nils-herrmann/clean_scirepeval huggingface dataset:
FRESH MILK FERMENTED IN BAMBOO Dendrocalamus asper AND Gigantochloa apus AS A PROBIOTIC OF LACTIC ACID BACTERIA Ni
Year: 2019

Research of potentiality of probiotic lactic acid bacteria (LAB) which are isolated from fresh milk Etawa goat fermented in Betung bamboo (Dendrocalamus asper) and Tali bamboo (Gigantochloa apus) have been conducted. The research aims to determine potentiality of probiotic LAB from fresh milk Etawa goat fermented in Betung bamboo (Dendrocalamus asper) and Tali bamboo (Gigantochloa apus) Type of this research is descriptive explorative. The probiotic test refers to Resistance of isolated bacteria in pH 3.0 and 0.3% bile salt. Both test is performed by using the pour plate method. Antibacterial activity test are performed by using agar diffusion method. The resistance of LAB obtained from Betung bamboo in pH 3.0 is 99.53% while the resistance of LAB in 0.3% bile salt is 94.74% The resistance of LAB obtained from Tali bamboo in pH 3.0 is 95.47% while the resistance of LAB in 0.3% bile salt is 97.96% Isolates obtained from Betung bamboo intermediately inhibited Escherichia coli growth (8 10 mm clear zone) whereas isolates obtained from Tali bamboo did not inhibited Escherichia coli growth. It is concluded that LAB isolated form fresh milk Etawa goat fermented in Betung bamboo is potentially candidate can be developed as a probiotic bacteria.
Efficient Piecewise Training of Deep Structured Models for Semantic Segmentation
Venue: 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
Year: 2016

Recent advances in semantic image segmentation have mostly been achieved by training deep convolutional neural networks (CNNs) We show how to improve semantic segmentation through the use of contextual information, specifically, we explore 'patch patch' context between image regions, and 'patch background' context. For learning from the patch patch context, we formulate Conditional Random Fields (CRFs) with CNN based pairwise potential functions to capture semantic correlations between neighboring patches. Efficient piecewise training of the proposed deep structured model is then applied to avoid repeated expensive CRF inference for back propagation. For capturing the patch background context, we show that a network design with traditional multi scale image input and sliding pyramid pooling is effective for improving performance. Our experimental results set new state of the art performance on a number of popular semantic segmentation datasets, including NYUDv2, PASCAL VOC 2012, PASCAL Context, and SIFT flow. In particular, we achieve an intersection overunion score of 78:0 on the challenging PASCAL VOC 2012 dataset.
A fusion of research informed teaching and teaching informed research: Designing a scalable online ecosystem for new partnerships in educational research
Venue: Australasian Journal of Educational Technology
Year: 2020

This paper examines the unbundling of research in the context of the Teachers' Research Exchange (T REX) platform, the national flagship Internet platform in Ireland for teachers to access, use and engage in research. This paper draws on two theoretical frameworks: Laurillard's conversational framework and Salmon's five stage model. The first highlights the experimental, exploratory and creative aspects of developing an overall framework to structure the digital learning environment for teachers as researchers. The second explores the future of higher education and systems like T REX in the era of digital transformation and the emerging context of Education 4.0. The original contribution is the integration of these frameworks in the creation of an open and shared learning platform so that schools and teachers can be supported to access, collaborate in and undertake research. In particular, the paper outlines signature features of the T REX design and the way it has emerged over the last 5 years as an innovative platform to mediate the fusion of research and teaching in education. The paper highlights key issues and challenges in unbundling the traditional university, particularly in the context of educational research, but also the imperative of doing so, in the emerging era of Education 4.0. Implications for practice or policy: Supporting teachers' research engagement is increasingly identified as a key priority in the improvement of educational standards and systems. T REX is of value to educationalists interested in a shared learning and open online teacher research platform. There should be a dialogic approach to the relationship between teaching and research, where each informs and enhances the other.
Optimal Recommendation in the Presense of Comparison Shopping
Year: 2012

Recommender systems have been widely used by online stores to suggest items of interest to users. These systems often identify a subset of items from a much larger set that best matches the user's interest. A key concern with existing approaches is over specialization, which results in returning items that are too similar to each other. Unlike existing solutions that rely on diversity metrics to reduce similarity among recommended items, we propose to use choice probability to measure the overall quality of recommendation lists, which unifies the desire to achieve both relevancy and diversity in generating recommendations. We first define the recommendation problem in the discrete choice setting. We then develop an efficient greedy algorithm that solves the problem to optimum in O(KlogK) time, where K stands for the number of wanted items. Using data from a major travel website, we show that recommendation lists generated by our algorithm outperform existing strong baselines. Sensitivity analysis is also conducted to evaluate the robustness of our solution.
Ki 67 Expression as a Factor Predicting Recurrence of Ductal Carcinoma In Situ of the Breast: A Systematic Review and Meta Analysis
Venue: Clinical breast cancer
Year: 2018

Ki 67 is a marker of proliferating cells; in this meta analysis we aimed to examine whether Ki 67 expression can predict recurrence rates of breast ductal carcinoma in situ (DCIS)  This systematic review and meta analysis was performed according to the Preferred Reporting Items for Systematic Reviews and Meta Analyses guidelines. Eligible articles were sought in MEDLINE up to April 30, 2017. Random effects (DerSimonian Laird) models were used for the calculation of pooled relative risk (RR) estimates; meta regression analysis was also performed. Separate analyses were performed according to Ki 67 expression cutoff levels, invasiveness of recurrence, and adjustment of studies.  Ten eligible cohort studies were synthesized; a significant association between Ki 67 expression and DCIS recurrence was noted for the Ki 67 cutoff at 10% (RR 1.66; 95% confidence interval [CI] 1.14 2.42) as well as the Ki 67 cutoff at 14% (RR 1.67; 95% CI, 1.01 2.77) Subanalysis on unadjusted (RR 1.48; 95% CI, 1.06 2.07) and adjusted studies (RR 2.19; 95% CI, 1.42 3.38) replicated the statistically significant findings. Ki 67 expression predicted the risk of invasive (RR 1.53; 95% CI, 1.14 2.06) and noninvasive (RR 1.59; 95% CI, 1.19 2.13) recurrence.  This meta analysis highlights Ki 67 expression as a predictor of DCIS recurrence; nevertheless, additional adjusted studies, with adequate follow up periods, stemming from various world regions seem to be needed on this topic.
Serum iron metabolites in an opportunistic sample of different captive primate species
Year: 2012

Previous reports in the literature indicate that primate species differ in their iron metabolism. Analyses were carried out of 229 serum samples of 18 different species, including marmosets, lemurs, woolly monkeys, colobines, macaques, baboon and great apes, for transferrin saturation %TS) an indicator of iron absorption. In correspondence with our expectations based on the literature, lemurs and marmosets had significantly higher %TS values than great apes and macaques/baboon. The findings corroborate patterns previously described in individual studies, and underline that further efforts should be made to understand the reasons and consequences of these differences in iron metabolism.
Data mining in education
Venue: Wiley Interdiscip. Rev. Data Min. Knowl. Discov.
Year: 2013

Applying data mining (DM) in education is an emerging interdisciplinary research field also known as educational data mining (EDM) It is concerned with developing methods for exploring the unique types of data that come from educational environments. Its goal is to better understand how students learn and identify the settings in which they learn to improve educational outcomes and to gain insights into and explain educational phenomena. Educational information systems can store a huge amount of potential data from multiple sources coming in different formats and at different granularity levels. Each particular educational problem has a specific objective with special characteristics that require a different treatment of the mining problem. The issues mean that traditional DM techniques cannot be applied directly to these types of data and problems. As a consequence, the knowledge discovery process has to be adapted and some specific DM techniques are needed. This paper introduces and reviews key milestones and the current state of affairs in the field of EDM, together with specific applications, tools, and future insights.
SNP based linkage mapping for validation of adult plant stripe rust resistance QTL in common wheat cultivar Chakwal 86
Venue: The Crop Journal
Year: 2019

Wheat crops in China are constantly challenged by stripe rust. Deployment of cultivars with diverse resistances is the best strategy to control the disease. A recombinant inbred line (RIL) population derived from a cross between the resistant cultivar Chakwal 86 and susceptible landrace Mingxian 169 was studied in multiple environments to examine the underlying genetics and to identify quantitative trait loci (QTL) for stripe rust resistance. One hundred and twenty eight RILs were genotyped with wheat 35K SNP array and a genome wide linkage map with 1480 polymorphic SNP markers, or bins, was constructed. Two major QTL on chromosomes 1BL and 3BS, and one minor QTL on 6BS had significant effects in reducing stripe rust severity. The QTL were validated using composite interval mapping (CIM) and inclusive composite interval mapping (ICIM) These methods explained 59.0% 74.1% of the phenotype variation in disease response. The QTL on chromosome 1BL was confirmed to be Yr29/Lr46 and the one on 3BS was the resistance allele identified in CIMMYT germplasm but was not Yr30/Sr2. The QTL on 6BS probably corresponded to previously known QTL. RILs with combined QTL were more resistant than those with single or no QTL. Kompetitive allele specific PCR (KASP) assays for the QTL with largest effect QTL on chromosome 3BS were performed on a subset of RILs and 150 unrelated wheat lines. The QTL on 3BS with its linked KASP markers can be used in marker assisted selection to improve stripe rust resistance in breeding programs.
Coastal climate change, soil salinity and human migration in Bangladesh
Venue: Nature Climate Change
Year: 2018

Climate change is not only altering weather patterns but also accelerating sea level rise, leading to increased inundation and saline contamination of soils. Given projected sea level rise, it is imperative to examine the extent to which farmers in coastal Bangladesh can adapt by diversifying economic activities before resorting to migration within and across borders. Here, to identify patterns in how households adapt to increased sea/freshwater flooding and soil salinity, we analyse nationally representative socioeconomic and migration data against a suite of environmental variables constructed at the sub district level. Our results show that inundation alone has negligible effects on migration and agricultural production. However, gradual increases in soil salinity correspond to increasing diversification into aquaculture and internal migration of household members. Salinity is also found to have direct effects on internal and international migration even after controlling for income losses, with mobility restricted to certain locations within Bangladesh. Our study suggests that migration is driven, in part, by the adverse consequences of salinity on crop production.Projected sea level rise and increased flooding threaten coastal agriculture. Gradual increases in soil salinity, but not inundation alone, are shown to correspond to increasing diversification into aquaculture and higher levels of internal migration.
Shalyaja Bhagandara (Fistula in ano due to Foreign body) A Rare Case
Year: 2019

According to Ayurveda with special reference to Sushruta Samhita there are five types of Bhagandara (Fistula in ano) Amongst which Unmargi Bhagandara or Shalyaja Bhagandara is described. These types of fistula in ano develop due to a foreign body. There are few reported cases where in Fistula in ano developed due to foreign body lodged in perianal area such as an ingested (bone) a forgotten surgical material, etc. A 43 year male patient came with complaint of intermittent pus discharge and pain around anal verge since 1 year. On examination an external opening was seen at 1 o'clock position which was found connected radially to the internal opening at 1 o'clock.  So, Fistulotomy and Ksharkarma (Cauterisation) was advised. During surgery two pieces of stones were found.  The stones were extracted and the post operative wound healed completely within 15 days.
Revisit Batch Normalization: New Understanding from an Optimization View and a Refinement via Composition Optimization
Venue: ArXiv
Year: 2018

Batch Normalization (BN) has been used extensively in deep learning to achieve faster training process and better resulting models. However, whether BN works strongly depends on how the batches are constructed during training and it may not converge to a desired solution if the statistics on a batch are not close to the statistics over the whole dataset. In this paper, we try to understand BN from an optimization perspective by formulating the optimization problem which motivates BN. We show when BN works and when BN does not work by analyzing the optimization problem. We then propose a refinement of BN based on compositional optimization techniques called Full Normalization (FN) to alleviate the issues of BN when the batches are not constructed ideally. We provide convergence analysis for FN and empirically study its effectiveness to refine BN.
Silk fibroin as biomaterial for bone tissue engineering.
Venue: Acta biomaterialia
Year: 2016

Silk fibroin (SF) is a fibrous protein which is produced mainly by silkworms and spiders. Its unique mechanical properties, tunable biodegradation rate and the ability to support the differentiation of mesenchymal stem cells along the osteogenic lineage, have made SF a favorable scaffold material for bone tissue engineering. SF can be processed into various scaffold forms, combined synergistically with other biomaterials to form composites and chemically modified, which provides an impressive toolbox and allows SF scaffolds to be tailored to specific applications. This review discusses and summarizes recent advancements in processing SF, focusing on different fabrication and functionalization methods and their application to grow bone tissue in vitro and in vivo. Potential areas for future research, current challenges, uncertainties and gaps in knowledge are highlighted.  Silk fibroin is a natural biomaterial with remarkable biomedical and mechanical properties which make it favorable for a broad range of bone tissue engineering applications. It can be processed into different scaffold forms, combined synergistically with other biomaterials to form composites and chemically modified which provides a unique toolbox and allows silk fibroin scaffolds to be tailored to specific applications. This review discusses and summarizes recent advancements in processing silk fibroin, focusing on different fabrication and functionalization methods and their application to grow bone tissue in vitro and in vivo. Potential areas for future research, current challenges, uncertainties and gaps in knowledge are highlighted.
Early Fusion of Camera and Lidar for robust road detection based on U Net FCN
Venue: 2018 IEEE Intelligent Vehicles Symposium (IV)
Year: 2018

Automated vehicles rely on the accurate and robust detection of the drivable area, often classified into free space, road area and lane information. Most current approaches use monocular or stereo cameras to detect these. However, LiDAR sensors are becoming more common and offer unique properties for road area detection such as precision and robustness to weather conditions. We therefore propose two approaches for a pixel wise semantic binary segmentation of the road area based on a modified U Net Fully Convolutional Network (FCN) architecture. The first approach UView Cam employs a single camera image, whereas the second approach UGrid Fused incorporates a early fusion of LiDAR and camera data into a multi dimensional occupation grid representation as FCN input. The fusion of camera and LiDAR allows for efficient and robust leverage of individual sensor properties in a single FCN. For the training of UView Cam, multiple publicly available datasets of street environments are used, while the UGrid Fused is trained with the KITTI dataset. In the KITTI Road/Lane Detection benchmark, the proposed networks reach a MaxF score of 94.23% and 93.81% respectively. Both approaches achieve realtime performance with a detection rate of about 10 Hz.
Influence of the leakage flux of the transformer on the welding current of high frequency resistance spot welding systems
Venue: 2018 IEEE Conference of Russian Young Researchers in Electrical and Electronic Engineering (EIConRus)
Year: 2018

High frequency resistance spot welding systems are considered. They have full wave centre tapped output rectifiers and three winding transformer. The leakage flux of three winding transformer including in the systems, influences on the welding current. For calculation of welding current the three winding transformer is replaced by two two winding ones. The mutual influence of the magnetic leakage fields of these two winding transformers on welding current is investigated. It is shown that welding current depends both on the modulus and the sign of the coupling factor of the magnetic leakage fields of these two winding transformers. The paper presents the dependences of the welding current from the coupling factor. They can be used for creation of economical transformers.
An Interdisciplinary approach to Finding and Using Data for Complex Environmental Modelling Problems:A Soil System Example
Year: 2017

Environmental processes are often complex, as they involve non linear interactions between biological, chemical and physical processes across a range of temporal and spatial scales. Soil systems are one example of this complexity. Soils emerge from the interaction between geology, biology (plant and microbial processes) biogeochemistry, hydrology, and climate. Further complexity arises from the feedbacks between soil and human systems. Soils underpin society: they provide the vast majority of our food, the regulate water flows and quality, as a carbon store they are important for climate regulation. Through agriculture, land use change and pollution, human actions over the last have modified soils for centuries. Yet soil sustainability is often overlooked in policy and private sectors  To understand these complex systems, simulation models are needed to integrate the processes that shape soils and influence their sustainability and that of our ecosystems and societies. To date, much of the modelling of soils has existed within one domain (e.g. either carbon cycle models, soil water flow or quality, soil erosion or formation) We aim to develop a more comprehensive model of the soil system by coupling soil biogeochemistry (N14CP, see  with erosion and hydrological processes, to create a model that can be used to simulate soil functioning and response under changing environmental conditions. The model can then be used as part of an integrated risk assessment process (for an example of an integrated risk assessment approach used in the flood management domain see  to inform decision making, enabling sustainable soil management and ensuring continued provision of vital soil services such as food provision and carbon storage. Large, or even vast, amounts of data from diverse sources are required to not only act as model input, but also to determine initial conditions, parameter values and to calibrate and validate models. Searching for this data can be incredibly time consuming and takes time away from furthering our understanding within environmental science. Standard methods of data searching can often be unsuccessful and miss sources of useful data. We are interested in the potential of semantic web technologies to enable more efficient discovery and querying of data sources. This paper addresses the important and under researched area of how to manage data associated with such large scale integrated modelling experiments. In particular, we are adopting an interdisciplinary approach to determine how digital technologies might contribute to environmental science. The overall goal of the paper is to evaluate the potential effectiveness of semantic web technology in addressing the needs of complex environmental modelling.
Gender differences in evaluation of new business opportunity: A stereotype threat perspective
Year: 2014

This study draws on stereotype threat theory to explore differences between men and women on evaluation of new business opportunities. Two controlled experiments, one with business students in Turkey and another with working professionals in the United States, were conducted. Participants were randomly assigned to specific experimental conditions and their assessment of a new business opportunity was measured after presentation of stereotypical information. As predicted, men reported higher opportunity evaluation than women when no gender stereotypical information was presented, whereas men and women evaluated the business opportunity equally favorably when entrepreneurs were described using gender neutral attributes. Interestingly, gender differences in opportunity evaluation were exacerbated when entrepreneurship was linked to masculine stereotypical information, and reversed in favor of women when entrepreneurship was linked to feminine stereotypical information. Practical implications and directions for future research are discussed.
Telemedicine for diabetes care in India during COVID19 pandemic and national lockdown period: Guidelines for physicians
Venue: Diabetes Metabolic Syndrome: Clinical Research Reviews
Year: 2020

In view of restrictions on mobility of patients because of COVID 19 pandemic, face to face consultations are difficult. We sought to study the feasibility of telemedicine in this scenario.  PubMed database and Google Scholar were searched using the key terms 'telemedicine' 'diabetes' 'COVID 19'up to 31ST March 2020. In addition, existing guidelines including those by Ministry of Health and Family Welfare (MOHFW) Government of India were accessed.  We discuss evidence and general guidelines regarding role of telemedicine in patients with diabetes along with its utility and limitations.  Telemedicine is a useful tool for managing patients of diabetes during this lockdown period. However, there is limited data and further research is required.