Miracle-dz/UniFAR-FTUs huggingface dataset:
Digital Platforms as a Key Tool for the Transformation of Regional Economy

Purpose: The presented article is aimed at exploring the possibilities of utilizing digital platforms as a key tool for transforming the regional economy, based on the classification of types and revealing the advantages of digital platforms. Design/Methodology/Approach: In the current conditions of regional transformation and the integration of traditional production and marketing processes into the electronic market conditions, any economic agents including commercial enterprises, non-profit organizations and public authorities, should use the advantages of digital platforms in the process of satisfying the needs of customers and the interests of stakeholders. The methodological grounds of this study are based on a systematic approach and a classification method, which are used to substantiate the advantages and possible limitations of introducing digital platforms into the activities of economic and social actors of the regional economy. Findings: The following advantages of digital platforms as a tool for the development of the regional economy are revealed: consolidation in one place of the external resources of various business partners; the ability to seamlessly integrate new functional modules into an existing platform; economies of scale on the demand side; reduction in transaction costs; platform value growth with an increase in the number of users; increased returns as the network effect of the digital ecosystem develops; opportunities for quick monetization and capitalization of the service; the presence of control of all business processes of the digital ecosystem. It is proposed to highlight innovative, informational, communication, commercial, professional digital platforms and digital government services platforms. Practical implications: In practice, the results of the study will help to determine the vectors for the further implementation of various types of digital platforms in the economy of the region, taking into account the interests of owners, participants, users and stakeholders of regional electronic platforms. Originality/value: The introduction of digital platforms into the economic system will be the driver of the development of the regional economy due to faster and better satisfaction of the needs of owners, participants, users and stakeholders of electronic platforms.
Long-term Persuasive Effects in Narrative Communication Research: A Meta-Analysis

This meta-analysis builds on the broad and diverse research on the persuasive effects of narrative communication. Researchers have found that narratives are a particularly effective type of message that often has greater persuasive effects than non-narratives immediately after exposure. The present study meta-analyzes whether this greater persuasive power persists over time. Results are based on k1 = 14 studies with k2 = 51 effect sizes for immediate measurement (N = 2,834) and k2 = 66 effect sizes for delayed measurement (N = 2,459). They show that a single narrative message has a stronger persuasive impact than a non-narrative message on attitudes and intentions at immediate as well as on attitudes, intentions, and behaviors at delayed measurement. Both message types did not differently affect the participants’ beliefs. Meta-analytic structural equation modeling confirms transportation as a mediator of immediate persuasive effects.
A Novel Statistical Learning-Based Methodology for Measuring the Goodness of Energy Profiles of Applications Executing on Multicore Computing Platforms

Accurate energy profiles are essential to the optimization of parallel applications for energy through workload distribution. Since there are many model-based methods available for efficient construction of energy profiles, we need an approach to measure the goodness of the profiles compared with the ground-truth profile, which is usually built by a time-consuming but reliable method. Correlation coefficient and relative error are two such popular statistical approaches, but they assume that profiles be linear or at least very smooth functions of workload size. This assumption does not hold true in the multicore era. Due to the complex shapes of energy profiles of applications on modern multicore platforms, the statistical methods can often rank inaccurate energy profiles higher than more accurate ones and employing such profiles in the energy optimization loop of an application leads to significant energy losses (up to 54% in our case). In this work, we present the first method specifically designed for goodness measurement of energy profiles. First, it analyses the underlying energy consumption trend of each energy profile and removes the profiles that exhibit a trend different from that of the ground truth. Then, it ranks the remaining energy profiles using the Euclidean distances as a metric. We demonstrate that the proposed method is more accurate than the statistical approaches and can save a significant amount of energy.
Detecting Students’ Flow States and Their Construct Through Electroencephalogram: Reflective Flow Experiences, Balance of Challenge and Skill, and Sense of Control

Previous studies measured flow states using students’ self-reported experiences, resulting in issues regarding nonobjective and nonreal-time data. Thus, this study used an electroencephalogram (EEG) to measure the EEG-detected real-time flow states (EEG-Fs) of 30 students from the 4th and 5th grades. Their EEG measurements, self-reported reflective flow experiences (SR-Fs), grade levels (GLs), balance of challenge and skill (BCS), and sense of control, represented by their overall test performance (OA-tp) and momentary test performance (MOM-tp), were analyzed to establish their EEG-F’s construct. Based on the results of a chi-square test, the EEG-F correlates significantly with SR-F, BCS, OA-tp, and MOM-tp. A J48 decision tree analysis and logistic regression further revealed that in-flow experiences (in-EEG-F) were detected when students had high SR-Fs, where the BCS contributed to flow states. In particular, students with a low-challenge/high-skill BCS demonstrated an in-EEG-F state upon having a high OA-tp. For high-challenge/high-skill, the in-EEG-F state was determined through their MOM-tp. Through the EEG and flow state construct, this study revealed a whole-part association between students’ momentary and overall reflective flow experiences and identified viable paths for inducing students’ EEG-Fs, which can contribute to future e-learning development when integrated with a brain-computer interface for e-learning or e-evaluation systems.
A Boundary in the H-R Diagram between Late-Type Stars with and without High Temperature Outer Atmospheres

The Linsky-Haisch hypothesis (1979) that a boundary exists in the H-R diagram separating yellow giants from red giants and supergiants is tested. IUE 1150-2000 A low-resolution spectra of 10 stars chosen to constitute a reverse bias sample are presented and discussed. Despite the bias, weak C IV emission indicative of high-temperature plasma was observed in four of the six stars chosen to be probable red stars, while no C IV emission was detected in the four stars chosen to be yellows. In a second test using the entire sample of 39 stars, nearly all of the yellow giants and supergiants were found to have an emission feature at 1549 A, which is attributed to C IV. The large magnitude dispersion could be attributed to temporal or spatial variability, differing magnetic field strengths and geometries, or age-related effects during post-main-sequence evolution. It is concluded that the Linsky-Haisch transition region boundary is a real phenomenon.
Comparative study of classification methods for diagnosis of Skin Cancer

With advent of technology, there is a side effect that affect human life and induces many diseases. People across globe are widely using the recent technology for the ease in the lifestyle. But this is inducing huge amount of pollution and unhealthy food and thereby use of medication (many a times self medication) results in allergies that may lead to skin cancer. If the skin cancer is treated in primary stages mortality rate across globe due to skin cancer can be reduced. This paper is an attempt to summarize the reported work and put forth a pathway to researchers to generate interest in area of medical imaging, recognizing the cancerous cells from the captured image and then using an expert system with a set of classifiers to predict whether the image has cancerous cells or not. Imaging, image segmentation and then application of classifier are the key steps in diagnosis of skin
Sperm whale presence observed using passive acoustic monitoring from gliders of opportunity

Habitat use of the endangered Mediterranean sperm whale subpopulation remains poorly understood, especially in winter. The sustained presence of oceanographic autonomous underwater vehicles in the area presents an opportunity to improve observation effort, enabling collection of valuable sperm whale distribution data, which may be crucial to their conservation. Passive acoustic monitoring loggers were deployed on vertically-profiling oceanographic gliders surveying the north-western Mediterranean Sea during winter 2012-2013 and June 2014. Sperm whale echolocation usual click trains, characteristic of foraging activity, were detected and classified in the recordings, providing information about presence of sperm whale along the glider tracks. Widespread presence of sperm whales in the north-western Mediterranean Sea was confirmed. Winter observations suggest different foraging strategies between the Ligurian Sea, where mobile and scattered individuals forage at all times of day, and the Gulf of Lion, where larger aggregations target intense oceanographic features in the open ocean such as fronts and mixing events, with reduced acoustic presence at dawn. This study demonstrates the ability to successfully observe sperm whale behaviour from passive acoustic monitoring gliders. We identify possible mission design improvements that would lead to benefit from passive acoustic monitoring glider surveys to significantly improve sperm whale population monitoring and habitat use.
The Link Model of ERP-Usage, Absorptive-Capacity, and Task-Technology-Fit, to Task-Innovation in ERP User

An ERP system contains millions of information that is useful for its users. The use of an ERP system in an organization is usually mandatory, and users are required to use and understand the workings of an ERP system in completing their routine tasks. As soon as employees complete training classes and ERP systems are used routinely and intensely in their daily work (Go-Live), employees are required to obtain new knowledge or skills from the ERP system that has just been installed and complete their work differently by innovate their way in doing tasks. However, there have been many cases of the ERP system that led to the failure of users to improve their work habits. The purpose of this research is to develop a model that can explain the influence of absorptive knowledge capacity, the ‘fit’ of the technology with the work, and the decision-making process to the task innovation of ERP users. The model was built by adapting the model Task-Technology Fit and Absorptive Capacity of previous studies related to MIS. The collection of the data by a survey conducted on ten organizations ERP system users in Java and Sulawesi. Four hundred questionnaires distributed to 10 organizations ERP users engaged in mining, food processing, banking, public services, telecommunications, and government offices. Three hundred forty-five questionnaires were returned, with 342 questionnaires able to be processed. Data were analyzed using multivariate statistical techniques Structural Equation Modelling (SEM)-PLS. This research has developed a potential model in the role of studying the effects of ERP Usage, Absorptive Capacity, and Task-Technology Fit to Task Innovation in the context of enterprise applications at the level of individual analysis. Reasons and contributing factors were described in the final section of this study.
A connectionist approach to analogy. On the modal meaning of periphrastic do in Early Modern English

Abstract This paper innovatively charts the analogical influence of the modal auxiliaries on the regulation of periphrastic do in Early Modern English by means of Convolutional Neural Networks (CNNs), a flavour of connectionist models known for their applications in computer vision. CNNs can be harnessed to model the choice between competitors in a linguistic alternation by extracting not only the contexts a construction occurs in, but also the contexts it could have occurred in, but did not. Bearing on the idea that two forms are perceived as similar if they occur in similar contexts, the models provide us with pointers towards potential loci of analogical attraction that would be hard to retrieve otherwise. Our analysis reveals clear functional overlap between do and all modals, indicating not only that analogical pressure was highly likely, but even that affirmative declarative do functioned as a modal auxiliary itself throughout the late 16th century.
What localizes beneath: A metric multisensor localization and mapping system for autonomous underground mining vehicles

Robustly and accurately localizing vehicles in underground mines is particularly challenging due to the unavailability of GPS, variable and often poor lighting conditions, visual aliasing in long tunnels, and airborne dust and water. In this paper, we present a novel, infrastructure‐less, multisensor localization method for robust autonomous operation within underground mines. The proposed method integrates with existing mine site commissioning and operation procedures and includes both an offline map‐building process and an online localization algorithm. The approach combines the strengths of visual‐based place recognition, LIDAR‐based localization, and odometry in a particle filter fusion process. We provide an extensive experimental validation using new large data sets acquired in two operational Australian underground hard‐rock mines (including a 600m‐deep multilevel mine with approximately 33 km of mapping data and 7 km of vehicle localization) by actual mining vehicles during production operations. We demonstrate a significant increase in localization accuracy over prior state‐of‐the‐art SLAM research systems and real‐time operation, with processing times in the order of 10 Hz. We present results showing a mean error of 0.68 m from the Queensland Mine data set and 1.32 m from the New South Wales Mine data set and at least 86% reduction in error compared with prior state of the art. We also analyze the impact of the particle filter parameters with respect to localization accuracy. Together this study represents a new approach to positioning systems for currently deployed autonomous vehicles within underground mine environments.
Printing and coating MXenes for electrochemical energy storage devices

Sina Abdolhosseinzadeh, Jakob Heier and Chuanfang (John) Zhang 1 Laboratory for Functional Polymers, Swiss Federal Laboratories for Materials Science and Technology (Empa), Überlandstrasse 129, Dübendorf CH-8600 Switzerland 2 Institute of Materials Science and Engineering, Ecole Polytechnique Fédérale de Lausanne (EPFL), Station 12, Lausanne CH-1015 Switzerland 3 Author to whom any correspondence should be addressed.
Use of trainable wavelet transform with adaptive threshold filtering for noise reduction of speech signals

A method of noise reduction based on an adaptive threshold filtering over trainable wavelet transform was proposed. The restrictions on the trainable transform filter parameters were provided by a set of quadratic regularization terms. As an analog of the “hard” and “soft” threshold functions we used their smooth infinitely differentiable versions. The parameters of trainable wavelet transform, and threshold values were estimated by backpropagation and gradient-based optimization algorithm with an adaptive momentum estimation. Results of the proposed method were compared with an approach based on fixed discrete wavelet transform and non-adaptive global and level-depended threshold algorithms on the model problem. We used signal-to-noise ratio between suppressed and clean signals to numerically estimate the efficiency of noise reduction. We showed that the best results were obtained when the proposed trainable method with Daubechies 4 wavelet filters fine-tuning and adaptive level-dependent thresholding were applied.
Heritage attractions, competitive pressures and adaptation: the case of the British seaside pier

ABSTRACT Heritage visitor attractions operate in a dynamic environment requiring them to respond to changing circumstances which they cannot control. This paper focuses on adaptation strategies adopted by one type of heritage resource: the seaside pier in the UK. As part of the heritage of mass tourism itself, piers were created in a specific context and, as that context has changed, they have needed to adapt to remain viable as attractions. A combination of observational field visits to all UK piers and in-depth interviews with selected pier owners was used to examine strategies of adaptation. Three pier categories were identified, characterised by different adaptation strategies and experiences offered to visitors. The Family Entertainment Pier has adopted a strategy of consolidation, offering a traditional product with some diversification to suit contemporary consumer tastes. The Twenty-first Century Innovator Pier has followed a strategy of differentiation, focussed on contemporary entertainments and catering. The Heritage Pier has differentiated by offering a nostalgic and uncommercialised experience of the traditional seaside holiday. These strategies enable individual piers to remain competitive as attractions but some have more robust business models and are in a stronger financial position to deal with ongoing and future challenges.
A tuned Holt-Winters white-box model for COVID-19 prediction

The year 2020 has become memorable the moment the novel COVID-19 spread massively around the world to become a pandemic. In this paper, we analyse and predict the future trend of the COVID-19 cases for the top ten countries with the highest number of confirmed cases to date and the top ten countries with the highest growth percentage within the last month. Since many recent works have proposed that the COVID-19 pattern follows an exponential distribution, we use a tuned approach to the Holt-Winters' additive method as a white-box model. Based on the analysis, we found that most of the countries are still presenting an increasing trend of confirmed cases in the near future. Apart from vaccine and drug development, measures such as vigilance, strategic governmental actions, public awareness, and social distancing are unarguably continuously needed to handle the spreading of COVID-19 and avoid or curb the next wave of the outbreak.
Effects of Growth Substrate and Container Size on Cutting Production from Mojave Sage Stock Plants

Stock plant productivity is an important concern for growers of mojave sage (Salvia pachyphylla) because this species produces more woody growth as the plant ages. The objective of the study was to determine the best growth substrate and container size combination to maximize stock plant productivity. A secondary objective was to determine whether the stock plant treatments influenced the rooting of vegetative cuttings. Three different container sizes (3, 12, and 15.5 qt) and four soilless substrates composed primarily of bark, peat, and perlite (substrate 1); bark, peat, and vermiculite (substrate 2); bark, peat, and coarse perlite (substrate 3); and peat (substrate 4) were used. The stock plant experiment was conducted using 12 treatment combinations, and a subset of those stock plants was selected randomly for the rooting study that immediately followed the stock plant experiment. Stock plants responded to substrate treatments differently. The most successful stock plants, which produced more cuttings per plant and per square foot, as well as larger cuttings, were those grown in substrate 3. Regardless of substrate, the highest number of cuttings per square foot was obtained from stock plants grown in 3-qt containers, indicating that the smaller containers allow for the most efficient use of space when growing mojave sage stock plants for 4 to 6 months. The rooting of vegetative cuttings was successful (88% to 100% of cuttings rooted after 4 weeks under mist) for all treatment combinations.
Inference of population effect and progeny selection via a multi-trait index in soybean breeding

The selection of superior genotypes of soybean entails a simultaneous evaluation of a number of favorable traits that provide a comparatively superior yield. Disregarding the population effect in the statistical model may compromise the estimate of variance components and the prediction of genetic values. The present study was undertaken to investigate the importance of including population effect in the statistical model and to determine the effectiveness of the index based on factor analysis and ideotype design via best linear unbiased prediction (FAI-BLUP) in the selection of erect, early, and highyielding soybean progenies. To attain these objectives, 204 soybean progenies originating from three populations were examined for various traits of agronomic interest. The inclusion of the population effect in the statistical model was relevant in the genetic evaluation of soybean progenies. To quantify the effectiveness of the FAI-BLUP index, genetic gains were predicted and compared with those obtained by the Smith-Hazel and Additive Genetic indices. The FAI-BLUP index was effective in the selection of progenies with balanced, desirable genetic gains for all traits simultaneously. Therefore, the FAI-BLUP index is an adequate tool for the simultaneous selection of important traits in soybean breeding.
COVID-19 mortality and long-term care: a UK comparison

Authors David Bell (University of Stirling), Adelina Comas-Herrera (Care Policy and Evaluation Centre, London School of Economics and Political Science), David Henderson (Edinburgh Napier University, Scottish Centre for Administrative Data Research), Siôn Jones (LE Wales and London Economics) Elizabeth Lemmon (University of Edinburgh, Edinburgh Health Economics), Mirko Moro (University of Stirling), Siobhán Murphy (Centre for Public Health, Queens University Belfast), Dermot O’Reilly (Centre for Public Health, Queens University Belfast), Pietro Patrignani (LE Wales and London Economics)
Speeding Up COVID-19 Detection Using Shaker-Mill Homogenization and a Direct-to-PCR Workflow

Accurate and timely testing has become an essential measure in combatting the COVID-19 global pandemic. Currently, polymerase chain reaction (PCR) based assays are the most relied on methods for SARS-CoV-2 detection. This traditional workflow involves a viral RNA extraction from the viral transport media storing nasopharyngeal swabs collected from patients, followed by PCR based detection. While accurate, this methodology is time consuming and resource heavy, causing for delays in receiving results or limited access to testing. Herein, we demonstrate a validated method for SARS-CoV-2 detection from viral transport media using a two-step, direct-to-PCR workflow revolving around shaker-mill homogenization. This method completely bypasses the extraction steps of the traditional workflow, replacing it with 30 seconds of mechanical disruption sufficient to allow for COVID-19 detection with a 96.43% sensitivity and 100% specificity when compared to traditional extraction to PCR based methods.