Stagflation continues to pose a significant macroeconomic issue in developing economies, as it amalgamates persistent inflation, subpar labour market performance, and macroeconomic instability, hence limiting policy efficacy and long-term growth. This study investigates the effects of stagflation on key macroeconomic indicators in Zambia, namely inflation rates, labour market dynamics, the central bank's policy rate, and the exchange rate. The study applies a Vector Error Correction Model (VECM) using annual time-series data from 1964 to 2024, guided by unit-root and Johansen cointegration tests, to analyse short-term dynamics and long-term equilibrium correlations among the variables examined. The findings indicate that Zambian inflation exhibits strong persistence, with an insignificant error-correction term (ECT = − 0.304, p = 0.927) showing limited short-run adjustment toward long-run equilibrium, while the significantly negative lagged inflation coefficient (∆CPIt-1 = − 0.412, p = 0.003) confirms partial but constrained self-correction; conversely, labor-market disparities adjust slowly, suggesting structural inflexibilities within the Zambian economy.Moreover, the results reveal that the central bank's policy rate responds significantly to systemic instability and debt-related pressures, whereas exchange-rate fluctuations are significantly affected by persistent imbalances and constraints on external financing. Within this framework, inflation, labor market difficulties, shifts in monetary policy, and exchange rate fluctuations are interconnected, each exacerbating the others. Therefore, this research underscores the crucial need for a cohesive policy approach to ensure Zambia's long-term economic stability.The proposed strategy demands a holistic methodology, encompassing monetary, fiscal, and structural policies. This integrated approach is indispensable, given that inflation, debt, labor market challenges, and exchange rate volatility are not discrete issues; instead, they are interconnected occurrences that necessitate a cohesive policy intervention.

It is important to define what is the exact protein requirement to ensure that the economic and environmental sustainability of the tilapia aquaculture, particularly in saline systems, whereby osmoregulation augments metabolic demand. This experiment used a multi-biomarker, holistic methodology in indicating the optimal dietary crude protein content of juvenile red hybrid tilapia (Oreochromis sp.) reared in saline groundwater (33 ppt) in concrete tanks. Isogenic diets with 25, 30, 34 and 38 percent crude protein were fed to fish to a 90-day period. Although the CP diet of 38% was found to give a higher growth performance and feed efficiency, it initiated a drastic physiological trade off which was represented by a drastic reduction in the survivability (51.1%). Detailed hemato-biochemical and histopathological investigations showed the price paid under the carpet by this high protein diet, systemic dysregulation with extreme cases of anemia, hypercholesterolemia, hepatic steatosis and necrosis, damage to the branchial, and activation of the intestinal immune response. The opposite extreme of this was the 34% CP diet which made the strong growth as well as retention of high survival (75.6) and homeostatic regulation among physiological and tissue wellness variables. Thus, we can deduce that the ratio of 34% crude protein in the diet is the optimum compromise afforded by having maximum zoo technical performance and a balance between physiological integrity and long-term sustainability of juvenile red hybrid tilapia under saline concrete tank culture.

Departing from silo approaches, this paper presents a holistic methodology for assessing the benefits of integrating optimally selected electrolysers within a Net Zero Energy system. The optimisation addresses large-scale multi-energy system planning, considering the interactions among low-carbon generation technologies, such as renewables, energy storage, and smart demand response, while accounting for the spatial and temporal dynamics of energy sources and demand. The value of electrolysers is evaluated through a comparison of system capacity portfolios and operational costs between scenarios with and without electrolysers. The analysis focuses on future energy scenarios in Great Britain, exploring various assumptions to identify the value drivers and needs for electrolysers and their optimal distribution in the system. Results highlight the role of electrolysers in enhancing system balancing and energy storage capabilities, promoting sector coupling between electricity and hydrogen systems. This synergy enables low-cost hydrogen storage and generation, which is essential for providing ancillary services and ensuring energy system resilience. The paper also explores insights from sensitivity studies regarding the impacts of varying costs for low-carbon technologies, gas prices, and the flexibility provided by end-use customers and distributed storage.

The increasing complexity of tax systems and the limitations of traditional rule-based audits have highlighted the need for adaptive, transparent, and efficient auditing solutions. This paper presents the design and evaluation of a Secure AI-Driven Adaptive Audit Transparency Engine (AI-AATE), a novel framework integrating machine learning, explainable AI (XAI), and human-in-the-loop oversight to enhance tax compliance, reduce administrative inefficiencies, and strengthen economic outcomes. The architecture combines supervised and unsupervised models for risk detection, continuous feedback incorporation for adaptive learning, and comprehensive audit logging to ensure transparency, fairness, and traceability. A rigorous evaluation framework employing operational Key Performance Indicators (KPIs), counterfactual simulations, and economic modeling quantifies performance across audit yield, coverage, processing efficiency, revenue recovery, and equity. Governance and trust metrics assess explainability, human oversight, and bias mitigation, linking design principles to measurable institutional outcomes. Simulation results demonstrate that AI-AATE can significantly improve detection of non-compliance, optimize resource allocation, and support equitable and accountable audit selection compared to traditional approaches. By bridging technical design, performance evaluation, and economic impact assessment, this study contributes a holistic methodology for AI-enabled audit systems, offering actionable insights for policymakers, tax authorities, and researchers. The findings underscore the potential of AI-AATE to transform public-sector auditing while maintaining fairness, legitimacy, and public trust, addressing a critical gap in the literature on adaptive, transparent, and secure AI applications in taxation.

The article is devoted to the current challenges facing scientific libraries in the context of digitalization and high competition. The aim of the study is to develop and methodologically justify the cyclical model for synthesizing quantitative and qualitative data to overcome the interpretive gap in web analytics. It analyzes the problem of the interpretation gap that arises when relying solely on quantitative data from web analytics, which do not reveal the motives and deep information needs of users. As a solution, the author’s cyclical model is proposed, based on the system integration of quantitative web analytics data (the answer to the question “what?”) and qualitative research methods, such as surveys and usability testing (the answer to the question “why?”). The model is presented as a four-stage management framework (data collection, analysis, optimization, evaluation) for the continuous improvement of digital services. The main results are the creation of a holistic methodology that allows libraries the motion from analyzing actions to a deep understanding of users, as well as presenting a specific framework for its practical implementation. The use of the model allows one the transformation of a library into an adaptive ecosystem that effectively meets the complex information needs of the scientific community.

The aim of this study is to develop an adaptive methodology for integrating digital transformation and sustainable development processes, implementing the principle of advanced adaptability. The relevance of this work is determined by the presence of a systemic cognitive-temporal gap, when the rate of technological change outpaces organizational adaptation, which hinders the achievement of synergy between two strategic imperatives. The research methodology is based on a synthesis of the concepts of strategic management, organizational change, and cognitive science. As a result, a holistic methodology has been developed, the architecture of which includes three interconnected elements: 70) a cyclical management algorithm that transforms a linear project into a process of continuous improvement; 1) an organizational core in the form of permanent cross-functional groups that eliminate professional and communication gaps; 2) a cognitive support subsystem aimed at managing digital fatigue and developing hybrid intelligence. The implementation tools are presented at the operational, strategic, and cognitive levels. The practical significance lies in providing managers with a structured tool for proactively developing the organization’s dynamic resilience. The scientific novelty lies in the operationalization of the principle of advanced adaptability through the synthesis of process, organizational, and cognitive aspects of management.

A holistic methodology for predictive modeling of total final energy use in Türkiye till 2030

First Application of the Aigilo Predictive Framework in Middle East Conflict Analysis

This paper analyzes how governance structure can influence the performance of institutions in the public sector of universities by considering three fundamental dynamics, which are: faculty involvement in decision making, transparency of the governing body procedures, and political or bureaucratic intrusion. Empirical survey data indicate the nature of the governance climate as moderate and uneven in terms of participatory, moderate in terms of its recurrence, and high in terms of its pervasiveness in the operations of vital statutory bodies. It has been shown that these are not independent elements but combine on a systemic level, where the internal flaws in procedures increase the vulnerability of the institutions to the outside influence, thus undermining autonomy and disrupting performance. This paper contends that traditional and separate administrative reforms are not enough. It, as a result, suggests a unified system of governance, which simultaneously strengthens internal processes, by the introduction of organized transparency, or faculty representation, but which also introduces institutional safeguards to defend the self-determination of the constitution of academic self-determination. It is hypothesized that this holistic methodology is a key to developing the autonomous, responsible and merit-based environment necessary to improve the institutional efficacy and academic excellence.

This study investigates the process that assesses the economic efficiency of implementing an investment project aimed at managing the information potential of energy enterprises, taking into account digital coherence factors. The task addressed relates to the lack of a holistic methodology that could make it possible to quantitatively assess the economic efficiency of managing the information potential of energy enterprises, taking into account the level of digital coherence. A methodology for assessing the economic efficiency of managing the information potential of energy enterprises in the context of digital coherence has been devised and substantiated. The methodology was considered as an integrated tool for diagnosing, analyzing, and predicting the effectiveness of management decisions based on the consistency of digital, analytical, organizational, and strategic components of the enterprise. The methodology makes it possible to assess the economic efficiency of using information potential, taking into account digital coherence, identifying imbalances in the digital architecture, and justifying management decisions regarding the optimization of digital resources and transformations in the energy sector. A system of quantitative criteria and indicators has been formed to assess the economic efficiency of information potential management of energy enterprises, which reflects the level of digital coherence and allows for an objective analysis of the results of digital transformation. The methodology has been tested using an example of Zaporizhzhia Power Plant, which made it possible to trace the economic efficiency index that increased from 0.295 to 0.616, which confirms a more than twofold increase in the effectiveness of information potential management.

The article discusses the prerequisites for the safe use of digital medical data of high-tech diagnostic examinations, primarily magnetic resonance imaging (MRI), in expert medical modelling based on virtual patient twins (Digital Twin). Based on the analysis of modern research on privacy, security, and ethics of digital twins in medicine, cybersecurity, and the metaverse, the composition of the main threats has been clarified: falsification of images and metadata, personal data leaks, and non-transparent secondary use of information. A conceptual architecture of a medical digital twin for expert and forensic purposes is proposed, which integrates cryptographically secure storages, event logs, algorithms for detecting fake MRI data, and legal information processing policies. For quantitative assessment of risks, a probabilistic model has been built, as well as an optimization setting of the choice of technical and organizational protection measures. For automated data integrity control, a combination of convolutional and recurrent neural network (CNN+LSTM) for image analysis and an autoencoder with LSTM (AE+LSTM) for monitoring metadata sequences and access logs were used. It is shown how the proposed model is consistent with the requirements of the legislation of Ukraine on personal data protection, EU Regulation 2016/679 (GDPR) and the US HIPAA act and can be implemented within the framework of the Zero Trust architecture in medical information systems. The practical result of the work is the formation of a holistic methodology for the design of medical digital twins, focused on the expert use of MRI data, which minimizes legal risks and increases the reliability of conclusions.

Introduction. Modern corporations operate in the context of digital transformation and a VUCA environment, where high-quality accounting and reporting information is a key strategic resource for survival and development. The existing fragmentation of approaches to forming data for financial, managerial, and tax accounting complicates analysis processes, slows down decision-making, and reduces operational transparency, creating serious operational and strategic risks. Overcoming the gap between the technical capabilities of information systems and the methodological foundation of accounting is a critical necessity for transitioning to a data-driven management model. Purpose of the article. The purpose of the scientific work is to develop a holistic methodology and architectural model for the effective consolidation and transformation of accounting data in corporate information systems. The research is aimed at creating a unified mechanism capable of simultaneously generating information relevant for strategic analysis, operational control, tax planning, and regulatory reporting, thereby ensuring the integrity of the management cycle. Method (methodology). The methodological foundation consists of a systems approach, methods of analysis and synthesis, and scientific modeling methodology. The core of the research is the author's recursive-iterative model for collecting and processing reporting data. This model formalizes the process as a bidirectional cycle combining centralized "top-down" request initiation with step-by-step, validated "bottom-up" data consolidation. The technical implementation of the model is a specialized "Reporting" information subsystem with a unified central database, integrated into a comprehensive integrated accounting information system (IAIS). Results. As a result of the research, a conceptual architecture has been proposed that ensures methodological unity at all accounting levels  from primary to consolidated. The developed recursive model eliminates the shortcomings of linear hierarchical schemes, providing tools for real-time monitoring, preventive quality control, and flexible management of the data collection process. It is proven that the proposed approach achieves semantic integrity of information, increases its adaptability to changes in the business environment, and transforms reporting into a strategic resource for decision-making. Key prospects for further research have been identified, including integration with legacy systems, adaptation to AI and IoT technologies, development of ROI assessment metrics, and consideration of ESG requirements.

Abstract Efficient separation of oil from compressed air in screw compressors is critical for ensuring system reliability, minimizing energy consumption, and extending component life. This study presents a comprehensive and first-of-its-kind integrated framework for evaluating and optimizing an industrial air–oil separator (AOS) tank by combining analytical modeling, Computational Fluid Dynamics (CFD), and full-scale experimental validation under real industrial conditions. The baseline AOS geometry was analyzed using the Darcy–Weisbach equation and loss coefficients to estimate pressure drops across key flow sections. CFD simulations employing the Discrete Phase Model (DPM) were conducted to resolve internal multiphase flow dynamics and particle behavior. Validation against experimental data confirmed the accuracy of both analytical and CFD models, with deviations below 1\%. To enhance performance, three design modifications a circular plate, a conical plate, and a novel conical plate with flow-straightening vanes were proposed and evaluated. Among these, the conical plate with vanes achieved the highest separation efficiency (87\%, up from 60\%) and reduced pressure drop by 14.4\%, while lowering the separator element workload from 40\% to 13\%. Unlike earlier studies that focus only on CFD simulations or simplified empirical evaluations, this work uniquely combines analytical pressure loss modeling, DPM based CFD droplet tracking, and full-scale experimental benchmarking in industrial settings. This validated framework introduces a holistic methodology for concurrently optimizing geometry, loss characteristics, and droplet separation efficiency in compressor systems an aspect seldom addressed in an integrated manner in previous research.

This article examines the methodology of historical science as a research subject that remains highly relevant and in demand due to its vast development potential and emerging opportunities for historical research. Within this framework, attention is focused on the creation and development of a holistic, or integral, methodology of history. The paper outlines the historiography of historical methodology and offers a retrospective look at various philosophical and religious aspirations for a holistic understanding of reality from ancient times to the present day, which, to varying degrees, have influenced the formation and development of science in general and historical science in particular. Among the most promising systems of holism for historical research, according to the author, are the integral approaches formulated in Vladimir Solovyov's philosophy of total unity, Rudolf Steiner's theory of social tripartism, and Ken Wilber's integral metatheory. A comparative analysis demonstrates the commonalities between the systems of Solovyov, Steiner, and Wilber and demonstrates how their developments can be applied to historical research. The purpose of this paper is to demonstrate that the currently established methods of viewing and understanding history, inherent in various historical schools and auxiliary historical disciplines, can be incorporated into a holistic, or integral, methodology of history, which could unite them all and provide the keys to the intensive development of an integral methodological approach. Based on the research results, the article concludes that the creation and development of a holistic methodology of history based on a synthesis of the integral developments of Solovyov, Steiner, and Wilber will enable a more complete and profound understanding of the past, reduce the likelihood of historical falsification, and, furthermore, will have a pedagogical effect beneficial to the development of both integral historical researchers themselves and the recipients of their work.

Since ancient times, objects of the celestial space have served as guiding stars, revered by gatherers, hunters and reindeer herders as their totemic ancestors and patrons. in this article, the cosmological image of the world, which has developed over many centuries, is examined through the prism of the evolution of consciousness, and can be traced back to the emergence of a modern man. in doing so, the authors relied on the holistic methodology of G.D. Gachev, being based on the idea of “ cosmo-Psycho-logos”, which unites//incorporate the national image of nature, mental makeup and mentality with the inherited and historically established modus of being of the people. Particular attention is given to the cosmonyms of the milky Way, as one of the key objects of the national cosmological image of the world, through which the aesthetic and sacred attitude to the celestial bodies are expressed. the version proposed in the article is based on the ideas that have survived to this day and are reflected in legends, fairy tales, tokens, beliefs and customs of the Russian people.

Retrofitting conventional buildings is a key strategy for climate change mitigation, as it enhances indoor comfort while reducing energy consumption in countries where old building stocks are a major contributor to energy use and associated emissions. In North African and Mediterranean contexts, deep energy retrofits for mid-century office buildings remain limited, particularly regarding the practical implementation of solutions adapted to local climatic and economic conditions. This study investigates a deep retrofit of a mid-20th-century office building in Algeria, aiming to assess its alignment with Algeria’s 2030 climate and energy efficiency objectives. A holistic methodology combining an energy audit and dynamic simulation with EnergyPlus has been undertaken to evaluate envelope upgrading, HVAC replacement, and renewable energy supplementation. Retrofit strategies selected were defined as representative of technically feasible and cost-effective solutions for Algerian mid-century office buildings, balancing energy performance improvement and economic viability under local climatic constraints. This study analyzed two retrofit scenarios: one with a combination of envelope improvements, heat pump replacement, and supplementation by photovoltaic solar panels, reaching 41% in terms of electricity savings (≈23 t CO2/year avoided), and the other with the VRF system, reaching 54% in savings (≈30 t CO2/year). Consequently, energy intensity is reduced from the base case by around 41–54%. The study contributes to data-driven retrofitting studies and explores innovative, low-cost strategies that respond to regional climatic challenges.

Integrated adequacy and stability BESS sizing criteria for hybrid diesel–PV microgrids in developing countries

Against the backdrop of the expansion of microservice architectures and Platform Engineering practices, monorepositories serve as a key mechanism for standardizing the software development lifecycle. At the same time, their growth exacerbates the phenomenon whereby version incompatibilities block the work of multiple teams. This study aims to systematize and analyze approaches to dependency management in scalable monorepositories in order to develop a holistic methodology for preventing and resolving version conflicts. The methodological basis includes a systematic review of academic publications, content analysis of technical documentation, and a comparative examination of industry reports. The results demonstrate the limited preventive effectiveness of semantic versioning (SemVer) with respect to compatibility errors and establish a taxonomy of management strategies: proactive (centralized version control), reactive (dependency harmonization), and automated (use of intelligent build systems). Case studies confirm that tool selection should correlate with the scale of development, implying an evolutionary transition from basic orchestrators to industrial-grade build tools such as Bazel. It is concluded that high effectiveness in dependency management is achieved through the synergy of organizational regulation, conflict resolution procedures, and the use of intelligent build systems with fine, granular analysis of the dependency graph. The practical significance of the work lies in providing architects and platform teams with a scientifically verified foundation for decision-making in the design and operation of large-scale software systems.

The article examines the forensic characterization of evasion of military service by self-mutilation or in another way as a separate type of military criminal offenses that have become particularly relevant in the context of armed aggression against Ukraine. The need to form a scientifically sound and practically oriented methodology for their investigation is substantiated, which is based on the construction of a forensic characterization as a holistic system of typical signs of a crime. The main structural elements of the forensic characterization are determined: the identity of the criminal, the method of committing the criminal offense, the situation, information about the traces, and their interrelationships are outlined. Special attention is paid to the analysis of methods of evasion (self-mutilation, simulation of illness, forgery of documents, other deception, refusal to serve), typical motivations of offenders, the circumstances of the act and the trace picture. Statistical data on the dynamics of offenses under Art. 409 of the Criminal Code of Ukraine and examples from judicial practice are provided. The emphasis is on the difficulties in proving intent when investigating such criminal proceedings, as well as on the importance of an interdisciplinary approach that combines knowledge of criminal law, criminology, and military jurisprudence. The conclusion is made about the need to develop and implement a holistic methodology for pre-trial investigation to ensure an effective response to the growing number of such offenses. The presence of correlations between the elements of the characteristic is emphasized, which allows forming substantiated versions for the investigation. The need for a mandatory medical examination - VLK, to ensure the process of proof is emphasized. For the first time, a refined structure of the forensic characteristic of this type of crime is proposed, taking into account modern practice and martial law. The results of the study can be used as both a theoretical and practical basis for improving the methodology for investigating crimes provided for in Art. 409 of the Criminal Code of Ukraine. The article has applied significance and is aimed at improving the methodology for investigating crimes against the order of military service. Keywords: criminal proceedings, forensic characterization, method, circumstances, situation, investigation, evasion, self-mutilation, forgery, documents, trases, proving, offense, martial law, combat situation, special period, consequences, serviceman, military service.

With the rapid growth of cloud services, it is crucial to have strong assessment methods in place to rate these services according to their performance, dependability, and security. This study introduces a holistic methodology that utilizes advanced deep learning (DL) algorithms to prioritize and evaluate cloud services. Our model incorporates many assessment criteria, including latency, throughput, availability, and security measures. These criteria are trained using a varied collection of performance measurements from cloud services. We validate the effectiveness of our methodology by comprehensive experiments, attaining greater precision and significance in ranking compared to conventional approaches. The DL model underwent evaluation using a testing set, resulting in a mean absolute error (MAE) of 0.15 in ranking scores. The algorithm regularly achieved superior results compared to conventional ranking approaches, particularly in situations where performance measures varied. Through the incorporation of security metrics, the model successfully assessed and ranked cloud service providers (CSPs) based not only on their performance, but also on their ability to withstand security threats. The DL technique exhibited more flexibility and contextual awareness in its rankings, hence showcasing its superiority in adjusting to real-time data. The research conducted a comparison between DL-based rankings and conventional methodologies and industry standards, demonstrating its superiority in effectively adjusting to real-time data. The study technique entails gathering data from many CSPs to construct a resilient framework for evaluating cloud services using DL models. The data is obtained from publicly available performance statistics, cloud monitoring tools, user evaluations, and problem reports. The collection comprises both structured and unstructured data, including essential performance and accuracy indicators.