nils-herrmann/clean_scirepeval huggingface dataset:
Users Regard Operator Training as Critical for Successful POCT Applications.
Venue: Clinical laboratory
Year: 2020

Point of care tests (POCT) measure analytes close to the patients and are a complementary supplement to the test menu of medical laboratories. However, the involvement of many different stakeholders makes it challenging to ensure reliable results.  In a survey, we asked experienced POCT users how they control their total POCT process and what factors they consider essential for success. Results were verified in four in depth interviews.  Overall, 73 German participants from various medical disciplines completed the survey. All but one participant regarded operator training as important but only half of the participants' institutions conducted operator training on a regular basis. Participants often requested e learning, but face to face teaching is still preferred. Twenty one percent of participants already used e learning and reported mixed satisfaction. Fifty five percent of the participants never refer to the quality management manual. Instead, 94% stated that if a POCT error arises a contact person for POCT is always available at their workplace. The majority of participants think that external and, in particular, internal quality controls are important for POCT. Only a few difficulties for performing quality control such as "temporal expenditure" and "lack of information about the importance of internal quality control" were commonly mentioned. For future developments, participants expect evolution and improvements especially with regard to "measurement quality and reliability" The answers of the experts in the in depth interviews largely corresponded with the participants of the survey.  The importance of operator training is well established and confirmed in this work. How to conduct this training is less certain, but the answers in this survey suggest some form of blended learning with e learning and practical elements. The discrepancy between the high importance that guidelines and other normative documents place on written information and their low practical usage was striking.
Multifunctional Effects of a Small Molecule STAT3 Inhibitor on NASH and Hepatocellular Carcinoma in Mice
Venue: Clinical Cancer Research
Year: 2017

The incidence of hepatocellular carcinoma is increasing in the United States, and liver cancer is the second leading cause of cancer related mortality worldwide. Nonalcoholic steatohepatitis (NASH) is becoming an important risk for hepatocellular carcinoma, and most patients with hepatocellular carcinoma have underlying liver cirrhosis and compromised liver function, which limit treatment options. Thus, novel therapeutic strategies to prevent or treat hepatocellular carcinoma in the context of NASH and cirrhosis are urgently needed.  Constitutive activation of STAT3 is frequently detected in hepatocellular carcinoma tumors. STAT3 signaling plays a pivotal role in hepatocellular carcinoma survival, growth, angiogenesis, and metastasis. We identified C188 9, a novel small molecule STAT3 inhibitor using computer aided rational drug design. In this study, we evaluated the therapeutic potential of C188 9 for hepatocellular carcinoma treatment and prevention.  C188 9 showed antitumor activity in vitro in three hepatocellular carcinoma cell lines. In mice with hepatocyte specific deletion of Pten (HepPten mice) C188 9 treatment blocked hepatocellular carcinoma tumor growth, reduced tumor development, and reduced liver steatosis, inflammation, and bile ductular reactions, resulting in improvement of the pathological lesions of NASH. Remarkably, C188 9 also greatly reduced liver injury in these mice as measured by serum aspartate aminotransferase and alanine transaminase levels. Analysis of gene expression showed that C188 9 treatment of HepPten mice resulted in inhibition of signaling pathways downstream of STAT3, STAT1, TREM 1, and Toll like receptors. In contrast, C188 9 treatment increased liver specification and differentiation gene pathways.  Our results suggest that C188 9 should be evaluated further for the treatment and/or prevention of hepatocellular carcinoma.
To tune or not to tune: Detecting orbital variability in Oligo Miocene climate records
Year: 2012

article i nfo Article history: We address the problem of detecting quasi periodic variability at orbital frequencies within pre Pleistocene climate records using depth derived and orbitally tuned chronologies. Many studies describing orbital vari ability in pre Pleistocene sediment hosted isotope records employ climatic records that are set on orbitally tuned chronologies, without accounting for the bias in spectral power estimates introduced by orbital tuning. In this study we develop a method to quantify the effects of tuning upon spectral estimates and, in particular, to more properly determine the statistical significance of spectral peaks associated with orbital frequencies. We apply this method to two marine sediment d 18 O records spanning the Oligo Miocene, from ODP cores 1090 and 1218. We find that using linear age depth relationships reveals statistically significant spectral peaks matching eccentricity in core 1090, and obliquity and precession in core 1218, where the last appears most significant. Tuning the chronologies to the orbital solutions of Laskar et al. (2004) increases the statis tical significance of the precession peak, whereas the obliquity and eccentricity peaks become indistinguish able from those expected from tuning noise. This result can be understood in that tuning records with high signal to noise ratios tends to lead to more significant spectral peaks, whereas a linear age depth relationship is better suited for detecting peaks when signal to noise ratios are low. We also demonstrate this concept using synthetic records.
Machine learning models and bankruptcy prediction
Venue: Expert Syst. Appl.
Year: 2017

Machine learning models show improved bankruptcy prediction accuracy over traditional models.Various models were tested using different accuracy metrics.Boosting, bagging, and random forest models provide better results. There has been intensive research from academics and practitioners regarding models for predicting bankruptcy and default events, for credit risk management. Seminal academic research has evaluated bankruptcy using traditional statistics techniques (e.g. discriminant analysis and logistic regression) and early artificial intelligence models (e.g. artificial neural networks) In this study, we test machine learning models (support vector machines, bagging, boosting, and random forest) to predict bankruptcy one year prior to the event, and compare their performance with results from discriminant analysis, logistic regression, and neural networks. We use data from 1985 to 2013 on North American firms, integrating information from the Salomon Center database and Compustat, analysing more than 10,000 firm year observations. The key insight of the study is a substantial improvement in prediction accuracy using machine learning techniques especially when, in addition to the original Altmans Z score variables, we include six complementary financial indicators. Based on Carton and Hofer (2006) we use new variables, such as the operating margin, change in return on equity, change in price to book, and growth measures related to assets, sales, and number of employees, as predictive variables. Machine learning models show, on average, approximately 10% more accuracy in relation to traditional models. Comparing the best models, with all predictive variables, the machine learning technique related to random forest led to 87% accuracy, whereas logistic regression and linear discriminant analysis led to 69% and 50% accuracy, respectively, in the testing sample. We find that bagging, boosting, and random forest models outperform the others techniques, and that all prediction accuracy in the testing sample improves when the additional variables are included. Our research adds to the discussion of the continuing debate about superiority of computational methods over statistical techniques such as in Tsai, Hsu, and Yen (2014) and Yeh, Chi, and Lin (2014) In particular, for machine learning mechanisms, we do not find SVM to lead to higher accuracy rates than other models. This result contradicts outcomes from Danenas and Garsva (2015) and Cleofas Sanchez, Garcia, Marques, and Senchez (2016) but corroborates, for instance, Wang, Ma, and Yang (2014) Liang, Lu, Tsai, and Shih (2016) and Cano etal. (2017) Our study supports the applicability of the expert systems by practitioners as in Heo and Yang (2014) Kim, Kang, and Kim (2015) and Xiao, Xiao, and Wang (2016)
Detection of Knocking Combustion Using the Continuous Wavelet Transformation and a Convolutional Neural Network
Year: 2021

The phenomenon of knock is an abnormal combustion occurring in spark ignition (SI) engines and forms a barrier that prevents an increase in thermal efficiency while simultaneously reducing CO2 emissions. Since knocking combustion is highly stochastic, a cyclic analysis of in cylinder pressure is necessary. In this study we propose an approach for efficient and robust detection and identification of knocking combustion in three different internal combustion engines. The proposed methodology includes a signal processing technique, called continuous wavelet transformation (CWT) which provides a simultaneous analysis of the in cylinder pressure traces in the time and frequency domains with coefficients. These coefficients serve as input for a convolutional neural network (CNN) which extracts distinctive features and performs an image recognition task in order to distinguish between non knock and knock. The results revealed the following: (i) The CWT delivered a stable and effective feature space with the coefficients that represents the unique time frequency pattern of each individual in cylinder pressure cycle; (ii) the proposed approach was superior to the state of the art threshold value exceeded (TVE) method with a maximum amplitude pressure oscillation (MAPO) criterion improving the overall accuracy by 6.15 percentage points (up to 92.62% and (iii) The CWT CNN method does not require calibrating threshold values for different engines or operating conditions as long as enough and diverse data is used to train the neural network.
Multifinger haptic interface for bimanual manipulation of virtual objects
Venue: 2009 IEEE International Workshop on Haptic Audio visual Environments and Games
Year: 2009

A multi finger haptic interface called MasterFinger for bimanual manipulation of virtual objects is described in this paper. The index fingers and the thumbs are inserted in their corresponding thimbles in order to perform virtual manipulations. A gimble with 3 rotational degree of freedom connects the thimble to a serial parallel mechanical structure that is made up of 3 electro mechanical actuators. This mechanism thereby provides 6 DoF of movement with force reflection in all directions. A distributed architecture has been implemented to simulate bimanual manipulations. This architecture is based on the use of a specific controller for each finger and a server computer that simulates the virtual manipulation in real time. Some experiments that evaluate the performance of the MasterFinger are described.
Children's CBT skills, metacognition, empathy, and theory of mind
Venue: Journal of Children's Services
Year: 2019

PurposeCognitive Behavioural Therapy (CBT) is an evidence based treatment for common mental health problems that affect children, young people and adults. The suitability of CBT for children has been questioned because it requires children to think about their thoughts, feelings and behaviours. The purpose of this paper is to investigate which cognitive and affective capacities predict children's ability to relate thoughts, feelings and behaviours. total of 59 typically developing children aged between 8 and 11 years took part in the study. CBT skills were assessed on a story task that required children to relate the character's thoughts to their feelings and behaviours. Children also completed an assessment of IQ, a feeling of knowing metamemory task that assessed metacognition, and a higher order theory of mind task. Furthermore, parents rated their child's empathy on the children's empathy quotient. findings suggest that CBT is developmentally appropriate for 8 11 year old children; however, young children and children with mental health problems may have impaired metacognition and CBT skills. Metacognition and empathy may moderate the efficacy of child CBT and warrant further investigation in clinical trials.valueThis study provides evidence for the cognitive and affective skills that might predict the outcome of CBT in children. Metacognition and empathy predict children's ability to relate thoughts, feelings and behaviours, and therefore may moderate the efficacy of CBT.
[The uncanny]
Venue: Annales francaises d'anesthesie et de reanimation
Year: 2011

frightening, yet familiar. He uses the German word and its opposite, to illustrate unheimlich heimlich, this point. Within the many definitions for the word reaches a point in which it means its unheimlich, exact opposite, without the word changing itself. means both "familiar" and "unfamiliar," and Unheimlich translates into English as "uncanny." Freud uses Hoffmann's story titled "The Sand Man" to explore what is uncanny, focusing on images and events that he believes are related to the fear of castration. These include: the loss of sight, specifically through the removal of eyes by the Sandman, and severed limbs. He connects this fear of castration to males' belief that "there is something uncanny about the female genital organs." According to Freud, the female's genital organs are uncanny because they (1)
Evaluation of Cabbage (Brassica oleracea) Varieties suited for Off Season Cultivation in Rain Shelters of Kerala
Year: 2019

Cabbage is one of the most important of the green leafy vegetables grown under temperate to tropical climate conditions. It is a cool season crop which grows best under cool, moist weather conditions. There is much variation exists among different cabbage types. In this experiment the cabbage is tried to cultivate before their normal season i.e. off season cultivation is aimed so that, the supply of produce is less and prices are high. For this the performance of five improved cabbage varieties during off season was assessed in rain shelters. The selected cabbage varieties were NS183, NS43, Kalyani, Green Challenger and Green Voyager. Number of replications was ten. Various biometric, yield characters, pest incidence, BC ratio were noted during the study. The trial revealed that cabbage variety Green Voyager performs better under rain shelters and is the best variety for off season cultivation in Kerala. The variety Green Voyager recorded highest plant height and leaf number at all stages of growth. The highest leaf length and leaf girth was reported by variety Green Voyager. Highest B: C ratio was obtained from Green Voyager and lowest from NS 43. Highest pestincidence was reported from NS43 and lowest from Green Voyager. Head length and width was highest in Green Voyager followed by variety Kalyani. Crop duration was lowest in Green Challenger and highest in Kalyani. Days to heading were lowest in Green Voyager and highest in Green Challenger. Highest yield per plant was noticed in Green Voyager and lowest from variety NS43.
An Empirical Study on API Usages
Venue: IEEE Transactions on Software Engineering
Year: 2019

API libraries provide thousands of APIs, and are essential in daily programming tasks. To understand their usages, it has long been a hot research topic to mine specifications that formally define legal usages for APIs. Furthermore, researchers are working on many other research topics on APIs. Although the research on APIs is intensively studied, many fundamental questions on APIs are still open. For example, the answers to open questions, such as which format can naturally define API usages and in which case, are still largely unknown. We notice that many such open questions are not concerned with concrete usages of specific APIs, but usages that describe how to use different types of APIs. To explore these questions, in this paper, we conduct an empirical study on API usages, with an emphasis on how different types of APIs are used. Our empirical results lead to nine findings on API usages. For example, we find that single type usages are mostly strict orders, but multi type usages are more complicated since they include both strict orders and partial orders. Based on these findings, for the research on APIs, we provide our suggestions on the four key aspects such as the challenges, the importance of different API elements, usage patterns, and pitfalls in designing evaluations. Furthermore, we interpret our findings, and present our insights on data sources, extraction techniques, mining techniques, and formats of specifications for the research of mining specifications.
Sample Efficient Reinforcement Learning with Maximum Entropy Mellowmax Episodic Control
Venue: ArXiv
Year: 2019

Deep networks have enabled reinforcement learning to scale to more complex and challenging domains, but these methods typically require large quantities of training data. An alternative is to use sample efficient episodic control methods: neuro inspired algorithms which use non /semi parametric models that predict values based on storing and retrieving previously experienced transitions. One way to further improve the sample efficiency of these approaches is to use more principled exploration strategies. In this work, we therefore propose maximum entropy mellowmax episodic control (MEMEC) which samples actions according to a Boltzmann policy with a state dependent temperature. We demonstrate that MEMEC outperforms other uncertainty and softmax based exploration methods on classic reinforcement learning environments and Atari games, achieving both more rapid learning and higher final rewards.
Bioadhesives in neurosurgery: a review.
Venue: Journal of neurosurgery
Year: 2019

Neurosurgery presents unique surgical challenges arising from delicate neural structures, limited accessibility, and the risk of CSF leakage that can lead to CNS infections. Sutures and staples may have limited applicability in the complex anatomical constraints of cranial and spinal surgeries, especially in trauma settings when time is of the essence. Surgical bioadhesives are emerging as attractive alternatives because they avoid traumatic application methods, provide a stress distributed fixation, and provide good cosmesis and outcomes. This article presents the history of the development of surgical bioadhesives, and is also a review of current applications of commercial surgical bioadhesives within neurosurgical procedures and the unmet clinical needs that should be addressed in bioadhesives technologies.  A PubMed literature search was performed using the terms "(glue OR bioadhesive OR fibrin OR tisseel OR evicel OR tachosil OR cyanoacrylate OR duraseal OR bioglue) AND (neurosurgery OR spine OR spinal OR dural OR microvascular decompression OR transsphenoidal OR endovascular)" Of 2433 records screened, 168 studies were identified that described the use of bioadhesives in neurosurgical procedures.  The greatest number of studies describing the use of bioadhesives in neurosurgery were identified for endovascular embolization, followed by dural closure and transsphenoidal surgeries. Other common areas of application were for microvascular decompression, skin closure, peripheral nerve repair, and other novel uses. Numerous case reports were also identified describing complications associated with bioadhesive use.  Despite the paucity of approved indications, surgical bioadhesive use in neurosurgical procedures is prevalent. However, current bioadhesives still each have their own limitations and research is intense in the development of novel solutions.
Implicit Life Event Discovery From Call Transcripts Using Temporal Input Transformation Network
Venue: IEEE Access
Year: 2019

Customer agent conversations (i.e. call transcripts) are invaluable source for companies as they convey direct information from their customers implicit and explicit behaviour. Identifying customer related events is an important task in customer services which is possible from the call transcripts. However, call centers produces a vast amount of transcripts which makes the manual or semi manual processing of such raw datasets quite challenging. Furthermore, customer agent call transcripts tend not to explicitly denote events that might be beneficial to customer services. Albeit being highly researched across multiple domains in the literature, event detection, especially implicit life event detection have not been well examined from call transcripts due to a lack of proper large scale dataset. In this research, we propose a novel deep learning approach based on latent topic modeling and deep recurrent neural networks with memory units to automatically detect implicit events from a customer's history of call transcripts. These implicit events are detected prior to the report date of that event thereby not containing any explicit topic/feature. We provide a case study on a real life, large scale data of more than 800K call transcripts from a large financial services company in the U.S. to examine the practical features and challenges of this problem. The evaluation results demonstrate the potential applicability of our implicit life event detection as it achieves a macro recall score of 53 (macro f1 of 47.5) on a highly imbalanced test set, negative samples are 95% of the data. Our model beats the the state of the art text classification benchmarks by macro f1 score of 5.6 and macro recall of 8.8 on average, and performs better than the ensemble of all single document and sequential classification benchmarks albeit being significantly less complex. The comparison results show the importance as well as our model's capability of capturing the mutual information of a sequence of call transcripts in detecting the implicit life events.
THE INFLUENCE OF TRANSFORMATIONAL LEADERSHIP AND WORK MOTIVATION ON EMPLOYEE PERFORMANCE MEDIATED BY JOB SATISFACTION
Year: 2018

This research aims to examine and to analyze the influence of transformational leadershipand work motivation on employee performance Hotel Kartika Graha Malang in Indonesia either through or without through job satisfaction.This study used a sample of 78 employees of Hotel Kartika Graha Malang in Indonesia. This research applies quantitative analysis by using explanatory study. The data is analyzed by using PLS for significant test and Sobel test for mediating test. The result shows that transformational leadership has no significant influence on employee performance but has significant influence toward job satisfaction. Nevertheless, work motivation has a significant influence on both employee performance and job satisfaction. Moreover, job satisfaction has a significant influence on employee performance. Job satisfaction is also fully mediated the influence of transformational leadership toward employee performance and partially mediated the influence of work motivation toward employee performance. The indicator that causes the non significant influence of the transformational leadership toward employee performance is an individualized consideration, which is not running optimally in Hotel Kartika Graha.