Miracle-dz/UniFAR-FTUs huggingface dataset:
FAIR Enough? Enhancing the Usage of Enterprise Data with Data Catalogs

With increasing relevance of data as a strategic asset, companies strive to make data FAIR, i.e. findable, accessible, interoperable and reusable. Data catalogs are considered an important means to realize these aspirations. However, data catalogs are still a novel and loosely defined concept, which lacks empirical studies on their implementation. Against this background, this study aims at fostering the understanding of data catalogs as a means of increasing data exposure and usage in enterprises. Based on a qualitative and explorative study involving 12 multi-national enterprises, we assess data catalog initiatives based on their scope, goals, and users. We propose a taxonomy of data catalog initiatives and present 3 detailed case studies that illustrate typical approaches to data catalogs. Our findings contribute to the ongoing discourse on the FAIR principles by elaborating on their significance in the enterprise context and analyzing their operationalization by means of data catalogs.
The May Fourth Movement and Provincial Warlords: A Reexamination

This article reexamines the relationship between the May Fourth Movement and warlordism by questioning the long-held assumption that the movement was from its inception as anti-warlord as it was anti-imperialist. In particular, it shows that popular nationalism before and at the beginning of the May Fourth Movement received continual support from many provincial warlords under and beyond the nominal control of the Beijing government. It demonstrates how and why these provincial warlords endorsed the patriotism of the movement at the national and international levels, even as they suppressed radical protests in their political domains. This analysis uncovers the long-obscured fact that the May Fourth Movement succeeded with crucial support from such provincial warlords because of its broad nationalism and the disunity of warlord politics. This rediscovery also reveals how military involvement in the movement changed the trajectory of both warlord politics and anti-warlordism in modern China.
Microfabricated Isothermal Eg-Fet Sensor For Lamp Mediated Crispr/Cas12a Detection Of Hepatitis C Virus

A highly sensitive and rapid platform for the detection of hepatitis C virus (HCV) has been developed by combining loop-mediated isothermal amplification (LAMP) with the use of clustered regularly interspaced short palindromic repeats of associated protein 12 (CRISPR/cas12a). An isothermal sensor which serves as an indium tin oxide (ITO) based extended gate field-effect transistor (EG-FET) and is equipped with a polymethyl methacrylate (PMMA) microwell, is used to detect the potential change on the sensing surface, induced by the LAMP/CRISPR reaction. The immobilized signal reporter, which is single-strand deoxyribonucleic acid (ssDNA) on the ITO sensing surface, is cleaved by the activated CRISPR/cas12a. This trans-cleavage occurs due to the highly specific base-pairing of a nucleic acid target to a complementary guide ribonucleic acid (gRNA). The result is a significant potential change within four minutes. The diagnostic platform that is established in this study can be applied to the detection of other virus/bacteria through the use of different pathogen-specific LAMP/CRISPR assays. Therefore, this technique provides a rapid yet high performance platform for the detection of label-free pathogens.
NoC Performance Estimation Based on Queueing Theory for Real Scientific Applications

The estimation of performance metrics of a Network-on-Chip (NoC) is an increasingly important aspect of Multiprocessor System-on-Chip (MPSoC) design. As systems grow in number of components and features, the correct design of the Network-on-Chip has a central role in meeting the performance requirements of the system, because it is responsible for the efficient movement of data inside the MPSoC. In this paper a software tool called NoCSimulator which is based in queueing theory for the estimation of latency and throughput of a NoC is proposed. The contribution of this tool is its ability to generate stochastic traffic patterns based on real data traffic of scientific applications, not only synthetic traffic as similar tools reported in literature. This makes it possible to study NoC performance under real working conditions, which gives better insights about NoC behavior, and improves the design space exploration at early stages of system development. NoCSimulator was validated using two state-of-the-art cycle-accurate simulators (BookSim and Garnet). NoCSimulator, based in queueing theory, does not model hardware aspects of the NoC as a cycle-accurate simulator, so, long simulation times of complex systems can be avoided during design space exploration.
Analysis of ionic channel currents under nsPEFs-stimulation by a circuital model of an excitable cell

In this paper, the circuital model of the plasma membrane of a giant squid axon, implementing the Hodgkin and Huxley equations for the initiation of action potentials, is adopted to analyze the behavior of ionic channel currents under ns pulsed electric fields (nsPEFs) stimuli. The time behavior of the currents of sodium, potassium and leakage channels is computed upon the application of liminal, subliminal and supraliminal, 100 ns long pulses. The here adopted approach allows to give insight into mechanistic details of electrostimulation of excitable cells by nsPEFs, that has been recently demonstrated by experimental studies.
Drug-Drug Interaction Extraction from Biomedical Texts via Relation BERT

There is a large number of drugs introduced every year and a number of interactions between drugs also has quick growth. As a result, biomedical texts following new drugs and interactions expand [15]. Several published studies of drug safety have revealed that drug-drug interactions (DDIs) may be detected too late, when millions of patients have already been exposed [25]. Therefore, the management of drug drug interactions is critical issue since the importance of known drug drug interaction and the giant amount of available information around them [5]. Thus, the issue creates an imperative need for the development of high-reliable automatic DDI extraction methods while manual DDI extraction is time-consuming and could lead to out-of-date information. However, the accuracy of the current automatic DDI extraction method is still insufficient for the practical application. In this research, we explore the Relation Bidirectional Encoder Representations from Transformers (Relation BERT) architecture [32] to detect and classify DDIs from biomedical texts using the DDI extraction 2013 corpus [5] and present three proposed models namely R-BERT∗, R-BioBERT1, and R-BioBERT2. From our knowledge, we are the first to investigate the potential of Relation BERT for the aim of accuracy improvement in DDI extraction. By using the state-of-the-art word representation method, three models produce macro-average F1-score of over 79%. Moreover, the accuracy of extracting Advice and Mechanism interaction achieves 90.63% and 97% respectively in terms of F1-score. The high accuracy of the model in Advice and Mechanism interaction creates motivation for wide application of automatic DDI extraction to the practice with high-reliable and humanless.
Linking Marketing to Financial Performance and Firm Value

In January 2002, Donald R. Lehmann, Executive Director of the Marketing Science Institute, submitted a proposal for a JM Special Section, “Linking Marketing to Financial Performance and Firm Value. ” The proposal included activities to promote interactions among marketing academics and practitioners, designed to advance research on this topic. I was excited about the opportunity to stimulate and publish new research, and after extensive discussions, the American Marketing Association and the Marketing Science Institute formally agreed to cosponsor the Special Section. Authors submitted their manuscripts through a paper competition as well as directly through JM. Donald R. Lehmann, the Consulting Editor, and a panel of distinguished scholars reviewed every submission. The panel included Tim Ambler, Gregory S. Carpenter, Robert Jacobson, V. Kumar, Roland T. Rust, and Rajendra K. Srivastava. All submissions underwent JM's standard double-blind review process under my editorship, and members of JM's Editorial Review Board served as reviewers. I am pleased to have this opportunity to acknowledge the important contributions of these many individuals: both authors and reviewers. As the subsequent essay suggests, three years’ hard work has produced a thought-provoking collection of articles. I hope they will generate further intellectual inquiry and debate about the link between marketing and financial performance in the business community.
Patterns of Population Structure Vary Across the Range of the White Sturgeon

Abstract Critical unknowns surrounding the basic biology of White Sturgeon Acipenser transmontanus have hindered management and conservation efforts. Population genetic data may be used to address some of these uncertainties, but previous examinations of population structure in White Sturgeon have been limited by the highly duplicated nature of the species’ genome. We report results from an analysis of White Sturgeon population structure within and among drainages using 13 polysomic microsatellite loci. Genetic diversity levels varied widely among regions, and the lowest levels were observed in the endangered Kootenai River Distinct Population Segment and the highest levels were detected in regions with access to estuarine and marine habitat. Patterns of within-drainage population structure varied, and no structure was detected in the Sacramento–San Joaquin drainage and a complex pattern of isolation by distance was revealed in the Columbia–Snake River drainage. These results contrast a previously publish.
An optimum transformation for somatic cell concentration in milk.

Abstract Hypothesis testing in analysis of variance requires that the errors be distributed normally and that subclass variances be homogeneous. The purpose of this study was to find the transformation for somatic cell concentration which meets these characteristics of hypothesis testing. Data consisted of 51,800 monthly tests from 52 herds on Dairy Herd Improvement in Wisconsin. Cell concentration by the Filter-DNA method was thousands of cells/ml. Analysis of untransformed data revealed extensive departure from normality and homogeneity. The family of transformations was Y′ = (Y+M) L for L≠0 or Y′ = Log e (Y+M) for L=0, where Y was the untransformed cell concentration. The analysis included L ranging from −. 7 to +. 6 and M ranging from 0 to 20. The maximum likelihood estimate of L was zero. By log transformation, student's t for skewness and kurtosis and Chi square for heterogeneity of variance were not different from zero. Adding a constant of 10 before taking the log caused a small improvement in tests of normality and heterogeneity of variance.
Conceptualization of an Integrated Procedure Model for Business Process Monitoring and Prediction

Predictive Process Monitoring (PPM) aims to improve operational business processes through the prediction of future behavior and process-related performance indicators. It includes a set of techniques to predict the future behavior of business processes based on knowledge of previously executed process instances. The need for rapid intelligent support in business process management brings growing attention to the field of PPM. A variety of machine learning (ML) based PPM techniques have been proposed by researchers to tackle several PPM-related prediction tasks, such as next activity, time, risk, or outcome. However, adopting such a PPM technique involves many complex tasks, including the selection and parametrization of a machine learning algorithm. Additionally, the novel domain lacks a set of guidelines on how to implement the methods to enable a broad and non-expert user group to benefit from it. As a remedy, this paper proposes a procedure model, which can serve as a step by step framework during the implementation and application of PPM. We conclude our work by demonstrating the applicability of our procedure model and by outlining our case study-based evaluation concept.
Co-creation of Virtual Reality Re-usable Learning objectives of 360° video scenarios for a Clinical Skills course

This paper presents a new approach for teaching the clinical skills course by exploiting the potential of a Virtual Reality application utilizing 360° videos with medical content for an undergraduate level course. We discuss the methodology prospects and identify the potential role of Virtual Reality reusable e-resources into learning and practicing clinical skills and the concept of embedding these processes to a real assess-educate-assess cycle that uses digital tools to optimize outcome. Scenarios, were developed in the context of the CoViRR Erasmus+ project, offering new insights for the digital integration in learning and teaching. The initial results of our analysis validated the effective design of a technologically enhanced learning tool as an addition to the existing methods used in medical education. Future work should integrate the application into the classroom and evaluate its effectiveness with real end-users.
A distributed IoT system for modelling dynamics in smart environments

This paper studies the possibilities of fusing the concepts of smart environments and continuous and discrete systems simulation into a single system where identification of parameters, dynamics modelling and the choice of adequate control actions can be performed in real-time. The goal is to develop a model of a distributed IoT system capable of intelligent and dynamic adjusting of structure and system parameters with respect to the set goals. This goal is achieved in two phases. In the first phase, the modelling of the nodes in the smart environment is performed through defining interconnected IoT devices that can gather data via sensors, perform control actions via actuators, and perform basic computing operations. In the second phase, hierarchical modelling of smart environments is performed by developing complex mathematical models that use collected data to calculate performance indicators, perform simulations, initiate control actions and support decision making. Both phases are supported by specific software components, some running in the cloud and some distributed on the edge devices. The developed approach is applied for modelling the system for traffic noise monitoring in a smart city.
Production of epipelic algae before and during lake fertilization in a subarctic lake

Epipelic production was studied over three years in Lake Gunillajaure, a small, subarctic lake in northern Sweden. In 1977 the lake was in its natural state and in 1978 and 1979 it was continuously fertilized with phosphorus and nitrogen. The fertilizations in 1978 and 1979 did not increase the annual epipelic production measured with 14C technique. Also the seasonal production pattern was the same during the three years. The variation in production with depth was similar all three years with mean values of 85 mg C m-2 d-1 at 2 m and 1 mg C m-2 d-1 at 11 m. Light penetration and water temperature were found to be the most important environmental factors affecting epipelic production. A long turnover time, between I months and 14 yr at different depths, of the probably well adapted epipelic community is suggested to be an explanation to the absence of a response to the fertilization.
GeoRSA: Geospatial Recommendation System using Sentimental Analysis

Because of the large amount of information available about locations points of interest on the internet and social media platforms, selecting and visiting places has become difficult for tourist. The opinion, reviews, or feedback of the users about location is available on social media. The feedback of users shared in the form of textual data needs to be analyzed using sentimental analysis. Sentiment analysis can categorize the textual data into different manners like negative, positive, neutral, etc. The sentiment analysis would be applied to both user comments or reviews about the location and location features or characteristics. Then after the sentiment analysis is over, the recommendation or suggestion of the user will take place. This paper discusses the sentiment analysis and different models used or recommendation models in geospatial recommendation systems. Here Bi-LSTM provides nearly 84.7 % accuracy with minimum mean square error (MSE) value as compared to other methods whereas Random Forest methods obtains 85% accuracy with an MSE value of 0.87.
ECOPHYSIOLOGY OF SEED GERMINATION IN ENDEMIC LABIATES OF CRETE

ABSTRACT Ecophysiological aspects of seed germination were investigated in four aromatic labiate plants of Crete Origanum dictamnus (dittany), Sideritis syriaca L. ssp. syriaca (Cretan mountain tea), Salvia pomifera L. ssp. pomifera (gall-bearing sage), and Salvia fruticosa (three-lobed sage). Experiments were performed both at constant temperatures and darkness as well as under temperature and light conditions simulating those prevailing in nature during the main germination periods (i.e., start and middle of the rainy season, November and February-March, respectively). In three out of the four species, no particular dormancy was revealed and germination occurred rather promptly, although in a rather narrow range of cool temperatures and at a relatively slow rate; both characteristics determine and/or support an early, autumn seed germination and seedling establishment. In the fourth plant, Sideritis syriaca, germination was manifested at relatively warm temperatures and at a considerably faster rate, in.
TDD4Fog: A Test-Driven Software Development Platform for Fog Computing Systems

As an ideal infrastructure for smart services, Fog Computing is becoming the next wave of IT investment harnessing the successful models of Cloud Computing and latest technologies such as 5G and Internet of Things (IoT). However, the development of Fog Computing systems is a big challenge due to its complex, heterogeneous and distributed nature. Currently, there are a few SDKs released by some public Cloud service providers to support the development of Fog services in a top-down fashion as the key motive is to leverage their business Cloud services. However, Fog Computing systems are usually designed in a bottom-up fashion as the major functionalities are centred around the Edge Nodes and the End Devices. Meanwhile, significant efforts are required to verify the conformance of software behaviours as the collaboration between the End Devices, Edge Nodes and Cloud Servers is vital to the success of a Fog Computing System. Therefore, a holistically designed software development platform is urgently required. In this paper, we propose TDD4Fog, a test-driven software development platform for Fog Computing systems. Following the Test-Driven Development (TDD) methodology and a bottom-up design fashion, TDD4Fog supports the microservice architecture and provides the Test-Driven utilities such as metamorphic testing, mutation testing and random testing for the whole software development lifecycle of Fog Computing systems. To demonstrate the feasibility of TDD4Fog, we have presented some preliminary results on the key components of TDD4Fog and discussed some important future research directions.
Bankruptcy Prediction Using Survival Analysis Technique

Currently, there is an extensive set of bankruptcy prediction models, but almost all of them are classification based, i.e., they allow to estimate the posterior probability that a particular firm will fail, given its financial characteristics. The expected time to failure is not considered explicitly. On the other hand, there is a survival analysis that deals with the time of the occurrence of the event of interest (while this event may not occur during observation). However, despite its popularity in the medical and technical sciences, survival analysis is relatively rarely used in predicting financial failure. Even when it is applied, most authors use the simplest form of a model. The goal of our work is to evaluate the applicability of survival analysis to bankruptcy prediction. We compare a few state-of-art statistical and machine learning models using a real dataset. Our findings confirm that survival analysis allows (1) to extract from given data valuable information regarding the dynamics of risks and (2) to estimate the impact of features.
An Improved Variable Neighbourhood Search Algorithm for Selective Dial-a-Ride Problems

Various alternative means of transportation are emerging aiming to combat traffic congestion. Among these solutions, car sharing and pooling schemes are currently gaining in popularity. Such schemes require computationally tractable algorithms for the efficient allocation of resources. Towards such an aim, the Variable Neighbourhood Search has emerged as the leading algorithmic solution. Aiming to speed up its convergence, this paper introduces two novelties to this algorithm. Results based on a realistic simulation scenario in a densely populated area demonstrate the added accuracy obtained using these novelties in a time-sensitive application.
An Experimental Validation of the Naïve Approach to Angle of Arrival Estimation for Green WSNs

In this work, the so-called naïve approach to Angle of Arrival (AoA) estimation is discussed and experimentally characterized. Mathematical evaluations are proposed for Uniform Linear Arrays antennas allowing to evaluate advantages and disadvantages coming from this method. Experiments validate the approach and give a practical proof of its limits. The great advantage of the proposed method is that it requires only a trigonometric function inversion and simple computations like multiplications. This allows using specific and classical computational complexity reduction, like CORDIC, to solve the problem and giving the possibility to employ the method in full-hardware AoA estimation systems.
Deadline-aware Scheduling in Cloud-Fog-Edge Systems

Nowadays robots are one of the most important technologies and face recognition is crucial for human-robot interaction. Face recognition has direct benefits in the commercial and law enforcement fields and it enables robots to perform a wide variety of roles such as assistance, search and rescue, military and so on. In this paper we propose a Cloud-Edge architecture for robots that enables the use case of face recognition in deadline constrained environments. We formulate a mathematical model for a Data Capsule which represents structured units of data in a time series. We design the components on each layer of the architecture. We propose a deadline aware scheduler in the Fog that acts as a proxy for the processing platforms in the Cloud and we design two face recognition applications, one in the Edge for robots that is implemented with eigenfaces and one in the Cloud for the processing platforms with deep neural networks (DNN). We evaluate the performance of the face recognition applications by running a workload that consists of a well-known labelled image data set. We test the ability of the Fog scheduler to launch jobs on time when strict deadlines are in place and it runs a heavy workload of jobs.