Analytical Foundation Research Articles

Business Model Adaptation For Foreign Company To Navigate Local Content Requirements In Indonesia’s Upstream Oil and Gas Industry In 2034 Using Inductive Scenario Planning Approach (Case Study Tas)

Complying with local content regulations in the upstream oil and gas industry presents a significant challenge. These regulations aim to enhance local capability, and the industry's yearly transaction value greatly impacts the local industry, necessitating the use of local products. However, despite requiring high technology, this industry mostly relies on imported products. To address this challenge, our research employs a systematic approach that combines scenario planning with Analytic Hierarchy Process (AHP) analysis. Our primary goal is to develop a comprehensive framework that empowers organizations to optimize their business strategies within the context of localization projects. By integrating scenario planning, which allows for the exploration of diverse future scenarios, with AHP analysis, which offers a structured analytical foundation for evaluating and selecting strategies, our research equips decision-makers with a powerful toolset. Through extensive case studies and meticulous analysis, our research sheds light on the nuanced decision-making processes and strategic adaptations that can be achieved through the synergy of scenario planning and AHP analysis in the domain of localization projects. Our findings and insights provide practical guidance to decision-makers grappling with the complexities of local content compliance and the long-term sustainability of business strategies.

Analysis of elastic stresses and thermal creep effect for a composite disk under the parabolic distribution of temperature using the Rayleigh-Ritz method with trigonometric Ritz functions

This research provides a detailed examination of the thermal creep effect on elastic stresses in a composite disk exposed to a parabolic temperature distribution. The disk, reinforced with steel fibers, is treated as a thermo-elastic composite material. The Rayleigh-Ritz method, a widely recognized numerical approach for solving boundary value problems, is utilized to derive analytical solutions for the radial and tangential stresses. This method employs the principle of minimum potential energy to approximate displacement fields using Trigonometric Ritz Functions, which are subsequently applied to calculate the stress components. The parabolic temperature distribution imparts a non-uniform thermal load across the disk, significantly affecting the stress distribution. The study explores how the thermal creep effect, intrinsic to the composite material, interacts with thermal variations, resulting in intricate stress patterns. By adjusting temperature parameters, the analysis demonstrates the sensitivity of stress components to thermal gradients. Comparative assessments are performed for various temperature profiles, identifying critical areas of stress concentration. The findings offer significant insights into the behavior of thermo-elastic composites under non-uniform thermal conditions, providing a robust analytical foundation for future research and engineering applications. This research emphasizes the necessity of considering both thermal creep effects and Trigonometric Ritz Functions in the design and analysis of composite structures subjected to varying temperature loads.

Analyzing shifts in structural dependence between oil prices and exchange rates in oil-importing economies

PurposeThe primary purpose of this study is to unveil the relationship between oil prices and exchange rates, with a specific focus on five major oil-importing countries. By examining this relationship, the research aims to provide valuable insights for policymakers, investors and stakeholders operating in the global economic landscape.Design/methodology/approachThe study employs a methodological approach to ensure robust and reliable findings. First, we assess the stationarity of the time series data to establish a solid analytical foundation. Subsequently, we construct GARCH(1,1) models to capture the persistence of the volatilities inherent in the data. Building upon this, we propose the novel application of the Markov-switching R-vine copula approach, which enables us to capture structural changes and measure the dependencies between oil prices and exchange rates.FindingsOur findings uncover significant negative relationships between oil prices and exchange rates across the examined economies while revealing varying degrees of interdependency among these variables. Notably, we elucidate distinct tail dependence structures, encompassing both symmetric and asymmetric aspects, which hold profound implications for risk assessment and portfolio management strategies. Furthermore, this study confirms the presence of regime-switching dynamics, elucidating how the co-movement patterns between oil prices and exchange rates evolve across different states or regimes, reflecting the dynamic nature of these interconnected markets.Originality/valueThe originality and value of this study lie in its comprehensive approach to understanding the relationship between oil prices and exchange rates. By accounting for structural changes and regime-switching behaviors, the research provides a nuanced understanding of the complex dynamics at play. The novel application of the Markov-switching R-vine copula approach contributes to the methodological advancement in this field of study. Furthermore, the insights derived from this research offer practical implications for policymakers, investors and stakeholders navigating the complexities of the global economic landscape, enabling them to make informed decisions and develop effective strategies to mitigate risks and capitalize on opportunities.

Carbon Footprints and Income Inequality: A Panel Data Analysis of the 66 Largest Carbon Emitting Nations

In this study, we delved into the complex interaction between carbon emissions and critical economic and environmental factors using Two-step Sys-GMM Dynamic Panel Data Estimation. Our comprehensive dataset encompassed 66 major carbon-emitting nations spanning 1990-2020, providing a robust analytical foundation. Our key findings revealed a noteworthy positive relationship between income inequality, as measured by the Gini index, and carbon emissions across all models. This implies that higher income inequality is linked to increased carbon emissions, possibly due to consumption disparities and resource utilization. Furthermore, the relationship between income and emissions displayed intriguing dynamics. While income per capita exhibited a positive association with emissions, suggesting that growing economies tend to emit more, the squared income per capita variable showcased an inverted U-shaped pattern. This suggests that emissions increase at a diminishing rate as income levels reach a certain threshold, aligning with the Environmental Kuznets Curve hypothesis. Surprisingly, we observed that rising income inequality contributed to long-term carbon reduction, hinting at potential shifts in consumption behavior driven by environmental policies as nations develop. Our analysis also highlighted the roles of energy consumption and urbanization in elevating emissions, emphasizing the need for energy efficiency and sustainable urban planning. Conversely, a higher share of renewable energy was linked to reduced emissions, underscoring the urgency of transitioning to cleaner energy sources. Furthermore, net foreign direct investment and international trade exhibited positive associations with carbon emissions, emphasizing the environmental consequences of globalization. Our research underscores the potential of targeted policies addressing income inequality to promote social equity while reducing emissions. Striking a balance between economic growth and environmental sustainability remains crucial, given the intricate dynamics between income and emissions.

Identification of Urban Renewal Potential Areas and Analysis of Influential Factors from the Perspective of Vitality Enhancement: A Case Study of Harbin City’s Core Area

In the context of people-centered and sustainable urban policies, identifying renewal potential based on vitality enhancement is crucial for urban regeneration efforts. This article collected population density data, house price data, and built environment data to examine the spatial pattern characteristics of Harbin’s core area using spatial autocorrelation analysis. Building on these findings, a geographically weighted regression (GWR) model was constructed to further analyze the influencing mechanisms of the relevant factors. The analysis revealed significant spatial development imbalances within Harbin’s core area, characterized by differentiated and uneven development of social and economic vitality between the old city and newly constructed areas. Notably, in certain regions, the construction intensity does not align with the levels of social and economic vitality, indicating potential opportunities for urban renewal. Furthermore, the examination of key influencing factors highlighted that the accessibility of commercial facilities and development intensity had the most substantial positive impact on social vitality. In contrast, the age of construction and the distribution of educational facilities demonstrated a strong positive correlation with economic vitality. By clearly delineating specific areas with urban renewal potential, this study provided a detailed characterization of the urban development pattern in Harbin. Additionally, by depicting the local variations in influencing factors, it established analytical foundations and objective references for urban planning in targeted locations. Ultimately, this research contributes new insights and frameworks for urban renewal analyses applicable to other regions.

Ontological Airspace-Situation Awareness for Decision System Support

Air Traffic Management (ATM) has become complicated mainly due to the increase and variety of input information from Communication, Navigation, and Surveillance (CNS) systems as well as the proliferation of Unmanned Aerial Vehicles (UAVs) requiring Unmanned Aerial System Traffic Management (UTM). In response to the UTM challenge, a decision support system (DSS) has been developed to help ATM personnel and aircraft pilots cope with their heavy workloads and challenging airspace situations. The DSS provides airspace situational awareness (ASA) driven by knowledge representation and reasoning from an Avionics Analytics Ontology (AAO), which is an Artificial Intelligence (AI) database that augments humans’ mental processes by means of implementing AI cognition. Ontologies for avionics have also been of interest to the Federal Aviation Administration (FAA) Next Generation Air Transportation System (NextGen) and the Single European Sky ATM Research (SESAR) project, but they have yet to be received by practitioners and industry. This paper presents a decision-making computer tool to support ATM personnel and aviators in deciding on airspace situations. It details the AAO and the analytical AI foundations that support such an ontology. An application example and experimental test results from a UAV AAO (U-AAO) framework prototype are also presented. The AAO-based DSS can provide ASA from outdoor park-testing trials based on downscaled application scenarios that replicate takeoffs where drones play the role of different aircraft, i.e., where a drone represents an airplane that takes off and other drones represent AUVs flying around during the airplane’s takeoff. The resulting ASA is the output of an AI cognitive process, the inputs of which are the aircraft localization based on Automatic Dependent Surveillance–Broadcast (ADS-B) and the classification of airplanes and UAVs (both represented by drones), the proximity between aircraft, and the knowledge of potential hazards from airspace situations involving the aircraft. The ASA outcomes are shown to augment the human ability to make decisions.

BASIC APPROACHES TO THE FORMATION OF THE TRANSPORT MODEL OF THE CITY

The pace of development in modern cities and the continuous increase in motorization lead to the aggravation of various issues, such as the creation of rational traffic management, the allocation of parking spaces, the quality assurance of road surfaces, and the protection of the environment. Solving these problems requires «correct» systematic management, effective organization of traffic flows, and optimal planning of transportation infrastructure. To manage the transport system and make optimal decisions in transport planning, specific systematic knowledge about the current transport situation and possible scenarios of its development are needed. Тhe lack of a quantitative description of the transport situation significantly complicates an already challenging selection among options for the development of the urban transport system. Bridging the gap between the desired and actual outcomes can be achieved through modern planning tools and impact assessments of decisions, particularly through a unified city transport model. As experience shows, a city transport model used to support decision-making in strategic transport planning is the optimal tool for quantitative assessment of proposed options for transport network development, their subsequent comparison, and well-founded conclusions regarding the feasibility of investments in transport infrastructure projects. Transport models based on modern information technology represent powerful computational software systems that, based on the functional and spatial characteristics of the city combined with all available data on transport demand and supply, calculate the most probable distribution of traffic and passenger flows across the street network. These calculations then form the basis for forecasting city development and provide the necessary analytical foundation for decision-making in the development of urban transport infrastructure. «Correct» strategic management of the city's transport system, among other things, directly affects the quality of transport services for the population and road safety. The main advantage of transport models is that they allow experimentation not with people, but only with a computer representation of their daily behavior. Keywords: transportation model, transport demand, transport supply, transport zone, demand layer.

Why not Designing Multidirectional Laminates with In-Plane Strength Design Sheets Applying the UD Criteria of TSAI–WU and Cuntze? Part1: Analytical Foundation

Why not Designing Multidirectional Laminates with In-Plane Strength Design Sheets Applying the UD Criteria of TSAI–WU and Cuntze? Part1: Analytical Foundation

Mathematical Model of a Rectangular Double Negative Meta‐Structure for 5G Applications

AbstractThis article introduces the mathematical formulation of a proven rectangular double negative (DNZ) metamaterial (MTM) structure using a lumped element circuit model in terms of RLC components for millimeter‐wave (mmWave) 28 GHz applications. The structure is developed on a 0.254 mm‐thick substrate material: Rogers RT/duroid 5880 with an area of 8.7 mm2. The model accounts for different losses inherent in the system; a series resistance to take into account the losses in the conductor and a shunt resistance to describe the losses in the dielectric substrate, addressing the finite conductivity of conducting materials, the finite resistivity of the dielectric material, and the presence of a dielectric substrate with metallic rings on top. By incorporating these factors, it is possible to precisely predict the resonance frequency associated with this specific structure, however limits the formulation from being applied to other shapes. Numerical validation demonstrates a good agreement with analytical predictions, affirming the model's reliability. The study provides a robust analytical foundation and numerical validation for the double negative metamaterial unit cell, advancing the mmWave 5G wireless technology field.

Analysis of the Flash Phenomenon in Rotor Targets with a Scatter Point Model

This paper thoroughly investigates the occurrence of the flash phenomenon in rotor targets. The flash phenomenon is discernible in rotor targets across the time and frequency domains. This phenomenon is characterized by the modulation of signal amplitude through the Singer function, resulting in periodic peaks in the time domain. The appearance of these peaks in the time domain corresponds to the presence of a corresponding frequency band in the time–frequency domain. The influence of interference between scattering points on the amplitude and phase of echo is investigated by employing the echo scattering point model and the echo analytical expression as analytical foundations. A more comprehensive examination of the mechanisms that contribute to the emergence of the flash phenomenon in the time and time–frequency domains is undertaken. Furthermore, its objective is to verify the validity of the analysis performed on the mechanism of the phenomenon. Based on theoretical investigations and simulation results, it can be concluded that the flash phenomenon observed in rotor targets is due to interference between particular scattering points. Fundamentally, it can be comprehended as a phenomenon of interference. This research achievement has specific theoretical and practical value in detecting and recognizing rotor targets.

A robust approach to transformer fault diagnosis: integrating time-current loci analysis with statistical feature extraction

The accurate differentiation of inrush currents and inter-turn faults in power transformers is critical for ensuring the reliability and safety of electrical power systems. Traditional methods often face challenges in distinguishing between these conditions due to their overlapping characteristics, leading to potential misoperations and system instability. This paper presents a novel solution through the introduction of the time-current loci (TIL) method, which effectively addresses this critical issue by providing a robust mechanism for classifying normal operating conditions, inrush currents, and inter-turn faults. The TIL method involves plotting time against current over a single cycle, generating distinct loci patterns that serve as a visual and analytical foundation for classification. By extracting and analyzing key statistical features from these loci, the method enhances the accuracy of fault detection. Specifically, the rate of change in time and current is used to determine the orientation of the TIL curve, with additional features such as the mean orientation and skewness being computed to capture the unique geometric properties associated with each operating condition. This approach simplifies the analysis process, eliminating the need for complex machine learning algorithms and reducing computational demands, making it highly suitable for real-time implementation. Experimental validation was carried out using a 1 kVA, 230 V/230 V transformer under various operating conditions, and the results demonstrated the effectiveness of the TIL method in reliably distinguishing between normal conditions, inrush currents, and inter-turn faults. The visual nature of the loci plots not only aids in accurate classification but also provides an intuitive understanding of transformer behavior, facilitating quick and informed decision-making by operators. This advancement addresses a critical challenge in transformer protection, offering a more reliable and efficient solution compared to traditional techniques.

Double Moving Average and Double Exponential Smoothing Method in Sales Forecasting

At present, predictions concerning sales are predominantly based on historical sales data, a practice that frequently yields inaccurate results. Such inaccuracies can lead to substantial financial losses, compelling organizations to lower the capital expenditures associated with certain products to offset these losses. This issue primarily arises from the failure to employ an appropriate forecasting methodology, resulting in estimations that lack reliable analytical foundations. This research aims to evaluate the efficacy of the Double Moving Average method compared to the Double Exponential Smoothing technique for sales forecasting, specifically through a case study involving herbal products. This study also seeks to analyze and compute the Mean Absolute Percentage Error (MAPE) for each forecasting method based on prior observations and research. The analysis draws on a dataset comprising 200 sales transactions from the five top-selling products collected between April 2022 and April 2024. The outcomes of this investigation provide MAPE values derived from the sales data, followed by a comprehensive summation of the calculated MAPE for each method. For June 2024, the results recorded are slightly higher in some cases. For example, the DES result for HNI Eucalyptus Oil was 42.65 with a MAPE of 0.46, while the DMA was 44.44 with a MAPE of 0.44.

A Critical Discourse Analysis of Women’s Representation in Maysaloon Hadi’s Novel The Black Eyes

This study applies a discourse analysis framework to explore the portrayal of women in Maysloon Hadi’s novel (The Black Eyes) (2011), using Critical Discourse Analysis (CDA) and Norman Fairclough’s tri-dimensional model (1989) as the analytical foundation. It investigates the roles and challenges women face in the novel. While there is growing interest in the portrayal of women in literature, Iraqi literature—especially from the perspective of Iraqi women writers remains underexplored. Hadi’s *The Black Eyes* provides a unique case to examine this intersection. Despite the novel’s rich narrative, which offers insight into Iraqi women’s lives, there is a lack of comprehensive CDA to understand how its language constructs and communicates the multifaceted roles, challenges, and images of women. The research fills this gap by analyzing specific passages from the novel using Fairclough’s model. The findings reveal a nuanced portrayal of women and underscore the value of CDA in analyzing such works. Further research is recommended to apply CDA to other Iraqi literary texts for broader insights.

Exploring Multivariate Statistics: Unveiling the Power of Eigenvalues in Wishart Distribution Analysis

This research examines how degrees of freedom and covariance matrix configurations affect Wishart distributed matrix eigenvalue distributions. We use the energy distance metric to compare eigenvalue distributions in Identity, Diagonal, and Structured covariance matrices. Through extensive simulations, we show that degrees of freedom and covariance matrix architectures greatly affect eigenvalue dispersion and energy distance distributions. Statistical models are more reliable with smaller, more stable distributions from higher degrees of freedom. We analyze the eigenvalue distributions of NextEra Energy (NEE), Enphase Energy (ENPH), First Solar (FSLR), SunPower (SPWR), and Brookfield Renewable Partners (BEP) stocks using this analytical methodology. Daily returns and covariance matrices were calculated using daily closing prices from January 1, 2018, to January 1, 2023. Our findings support simulation studies indicating larger degrees of freedom lead to more stable energy distance distributions.The findings of this study are useful in finance, genetics, and environmental studies, where stability and variability of the covariance structure are important. The requirement for higher-dimensional settings and real-world datasets to validate the theoretical framework are our research’s constraints. This research expands Wishart distribution knowledge and provides a solid analytical foundation for data analysis and model fitting in numerous scientific fields.

ANALISIS INTEGRASI MATERI SEJARAH DAN KEBERAGAMAAN DALAM PRAKTIK PENDIDIKAN DI INDONESIA

This research is motivated by Indonesia's long history based on diversity of cultures, languages, religions, ethnicities, races, and customs. The integration of historical and religious materials in educational practices in Indonesia aims to understand the learning content that will shape the character of students as grassroots individuals with a nationalist spirit and religious moral values. In this study, the author employs a qualitative literature approach by collecting various relevant data sources. The theory of multicultural education, consisting of five elements: content integration, understanding the construction process of knowledge, reducing prejudice, equity pedagogy, and strengthening school culture as well as social structure by James A. Banks, serves as the analytical foundation for this research. The results indicate that historical materials also contain teachings on being tolerant and open to various differences in race, ethnicity, tribe, and culture. History learning that incorporates religious practices will create a harmonious environment by applying the principles of mutual respect, equality, and togetherness.

Integrated Thermal Proteome Profiling and Affinity Ultrafiltration Mass Spectrometry (iTPAUMS): A Novel Paradigm for Elucidating the Mechanism of Action of Natural Products.

Natural products (NPs) are foundational to drug discovery, offering a rich repertoire of molecular diversity with multifaceted modes of action against a broad array of targets. Despite their potential, deconvoluting the intricate mechanism of action (MoA) of NPs, characterized by their multicomponent, multitarget, and multilevel interactions, remains a formidable challenge. Here, we introduce an innovative pipeline called integrated thermal proteome profiling and affinity ultrafiltration mass spectrometry (iTPAUMS). This approach combines the high-throughput capacity of thermal proteome profiling (TPP) with the specificity of affinity ultrafiltration mass spectrometry (AUMS), creating a powerful toolkit for elucidating complex MoAs of NPs. Significantly, our investigation represents a pioneering application of TPP to delineate the target group of NPs mixtures and overcome the long-standing obstacle of mapping specific component-target interactions through AUMS. Our findings demonstrate the utility of iTPAUMS in constructing a comprehensive component-target atlas, providing a robust analytical foundation for unraveling the intricate pharmacological landscapes of NPs and advancing drug discovery.

Conceptual, Mathematical, and Analytical Foundations for Mission Engineering and System of Systems Analysis

Conceptual, Mathematical, and Analytical Foundations for Mission Engineering and System of Systems Analysis

A Gated TIMS FTICR MS Instrument to Decipher Isomeric Content of Complex Organic Mixtures.

Modern research faces increasingly complex materials with a constant need for new analytical strategies that can provide deeper levels of chemical insight. Ultrahigh resolution mass spectrometry (MS), particularly Fourier transform ion cyclotron resonance (FTICR) MS, has provided a robust analytical foundation. However, MS alone offers limited structural information. Here, we present the first implementation and results from an FTICR MS with fully integrated dual accumulation analysis with gated trapped ion mobility spectrometry (gTIMS) capability. The drastically extended charge capacity and parallel accumulation facilitate the analysis of complex mixtures. We achieved a high dynamic range of 4 orders of magnitude within a single FTICR acquisition event. Simultaneously, the valuable linear relationship between the TIMS elution voltage and reduced mobility was retained over a wide mobility range. Benchmarking the instrument performance with Suwannee River fulvic acid (SRFA) by variable ramp gTIMS analysis allowed separation and unambiguous assignment of different charge state distributions. Application to bio-oils has proven the capability to distinguish the isomeric diversity in these ultracomplex samples, while maintaining the expected FTICR MS resolving power and mass accuracy. Valuable information about the molecular distribution, isomeric diversity, and main molecular differences could directly be extracted within the analysis time of a classical "dilute and shoot" direct infusion experiment. The development of this fully integrated and flexible gTIMS with FTICR MS analysis possesses the potential to significantly change the current landscape of high-resolution mass spectrometric analysis of complex mixtures through the added insight of isomeric complexity afforded by TIMS. The exploration of the added IMS dimension promises transformative effects across diverse fields including energy transition, environmental studies, and biological research.

Modelling the growth of the network value

Modelling the growth of the network value

Analysis of the free vibration of a beam on a deformable support using a generalised analytical continuum model

ABSTRACT This paper deals with the application of a generalised analytical continuum foundation model recently proposed by the senior author to the dynamic analysis of beams. Specifically, analytical and numerical solutions of the problem of the free vibration of a beam on an equivalent two-parameter model and a simplified Winkler-type model are presented. In the two-parameter representation, a novel approach is used in which the shear parameter is taken care of by moving it from the soil-beam interface to the beam proper in the form of a geometric stiffness so that it serves as an enhancement to the conventional elastic beam stiffness. By doing so, the familiar Winkler support is maintained while the shear interaction among the springs is realised at the same time. Additional end springs are introduced to account for the shear continuity extending beyond the ends. The analytical and numerical results are compared with those obtained from the finite element software, Abaqus, by means of which the calibration factor is determined. The model is validated using measured data from the literature. The results are also compared with other existing models. Both the single- and two-parameter variants of the model performed remarkably well, especially the two-parameter variant.