nils-herrmann/clean_scirepeval huggingface dataset:
Sex differences in immune responses that underlie COVID 19 disease outcomes
Venue: Nature
Year: 2020

There is increasing evidence that coronavirus disease 2019 (COVID 19) produces more severe symptoms and higher mortality among men than among women 1 5 However, whether immune responses against severe acute respiratory syndrome coronavirus (SARS CoV 2) differ between sexes, and whether such differences correlate with the sex difference in the disease course of COVID 19, is currently unknown. Here we examined sex differences in viral loads, SARS CoV 2 specific antibody titres, plasma cytokines and blood cell phenotyping in patients with moderate COVID 19 who had not received immunomodulatory medications. Male patients had higher plasma levels of innate immune cytokines such as IL 8 and IL 18 along with more robust induction of non classical monocytes. By contrast, female patients had more robust T cell activation than male patients during SARS CoV 2 infection. Notably, we found that a poor T cell response negatively correlated with patients' age and was associated with worse disease outcome in male patients, but not in female patients. By contrast, higher levels of innate immune cytokines were associated with worse disease progression in female patients, but not in male patients. These findings provide a possible explanation for the observed sex biases in COVID 19, and provide an important basis for the development of a sex based approach to the treatment and care of male and female patients with COVID 19. Male patients with COVID 19 have higher plasma levels of innate immune cytokines and chemokines such as IL 8, IL 18 and CCL5 and more non classical monocytes than female patients, whereas female patients mount robust T cell activation maintained even in older age.
Technostress and the hierarchical levels of personality: a two wave study with multiple data samples
Venue: Eur. J. Inf. Syst.
Year: 2019

Even though IS use has numerous benefits for users and organisations, such as improved user performance and greater productivity, an increasing number of users experience technostress. Since technostress can result in decreased user well being, it is important to understand what leads users to perceive it. Recent technostress research points to the relationship between personality traits and the perception of technostress as a research gap. Given that personality traits are structured hierarchically, we study how and which levels of user personality influence the perception of technostress. In developing our research model, we select personality traits from the three hierarchical levels of personality: neuroticism, personal innovativeness in IT (PIIT) and IT mindfulness. The results of 2 two wave studies analysing data collected in an organisational setting (sample 1) and through mTurk (sample 2) reveal that all three personality traits influence the perception of technostress, with IT mindfulness having the strongest impact. This study contributes by revealing that user personality and, primarily, IT mindfulness influence the perception of technostress. Additionally, our findings reveal an inverted u curved influence of techno stressors on user performance, deepening our understanding of how the perception of technostress influences user reactions.
Aldosterone breakthrough in dogs with naturally occurring myxomatous mitral valve disease.
Venue: Journal of veterinary cardiology the official journal of the European Society of Veterinary Cardiology
Year: 2017

Aldosterone breakthrough (ABT) is the condition in which angiotensin converting enzyme inhibitors (ACEIs) and/or angiotensin receptor blockers fail to effectively suppress the activity of the renin angiotensin aldosterone system. The objective of this study was to determine if ABT occurs in dogs with naturally occurring myxomatous mitral valve disease receiving an ACEI, using the urine aldosterone to creatinine ratio (UAldo:C) as a measure of renin angiotensin aldosterone system activation.  This study includes 39 dogs with myxomatous mitral valve disease. C cut off definition (derived from a normal population of healthy, adult, and client owned dogs) was used to determine the prevalence of ABT in this population. Spearman analysis and univariate logistic regression were used to evaluate the relationship between UAldo:C and ABT (yes/no) and eight variables (age, serum K+ concentration, serum creatinine concentration, ACEI therapy duration and ACEI dosage, furosemide therapy duration and furosemide dosage, and urine sample storage time) Finally, the UAldo:C in dogs receiving spironolactone, as part congestive heart failure (CHF) therapy, was compared to dogs with CHF that were not receiving spironolactone.  The prevalence of ABT was 32% in dogs with CHF and 30% in dogs without CHF. There was no relationship between either the UAldo:C or the likelihood of ABT and the eight variables. Therapy with spironolactone lead to a significant elevation of the UAldo:C.  Using the UAldo:C and a relatively stringent definition of ABT, it appears that incomplete RAAS blockade is common in dogs with MMVD receiving an ACEI. The prevalence of ABT in this canine population mirrors that reported in humans. While the mechanism of ABT is likely multifactorial and still poorly understood, the proven existence of ABT in dogs offers the potential to improve the prognosis for MMVD with the addition of a mineralocorticoid receptor blocker to current therapeutic regimens.  30% of dogs being treated for heart disease and CHF satisfied the definition of ABT. Identifying patient subpopulations experiencing ABT may help guide future study design and clinical decision making.
Mechanisms of enhanced methane emission due to introduction of Spartina anglica and Phragmites australis in a temperate tidal salt marsh
Year: 2020

Introductions of exotic plants are widespread ecological threats disturbing the carbon and nutrient cycles in global coastal wetlands. Previous studies have found that the invasion has enhanced methane emission through increased primary production and a changed methanogenic pathway. However, the previous studies did not determine the depth profiles of the changes while the depth in the soil profile to which the disturbance propagates is one of the critical issues to be addressed. In this study, we illustrated the depth profiles of the effect of the introductions of S. anglica and P. australis on the potential methane production, soil biogeochemistry, and soil microbial community which play a substantial role in enhanced methane emission due to the invasions. The in situ field measurements and anaerobic incubation were conducted to determine the mechanistic differences of depth profiles. Both introductions of S. anglica and P. australis increased methane emission through different microbial mechanisms depending on depth profile. Potential methane production was stimulated in shallow and deep soil layer of S. anglica and P. australis introduced marshes, respectively. S. anglica increased the contribution of methylotrophic methanogenesis in top soil layers while P. australis decreased competitive inhibition by sulfate reducers in all soil layers. These results emphasize the importance of below ground microbial activity and vertical distribution in carbon cycles in tidal marshes. Further plant invasion studies should focus on depth profiles of microbial processes, as their distribution and activity vary substantially across soil depths.
Deep Dual Channel Neural Network for Image Based Smoke Detection
Venue: IEEE Transactions on Multimedia
Year: 2020

Smoke detection plays an important role in industrial safety warning systems and fire prevention. Due to the complicated changes in the shape, texture, and color of smoke, identifying the smoke from a given image still remains a substantial challenge, and this has accordingly aroused a considerable amount of research attention recently. To address the problem, we devise a new deep dual channel neural network (DCNN) for smoke detection. In contrast to popular deep convolutional networks (e.g. Alex Net, VGG Net, Res Net, and Dense Net and the DNCNN that is specifically devoted to detecting smoke) our proposed end to end network is mainly composed of dual channels of deep subnetworks. In the first subnetwork, we sequentially connect multiple convolutional layers and max pooling layers. Then, we selectively append the batch normalization layer to each convolutional layer for overfitting reduction and training acceleration. The first subnetwork is shown to be good at extracting the detailed information of smoke, such as texture. In the second subnetwork, in addition to the convolutional, batch normalization, and max pooling layers, we further introduce two important components. One is the skip connection for avoiding the vanishing gradient and improving the feature propagation. The other is the global average pooling for reducing the number of parameters and mitigating the overfitting issue. The second subnetwork can capture the base information of smoke, such as contours. We finally deploy a concatenation operation to combine the aforementioned two deep subnetworks to complement each other. Based on the augmented data obtained by rotating the training images, our proposed DCNN can promptly and stably converge to the perfect performance. Experimental results conducted on the publicly available smoke detection database verify that the proposed DCNN has attained a very high detection rate that exceeds 99.5% on average, superior to state of the art relevant competitors. Furthermore, our DCNN only employs approximately one third of the parameters needed by the comparatively tested deep neural networks. The source code of DCNN will be released at
Learning by Aligning: Visible Infrared Person Re identification using Cross Modal Correspondences
Venue: ArXiv
Year: 2021

We address the problem of visible infrared person reidentification (VI reID) that is, retrieving a set of person images, captured by visible or infrared cameras, in a crossmodal setting. Two main challenges in VI reID are intraclass variations across person images, and cross modal discrepancies between visible and infrared images. Assuming that the person images are roughly aligned, previous approaches attempt to learn coarse imageor rigid part level person representations that are discriminative and generalizable across different modalities. However, the person images, typically cropped by off the shelf object detectors, are not necessarily well aligned, which distract discriminative person representation learning. In this paper, we introduce a novel feature learning framework that addresses these problems in a unified way. To this end, we propose to exploit dense correspondences between cross modal person images. This allows to address the cross modal discrepancies in a pixel level, suppressing modality related features from person representations more effectively. This also encourages pixel wise associations between cross modal local features, further facilitating discriminative feature learning for VI reID. Extensive experiments and analyses on standard VI reID benchmarks demonstrate the effectiveness of our approach, which significantly outperforms the state of the art.
Enabling effective workflow model reuse: A data centric approach
Venue: Decis. Support Syst.
Year: 2017

With increasingly widespread adoption of workflow technology as a standard solution to business process management, a large number of workflow models have been put in use in companies in the era of electronic commerce. These workflow models form a valuable resource for workflow domain knowledge, which should be reused to support workflow model design. However, current workflow modeling approaches do not facilitate workflow model reuse as a fundamental requirement, leading to a research gap in effective workflow model reuse. In this paper, we propose a novel approach called Data centric Workflow Model Reuse framework (DWMR) to provide a solution to workflow model reuse. DWMR compliments existing control flow focused workflow modeling approaches by explicitly storing workflow data information, such as data dependency, data task relationships, and data similarity scores. DWMR also provides data driven workflow model search and composition algorithms to satisfy user query requirements by automatically combining multiple workflow models. We demonstrate the feasibility of the DWMR approach by applying it to data from a well known industry workflow model repository. We propose a formal data driven approach (DWMR) to facilitate efficient workflow model reuse.DWMR compliments existing control flow focused workflow modeling approaches by explicitly storing workflow data information.DWMR provides data driven workflow model search and composition algorithms to automatically satisfy user query requirements.The result generated by our approach has a high accuracy in terms of precision and recall, comparing with human experts.
The effect of pre operative autologous blood donation self transfusion on hormone and postpartum convalescence in Lying in women.
Venue: International journal of clinical and experimental medicine
Year: 2015

The aim of the study is to investigate the feasibility of pre operative autologous blood donation (PABD) self transfusion on the postpartum recovery and the endocrine in lying in women. The PABD is carried out on 70 pregnant women who have high risk of postpartum hemorrhage. Those 70 subjects were divided into three groups: 33 cases of PABD self transfusion during the Cesarean section; 16 cases of PABD self transfusion as a physiological means and 21 cases without transfusion. Serum levels of Estradiol (E2) Progesterone (P) Prolactin (PRL) hormone are evaluated 48 hours before and after labor; Postpartum colostrum timing, milk yield, short term and long term uterine contraction are observed among the cases. No significance were observed among the three groups on E2, P, PRL hormone 48 hours before and after labor. The PRL concentration in PABD self transfusion group is higher than that in the group without self transfusion 48 hours after labor. Using different PABD self transfusion strategies, significant difference of the initial milk yield time were observed in the three groups (F=6.035 P=0.004) but the milk yield is no significant different on second day and third day. The self transfusion of PABD has little influence on uterine contraction. For the women who underwent Cesarean Section, the PABD self transfusion is conducive to the increase of PRL level. The PABD self transfusion advances the commencement time of milk yield, while with little effect on neither milk yield volume nor uterine contraction.
ADAPTING TO OR CHANGING OUR STUDENTS? Millennials" Learning Behaviour and Its Implications on Educational Management
Year: 2019

Understanding their students is a part of educators' task. Knowledge of their students' social context of development may furnish teachers' effort to engage their students to active learning process. Moreover, the learner centred education paradigm needs teachers to be more concern on students as active learners by creating learning environment and educational management which are relevant to students' characteristics. Recently, the new generation, the millennials, are flourishing higher education. They are different from their predecessor cohorts as the millennials are digital natives who are born and grown by information, communication and technology. Many studies on millennial students indicate needs for teachers to change and follow the demands of this new generation. However, education is not always about just accepting recent generation's all styles and behavior. It involves broader process of changing, adapting and enhancing. This study aims to explore several teachers' lived experience on managing millennials' learning process, behaviour and the consequences for educational management and teachers' professional development. The data are collected through direct observation and reflective notes on daily teaching practices in language and social sciences classrooms. The study indicates that balancing between adapting and changing teaching learning process is required. There are several areas should be re considered. The first area is teachers should manage the on line and off line learning. Secondly, some positive technological impacted characters should be balanced by reviving previous characters or mindset. Thirdly, re designing learning environment should be taken into account. Fourthly, the educators should also adapt to technological changes and improve their knowledge and skills.
COSMETIC COMPOSITIONS FOR REDUCING OR PREVENTING SIGNS OF CELLULITE Inventors: Alexander P. Znaiden, Trumbull;
Year: 2017

of FR 2554 344 published May 10, 1985.  published Jun. 23, 1994. Kligman, A. "Early Destructive Effect of Sunlight on Human Skin" JAMA, (Dec. 29, 1969) vol. 210, Structural Alterations in Exposed and Unex posed Aged Skin' Journal of Investigative Dermatology, (1979) vol. 73, pp. 59 66. Smith, J. et al. "Alterations in Human Dermal Connective Tissue with Age and Chronic Sun Damage' Journal of Investigative Dermatology, (1962) vol. 39, pp. 347 350. Shuster, S. et al. "The Influence of Age and Sex on Skin Thickness, Skin Collagen and Density' British Journal of Dermatology (1975) vol. 93, pp. 639 643. Griffiths, Christopher E. et al. "Restoration of Collagen Formation in Photodamaged Human Skin by Tretinoin (Ret inoic Acid)" The New England Journal of Medicine (1993) vol. 329, pp. 530 535. Chen, S. et al. "Effects of All Trans Retinoic Acid on UVB Irradiated and Non Irradiated Hairless Mouse Skin' Society for Investigative Dermatology, (1992) vol. 98, pp. 248 254. Nakagawa, et. al. "Long Term Culture of Fibroblasts in Contracted Collagen  (1989) vol. 93, pp. 792 798. Jutley, J. K. et al. "Influence of Retinoic Acid and TGF o. on Dermal Fibroblast Proliferation and Collagen Production in Monolayer Cultures and Dermal Equivalents' Matrix, (1993) vol. 13, pp. 235 24) Co pending application Serial No. 08/393,979. Co pending application Serial No. 08/394,122. Primary Examiner Sallie M. Gardner Attorney, Agent, or Firm Rimma Mitelman
A Decision Transformer Fault Diagnostics System Based on Dissolved Gas Analysis
Venue: 2019 21st International Middle East Power Systems Conference (MEPCON)
Year: 2019

Dissolved gas analysis (DGA) is considered one of the simplest and most widely used tools to diagnose incipient faults of power transformers. Several methods have been proposed for DGA in power transformers, such as Roger's ratio method, IEC method and Duval Triangle method. However, these methods suffer from the low accuracy or no decision problems. Accordingly, in this paper, a combined method is designed and built using MATLAB/SIMULINK to construct enhanced DGA diagnostics of transformer faults. This method will be referred as Decision Transformer Fault Diagnostics (DTFD) method. DTFD method is built based on the conventional methods, but, with a combined framework in order to give an enhanced accuracy. First, each individual method is built separately with an output representing the fault in a matrix form. Then, the outputs of all individual methods are used as the input to DTFD system. Through this method, the fault states of each matrix are compared giving a combined matrix. The fault state that has the highest number within the matrix represents the dominant fault. 386 dataset samples are used to validate the proposed method. A comparison of DTFD method with conventional ones indicated that the proposed method has the highest classification.
A User interface Environment as a Support in Maths Teaching for Deaf Children
Venue: ICEIS
Year: 2010

The use of theories stemming from different areas of knowledge has contributed both to knowledge acquisition since these theories help perceive individuals more effectively and to the development of educational software. This is the case, for instance, of cognitive theories, which in turn assist in terms of both learning support and the clarification of possible needs in the acquisition of a given concept. In the present paper, we propose a discussion about the help function (Leite, Borba and Gomes, 2008) provided by educational software, focusing deaf students. This paper also presents an enhancement for the interface design of the help function, based on the Conceptual Fields Theory and the Theory of Multiple External Representations. This interface design enhancement is created by carrying out a new analysis regarding the display of a help function through diagrams. In short, in the present paper we derive important contributions from these two theoretical frameworks so as to provide deaf students with support in the acquisition of mathematical concepts, considering that these deaf students are put in an inclusive context.
Guan Yu Hu Lian Wang Gong Si Ji Xiao Guan Li (OKR)Chuang Xin De Tao Lun
Year: 2014

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Intelligent fuzzy sliding mode controller based on FPGA for the speed control of a BLDC motor
Year: 2020

Brushless DC (BLDC) motors are one of the most widely used motors for various industrial applications due to their high efficiency, high torque to weight ratio and elimination of mechanical commutator. These motors operate in wide range of speeds and necessitate precise speed control techniques, for their nonlinear model, insenseitive to parameter variations and external disturbances, when used in various sensitive applications. Conventional PI and other existing controllers produce high overshoot and increased rise time and settling time. The performance of BLDC motor is enhanced using a Fuzzy Sliding Mode Controller (FSMC) whose gain is intelligently varied with the help of a Fuzzy Inference System (FIS) For this purpose, a suitable FSMC is designed, simulated and implemented using FPGA. The simulation results are validated using Hardware in the loop (HIL) simulation as well as actual hardware implementation. Great improvement in the transient performance is achieved when compared to chatter free SMC, Fuzzy PI and conventional PI controller.
Viziometrics: Analyzing Visual Information in the Scientific Literature
Venue: IEEE Transactions on Big Data
Year: 2018

Scientific results are communicated visually in the literature through diagrams, visualizations, and photographs. These information dense objects have been largely ignored in bibliometrics and scientometrics studies when compared to citations and text. In this paper, we use techniques from computer vision and machine learning to classify more than 8 million figures from PubMed into five figure types and study the resulting patterns of visual information as they relate to scholarly impact. We find that the distribution of figures and figure types in the literature has remained relatively constant over time, but can vary widely across field and topic. Remarkably, we find a significant correlation between scientific impact and the use of visual information, where higher impact papers tend to include more diagrams, and to a lesser extent more plots. To explore these results and other ways of extracting this visual information, we have built a visual browser to illustrate the concept and explore design alternatives for supporting viziometric analysis and organizing visual information. We use these results to articulate a new research agenda viziometrics to study the organization and presentation of visual information in the scientific literature.