Search results for: resolution digital data

Authors: James Moir

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The mainstay of Big Data is prediction in that it allows practitioners, researchers, and policy analysts to predict trends based upon the analysis of large and varied sources of data. These can range from changing social and political opinions, patterns in crimes, and consumer behaviour. Big Data has therefore shifted the criterion of success in science from causal explanations to predictive modelling and simulation. The 19th-century science sought to capture phenomena and seek to show the appearance of it through causal mechanisms while 20th-century science attempted to save the appearance and relinquish causal explanations. Now 21st-century science in the form of Big Data is concerned with the prediction of appearances and nothing more. However, this pulls social science back in the direction of a more rule- or law-governed reality model of science and away from a consideration of the internal nature of rules in relation to various practices. In effect Big Data offers us no more than a world of surface appearance and in doing so it makes disappear any context-specific conceptual sensitivity.

Keywords: big data, appearance, disappearance, surface, epistemology

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Authors: Stepan Papacek, Jiri Jablonsky, Radek Kana, Ctirad Matonoha, Stefan Kindermann

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FRAP (Fluorescence Recovery After Photobleaching) is a widely used measurement technique to determine the mobility of fluorescent molecules within living cells. While the experimental setup and protocol for FRAP experiments are usually fixed, data processing part is still under development. In this paper, we formulate and solve the problem of data selection which enhances the processing of FRAP images. We introduce the concept of the irrelevant data set, i.e., the data which are almost not reducing the confidence interval of the estimated parameters and thus could be neglected. Based on sensitivity analysis, we both solve the problem of the optimal data space selection and we find specific conditions for optimizing an important experimental design factor, e.g., the radius of bleach spot. Finally, a theorem announcing less precision of the integrated data approach compared to the full data case is proven; i.e., we claim that the data set represented by the FRAP recovery curve lead to a larger confidence interval compared to the spatio-temporal (full) data.

Keywords: FRAP, inverse problem, parameter identification, sensitivity analysis, optimal experimental design

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Authors: Muhammad Faisal Khilji, Rana Shoaib Hamid

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Subdural hematoma (SDH) is an extremely rare complication of immune thrombocytopenic purpura (ITP). We present a case of a 34 years old female who presented to the Emergency department of a tertiary care hospital with complaints of headache, on and off gums bleeding and upper respiratory tract symptoms for the last two weeks. Examination was unremarkable except some purpura over limbs. Investigations revealed zero platelets and peripheral film suggestive of ITP. Computerized tomography (CT) brain revealed bilateral SDH in the frontal areas extending into Falx cerebri. Impression of ITP with SDH was made. Patient was treated with intravenous immunoglobulin (IVIg), methyl prednisolone and initial Platelets transfusion. Patient recovered uneventfully with platelets reaching normal levels within a few days and resolution of SDH without surgery.

Keywords: headache, immune thrombocytopenia, purpura, subdural hematoma

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Authors: Nabilah Filzah Mohd Radzuan, Zalinda Othman, Azuraliza Abu Bakar, Abdul Razak Hamdan

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Uncertain data is believed to be an important issue in building up a prediction model. The main objective in the time series uncertainty analysis is to formulate uncertain data in order to gain knowledge and fit low dimensional model prior to a prediction task. This paper discusses the performance of a number of techniques in dealing with uncertain data specifically those which solve uncertain data condition by minimizing the loss of compression properties.

Keywords: compression properties, uncertainty, uncertain time series, mining technique, weather prediction

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Authors: Maram Saleh

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Knowledge has become an essential resource in today’s economy and become the most important asset of maintaining competition advantage in organizations. The importance of knowledge has made organizations to manage their knowledge assets and resources through all multiple knowledge management stages such as: Knowledge Creation, knowledge storage, knowledge sharing and knowledge use. Researches on data mining are continues growing over recent years on both business and educational fields. Data mining is one of the most important steps of the knowledge discovery in databases process aiming to extract implicit, unknown but useful knowledge and it is considered as significant subfield in knowledge management. Data miming have the great potential to help organizations to focus on extracting the most important information on their data warehouses. Data mining tools and techniques can predict future trends and behaviors, allowing businesses to make proactive, knowledge-driven decisions. This review paper explores the applications of data mining techniques in supporting knowledge management process as an effective knowledge discovery technique. In this paper, we identify the relationship between data mining and knowledge management, and then focus on introducing some application of date mining techniques in knowledge management for some real life domains.

Keywords: Data Mining, Knowledge management, Knowledge discovery, Knowledge creation.

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Authors: Quentin Lobbe, David Chavalarias, Alexandre Delanoe

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The ICT revolution has given birth to an unprecedented world of digital traces and has impacted a wide number of knowledge-driven domains such as science, education or policy making. Nowadays, we are daily fueled by unlimited flows of articles, blogs, messages, tweets, etc. The internet itself can thus be considered as an unsteady hyper-textual environment where websites emerge and expand every day. But there are structures inside knowledge. A given text can always be studied in relation to others or in light of a specific socio-cultural context. By way of their textual traces, human beings are calling each other out: hypertext citations, retweets, vocabulary similarity, etc. We are in fact the architects of a giant web of elements of knowledge whose structures and shapes convey their own information. The global shapes of these digital traces represent a source of collective knowledge and the question of their visualization remains an opened challenge. How can we explore, browse and interact with such shapes? In order to navigate across these growing constellations of words and texts, interdisciplinary innovations are emerging at the crossroad between fields of social and computational sciences. In particular, complex systems approaches make it now possible to reconstruct the hidden structures of textual knowledge by means of multi-scale objects of research such as semantic maps and phylomemies. The phylomemy reconstruction is a generic method related to the co-word analysis framework. Phylomemies aim to reveal the temporal dynamics of large corpora of textual contents by performing inter-temporal matching on extracted knowledge domains in order to identify their conceptual lineages. This study aims to address the question of visualizing the global shapes of online political discussions related to the French presidential and legislative elections of 2017. We aim to build phylomemies on top of a dedicated collection of thousands of French political tweets enriched with archived contemporary news web articles. Our goal is to reconstruct the temporal evolution of online debates fueled by each political community during the elections. To that end, we want to introduce an iterative data exploration methodology implemented and tested within the free software Gargantext. There we combine synchronic and diachronic axis of visualization to reveal the dynamics of our corpora of tweets and web pages as well as their inner syntagmatic and paradigmatic relationships. In doing so, we aim to provide researchers with innovative methodological means to explore online semantic landscapes in a collaborative and reflective way.

Keywords: online political debate, French election, hyper-text, phylomemy

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Authors: Nabil Jallouli, Luisa M. Pastrana-Martínez, Ana R. Ribeiro, Nuno F. F. Moreira, Joaquim L. Faria, Olfa Hentati, Adrián M. T. Silva, Mohamed Ksibi

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Degradation and mineralization of ibuprofen (IBU) were investigated using Ultraviolet (UV) Light Emitting Diodes (LEDs) in TiO2 photocatalysis. Samples of ultrapure water (UP) and a secondary treated effluent of a municipal wastewater treatment plant (WWTP), both spiked with IBU, as well as a highly concentrated IBU (230 mgL-1) pharmaceutical industry wastewater (PIWW), were tested in the TiO2/UV-LED system. Three operating parameters, namely, pH, catalyst load and number of LEDs were optimized. The process efficiency was evaluated in terms of IBU removal using high performance liquid chromatography (HPLC) and ultra-high performance liquid chromatography coupled to tandem mass spectrometry (UHPLC-MS/MS). Additionally, the mineralization was investigated by determining the dissolved organic carbon (DOC) content. The chemical structures of transformation products were proposed based on the data obtained using liquid chromatography with a high resolution mass spectrometer ion trap/time-of-flight (LC-MS-IT-TOF). A possible pathway of IBU degradation was accordingly proposed. Bioassays were performed using the marine bacterium Vibrio fischeri to evaluate the potential acute toxicity of original and treated wastewaters. TiO2 heterogeneous photocatalysis was efficient to remove IBU from UP and from PIWW, and less efficient in treating the wastewater from the municipal WWTP. The acute toxicity decreased by ca. 40% after treatment, regardless of the studied matrix.

Keywords: acute toxicity, Ibuprofen, UV-LEDs, wastewaters

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Authors: Murat Yazici

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Anomalies are irregularities found in data that do not adhere to a well-defined standard of normal behavior. The identification of outliers or anomalies in data has been a subject of study within the statistics field since the 1800s. Over time, a variety of anomaly detection techniques have been developed in several research communities. The cluster analysis can be used to detect anomalies. It is the process of associating data with clusters that are as similar as possible while dissimilar clusters are associated with each other. Many of the traditional cluster algorithms have limitations in dealing with data sets containing categorical properties. To detect anomalies in categorical data, fuzzy clustering approach can be used with its advantages. The fuzzy k-Mode (FKM) clustering algorithm, which is one of the fuzzy clustering approaches, by extension to the k-means algorithm, is reported for clustering datasets with categorical values. It is a form of clustering: each point can be associated with more than one cluster. In this paper, anomaly detection is performed on two simulated data by using the FKM cluster algorithm. As a significance of the study, the FKM cluster algorithm allows to determine anomalies with their abnormality degree in contrast to numerous anomaly detection algorithms. According to the results, the FKM cluster algorithm illustrated good performance in the anomaly detection of data, including both one anomaly and more than one anomaly.

Keywords: fuzzy k-mode clustering, anomaly detection, noise, categorical data

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Authors: Mohammad R. Kavian Nezhad, Carlos F. Lange, Brian A. Fleck

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This research presents the validation study of a computational fluid dynamics (CFD) model developed to simulate the scalar dispersion emitted from rooftop sources around the buildings at the University of Alberta North Campus. The ANSYS CFX code was used to perform the numerical simulation of the wind regime and pollutant dispersion by solving the 3D steady Reynolds-averaged Navier-Stokes (RANS) equations on a building-scale high-resolution grid. The validation study was performed in two steps. First, the CFD model performance in 24 cases (eight wind directions and three wind speeds) was evaluated by comparing the predicted flow fields with the available data from the previous measurement campaign designed at the North Campus, using the standard deviation method (SDM), while the estimated results of the numerical model showed maximum average percent errors of approximately 53% and 37% for wind incidents from the North and Northwest, respectively. Good agreement with the measurements was observed for the other six directions, with an average error of less than 30%. In the second step, the reliability of the implemented turbulence model, numerical algorithm, modeling techniques, and the grid generation scheme was further evaluated using the Mock Urban Setting Test (MUST) dispersion dataset. Different statistical measures, including the fractional bias (FB), the geometric mean bias (MG), and the normalized mean square error (NMSE), were used to assess the accuracy of the predicted dispersion field. Our CFD results are in very good agreement with the field measurements.

Keywords: CFD, plume dispersion, complex urban geometry, validation study, wind flow

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Authors: Victor Onomza Waziri, John K. Alhassan, Idris Ismaila, Noel Dogonyara

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This paper describes the problem of building secure computational services for encrypted information in the Cloud. Computing without decrypting the encrypted data; therefore, it meets the yearning of computational encryption algorithmic aspiration model that could enhance the security of big data for privacy or confidentiality, availability and integrity of the data and user’s security. The cryptographic model applied for the computational process of the encrypted data is the Fully Homomorphic Encryption Scheme. We contribute a theoretical presentations in a high-level computational processes that are based on number theory that is derivable from abstract algebra which can easily be integrated and leveraged in the Cloud computing interface with detail theoretic mathematical concepts to the fully homomorphic encryption models. This contribution enhances the full implementation of big data analytics based on cryptographic security algorithm.

Keywords: big data analytics, security, privacy, bootstrapping, Fully Homomorphic Encryption Scheme

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Authors: Muhammad Yasir Wadood, Fatemeh Babaeian

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By developing ultra-wideband (UWB) systems, there is a high demand for UWB filters with low insertion loss, wide bandwidth, and having a planar structure which is compatible with other components of the UWB system. A microstrip interdigital filter is a great option for designing UWB filters. However, the presence of via holes in this structure creates difficulties in the fabrication procedure of the filter. Especially in the higher frequency band, any misalignment of the drilled via hole with the Microstrip stubs causes large errors in the measurement results compared to the desired results. Moreover, in this case (high-frequency designs), the line width of the stubs are very narrow, so highly precise small via holes are required to be implemented, which increases the cost of fabrication significantly. Also, in this case, there is a risk of having fabrication errors. To combat this issue, in this paper, a via-less UWB microstrip filter is proposed which is designed based on a modification of a conventional inter-digital bandpass filter. The novel approaches in this filter design are 1) replacement of each via hole with a quarter-wavelength open circuit stub to avoid the complexity of manufacturing, 2) using a bend structure to reduce the unwanted coupling effects and 3) minimising the size. Using the proposed structure, a UWB filter operating in the frequency band of 3.9-6.6 GHz (1-dB bandwidth) is designed and fabricated. The promising results of the simulation and measurement are presented in this paper. The selected substrate for these designs was Rogers RO4003 with a thickness of 20 mils. This is a common substrate in most of the industrial projects. The compact size of the proposed filter is highly beneficial for applications which require a very miniature size of hardware.

Keywords: band-pass filters, inter-digital filter, microstrip, via-less

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Authors: Josie West

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This paper focuses on the conflicts and utility of Marxist Feminist frames for sex work research, drawing on findings uncovered through in-depth interviews with online sex workers, alongside critical discourse analysis of media and political commentary. It brings the critical perspective of women into digital workerism and gig economy dialogue who, despite their significant presence within online work, have been overlooked. The autonomist Marxist concept of class composition is adopted to unpack the social, technical and political composition of this often-invisible segment of the service sector. Autonomism makes visible the perspective of workers engaged in processes of mobilization and demobilizaiton. This allows researchers to find everyday forms of resistance which occur within and outside trade unions. On the other hand, Marxist feminist arguments about invisibility politics can generate unhelpful allegories about sex work as domestic labour within the reproductive sphere. Nick Srnicek’s development of Marx’s notion of infrastructure rents helps theorize experiences of unpaid labour within online sex work. Moreover, debates about anti-work politics can cause conflict among sex workers fighting for the labour movement and those rejecting the capitalist work ethic. This illuminates’ tensions caused by white privilege and differing experiences of sex work. The monopolistic and competitive nature of sex work platforms within platform capitalism, and the vulnerable position of marginalised workers within stigmatized/criminalised markets, complicates anti-work politics further. This paper is situated within the feminist sex wars and the intensely divisive question of whether sex workers are victims of the patriarchy or symbols of feminist resistance. Camgirls are shown to engage in radical tactics of resistance against their technical composition on popular sex work platforms. They also engage in creative acts of resistance through performance art, in an attempt to draw attention to stigma and anti-criminalization politics. This sector offers a fascinating window onto grassroots class-action, alongside education about ‘whorephobia.’ A case study of resistance against Only Fans, and a small workers co-operative which emerged during the pandemic, showcases how workers engage in socialist and political acts without the aid of unions. Workers are victims of neoliberalism and simultaneous adopters of neoliberal strategies of survival. The complex dynamics within unions are explored, including tensions with grass-roots resistance, financial pressures and intersecting complications of class, gender and race.

Keywords: autonomist marxism, digital labor, feminism, neoliberalism, sex work, platform capitalism

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Authors: Sarisa Pinkham, Kanyarat Bussaban

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The research aims to approximate the amount of daily rainfall by using a pixel value data approach. The daily rainfall maps from the Thailand Meteorological Department in period of time from January to December 2013 were the data used in this study. The results showed that this approach can approximate the amount of daily rainfall with RMSE=3.343.

Keywords: daily rainfall, image processing, approximation, pixel value data

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Authors: Timna Mayer

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This paper explores the vast potential of self-directed learning in violin pedagogy. Based in practice and drawing on concepts from neuropsychology, the author, a violinist and teacher, outlines five learning principles. Self-directed learning is defined as an ongoing process based on problem detection, definition, and resolution. The traditional roles of teacher and student are reimagined within this context. A step-by-step guide to applied self-directed learning suggests a model for both teachers and students that realizes student independence in the classroom, leading to higher-level understanding and more robust performance. While the value of self-directed learning is well-known in general pedagogy, this paper is novel in applying the approach to the study of musical performance, a field which is currently dominated by habit and folklore, rather than informed by science.

Keywords: neuropsychology and musical performance, self-directed learning, strategic problem solving, violin pedagogy

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Authors: Kenneth Harper

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The rapid growth of data-driven decision-making across various industries necessitates advanced solutions to ensure data integrity, scalability, and security. This study introduces a decentralized data platform built on blockchain technology to improve data management processes in high-volume environments such as healthcare and financial services. The platform integrates blockchain networks using Cosmos SDK and Polkadot Substrate alongside decentralized storage solutions like IPFS and Filecoin, and coupled with decentralized computing infrastructure built on top of Avalanche. By leveraging advanced consensus mechanisms, we create a scalable, tamper-proof architecture that supports both structured and unstructured data. Key features include secure data ingestion, cryptographic hashing for robust data lineage, and Zero-Knowledge Proof mechanisms that enhance privacy while ensuring compliance with regulatory standards. Additionally, we implement performance optimizations through Layer 2 scaling solutions, including ZK-Rollups, which provide low-latency data access and trustless data verification across a distributed ledger. The findings from this exercise demonstrate significant improvements in data accessibility, reduced operational costs, and enhanced data integrity when tested in real-world scenarios. This platform reference architecture offers a decentralized alternative to traditional centralized data storage models, providing scalability, security, and operational efficiency.

Keywords: blockchain, cosmos SDK, decentralized data platform, IPFS, ZK-Rollups

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Authors: Mohammad Javad Mollakazemi, Farhad Asadi, Aref Ghafouri

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In this paper, we considered and applied parametric modeling for some experimental data of dynamical system. In this study, we investigated the different distribution of output measurement from some dynamical systems. Also, with variance processing in experimental data we obtained the region of nonlinearity in experimental data and then identification of output section is applied in different situation and data distribution. Finally, the effect of the spanning the measurement such as variance to identification and limitation of this approach is explained.

Keywords: Gaussian process, nonlinearity distribution, particle filter, system identification

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Authors: Jaya Mathew

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Many organizations are faced with the challenge of how to analyze and build Machine Learning models using their sensitive telemetry data. In this paper, we discuss how users can leverage the power of R without having to move their big data around as well as a cloud based solution for organizations willing to host their data in the cloud. By using ScaleR technology to benefit from parallelization and remote computing or R Services on premise or in the cloud, users can leverage the power of R at scale without having to move their data around.

Keywords: predictive maintenance, machine learning, big data, cloud based, on premise solution, R

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Authors: Sven Gehrke, Johannes Ruhland

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Data is the oil of our time, without them progress would come to a hold [1]. On the other hand, the mistrust of data mining is increasing [2]. The paper at hand shows different aspects of the concept of trust and describes the information asymmetry of the typical stakeholders of a data mining project using the CRISP-DM phase model. Based on the identified influencing factors in relation to trust, problematic aspects of the current approach are verified using various interviews with the stakeholders. The results of the interviews confirm the theoretically identified weak points of the phase model with regard to trust and show potential research areas.

Keywords: trust, data mining, CRISP DM, stakeholder management

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Authors: K. Thiagarajan, K. Saranya, A. Veeraiah, B. Sudha

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This paper presents a novel algorithm for secure, reliable and flexible transmission of big data in two hop wireless networks using cooperative jamming scheme. Two hop wireless networks consist of source, relay and destination nodes. Big data has to transmit from source to relay and from relay to destination by deploying security in physical layer. Cooperative jamming scheme determines transmission of big data in more secure manner by protecting it from eavesdroppers and malicious nodes of unknown location. The novel algorithm that ensures secure and energy balance transmission of big data, includes selection of data transmitting region, segmenting the selected region, determining probability ratio for each node (capture node, non-capture and eavesdropper node) in every segment, evaluating the probability using binary based evaluation. If it is secure transmission resume with the two- hop transmission of big data, otherwise prevent the attackers by cooperative jamming scheme and transmit the data in two-hop transmission.

Keywords: big data, two-hop transmission, physical layer wireless security, cooperative jamming, energy balance

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Authors: Ninel Kokanyan, Edvard Kokanyan, Anush Movsesyan, Marc D. Fontana

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Lithium Niobate (LN) is one of the widely used ferroelectrics having a wide number of applications such as phase-conjugation, holographic storage, frequency doubling, SAW sensors. Furthermore, the possibility of doping with rare-earth ions leads to new laser applications. Ho and Tm dopants seem interesting due to laser emission obtained at around 2 µm. Raman spectroscopy is a powerful spectroscopic technique providing a possibility to obtain a number of information about physicochemical and also optical properties of a given material. Polarized Raman measurements were carried out on Ho and Tm doped LN crystals with excitation wavelengths of 532nm and 785nm. In obtained Raman anti-Stokes spectra, we detect expected modes according to Raman selection rules. In contrast, Raman Stokes spectra are significantly different compared to what is expected by selection rules. Additional forbidden lines are detected. These lines have quite high intensity and are well defined. Moreover, the intensity of mentioned additional lines increases with an increase of Ho or Tm concentrations in the crystal. These additional lines are attributed to emission lines reflecting the photoluminescence spectra of these crystals. It means that in our case we were able to detect, within a very good resolution, in the same Stokes spectrum, the transitions between the electronic states, and the vibrational states as well. The analysis of these data is reported as a function of Ho and Tm content, for different polarizations and wavelengths, of the incident laser beam. Results also highlight additional information about π and σ polarizations of crystals under study.

Keywords: lithium niobate, Raman spectroscopy, luminescence, rare-earth ions doped lithium niobate

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Authors: Maayan Nakash

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The modern workplace is undergoing a revolution, with Generation Z (Gen-Z) at the forefront of this transformative shift. However, empirical investigations specifically targeting the workplace preferences of this generation remain limited. Through direct examination of their tendencies via a survey approach, this study offers vital insights for aligning organizational policies and practices. The results presented in this paper are part of a comprehensive study that explored Gen Z's viewpoints on various employment market aspects, likely to decisively influence the design of future work environments. Data were collected via an online survey distributed among a cohort of 461 individuals from Gen-Z, born between the mid-1990s and 2010, consisting of 241 males (52.28%) and 220 females (47.72%). Responses were gauged using Likert scale statements that probed preferences for teamwork versus individual work, virtual versus personal interactions, and open versus private workspaces. Descriptive statistics and analytical analyses were conducted to pinpoint key patterns. We discovered that a high proportion of respondents (81.99%, n=378) exhibited a preference for teamwork over individual work. Correspondingly, the data indicate strong support for the recognition of team-based tasks as a tool contributing to personal and professional development. In terms of communication, the majority of respondents (61.38%) either disagreed (n=154) or slightly agreed (n=129) with the exclusive reliance on virtual interactions with their organizational peers. This finding underscores that despite technological progress, digital natives place significant value on physical interaction and non-mediated communication. Moreover, we understand that they also value a quiet and private work environment, clearly preferring it over open and shared workspaces. Considering that Gen-Z does not necessarily experience high levels of stress within social frameworks in the workplace, this can be attributed to a desire for a space that allows for focused engagement with work tasks. A One-Sample Chi-Square Test was performed on the observed distribution of respondents' reactions to each examined statement. The results showed statistically significant deviations from a uniform distribution (p<.001), indicating that the response patterns did not occur by chance and that there were meaningful tendencies in the participants' responses. The findings expand the theoretical knowledge base on human resources in the dynamics of a multi-generational workforce, illuminating the values, approaches, and expectations of Gen-Z. Practically, the results may lead organizations to equip themselves with tools to create policies tailored to Gen-Z in the context of workspaces and social needs, which could potentially foster a fertile environment and aid in attracting and retaining young talent. Future studies might include investigating potential mitigating factors, such as cultural influences or individual personality traits, which could further clarify the nuances in Gen-Z's work style preferences. Longitudinal studies tracking changes in these preferences as the generation matures may also yield valuable insights. Ultimately, as the landscape of the workforce continues to evolve, ongoing investigations into the unique characteristics and aspirations of emerging generations remain essential for nurturing harmonious, productive, and future-ready organizational environments.

Keywords: workplace, future of work, generation Z, digital natives, human resources management

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Authors: Yaxin Bi, Peter Nicholl

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The rapid advancement of Information and communication technologies (ICT) is extremely influencing every aspect of Higher Education. It has transformed traditional teaching, learning, assessment and feedback into a new era of Digital Education. This also introduces many challenges in capturing and understanding student engagement with their studies in Higher Education. The School of Computing at Ulster University has developed a Feedback And Notification (FAN) Online tool that has been used to send students links to personalized feedback on their submitted assessments and record students’ frequency of review of the shared feedback as well as the speed of collection. The feedback that the students initially receive is via a personal email directing them through to the feedback via a URL link that maps to the feedback created by the academic marker. This feedback is typically a Word or PDF report including comments and the final mark for the work submitted approximately three weeks before. When the student clicks on the link, the student’s personal feedback is viewable in the browser and they can view the contents. The FAN tool provides the academic marker with a report that includes when and how often a student viewed the feedback via the link. This paper presents an investigation into student engagement through analyzing the interaction timestamps and frequency of review by the student. We have proposed an approach to modeling interaction timestamps and use sentiment classification techniques to analyze the data collected over the last five years for a set of modules. The data studied is across a number of final years and second-year modules in the School of Computing. The paper presents the details of quantitative analysis methods and describes further their interactions with the feedback overtime on each module studied. We have projected the students into different groups of engagement based on sentiment analysis results and then provide a suggestion of early targeted intervention for the set of students seen to be under-performing via our proposed model.

Keywords: feedback, engagement, interaction modelling, sentiment analysis

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Authors: Yudhitya Kusumawati, Yuki Mitsuya, Tomooki Shiba, Mitsuru Uesaka

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After the Fukushima Daiichi nuclear power reactor incident, there are a lot of unaccountable nuclear fuel debris in the reactor core area, which is subject to safeguard and criticality safety. Before the actual precise analysis is performed, preliminary on-site screening and mapping of nuclear debris activity need to be performed to provide a reliable data on the nuclear debris mass-extraction planning. Through a collaboration project with Japan Atomic Energy Agency, an on-site nuclear debris screening system by using dual energy X-Ray inspection and neutron energy resonance analysis has been established. By using the compact and mobile pulsed neutron source constructed from 3.95 MeV X-Band electron linac, coupled with Tungsten as electron-to-photon converter and Beryllium as a photon-to-neutron converter, short-distance neutron Time of Flight measurement can be performed. Experiment result shows this system can measure neutron energy spectrum up to 100 eV range with only 2.5 meters Time of Flightpath in regards to the X-Band accelerator’s short pulse. With this, on-site neutron Time of Flight measurement can be used to identify the nuclear debris isotope contents through Neutron Resonance Transmission Analysis (NRTA). Some preliminary NRTA experiments have been done with Tungsten sample as dummy nuclear debris material, which isotopes Tungsten-186 has close energy absorption value with Uranium-238 (15 eV). The results obtained shows that this system can detect energy absorption in the resonance neutron area within 1-100 eV. It can also detect multiple elements in a material at once with the experiment using a combined sample of Indium, Tantalum, and silver makes it feasible to identify debris containing mixed material. This compact neutron Time of Flight measurement system is a great complementary for dual energy X-Ray Computed Tomography (CT) method that can identify atomic number quantitatively but with 1-mm spatial resolution and high error bar. The combination of these two measurement methods will able to perform on-site nuclear debris screening at Fukushima Daiichi reactor core area, providing the data for nuclear debris activity mapping.

Keywords: neutron source, neutron resonance, nuclear debris, time of flight

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Authors: Romeo Botes, Imelda Smit

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In today’s modern age of technology vast amounts of data needs to be processed in real-time to keep users satisfied. This data comes from various sources and in many formats, including electronic and mobile devices such as GPRS modems and GPS devices. They make use of different protocols including TCP, UDP, and HTTP/s for data communication to web servers and eventually to users. The data obtained from these devices may provide valuable information to users, but are mostly in an unreadable format which needs to be processed to provide information and business intelligence. This data is not always current, it is mostly historical data. The data is not subject to implementation of consistency and redundancy measures as most other data usually is. Most important to the users is that the data are to be pre-processed in a readable format when it is entered into the database. To accomplish this, programmers build processing programs and scripts to decode and process the information stored in databases. Programmers make use of various techniques in such programs to accomplish this, but sometimes neglect the effect some of these techniques may have on database performance. One of the techniques generally used,is to pull data from the database server, process it and push it back to the database server in one single step. Since the processing of the data usually takes some time, it keeps the database busy and locked for the period of time that the processing takes place. Because of this, it decreases the overall performance of the database server and therefore the system’s performance. This paper follows on a paper discussing the performance increase that may be achieved by utilizing array lists along with a pull-process-push data processing technique split in three steps. The purpose of this paper is to expand the number of clients when comparing the two techniques to establish the impact it may have on performance of the CPU storage and processing time.

Keywords: performance measures, algorithm techniques, data processing, push data, process data, array list

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Authors: Sapir Handelman

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Brief Solution Focused Negotiation is a powerful conflict resolution tool. It can be applied in almost all dimensions of our social life, from politics to family. The initiative invites disputing parties to negotiate practical solutions to their conflict. The negotiation is conducted in a framework of rules, structure, and timeline. The paper presents a model of Brief Solution Focused Negotiation that rests on three pillars: Transformation – turning opposing parties into a negotiating cooperative; Practicality – focusing on practical solutions to a negotiable problem; Discovery – discovering key game changers. This paper introduces these three building blocks. It demonstrates the potential contribution of each one of them to negotiation success. It shows that an effective combination of these three elements has the greatest potential to build, maintain and successfully conclude Brief Solution Focused Negotiation.

Keywords: conflict, negotiation, negotiating cooperative, game changer

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Authors: Nabilah Ibrahim, Hafiz Mohd Zaini, Wan Fatin Liyana Mutalib

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Ultrasound equipment or machine is capable to scan in two dimensional (2D) areas. However there are some limitations occur during scanning an object. The problem will occur when scanning process that involving the asymmetric object. In this project, the ultrasound probe holder for asymmetric reflector scanning in 3D image is proposed to make easier for scanning the phantom or object that has asymmetric shape. Initially, the constructed asymmetric phantom that construct will be used in 2D scanning. Next, the asymmetric phantom will be interfaced by the movement of ultrasound probe holder using the Arduino software. After that, the performance of the ultrasound probe holder will be evaluated by using the various asymmetric reflector or phantom in constructing a 3D image

Keywords: ultrasound 3D images, axial and lateral resolution, asymmetric reflector, Arduino software

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Authors: Nkongho Ayuketang Arreyndip, Ebobenow Joseph

Abstract:

In this paper, temperature extremes are forecast by employing the block maxima method of the generalized extreme value (GEV) distribution to analyse temperature data from the Cameroon Development Corporation (CDC). By considering two sets of data (raw data and simulated data) and two (stationary and non-stationary) models of the GEV distribution, return levels analysis is carried out and it was found that in the stationary model, the return values are constant over time with the raw data, while in the simulated data the return values show an increasing trend with an upper bound. In the non-stationary model, the return levels of both the raw data and simulated data show an increasing trend with an upper bound. This clearly shows that although temperatures in the tropics show a sign of increase in the future, there is a maximum temperature at which there is no exceedance. The results of this paper are very vital in agricultural and environmental research.

Keywords: forecasting, generalized extreme value (GEV), meteorology, return level

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Authors: Nadine Rashed, A. Samer Ezeldin, Engy Serag

Abstract:

The principle of good faith plays a critical role in shaping contractual relationships, yet its application varies significantly across different types of construction contracts and legal systems. This paper presents a comparative analysis of how various construction contracts perceive the principle of good faith, a fundamental aspect that influences contractual relationships and project outcomes. The primary objective of this analysis is to examine the differences in the application and interpretation of good faith across key construction contracts, including JCT (Joint Contracts Tribunal), FIDIC (Fédération Internationale des Ingénieurs-Conseils), NEC (New Engineering Contract), and ICE (Institution of Civil Engineers) Contracts. To accomplish this, a mixed-methods approach will be employed, integrating a thorough literature review of current legal frameworks and academic publications with primary data gathered from a structured questionnaire aimed at industry professionals such as contract managers, legal advisors, and project stakeholders. This combined strategy will enable a holistic understanding of the theoretical foundations of good faith in construction contracts and its practical effects in real-world contexts. The findings of this analysis are expected to yield valuable insights into how varying interpretations of good faith can impact project performance, dispute resolution, and collaborative practices within the construction industry. This paper contributes to a deeper understanding of how the principle of good faith is evolving in the construction industry, providing insights for contract drafters, legal practitioners, and project managers seeking to navigate the complexities of contractual obligations across different legal systems.

Keywords: construction contracts, contractual obligations, ethical practices, good faith

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Authors: Abdulrahman K. Alshegaifi, Chun-Hsi Huang

Abstract:

Treating data based on its location in memory has received much attention in recent years due to its different properties, which offer important aspects for cache utilization. Stack data and non-stack data may interfere with each other’s locality in the data cache. One of the important aspects of stack data is that it has high spatial and temporal locality. In this work, we simulate non-unified cache design that split data cache into stack and non-stack caches in order to maintain stack data and non-stack data separate in different caches. We observe that the overall hit rate of non-unified cache design is sensitive to the size of non-stack cache. Then, we investigate the appropriate size and associativity for stack cache to achieve high hit ratio especially when over 99% of accesses are directed to stack cache. The result shows that on average more than 99% of stack cache accuracy is achieved by using 2KB of capacity and 1-way associativity. Further, we analyze the improvement in hit rate when adding small, fixed, size of stack cache at level1 to unified cache architecture. The result shows that the overall hit rate of unified cache design with adding 1KB of stack cache is improved by approximately, on average, 3.9% for Rijndael benchmark. The stack cache is simulated by using SimpleScalar toolset.

Keywords: hit rate, locality of program, stack cache, stack data

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Authors: Timothy Scheinert, Eliza Wright

Abstract:

Background: Link Health is a Boston-based non-profit leveraging in-person and digital platforms to promote health equity. Its primary aim is to financially support low-income individuals through enrollment in federal benefit programs. This study examines the monetary impact of enrollment in several benefit programs. Methodologies: Approximately 17,000 individuals have been screened for eligibility via digital outreach, community events, and in-person clinics. Enrollment and financial distributions are evaluated across programs, including the Affordable Connectivity Program (ACP), Lifeline, LIHEAP, Transitional Aid to Families with Dependent Children (TAFDC), and the Supplemental Nutrition Assistance Program (SNAP). Major Findings: A total of 1,895 individuals have successfully applied, collectively distributing an estimated $1,288,152.00 in aid. The largest contributors to this sum include: ACP: 1,149 enrollments, $413,640 distributed annually. Child Care Financial Assistance (CCFA): 15 enrollments, $240,000 distributed annually. Lifeline: 602 enrollments, $66,822 distributed annually. LIHEAP: 25 enrollments, $48,750 distributed annually. SNAP: 41 enrollments, $123,000 distributed annually. TAFDC: 21 enrollments, $341,760 distributed annually. Conclusions: These results highlight the role of targeted outreach and effective enrollment processes in promoting access to federal benefit programs. High enrollment rates in ACP and Lifeline demonstrate a considerable need for affordable broadband and internet services. Programs like CCFA and TAFDC, despite lower enrollment numbers, provide sizable support per individual. This analysis advocates for continued funding of federal benefit programs. Future efforts can be made to develop screening tools that identify eligibility for multiple programs and reduce the complexity of enrollment.

Keywords: benefits, childcare, connectivity, equity, nutrition

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