Different Sources Of Information Research Articles

Interactive Modelling of lost Landmarks – Qishla of Mosul A Case Study

The recent technological development in many fields has made a significant contribution to the reconstruction of a historical buildings. The matter becomes complicated when the buildings are destroyed, and it is required to collect as many different sources of information as possible. The research problem lies in the absence of studies on reconstructing the lost historical landmarks in the old Mosul city in an interactive 4D modelling. This study aims to develop a strategy to reconstruct historical cultural landmarks in an interactive digital way, based on the collection and integration of multiple sources of information, in addition to an attempt to extrapolate the lost elements based on similar data and various available information, and all this using available software techniques to create interactive environments. To produce documents that can be used in the process of restoring the real origin as an advanced engineering document, used for future periodic maintenance and preservation purposes. The study seeks to reconstruct the military Qishla building in the completely destroyed in Mosul, as a case study, using multiple tools and techniques, in parallel with the techniques Modern digital, and various CAD programs for engineering and parametric drawing and Ar-media augmented reality app.

Prehistory, neuroscience, and evolutionary anthropology: a personal journey.

The relationship between anthropology and neuroscience has always been friendly but controversial, because they embrace inclusive common topics (human beings and their brains) although following distinct approaches, often more holistic and speculative in the former field, more reductionist and quantitative in the latter. In recent decades, novel disciplines have been proposed to bridge the gap between anthropology and neuroscience, mostly taking into account their common interest in human evolution. Paleoneurology deals with the study of brain anatomy in extinct species. Neuroarchaeology concerns the study of brain functions associated with behaviours that are of interest according to the archaeological record. Cognitive archaeology investigates the evolution of those behaviours following methods and theories in psychology. These new fields can provide quantitative and experimental support to topics that, to date, have been largely discussed only on a theoretical basis. Nonetheless, working with extinct species necessarily involves many limitations. Consistent theories on the evolution of our cognitive abilities must rely on the integration of different sources of information, on parallel and independent evidence from different fields, and on a proper attitude: openness and caution.

Impact on Potable Water Consumption Due to Massive Migrations: The Case of Bogotá, Colombia

One of the most important aspects for the regulation of a water distribution system in a city is the accurate projection of the population to be supplied. Accordingly, it is necessary to know the social, cultural, and economic characteristics of the inhabitants, as well as the industrial, commercial, tourist and migratory dynamics to understand a city’s future development. Generally, population growth is continuous and usually follows a trend that makes it possible to extrapolate the future population, thus predicting the demand for drinking water based on per capita supply. However, forced migrations challenge this assumption. The present work aims to collect and analyze information related to Venezuelan migration, in the southern zone of Bogotá, Colombia. First of all, in this project, the demographic characteristics of the migrant population are defined, and the different sources of information collected during the study are used to estimate the total number of migrants in Bogotá. Then, new methodologies for tracking people using social media data connections, geolocation from active connections, and emerging platforms are shown. This is followed by an explanation of two approaches used to estimate the number of migrants from 2014 to 2020 in the boroughs of: Kennedy, Bosa, Tunjuelito, Usme, Ciudad Bolivar and the municipality of Soacha. Finally, using mathematical and statistical approximations, the study shows that the increases in water consumption in the city coincides with the flow rates needed to supply the migrant population. The results indicate that during the first half of 2021 the flow demanded by the migrant population in the studied localities was around 8 Mgal/d (350 L/s), which is 35.1% of the total flow demanded by the entire migrant population in Bogotá. The migrant water consumption was between 4.8% and 6.1% of the total demand for the city.

Emotion in Entrepreneurship Education: Passion in Artistic Entrepreneurship Practice

Although emotions have been recognized as fundamental for entrepreneurship and educational practices in general, the role of emotions in entrepreneurship education (EE) remains overlooked in entrepreneurship scholarship, particularly in the field of EE in the arts, where entrepreneurs are driven by their passion and strong emotional connections to their artistic projects. In this article, we discuss how emotion, and in particular passion, impacts entrepreneurship educational practices in the context of artistic entrepreneurship. Our 24-month inductive practice-based study, which used different sources of information (documents produced by the participants, direct observation, videos and semi-structured interviews) resulted in the identification of two dynamics of passion (transforming and contagious) and three aspects in which passion affected the EE process (motivation, collaboration and resilience). We conclude that our results can enrich studies in EE from different paths: discussing the relations between impacts of passion and other widely discussed constructs, broadening the understanding of passion as a dynamic and sociocultural emotion and highlighting the role of pedagogical practices in this context. Additionally, we broaden current understandings of the importance of passion for EE in the arts field.

Measurement and Analysis with KPIs based on an AMI system

This paper presents the development of a series of key performance indicators (KPI´s) for the electrical system of the campus of the National University of Colombia based on the deployed smart metering infrastructure (AMI). In order to develop the proposed indicators, it was necessary to use different sources of information to complement the data provided by the AMI system. For each of the proposed indicators is presented the way in which each selected indicator is calculated, and an analysis of the behavior obtained for each KPI. It was possible to observe how, based on the results obtained from the different indicators proposed, periods of inefficiency in terms of electricity consumption were identified. Finally, the conclusions obtained during the development of the project are presented.

Physics Teachers' Implementation of Competence-Based Curriculum through the use of Inquiry-Based Teaching and Learning: A Case of Lower Secondary Schools in Gakenke District

This study investigates how physics teachers of lower secondary schools practice inquiry-based teaching and learning (IBTL) in their physics teaching. The study used qualitative techniques such as open-ended questionnaires, interviews, and observation to collect data. The study participants were three senior-one physics teachers purposefully selected from three lower secondary schools of Gakenke District located in the Northern Province of Rwanda. Collected data were analyzed using content analysis. The study's findings revealed that study participants know about and how IBTL should be implemented. However, they were limited only on traditional methods of teaching and did not tackle five practices of inquiry-based teaching and learning in their physics teaching, which include engaging students in scientifically oriented questions, allowing students to give priority to evidence, allowing students to give explanations from evidence, and to communicate and justify their explanations and evaluating explanations from evidence. Therefore, teachers were found not effectively implementing the current competence-based curriculum (CBC) in Rwanda. Teachers are recommended to ask questions that require students to compare and contrast for practicing inquiry practice of evaluating explanations from evidence and to allow students to use different sources of information rather than using physics books only.

Media Consumption During the COVID-19 Pandemic and the Role of Media Systems: A Comparative Study

Global crisis events like the coronavirus pandemic can have an effect on the media and the habits of their audience. The need to find information about the virus and imposed restrictions, as well as the intensity of the COVID-19 coverage and the need to verify the truthfulness of news, make people use many different sources of information. People use media in different ways depending on where they live and the type of political regime and model of media system in their country. This paper aims to analyze whether the model of media system affected people’s selection of sources of information on the virus in Poland, Italy, France, Czech Republic, Sweden and the United Kingdom, and if the pandemic could have changed the media consumption. Based on an online survey conducted with 9541 respondents from six countries, and reports on media use in Poland in 2019 and 2020, we argue that the model of media system did not determine the changes we observed in media use. The nature of the pandemic was more important in this respect, and, as our research shows, led to an increase in media use in the studied countries – especially websites and social media. The case of Poland also showed that the use of public service increased, which can be interpreted as the need for the public to learn about the government’s opinions and restrictions, and to verify

Digital twins for decoding human-building interaction in multi-domain test-rooms for environmental comfort and energy saving via graph representation

Human comfort studies are a complex topic given their interdisciplinary and multi-stimuli nature. The integration of different sources of information from multi-domain experiments for occupants’ environmental perception investigation can change the way that researchers perceived the interactions among building agents. This study aims to establish a new method for digital twin development from a test room for human-comfort and energy behavior analysis, through a Graph Neural Network. An as-monitored BIM model was integrated with monitoring information, i.e., environmental data from the facility, physiological signals, and survey answers from occupants, and the relationships between the building agents were outlined, based on the experiment purposes. A real-world experiment was adopted to demonstrate the procedure and results, connecting the information from experiments and a test room facility employed to the study. The original approach filled a recurrent gap in human-comfort studies: the integration of all the sources of information in a single visual platform through Graph Neural Networks, allowing the recognition of patterns, connections, and the influence of the diverse stimuli in the human perception of the surrounding environment. In complex building systems, with large sensing structures, high occupancy and multi-source environmental stimuli, new conclusions can be drawn to a full understanding of the human-building interactions.

Assessing the Impact of Use and Trust in Different Sources of Information on COVID-19 Vaccination Uptake in Saudi Arabia (SA) Using the COVID-19 Vaccine Hesitancy and Resistance in SA (CoV-HERSA) Tool.

COVID-19 vaccination has been the cornerstone measure to tackle the severe morbidity and mortality of the ongoing global pandemic. However, vaccine hesitancy and resistance were observed in different populations, including Saudi Arabia (SA), yet such hesitancy was not accurately measured, nor were its influencing factors determined. The COVID-19 Vaccine Hesitance and Resistance in SA (CoV-HERSA) tool was developed, validated, and distributed to 387 participants to accurately measure their COVID-19 vaccine hesitancy and its influencing factors. Different chronic conditions affected participants' CoV-HERSA differently, with those having autoimmune disorders having the highest CoV-HERSA scores. Previous exposure to COVID-19 significantly increased the CoV-HERSA scores. The use and trust of the different sources of information had a significant influence on the CoV-HERSA scores. Those who used newspapers and healthcare practitioners as their source of information had the highest CoV-HERSA scores, while those who relied on family/friends had the lowest scores. The CoV-HERSA is a validated tool that accurately reflects participants' willingness and hesitancy to COVID-19 vaccination and can be used to explore the influence of different factors on the participants' attitudes towards the COVID-19 vaccine.

Assessing the Repeatability of Multi-Frequency Multi-Layer Brain Network Topologies Across Alternative Researcher's Choice Paths.

There is a growing interest in the neuroscience community on the advantages of multilayer functional brain networks. Researchers usually treated different frequencies separately at distinct functional brain networks. However, there is strong evidence that these networks share complementary information while their interdependencies could reveal novel findings. For this purpose, neuroscientists adopt multilayer networks, which can be described mathematically as an extension of trivial single-layer networks. Multilayer networks have become popular in neuroscience due to their advantage to integrate different sources of information. Here, Ιwill focus on the multi-frequency multilayer functional connectivity analysis on resting-state fMRI(rs-fMRI) recordings. However, constructing a multilayer network depends on selecting multiple pre-processing steps that can affect the final network topology. Here, I analyzed the rs-fMRI dataset from a single human performing scanning over a period of 18months (84 scans in total), and the rs-fMRIdataset containing 25 subjects with 3 repeat scans. I focused on assessing the reproducibility of multi-frequency multilayer topologies exploring the effect of two filtering methods for extracting frequencies from BOLD activity, three connectivity estimators, with or without a topological filtering scheme, and two spatial scales. Finally, I untangled specific combinations of researchers' choices that yield consistently brain networks with repeatable topologies, giving me the chance to recommend best practices over consistent topologies.

The Real-World Experiences of Persons With Multiple Sclerosis During the First COVID-19 Lockdown: Application of Natural Language Processing.

The increasing availability of "real-world" data in the form of written text holds promise for deepening our understanding of societal and health-related challenges. Textual data constitute a rich source of information, allowing the capture of lived experiences through a broad range of different sources of information (eg, content and emotional tone). Interviews are the "gold standard" for gaining qualitative insights into individual experiences and perspectives. However, conducting interviews on a large scale is not always feasible, and standardized quantitative assessment suitable for large-scale application may miss important information. Surveys that include open-text assessments can combine the advantages of both methods and are well suited for the application of natural language processing (NLP) methods. While innovations in NLP have made large-scale text analysis more accessible, the analysis of real-world textual data is still complex and requires several consecutive steps. We developed and subsequently examined the utility and scientific value of an NLP pipeline for extracting real-world experiences from textual data to provide guidance for applied researchers. We applied the NLP pipeline to large-scale textual data collected by the Swiss Multiple Sclerosis (MS) registry. Such textual data constitute an ideal use case for the study of real-world text data. Specifically, we examined 639 text reports on the experienced impact of the first COVID-19 lockdown from the perspectives of persons with MS. The pipeline has been implemented in Python and complemented by analyses of the "Linguistic Inquiry and Word Count" software. It consists of the following 5 interconnected analysis steps: (1) text preprocessing; (2) sentiment analysis; (3) descriptive text analysis; (4) unsupervised learning-topic modeling; and (5) results interpretation and validation. A topic modeling analysis identified the following 4 distinct groups based on the topics participants were mainly concerned with: "contacts/communication;" "social environment;" "work;" and "errands/daily routines." Notably, the sentiment analysis revealed that the "contacts/communication" group was characterized by a pronounced negative emotional tone underlying the text reports. This observed heterogeneity in emotional tonality underlying the reported experiences of the first COVID-19-related lockdown is likely to reflect differences in emotional burden, individual circumstances, and ways of coping with the pandemic, which is in line with previous research on this matter. This study illustrates the timely and efficient applicability of an NLP pipeline and thereby serves as a precedent for applied researchers. Our study thereby contributes to both the dissemination of NLP techniques in applied health sciences and the identification of previously unknown experiences and burdens of persons with MS during the pandemic, which may be relevant for future treatment.

On the Impact of News for Reliable Stock Market Predictions: An LSTM-based Ensemble using FinBERT Word-Embeddings

Stock market (SM) prediction methods can be divided into two categories based on the number of information sources used: single-source methods and dual-source approaches. To estimate the price of a stock, single-source approaches rely solely on numerical data. The Efficient Market Hypothesis (EMH), [1]. States that the stock price will represent all important information. Different sources of information might complement one another and influence the stock price. Machine learning and deep learning techniques have long been used to anticipate stock market movements, [2], [3]. The researcher gathered the dataset, [4], [5], [6], [7]. The dataset contains the date of the reading, the opening price, the high and low value of the stock, news about the stock, and the volume. The researcher uses a variety of machine Learning and deep learning approaches to compare performance and prediction error rates, in addition, the researcher also compared the effect of adding the news text as a feature and as a label model. and using a dedicated model for news sentiment analysis by applying the FinBERT word embedding and using them to construct a Long Short-Term Memory (LSTM). From our observation, it is evident that Deep learning-based models performed better than their Machine learning counterparts. The author shows that information extracted from news sources is better at predicting rather than its direction of price movement. And the best-performing model without news is the LSTM with an RMSE of 0.0259 while the best-performing model with news is the LSTM with a stand-alone and LSTM model for news yields RMSE of 0.0220.

Preference for prior probabilities of positions under incongruent visual information.

People's expectations help them make judgments about the world. In the area of spatial memory, the interaction of existing knowledge with incoming information is best illustrated in the category effect, a bias in positioning a target toward the prototypical location of its region (Huttenlocher et al., 1991). According to Bayesian principles, these biased judgments are weighted averages of a target's metric code (incoming data) and spatial categorical code (prior expectation). While previous research in the category effect generally focused on presence and reliability of different sources of information, we examined a scenario in which prior expectation is violated. Specifically, we investigated the role of prior expectations in location memory when these conflict with visual perception. We found that people favored their previous knowledge about where a target ought to be over the visual data on the actual target location. Hence, our work contributes to the literature by demonstrating the dominance of prior expectations over incongruent visual cues, and the data extend the context effect to spatial memory. (PsycInfo Database Record (c) 2022 APA, all rights reserved).

Social differentiation in career decision-making processes and higher education intentions: the role of family background and school composition in the use of different sources of information

ABSTRACT The way in which young people’s post-school intentions and pathways reflect their social background has been the subject of a good deal of research. However, much less attention has been paid to social differentiation in the amount and kind of career guidance information received by young people and its role in reinforcing or ameliorating social differentiation in transitions. This article contributes by examining the extent to which sources of information and guidance activities vary by social background and school social mix and the consequences of this variation for intentions to go on to higher education. The paper draws on data from a large-scale longitudinal study of young people in Ireland, the Growing Up in Ireland study. It finds that young people from more advantaged backgrounds are much more reliant on their parents as a source of information and advice, reflecting access to insider knowledge of the educational system, and are more likely to pay for private guidance services. In contrast, young people from less advantaged backgrounds and those attending schools with a concentration of working-class students are more reliant on school-based sources of information, though these resources are insufficient to boost their chances of making the transition to higher education.

A WEB-BASED PLANNING PERMIT ASSESSMENT PROTOTYPE: ITWIN4PP

Abstract. The current process of issuing planning permits mostly relies on checking Land-use Regulations (LuRs) against two-dimensional (2D) analogue or digital proposed development plans. Checking three-dimensional (3D) LuRs within 2D proposed development plans results in challenges for decision-makers to understand LuRs’ limits and the impacts of the proposed developments on existing buildings in their surrounded proximity. Given the advancement of 3D geospatial technologies, to overcome such challenges and facilitate the process of issuing planning permits, 3D digital approaches should be developed for effective 3D storage, analysis, and visualisation of 3D LuRs and detection of their potential conflicts. This paper, as part of an internship project with Bentley systems, aims to design and develop a web-based 3D visualisation prototype called iTwin4PP for issuing planning permits using Bentley iTwin platform. This prototype first demonstrates how 3D LuRs related to planning approval can be modelled automatically in 3D and combined with an integrated BIM-GIS environment including BIM designs of the proposed developments and GIS models of planning/city-data. Then, the prototype considers the possibility of 3D spatial analyses (especially proximity analysis) for verifying 3D LuRs automatically to detect potential spatio-semantic conflicts that may arise between modelled LuRs and physical/planning objects. Five LuRs subject to planning approval in Victorian jurisdiction, in Australia, including height limits, energy efficiency protection, overshadowing open space, noise impacts, and overlooking are highlighted. While these LuRs are specific to Melbourne’s planning scheme ordinance, we believe that the prototype and encountered challenges in integrating different sources of information especially BIM and GIS, modelling 3D LuRs, and detecting their potential conflicts are common and can be applied in other jurisdictions.

A New Approach to Decision-Making Problem under Complex Pythagorean Fuzzy Information

In daily life, decision-making (DM) problem is a complicated work related to uncertainties and vagueness. To overcome these imprecisions, many fuzzy sets and theories have been presented by different scholars. Probabilistic models are the communal models proposed for the management of uncertainties. On the other hand, if these uncertainties are not probabilistic in nature, then other models such as fuzzy linguistic and fuzzy logic are developed. Here, a new approach known as the complex Pythagorean fuzzy Maclaurin symmetric mean (CPFMSM) operator is used to handle these uncertainties in DM issues. This complex Pythagorean fuzzy set (CPFS) is a modified form of the Pythagorean fuzzy set (PFS) and of the complex intuitionistic fuzzy set (CIFS). The aggregation operators have the ability to combine different sources of information. Therefore, an aggregation operator known as the MSM operator is utilized under the complex Pythagorean fuzzy (CPF) environment to extend the theory and applications of traditional MSM. For this purpose, we devised new operators known as CPFMSM and CPF dual Maclaurin symmetric mean (CPFDMSM) to aggregate CPF data. To evaluate an emergency program, the MAGDM approach is used, which is based on the newly introduced operators. Furthermore, the viability and applicability of the propounded method are certified by a detailed analysis with the other approaches researched in the past.

Quantitative methods to determine the student workload: II. Statistical models for the microcurricular performance indicators.

Previously, we observed that the student workload follows an inverse relation with the learning rate (an application of the kinematic notion of speed contextualized to the learning process). Motivated by this finding, we propose a quantitative estimation of the learning rate using a different source of information: the historical records of final grades of a given course. According to empirical data analyzed in other similar studies, the distribution functions of final grades exhibit a regular pattern: a Gaussian behavior for the approval region and a homogeneous distribution for the failed one. This fact is combined with the incidence of student elimination-desertion rules for introducing two simple agent-based models. Our analysis is complemented by revisiting the performance indicators typically employed to characterize the student promotion and progression. We discuss some other performance indicators to characterize the learning advancement of students: the group learning rate and the learning curve. We compare the results of Monte Carlo simulations with empirical data, observing a good agreement in the behavior of performance indicators derived from these sources. This analysis suggests an adaptive method for the readjustment of the student workload (the number of academic credits) considering the group learning rates during a follow-up period, which resembles the readjustment of prices of goods (and services) in correspondence with the evolution of supply and demand.

Key Information Sources and their Utility for Upliftment of Members of Tribal Farmer Producer Groups

Information is the crucial component in every walk of life. Information channels for and between the farmersare vital for increasing the agricultural yields. The present study in Seethampeta block of Srikakulam districtin Andhra Pradesh was focused on identifying the utility of different sources of information. A sample of145 respondents was selected through multi-stage proportionate sampling method. The data were collectedwith the help of well-structured interview schedule through personal interview method. The findings ofthe study elicited that progressive farmers served as regular source of information for majority (70.34 %) ofthe tribal FPG members and among the occasional source of information Subject Matter Specialists fromKVK were preferred by exactly four-fifths (80.00 %) of the members of tribal FPGs. Radio was never preferredas information source by 77.24 per cent of respondents. Information utility and information score werehigh for progressive farmers with values of 0.84 and 2.25 respectively. The study concludes by saying thatwhatever information is available to the members of tribal FPGs, it should be communicated at right timein right form and right channel which can improve the utility of information and thus the quality of work.

Incorporation of spatial- and connectivity-based cortical brain region information in regularized regression: Application to Human Connectome Project data.

Studying the association of the brain's structure and function with neurocognitive outcomes requires a comprehensive analysis that combines different sources of information from a number of brain-imaging modalities. Recently developed regularization methods provide a novel approach using information about brain structure to improve the estimation of coefficients in the linear regression models. Our proposed method, which is a special case of the Tikhonov regularization, incorporates structural connectivity derived with Diffusion Weighted Imaging and cortical distance information in the penalty term. Corresponding to previously developed methods that inform the estimation of the regression coefficients, we incorporate additional information via a Laplacian matrix based on the proximity measure on the cortical surface. Our contribution consists of constructing a principled formulation of the penalty term and testing the performance of the proposed approach via extensive simulation studies and a brain-imaging application. The penalty term is constructed as a weighted combination of structural connectivity and proximity between cortical areas. Simulation studies mimic the real brain-imaging settings. We apply our approach to the study of data collected in the Human Connectome Project, where the cortical properties of the left hemisphere are found to be associated with vocabulary comprehension.

Multimodal recognition of frustration during game-play with deep neural networks

Frustration, which is one aspect of the field of emotional recognition, is of particular interest to the video game industry as it provides information concerning each individual player’s level of engagement. The use of non-invasive strategies to estimate this emotion is, therefore, a relevant line of research with a direct application to real-world scenarios. While several proposals regarding the performance of non-invasive frustration recognition can be found in literature, they usually rely on hand-crafted features and rarely exploit the potential inherent to the combination of different sources of information. This work, therefore, presents a new approach that automatically extracts meaningful descriptors from individual audio and video sources of information using Deep Neural Networks (DNN) in order to then combine them, with the objective of detecting frustration in Game-Play scenarios. More precisely, two fusion modalities, namely decision-level and feature-level, are presented and compared with state-of-the-art methods, along with different DNN architectures optimized for each type of data. Experiments performed with a real-world audiovisual benchmarking corpus revealed that the multimodal proposals introduced herein are more suitable than those of a unimodal nature, and that their performance also surpasses that of other state-of-the–art approaches, with error rate improvements of between 40% and 90%.