Fundamental Concepts Research Articles

DESIGN ELEMENTS THAT QUALIFY HOUSING FOR SOCIAL INTEREST: CASE STUDY IN THE MUNICIPALITY OF CURIONÓPOLIS-PA

This article presents a study about the design limitations regarding Social Housing, observing approaches of flexibility, functionality, and adaptability included in the context of project aspects for family residences. These project elements can expand the perspectives for proposals of adapting the residences according to real families' demands and needs. This way, this research aims to carry out an approach about the main project elements to potentialize the quality of social housing from a bibliographical review, which grounded analyses in case studies carried out in Curionópolis-PA. The results comprise a synthesis of the fundamental concepts for the proposition of an alternative that comprehends the performance of these residences directed to the low-income public.

Multimodal emotion recognition: A comprehensive review, trends, and challenges

AbstractAutomatic emotion recognition is a burgeoning field of research and has its roots in psychology and cognitive science. This article comprehensively reviews multimodal emotion recognition, covering various aspects such as emotion theories, discrete and dimensional models, emotional response systems, datasets, and current trends. This article reviewed 179 multimodal emotion recognition literature papers from 2017 to 2023 to reflect on the current trends in multimodal affective computing. This article covers various modalities used in emotion recognition based on the emotional response system under four categories: subjective experience comprising text and self‐report; peripheral physiology comprising electrodermal, cardiovascular, facial muscle, and respiration activity; central physiology comprising EEG, neuroimaging, and EOG; behavior comprising facial, vocal, whole‐body behavior, and observer ratings. This review summarizes the measures and behavior of each modality under various emotional states. This article provides an extensive list of multimodal datasets and their unique characteristics. The recent advances in multimodal emotion recognition are grouped based on the research focus areas such as emotion elicitation strategy, data collection and handling, the impact of culture and modality on multimodal emotion recognition systems, feature extraction, feature selection, alignment of signals across the modalities, and fusion strategies. The recent multimodal fusion strategies are detailed in this article, as extracting shared representations of different modalities, removing redundant features from different modalities, and learning critical features from each modality are crucial for multimodal emotion recognition. This article summarizes the strengths and weaknesses of multimodal emotion recognition based on the review outcome, along with challenges and future work in multimodal emotion recognition. This article aims to serve as a lucid introduction, covering all aspects of multimodal emotion recognition for novices.This article is categorized under: Fundamental Concepts of Data and Knowledge > Human Centricity and User Interaction Technologies > Cognitive Computing Technologies > Artificial Intelligence

Transfer Learning Enabled Modeling Paradigm for PVT-aware Circuit Performance Estimation

Designing robust performance models for modern complex digital circuits in the face of rapidly accelerating process variations is a critical yet demanding task. This paper introduces an efficient statistical performance modeling approach for VLSI digital circuits that incurs minimal computational expense. The fundamental concept involves capitalizing on knowledge gained from circuit modeling in one technology node to streamline the modeling process in another. This is achieved by merging previously established statistical models of process technology with a limited set of simulation data from a subsequent process technology through transfer learning. Comprehensive experiments conducted across diverse technology nodes demonstrate that the proposed framework is robust, precise, efficient in data usage, and computationally superior to other cutting-edge performance modeling techniques.

Sustainable Sip: Learning Concepts Of Business Sustainability Through A Hybrid Game

We present Sustainable Sip, a hybrid multiplayer round-based game designed to provide an experiential learning environment for university-level students at the University of Queensland, Australia. It aims to facilitate literacy around the fundamental concepts of business sustainability, specifically the tensions and interplay between economics and sustainability, and systems thinking. This paper represents stage 1 of this project (design, development, testing). Impact assessment will be covered by stage 2 (application), which is outside the scope of this paper. Each player manages a coffee shop in a competitive market, seeking to maximise their customer base, whilst minimising the production of waste (unsold coffee tokens). Each round, players must decide how much coffee (inventory) to buy from the wholesaler, and the price to sell coffee to customers. Players aim to balance the dimensions of economic performance, environmental sustainability, and customer loyalty; the player that best achieves this wins the ‘Sustainable Sipper’ award. ‘Customer loyalty’ is a central concept, calculated in the app for each player and is influenced by the shop’s coffee price, quantity of waste the shop produces, and a player’s ability to meet customer demand. The game design explicitly draws from system thinking, user-centred and hybrid design, and from self-determination theory (motivation). Sustainable Sip has undergone early testing with preliminary feedback highlighting that the gameplay is simple, intuitive and interactive, aspects that are attributable in part to the game design and the hybrid approach.

Generative Adversarial Network Based Music Generation

Music has been an integral part of human civilization personally and culturally. Historically, music has been generated using various instruments, or natural sounds like water drops, or unconventional musical instruments like metal or glass-wares. At present, technologies like Musical Instrument Digital Interface (MIDI) are used to generate music electronically. This research investigates the use of Generative Adversarial Networks (GANs) for beginner-friendly music production. This model uses Long Short-Term Memory (LSTM) generator and Patch GAN as discriminator for the GAN architecture. The generator consists of input layer, embedding layer, LSTM layer and generates output with a SoftMax function. Similarly, the discriminator consists of a convolution layer, the output of which is averaged by the global average pooling layer, and output is generated by the sigmoid function. The model is trained on Maestro MIDI dataset. We make the process understandable by delving into the implementation specifics and outlining the fundamental concepts of music. Our effective model highlights the potential of GANs in music composition by producing cohesive music. After training for 50 epochs, the model exhibited a remarkable precision of 91.82 percent. This project uses the combination of Artificial Intelligence (AI) with music theory to provide intriguing new opportunities in the field of music. The model can be beneficial for different industries like gaming, music, entertainment, education, etc.

The Game of Dominoes as Teaching-learning Method of Basic Concepts of Differential Calculus

This paper will present teaching-learning mathematics strategies, specifically differential calculus in a variable, through a fun and competitive space in which action is manifested by the student and not only the repetition of information by the teacher. This action refers to motivate, problematise, summarise, and coordinate a dominoes game whose thematic cards are designed around the basic and main contents of differential calculus. The strategies for teaching this area are diverse and precisely the game of dominoes is one of the most used strategies in the practice of mathematics because it stimulates logical reasoning and mental skills. The objective is to identify the way in which the game of dominoes affects the learning and understanding of fundamental concepts of differential calculus in a variable by means of experimentation carried out on students of the first semester of the School of Engineering and Sciences of Technological of Monterrey. Finally, the results of this study will be presented and the use of this strategy in other topics around mathematics will be recommended to facilitate logical and meaningful learning in students.

A Survey on Federated Unlearning: Challenges, Methods, and Future Directions

In recent years, the notion of “the right to be forgotten” (RTBF) has become a crucial aspect of data privacy for digital trust and AI safety, requiring the provision of mechanisms that support the removal of personal data of individuals upon their requests. Consequently, machine unlearning (MU) has gained considerable attention which allows an ML model to selectively eliminate identifiable information. Evolving from MU, federated unlearning (FU) has emerged to confront the challenge of data erasure within federated learning (FL) settings, which empowers the FL model to unlearn an FL client or identifiable information pertaining to the client. Nevertheless, the distinctive attributes of federated learning introduce specific challenges for FU techniques. These challenges necessitate a tailored design when developing FU algorithms. While various concepts and numerous federated unlearning schemes exist in this field, the unified workflow and tailored design of FU are not yet well understood. Therefore, this comprehensive survey delves into the techniques and methodologies in FU providing an overview of fundamental concepts and principles, evaluating existing federated unlearning algorithms, and reviewing optimizations tailored to federated learning. Additionally, it discusses practical applications and assesses their limitations. Finally, it outlines promising directions for future research.

Internet of Things

The Internet of Things (IoT) represents a transformative trend in technology, enabling interconnected devices to communicate and exchange data seamlessly. This paper explores the fundamental concepts, current applications, challenges, and future prospects of IoT. It examines the impact of IoT on various industries, including healthcare, manufacturing, and smart cities, and discusses the security and privacy concerns associated with this technology. The study concludes with insights into future advancements and the potential societal implications of widespread IoT adoption

Electrocatalytic Nitrate Reduction for Brackish Groundwater Treatment: From Engineering Aspects to Implementation

In recent years, nitrate has emerged as a significant groundwater pollutant due to its potential ecotoxicity. In particular, nitrate contamination of brackish groundwater poses a serious threat to both ecosystems and human health and remains difficult to treat. A promising, sustainable, and environmentally friendly solution when biological treatments are not applicable is the conversion of nitrate to harmless nitrogen (N2) or ammonia (NH3) as a nutrient by electrocatalytic nitrate reduction (eNO3R) using solar photovoltaic energy. This review provides a comprehensive overview of the current advances in eNO3R for the production of nitrogen and ammonia. The discussion begins with fundamental concepts, including a detailed examination of the mechanisms and pathways involved, supported by Density Functional Theory (DFT) to elucidate specific aspects of ammonium and nitrogen formation during the process. Furthermore, the integration of artificial intelligence (AI) and machine learning (ML) offers promising advancements in enhancing the predictive power of DFT, accelerating the discovery and optimization of novel catalysts. In this review, we also explore various electrode preparation methods and emphasize the importance of in situ characterization techniques to investigate surface phenomena during the reaction process. The review highlights numerous examples of copper-based catalysts and analyses their feasibility and effectiveness in ammonia production. It also explores strategies for the conversion of nitrate to N2, focusing on nanoscale zerovalent iron as a selective material and the subsequent oxidation of the produced ammonia. Finally, this review addresses the implementation of the eNO3R process for the treatment of brackish groundwater, discussing various challenges and providing reasonable opinions on how to overcome these obstacles. By synthesizing current research and practical examples, this review highlights the potential of eNO3R as a viable solution to mitigate nitrate pollution and improve water quality.

7870 Exploring The Role Of ChatGPT In Pediatric Endocrinology Education; Exam Preparation And Question Generation

Abstract Disclosure: J. Tarkoff: None. A.G. Martinez Sanchez: None. Large language models (LLMs) hold substantial promise for improving physician knowledge and expertise. Their role in medical education and the generation of diverse diagnoses can be crucial, potentially leading to positive effects on clinical outcomes. The New England Journal of Medicine pediatric case challenges have examined ChatGPT [1]. Yet, its performance in specialized areas like Pediatric Endocrinology remains unexplored. This study assesses the effectiveness of ChatGPT 4 in responding to questions from the Pediatric Endocrine Self-Assessment Program (PESAP). It also examines if the AI is suitable for creating an educational quiz for residents and fellows. Methods: ChatGPT 4 underwent testing with questions from the 2021-2022 version of PESAP, utilizing the prompt: “Can you assist from the perspective of a pediatric endocrinologist with the following patient case”. Responses were evaluated for initial correctness, and performance was analyzed across various competency categories corresponding to the 7 “umbrella sections” of the tool (Adrenal, Bone, Carbohydrate and Lipid Metabolism/Obesity, Growth, Pituitary, Reproductive System, and Thyroid). Subsequently, we personalized the model by incorporating the questions and detailed responses from the PESAP. This customization resulted in a model that was then utilized to create a 10-question proof-of-concept quiz for four board-certified pediatric endocrinologists. The quiz included a scoring system designed to measure the extent and depth of ChatGPT knowledge. Results: ChatGPT 4 accurately answered 52% of PESAP questions, demonstrating varying performance across specific categories, ranging from 30% (Adrenal) to 78% (Reproductive System). In 16 questions, ChatGPT 4 did not provide an initial answer, requiring a specific request for a response. For questions related to thyroid cancer, explicit prompts were necessary to instruct responses based on the American Thyroid Association 2015 guidelines. In the endocrinologist quiz, the average score was 80%, ranging from 60% to 100%. Discussion: Currently, using ChatGPT 4 as the final diagnostic tool, especially in pediatric endocrinology, should be approached with caution, based on our assessment. However, when incorporating distinct Pediatric Endocrinology case studies, ChatGPT 4 successfully generated valuable educational questions, reinforcing fundamental concepts in the field. We anticipate that as LLMs advance and receive direct medical training, the out-of-the-box diagnostic accuracy will improve, leading to a transformative impact on medical education through this technology.

National Council on Radiation Protection (NCRP) 2024 annual meeting: advanced and small modular nuclear power reactors**The authors have confirmed that any identifiable participants in this study have given their consent for publication.

On 25-26 March 2023, the U.S. National Council on Radiation Protection and Measurements (NCRP) held its 2024 annual meeting in Bethesda, Maryland, USA. The NCRP dates from 1929, and this meeting celebrated the 60th anniversary of receiving a U.S. Congressional Charter. For this annual meeting the NCRP felt it was essential to provide a briefing about advanced and small modular nuclear reactors (SMRs). The Journal of Radiological Protection is delighted to publish the following synopsis of material presented at the U.S. NCRP meeting. This synopsis is divided into five sections. The first section provides an overview of the whole meeting together with summaries of two context setting overview papers. The following four sessions of this synopsis are specific to advanced and small modular nuclear power reactors. The meeting also included keynote presentations by three of NCRP annual award recipients. The meeting topical areas were Technology Overview and Critical Issues. The individual papers laid the groundwork to understanding reactor technologies, terminology, and the fundamental concepts and processes for electrical generation. The perspectives of the U.S. Environmental Protection Agency and states, through the Conference of Radiation Control Program Directors were provided. The papers included a discussion of diverse topics including potential emergency preparedness considerations, radiological survey requirements, an evaluation of the future of nuclear power, the economics of reactors (both large and small), and the critical issues identified by the recent National Academies of Sciences' study on advanced reactors. The summary papers were developed to briefly document the major points and concepts presented during the oral papers presented at the 2024 NCRP Annual Meeting. The meeting heralded the dawn of a new era for commercial nuclear power.

Hydrophobic assembly of molecular catalysts at the gas-liquid-solid interface drives highly selective CO2 electromethanation.

Molecular catalysts offer tunable active and peripheral sites, rendering them ideal model systems to explore fundamental concepts in catalysis. However, hydrophobic designs are often regarded as detrimental for dissolution in aqueous electrolytes. Here we show that established cobalt terpyridine catalysts modified with hydrophobic perfluorinated alkyl side chains can assemble at the gas-liquid-solid interfaces on a gas diffusion electrode. We find that the self-assembly of these perfluorinated units on the electrode surface results in a catalytic system selective for electrochemical CO2 reduction to CH4, whereas every other cobalt terpyridine catalyst reported previously was only selective for CO or formate. Mechanistic investigations suggest that the pyridine units function as proton shuttles that deliver protons to the dynamic hydrophobic pocket in which CO2 reduction takes place. Finally, integration with fluorinated carbon nanotubes as a hydrophobic conductive scaffold leads to a Faradaic efficiency for CH4 production above 80% at rates above 10 mA cm-2-impressive activities for a molecular electrocatalytic system.

ANALYZING COLLAGE AS A COMMUNICATION ACT IN THE DIGITAL AGE BASED ON THE DADAISM RENEWAL MOVEMENT'S SPIRIT

Collage combines the semiotic tradition of Roland Barthes, the phenomenological tradition of Edmund Husserl, the critical tradition of Karl Marx, and the point of view of Jurgen Habermas in visual communication. The collage technique creates trans-aesthetic elements and double codes, a Dadaist method of encoding signs. Building on previous research, this study explores collage art as a communication act and its process in the digital era. Digital photography technology and computer software facilitate quicker and easier creation of collage art, emphasizing the fundamental concept of "paste." This pasting-sticking process mirrors the communication process of art and design in the digital era, illustrating cyclical communication capable of transcending space and time. The research has several conclusions. (1) By enhancing visual communication strategies and efficiency based on the spirit of the Dadaism renewal movement, unique collages can be created as a personal sign of message(s). (2) Collages as communication act in the digital era represent dialogue in trans-aesthetics code. (3) On the other hand, with the spirit of Dadaism that contains a free mind, open creation, and emphasis on social-cultural issues, collages as a communication process are also possible to frame messages and create fallacy.

Overview of Single-Subject Design in Special Education Studies

In recent years, there has been an increase in the use of single-subject design in special education research. This paper provides an insightful overview of the application of single-subject design within special education studies. The paper delves into an exploration of the fundamental concepts underlying single-subject design, its historical origins, and the wide range of purposes it serves. Furthermore, an examination of the strengths and weaknesses inherent to single-subject design is undertaken. Finally, an evaluation of withdrawal design, multiple baseline design, and alternating treatment design and their special education research application are offered. In this evaluation, the researcher specifically examines how these designs can be applied in special education research contexts. By exploring these variations, this paper provides insights into the diverse ways single-subject design can be applied to address the unique research needs and challenges in special education.

Application and Impact of Marketing Mechanisms in the Perspective of New Public Management

This paper explores the application and impact of marketing mechanisms within the framework of New Public Management (NPM). It begins by outlining the fundamental concepts of NPM and its differences from traditional public management, highlighting its significance in adapting to the era of globalization and knowledge economy. The paper then analyzes the specific application of marketing mechanisms in China's public sector, particularly in the promotion and optimization of e-government service platforms, demonstrating how market research and data analysis enhance the quality and efficiency of public services. Finally, the paper summarizes the value created by marketing mechanisms for the public sector and emphasizes the necessity of continuous optimization in practice to balance efficiency with equity, and market mechanisms with public interest.

Mastering meta-analysis in Microsoft Excel with MetaXL add-in: A comprehensive tutorial and guide to meta-analysis.

Meta-analysis, a powerful technique for combining effect estimates from multiple studies, enhances statistical power and precision. However, its adoption can be hindered by challenges in statistical interpretation and the complexity of specialized software. MetaXL, a freely available Microsoft Excel add-in, aims to mitigate these barriers by providing comprehensive support and facilitating seamless integration of meta-analytical results into research publications. This tutorial illustrates the practical application of MetaXL for synthesizing meta-analytical evidence, with a focus on common effect sizes and their presentation. This paper reintroduce MetaXL's functions and provide concise explanations of common effect sizes employed in meta-analysis. The tutorial delves into fundamental concepts such as the selection of appropriate effect sizes for pooling and the choice of meta-analytical models. Eight illustrative examples are presented, incorporating diverse effect sizes and data formats, including scenarios involving incidence rate ratios, weighted and standardized mean differences, hazard ratios, and prevalence. Additionally, key concepts in network meta-analysis are discussed, along with their implementation in MetaXL. MetaXL provides convenient access to data formatting templates tailored to various data types and effect sizes encountered in included studies. This tutorial offers researchers, particularly those with limited resources, detailed explanations and insights into commonly used methodologies for pooling effect sizes. Furthermore, it introduces the new Excel functions that comes with the MetaXL add-in. Accurate population of this function and adherence to the correct format are essential to ensure error-free analyzes.

Designing classroom game for improving Kirchhoff's second law mastering

Kirchhoff’s second law is a fundamental concept in electricity and electronics. Therefore, DBR (Design-Based Research) which comprising 3 phases: Analysis, Design and Evaluation, was conducted to support mastery of the law. The sample consisted of 138 first-semester college students. In the Analysis phase, it was found that 63% of the students did not master Kirchhoff’s second law due to the following reasons: 1) They had forgotten about the law (66.3%), 2) The explanation of the law was difficult to understand (30.3%), and 3) They were not motivated to study the law (3.4%). In the Design phase, a classroom game named 'Time Bomb Defusing' was constructed to address these issues. In the game, Kirchhoff’s second law was used by students to identify the secret wires (wires that should be cut) and the decoys (wires that should not be cut) of dummy time bombs to defuse them. In the Evaluation phase, the game was reviewed by the sample, and the results were as follows: 1) Learning Kirchhoff’s second law through the game was perceived as enjoyable (87.7%), indicating that the law would be remembered longer, 2) The explanation in the game about using Kirchhoff’s second law to defuse dummy time bombs was easily understood (69.6%), and 3) Students were interested in using Kirchhoff’s second law to defuse dummy time bombs in the game (93.5%), indicating that the game stimulated their interest in studying the law. Based on these findings, it can be concluded that the 'Time Bomb Defusing' game has the potential to improve mastery of Kirchhoff’s second law.

A-101 Is That Unexpected? Education on Handling Real World Complications with Tightly Controlled Analytes During Method Evaluation

Abstract Background A common activity in clinical laboratories is performance evaluations. Given the importance for a new analyzer being put into clinical use, this exercise typically requires extensive preparation with significant time and resources invested to bring on new instrumentation. During early adoption, studies examined real-world performance and comparability of the Atellica® CI Analyzer (Atellica CI) to clinically used instruments. Precision, linearity, and method comparison (MC) studies, conducted in accordance with CLSI protocols, were completed at several locations installing new instruments to examine the analytical performance of common clinical laboratory assays. This work examines the impact of using only unmodified specimens to verify performance during method comparison for tightly controlled biological analytes like electrolytes and illustrates the impact of the tested sample range on regression analysis. Methods Testing was completed using unmodified, de-identified, remnant patient serum specimens. The MC studies for these electrolytes were completed over multiple days using single replicates from at least 40 individual specimens, on two instruments, the Atellica CI and the Dimension® EXL in accordance with CLSI EP09-3A. The effect of sample range on regression was demonstrated through data restriction and inclusion of modified sample results within the data analysis. Data analysis was completed using Analyse-it for Microsoft Excel (version 6.15.4). Results Representative method comparison data for sodium, chloride, and potassium found slope differences were more pronounced when sample concentrations were more limited. Individual assay slopes were determined using Deming linear regression model and comparisons between restricted samples show the effects of sample range on regression and correlation parameters. Conclusions Understanding the relationship between sample range and regression analysis is helpful to illustrate an important fundamental concept of method comparison. These examples illustrate the importance in covering as much of the analytical measuring interval as possible and the impact on regression analysis when samples are narrowly grouped.

Education in Cybersecurity – A Case Study

The inclusion of soft skills linked to Cybersecurity and cyber defense in the academic curricula of higher education courses is a very current issue. Several studies have been carried out in this regard, given the increasing importance of creating awareness about good practices in accessing computer networks, particularly the Internet. These studies primarily consist of recommendations on building the curricula of such courses, guiding the correct ways to do this. Our approach here is from the perspective of the students who may be the target of these cybersecurity skills. In this sense, we are developing a study in a Portuguese higher education institution to understand what higher education students think about this issue and their perception of the need for training in the area. We will ask students from various areas of education about this topic. We will then present an extended study when the preliminary results of a pilot test study are completed and shown. We questioned 423 students from the Informatics Engineering Degree course and analyzed the data obtained in the survey. Since then, this analysis of the data obtained in this study allows us to conclude that the students who participated in this study are very aware of issues related to cyber security and the need to be taught at least the fundamental concepts of cyber hygiene and cyber protection to all to all higher education students. In addition to this first conclusion, most of these students are concerned about the causes and consequences of cyber-attacks and the lack of knowledge that most people have about this problem.

Explaining maternal antenatal attachment by psychological, clinical and sociodemographic factors: a path analysis study

BackgroundAntenatal attachment is a fundamental concept relative to human transition to parenthood and may be influenced by several factors. The aim of this study is to find the best model to explain maternal antenatal attachment based on the interaction among sociodemographic, clinical, and psychological variables.MethodsPregnant women (N = 407) were interviewed during the second trimester of pregnancy while waiting for medical consultations. A sociodemographic, obstetric, and psychometric protocol was used. Statistical analyses: path analysis. Independent variables: psychopathological symptomatology, depression, anxiety, stress, occupational stress, attitudes about pregnancy and motherhood, coping styles, marital satisfaction, sociodemographic, and clinical variables. Dependent variables: maternal antenatal attachment in its several dimensions; quality of attachment (QA), intensity of preoccupation (IP), and global attachment (GA).ResultsDepression, anxiety, attitudes, and marital satisfaction explained 37% of maternal antenatal QA. Age, depression, anxiety, attitudes, and marital satisfaction explained 26% of maternal antenatal IP. Age, depression, anxiety, attitudes, and marital satisfaction explained 34% of the statistical variance of maternal antenatal GA.ConclusionsFactors like emotional states (depression and anxiety), attitudes towards pregnancy and motherhood, marital satisfaction, and a sociodemographic variable (age) contribute significantly for the explanation of maternal antenatal attachment.