Search results for: non-linear dynamics features

Authors: Jiajia Yao, GuanLin Wu, Fang liu, JunShuai Xue, JinCheng Zhang, Yue Hao

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Due to its outstanding material properties like high thermal conductivity and ultra-wide bandgap, Aluminum nitride (AlN) has the promising potential to provide high breakdown voltage and high output power among III-nitrides for various applications in electronics and optoelectronics. This work presents material growth and characterization of strained channel Aluminum nitride/Gallium nitride (AlN/GaN) heterostructures grown by plasma-assisted molecular beam epitaxy (PA-MBE) on AlN-on-sapphire templates. To improve the crystal quality and manifest the ability of the PA-MBE approach, a thick AlN buffer with a thickness of 180 nm is first grown on AlN template, which acts as a back-barrier to enhance the breakdown characteristic and isolates the leakage path existing in the interface between AlN epilayer and AlN template, as well as improve the heat dissipation. The grown AlN buffer features a root-mean-square roughness of 0.2 nm over a scanned area of 2×2 µm2 measured by atomic force microscopy (AFM), and exhibits full-width at half-maximum of 95 and 407 arcsec for the (002) and (102) plane the X-ray rocking curve, respectively, tested by high resolution x-ray diffraction (HR-XRD). With a thin and strained GaN channel, the electron mobility of 294 cm2 /Vs. with a carrier concentration of 2.82×1013 cm-2 at room temperature is achieved in AlN/GaN double-channel heterostructures, and the depletion capacitance is as low as 14 pF resolved by the capacitance-voltage, which indicates the promising opportunities for future applications in next-generation high temperature, high-frequency and high-power electronics with a further increased electron mobility by optimization of heterointerface quality.

Keywords: AlN/GaN, HEMT, MBE, homoepitaxy

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Authors: Frederic Heymes, Michael Albrecht Birk, Roland Eyssette

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Boiling Liquid Expanding Vapor Explosion (BLEVE) has been a well know industrial accident for over 6 decades now, and yet it is still poorly predicted and avoided. BLEVE is created when a vessel containing a pressure liquefied gas (PLG) is engulfed in a fire until the tank rupture. At this time, the pressure drops suddenly, leading the liquid to be in a superheated state. The vapor expansion and the violent boiling of the liquid produce several shock waves. This works aimed at understanding the contribution of vapor ad liquid phases in the overpressure generation in the near field. An experimental work was undertaken at a small scale to reproduce realistic BLEVE explosions. Key parameters were controlled through the experiments, such as failure pressure, fluid mass in the vessel, and weakened length of the vessel. Thirty-four propane BLEVEs were then performed to collect data on scenarios similar to common industrial cases. The aerial overpressure was recorded all around the vessel, and also the internal pressure changed during the explosion and ground loading under the vessel. Several high-speed cameras were used to see the vessel explosion and the blast creation by shadowgraph. Results highlight how the pressure field is anisotropic around the cylindrical vessel and highlights a strong dependency between vapor content and maximum overpressure from the lead shock. The time chronology of events reveals that the vapor phase is the main contributor to the aerial overpressure peak. A prediction model is built upon this assumption. Secondary flow patterns are observed after the lead. A theory on how the second shock observed in experiments forms is exposed thanks to an analogy with numerical simulation. The phase change dynamics are also discussed thanks to a window in the vessel. Ground loading measurements are finally presented and discussed to give insight into the order of magnitude of the force.

Keywords: phase change, superheated state, explosion, vapor expansion, blast, shock wave, pressure liquefied gas

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Authors: Dhanuja S. Patil, Sanjay B. Waykar

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Event detection is a standout amongst the most key parts for distinctive sorts of area applications of video data framework. Recently, it has picked up an extensive interest of experts and in scholastics from different zones. While detecting video event has been the subject of broad study efforts recently, impressively less existing methodology has considered multi-model data and issues related efficiency. Start of soccer matches different doubtful circumstances rise that can't be effectively judged by the referee committee. A framework that checks objectively image arrangements would prevent not right interpretations because of some errors, or high velocity of the events. Bayesian networks give a structure for dealing with this vulnerability using an essential graphical structure likewise the probability analytics. We propose an efficient structure for analysing and summarization of soccer videos utilizing object-based features. The proposed work utilizes the t-cherry junction tree, an exceptionally recent advancement in probabilistic graphical models, to create a compact representation and great approximation intractable model for client’s relationships in an interpersonal organization. There are various advantages in this approach firstly; the t-cherry gives best approximation by means of junction trees class. Secondly, to construct a t-cherry junction tree can be to a great extent parallelized; and at last inference can be performed utilizing distributed computation. Examination results demonstrates the effectiveness, adequacy, and the strength of the proposed work which is shown over a far reaching information set, comprising more soccer feature, caught at better places.

Keywords: summarization, detection, Bayesian network, t-cherry tree

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Authors: Cosmas Pandit Pagwiwoko, Ammar Khaled Abdelaziz Abdelsamia

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Flutter as a phenomenon of flow-induced and self-excited vibration has to be recognized considering its harmful effect on the structure especially in a stage of aircraft design. This phenomenon is also important for a wind energy harvester based on the fluttering surface due to its effective operational velocity range. This multi-physics occurrence can be presented by two governing equations in both fluid and structure simultaneously in respecting certain boundary conditions on the surface of the body. In this work, the equations are resolved separately by two distinct solvers, one-time step of each domain. The modelling and simulation of this flow-structure interaction in ANSYS show the effectiveness of this loosely coupled method in representing flutter phenomenon however the process is time-consuming for design purposes. Therefore, another technique using the same weak coupled aero-structure is proposed by using system dynamics approach. In this technique, the aerodynamic forces were calculated using singularity function for a range of frequencies and certain natural mode shapes are transformed into time domain by employing an approximation model of fraction rational function in Laplace variable. The representation of structure in a multi-degree-of-freedom coupled with a transfer function of aerodynamic forces can then be simulated in time domain on a block-diagram platform such as Simulink MATLAB. The dynamic response of flutter at certain velocity can be evaluated with another established flutter calculation in frequency domain k-method. In this method, a parameter of artificial structural damping is inserted in the equation of motion to assure the energy balance of flow and vibrating structure. The simulation in time domain is particularly interested as it enables to apply the structural non-linear factors accurately. Experimental tests on a fluttering airfoil in the wind tunnel are also conducted to validate the method.

Keywords: flutter, flow-induced vibration, flow-structure interaction, non-linear structure

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Authors: Jonas Hamann, Siavash Saki, Tobias Hagen

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Discretization of urban areas is a crucial aspect in many spatial analyses. The process of discretization of space into subspaces without overlaps and gaps is called tessellation. It helps understanding spatial space and provides a framework for analyzing geospatial data. Tessellation methods can be divided into two groups: regular tessellations and irregular tessellations. While regular tessellation methods, like squares-grids or hexagons-grids, are suitable for addressing pure geometry problems, they cannot take the unique characteristics of different subareas into account. However, irregular tessellation methods allow the border between the subareas to be defined more realistically based on urban features like a road network or Points of Interest (POI). Even though Python is one of the most used programming languages when it comes to spatial analysis, there is currently no library that combines different tessellation methods to enable users and researchers to compare different techniques. To close this gap, we are proposing TessPy, an open-source Python package, which combines all above-mentioned tessellation methods and makes them easily accessible to everyone. The core functions of TessPy represent the five different tessellation methods: squares, hexagons, adaptive squares, Voronoi polygons, and city blocks. By using regular methods, users can set the resolution of the tessellation which defines the finesse of the discretization and the desired number of tiles. Irregular tessellation methods allow users to define which spatial data to consider (e.g., amenity, building, office) and how fine the tessellation should be. The spatial data used is open-source and provided by OpenStreetMap. This data can be easily extracted and used for further analyses. Besides the methodology of the different techniques, the state-of-the-art, including examples and future work, will be discussed. All dependencies can be installed using conda or pip; however, the former is more recommended.

Keywords: geospatial data science, geospatial data analysis, tessellations, urban studies

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Authors: Bella Japoshvili, Zhanetta Shubitidze, Ani Bikashvili, Sophio Gabelashvili, Marina Gioshvili, Levan Mumladze

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Paravani and Saghamo Lakes are oligotrophic lentic systems located in Javakheti plateau (South Georgia) at 2073 m and 1996 m a.s.l. respectively. Javakheti plateau is known as a lakes region as there are located almost 60 small and medium size lakes. Paravani Lake is the biggest lake by its surface area in Georgia, 37 km 2. The Saghamo Lake is smaller and its surface area consists 4.58 km2. These two lakes are connected with Paravani River, because of this the main hydrobiological and ichthyological features are the same. More than 15-30 years were not studied macroinvertebrates of these lakes. Even the existing information is lack and very limited. The aim of our study was to identify main macroinvertebrate groups inhabiting both lakes and to compare obtaining results to existing information. Our investigation was carried out during 2014 and 2015, in 3 seasons of the year, in winter because of severe condition samples were not taken. Kick-net and Petersen grab were used for material collecting, 4 sites from Paravani Lake and 3–from Saghamo Lake were sampled. Collected invertebrates were fixed in ethanol and late taken to the laboratory, where organisms were identified to the lowest taxon possible, usually family. By our results identified 14 taxa for Paravani Lake and 12 taxa for Saghamo Lake. Our results differ from previous information; for Saghamo Lake previously 13 taxa and for Paravani Lake 12 taxa were described. The percentage of the groups also differ from existing information. Our investigation showed that in Paravani Lake most abundant are Apmhipoda, Hydrachnidae, and Hemiptera, in our samples the number of individuals for those 3 taxa was more than thousand, in each. For Saghamo Lake numerous taxon was Amphipoda-36.3%, following by Ephemeroptera-11.37%, Chironomidae-10.5% and Hydrachnidae-7.03% respectively. We also identified the dominant taxon for all studied seasons. Autumn is the period when the diversity of macroinvertebrates are higher in both lakes.

Keywords: Georgia, lakes, macroinvertebrates, monitoring

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Authors: Miranda I. Dosunmu, Orok E. Oyo-Ita, Inyang O. Oyo-Ita

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Polycyclic aromatic hydrocarbons (PAHs) are a class of priority listed organic pollutants due to their carcinogenicity, mutagenity, acute toxicity and persistency in the environment. The distribution and historical changes of PAHs contamination in recent sediment cores from the Imo River were investigated using gas chromatography coupled with mass spectrometer. The concentrations of total PAHs (TPAHs) ranging from 402.37 ng/g dry weight (dw) at the surface layer of the Estuary zone (ESC6; 0-5 cm) to 92,388.59 ng/g dw at the near surface layer of the Afam zone (ASC5; 5-10 cm) indicate that PAHs contamination was localized not only between sample sites but also within the same cores. Sediment-depth profiles for the four (Afam, Mangrove, Estuary and illegal Petroleum refinery) cores revealed irregular distribution patterns in the TPAH concentrations except the fact that these levels became maximized at the near surface layers (5-10 cm) corresponding to a geological time-frame of about 1996-2004. This time scale coincided with the period of intensive bunkering and oil pipeline vandalization by the Niger Delta militant groups. Also a general slight decline was found in the TPAHs levels from near the surface layers (5-10 cm) to the most recent top layers (0-5 cm) of the cores, attributable to the recent effort by the Nigerian government in clamping down the illegal activity of the economic saboteurs. Therefore, the recent amnesty period granted to the militant groups should be extended. Although mechanism of perylene formation still remains enigmatic, examination of its distributions down cores indicates natural biogenic, pyrogenic and petrogenic origins for the compound at different zones. Thus, the characteristic features of the Imo River environment provide a means of tracing diverse origins for perylene.

Keywords: perylene, historical trend, distribution, origin, Imo River

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Authors: Nasim Eslamirad, Mahdi Rasoulinezhad, Francesco De Luca, Sadok Ben Yahia, Kimmo Sakari Lylykangas, Francesco Pilla

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With the ongoing urbanization, cities face increasing environmental challenges impacting human well-being. To tackle these issues, data-driven approaches in urban analysis have gained prominence, leveraging urban data to promote sustainability. Integrating Machine Learning techniques enables researchers to analyze and predict complex environmental phenomena like Urban Heat Island occurrences in urban areas. This paper demonstrates the implementation of data-driven approach and interpretable Machine Learning algorithms with interpretability techniques to conduct comprehensive data analyses for sustainable urban design. The developed framework and algorithms are demonstrated for Tallinn, Estonia to develop sustainable urban strategies to mitigate urban heat waves. Geospatial data, preprocessed and labeled with UHI levels, are used to train various ML models, with Logistic Regression emerging as the best-performing model based on evaluation metrics to derive a mathematical equation representing the area with UHI or without UHI effects, providing insights into UHI occurrences based on buildings and urban features. The derived formula highlights the importance of building volume, height, area, and shape length to create an urban environment with UHI impact. The data-driven approach and derived equation inform mitigation strategies and sustainable urban development in Tallinn and offer valuable guidance for other locations with varying climates.

Keywords: data-driven approach, machine learning transparent models, interpretable machine learning models, urban heat island effect

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Authors: Małgorzata Osowiecka

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Negative emotions are rather seen as creativity inhibitor. On the other hand, it is worth noting that negative emotions may be good for our functioning. Negative emotions enhance cognitive resources and improve evaluative processes. Moreover maintaining a negative emotional state allow for cognitive reinterpretation of the emotional stimuli, what is good for our creativity, especially cognitive flexibility. Writing a diary or writing about difficult emotional experiences in general can be the way to not only improve psychical health, but also – enhance creative behaviors. Thanks to translating difficult emotions to the verbal level and giving them ‘a name’ or ‘a label’, we can get easier access to both emotional content of an experience and to the semantic content, without the need of speaking out loud. Expressive writing improves academic results and the efficiency of working memory. The classical method of writing about emotions consists in a long-term process of describing negative experiences. Present research demonstrate the efficiency of this process over a shorter period of time - one writing session, on school children sample. Participants performed writing task. Writing task had two different topics: emotions connected with their negative emotions (expressive writing) and content not connected with negative emotional state (writing about one’s typical day). Creativity was measured by Guilford’s Alternative Uses Task. Results have shown that writing about negative emotions results in the higher level of divergent thinking in all three parameters: fluency, flexibility and originality. After the writing task mood of expressive writing participants remained negative more than the mood of the controls. Taking an expressive action after a difficult emotional experience can support functioning, which can be observed in enhancement of divergent thinking. Writing about emotions connected with negative experience makes one more creative, than writing about something unrelated with difficult emotional moments. Research has shown that young people should not demonize negative emotions. Sometimes, properly applied, negative emotions can be the basis of creation. Preparation was supported by a The Young Scientist University grant titled ‘Dynamics of emotions in the creative process’ from The Polish Ministry of Science and Higher Education.

Keywords: creativity, divergent thinking, emotions, expressive writing

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Authors: Ndagi Salihu

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Corporate governance mechanisms are the control measures for ensuring that all the interests groups are equally represented and management are working towards wealth creation in the interest of all. Therefore, there are many empirical studies during the last three decades on corporate governance and firm performance. However, little is known about the effects of family ownership on the relationship between corporate governance and firm performance, especially in the developing economy like Nigeria. This limit our understanding of the unique governance dynamics of family ownership with regards firm performance. This study examined the impact of family ownership on the relationship between governance mechanisms and financial performance of publicly listed companies in Nigeria. The study adopted quantitative research methodology using correlational ex-post factor design and secondary data from annual reports and accounts of a sample of 23 listed companies for a period of 5 years (2014-2018). The explanatory variables are the board size, board composition, board financial expertise, and board audit committee attributes. Financial performance is proxy by Return on Assets (ROA) and Return on Equity (ROE). Multiple panel regression technique of data analysis was employed in the analysis, and the study found that family ownership has a significant positive effect on the relationships between corporate governance mechanisms and financial performance of publicly listed firms in Nigeria. This finding is the same for both the ROA and ROE. However, the findings indicate that board size, board financial expertise, and board audit committee attributes have a significant positive impact on the ROA and ROE of the sample firms after the moderation. Moreover, board composition has significant positive effect on financial performance of the sample listed firms in terms of ROA and ROE. The study concludes that the use of family ownership in the control of firms in Nigeria could improve performance by reducing the opportunistic actions managers as well as agency problems. The study recommends that publicly listed companies in Nigeria should allow significant family ownership of equities and participation in management.

Keywords: profitability, board characteristics, agency theory, stakeholders

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Authors: John Akinwumi Makinde

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Nigerian manufacturing industry, particularly the fast moving consumer producing firms play vital roles in Nigerian economy. This sector’s product acceptability is given very little attention along with social media advertising that communicate product information to audience across the globe need to be documented. Procter and Gamble Plc operate in Nigeria with appreciable number of fast moving consumer goods that service Nigerian economy. Social media advertising disposition of the company and product acceptability of the company deserve some elucidations. This study therefore examined the impact of social media advertising on product acceptability of FMCG in Nigerian manufacturing industry, using Procter and Gamble Plc as case study. The study employed the case study type of descriptive survey research design. The population consisted of 235 customers of G&P Plc, which were selected through random sampling method. A total of 235 copies of questionnaires titled 'Social Media Advertising and Product Acceptability (SMA-PA) Questionnaire' was administered and retrieved. Data generated were analysed using frequency distribution and regression analysis at 0.05 level. It was found that social media advertising positively and significantly motivated customers to buy product of P&G Plc (r =.147**, N= 235, p(.000) < .01). Findings also showed that social media advertising has significant impact on product acceptability of FCMG in P&G Plc (F(2,61)=22.250; R2=.629; P(.000) < .05). The study concluded that social media advertising is a determinant factor of consumer decision to accept fast moving consumer goods in Nigerian manufacturing industry. It is recommended that with the growing market of FMCG, there is need to educate the market with the product unique features, standard and quality on social media. Finally, Fast Moving Consumer Goods firms should deploy excellent marketing mix on social media.

Keywords: advertising, fast moving consumer goods, manufacturing industry, product acceptability, social media

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Authors: Rachel Y. Zhang, Christopher K. Anderson

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A critical aspect of revenue management is a firm’s ability to predict demand as a function of price. Historically hotels have used simple time series models (regression and/or pick-up based models) owing to the complexities of trying to build casual models of demands. Machine learning approaches are slowly attracting attention owing to their flexibility in modeling relationships. This study provides an overview of approaches to forecasting hospitality demand – focusing on the opportunities created by machine learning approaches, including K-Nearest-Neighbors, Support vector machine, Regression Tree, and Artificial Neural Network algorithms. The out-of-sample performances of above approaches to forecasting hotel demand are illustrated by using a proprietary sample of the market level (24 properties) transactional data for Las Vegas NV. Causal predictive models can be built and evaluated owing to the availability of market level (versus firm level) data. This research also compares and contrast model accuracy of firm-level models (i.e. predictive models for hotel A only using hotel A’s data) to models using market level data (prices, review scores, location, chain scale, etc… for all hotels within the market). The prospected models will be valuable for hotel revenue prediction given the basic characters of a hotel property or can be applied in performance evaluation for an existed hotel. The findings will unveil the features that play key roles in a hotel’s revenue performance, which would have considerable potential usefulness in both revenue prediction and evaluation.

Keywords: hotel revenue, k-nearest-neighbors, machine learning, neural network, prediction model, regression tree, support vector machine

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Authors: Mahtab Makaremi Masouleh, Günter Wozniak

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This work is part of a major research project concerning the design of a light temporary installable textile flood control structure. The motivation for this work is the great need of applying light structures for the protection of coastal areas from detrimental effects of rapid water runoff. The prime objective of the study is the numerical analysis of the interaction among free surface water flow and slender shaped pliable structures, playing a key role in safety performance of the intended system. First, the behavior of down scale membrane is examined under hydrostatic pressure by the Abaqus explicit solver, which is part of the finite element based commercially available SIMULIA software. Then the procedure to achieve a stable and convergent solution for strongly coupled media including fluids and structures is explained. A partitioned strategy is imposed to make both structures and fluids be discretized and solved with appropriate formulations and solvers. In this regard, finite element method is again selected to analyze the structural domain. Moreover, computational fluid dynamics algorithms are introduced for solutions in flow domains by means of a commercial package of Star CCM+. Likewise, SIMULIA co-simulation engine and an implicit coupling algorithm, which are available communication tools in commercial package of the Star CCM+, enable powerful transmission of data between two applied codes. This approach is discussed for two different cases and compared with available experimental records. In one case, the down scale membrane interacts with open channel flow, where the flow velocity increases with time. The second case illustrates, how the full scale flexible flood barrier behaves when a massive flotsam is accelerated towards it.

Keywords: finite element formulation, finite volume algorithm, fluid-structure interaction, light pliable structure, VOF multiphase model

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Authors: Wessam Fekry Ibrahim Moussa

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The northwest coast of Egypt embraces about 500 km of the Mediterranean coastline. The area covered extends from Alexandria on the East to the village of Sallum at Egypt's border with Libya in the west with an average depth of 20-70 km. When one looks at this long strip of land, one is struck by the fact that, from the archaeological point of view, one knows relatively little about this region during ancient times, its history, villages, inhabitants, and heritage. According to classical writers, in antiquity, the area seemed to be more populated and characterized by its rich buildings and inhabitants. They mentioned several Greco-Roman towns and harbors scattered along the coast nearly 2 thousand years ago. Strabo, for instance, in his book 17, confirmed the existence of about 12 several clusters along the coast, which varied between cities, villages, harbors, and small islands. Claudius Ptolemaeus also enumerated many marina sites as well as some small cities and villages. Unfortunately, nowadays, most of them have been lost either due to the extensive development of the north coast, Natural Disasters, or Erosion Factors. However, recent excavations carried out within the area revealed just a little of these settlements. The aim of this study is to reveal the secrets of the hidden heritage of those ancient sites and shed light on the role they played in the past, as some of them used to be stops on the trade route between Libya and Egypt (Strabo 17) or major centers for some of the international imports. The study will explore the archeological evidence using the analytical methodology to analyze each site and identify its features and significances in order to conclude the importance and role it once played during the past. Findings could be used by authorities and policymakers to utilize these heritage resources to improve cultural tourism within the area and enhance the tourist's experience.

Keywords: Greco Roman, heritage, ancient cities, north west coast

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Authors: Debarati Bhaumik, Diptish Dey

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Multilevel models, increasingly deployed in industries such as insurance, food production, and entertainment within functions such as marketing and supply chain management, need to be transparent and ethical. Applications usually result in binary classification within groups or hierarchies based on a set of input features. Using open-source datasets, we demonstrate that popular explainability methods, such as SHAP and LIME, consistently underperform inaccuracy when interpreting these models. They fail to predict the order of feature importance, the magnitudes, and occasionally even the nature of the feature contribution (negative versus positive contribution to the outcome). Besides accuracy, the computational intractability of SHAP for binomial classification is a cause of concern. For transparent and ethical applications of these hierarchical statistical models, sound audit frameworks need to be developed. In this paper, we propose an audit framework for technical assessment of multilevel regression models focusing on three aspects: (i) model assumptions & statistical properties, (ii) model transparency using different explainability methods, and (iii) discrimination assessment. To this end, we undertake a quantitative approach and compare intrinsic model methods with SHAP and LIME. The framework comprises a shortlist of KPIs, such as PoCE (Percentage of Correct Explanations) and MDG (Mean Discriminatory Gap) per feature, for each of these three aspects. A traffic light risk assessment method is furthermore coupled to these KPIs. The audit framework will assist regulatory bodies in performing conformity assessments of AI systems using multilevel binomial classification models at businesses. It will also benefit businesses deploying multilevel models to be future-proof and aligned with the European Commission’s proposed Regulation on Artificial Intelligence.

Keywords: audit, multilevel model, model transparency, model explainability, discrimination, ethics

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Authors: Isaac K. E. Ampomah, Seong-Bae Park, Sang-Jo Lee

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Over the past decade, there have been promising developments in Natural Language Processing (NLP) with several investigations of approaches focusing on Recognizing Textual Entailment (RTE). These models include models based on lexical similarities, models based on formal reasoning, and most recently deep neural models. In this paper, we present a sentence encoding model that exploits the sentence-to-sentence relation information for RTE. In terms of sentence modeling, Convolutional neural network (CNN) and recurrent neural networks (RNNs) adopt different approaches. RNNs are known to be well suited for sequence modeling, whilst CNN is suited for the extraction of n-gram features through the filters and can learn ranges of relations via the pooling mechanism. We combine the strength of RNN and CNN as stated above to present a unified model for the RTE task. Our model basically combines relation vectors computed from the phrasal representation of each sentence and final encoded sentence representations. Firstly, we pass each sentence through a convolutional layer to extract a sequence of higher-level phrase representation for each sentence from which the first relation vector is computed. Secondly, the phrasal representation of each sentence from the convolutional layer is fed into a Bidirectional Long Short Term Memory (Bi-LSTM) to obtain the final sentence representations from which a second relation vector is computed. The relations vectors are combined and then used in then used in the same fashion as attention mechanism over the Bi-LSTM outputs to yield the final sentence representations for the classification. Experiment on the Stanford Natural Language Inference (SNLI) corpus suggests that this is a promising technique for RTE.

Keywords: deep neural models, natural language inference, recognizing textual entailment (RTE), sentence-to-sentence relation

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Authors: Sangyeop Shin, D. C. M. Kulatunga, S. H. S. Dananjaya, Chamilani Nikapitiya, Jehee Lee, Mahanama De Zoysa

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Saprolegniasis is one of the most devastating fungal diseases in freshwater fish which is caused by species in the genus Saprolegnia including Saprolegnia parasitica. In this study, we isolated the strain of S. parasitica from diseased rainbow trout in Korea. Morphological and molecular based identification confirmed that isolated fungi belong to the member of S. parasitica, supported by its typical fungal features including cotton-like whitish mycelium, zoospores (primary and secondary) and phylogenetic analysis with internal transcribed spacer (ITS) region. Pathogenicity of isolated S. parasitica was developed in embryo, larvae, juvenile and adult zebrafish as a disease model. Up regulation of host genes encoding ZfTnf-α, Zfc-Rel, ZfIl-12, ZfLyz-c, Zfβ-def, and ZfHsp-70 was identified in zebrafish larvae after experimental challenge of S. parasitica showing the host immune responses against the S. parasitica. Survival of the juveniles upon fungal infection might be due to the increased immune protection in the host. Investigation of antifungal susceptibility of S. parasitica with natural lawsone (2-hydroxy-1,4-naphthoquinone) revealed the minimum inhibitory concentration (MIC) and percentage inhibition of radial growth (PIRG %) as 200 µg/mL and 31.8%, respectively. Lawsone was able to change the membrane permeability, and cause irreversible damage and disintegration to the cellular membranes of S. parasitica which might have effect on fungi growth inhibition. Moreover, the mycelium exposed to lawsone (MIC level) changed the transcriptional responses of S. parasitica genes. Overall results indicate that lawsone could be a potential and novel anti-S. parasitica agent for controlling S. parasitica infection.

Keywords: host-pathogen interactions, lawsone, rainbow trout, Saprolegnia parasitica, Saprolegniasis, zebrafish

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Authors: Jafar Akbari

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Quinolines are very important compounds partially because of their pharmacological properties which include wide applications in medicinal chemistry. notable among them are antimalarial drugs, anti-inflammatory agents, antiasthamatic, antibacterial, antihypertensive, and tyrosine kinase inhibiting agents. Despite quinoline usage in pharmaceutical and other industries, comparatively few methods for their preparation have been reported.The Friedlander annulation is one of the simplest and most straightforward methods for the synthesis of poly substituted quinolines. Although, modified methods employing lewis or br¢nsted acids have been reported for the synthesis of quinolines, the development of water stable acidic catalyst for quinoline synthesis is quite desirable. One of the most remarkable features of ionic liquids is that the yields can be optimized by changing the anions or the cations. Recently, sulfonic acid functionalized ionic liquids were used as solvent-catalyst for several organic reactions. We herein report the one pot domino approach for the synthesis of quinoline derivatives in Friedlander manner using TSIL as a catalyst. These ILs are miscible in water, and their homogeneous system is readily separated from the reaction product, combining advantages of both homogeneous and heterogeneous catalysis. In this reaction, the catalyst plays a dual role; it ensures an effective condensation and cyclization of 2-aminoaryl ketone with second carbonyl group and it also promotes the aromatization to the final product. Various types of quinolines from 2-aminoaryl ketones and β-ketoesters/ketones were prepared in 85-98% yields using the catalytic system of SO3-H functionalized ionic liquid/H2O. More importantly, the catalyst could be easily recycled for five times without loss of much activity.

Keywords: antimalarial drugs, green chemistry, ionic liquid, quinolines

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Authors: Karla Hrbackova, Anna Petr Safrankova, Jakub Hladik

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Machiavellianism as social phenomenon reveals certain methods of communication that man puts in contact with other people in both personal and working life (and work with clients). Especially at the helping-profession students is honesty, openness or low motivation for personal benefit important part of the profession. The aim of a study is to expose intercultural differences influence of Machiavellianism between Czech and Slovak university students and to determine whether these differences manifest themselves differently among students focusing on management and social profession (by helping degrees). The research involved 1120 university students from the Czech Republic and the Slovak Republic. The measurement of Machiavellianism used a test Mach IV. The test contained 20 statements divided into four central dimensions of Machiavellianism: flattery, deception and falsehood, immorality and cynicism. We found out, that Czech and Slovak's student expressed a similar attitude toward Machiavellianism as a means of manipulation in the interpersonal relationship, but this is not true in all dimensions. Czech and Slovak's students perceive differently flattery, deception and falsehood and cynicism too. In addition to specific features of cultural conditionality, we found out differences depending on the helping profession. Students focusing on managerial occupation (lower level of helping) tend towards greater acceptance of the use flattery as a means of communication than students specializing in the social profession (high-grade assisting). If they are an intercultural and interdisciplinary influences combined and effect on the perception of Machiavellianism together, then their significant importance for the perception of flattery and cynicism. The result will help us to reveal certain specific traits that students apply communication and with whom we can continue to work during their training.

Keywords: use of manipulative strategies, Machiavellianism, helping-profession students, professional training of students

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Authors: Aliyu Damri

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This study analyzed the emergence of WhatsApp and how employees at the Federal Radio Corporation of Nigeria, Kaduna defined the concept base on their vast broadcasting experiences for over five decades and application of the phenomenon to the radio station. It also analyzed the nature, patterns, dimensions, features, challenges as well as the effects of WhatsApp as a social networking site with specific interest on the radio outlet. Also, the study identified how the radio organization responded to the challenges in an attempt to adapt to the new pattern of broadcasting characterized by many technological transformations. The study further explained in details such skills journalists need to function optimally using WhatsApp as well as the impacts of the WhatsApp on radio broadcasting. It used a combination of published materials, focus group discussion, in depth interviews and participant observation on the activities of the radio stations to address the research questions. The data generated provided insight to better understand the challenges posed to FRCN Kaduna as a result of WhatsApp application and how FRCN Kaduna responded to the challenges. It also provided information on the skills journalists need to function optimally in using WhatsApp application in the radio station. The interview and focus group discussion’s transcripts and the published materials were analyzed along thematic pattern related to the research questions in the study. The dominant response relied heavily on change in the radio station’s organizational and technical integration of newsrooms, the use of a multiskilled workforce, application of a flexible and user-friendly technology in all aspects of production, expansion of the station’s services in to new media such as internet and mobile phones as well as sharing of ideas across different units in the radio outfit.

Keywords: broadcasting, challenge, northern Nigeria, radio, WhatsApp application

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Authors: C. Busuioc, S. I. Jinga, E. Pavel

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The continuous need of more non-volatile memories with a higher storage capacity, smaller dimensions and weight, as well as lower costs, has led to the exploration of optical lithography on active media, as well as patterned magnetic composites. In this context, optical lithography is a technique that can provide a significant decrease of the information bit size to the nanometric scale. However, there are some restrictions that arise from the need of breaking the optical diffraction limit. Major achievements have been obtained by employing a vitoceramic material as active medium and a laser beam operated at low power for the direct writing procedure. Thus, optical discs with ultra-high density were fabricated by a conventional melt-quenching method starting from analytical purity reagents. They were subsequently used for 3D recording based on their photosensitive features. Naturally, the next step consists in the elucidation of the composition and structure of the active centers, in correlation with the use of silver and rare-earth compounds for the synthesis of the optical supports. This has been accomplished by modern characterization methods, namely transmission electron microscopy coupled with selected area electron diffraction, scanning transmission electron microscopy and electron energy loss spectroscopy. The influence of laser diode parameters, silver concentration and fluorescent compounds formation on the writing process and final material properties was investigated. The results indicate performances in terms of capacity with two order of magnitude higher than other reported information storage systems. Moreover, the fluorescent photosensitive vitroceramics may be integrated in other applications which appeal to nanofabrication as the driving force in electronics and photonics fields.

Keywords: data storage, fluorescent compounds, laser writing, vitroceramics

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Authors: Tanvi P. Medshinge, Prasad Vaidya, Monisha E. Royan

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In India, cooling loads in residential sector is a major contributor to its total energy consumption. Due to the increasing cooling need, the market penetration of air-conditioners is further expected to rise. Natural Ventilation (NV), however, possesses great potential to save significant energy consumption especially for residential buildings in moderate climates. As multifamily residential apartment buildings are designed by repetitive use of prototype designs, deriving individual NV based design prototype solutions for a combination of different wind incidence angles and orientations would provide significant opportunity to address the rise in cooling loads by residential sector. This paper presents the results of NV performance of a selected prototype apartment design with a cluster of four units in Pune, India, and an attempt to improve the NV performance through design modifications. The water table apparatus, a physical modelling tool, is used to study the flow patterns and simulate wind-induced NV performance. Quantification of NV performance is done by post processing images captured from video recordings in terms of percentage of area with good and poor access to ventilation. NV performance of the existing design for eight wind incidence angles showed that of the cluster of four units, the windward units showed good access to ventilation for all rooms, and the leeward units had lower access to ventilation with the bedrooms in the leeward units having the least access. The results showed improved performance in all the units for all wind incidence angles to more than 80% good access to ventilation. Some units showed an additional improvement to more than 90% good access to ventilation. This process of design and performance evaluation improved some individual units from 0% to 100% for good access to ventilation. The results demonstrate the ease of use and the power of the water table apparatus for performance-based design to simulate wind induced NV.  

Keywords: fluid dynamics, prototype design, natural ventilation, simulations, water table apparatus, wind incidence angles

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Authors: Anindita Sen

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Oral fluid is a promising diagnostic matrix for drugs of abuse compared to urine and serum. Detection of methamphetamine in oral fluid would pave way for the easy evaluation of impairment in drivers during roadside drug testing as well as ensure safe working environments by facilitating evaluation of impairment in employees at workplaces. A membrane-based point-of-care (POC) friendly pre-treatment technique has been developed which aided elimination of interferences caused by salivary proteins and facilitated the demonstration of methamphetamine detection in saliva using a gold nanoparticle based colorimetric aptasensor platform. It was found that the colorimetric response in saliva was always suppressed owing to the matrix effects. By navigating the challenging interfering issues in saliva, we were successfully able to detect methamphetamine at nanomolar levels in saliva offering immense promise for the translation of these platforms for on-site diagnostic systems. This subsequently motivated the development of a handheld portable light meter device that can reliably transduce the aptasensors colorimetric response into absorbance, facilitating quantitative detection of analyte concentrations on-site. This is crucial due to the prevalent unreliability and sensitivity problems of the conventional drug testing kits. The fabricated light meter device response was validated against a standard UV-Vis spectrometer to confirm reliability. The portable and cost-effective handheld detector device features sensitivity comparable to the well-established UV-Vis benchtop instrument and the easy-to-use device could potentially serve as a prototype for a commercial device in the future.

Keywords: aptasensors, colorimetric gold nanoparticle assay, point-of-care, oral fluid

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Authors: Reema Tiwari, U. C. Kulshrestha

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As a key regulator of atmospheric oxidative capacity and secondary aerosol formations, the signatures of reactive nitrogen (Nr) emissions are becoming increasingly evident in the cascade of air pollution, acidification, and eutrophication of the ecosystem. However, their accurate estimates in N budget remains limited by the photochemical conversion processes where occurrence of differential atmospheric residence time of gaseous (NOₓ, HNO₃, NH₃) and particulate (NO₃⁻, NH₄⁺) Nr species becomes imperative to their spatio temporal evolution on a synoptic scale. The present study attempts to quantify such interactions under tropical conditions when low anticyclonic winds become favorable to the advections from west during winters. For this purpose, a diurnal sampling was conducted using low volume sampler assembly where ambient concentrations of Nr trace gases along with their ionic fractions in the aerosol samples were determined with UV-spectrophotometer and ion chromatography respectively. The results showed a spatial gradient of the gaseous precursors with a much pronounced inter site variability (p < 0.05) than their particulate fractions. Such observations were confirmed for their limited photochemical conversions where less than 1 ratios of day and night measurements (D/N) for the different Nr fractions suggested an influence of boundary layer dynamics at the background site. These phase conversion processes were further corroborated with the molar ratios of NOₓ/NOᵧ and NH₃/NHₓ where incomplete titrations of NOₓ and NH₃ emissions were observed irrespective of their diurnal phases along the sampling transect. Their calculations with equilibrium based approaches for an NH₃-HNO₃-NH₄NO₃ system, on the other hand, were characterized by delays in equilibrium attainment where plots of their below deliquescence Kₘ and Kₚ values with 1000/T confirmed the role of lower temperature ranges in NH₄NO₃ aerosol formation. These results would help us in not only resolving the changing atmospheric inputs of reduced (NH₃, NH₄⁺) and oxidized (NOₓ, HNO₃, NO₃⁻) Nr estimates but also in understanding the dependence of Nr mixing ratios on their local meteorological conditions.

Keywords: diurnal ratios, gas-aerosol interactions, spatial gradient, thermodynamic equilibrium

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Authors: Chelsea Peterson-Salahuddin

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Over the past decade, news outlets have increasingly used social media as a means to create and distribute news content to audiences. Ephemerality, the transitory nature of media, and mobility, media viewing on mobile technologies, are two increasingly salient attributes of social media content; yet little is known about how these features influence news selection practices of news outlets when distributing news via social media. To account for this gap, this study examines the influences of ephemerality and mobility on social media news content on the social media application Snapchat, in order to understand how these qualities of digital media influence and shape news content. Findings from this study suggest that understandings of ephemerality and mobility play a key role in influencing social media news. This paper suggests that as these factors become increasingly salient in our dominant news viewing environments, being able to understand how they manifest themselves in online news reporting practices is critical for both scholars and practitioners of news as they aim to understand what 'newsworthiness' means in the current, digital age. Findings from this study also enhance our current understandings of how the technological affordances of online and digital media platforms play a key role in shaping the kinds being produced and what information is being prioritized and highlighted in our contemporary news media environment. This is especially important in our current era where new mediums and technologies for news dissemination are continuously arising, and reorienting our understandings of what is considered ‘news'. As a key site of mass communication, discourse, and stories highlighted in the news do critical work in defining culture and ideology. Thus, better understanding the contours of news in our contemporary moment is critical in understanding cultural norms and meaning-making.

Keywords: content analysis, ephemerality, mobile communication, social media news

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Authors: Isabel Lopez-Oliva, Akshay Paropkari, Shweta Saraswat, Stefan Serban, Paola de Pablo, Karim Raza, Andrew Filer, Iain Chapple, Thomas Dietrich, Melissa Grant, Purnima Kumar

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Objective: We aimed to explicate the role of the subgingival microbiome in the causal link between rheumatoid arthritis (RA) and periodontitis (PD). Methods: Subjects with/without RA and with/without PD were randomized for treatment with scaling and root planing (SRP) or oral hygiene instructions. Subgingival biofilm, gingival crevicular fluid, and serum were collected at baseline and at 3- and 6-months post-operatively. Correlations were generated between 72 million 16S rDNA sequences, immuno-inflammatory mediators, circulating antibodies to oral microbial antigens, serum inflammatory molecules, and clinical metrics of RA. The dynamics of inter-microbial and host-microbial interactions were modeled using differential network analysis. Results: RA superseded periodontitis as a determinant of microbial composition, and DAS28 score superseded the severity of periodontitis as a driver of microbial assemblages (p=0.001, ANOSIM). RA subjects evidenced higher serum anti-PPAD (p=0.0013), anti-Pg-enolase (p=0.0031), anti-RPP3, anti- Pg-OMP and anti- Pi-OMP (p=0.001) antibodies than non-RA controls (with and without periodontitis). Following SRP, bacterial networks anchored by IL-1b, IL-4, IL-6, IL-10, IL-13, MIP-1b, and PDGF-b underwent ≥5-fold higher rewiring; and serum antibodies to microbial antigens decreased significantly. Conclusions: Our data suggest a circular relationship between RA and PD, beginning with an RA-influenced dysbiosis within the healthy subgingival microbiome that leads to exaggerated local inflammation in periodontitis and circulating antibodies to periodontal pathogens and positive correlation between severity of periodontitis and RA activity. Periodontal therapy restores host-microbial homeostasis, reduces local inflammation, and decreases circulating microbial antigens. Our data highlights the importance of integrating periodontal care into the management of RA patients.

Keywords: rheumatoid arthritis, periodontal, subgingival, DNA sequence analysis, oral microbiome

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Authors: Juliana Lukša, Bazilė Ravoitytė, Elena Servienė, Saulius Serva

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Totiviridae L-A virus is a persistent Saccharomyces cerevisiae dsRNA virus. It encodes the major structural capsid protein Gag and Gag-Pol fusion protein, responsible for virus replication and encapsulation. These features also enable the copying of satellite dsRNAs (called M dsRNAs) encoding a secreted toxin and immunity to it (known as killer toxin). Viral capsid pore presumably functions in nucleotide uptake and viral mRNA release. During cell division, sporogenesis, and cell fusion, the virions remain intracellular and are transferred to daughter cells. By employing high throughput RNA sequencing data analysis, we describe the influence of solely L-A virus on the expression of genes in three different S. cerevisiae hosts. We provide a new perception into Totiviridae L-A virus-related transcriptional regulation, encompassing multiple bioinformatics analyses. Transcriptional responses to L-A infection were similar to those induced upon stress or availability of nutrients. It also delves into the connection between the cell metabolism and L-A virus-conferred demands to the host transcriptome by uncovering host proteins that may be associated with intact virions. To better understand the virus-host interaction, we applied differential proteomic analysis of virus particle-enriched fractions of yeast strains that harboreither complete killer system (L-A-lus and M-2 virus), M-2 depleted orvirus-free. Our analysis resulted in the identification of host proteins, associated with structural proteins of the virus (Gag and Gag-Pol). This research was funded by the European Social Fund under the No.09.3.3-LMT-K-712-19-0157“Development of Competences of Scientists, other Researchers, and Students through Practical Research Activities” measure.

Keywords: totiviridae, killer virus, proteomics, transcriptomics

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Authors: Bahareh Golchin, Nooshin Riahi

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One of the most significant issues as attended a lot in recent years is that of recognizing the sentiments and emotions in social media texts. The analysis of sentiments and emotions is intended to recognize the conceptual information such as the opinions, feelings, attitudes and emotions of people towards the products, services, organizations, people, topics, events and features in the written text. These indicate the greatness of the problem space. In the real world, businesses and organizations are always looking for tools to gather ideas, emotions, and directions of people about their products, services, or events related to their own. This article uses the Twitter social network, one of the most popular social networks with about 420 million active users, to extract data. Using this social network, users can share their information and opinions about personal issues, policies, products, events, etc. It can be used with appropriate classification of emotional states due to the availability of its data. In this study, supervised learning and deep neural network algorithms are used to classify the emotional states of Twitter users. The use of deep learning methods to increase the learning capacity of the model is an advantage due to the large amount of available data. Tweets collected on various topics are classified into four classes using a combination of two Bidirectional Long Short Term Memory network and a Convolutional network. The results obtained from this study with an average accuracy of 93%, show good results extracted from the proposed framework and improved accuracy compared to previous work.

Keywords: emotion classification, sentiment analysis, social networks, deep neural networks

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Authors: James Ormrod

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This paper is concerned with volunteer livestock checking. Based on a pilot study consisting of ‘go-along’ interviews with livestock checkers, it argues that there are limitations to the insights that can be generated from approaches to ‘discourse analysis’ that would focus only on the verbalizable aspects of the practice. Volunteer livestock checking takes place across Europe as part of conservation projects aimed at maintaining particular habitats through the reintroduction of grazing animals. Volunteers are variously called ‘urban shepherds’, because these practices often take place on urban fringes, or ‘lookerers’, as their role is to make visual checks on the animals. Pilot research that took place on the South Downs (a chalk downland habitat on the South Coast of the UK) involved researchers accompanying volunteers as they checked on livestock. They were asked to give an account of what they were doing and then answer semi-structured interview questions. Participants drew on popular discourses on conservation and biodiversity, as framed by the local council who run the programme. They also framed their relationships to the animals in respect to the more formal limitations of their role as identified through the conservation programme. And yet these discourses, significant as they are, do not adequately explain why volunteers are drawn to, and emotionally invested in, lookering. The methodology employed allowed participants instead to gesture to features of the landscape and to recall memories, and for the researchers to see how volunteers interacted with the animals and the landscape in embodied and emotionally loaded ways. The paper argues that a psychosocial perspective that pays attention to the contradictions and tensions made visible through this methodology helps develop a fuller understanding of volunteer livestock checking as a social practice.

Keywords: conservation, human-animal relations, lookering, volunteering

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Authors: Dimitra Marini, Konstantinos Bakogiannis, Spyros Polychronopoulos, Georgios Kouroupetroglou

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Archaemusicology cannot entirely depend on the study of the excavated ancient musical instruments as most of the time their condition is not ideal (i.e., missing/eroded parts) and moreover, because of the concern damaging the originals during the experiments. Researchers, in order to overcome the above obstacles, build replicas. This technique is still the most popular one, although it is rather expensive and time-consuming. Throughout the last decades, the development of physical modeling techniques has provided tools that enable the study of musical instruments through their digitally simulated models. This is not only a more cost and time-efficient technique but also provides additional flexibility as the user can easily modify parameters such as their geometrical features and materials. This paper thoroughly describes the steps to create a physical model of a woodwind ancient Greek instrument, Plagiaulos. This instrument could be considered as the ancestor of the modern flute due to the common geometry and air-jet excitation mechanism. Plagiaulos is comprised of a single resonator with an open end and a number of tone holes. The combination of closed and open tone holes produces the pitch variations. In this work, the effects of all the instrument’s components are described by means of physics and then simulated based on digital waveguides. The synthesized sound of the proposed model complies with the theory, highlighting its validity. Further, the synthesized sound of the model simulating the Plagiaulos of Koile (2nd century BCE) was compared with its replica build in our laboratory by following the scientific methodologies of archeomusicology. The aforementioned results verify that robust dynamic digital tools can be introduced in the field of computational, experimental archaemusicology.

Keywords: archaeomusicology, digital waveguides, musical acoustics, physical modeling

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