nils-herrmann/clean_scirepeval huggingface dataset:
Deep learning based breast cancer classification through medical imaging modalities: state of the art and research challenges
Venue: Artificial Intelligence Review
Year: 2019

Breast cancer is a common and fatal disease among women worldwide. Therefore, the early and precise diagnosis of breast cancer plays a pivotal role to improve the prognosis of patients with this disease. Several studies have developed automated techniques using different medical imaging modalities to predict breast cancer development. However, few review studies are available to recapitulate the existing literature on breast cancer classification. These studies provide an overview of the classification, segmentation, or grading of many cancer types, including breast cancer, by using traditional machine learning approaches through hand engineered features. This review focuses on breast cancer classification by using medical imaging multimodalities through state of the art artificial deep neural network approaches. It is anticipated to maximize the procedural decision analysis in five aspects, such as types of imaging modalities, datasets and their categories, pre processing techniques, types of deep neural network, and performance metrics used for breast cancer classification. Forty nine journal and conference publications from eight academic repositories were methodically selected and carefully reviewed from the perspective of the five aforementioned aspects. In addition, this study provided quantitative, qualitative, and critical analyses of the five aspects. This review showed that mammograms and histopathologic images were mostly used to classify breast cancer. Moreover, about 55% of the selected studies used public datasets, and the remaining used exclusive datasets. Several studies employed augmentation, scaling, and image normalization pre processing techniques to minimize inconsistencies in breast cancer images. Several types of shallow and deep neural network architecture were employed to classify breast cancer using images. The convolutional neural network was utilized frequently to construct an effective breast cancer classification model. Some of the selected studies employed a pre trained network or developed new deep neural networks to classify breast cancer. Most of the selected studies used accuracy and area under the curve metrics followed by sensitivity, precision, and F measure metrics to evaluate the performance of the developed breast cancer classification models. Finally, this review presented 10 open research challenges for future scholars who are interested to develop breast cancer classification models through various imaging modalities. This review could serve as a valuable resource for beginners on medical image classification and for advanced scientists focusing on deep learning based breast cancer classification through different medical imaging modalities.
Learning strategies in CLIL classrooms: how does strategy instruction affect reading competence over time?
Year: 2018

This article reports on an intervention study of reading comprehension among young learners of English as a third language (L3) in a multilingual (Spanish Basque English) context in the Basque Country. The study involves a pre test post test design, with an intervention of 7 weeks using two intact groups of participants that served as experimental and control groups in CLIL classrooms, where a number of subjects are taught through the L3: English. Findings indicate that reading awareness and strategy use were enhanced following the training. While both the control and the experimental group improved over time, the experimental group showed statistically significant gains in reading comprehension, outperforming the control group. Furthermore, this difference was maintained over a two year period, with the experimental group showing a marked improvement in reading competence. This study has implications for the fields of learning strategies and instructional strategies with respect to children learning a foreign language in CLIL contexts.
Thrust washer for planet gears of a planetary gear
Year: 2008

Thrust washer (7, 8) of a planetary gear (1) in which planetary gears (4) via a bearing assembly (5) on a Planetenradbolzen (6) are rotatably mounted and the thrust washer (7, 8) at least in one axial direction, a planetary gear (4 limited. The at least one thrust washer (7, 8) is designed such that the thrust washer (7, 8) limits all the planet gears (4) of a planetary gear (1) in an axial direction.
Automating Procurement Contracts in the Healthcare Supply Chain Using Blockchain Smart Contracts
Venue: IEEE Access
Year: 2021

Effectively managing the healthcare supply chain (HCSC) process is crucial for healthcare providers not only during pandemics such as COVID 19 but also in their normal operations. Despite significant advances in new technologies and treatment options providers still suffer from poor procurement, ordering, forecasting, and distribution practices. Group Purchasing Organizations (GPOs) are an important stakeholder in HCSC and benefit providers with cost savings, volume discounts, and vendor selection. However, the current GPO contract process is time consuming and lacks efficiency. Hence, our proposed solution integrates blockchain technology and decentralized storage to promote transparency, streamlines communication with stakeholders, and minimize the procurement timeline while avoiding pricing discrepancies and inaccuracies. Our solution connects all the stakeholders such as manufacturer, GPO, distributor, and provider using Ethereum network. In this paper, we propose a blockchain solution using smart contracts to automate the GPO contract process. We propose a generic framework for contracting process in the HCSC with detailed algorithms depicting various interactions among HCSC stakeholders. The smart contract code was developed and tested using Remix IDE and the code is publicly shared via Github. We discuss various security risks and present detailed cost analysis of various transactions incurred by the stakeholders. Our analysis demonstrates that the proposed blockchain based solution is economically feasible as only a minimal transaction fee is expended by the stakeholders in the distributed network.
Fast HR Enhancing service excellence
Year: 2011

We are a people who love speed. From the Inc 500 that extols the virtues of fast growth firms to sports, where a record number of more than 100 million people are fans of NASCAR, speed is important. Going fast is not just a USA phenomenon; consider the numbers of BMW and Porsche owners (and others) who flock to the Autobahn to 'put the pedal to the metal, as they say, in an effort to experience the thrill of high speed driving. Fast also characterizes the business environment most firms work in today. The pace of change is escalating, the speed at which new products are brought to market is faster, and the adoption of new technologies have led to breakthrough ways of communicating and doing work. Globalization and technology have contributed to speeding up almost everything we do. The Blackberry, iPhone, iPad, laptop computers, batteries that last much longer than in the past, smaller handheld units and fast airplane travel all contribute to business and people going faster. What about the human resource (HR) function? Is it fast? Type in the term 'fast HR' into your favorite browser, and you will find many articles and references to fast heart rate. You will be lucky to find anything that puts the words 'fast' and 'human resources' together, because in general, high speed is not a term that is married to the HR function. In this paper, two studies of fast HR will be reported. The first one is a survey of executives; in this piece of work, we found that about 62% of the respondents said their HR functions were slow; more telling are the comments these executives wrote. Even worse was some of the feedback from HR executives, claiming they want to leave their own jobs and companies because the slowness at which they respond to the business's needs is demoralizing and is leading them to rethink careers in HR. In the second study reported in this paper, I briefly review a body of findings from firm level studies of fast growth firms showing the impact of HR on firm performance using metrics like earnings and stock price growth. Both the fast HR survey findings and the firm level research studies tell a story about the way in which HR helps firms when the organization is going fast. However, when business slows down, something happens, and HR has a negative effect on the company's performance. The remainder of the paper discusses a solution to the 'something' about HR that leads to negative outcomes in an organization. That something is big, bulky HR tools and systems that slow down managers and business process. The two research studies discussed demonstrate that speed is important for HR, and in order to help HR take action and go faster, in this paper, I review a body of learning from extreme and agile programming. This work is being used by software developers to speed up and improve the delivery of their programming code. There are lessons learned from the discipline and process used in both agile and extreme programming that companies like Telefonica, American Express, and the Knowland Group are using to improve how HR services are delivered to their internal stakeholders.
Using Transfer Learning Approach to Implement Convolutional Neural Network model to Recommend Airline Tickets by Using Online Reviews
Venue: 2020 15th International Workshop on Semantic and Social Media Adaptation and Personalization (SMA
Year: 2020

Social Media provides an opportunity for people to share their idea about different aspects of life. Traveling is one of the essential aspects of life. In this paper, we use Bidirectional Encoder Representations from Transformers(BERT) for sentiment classification of online reviews to implement a Convolutional Neural Network model that can recommend airline tickets. Different sentiment classification models are compared based on people's online reviews from six online social platforms. The new model can classify airline tickets as an economical choice or not economical choice ticket based on various aspects of the trips, including airline customer satisfaction, travel destination, hotel information, restaurants, and tourist attractions. This work contribution is two fold: first, it examines the importance of choosing a transfer learning model for sentiment classification of online social platforms in the recommender system's prediction accuracy. Second, the implementation of the Convolutional Neural Network model, which can classify airline tickets based on data generated by multiple online social platforms. Using the natural language processing approach to use the transfer learning model to improve the CNN model's prediction accuracy is a new approach to use online social platforms to recommend economical airline tickets to consumers.
Changes in microbiological attributes of a red latosol under different cropping systems
Year: 2018

The objective of this study was to evaluate changes in microbiological attributes of soils under different growing systems. Three cropping systems were evaluated (1) no tillage system (NTS) (2) conventional tillage system (CTS) and (3) newly scarified no tillage system (SNTS) The three systems were maintained for 20 years. In addition, a primary forest (PF) fragment was used as a reference. Soil samples were collected at depths of 0.0 0.1 m, 0.1 0.2 m, and 0.2 0.3 m. The following variables were measured: microbial biomass carbon (MBC) soil respirometry (RESP) using the fumigation and incubation method; microbial biomass nitrogen (MBN) using fumigation and extraction; metabolic quotient (qCO2) using the RESP MBC ratio; and number of spores (NS) of mycorrhizal fungi by plate counting of spores extracted from the soil by wet sieving. Treatments were arranged in a completely randomized block design with five replicates. The different management systems affected the soil microbiota, especially in the superficial layer of 0.0 0.1 m. At a depth of 0.0 0.2 m, the PF presented significantly higher values for all analyzed attributes, except for NS. There were no significant differences in the studied characteristics between the NTS and SNTS at the three depths, indicating that scarification performed only once in the NTS was not sufficient to produce changes in soil microbiological attributes. However, the adoption of the CTS for 20 years promoted a decrease in MBC and MBN. RESP and NS were not significantly different between the cropping systems. Principal component analysis indicated a significant difference in microbiological characteristics between the PF and the areas under management. Therefore, different growing systems change the soil microbiota, and the lower the degree of tilling (NTS and SNTS) the smaller the changes in soil microbiological attributes. Soil preparation practices used in conventional tilling negatively affect soil microbial biomass, decreasing the levels of MBC and MBN.
Stable Organic Inorganic Perovskite Solar Cells without Hole Conductor Layer Achieved via Cell Structure Design and Contact Engineering
Year: 2016

Within the past few years, the record efficiency of inorganic organic perovskite solar cell (PSC) has improved rapidly up to over 20% However, the viability of commercialization of the PSC technology has been seriously questioned due to the moisture and thermal induced instabilities. Here, it is demonstrated that these issues may be mitigated via cell structure design and contact engineering. By employing the hole conductor layer free cell structure and a bi layer back contact consisting of a carbon/CH3NH3I composite layer and a compact hydrophobic carbon layer, the PSCs have shown excellent stability, inhibiting moisture ingression and heat induced perovskite degradation. It is found that, the unique bi layer contact enables the optimization of perovskite absorbers during thermal stress. As a result, instead of degradation, the devices present enhanced performance under heating at 100 degC for 30 min. The best performing cell shows a final efficiency of 13.6% from an initial efficiency of 11.3% after thermal stress. Upon encapsulation, these cells can even retain 90% of the initial efficiencies after water exposure and over 100% initial efficiency under thermal stress at 150 degC for half an hour. This approach provides a facile way for stabilizing the PSCs and opens a door for viable commercialization of the emerging PSC technology.
The Future of Reef Ecosystems in the Gulf of Mexico: Insights From Coupled Climate Model Simulations and Ancient Hot House Reefs
Venue: Front. Mar. Sci.
Year: 2019

Shallow water coral reefs and deep sea coral communities are sensitive to current and future environmental stresses, such as changes in sea surface temperatures (SST) salinity, carbonate chemistry, and acidity. Over the last half century, some reef communities have been disappearing at an alarming pace. This study focuses on the Gulf of Mexico, where the majority of shallow coral reefs are reported to be in poor or fair condition. We analyze the RCP8.5 ensemble of the Community Earth System Model v1.2 to identify monthly to decadal trends in mean Gulf of Mexico SST. Secondly, we examine projected changes in ocean pH, carbonate saturation state, and salinity in the same coupled model simulations. We find that the joint impacts of predicted higher temperatures and changes in ocean acidification will severely degrade Gulf of Mexico reef systems by the end of the 21st century. SSTs are likely to warm by 2.5 to 3C; while corals do show signs of an ability to evolve toward higher temperatures, current coral species and reef systems are likely to suffer major bleaching events in coming years. We contextualize future changes with ancient reefs from paleoclimate analogues, periods of Earth's past that were also exceptionally warm, specifically rapid `hyperthermal" events. Ancient analogue events are often associated with extinctions, reef collapse, and significant ecological changes, yet reef communities managed to survive these events on evolutionary timescales. Finally, we review research which discusses the adaptive potential of the Gulf of Mexico's coral reefs, meccas of biodiversity and oceanic health. We assert that the only guaranteed solution for long term conservation and recovery is substantial, rapid reduction of anthropogenic greenhouse gas emissions.
Pretreatment with dexmedetomidine alleviates lung injury in a rat model of intestinal ischemia reperfusion
Venue: Molecular medicine reports
Year: 2020

The aim of the present study was to investigate the antioxidant mechanisms of dexmedetomidine against lung injury during intestinal ischemia reperfusion (IIR) in rats. The model of IIR induced acute lung injury was established by occluding the superior mesenteric artery (SMA) for 1 h and reperfusing for 2 h using Sprague Dawley rats. Pathological examination was used to assess the extent of the lung injury. Oxidative stress was evaluated by measuring malondialdehyde, myeloperoxidase and superoxide dismutase in the lung and plasma. The proinflammatory cytokines tumor necrosis factor a and interleukin 6 were determined via an enzyme linked immunosorbent assay. The mRNA and protein expression of nuclear factor erythroid 2 related factor 2 (Nrf2) and heme oxygenase 1 (HO 1) were determined using a reverse transcription quantitative polymerase chain reaction and western blotting. Pretreatment with dexmedetomidine significantly inhibited the oxidative stress response and proinflammatory factor release caused by IIR compared with the normal saline group (MDA and SOD in lung and plasma, P<0.05; MPO, IL 1b and TNF a in lung and plasma, P<0.05) Dexmedetomidine improved pulmonary pathological changes in IIR rats compared with the normal saline group. Investigations into the molecular mechanism revealed that dexmedetomidine increased the expression levels of Nrf2 and HO 1 via activating a2 adrenergic receptors compared with the normal saline group. The antagonism of a2 adrenergic receptors may reverse the protective effect of dexmedetomidine on lung injury during IIR, including decreasing the expression levels of Nrf2 and HO 1, elevating the oxidative stress response and increasing the proinflammatory factor release. In conclusion, pretreatment with dexmedetomidine demonstrated protective effects against lung injury during IIR via a2 adrenergic receptors. The Nrf2/HO 1 signaling pathway may serve a function in the protective effect of dexmedetomidine.
Simultaneous localization, mapping, and path planning for unmanned vehicle using optimal control
Year: 2018

Among the huge number of functionalities that are required for autonomous navigation, the most important are localization, mapping, and path planning. In this article, investigation of the path planning problem of unmanned ground vehicle is based on optimal control theory and simultaneous localization and mapping. A new approach of optimal simultaneous localization, mapping, and path planning is proposed. Our approach is mainly affected by vehicle's kinematics and environment constraints. Simultaneous localization, mapping, and path planning algorithm requires two main stages. First, the simultaneous localization and mapping algorithm depends on the robust smooth variable structure filter estimate accurate positions of the unmanned ground vehicle. Then, an optimal path is planned using the aforementioned positions. The aim of the simultaneous localization, mapping, and path planning algorithm is to find an optimal path planning using the Shooting and Bellman methods which minimizes the final time of the unmanned ground vehicle path tracking. The simultaneous localization, mapping, and path planning algorithm has been approved in simulation, experiments, and including real data employing the mobile robot Pioneer P 3 AT The obtained results using smooth variable structure filter simultaneous localization and mapping positions and the Bellman approach show path generation improvements in terms of accuracy, smoothness, and continuity compared to extended Kalman filter simultaneous localization and mapping positions.
Attitudes of college students towards online advertisement in social networking sites: a structural equation modelling approach
Year: 2019

Social media and social networking sites (SNSs) are now considered to be an integral part of the marketing mix of firms and organisations across the world. Advertisement in SNSs takes a growing part of the advertising budget, replacing traditional media, due to its characteristics in terms of ROI and interactivity. However, the attitudes of consumers towards advertisement affect the efficiency of advertisements in SNSs as well. In this paper, we are examining the attitudes of 512 college students in the region of Western Macedonia towards advertisement in the SNSs. In accordance with relevant literature, five main categories of attitudes towards SNSs advertisement are identified, namely information, entertainment, credibility, irritation, value corruption through confirmatory factor analysis, and the effect they have on the attitudes towards advertisement and advertisement value is assessed with the use of a structural equation model. Our results validate the positive effect that entertainment, information and credibility have in advertisement value.
En Erken Devirlerden Turk Mezarlarina Olu Yakimi Uygulamasi
Year: 2020

Turklerin binlerce yillik mezar gomu uslubu olarak inhumasyon uygulamasini kullandiklari, atalar kultu inanisina gore olulerini kurganlara gomdukleri bilinmektedir. Diger taraftan Turkler arasinda olu yakiminin uygulanip uygulanmadigi meselesi uzerine tartismalar mevcuttur. Cin kaynaklari Turklerin olu yakimi uygulamasini yaptiklarini ifade etmektedirler. Arkeolojik olarak da Turklerde olu yakimi uygulamasinin kullanildigi gorulmektedir. Boylece yazili kaynaklar ve maddi kultur unsurlari birbirlerini desteklemis bulunmaktadir. Turklerin yurt tuttugu engin bozkir cografyasinda Ural havalisinden Hakasya topraklarina kadar Srubna Andronovo Kulturu doneminden Karasuk Kulturu ve Tagar Tastik Kulturu ve beraberinde Turk boylarinin sahneye cikisi ve uyguladiklari olu gomme uygulamalarini mezar envanterleri uzerinden inceleyerek, kremasyon uygulamasinin en erken hangi donem kullanildiginin ve ne zaman terk edildiginin cevaplarini ortaya koymaya calisacagiz.
Performance Analysis of One Model of Communication and Information System in Military Operation
Venue: Defence Science Journal
Year: 2019

This paper presents a model of communication and information system in military operations. Here OPNET MODELER simulation package is applied because it is suitable for network modelling, topology and capacity planning. Simulation of different types of IP traffic and monitor their performance to optimise the functionality of network elements, management performance network applications, and as well as in research and development of new network technologies. Application of the method of mass service are determined by the capacity needed for voice transmission on the links in the model and using the OPNET MODELER simulation program are analysed performance modeled communication information system in data transmission. The results of the simulation are presented through target the service settings: workload links communication and information system, e mail download response time, http page download response time and packet loss in data transfer. The aim of the research has shown that modeled communication information system with defined elements (nodes) the capacity of links (according to the specification of telecommunication devices) and defined traffic can respond to the requirements of command forces in the military operation in terms of telecommunication service. The results of the analysed service target parameters show that modeled communication and information system provides an efficient flow of information and the tra nsfer of voice and IP data for the needs of command and control in military operations.