Search results for: organisational features

Authors: K. N. Dinesh Babu, P. K. Gargava

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Energization of a transformer results in sudden flow of current which is an effect of core magnetization. This current will be dominated by the presence of second harmonic, which in turn is used to segregate fault and inrush current, thus guaranteeing proper operation of the relay. This additional security in the relay sometimes obstructs or delays differential protection in a specific scenario, when the 2^nd harmonic content was present during a genuine fault. This kind of scenario can result in isolation of the transformer by Buchholz and pressure release valve (PRV) protection, which is acted when fault creates more damage in transformer. Such delays involve a huge impact on the insulation failure, and chances of repairing or rectifying fault of problem at site become very dismal. Sometimes this delay can cause fire in the transformer, and this situation becomes havoc for a sub-station. Such occurrences have been observed in field also when differential relay operation was delayed by 10-15 ms by second harmonic blocking in some specific conditions. These incidences have led to the need for an alternative solution to eradicate such unwarranted delay in operation in future. Modern numerical relay, called as intelligent electronic device (IED), is embedded with advanced protection features which permit higher flexibility and better provisions for tuning of protection logic and settings. Such flexibility in transformer protection IEDs, enables incorporation of alternative methods such as dynamic switching of second harmonic feature for blocking the differential protection with additional security. The analysis and precautionary measures carried out in this case, have been simulated and discussed in this paper to ensure that similar solutions can be adopted to inhibit analogous issues in future.

Keywords: differential protection, intelligent electronic device (IED), 2nd harmonic inhibit, inrush inhibit

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Authors: Ling Xu, Giuseppe Loprencipe, Antonio D'Andrea

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Asphalt pavement with recycled and sustainable materials has become the most commonly adopted strategy for road construction, including reclaimed asphalt pavement (RAP) and crumb rubber (CR) from waste tires. However, the adhesion and cohesion characteristics of rubberized reclaimed asphalt pavement were still ambiguous, resulting in deteriorated adhesion behavior and life performance. This research investigated the effect of bonding characteristics on rutting resistance and moisture susceptibility of rubberized reclaimed asphalt pavement in terms of two RAP sources with different oxidation levels and two tire rubber with different particle sizes. Firstly, the binder bond strength (BBS) test and bonding failure distinguishment were conducted to analyze the surface behaviors of binder-aggregate interaction. Then, the compatibility and penetration grade of rubberized RAP binder were evaluated by rotational viscosity test and penetration test, respectively. Hamburg wheel track (HWT) test with high-temperature viscoelastic deformation analysis was adopted, which illustrated the rutting resistance. Additionally, a water boiling test was employed to evaluate the moisture susceptibility of the mixture and the texture features were characterized with the statistical parameters of image colors. Finally, the colloid structure model of rubberized RAP binder with surface interaction was proposed, and statistical analysis was established to release the correlation among various indexes. This study concluded that the gel-phase colloid structure and molecular diffusion of the free light fraction would affect the surface interpretation with aggregate, determining the bonding characteristic of rubberized RAP asphalt.

Keywords: bonding characteristics, reclaimed asphalt pavement, rubberized asphalt, sustainable material

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Authors: Qingsong Xiong, Cheng Yuan, Qingzhao Kong, Haibei Xiong

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Deep-learning-driven approaches based on vibration responses have attracted larger attention in rapid structural condition assessment while obtaining sufficient measured training data with corresponding labels is relevantly costly and even inaccessible in practical engineering. This study proposes an integrated label propagation network for structural condition assessment, which is able to diffuse the labels from continuously-generating measurements by intact structure to those of missing labels of damage scenarios. The integrated network is embedded with damage-sensitive features extraction by deep autoencoder and pseudo-labels propagation by optimized fuzzy clustering, the architecture and mechanism which are elaborated. With a sophisticated network design and specified strategies for improving performance, the present network achieves to extends the superiority of self-supervised representation learning, unsupervised fuzzy clustering and supervised classification algorithms into an integration aiming at assessing damage conditions. Both numerical simulations and full-scale laboratory shaking table tests of a two-story building structure were conducted to validate its capability of detecting post-earthquake damage. The identifying accuracy of a present network was 0.95 in numerical validations and an average 0.86 in laboratory case studies, respectively. It should be noted that the whole training procedure of all involved models in the network stringently doesn’t rely upon any labeled data of damage scenarios but only several samples of intact structure, which indicates a significant superiority in model adaptability and feasible applicability in practice.

Keywords: autoencoder, condition assessment, fuzzy clustering, label propagation

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Authors: S. Abdi Goudazri, R. Ziaie Moayed, A. Nazeri

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Loose soils normally are of weak bearing capacity due to their structural nature. Being exposed to heavy traffic loads, they would fail in most cases. To tackle the aforementioned issue, geotechnical engineers have come up with different approaches; one of which is making use of geosynthetic-reinforced soil-aggregate systems. As these polymeric reinforcements have highlighted economic and environmentally-friendly features, they have become widespread in practice during the last decades. The present research investigates the efficiency of four different types of these reinforcements in increasing the bearing capacity of two-layered soil sections using a series California Bearing Ratio (CBR) test. The studied sections are comprised of a 10 cm-thick layer of no. 161 Firouzkooh sand (weak subgrade) and a 10 cm-thick layer of compacted aggregate materials (base course) classified as SP and GW according to the United Soil Classification System (USCS), respectively. The aggregate layer was compacted to the relative density (Dr) of 95% at the optimum water content (Wopt) of 6.5%. The applied reinforcements were including two kinds of geocomposites (type A and B), a geotextile, and a geogrid that were embedded at the interface of the lower and the upper layers of the soil-aggregate system. As the standard CBR mold was not appropriate in height for this study, the mold used for soaked CBR tests were utilized. To make a comparison between the results of stress-settlement behavior in the studied specimens, CBR values pertinent to the penetrations of 2.5 mm and 5 mm were considered. The obtained results demonstrated 21% and 24.5% increments in the amount of CBR value in the presence of geocomposite type A and geogrid, respectively. On the other hand, the effect of both geotextile and geocomposite type B on CBR values was generally insignificant in this research.

Keywords: geosynthetics, geogrid, geotextile, CBR test, increasing bearing capacity

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Authors: Maryam Karimi, Rouzbeh Nazari

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Introduction: Understanding thermal distribution in the micro-urban climate has now been necessary for urban planners or designers due to the impact of complex micro-scale features of Urban Heat Island (UHI) on the built environment and public health. Hence, understanding the interrelation between urban components and thermal pattern can assist planners in the proper addition of vegetation to build-environment, which can minimize the UHI impact. To characterize the need for urban green infrastructure (UGI) through better urban planning, this study proposes the use of RayMan model to measure the impact of air quality and increased temperature based on urban morphology in the selected metropolitan cities. This project will measure the impact of build environment for urban and regional planning using human biometeorological evaluations (Tmrt). Methods: We utilized the RayMan model to estimate the Tmrt in an urban environment incorporating location and height of buildings and trees as a supplemental tool in urban planning and street design. The estimated Tmrt value will be compared with existing surface and air temperature data to find the actual temperature felt by pedestrians. Results: Our current results suggest a strong relationship between sky-view factor (SVF) and increased surface temperature in megacities based on current urban morphology. Conclusion: This study will help with Quantifying the impacts of the built environment and surface properties on surrounding temperature, identifying priority urban neighborhoods by analyzing Tmrt and air quality data at the pedestrian level, and characterizing the need for urban green infrastructure cooling potential.

Keywords: built environment, urban planning, urban cooling, extreme heat

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Authors: Taslim Khan, Ray Hua Horng, Rajendra Singh

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This study presents a comprehensive analysis of the design, fabrication, and performance evaluation of a solar-blind Schottky photodetector based on ZnGa₂O₄ grown via MOCVD, utilizing Ni/Au as the Schottky electrode. ZnGa₂O₄, with its wide bandgap of 5.2 eV, is well-suited for high-performance solar-blind photodetection applications. The photodetector demonstrates an impressive responsivity of 280 A/W, indicating its exceptional sensitivity within the solar-blind ultraviolet band. One of the device's notable attributes is its high rejection ratio of 10⁵, which effectively filters out unwanted background signals, enhancing its reliability in various environments. The photodetector also boasts a photodetector responsivity contrast ratio (PDCR) of 10⁷, showcasing its ability to detect even minor changes in incident UV light. Additionally, the device features an outstanding detective of 10¹⁸ Jones, underscoring its capability to precisely detect faint UV signals. It exhibits a fast response time of 80 ms and an ON/OFF ratio of 10⁵, making it suitable for real-time UV sensing applications. The noise-equivalent power (NEP) of 10^-17 W/Hz further highlights its efficiency in detecting low-intensity UV signals. The photodetector also achieves a high forward-to-backward current rejection ratio of 10⁶, ensuring high selectivity. Furthermore, the device maintains an extremely low dark current of approximately 0.1 pA. These findings position the ZnGa₂O₄-based Schottky photodetector as a leading candidate for solar-blind UV detection applications. It offers a compelling combination of sensitivity, selectivity, and operational efficiency, making it a highly promising tool for environments requiring precise and reliable UV detection.

Keywords: wideband gap, solar blind photodetector, MOCVD, zinc gallate

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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: 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: 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: Odunayo Olawuyi Fadodun

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Dielectric elastomers (DEs) are a type of intelligent materials with salient features like electromechanical coupling, lightweight, fast actuation speed, low cost and high energy density that make them good candidates for numerous engineering applications. This paper adopts a new nonlinear electroelastic constitutive theory to examine radial deformation of a pressurized thick-walled spherical shell of soft dielectric material with compliant electrodes on its inner and outer surfaces. A general formular for the internal pressure, which depends on the deformation and a potential difference between boundary electrodes or uniform surface charge distributions, is obtained in terms of special function. To illustrate the effects of an applied electric field on the mechanical behaviour of the shell, three different energy functions with distinct mechanical properties are employed for numerical purposes. The observed behaviour of the shells is preserved in the presence of an applied electric field, and the influence of the field due to a potential difference declines more slowly with the increasing deformation to that produced by a surface charge. Counterpart results are then presented for the thin-walled shell approximation as a limiting case of a thick-walled shell without restriction on the energy density. In the absence of internal pressure, it is obtained that inflation is caused by the application of an electric field. The resulting numerical solutions of the theory presented in this work are in agreement with those predicted by the generally adopted Dorfmann and Ogden model.

Keywords: constitutive theory, elastic dielectric, electroelasticity, finite deformation, nonlinear response, spherical shell

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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: 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: Kizito Ugochukwu Nwajeri

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This paper explores the application of hybrid block methods for solving boundary value problems (BVPs), which are prevalent in various fields such as science, engineering, and applied mathematics. Traditionally, numerical approaches such as finite difference and shooting methods, often encounter challenges related to stability and convergence, particularly in the context of complex and nonlinear BVPs. To address these challenges, we propose a hybrid block method that integrates features from both single-step and multi-step techniques. This method allows for the simultaneous computation of multiple solution points while maintaining high accuracy. Specifically, we employ a combination of polynomial interpolation and collocation strategies to derive a system of equations that captures the behavior of the solution across the entire domain. By directly incorporating boundary conditions into the formulation, we enhance the stability and convergence properties of the numerical solution. Furthermore, we introduce an adaptive step-size mechanism to optimize performance based on the local behavior of the solution. This adjustment allows the method to respond effectively to variations in solution behavior, improving both accuracy and computational efficiency. Numerical tests on a variety of boundary value problems demonstrate the effectiveness of the hybrid block methods. These tests showcase significant improvements in accuracy and computational efficiency compared to conventional methods, indicating that our approach is robust and versatile. The results suggest that this hybrid block method is suitable for a wide range of applications in real-world problems, offering a promising alternative to existing numerical techniques.

Keywords: hybrid block methods, boundary value problem, polynomial interpolation, adaptive step-size control, collocation methods

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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: 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: Sohrab Jafarpour, Hamed Farokhi, Mohammad Rahmati, Alireza Gholipour

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In the current study, a nonlinear fluid-structure interaction (FSI) biomechanical model of atherosclerosis in the left anterior descending (LAD) coronary artery is developed to perform a detailed sensitivity analysis of the geometrical features of an atheroma plaque. In the development of the numerical model, first, a 3D geometry of the diseased artery is developed based on patient-specific dimensions obtained from the experimental studies. The geometry includes four influential geometric characteristics: stenosis ratio, plaque shoulder-length, fibrous cap thickness, and eccentricity intensity. Then, a suitable strain energy density function (SEDF) is proposed based on the detailed material stability analysis to accurately model the hyperelasticity of the arterial walls. The time-varying inlet velocity and outlet pressure profiles are adopted from experimental measurements to incorporate the pulsatile nature of the blood flow. In addition, a computationally efficient type of structural boundary condition is imposed on the arterial walls. Finally, a non-Newtonian viscosity model is implemented to model the shear-thinning behaviour of the blood flow. According to the results, the structural responses in terms of the maximum principal stress (MPS) are affected more compared to the fluid responses in terms of wall shear stress (WSS) as the geometrical characteristics are varying. The extent of these changes is critical in the vulnerability assessment of an atheroma plaque.

Keywords: atherosclerosis, fluid-Structure interaction modeling, material stability analysis, and nonlinear biomechanics

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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: 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

Procedia PDF Downloads 133