Core Methodology Research Articles

RNA-Binding Proteins as Novel Effectors in Osteoblasts and Osteoclasts: A Systems Biology Approach to Dissect the Transcriptional Landscape.

Bone health is ensured by the coordinated action of two types of cells-the osteoblasts that build up bone structure and the osteoclasts that resorb the bone. The loss of balance in their action results in pathological conditions such as osteoporosis. Central to this study is a class of RNA-binding proteins (RBPs) that regulates the biogenesis of miRNAs. In turn, miRNAs represent a critical level of regulation of gene expression and thus control multiple cellular and biological processes. The impact of miRNAs on the pathobiology of various multifactorial diseases, including osteoporosis, has been demonstrated. However, the role of RBPs in bone remodeling is yet to be elucidated. The aim of this study is to dissect the transcriptional landscape of genes encoding the compendium of 180 RBPs in bone cells. We developed and applied a multi-modular integrative analysis algorithm. The core methodology is gene expression analysis using the GENEVESTIGATOR platform, which is a database and analysis tool for manually curated and publicly available transcriptomic data sets, and gene network reconstruction using the Ingenuity Pathway Analysis platform. In this work, comparative insights into gene expression patterns of RBPs in osteoblasts and osteoclasts were obtained, resulting in the identification of 24 differentially expressed genes. Furthermore, the regulation patterns upon different treatment conditions revealed 20 genes as being significantly up- or down-regulated. Next, novel gene-gene associations were dissected and gene networks were reconstructed. Additively, a set of osteoblast- and osteoclast-specific gene signatures were identified. The consolidation of data and information gained from each individual analytical module allowed nominating novel promising candidate genes encoding RBPs in osteoblasts and osteoclasts and will significantly enhance the understanding of potential regulatory mechanisms directing intracellular processes in the course of (patho)physiological bone turnover.

How Did Early Modern Scholars Study Early Maps?

The two standard Anglophone historiographies of the history of cartography (Skelton 1972, Harley 1987) situated the field’s origins in the early modern era, or even in the later Middle Ages. Yet “history” and “cartography” are both products of the profound changes in the intellectual foundations and institutions of European knowledge practices in the decades to either side of 1800. By contrast, this essay explores how early modern scholars variously engaged with maps from the past in terms of contemporary knowledge practices. [Part 1] It first outlines the early modern meanings of “history” and “geography” and then reviews how early modern scholars engaged with early maps in three distinct ways. Classical scholars used the Peutinger map and Ptolemy’s Geography to identify locations of ancient places in order to improve their comprehension of Classical texts. [Part 2] Some geographers wrote histories of voyages and travels that related past routes to contemporary maps, and others wrote historical narratives of the compilation of encyclopedic texts and maps of regional and world knowledge. [Part 3] Antiquaries opportunistically described and at times reproduced a variety of maps, charts, and plans that came to their attention, but without active searches for early maps. Overall, I demonstrate that before 1775 there was neither a systematic approach taken to the study of maps from the past nor any hint of the core methodology that would be adopted by the first historiographical mode of map history as it developed after 1830.

Optimizing smart home appliance energy monitoring using Factorial Hidden Markov Models for Internet of Behavior

The energy demand which is increasing globally strongly underlines the necessity of solutions that will improve power efficiency. This study addresses the research problem of enhancing energy management in smart homes through Non-Intrusive Load Monitoring (NILM), a method that enables consumers and companies to optimize their energy usage by disaggregating total household energy consumption into specific appliance-level data. The research focuses on the creation and establishment of an Internet of Behavior (IoB) model that encourages NILM to develop more energy-efficient and clever residential buildings as its main goal. The Factorial Hidden Markov Model (FHMM) is employed as the core methodology in this NILM process, facilitating the disaggregation of overall household energy usage into individual appliance consumption patterns. This method was used over the known Reference Energy Disaggregation Dataset (REDD) for comparing the actual energy consumption of the appliances with the projected estimates. The results are evidence that the FHMM method is an energy disaggregation technique that can provide important information about the customer usage of energy and, hence will help in the strategic shifting of high-energy appliances to non-peak hours for the user. This study's main value lies in the new application of IoB-based NILM methods to increase energy efficiency, which is the real solution for energy costs and the grid peak load problem. The results of the proposed method are compared with other methods and real data. The comparisons show the acceptable response of the proposed method when it is compared with the real data and is better than another method.

Hybrid Quantum or Purely Classical? Assessing the Utility of Quantum Feature Embeddings

Background As graph datasets—including social networks, supply chains, and bioinformatics data—grow in size and complexity, researchers are driven to search for solutions enhancing model efficiency and speed. One avenue that may provide a solution is Quantum Graph Learning (QGL), a subfield of Quantum Machine Learning (QML) that applies machine learning inspired or powered by quantum computing to graph learning tasks. Methods We reevaluate Quantum Feature Embeddings (QFE), a QGL methodology published by Xu et al. earlier this year. QFE uses Variational Quantum Circuits to preprocess node features and then sends them to a classical Graph Neural Network (GNN), with the goal of increasing performance and/or decreasing total model size. Xu et al. evaluated this methodology by comparing its performance with the performance of variously-sized classical models on the benchmark datasets PROTEINS and ENZYMES, and they report success. Our core methodology and learning task remain unchanged. However, we have made several changes to the experimental design that enhance the rigor of the study: 1) we include the testing of models with no embedder; 2) we conduct a thorough hyperparameter search using a state-of-the-art optimization algorithm; and 3) we conduct stratified five-fold cross-validation, which mitigates the bias produced by our small datasets and provides multiple test statistics from which we can calculate a confidence interval. Results We produce classical models that perform comparably to QFE and significantly outperform the small classical models used in Xu et al.’s comparison. Notably, many of our classical models achieve this using fewer parameters than the QFE models we trained. Xu et al. do not report their total model sizes. Conclusion Our study sheds doubt on the efficacy of QFE by demonstrating that small, well-tuned classical models can perform just as well as QFE, highlighting the importance of hyperparameter tuning and rigorous experimental design.

Causal relationship between immune cells and epilepsy mediated by metabolites analyzed through Mendelian randomization

Our study investigated the causal relationship between immune cells, metabolites, and epilepsy using two-sample Mendelian Randomization (MR) and mediation MR analysis of 731 immune cell traits and 1400 metabolites. Our core methodology centered on inverse-variance weighted MR, supplemented by other methods. This approach was crucial in clarifying the potential intermediary functions of metabolites in the genetic links between traits of immune cells and epilepsy. We found a causal relationship between immune cells and epilepsy. Specifically, the genetically predicted levels of CD64 on CD14-CD16- are positively correlated with the risk of epilepsy (p < 0.001, OR = 1.0826, 95% CI 1.0361–1.1312). Similarly, metabolites also exhibit a causal relationship with both immune cells (OR = 1.0438, 95% CI 1.0087–1.0801, p = 0.0140) and epilepsy (p = 0.0334, OR = 1.0897, 95% CI 1.0068–1.1795), and sensitivity analysis was conducted to further validate these relationships. Importantly, our intermediate MR results suggest that the metabolite Paraxanthine to linoleate (18:2n6) ratio may mediate the causal relationship between immune cell CD64 on CD14-CD16- and epilepsy, with a mediation effect of 5.05%. The results suggest the importance of specific immune cell levels and metabolites in understanding epilepsy’s pathogenesis, which is significant for its prevention and treatment.

WRF-Comfort: simulating microscale variability in outdoor heat stress at the city scale with a mesoscale model

Abstract. Urban overheating and its ongoing exacerbation due to global warming and urban development lead to increased exposure to urban heat and increased thermal discomfort and heat stress. To quantify thermal stress, specific indices have been proposed that depend on air temperature, mean radiant temperature (MRT), wind speed, and relative humidity. While temperature and humidity vary on scales of hundreds of meters, MRT and wind speed are strongly affected by individual buildings and trees and vary on the meter scale. Therefore, most numerical thermal comfort studies apply microscale models to limited spatial domains (commonly representing urban neighborhoods with building blocks) with resolutions on the order of 1 m and a few hours of simulation. This prevents the analysis of the impact of city-scale adaptation and/or mitigation strategies on thermal stress and comfort. To solve this problem, we develop a methodology to estimate thermal stress indicators and their subgrid variability in mesoscale models – here applied to the multilayer urban canopy parameterization BEP-BEM within the Weather Research and Forecasting (WRF) model. The new scheme (consisting of three main steps) can readily assess intra-neighborhood-scale heat stress distributions across whole cities and for timescales of minutes to years. The first key component of the approach is the estimation of MRT in several locations within streets for different street orientations. Second, mean wind speed and its subgrid variability are downscaled as a function of the local urban morphology based on relations derived from a set of microscale LES and RANS simulations across a wide range of realistic and idealized urban morphologies. Lastly, we compute the distributions of two thermal stress indices for each grid square, combining all the subgrid values of MRT, wind speed, air temperature, and absolute humidity. From these distributions, we quantify the high and low tails of the heat stress distribution in each grid square across the city, representing the thermal diversity experienced in street canyons. In this contribution, we present the core methodology as well as simulation results for Madrid (Spain), which illustrate strong differences between heat stress indices and common heat metrics like air or surface temperature both across the city and over the diurnal cycle.

Mental health, positive psychology and forgiveness: a positive autoethnographic case study of Everett Worthington

Purpose The purpose of this article is to elicit understanding of how forgiveness, religion and spirituality, and relationships can better our lives. It draws from the life of Everett L. Worthington, Jr, a positive psychologist and Commonwealth Professor Emeritus at Virginia Commonwealth University. He has published almost 50 books and over 500 scholarly articles or chapters. Design/methodology/approach This is a case study, followed by a ten-question interview. The core methodology is positive autoethnography which is embraced by Worthington to reveal life lessons from things he has done. Findings Worthington reveals a life honoring the interwoven lives of people. He has studied forgiveness intensively and finds it to be an essential way of making our way in the world and in a world community that all too often hosts hurt. Research limitations/implications An extensive literature has developed to understand what forgiveness is, how it comes about naturally and how the REACH Forgiveness method can help people who struggle to forgive themselves or others, and do it more quickly, thoroughly and frequently. Forgiving has psychological, social, spiritual and physical benefits to the forgiver. Practical implications This article is filled with practical information on how to forgive and how to pursue eudaemonia, which Worthington defines as virtue for oneself. Social implications Forgiveness has widespread social implications. Good relationships are those that can help form, maintain, grow and repair when damaged close emotional bonds. Forgiveness helps repair, maintain and grow those bonds. Originality/value Worthington has been instrumental in the establishment and growth of the subfield of forgiveness studies and in the study of humility.

How safety value is exchanged in Chinese modern megaprojects: A stakeholder value network perspective

To visualize and quantify these exchanges among primary stakeholders in megaprojects, this study introduces network stakeholder value network as the core methodology. Starting with a qualitative organizational safety behavior model encompassing 8 stakeholders and 61 value flows, it's built based on 3 organizational safety behavior categories. Moreover, questionnaire data from 356 experts quantifies the model, and the design structure matrix identifies 13845 safety value paths, including 4827 cycles. Analysis delves into critical cycles, stakeholders, flows, and organizational safety behavior types to refine safety strategies. The findings, based on Chinese megaprojects, suggest that contractors and governments must lead safety awareness efforts, with safety compliance paramount for all parties. The study's contributions are threefold: firstly, it broadens the scope to encompass organizational safety behavior; secondly, it introduces a novel approach to visualize and quantify safety value flows; and thirdly, it considers multiple types of value exchanges, underscores their significance in promoting safety value, and offers practical recommendations to strengthen construction safety performance.

Foodscape of Pakistan: A Qualitative Investigation of Intentions, Themes and Social Realities in Food Vlogging

This qualitative research paper delves into the captivating world of Pakistani food vloggers, investigating the profound how’s and whys behind their use of social media platforms to generate and share delectable food-related content. With a focus on comprehending the underlying intentions that drive these vloggers in creating their culinary masterpieces, the study conducted in-depth interviews with four renowned food vloggers: Adeel Chaudhry, Foodies by Ashir, Saqib Mobeen, and Hamza Haram. Employing thematic analysis as the core methodology, the research unveils the captivating reality crafted by food vloggers through their interactions with the audience. It draws inspiration from the symbolic interactionist perspective and the sociological theories of George Herbert Mead and Herbert Blumer. Within the intricate tapestry of these culinary journeys, three prominent themes emerge foodscapes, media influence, and psychological tools. This pioneering research not only offers profound insights into the domain of food vlogging and marketing but also presents a novel perspective on how individuals in the digital age construct their social realities through dynamic interactions with their followers. As this study uncovers the artistry behind the culinary creations and the strategic utilization of social media, it paves the way for further research and exploration in this enthralling realm of food vlogging.

Partial discharge localization in power transformer tanks using machine learning methods

This paper presents a comparison of machine learning (ML) methods used for three-dimensional localization of partial discharges (PD) in a power transformer tank. The study examines ML and deep learning (DL) methods, ranging from support vector machines (SVM) to more complex approaches like convolutional neural networks (CNN). Multiple case studies are considered, each with different attributes, including sensor position, frequency content of the PD signal, and size of the transformer tank. The paper focuses on predicting the PD location in three-dimensional space using single-sensor electric field measurements. Various aspects of each method are analyzed, such as the input signal, core methodology, correlation coefficient between the predicted location and the actual location, and root mean square error (RMSE). These features are discussed and compared across the different methods. The results indicate that the CNN model exhibits superior performance in terms of location accuracy among the methods considered.

Enhancing Flood Impact Analysis through the Integration of Landsat and MODIS Imagery

This article explores the efficacy of integrating Landsat and MODIS satellite imagery for comprehensive flood impact analysis. By employing advanced remote sensing technologies and sophisticated data processing techniques, this study offers a methodological framework that enhances the precision and depth of environmental analysis. The core methodology involves the systematic processing of satellite data, including radiometric and geometric corrections, combined with the use of analytical indices such as the Normalized Difference Water Index (NDWI) and the Enhanced Vegetation Index (EVI). These indices play a crucial role in accurately delineating water bodies and assessing the extent of flooding. The approach not only improves the reliability of flood mapping but also contributes to the broader understanding of environmental changes and aids in effective disaster management. Through this study, we demonstrate how strategic data integration can provide valuable insights for policymakers, enhancing responses to environmental crises.

Geospatial-based decision support system for prioritizing road segments for maintenance and rehabilitation

Given the growing demand for moving goods and people and the escalating number of vehicles on highways, issues related to road safety have become common concerns. Additionally, the aging of the road infrastructure and the intensification of climatic change effects reinforce the problem of road pavement serviceability. However, accessing funds for road maintenance and having the autonomy for pure technical decisions remain significant challenges for transport authorities. Thus, this paper addresses developing a geospatial-based systematic process for prioritizing highway segment candidates for maintenance and rehabilitation. The investigation was designed to facilitate the decision-making process for the state transportation authority to allocate funds over the 20.315 km of major highways in Minas Gerais, Brazil. The method consisted of ranking the highway segments regarding the geographic context to which each segment belongs, as well as the operational characteristics of the highways. The approach meets the United Nations Sustainable Development Goals by considering socio-economic and demographic perspectives along with other traditional engineering characteristics such as the available transport infrastructure, the traffic volume, the highway capacity, the pavement serviceability and the potential for deterioration, the condition of the surface drainage, and weather-related data. Transportation practitioners with expertise in pavement maintenance and rehabilitation and transportation managers contributed to the development by weighting the factors according to the best possible technical judgment regarding prioritizing funds. The GIS-oriented Delphi framework resulted in a priority index. The index allowed the elaboration of a ranking of priorities, which is vital information regarding public policies and the transparent use of public money for road maintenance and rehabilitation within the State of Minas Gerais. The model is robust and outputs geoinformation and tabular data to feed the decision-making process in an organized and transparent manner. Therefore, the core methodology can be easily transferable to other jurisdictions.

A unifying perspective on non-stationary kernels for deeper Gaussian processes

The Gaussian process (GP) is a popular statistical technique for stochastic function approximation and uncertainty quantification from data. GPs have been adopted into the realm of machine learning (ML) in the last two decades because of their superior prediction abilities, especially in data-sparse scenarios, and their inherent ability to provide robust uncertainty estimates. Even so, their performance highly depends on intricate customizations of the core methodology, which often leads to dissatisfaction among practitioners when standard setups and off-the-shelf software tools are being deployed. Arguably, the most important building block of a GP is the kernel function, which assumes the role of a covariance operator. Stationary kernels of the Matérn class are used in the vast majority of applied studies; poor prediction performance and unrealistic uncertainty quantification are often the consequences. Non-stationary kernels show improved performance but are rarely used due to their more complicated functional form and the associated effort and expertise needed to define and tune them optimally. In this perspective, we want to help ML practitioners make sense of some of the most common forms of non-stationarity for Gaussian processes. We show a variety of kernels in action using representative datasets, carefully study their properties, and compare their performances. Based on our findings, we propose a new kernel that combines some of the identified advantages of existing kernels.

AI-ENABLED CUSTOMER EXPERIENCE ENHANCEMENT IN BUSINESS

This scholarly investigation delves into the transformative impact of Artificial Intelligence (AI) on enhancing customer experience in the business realm. The study's purpose was to meticulously examine the integration, evolution, and strategic implications of AI in business operations, particularly in customer engagement. A comprehensive literature review and detailed case study analysis constituted the core methodology, focusing on peer-reviewed articles and practical examples from diverse business sectors. This approach facilitated a multi-dimensional exploration, capturing both the technological advancements in AI and the associated implementation challenges within various business contexts. Central findings from this research underscore AI's evolution from an emerging technological tool to a fundamental component in customer-centric business strategies. AI's capabilities in personalizing customer interactions, automating support systems, and leveraging predictive analytics have revolutionized business-customer dynamics. However, this evolution is not without its challenges, including data privacy concerns, ethical considerations, and the need for skilled AI expertise. The study concludes that AI is a strategic asset, necessitating thoughtful integration into business models. It emphasizes the importance of a collaborative approach, where AI specialists and industry experts work synergistically to tailor AI solutions to specific business needs. Ethical considerations and maintaining customer trust are highlighted as pivotal in AI deployment strategies. The study recommends continuous innovation, investment in AI infrastructure and talent, and adherence to ethical AI practices. These measures are essential for businesses to enhance customer experiences and drive sustainable growth in the digital age&#x0D; Keywords: Artificial Intelligence, Customer Experience, Business Strategy, AI Integration, Ethical Considerations.

EKONOMI MATEMATIKA: BAGAIMANA FUNGSI LINEAR MENENTUKAN KESIMBANGAN HARGA PASAR

The primary objective of this scientific inquiry is to thoroughly investigate and expound upon the practical application and implementation of linear function equations within the domain of economic mathematics. The central focus lies in dissecting the intricate modeling of market equilibrium prices, with a specific emphasis on the assessment of consumer and producer surpluses associated with essential commodities, such as rice. The core methodology of this study involves an in-depth literature review, which serves as the backbone for understanding the nuances of market equilibrium price models and the nuanced application of linear equations within economic mathematics. The economic and business disciplines are often considered semi-precise sciences, demanding a nuanced blend of qualitative and quantitative analyses to achieve a comprehensive understanding of their multifaceted dynamics. In this context, quantitative analysis in economics and business invariably necessitates the utilization of mathematics as an indispensable tool. Hence, this study consciously adopts a quantitative research approach, a methodological process rooted in the systematic acquisition of knowledge using numerical data as a tool to dissect and analyze crucial information relevant to the subject matter. Delving into the intricacies of supply and demand laws through the lens of linear functions, the study ultimately concludes that the adoption of mathematical models grounded in linear functions holds the potential to effectively determine the demand and supply functions, especially concerning staple commodities like rice. Moreover, it proves instrumental in identifying and comprehending market equilibrium, furnishing invaluable insights that can significantly contribute to a nuanced understanding and effective management of economic phenomena. In essence, this research aims not only to contribute to the theoretical discourse in economic mathematics but also to provide practical insights that can inform decision-making processes within the realms of economics and business.

Mechatronics in modern industrial applications: Delving into the integration of electronics, mechanics, and informatics

This study delves into the integration of electronics, mechanics, and informatics within the realm of mechatronics, emphasizing its pivotal role in modern industrial applications. The primary objective is to dissect the synergistic interplay of these disciplines and assess their collective impact on various industrial sectors. Employing a systematic literature review as the core methodology, the research draws on a wide array of peer-reviewed articles and academic journals, focusing on publications from the last two decades to ensure relevance and contemporaneity. Key findings reveal that mechatronics has significantly evolved from its conceptual inception to a cornerstone technology in industries such as manufacturing, automotive, and healthcare. The study highlights the advancements in mechatronic systems, including the integration of AI and IoT, which have led to enhanced efficiency, precision, and adaptability in industrial processes. Furthermore, the research identifies the economic, technological, and environmental impacts of these systems, underscoring their role in driving innovation and sustainability. The study concludes with actionable recommendations for industry stakeholders and policymakers, emphasizing the need for continuous investment in R&amp;D, workforce training, and the development of ethical standards and regulations. It also proposes future research directions, particularly focusing on the scalability, sustainability, and long-term societal impacts of mechatronic systems. This research contributes to a deeper understanding of mechatronics in the industrial domain, offering insights into its current applications and future potential.

The Alignment between International and National Academic Accreditations - An Application in Information Systems Bachelor Program at Kingdom of Saudi Arabia

This study proposes a method for consolidating various program accreditations into a unified accreditation process for a single academic program. The primary challenge lies in harmonizing the diverse requirements of multiple accreditations. In the context of academic technology programs in Saudi Arabia, this research focuses on the integration of NCAAA and ABET accreditations, with a specific application to the Information Systems at King Abdulaziz university. The core methodology employed in this study involves applying Bloom's taxonomy of learning to identify the synergies between the accreditation criteria of ABET and NCAAA. To ascertain the alignment of learning outcome measurement based on both accreditations, a questionnaire was administered to faculty members within the College of Computer Science and Information Technology at renowned universities in the Kingdom of Saudi Arabia. This article is intended for researchers specializing in the quality of education in computing and engineering, as well as professionals, developers, and officials with an interest in academic accreditations. Furthermore, this research seeks to establish a foundation for the seamless dissemination of educational content through websites and digital applications, adhering to the standards and principles of academic accreditation.

Supply Chain Resilience Meets Quality Management

Purpose: This study is aimed at supply chain resilience and quality management, which are critical in today's volatile global economy marked by various disruptions. The focus is on how these traditionally separate disciplines can be combined to enhance organizational performance.&#x0D; &#x0D; Theoretical framework: The theoretical framework revolves around the concepts of supply chain resilience, which includes elements like proactive risk assessment, adaptability, collaborative networks, strategic redundancies, and quality management principles. The research scrutinizes their interplay, revealing significant overlap and implications for organizational outcomes.&#x0D; &#x0D; Design/Methodology/Approach: An exploratory case study approach forms the core methodology, primarily utilizing secondary data to gain insights into real-world scenarios. This approach enables a deep understanding of the critical factors influencing supply chain resilience and the impact of quality management concepts on customer satisfaction and product/service quality in supply chain processes. The questionnaire is distributed to get insights into the company's practices.&#x0D; &#x0D; Findings: The findings indicate that integrating supply chain resilience with quality management leads to more sustained and improved organizational outcomes. A resilient supply chain is quicker to recover from disruptions, minimizing financial impacts. Concurrently, a steadfast focus on quality ensures customer satisfaction and loyalty. This integration offers synergistic benefits, contributing to operational efficiency, market competitiveness, and a more substantial brand reputation.&#x0D; &#x0D; Research, Practical &amp; Social implications: The study provides insights into critical factors of supply chain resilience and their interconnections with quality management concepts. These insights can inform strategies for enhancing resilience and quality management in supply chains. Practically, the study offers a guide for organizations to adopt a unified approach that optimizes supply chain resilience and quality management. This approach is particularly relevant for organizations seeking to improve operational efficiency and customer satisfaction through better risk management and quality assurance processes. Socially, the study highlights the importance of resilient and high-quality supply chains in today’s business landscape by maintaining Sustainable Developmental Goals.&#x0D; &#x0D; Originality/Value: This research highlights how resilience, characterized by adaptability and robust recovery in disruptive scenarios, and stringent quality management, crucial for meeting customer expectations, are fundamental in modern supply chains. The study emphasizes the importance of a risk management culture and the role of digital technologies in enhancing supply chain resilience, underscoring their vital contributions during crises like the COVID-19 pandemic. Thus, this work offers valuable insights into achieving more resilient and high-quality supply chains in today's challenging business environment through the integration of Quality Management Tools.

Enhancing Security in Database Grid Infrastructure for Storage Clusters

This research project primarily focused on improving security by addressing vulnerabilities and creating a cost-scalable storage cluster solution, for applications. The Oracle Grid Infrastructure for Storage Clusters (OGISC) represents cutting-edge advancements in this field. The research extensively explores the philosophy of OGISC. Explains its core methodology. Recognizing the weaknesses of existing security mechanisms, we propose a solution to enhance security measures. A key aspect of our investigation involves integrating Glusterfs, a system known for its ability to scale linearly. We delve into the architecture of the solution demystifying the storage scale-out processes to its operation. Our study goes beyond integration by developing an approach and metadata model specifically tailored for Glusterfs ensuring optimal performance and robustness. One noteworthy aspect of our research is the application of Glusterfs compression with OpenVPN. This exploration highlights the benefits derived from this integration emphasizing how OpenVPN enhances Glusterfs capabilities. Rigorous analysis stages serve as the foundation for our findings resulting in a forward-thinking solution. Finally, we conclude this research with an examination of avenues for future exploration in this dynamic field.

Vertical vs. Horizontal Fractal Dimensions of Roads in Relation to Relief Characteristics

This paper investigated the surface length of roads from both horizontal and vertical perspectives using the theory of fractal dimension of surfaces and curves. Three progressive experiments were conducted. The first demonstrated the magnitude of the differences between the planar road length and the DTM-derived surface road length and assessed its correlation with the DTM-calculated road slope. The second investigated the road distance complexity through the fractal dimension in both planar and vertical dimensions. The third related the vertical with the horizontal fractal dimension of roads across a range of distinct physiographic regions. The study contributed theoretically by linking the planimetric complexity to vertical complexity, with clear applications for advanced transportation studies and network analyses. The core methodology used geographic information systems (GIS) to integrate a high resolution (1 × 1 m) digital terrain model (DTM) with a road network layer. A novel concept, the vertical fractal dimension of roads was introduced. Both the vertical and horizontal fractal dimensions of the roads were calculated using the box-counting methodology. We conducted an investigation into the relationship between the two fractal dimensions using fourteen study areas within four distinct physiographic regions across Slovenia. We found that the average slope of a three-dimensional (3D) road was directly related to the length difference between 3D and two-dimensional (2D) roads. The calculated values for the vertical fractal dimension in the study areas were only slightly above 1, while the maximum horizontal fractal dimension of 1.1837 reflected the more sinuous properties of the road in plan. Variations in the vertical and horizontal fractal dimensions of the roads varied between the different physiographic regions.