Miracle-dz/UniFAR-FTUs huggingface dataset:
Communities Beyond Society: Divergence of Local Prehistories on the Bothnian Arc, Northern Europe

Abstract This article presents a comparison of material records of two nearby regions on the coast of the Bothnian Bay. The timeframe is 5300–2000 BCE. The focus is on regional differences, which indicate a schizmogenesis of communal identities. The study calls for a reorientation of research concerning Fennoscandian prehistory. More attention should be paid to localized prehistories. It is argued that when prehistoric society is used as a fundamental group category, especially in the context of forager communities, the modern concept of state society distorts the underlying framework. Focusing on the regional level by constructing local prehistoric narratives limits the anachronistic effect and allows the proliferation of local communal identities. Such local prehistories, when collated and compared, offer a pathway to understanding prehistoric stateless societies, which are misrepresented by simplistic material cultural zones and the inherent homogeny ingrained within the concept of society. In this paper, the analysis is focused on practices representing local traditions. Two divergent themes that arise from the local prehistoric narratives are the Late Mesolithic use of local stone materials and regional changes in Neolithic dwelling forms.
COVID-19 cases with delayed absorption of lung lesion

Abstract Objective Over 90% of the COVID-19 patients with computed tomographic (CT) manifestations showed radiological improvement on dissipating stage. Cases with refractory pulmonary infiltration were discussed in this study. Methods During hospitalization, chest CT scan and reverse transcriptase polymerase chain reaction (RT-PCR) test were repeatedly performed. While drawing parallels to RT-PCR, the impact of delayed absorption of lung lesions on length of hospital stay (LOS) and medical expense was investigated. Features for delayed absorption of lung lesions were identified using cox proportional hazard regression model. Results Cases with delayed absorption of lung lesions had a prolonged LOS (18.00 ± 4.90 vs 9.25 ± 4.20, p < 0.01) and increased medical expense (9124.55 ± 2421.31 vs 4923.88 ± 2218.56, p < 0.01). Time interval from admission to a negative RT-PCR (ATN) was also prolonged (13.29 ± 4.72 vs 9.25 ± 4.20, p = 0.03). The cox proportional hazard regression model indicated that imported cases bore high risk of delayed absorption of lung lesions (hazard ratio = 2.54, 95% confidence interval 1.05, 6.11, p = 0.04). Sensitivity analysis revealed similar pattern (hazard ratio = 6.64, 95% confidence interval 1.62, 27.18, p = 0.01). Conclusion Imported cases of COVID-19 were more likely to have refractory pulmonary infiltration, which subsequently prolongs LOS and increases medical expense.
Use of biofilter as pre-treatment of polluted river water for drinking water supply

Innovations in the biofiltration process can provide effective solutions to overcome crucial water pollution problems. The elimination of pollutants is a result of the combined effects of biological oxidation, adsorption and filtration processes. This research aims to evaluate the performance of quartz sand biofiltration for removing total suspended solids, turbidity, color, organic matter, and ammonium from polluted river water and develop an empirical model for designing quartz sand biofilters for the treatment of polluted river water. Experiments were conducted using two biofilter units filled with quartz sand as filter media. A set of experiments were performed to evaluate the effect of hydraulic retention time on biofilter performance in removing water contaminants. The kinetics of organic matter removal were also determined to describe the performance of the biofilter. The results show that biofiltration can significantly remove river water pollutants. Removal efficiency depends on the applied hydraulic retention time. At a hydraulic retention time of two hours, removal efficiencies of total organics, ammonium and total suspended solids were up to 78%, 82%, and 91%, respectively. A model for designing quartz sand biofiltration has been developed from the experimental data.
Transient dynamics of accelerating turbulent pipe flow

Abstract The transient dynamics of accelerating turbulent pipe flow has been examined using direct numerical simulation (DNS) data sets with a high spatiotemporal resolution, starting from low and moderate Reynolds numbers. The time-dependent evolution of the mean flow dynamics reveals that internal flows, during and after a rapid increase in the flow rate, experience four unambiguous transient stages: inertial, pre-transition, transition and core relaxation before they reach their final steady-state. The first stage is characterised by a rapid and substantial increment in the viscous forces within the viscous sublayer, together with the frozen behaviour of the existing turbulent eddies. The pre-transitional stage reveals a weak response of the turbulent inertia within the near-wall region, together with a rapid reduction in the viscous forces. At the third stage, termed transition, balanced growth in the magnitude of both the turbulent and the viscous forces within $y^{+0} \lesssim 50$ is observed. The final stage, referred to as core relaxation, shows a quasi-steady behaviour at $y^{+0} < 50$ and reveals the slow propagation of turbulence from the near-wall region into the wake region. A decomposition of the skin friction coefficient ($C_f$), using an FIK identity suitable for unsteady pipe flow, shows a progressive increment in the turbulent contribution during the core-relaxation period. Simultaneously, the unsteady contribution decreases proportionally, maintaining a plateau in $C_f$. The principal mechanism responsible for this slow regeneration in the wake is a temporal turbulence stratification at the inner region of the flow, together with a quiescent core, which maintains geometric coherence across extensive periods.
Sub-Hinze scale bubble production in turbulent bubble break-up

Abstract We study bubble break-up in homogeneous and isotropic turbulence by direct numerical simulations of the two-phase incompressible Navier–Stokes equations. We create the turbulence by forcing in physical space and introduce the bubble once a statistically stationary state is reached. We perform a large ensemble of simulations to investigate the effect of the Weber number (the ratio of turbulent and surface tension forces) on bubble break-up dynamics and statistics, including the child bubble size distribution, and discuss the numerical requirements to obtain results independent of grid size. We characterize the critical Weber number below which no break-up occurs and the associated Hinze scale $d_h$. At Weber number close to stable conditions (initial bubble sizes $d_0\approx d_h$), we observe binary and tertiary break-ups, leading to bubbles mostly between $0. 5d_h$ and $d_h$, a signature of a production process local in scale. For large Weber numbers ($d_0> 3d_h$), we observe the creation of a wide range of bubble radii, with numerous child bubbles between $0. 1d_h$ and $0. 3d_h$, an order of magnitude smaller than the parent bubble. The separation of scales between the parent and child bubble is a signature of a production process non-local in scale. The formation mechanism of these sub-Hinze scale bubbles relates to rapid large deformation and successive break-ups: the first break-up in a sequence leaves highly deformed bubbles which will break again, without recovering a spherical shape and creating an array of much smaller bubbles. We discuss the application of this scenario to the production of sub-Hinze bubbles under breaking waves.
Effect of heat transfer law on the performance of a generalized irreversible Carnot heat pump

The sole irreversibility considered in an endoreversible Carnot heat pump model is the loss of heat resistance between the heat pump and its surrounding heat reservoirs. This paper presents a generalized irreversible Carnot heat pump model by taking into account several additional internal irreversibilities of the heat pump, such as heat leakage, friction, turbulence and other undesirable irreversibility factors. This is done by means of a constant parameter and a constant coefficient, together with the loss of heat resistance. The relationship between the optimal heating load and the coefficient of performance (COP) is derived from a generalized heat transfer law Q = KΔ(T n ). The effect of heat leakage, internal irreversibility, and heat transfer law on the optimal performance of the generalized irreversible Carnot heat pump is investigated.
Organizational Learning and Innovation in the Tourism Industry as a Basis for Creating Value for Tourists

Abstract Organizational learning, innovation and creating value for tourists are the main issues in a successful tourism research. From that point of view, the aim of the study is to examine the relationship between the variables included in the research: organizational learning, innovation and creating value for tourists. The tested conceptual model didn’t fit well, so it was corrected based on the recommendations for a possible revision of the model. The analysis results indicated a significant impact of organizational learning on innovation and on creating value for tourists. Innovation, also, has a significant impact on creating value for tourists. Furthermore, the results indicated that innovation established the relationship between organizational learning and creating value for tourists. These results show the importance of organizational learning and innovation to improve creating value for tourists.
Caffeine Produces Neutral Effects on Extraverts’ and Introverts’ Performance of Fundamental Motor Skills

Abstract Extraverts are active and talkative, while introverts are quiet and calm. This difference has been attributed to the cortical activation level (arousal), which is low in extraverts and high in introverts. Thus, to reach an optimal level of arousal, extraverts seek stimulation and introverts avoid it. As caffeine increases arousal levels, our primary aim was to investigate the effect of caffeine on the performance of extraverted and introverted university students in the execution of stability, manipulative, and locomotor tasks. Considering the above, we evaluated side effects, such as restlessness and trembling of hands, we also analyzed the individual's perception regarding caffeine intake and the placebo. Forty two volunteers were classified as 21 extraverts and 21 introverts by the Eysenck Personality Questionnaire. Participants performed three tasks on two different days, having previously ingested caffeine and a placebo in counterbalanced order. A double-blind technique was employed. The dependent variable was the execution time to perform the tasks. The analyses of variance [2 (extraversion/introversion) x 2 (caffeine x placebo)] for each task did not show significant differences. Regarding the secondary aim, the chi-square test showed that introverts had a better perception of the substance they had ingested than did extraverts. Our findings indicate that in the execution of stability, manipulative, and locomotor tasks, either caffeine or the placebo produced the same effect, regardless of the participants' extraversion score. In addition, introverts were more sensitive to perceive which substance was ingested, caffeine or the placebo, than extraverts.
Metadiscourse to Foster Student Collective Responsibility for Deepening Inquiry

This study examined two complementary designs of metadiscourse to foster collective responsibility for sustained, progressive inquiry in two comparable Grade 5/6 classrooms that investigated astronomy. Class A’s metadiscourse focused on reviewing student questions to formulate deepening goals. Class B’s focused on co-monitoring disciplinary key concepts in readings that suggested possible areas for deeper discourse. Analyses of classroom videos and online discourse suggest the positive impact of such metadiscourse on sustained knowledge building, along with specific conditions required.
Learning analytics and governance of the digital learning process

E-learning platforms collect a large amount of data on online courses, which progressively evolve towards complex and increasingly heterogeneous educational models. It is then necessary to pass the training’s veriﬁcation as linked to the observable results (completion status and results of the test), in favor of a global representation of the phenomena that positively or negatively aﬀect the user experience. For this reason, we developed a model of analysis of the tracking data starting from the experiences of digital learning operators to create a tool capable of synthesizing all aspects of training. The developed Macro Index ( and sub-indexes) makes comparable (online, classroom, blended) courses through a uniﬁed analysis model. It can discriminate diﬀerent experiences and indicate the expected outcomes based on previous similar data, guiding the tutors in the diﬀerentiated intervention methods to support and facilitate learning.
Neural Approach for Named Entity Recognition

The work presents the results of bidirectional long short term memory (BiLSTM) neural network with conditional random fields (CRF) architecture for named entity recognition (NER) problem solving. NER is one of the natural language processing (NLP) tasks. The NER solution allows to recognize and identify specific entities that are relevant for searching in particular data domain. The generalized NER algorithm and neural approach for NER with BiLSTM-CRF model are presented. The use of CRF is responsible for prediction the appearance of searched named entities and improves the recognition quality indicators. The result of the neural network processing is input text information with recognized and designated named entities. It is proposed to use weakly structured resume text information to conduct experiments with BiLSTM-CRF model for named entities recognition. Ten types of named entities are chosen for neural network processing, such as: person, date, location, organization, etc. Own created corpus of resume documents marked manually was used as a data set for BiLSTM-CRF neural model training, validation and testing. Analysis of the adequacy of the proposed approach was carried out using precision, recall and balanced measure F1 metrics. The average recognition values on the testing set were: precision 79,06%, recall 71,51% and F1 75,09%. The best recognition scores were obtained for named entity “date”: precision 92,12%, recall 81,60%, F1 86,54%. The developed neural model and software have practical value for solving problem of resume summarizing and ranking candidates for work as they can be used to form an array of incoming data.
Models of Distributed Systems Testing Based on Energy Consumption in Behavior

Deviations from the standard power consumption often indicate malfunctions of technical systems. Continuous and periodic, instant and cumulative special analysis of energy consumption in the behavior of distributed information systems (DIS), compared with the features of the procedures they execute, can be an essential part of the means of online and offline testing in operability of DIS. This paper presents a model of behavioral testing and recognition of DIS, including a formal analysis of energy consumption. The model is based on behavioral experiments of an automata class and Petri nets, representing a reference DIS with added characteristics of energy consumption. The model has the features of behavioral individual and group energy characteristics, cross-sections-states, counters, spatial and temporal structures of energy consumption. The individuality of the characteristics presupposes the possibility of identifying the states of the reference model by its energy consumption, section-states are the subsets of its attainable states/positions identified in the reference model with characteristic (identifying) energy costs, counters are moving chips, that register energy consumption, as they move, and finally, energy consumption structures supposed and achievable topological structures of the movement of counters in the graph of the behavioral model.The model is designed to determine the energy conditions, used in the construction of methods of online and offline behavioral testing of the DIS performance, extended by the analysis of energy consumption.
Quotations, Coreference Resolution, andSentiment Annotations in Croatian NewsArticles: An Exploratory Study

This paper presents a corpus annotated for the task of direct-speech extraction in Croatian. The paper focuses on the annotation of the quotation, co-reference resolution, and sentiment annotation in SETimes news corpus in Croatian and on the analysis of its language-specific differences compared to English. From this, a list of the phenomena that require special attention when performing these annotations is derived. The generated corpus with quotation features annotations can be used for multiple tasks in the field of Natural Language Processing.
Insecticidal Effects from Ethanol Extracts of Root Peel, Stem Peel and Fruit Peel of Pomegranate (Punica granatum L.) on House Dust Mite

The purpose of this study was to prove insecticidal effect from ethanol extracts of root peel, stem peel and fruit peel of pomegranate(Punica granatum L.) on house dust mite. The insecticidal effects of each extracts on house dust mit were applied by direct contact method at different concentrations(1.0, 0.5, 0.25, 0.125, 0.0625㎎/40㎕) and exposure time of 24hours. At the concentration of extract 1㎎/40㎕, a complete mortality of 100% was observed from the root peel, stem peel, and fruit peel of pomegranate. At the concentration of extract 0.5㎎/40㎕, the high mortality of 88.00%, 100% and 93.10% was observed from the root peel, stem peel and fruit peel of pomegranate, respectively. At the concentration of extract 0.25㎎/40㎕, the highest mortality of 89.50% was observed from the root peel, followed by 83.25% in stem peel and 57.00% in fruit peel. At the concentration of extract 0.125㎎/40㎕, 68.30%, 55.00% and 69.57% of mortality were observed from root peel, stem peel and fruit peel of pomegranate, respectively. By the results, the ethanol extracts of root peel, stem peel and fruit peel of pomegranate were shown to present a highly powerful insecticidal effect on house dust mite. GC/MS analysis was used to identify pseudopelletierine, which is known as insecticidal active component in pomegranate. As the result, pseudopelletierine was found in the root peel. In order to verify the insecticidal effect of pomegranate, the insecticidal effects of the authentic pseudopelletierine and 11 terpene compounds contained in root peel, stem peel and fruit peel of pomegranate were measured. From the results, pseudopelletierin presented a higher insecticidal effect on house dust mite at the concentration higher than 0.5㎎/40㎕. The camphor showed a great mortality over 90% at all concentrations and delta-3Carene also showed a high mortality, which was higher than 70% at all concentrations. The beta-Pinene, trans-Caryophyllene, gammaTerpinene showed the mortality of 80% or higher at the concentration ranged from 1㎎/40㎕ to 0.125㎎/40㎕, and camphene showed the mortality of 60% or higher at all concentrations.
A computational framework for the analysis of the Uruguayan dictatorship archives

Between 1973 and 1985, a civic-military dictatorship ruled in Uruguay. Systematic violations of human rights marked this period. Project Cruzar.uy aims to develop tools and methodologies to analyze historical documents from that period. We present the advances in this ongoing project. We describe a set of tools to automatize the extraction and organization of information from the archives using computational tools including image processing, machine learning, natural language processing, information extraction and integration.
Image Formation Mechanism of Ultra Fine Grain Emulsion BB640 Processed with High Contrast Developer D8

Photographic emulsions based on silver halide grains and their developers are characterized by the H-D curve, relating density with exposure. The H-D curve depends on a number of parameters from both of the emulsion and of the developing process, and always has a typical shape. In this study we present a H-D curve obtained with an ultra fine emulsion, Colourholographics BB640, developed with a high contrast developer, Kodak D8, whose shape differs considerably from that of the standard curve. This process leads to very high values of density as well as to an apparent holographic overmodulation. Based on these results an image formation mechanism is proposed.
Deep Learning Based Recurrent Neural Networks to Enhance the Performance of Wind Energy Forecasting: A Review

In the modern era, deep learning is a powerful technique in the field of wind energy forecasting. The deep neural network effectively handles the seasonal variation and uncertainty characteristics of wind speed by proper structural design, objective function optimization, and feature learning. The present paper focuses on the critical analysis of wind energy forecasting using deep learning based Recurrent neural networks (RNN) models. It explores RNN and its variants, such as simple RNN, Long Short Term Memory (LSTM), Gated Recurrent Unit (GRU), and Bidirectional RNN models. The recurrent neural network processes the input time series data sequentially and captures well the temporal dependencies exist in the successive input data. This review investigates the RNN models of wind energy forecasting, the data sources utilized, and the performance achieved in terms of the error measures. The overall review shows that the deep learning based RNN improves the performance of wind energy forecasting compared to the conventional techniques.
OCR Workflow: Facing Printed Texts of Ancient, Medieval and Modern Greek Literature

Book digitization is being increasingly enhanced, as it facilitates not only the dissemination and preservation of cultural heritage but also the analysis of large amounts of textual data as well as the extraction and discovery of knowledge in a faster, dynamic and interactive way. Quite often, OCR, as the core technology of book digitization, has to address major dif-ﬁculties related to the condition of the primary source or to scanning issues. The main contribution of this paper is to provide an extensive study on Tesseract, an open-source OCR system, including image pre-processing and text post-processing methods, that overcome a variety of image handling problems. Additionally, a re-trained Greek language model, based on individual fonts training plus pairs of image-text training, is being provided. Finally, this paper proposes a pipeline of methods, including text line detection, that result in enhanced accuracy for Greek Literature documents, even when they consist of distorted pages, due to scanning issues or damaged physical material.
VH-TDMA: A description and verification of an instrument to measure aerosol particle hygroscopicity and volatility

ABSTRACT The aim of this study was to provide a description and characterize the performance of a volatility hygroscopicity tandem differential mobility analyzer (VH-TDMA) by calibration measurements. In our investigations, we used the two calibration standards most often used by the TDMA community: ammonium sulfate ((NH4)2SO4) and sodium chloride (NaCl) particles. The hygroscopic growth factors, volatility factors, and the hygroscopic growth factors of the nonvolatile particle core were measured in different relative humidity and thermal denuder temperature conditions. The measured hygroscopic growth and deliquescence relative humidities for ammonium sulfate and sodium chloride particles are in line with theory and results from previous measurements, as are the measurements for volatility and hygroscopicity of the particle core. We summarize the measurement campaigns, where the VH-TDMA has been deployed, and show the instrument is capable of measuring high quality data of atmospheric particle properties in various environments, in the laboratory and in the field. Copyright © 2017 American Association for Aerosol Research