Methodological Proposal Research Articles

Methodological Proposal for the Adaptation of the Living with Long-Term Conditions Scale to the Family Caregiver.

(1) Background: Living with long-term conditions affects both patients and family caregivers. To obtain a more complete overview of this phenomenon, a measurement instrument is needed that includes both perspectives. The aim is to adapt a scale to family caregivers of individuals with long-term conditions. (2) Methods: A methodological proposal is presented that illustrates the adaptation of the EC-PC scale to the family caregiver. Three phases are proposed: adaptation of the items, panel of experts, and pre-test. (3) Results: In the adaptation phase, the items from the original EC-PC were modified to adapt them to the family caregiver, and new items were added associated with the differences in living with LTC from the perspective of family caregivers. In the panel of experts phase, a universal agreement was reached related to the clarity, relevance, and essentiality of the items included. In the pre-test phase, the content of the scale was verified quantitatively and qualitatively. (4) Conclusions: The content of the items of version 5 of the EC-PC-Family showed a high index of inter-judge agreement. When a phenomenon affects both patients and their environment, such as living with LTC, it is necessary to include both perspectives in the measurement tools.

Robotic systems for upper-limb rehabilitation in multiple sclerosis: a SWOT analysis and the synergies with virtual and augmented environments.

The robotics discipline is exploring precise and versatile solutions for upper-limb rehabilitation in Multiple Sclerosis (MS). People with MS can greatly benefit from robotic systems to help combat the complexities of this disease, which can impair the ability to perform activities of daily living (ADLs). In order to present the potential and the limitations of smart mechatronic devices in the mentioned clinical domain, this review is structured to propose a concise SWOT (Strengths, Weaknesses, Opportunities, and Threats) Analysis of robotic rehabilitation in MS. Through the SWOT Analysis, a method mostly adopted in business management, this paper addresses both internal and external factors that can promote or hinder the adoption of upper-limb rehabilitation robots in MS. Subsequently, it discusses how the synergy with another category of interaction technologies - the systems underlying virtual and augmented environments - may empower Strengths, overcome Weaknesses, expand Opportunities, and handle Threats in rehabilitation robotics for MS. The impactful adaptability of these digital settings (extensively used in rehabilitation for MS, even to approach ADL-like tasks in safe simulated contexts) is the main reason for presenting this approach to face the critical issues of the aforementioned SWOT Analysis. This methodological proposal aims at paving the way for devising further synergistic strategies based on the integration of medical robotic devices with other promising technologies to help upper-limb functional recovery in MS.

Methodological Proposal for the Analysis of Coupling and Alteration Contacts

El presente trabajo describe una propuesta para el estudio de los contactos de acoplamiento y alteración. Empleando un procedimiento similar a los reportados en la literatura sobre seguimiento y captura (tracking and catching) se diseñó y adaptó una tarea en la que se presentaron dos situaciones en las que los participantes debían ajustarse por medio de su desplazamiento en la pantalla de una computadora a un objeto en movimiento. En la primera situación, las condiciones de ocurrencia del objeto en movimiento (velocidad o trayectoria) fueron constantes y no se modificaron por el patrón de respuesta desplegado por los participantes; en la segunda situación, el patrón de respuesta alteraba las condiciones de ocurrencia del objeto (su velocidad). La primera situación se consideró equivalente a una condición de acoplamiento; la segunda, a una de alteración. En los resultados se muestran ejemplares de las medidas molares que pueden obtenerse a partir de los registros. Se considera que el procedimiento es útil para la evaluación de los contactos de acoplamiento y alteración.

Situating data relations in the datafied home: A methodological approach

Studying datafication focusing on the microlevel of everyday life poses epistemological and methodological challenges. Indeed, the black-boxed nature of algorithms makes data inaccessible and unintelligible to the researcher. Therefore, this paper aims to advance a methodological proposal for addressing the situatedness of datafication in everyday life by framing mediatised relations as a proxy for data relations. In particular, this research adopts a “non-media-centric” approach and frames families as communicative figurations. By reporting a qualitative longitudinal study on the datafication of childhood and family life involving 20 families with at least 1 child aged 8 years or younger in Italy and by employing a mixed method constructivist grounded theory methodology that includes network methods, we analyse three families as exemplary of different network articulations. Such an approach, we argue, can help materialise the mediatised relations through, about, and around data that emerge in contemporary family life.

Novel Machine Learning Approach for DDoS Cloud Detection: Bayesian-Based CNN and Data Fusion Enhancements.

Cloud computing has revolutionized the information technology landscape, offering businesses the flexibility to adapt to diverse business models without the need for costly on-site servers and network infrastructure. A recent survey reveals that 95% of enterprises have already embraced cloud technology, with 79% of their workloads migrating to cloud environments. However, the deployment of cloud technology introduces significant cybersecurity risks, including network security vulnerabilities, data access control challenges, and the ever-looming threat of cyber-attacks such as Distributed Denial of Service (DDoS) attacks, which pose substantial risks to both cloud and network security. While Intrusion Detection Systems (IDS) have traditionally been employed for DDoS attack detection, prior studies have been constrained by various limitations. In response to these challenges, we present an innovative machine learning approach for DDoS cloud detection, known as the Bayesian-based Convolutional Neural Network (BaysCNN) model. Leveraging the CICDDoS2019 dataset, which encompasses 88 features, we employ Principal Component Analysis (PCA) for dimensionality reduction. Our BaysCNN model comprises 19 layers of analysis, forming the basis for training and validation. Our experimental findings conclusively demonstrate that the BaysCNN model significantly enhances the accuracy of DDoS cloud detection, achieving an impressive average accuracy rate of 99.66% across 13 multi-class attacks. To further elevate the model's performance, we introduce the Data Fusion BaysFusCNN approach, encompassing 27 layers. By leveraging Bayesian methods to estimate uncertainties and integrating features from multiple sources, this approach attains an even higher average accuracy of 99.79% across the same 13 multi-class attacks. Our proposed methodology not only offers valuable insights for the development of robust machine learning-based intrusion detection systems but also enhances the reliability and scalability of IDS in cloud computing environments. This empowers organizations to proactively mitigate security risks and fortify their defenses against malicious cyber-attacks.

Real-Time Point Recognition for Seedlings Using Kernel Density Estimators and Pyramid Histogram of Oriented Gradients

This paper introduces a new real-time method based on a combination of kernel density estimators and pyramid histogram of oriented gradients for identifying a point of interest along the stem of seedlings suitable for stem–stake coupling, also known as the ‘clipping point’. The recognition of a clipping point is a required step for automating the stem–stake coupling task, also known as the clipping task, using the robotic system under development. At present, the completion of this task depends on the expertise of skilled individuals that perform manual clipping. The robotic stem–stake coupling system is designed to emulate human perception (in vision and cognition) for identifying the clipping points and to replicate human motor skills (in dexterity of manipulation) for attaching the clip to the stem at the identified clipping point. The system is expected to clip various types of vegetables, namely peppers, tomatoes, and cucumbers. Our proposed methodology will serve as a framework for automatic analysis and the understanding of the images of seedlings for identifying a suitable clipping point. The proposed algorithm is evaluated using real-world image data from propagation facilities and greenhouses, and the results are verified by expert farmers indicating satisfactory performance. The precise outcomes obtained through this identification method facilitate the execution of other autonomous functions essential in precision agriculture and horticulture.

Developing a Social Vulnerability Index (SVI) for Risk Mapping

community, in Jilotepec, State of Mexico, with risk mapping purposes. Design/Methodology/Approach: A Social Vulnerability Index (SVI) was developed based on sociodemographic indicators, housing characteristics, and the conditions of the production systems. The information sources for the SVI indicators were obtained from official Basic Geostatistical Area (AGEB) censuses and from an online survey carried out in the community under study. The SVI was cross-referenced with data from four previously published natural hazard studies for risk mapping purposes. Results: The methodological proposal uses equal weight index statistical techniques to develop the SVI. Using online surveys is a clearly viable option for research studies that require to obtain more detailed data on housing characteristics, as well as the population’s perception of certain changes in precipitation and temperature patterns that are happening in their community and the measures, they take to face these natural phenomena. Study Limitations/Implications: Online surveys are relatively recent, which implies the need to design and implement validation and sampling mechanisms for the results. Findings/Conclusions: Risk mapping enabled the territorial visualization and identification of the communal vulnerability conditions, which facilitates a conceptual approach to the social reality of the population and will allow the formulation of potential future scenarios of climate change and the implementation of public policies.

Advancements in Unsupervised Learning: Mode-Assisted Quantum Restricted Boltzmann Machines Leveraging Neuromorphic Computing on the Dynex Platform

The integration of neuromorphic computing into the Dynex platform signifies a transformative step in computational technology, particularly in the realms of machine learning and optimization. This advanced platform leverages the unique attributes of neuromorphic dynamics, utilizing neuromorphic annealing - a technique divergent from conventional computing methods - to adeptly address intricate problems in discrete optimization, sampling, and machine learning. Our research concentrates on enhancing the training process of Restricted Boltzmann Machines (RBMs), a category of generative models traditionally challenged by the intricacy of computing their gradient. Our proposed methodology, termed “quantum mode training”, blends standard gradient updates with an off-gradient direction derived from RBM ground state samples. This approach significantly improves the training efficacy of RBMs, outperforming traditional gradient methods in terms of speed, stability, and minimized converged relative entropy (KL divergence). This study not only highlights the capabilities of the Dynex platform in progressing unsupervised learning techniques but also contributes substantially to the broader comprehension and utilization of neuromorphic computing in complex computational tasks.

Fourier transform infrared spectroscopy and machine learning for Porphyromonas gingivalis detection in oral bacteria.

Porphyromonas gingivalis, a Gram-negative anaerobic bacillus, is the primary pathogen in periodontitis. Herein, we cultivated strains of oral bacteria, including P. gingivalis and the oral commensal bacteria Actinomyces viscosus and Streptococcus mutans, and recorded the infrared absorption spectra of the gases released by the cultured bacteria at a resolution of 0.5cm-1 within the wavenumber range of 500-7500cm-1. From these spectra, we identified the infrared wavenumbers associated with characteristic absorptions in the gases released by P. gingivalis using a decision tree-based machine learning algorithm. Finally, we compared the obtained absorbance spectra of ammonia (NH3) and carbon monoxide (CO) using the HITRAN database. We observed peaks at similar positions in the P. gingivalis gases, NH3, and CO spectra. Our results suggest that P. gingivalis releases higher amounts of NH3 and CO than A. viscosus and S. mutans. Thus, combining Fourier transform infrared spectroscopy with machine learning enabled us to extract the specific wavenumber range that differentiates P. gingivalis from a vast dataset of peak intensity ratios. Our method distinguishes the gases from P. gingivalis from those of other oral bacteria and provides an effective strategy for identifying P. gingivalis in oral bacteria. Our proposed methodology could be valuable in clinical settings as a simple, noninvasive pathogen diagnosis technique.

Thermochemistry: teaching concepts from the construction of a collaborative website

The present work is about the development of a teaching methodology proposal from the use of technological resources, which was guided the development of a collaborative website on the Google Sites platform, from a literature review on directed sites, about the concepts of endothermic and exothermic reactions, as well as about the applicability of thermochemistry in everyday life. The project occurred with students from the 2nd grade of high school in a state school in Cariacica, Espírito Santo, which enabled the construction of a technological artifact and promoted the transmission of knowledge in an applicable way.

BERT-Based Medical Chatbot: Enhancing Healthcare Communication through Natural Language Understanding

The advent of modern technologies like Artificial Intelligence(AI), Internet of Things(IoT) and Deep Learning(DL) has ushered in a transformative era in healthcare, offering innovative solutions towards personalized healthcare by enhancing the quality of various medical services. Our proposed methodology involves the development of a BERT-based medical chatbot, leveraging cutting-edge deep learning technology to significantly enhance healthcare communication and accessibility. The traditional challenges faced by medical chatbots, such as imprecise understanding of medical conversations, inaccurate responses to jargon, and the inability to offer personalized feedback, are addressed through the utilization of Bidirectional Encoder Representations from Transformers (BERT). The performance metrics of our chatbot underscore its effectiveness. With an accuracy of 98%, the chatbot ensures a high level of precision in handling medical queries. The precision score of 97% attests to the accuracy and reliability of its responses. The AUC-ROC score of 97% indicates the chatbot's exceptional ability to predict specific diseases based on user queries and symptoms, showcasing its robust predictive power. Furthermore, a recall of 96% demonstrates the chatbot's capability to avoid missing cases in medical diagnoses, ensuring comprehensive coverage of potential conditions. The F1 score of 98% showcases the chatbot's proficiency in delivering accurate and personalized healthcare information, striking a harmonious balance between precision and recall. Our BERT-based medical chatbot not only addresses the limitations of traditional approaches but also achieves a remarkable performance with high accuracy, precision, predictive power, and comprehensive coverage, making it a valuable tool for advancing the quality of healthcare services.

GPOSYSH: Genomic Prediction of Oryza Sativa Yield and Subpopulation Using Hybrid Methods.

Most of the existing studies focus on genetic methods or Machine learning models, in this, we implemented a hybrid combination of genetic methods and machine learning models that accurately predicted phenotypic trait yield, height and subpopulation. Our proposed methodology for genomic prediction of yield in Oryza sativa (rice) involves a two-level classification approach. First, we classify biological sequences and cluster them using the UPGMA algorithm on a phylogenetic tree. Then, we use advanced machine learning techniques like Random Forest, and K-Nearest Neighbours to predict GEBVs with 85- 95% accuracy on rice subpopulations. we achieved an accuracy of 93% when compared with other stated literature in this paper. This approach overcomes limitations and effectively enhances crop breeding by capturing the genotype-phenotype relationship.

Fire Risk Analysis: A Proposal for Historic Buildings in Brazil

ABSTRACT Fire risk analysis is increasingly being applied to buildings as a tool for managing the risk of a fire event, and the extent of its consequences. However, such methods give different results depending on the fire code under which the building was designed, which can compromise the effectiveness of this type of analysis. Given that regulatory requirements for fire safety differ from place to place, methodologies focused on solely one legislation may not be applicable to buildings constructed under other legislation. This paper presents a proposal for a fire risk analysis methodology based on established parameters, independent of local legislation requirements. The method proposed in this article is called Cálculo Escalonar de Perigo de Incêndio — CEPI (Scalar Calculation of Fire Hazard). The aim is to make a free method, available to fire safety professionals; and to provide a practical tool for measuring fire safety in Brazilian heritage buildings.

METHODOLOGY FOR ASSESSING THE EFFECTIVENESS OF THE AIR DEFENSE SYSTEM

This academic article introduces a pioneering methodology for the comprehensive assessment of air defense systems, addressing existing shortcomings in evaluation approaches. The subject of this study revolves around the development and implementation of an advanced methodology for assessing air defense systems. еThe primary aim is to rectify existing evaluation shortcomings by introducing a holistic model that significantly enhances both efficiency and effectiveness in the evaluation of air defense systems. This methodology incorporates critical elements such as system description, mission, objectives, combat environment, threat, and concept. By explicitly considering factors like system flexibility, survivability, and operational concepts, the study surpasses traditional evaluations, providing a nuanced understanding of the capabilities inherent in air defense systems. The methodology places particular emphasis on the importance of threat analysis, addressing uncertainties related to enemy forces and tactics. Additionally, the study introduces an innovative evaluation model employing tactical scenarios to assess system reliability, availability, and durability, integrating combat environment factors and potential adversary combat options. The research contributes to the academic discourse by providing a systematic and thorough approach to air defense system evaluation, tailored to the complexities of modern warfare and in alignment with the evolving military and technological landscapes. The article suggests avenues for future research to delve into nuanced criteria selection and aggregation methods, aiming to further refine the proposed evaluation methodology.

Education for sustainable development in art, science, technology, and the environment: Is there a methodological proposal for school and higher education?

<p>This article aims to examine the relationship between art, science, technology, and the environment to transform the vitality of developing methodological strategies through education for sustainable development. The main goal is to generate a greater importance of a new demand through the selective didactic proposal in different educational systems, both for school education and for higher education. The new methodological foundations for rethinking and reviewing the knowledge of educational research through documentary study and sustainability in the general educational system. This model of sustainable development in education—schooling and higher education—involves actions and activities based on the concept of sustainable development. There is a new search for different modalities and educational subjects for sustainable development. The new methodological orientation in both educational systems makes it possible to create teaching tools that address sustainability, tolerance, and self-care responsibility towards the future of climate change.<strong></strong></p>

All in a moment: Continuous response measurement method for analyzing dynamic science communication content

Science communication strategy plays a crucial role in effectively conveying scientific information to target audiences. While various models exist for developing communication strategies, many focus on measuring the effectiveness of communication efforts and adjusting these based on audience feedback. However, traditional methods of evaluating communication effectiveness often measure one component at a time and do not consider how people make decisions in real-life situations. This paper proposes the use of continuous response measurement (CRM) as a method to evaluate science communication in agricultural social science research. CRM allows for real-time measurement of how individuals make decisions in response to dynamic communication content. This paper compares different types of CRM, including in-person and virtual dial CRM, and provides resources for researchers interested in implementing this methodology. The paper also discusses various research designs that can be used with CRM, such as experimental designs, survey designs, time-series designs, focus groups, and coding content and behavior. The benefits and limitations of CRM are outlined, highlighting the need for immediate feedback and real-time response in science communication campaigns. In-person CRM is discussed, including the selection of stimuli, response prompts, data collection procedures, and data analysis. Virtual CRM is also examined, highlighting its advantages in terms of flexibility and cost-effectiveness. The paper concludes by discussing data output and analysis methods for CRM data. Overall, this paper serves as a methodological proposal for the use of CRM in agricultural social science research, emphasizing the importance of real-time measurement and response in science communication.

A hybrid econometrics and machine learning based modeling of realized volatility of natural gas

Determining which variables affect price realized volatility has always been challenging. This paper proposes to explain how financial assets influence realized volatility by developing an optimal day-to-day forecast. The methodological proposal is based on using the best econometric and machine learning models to forecast realized volatility. In particular, the best forecasting from heterogeneous autoregressive and long short-term memory models are used to determine the influence of the Standard and Poor’s 500 index, euro–US dollar exchange rate, price of gold, and price of Brent crude oil on the realized volatility of natural gas. These financial assets influenced the realized volatility of natural gas in 87.4% of the days analyzed; the euro–US dollar exchange rate was the primary financial asset and explained 40.1% of the influence. The results of the proposed daily analysis differed from those of the methodology used to study the entire period. The traditional model, which studies the entire period, cannot determine temporal effects, whereas the proposed methodology can. The proposed methodology allows us to distinguish the effects for each day, week, or month rather than averages for entire periods, with the flexibility to analyze different frequencies and periods. This methodological capability is key to analyzing influences and making decisions about realized volatility.

Enhancing chemical synthesis: a two-stage deep neural network for predicting feasible reaction conditions

In the field of chemical synthesis planning, the accurate recommendation of reaction conditions is essential for achieving successful outcomes. This work introduces an innovative deep learning approach designed to address the complex task of predicting appropriate reagents, solvents, and reaction temperatures for chemical reactions. Our proposed methodology combines a multi-label classification model with a ranking model to offer tailored reaction condition recommendations based on relevance scores derived from anticipated product yields. To tackle the challenge of limited data for unfavorable reaction contexts, we employed the technique of hard negative sampling to generate reaction conditions that might be mistakenly classified as suitable, forcing the model to refine its decision boundaries, especially in challenging cases. Our developed model excels in proposing conditions where an exact match to the recorded solvents and reagents is found within the top-10 predictions 73% of the time. It also predicts temperatures within ± 20 °C of the recorded temperature in 89% of test cases. Notably, the model demonstrates its capacity to recommend multiple viable reaction conditions, with accuracy varying based on the availability of condition records associated with each reaction. What sets this model apart is its ability to suggest alternative reaction conditions beyond the constraints of the dataset. This underscores its potential to inspire innovative approaches in chemical research, presenting a compelling opportunity for advancing chemical synthesis planning and elevating the field of reaction engineering.Scientific contributionThe combination of multi-label classification and ranking models provides tailored recommendations for reaction conditions based on the reaction yields. A novel approach is presented to address the issue of data scarcity in negative reaction conditions through data augmentation.Graphical

Participatory integrated coastal management in island regions: a methodological proposal based on tourism

This study is aimed to understand political decision-making processes in two islands: Ilha Grande in the state of Rio de Janeiro, Brazil and Ile d’ Óléron in France. Our intention is to propose a participative coastal management model for insular regions. To elaborate management models, we believe it is necessary to make deep diagnosis relating scientific and local knowledge. Theoretically, concepts of interdisciplinarity and complexity need to be considered together with an embracing view, including socioenvironmental issues and social participation, present in public policies.

Children’s Thoughts on Wonder in Fairy Tales

AbstractFairy tales are a means to introduce the Polish language and social education exercises in Polish kindergartens and schools (early childhood literature education). In their methodological proposals, authors of school handbooks use fairy tales as a medium for teaching moral values. The Goldfish is an example of such an approach (Fałdzińska and Rączyńska, 2017). This article presents the thoughts of children aged six to nine (Polish kindergarten and primary school years one to three) constructed around the fairy tale in question, which, on the one hand, can be understood as a cautionary lesson about the importance of remaining satisfied with one’s station (Tatar 2014), and on the other serves as a meta-tale, exposing a rule of the genre’s poetics (Somoff, 2019). Assuming the role of observer-as-participant, intent on providing interpretations, the author of this study aims to identify children’s categories of wonder occurring in Aleksandr Afanasyev’s The Goldfish (Afanasyev, 1984). It was observed that children reflect on the rules of wonder in fairy tales (as culture-specific texts) and derive social meanings significant from the viewpoint of fairy tale categories. As such, they were not only limited to the lesson of condemning greed, which is often assigned to The Goldfish.