPC codeword over dies rather than packing a single LDPC codeword in one die. The system has to properly select the dies between which to split the LDPC codeword. The LDPC decoding has to be modified to account for the BER diversity. Simulations results show a large coding gain resulting from spreading a codeword, with minimal quality of service (QoS) impact. The improvement stems from the fact that the codeword splitting reduces the mean and the variance of the BER distribution. We also propose an LDPC code design which is customized to the codeword split across multiple dies. The proposed LDPC code design is on similar lines to its design for quasi static fading channel. The LDPC code can be designed using density evolution to obtain an optimal degree distribution for a given BER distribution. Simulation results for the custom designed LDPC codes validate the hypothesis by showing appreciable coding gains.
Energetic and exergetic analysis of solar powered lithium bromide water absorption cooling system
Year: 2017

This paper presents a comprehensive thermodynamic modeling of the solar powered lithium bromide water (LiBr H2O) absorption chiller system. The study examined the influence of the solar collector types on the collector efficiency and the useful heat gain by the collector for the best performance. The study also analyzed the effects of generator inlet temperature, effectiveness of the solution heat exchanger and the pump mass flow rate on the energetic and exergetic performance of the chiller system. The examined performance parameters were coefficient of performance, exergetic efficiency, exergy destruction, fuel depletion ratio and improvement potential. The study revealed that 'evacuated selective surface' collector type has resulted in higher efficiency and more useful heat gain than the single and double glazed collector types. Additionally, the increase in the heat exchanger effectiveness improved the system performance, while the increase in the pump mass flow rate reduced the chiller performance. Furthermore, the result also indicated that the main source of the exergy destruction is the solar collector. In the solar collector, 71.9% of the input exergy was destroyed which accounted for 84% of the total exergy loss. Additionally, 7.1% of the inlet exergy was lost in the generator which was equivalent to 8.3% of the total exergy loss. The overall exergetic improvement potential of the system was approximately 84.7%