Implementation Of Methodology Research Articles

Cartographic animations, a supporting technology for education

Abstract. To date, basic theoretical and methodological ideas of animated cartography have been formed, as well as general schemes for animated maps compilation. However, the technological issue remains relevant in this domain due to the abundance of modern tools that can be used for compilation of animated maps. Particularly, most of currently implemented cartographic animation methodologies require the use of a variety of specialized software, including geographic information systems (GISs), vector and raster graphical editors, video effects editing software, and others. So the data processing and conversion techniques are extremely complicated in some cased, when data converted from one software platform to another.In our study, we established investigation of popular desktop GISs functionality application as a full-cycle animated cartography environment. In such a context, we discover approaches to improve methodology of animated maps automated compilation. To prototype a full-cycle animation in GIS environment, we developed a set of animation algorithms implemented in QGIS and documented the algorithms using UML (Unified Modeling Language) diagrams.

Serum microRNA Biomarker Expression in HIV and TB: A Concise Overview.

Non-coding RNAs (ncRNAs), specifically MicroRNAs or miRNAs, are now under-stood to be essential regulators in the complex field of gene expression. By selectively bind-ing to certain mRNA targets, these tiny RNA molecules control the expression of genes., leading to mRNA degradation or translational repression. The discovery of miRNAs has sig-nificantly advanced biomedical research, particularly in elucidating the molecular mecha-nisms underlying various diseases and exploring innovative therapeutic approaches. Recent progress in miRNA research has provided insights into their biogenesis, functional roles, and potential clinical applications. Despite the absence of established methodologies for clinical implementation, miRNAs show great promise as diagnostic and therapeutic agents for a wide array of diseases. Their distinctive attributes, such as high specificity, sensitivity, and accessibility, position them as ideal candidates for biomarker development and targeted therapy. Achieving a comprehensive understanding of miRNA biology and functionality is crucial to fully harnessing their potential in medicine. Ongoing research efforts aim to un-ravel the intricate mechanisms of miRNA-mediated gene regulation and to develop novel approaches for utilizing miRNAs in disease diagnosis, prognosis, and treatment. This review provides a comprehensive analysis of current knowledge on miRNAs, focusing on their bio-genesis, regulatory mechanisms, and potential clinical applications. By synthesizing existing evidence and highlighting key research findings, this review aims to inspire further explora-tion into the diverse roles of miRNAs in health and disease. Ultimately, this endeavour could result in the development of innovative miRNA-based diagnostic and therapeutic strategies.

Numerical Treatment for the Caputo-Fabrizio Fractional Smoking Model using an Efficient Numerical Technique

Smoking is a prevalent social behavior widely practiced worldwide, especially in settings such as schools and some significant gatherings. Based on the World Health Organization (WHO), smoking is the third leading source of human mortality and the primary preventable cause of disease. This study introduces a highly efficient simulation technique to analyze and solve the Caputo-Fabrizio (CF) fractional smoking model. We numerically solve the fractional integral equations (FIEs) with Simpson’s 1/3 rule, an effective numerical integration technique. We concentrate on clarifying the stability/convergence of the proposed strategy. We juxtapose the outcomes derived using the Runge-Kutta method (RK4) with those obtained through the implemented methodology. The findings indicate that the used technique provides a simple and efficient instrument for simulating the solution of these models. The principal advantage of the proposed method is its dependence on a limited number of straightforward steps, devoid of long-term consequences or reliance on a perturbation parameter.

Comparative Analysis of Huffman Coding Implementations for Efficient Data Communication Using Greedy and Divide-and-Conquer Techniques

Efficient data compression techniques are required to minimize storage and processing overhead due to modern systems' growing amount of data. Huffman Coding is a lossless compression technique that maintains data integrity by assigning shorter bit codes to characters appearing frequently, reducing size. Our analysis focuses on two implementation methodologies: greedy technique and divide and conquer. To find efficient solutions, divide-and-conquer algorithms partition problems into smaller components. In contrast, greedy algorithms strive to attain the utmost attainable result at each level. Our extensive investigation centers on the timing and space intricacies of diverse methodologies, enabling a comparative analysis that underscores their respective merits and drawbacks.

Shape optimization for high efficiency metasurfaces: theory and implementation

Complex non-local behavior makes designing high efficiency and multifunctional metasurfaces a significant challenge. While using libraries of meta-atoms provide a simple and fast implementation methodology, pillar to pillar interaction often imposes performance limitations. On the other extreme, inverse design based on topology optimization leverages non-local coupling to achieve high efficiency, but leads to complex and difficult to fabricate structures. In this paper, we demonstrate numerically and experimentally a shape optimization method that enables high efficiency metasurfaces while providing direct control of the structure complexity through a Fourier decomposition of the surface gradient. The proposed method provides a path towards manufacturability of inverse-designed high efficiency metasurfaces.

5.A. Scientific session: From theory to action - Applying the Medical Research Council framework on complex interventions

Abstract The reasons for the workshop: Globally, public health problems such as obesity, poor mental health, and social inequality in health are on the rise. Tackling these significant challenges requires well-designed complex interventions. Interventions rarely follow linear sequences from intervention design toward large-scale implementation. Rather, they take non-linear trajectories by constantly adapting the intervention design according to the context. This thinking is outlined in the British Medical Research Council’s (MRC) 2021 framework for developing and evaluating complex interventions. This workshop encompasses four case projects, each corresponding to one of the four phases in the framework: i) development, ii) feasibility, iii) implementation, and iv) evaluation. However, recognising the deliberately generic nature of this framework, our workshop also encompasses co-creation, process evaluation, and implementation theories and methodology. Combining these methodologies presents challenges that necessitate clear argumentation and critical reflection. In this workshop, we will share the challenges encountered when applying the MRC 2021 framework and hopefully inspire future innovative opportunities in intervention research. Specifically, this workshop aims to: i) exchange challenges from intervention research methods and theories drawing on presenters’ experiences across four public health case projects. Presenters will critically reflect on the key concepts from the MRC framework including context, programme theory, engagement of stakeholders, uncertainties, and refinement of interventions. ii) discuss opportunities for future complex intervention research and outline best practices for using the MRC 2021 framework for complex interventions. The added value of organising the workshop: The audience attending this workshop will reflect on multiple on-the-ground experiences of applying methodology and principles from the MRC 2021 framework as well as methods and theories relevant to intervention research: co-creation, qualitative (e.g., ethnography, interviews) and quantitative (e.g. experimental, observational) methods applied to all core phases of complex interventions. At the end of the workshop, the audience will be invited to participate in an open, plenary discussion facilitated by the workshop’s chairs: two international experts on complex interventions, Reader Rhiannon Evans and Prof. Helle Terkildsen Maindal. The experts will encourage the presenters and audience to reflect critically on the case projects and ways forward for complex intervention research. Coherence between the presentations in relation to the workshop topic: The four case projects are guided by the MRC 2021 framework for complex interventions and involve public health interventions at the individual, interpersonal, and structural levels in Denmark and Canada. Key messages • Tackling public health challenges demands innovative and well-designed complex interventions combining research traditions, principles from health promotion, and cutting-edge best-practice frameworks. • The symposium will create a platform for cross-disciplinary knowledge exchange through case projects and expert-led joint discussions on how to apply the MRC 2021 framework for complex interventions.

Simulation of the growth and yield of the species Gmelina arborea Roxb

Forests are being threatened worldwide mainly due to the increase in demand for wood and that more than 50% is extracted from them. This is largely because forest plantations grow at a slow rate and supply the market. Given that, one of the fundamental goals of forestry research is the construction of growth and yield models to be used as a planning tool. In this way, the proposal of this work is to show how through the modeling and simulation of the growth of a forest stand, the uncertainty of investors is reduced because it allows to quantify the production of wood that will be obtained. The study was based on data obtained from inventories conducted between 2012 and 2015 in 31 permanent plots of Gmelina arborea Roxb., located in three municipalities of Tolima: Armero, Coello, and Guamo. Based on the inventory data, three regression models were implemented. These results will be particularly useful for silvicultural management planning since the implemented methodology can be applied in other regions of the world and with other varieties of forest species. As an additional stimulus for investors, in the simulation it is possible to quantify carbon capture in a technical way to be offered in the carbon bond market.

Longitudinal Analyses of Mental Health in Autistic Individuals: A Systematic Review.

Co-occurring mental health conditions affect autistic individuals at high rates, impacting their well-being and quality of life. Mental health conditions are often appropriate treatment targets that can improve the lives of autistic individuals. Because of this, there is growing interest in predictors of mental health and behavioral outcomes. Given the rapidly evolving evidence base and growing literature using longitudinal cohorts, it is unclear which predictors of symptoms of anxiety and depression are consistent, and which are not. Additionally, it is difficult to deduce which predictors of mental health symptoms at a given time also predict change over time. This can be partially due to the different statistical approaches that are implemented, including trajectory vs. non-trajectory methodologies. We conducted a systematic review to evaluate how non-trajectory and trajectory analyses inform our knowledge of how symptoms of anxiety and depression change over time. Additionally, we aimed to identify important predictors of change and later anxiety and depressive symptoms in autistic individuals. There is variability in symptoms of anxiety and depression in autistic individuals. Adaptive skills arose as significant predictors of change and of later symptoms of both anxiety and depression. Peer relationships in school age seem to be particularly important in predicting later symptoms of depression. This review provides evidence that there are different trajectories and different patterns of mental health symptoms over the lifespan, providing further evidence that autism is a developmental condition that changes over time in different ways for different people. Implications and future directions are further discussed.

Video Content Analysis for Detection of Road Traffic Congestion Pattern: An Optimized YOLOv8 Based Approach

This paper presents a new approach to Video Content Analysis (VCA) with a specific focus on detecting and analyzing road traffic congestion patterns. The implemented methodology deploys the YOLOv8 (You Only Look Once) object detection framework, which has been optimized to accurately and efficiently identify road traffic objects within video streams. The primary goal is to improve traditional VCA systems by capturing and understanding complex road congestion scenarios. This research develops a customized YOLO model optimized specifically for road traffic analysis. The optimization process addresses the challenges inherent in real-world traffic scenarios including variations in lighting conditions, diverse vehicle types and dynamic traffic flow patterns. The developed model seeks to enable robust and real-time detection of congestion-related events such as heavy congestion and normal congestion thereby contributing to improved traffic management and overall road safety. Moreover, the paper explores the integration of advanced techniques for congestion pattern analysis including vehicle detection, tracking, and counting. The combination of these features facilitates a comprehensive understanding of traffic dynamics and congestion evolution over time. The proposed approach is trained and evaluated on benchmark datasets and real-world video footage to assess its performance. Experimental results demonstrate the effectiveness of the optimized YOLOv8-based approach in accurately detecting and analyzing road traffic congestion patterns. The system exhibits promising capabilities in handling diverse and challenging scenarios thereby serving as a valuable tool for intelligent transportation systems, urban planning and traffic management. This research significantly contributes to the advancement of video-based traffic analysis, providing a robust solution for monitoring and mitigating congestion-related challenges on road networks.

РОЛЬ KPI ТА OKR В ЕФЕКТИВНОСТІ ДІЯЛЬНОСТІ КОМПАНІЇ

The article addresses the implementation and effective utilization of two crucial management methodologies - Key Performance Indicators (KPIs) and Objectives and Key Results (OKR) - in the context of the contemporary business environment. The significance of both methods is analyzed in the backdrop of increasing competition and constant industry changes, necessitating novel approaches to strategic management and the exploration of methods to enhance overall company efficiency. Various aspects of employing KPIs and OKRs in different types of companies and at various stages of their development are highlighted. Special attention is given to examining how both methodologies can coexist and interact to achieve maximum effectiveness in strategic management. A methodology for the joint implementation of KPIs and OKRs is proposed, taking into account the unique needs and characteristics of each approach. The outlined steps include analyzing the company's strategy, identifying key areas for measurement, formulating specific objectives through OKRs, and determining corresponding KPIs. Roles and significance of Key Performance Indicators and Objectives and Key Results in different types of companies are analyzed, demonstrating how their combined usage can create synergy and ensure strategic success. The overall conclusion of the article underscores the importance of establishing an integrated management system that combines the stability of KPIs with the flexibility of OKRs. The necessity for continuous improvement and adaptation of these methodologies in the context of a dynamic business environment is emphasized. In summary, the article makes a significant contribution to understanding and applying strategic management methodologies, fostering the development of modern approaches to enhancing overall business efficiency.

Analytical model of an active noise control system for performance analysis of adaptive algorithms in HVAC system

In real-world acoustic scenarios related to the problem with the low-frequency noise such as HVAC systems, the signal is a stochastic, non-stationary and time-varying process where the performance of the active noise control technique mainly depends on the characteristics of the adaptive filtering techniques. In this paper, a method for analytical modeling of active noise control of signals in MATLAB/Simulink is presented, in which the acquisition of audio signals is provided by using a National Instruments control and acquisition module. For the realization of the active noise control, an adaptive filtering system model for signal analysis has been developed, using three adaptive algorithms (LMS,RLS and NLMS). The analysis of the characteristics of each of the three investigated adaptive algorithms is performed according to four parameters: Mean Square Error (MSE), convergence speed, stability and robustness. The conclusions are presented through a comparative analysis of the results obtained from the implemented methodology. The individual characteristics and parameters that are observed for correct adaptation and optimal results of each of the adaptive filters are explained in detail.

COMPUTER DIAGNOSTICS OF THE CONDITION OF SHIP ROLLING BEARINGS DURING THEIR OPERATION

The results of statistical processing of vibration signals obtained during diagnostics of ship power plants during the operation process are offered. A distinctive feature of monitoring the technical condition of elements of ship power plants is the uncertainty in the fixation and duration of extreme loads. The peculiarity of computer vibration diagnostics of rotary mechanisms is determined, which is that, in particular, physical measurements of vibration signals require their use in mathematical models of the physical state of the bearing. Calculations of statistical characteristics of vibration signals in the time domain, such as standard deviation (Std), asymmetry (Skewness), kurtosis (Kurtosis), full range of oscillations (Peak2Peak), root mean square value (RMS), cross factor (CrestFactor), form- factor (ShapeFactor), impulse factor (ImpulseFactor), marginal factor (MarginFactor), energy (Energy). The variety of diagnostic signals leads to the need to merge indicators of different dimensions without losing available information into a single generalizing indicator, for which the method of principal components is used in the work. The methodology of statistical processing and practical implementation of diagnostics of vibration signals during the analysis of damage evolution of rolling bearings of turbochargers of marine power plants are described. A new computer information parameter of vibration diagnostics based on the analysis of the first principal component in the time domain and spectral excess in the frequency domain has been identified and experimentally confirmed. The use of new diagnostic parameters makes it possible not only to judge the degree of damage to the rolling bearing, but also to observe the dynamics of the development of the defect and make a relative forecast of the working life of the rolling bearing. A real practical situation is considered - computer diagnostics of vibration signals of ship rolling bearings of a point defect of an external defect of a rolling bearing ring. Determining the applicability of the principal component method to other types of defects showed their potential for vibrodiagnosis of defects located on the inner ring of the bearing and when the ball falls out of the holder.

Inova Lab Una -Financial Education: Proposal Based on Teaching, Mobile App and Augmented Reality to Raise Awareness and Financial Control

The Brazilian population demonstrates a low level of financial planning, which results in financial problems, unpayable debts, credit restrictions and problems that result in a low quality of life and access to resources. This issue also implies unfeasibility for investments, such as education and professional development. Currently, new laws have been instituted by the Brazilian government, promulgating this approach in basic education. Economics was brought into educational institutions among the cross-cutting themes. This paper aims to present the development of an educational solution of an application for teaching and practicing financial education based on schools. Based on development in mobile solutions, with foundations in financial education and scoring in pedagogical terms, an app with capabilities and information that are weighted by teaching and finance was built. In its implementation methodology, tools such as BPMN (Business Process Model and Notation) and an Adapted Canvas were used to define the project scope. An experimental process with AR (Augmented Reality), for an initial evaluation, connecting a gamified and playful association for the efforts of users to be connected. This first stage of the research aims to academically substantiate and demonstrate the foundations of this solution, which is mainly aimed at Brazilian public education, which is so lacking in investments and accessible tools to optimize the learning of its students.

Efficient End-to-End Convolutional Architecture for Point-of-Gaze Estimation.

Point-of-gaze estimation is part of a larger set of tasks aimed at improving user experience, providing business insights, or facilitating interactions with different devices. There has been a growing interest in this task, particularly due to the need for upgrades in e-meeting platforms during the pandemic when on-site activities were no longer possible for educational institutions, corporations, and other organizations. Current research advancements are focusing on more complex methodologies for data collection and task implementation, creating a gap that we intend to address with our contributions. Thus, we introduce a methodology for data acquisition that shows promise due to its nonrestrictive and straightforward nature, notably increasing the yield of collected data without compromising diversity or quality. Additionally, we present a novel and efficient convolutional neural network specifically tailored for calibration-free point-of-gaze estimation that outperforms current state-of-the-art methods on the MPIIFaceGaze dataset by a substantial margin, and sets a strong baseline on our own data.

Evolution of Requirements Engineering in Agile Methodology – Literature Review

Requirements Agile approaches have transformed engineering. This paper shows how RE in Agile software development has evolved from documentation-heavy to collaborative, adaptable, and customer-focused. Agile was born in the mid-1990s when the industry realized it needed to respond to changing client needs and market volatility. This evolution includes iterative development, client interaction, and emphasizing communication above documentation, as discussed in the paper. By comparing conventional and Agile RE approaches, we demonstrate the benefits of adapting to change, working with customers, and delivering functional software faster. This analysis provides a persuasive description of Agile RE implementation methodologies and resources through a detailed literature review and real-world experiences. User stories and backlog refinement are notable techniques. The research finishes by exploring how these techniques affect team dynamics, project success, and customer satisfaction. RE's Agile difficulties and opportunities are also examined. The findings illuminate RE methods' successful adaptation to Agile projects' dynamic character. Software development is more responsive and effective due to this adaptation.

Реализация требований федеральной рабочей программы основного общего образования при обучении геометрии

The article is based on the main key ideas of formation, planned meta-subject results. The article considers the methodology of implementation of the requirements of the federal working program of basic general education in teaching mathematics, taking into account the goals of teaching mathematics. An approach to the formation of cognitive universal educational actions of students in teaching geometry is proposed, using educational tasks and educational assignments.

Tour and Travel Services Company Website Design with Enhanced Awareness of Transportation Safety

<p><em>Transportation safety is a critical aspect of the tour and travel industry. With increased focus on passenger safety and comfort, tour and travel companies must adopt information technology to enhance safety aspects. This article discusses the design and development of a tour and travel company website that is more safety-conscious, integrating important features such as NIK usage, passenger tracking, real-time notifications, and customer awareness education regarding safety. The design process employs the Analysis, Design, Development, Implementation, and Evaluation (ADDIE) methodology. In summary, the new features added to the booking system have greatly improved user satisfaction and operational efficiency for the company. The safety-conscious website has positively impacted passenger safety and comfort. Technologies like NIK integration, real-time tracking, and automatic notifications have made the service more reliable and user-friendly. However, there's still a need for better education on safety requirements for drivers and vehicles, as many users admit to lacking understanding in this area. Continued efforts to educate users transparently can enhance trust and safety in transportation services.</em></p>

Proactively Defending Enterprise Computer Systems Against Threats and Vulnerabilities

Cybersecurity remains a critical concern, even amidst global events like the COVID-19 pandemic. The rise of COVID-19 has deepened cybersecurity threats, with phishing emails and phone scams attempting to exploit the situation. This paper focuses on proactive strategies to protect enterprise environments against threats and vulnerabilities, specifically in Windows-based systems. Tracing threats and vulnerabilities to their source aims to address them in their early stages rather than after an attack has occurred. This research tackles three prevalent issues: phishing emails, vulnerability patching, and industrial internet-connected devices. Through analyzing various cyber defense models and vulnerability databases, this paper proposes frameworks to mitigate these issues effectively. The study includes a detailed examination of sources of threats and vulnerabilities, aiming to develop methodologies for practical implementation. Ultimately, the goal is to summarize best practices to enhance tool utilization and process improvement and propose new proactive defense methods. The research emphasizes the shift from reactive to proactive defense strategies to better protect enterprise networks.

Investigating the Role of Change Management in Achieving Sustainable Project Management in Construction Firms

Abstract Over the past years, the construction sector has seen significant transformation driven by sustainability and change management, as most construction companies strive to lower emissions, be future-proof, and slow down the rate of climate change. While these two disciplines have distinct goals, they share essential principles. Both change management and sustainable project management emphasize addressing the environmental, social, and economic aspects while maintaining ethics, transparency, and stakeholder demands. According to previous studies, incorporating sustainability concepts into project management systems could enhance organizational value, risk control, cost savings, stakeholders’ relationships, and long-term value generation. In the context of change management, integrating sustainability into project management systems strives to ensure that initiatives are not only financially feasible but also socially and ecologically responsible. Despite previous research in those fields, further investigation and application of the implementation methodologies are still recommended. The main aim of this paper is to investigate the relationship between sustainability in the project management field, its relation to change management procedures, and how it could be beneficial for the implementation process in the construction industry. A constructive literature review was adapted to develop a potential primary framework for future implementation, which facilitates future attempts at construction organization implementation processes.

StrainNet-LD: Large Displacement digital image correlation based on deep learning and displacement-field decomposition

First, a displacement-field decomposition strategy is proposed that effectively enhances the accuracy of digital image correlation (DIC) algorithms under large displacement or deformation conditions. The implementation methodologies of displacement-field decomposition under both Lagrangian and Eulerian descriptions are derived and presented. Second, displacement-field decomposition is applied to speckle image correlation algorithms based on deep learning, resolving the contradiction between registration capability of large displacement and the accuracy. A CUDA-accelerated and residual learning-based strain fitting algorithm is proposed for high-precision and real-time strain computation. This method is named “StrainNet for Large Displacement” (StrainNet-LD). StrainNet-LD achieves a calculation speed of 768 × 768 × 6 fps (768 × 768 × 3 fps with strain calculated simultaneously) on GPU with displacement MAE < 0.05 pixels and strain MAE < 0.002ε. Finally, rubber tensile tests, 3D motion measurement of composite shells, and morphology reconstruction experiments of speckle-free composite repair structures are conducted to validate the algorithm's robustness and efficiency. Some vital discussions of displacement-field decomposition are given in the Appendixes. The code and dataset are available at https://github.com/GW-Wang-thu/StrainNet-LD.