Search Results (96)

The escalating threat of climate change has intensified the global urgency to accurately predict carbon dioxide (CO₂) emissions for sustainable development, particularly in developing economies experiencing rapid industrialization and globalization. Traditional Extreme Learning Machines (ELMs) offer rapid learning but often yield unstable performance due to random parameter initialization. This study introduces a novel hybrid model, Red-Billed Blue Magpie Optimizer-tuned ELM (RBMO-ELM) which harnesses the intelligent foraging behavior of red-billed blue magpies to optimize input-to-hidden layer weights and biases. The RBMO algorithm is first benchmarked on 15 functions from the CEC2015 test suite to validate its optimization effectiveness. Subsequently, RBMO-ELM is applied to predict Indonesia’s CO₂ emissions using a multidimensional dataset that combines economic, technological, environmental, and globalization-driven indicators. Empirical results show that the RBMO-ELM significantly surpasses several state-of-the-art hybrid models in accuracy (higher R²) and convergence efficiency (lower error). A permutation-based feature importance analysis identifies social globalization, GDP, and ecological footprint as the strongest predictors underscoring the socio-economic influences on emission patterns. These findings offer both theoretical and practical implications that inform data-driven Artificial Intelligence (AI) and Machine Learning (ML) applications in environmental policy and support sustainable governance models. Full article

This study presents a novel methodological framework for analyzing circular economy potential in historic small towns, using Taurasi as an illustrative case to demonstrate framework applicability rather than providing a comprehensive quantitative assessment. The primary contribution is methodological: developing an integrated analytical framework that combines urban metabolism analysis, stakeholder engagement theory, and heritage preservation strategies. The framework addresses the specific challenges of implementing circular economy principles in heritage-rich contexts where traditional data collection methods may be insufficient. This research provides a replicable methodology for future comprehensive empirical studies in similar contexts. Full article

With the development of the marine economy and the increase in marine activities, deep saturation diving has gained significant attention. Helium speech communication is indispensable for saturation diving operations and is a critical technology for deep saturation diving, serving as the sole communication method to ensure the smooth execution of such operations. This study introduces deep learning into helium speech recognition and proposes a spectrogram-based dual-model helium speech recognition method. First, we extract the spectrogram features from the helium speech. Then, we combine a deep fully convolutional neural network with connectionist temporal classification (CTC) to form an acoustic model, in which the spectrogram features of helium speech are used as an input to convert speech signals into phonetic sequences. Finally, a maximum entropy hidden Markov model (MEMM) is employed as the language model to convert the phonetic sequences to word outputs, which is regarded as a dynamic programming problem. We use a Viterbi algorithm to find the optimal path to decode the phonetic sequences to word sequences. The simulation results show that the method can effectively recognize helium speech with a recognition rate of 97.89% for isolated words and 95.99% for continuous helium speech. Full article

In the context of an ever-evolving global economy, ASEAN companies face dynamic systemic risk that reshapes their financial interrelationships. This study examines the transmission of these risks using advanced graph theory techniques, particularly the measurement of eigenvector centrality based on Euclidean distances, combined with a hidden model that incorporates macroeconomic variables, such as GDP. The research focuses on identifying critical nodes within the corporate network, evaluating their contagion potential—both in terms of reinforcing resilience and amplifying vulnerabilities—and analyzing the influence of external factors on the network’s structure and behavior. The findings offer an innovative framework for managing systemic risk and provide strategic guidelines for the formulation of economic policies in emerging ASEAN markets. Full article

Mathematical modeling allows taking into account registered and hidden infections to make correct predictions of epidemic dynamics and develop recommendations that can reduce the negative impact on public health and the economy. A model for visible and hidden epidemic dynamics (published by the author in February 2025) has been generalized to account for the effects of re-infection and newborns. An analysis of the equilibrium points, examples of numerical solutions, and comparisons with the dynamics of real epidemics are provided. A stable quasi-equilibrium for the particular case of almost completely hidden epidemics was also revealed. Numerical results and comparisons with the COVID-19 epidemic dynamics in Austria and South Korea showed that re-infections, newborns, and hidden cases make epidemics endless. Newborns can cause repeated epidemic waves even without re-infections. In particular, the next epidemic peak of pertussis in England is expected to occur in 2031. With the use of effective algorithms for parameter identification, the proposed approach can ensure effective predictions of visible and hidden numbers of cases and infectious and removed patients. Full article

Background: Ocular diseases have been a severe problem worldwide, specifically in underdeveloped countries that do not have enough technology or economy to treat them. It would be beneficial to have software with low installation complexity and ease of use, allowing high efficacy in diagnosing eye diseases. This study aims to design and implement an algorithm based on deep learning to classify ocular diseases with high precision. Methods: This work describes digital image processing techniques for the easier handling of eye images; in particular, blur filters were used. The Canny filter was also applied to obtain the edges that allow the difference between the analyzed diseases. Once the images were pre-processed, a convolutional neural network of our own design was applied to perform the classification task. The validation algorithm used in this work was the hold-out algorithm (80–20). The metrics used to evaluate our proposal were the confusion matrix, accuracy, recall precision, and F1-score. Results: The dataset has five classes, namely, normal, cataract, diabetic retinopathy, glaucoma, and other retina diseases. The network architecture consists of 11 layers, including three convolutional layers, three max pooling layers, one batch normalization layer, one flattening layer, two hidden layers, and one output layer. This model resulted in 97% efficiency across all metrics. Conclusions: With the individual analysis of each metric, it can be observed that the proposed algorithm is capable of differentiating, first, images of healthy eyes from diseased ones and, second, adequately classifying eye diseases. Full article

Increasing wind power penetration will profoundly impact a power system’s operating mechanism. It is necessary to study a control strategy so that wind farms can use energy storage to improve their controllability to the level of traditional units. Therefore, this paper proposes a control strategy for wind storage systems based on temporal pattern attention (TPA) and bidirectional gated recurrent units (BiGRUs). The control strategy uses BiGRU to extract the time series information between the energy storage output, the actual output of the wind farm, and the energy storage state, which improves the control stability of a wind storage system. At the same time, TPA is introduced to assign different weights to the hidden layer state of the neural network to highlight the importance of local time series information to the current energy storage output, effectively improving the model performance and reducing the control deviation. Finally, the stability and superiority of the proposed control strategy are verified based on an actual wind farm dataset. The economy of the wind storage system with this control strategy improves significantly. Full article

Understanding how crises like the COVID-19 pandemic affect variable annuity pricing is crucial, especially in emerging markets like India. The motivation is that financial stability and risk management in these markets depend heavily on accurate pricing models. While prior research has primarily focused on Western markets, there is a significant gap in analyzing the impact of extreme volatility and regime-dependent dynamics on variable annuities in emerging economies. This study investigates how regime shifts during the COVID-19 pandemic influence variable annuity pricing in the Indian stock market, specifically using the Nifty 50 Index data from 7 September 2017 until 7 September 2023. Advanced methodologies, including regime-switching hidden Markov models, artificial neural networks, and Monte Carlo simulations, were applied to analyze pre- and post-COVID-19 market behavior. The regime-switching hidden Markov models effectively capture latent market regimes and their transitions, which traditional models often overlook, while neural networks provide flexible functional approximations that enhance pricing accuracy in highly non-linear environments. The Expectation–Maximization (EM) algorithm was employed to achieve robust calibration and enhance pricing accuracy. The analysis showed significant pricing variations across market regimes, with heightened volatility observed during the pandemic. The findings highlight the effectiveness of regime-switching models in capturing market dynamics, particularly during periods of economic uncertainty and turbulence. This research contributes to the understanding of variable annuity pricing under regime-dependent dynamics in emerging markets and offers practical implications for improved risk management and policy formulation. Full article

Vocal proponents claim that hydrogen will play a crucial role in the low-carbon energy future, a claim critics dismiss. Our approach to clarifying these disputes involves reviewing literature and policy documents, revisiting energy and hydrogen physics, and framing the hydrogen question within the context of failing climate and energy politics and actions aimed at reducing greenhouse gas emissions. Clarity about hydrogen’s role begins with knowing its peculiar properties, followed by numerical data on energy conversions and related losses, which reveal intractable hurdles in deploying a hydrogen energy economy. Thus, hydrogen derivatives like ammonia and synthetic hydrocarbon fuels emerge, but they sink the green hydrogen ambitions advertised to the public. Their dubious environmental and financial performance is hidden by substantial subsidies. The announced EU megaproject for producing 11 Mtons of green ammonia at the Caspian Sea in Kazakhstan contrasts with the 20 ktons realized project in Norway. While the Kazakhstani project promises grand results, its practical and financial feasibility is questionable. The Norwegian project shows the reality of green ammonia production. The article concludes that hydrogen’s economic and environmental feasibility remains challenging. Full article

The compressor blades of the gas turbine continually operate under extreme conditions, including elevated temperature, increased pressure, rapid rotation speed, and high-load environments, and are also subjected to complex vibrations, which inevitably lead to performance degradation and failures. Early fault warning based on historical operation data and real-time working conditions can enhance the safety and economy of gas turbines, preventing severe accidents. However, previous studies often faced challenges, such as a lack of fault data, imbalanced datasets, and low data utilization, which limited the accuracy of the algorithms. This study proposes a fault warning technique for gas turbine compressor blades based on AAKR-ACGAN. First, a digital twin model of the gas turbine is constructed using long-term operation data and simulation data from the mechanism model. Then, an auto-associative kernel regression (AAKR) model is used for the fault warning, monitoring multiple parameters to provide effective early warnings of potential faults. Additionally, an auxiliary classifier generative adversarial network (ACGAN) is employed to fully extract hidden data features of the fault points, balance the dataset, and accurately simulate the process of fault occurrence and development. The proposed approach is utilized for the early detection of faults in the compressor blades of a high-capacity gas turbine, and its precision and applicability are confirmed. The multisource early warning indicator can provide an early warning of a failure up to one year in advance of its occurrence. It was also able to detect a severe surge that occurred six months before the failure, which is speculated to be one of the causes that led to the failure. Full article

The impact of tourism on economic growth is a subject of interest to researchers as well as policy makers. Numerous studies have explored this relationship, often arriving at varying conclusions depending on the methods employed. Most of these studies, however, assume a symmetric relationship between tourism and economic growth. In this study, the Hatemi-J asymmetric causality test was used to test for short-run asymmetric causality between tourism receipts and economic growth in Turkey for the period 1990–2023, and unidirectional causality was found between the increase in tourism incomes and economic growth and between the decrease in tourism incomes and economic contraction. Additionally, the hidden co-integration test was applied to examine the asymmetric relationships between them in the long run, and the results reveal that an increase in tourism revenues provides resilience to the economy by mitigating contraction during economic downturns. This study contributes to the field by addressing the interaction of tourism and the economy in Turkey from an asymmetric perspective and by revealing previously unobserved relationships. The results provide partial support for the tourism-led growth hypothesis. In the long term, it is recommended that policymakers design tourism strategies aimed at enhancing resilience to economic shocks, thereby also strengthening the national economy. Diversified markets and products, well-structured incentives, and sustainable tourism practices are key elements in achieving this goal. Full article

The accurate and timely detection of cyber threats is critical to keeping our online economy and data safe. A key technique in early detection is the classification of unusual patterns of network behaviour, often hidden as low-frequency events within complex time-series packet flows. One of the ways in which such anomalies can be detected is to analyse the information entropy of the payload within individual packets, since changes in entropy can often indicate suspicious activity—such as whether session encryption has been compromised, or whether a plaintext channel has been co-opted as a covert channel. To decide whether activity is anomalous, we need to compare real-time entropy values with baseline values, and while the analysis of entropy in packet data is not particularly new, to the best of our knowledge, there are no published baselines for payload entropy across commonly used network services. We offer two contributions: (1) we analyse several large packet datasets to establish baseline payload information entropy values for standard network services, and (2) we present an efficient method for engineering entropy metrics from packet flows from real-time and offline packet data. Such entropy metrics can be included within feature subsets, thus making the feature set richer for subsequent analysis and machine learning applications. Full article

The separation of high-purity hydrogen from mixed gasses using dense metallic alloy membranes is essential for advancing a hydrogen-based economy. Palladium-based membranes exhibit outstanding catalytic activity and theoretically infinite hydrogen selectivity, but their high cost and limited performance in contaminant-rich environments restrict their widespread use. This study addresses these limitations by exploring strategies to develop cost-effective, high-performance alternatives. Key challenges include the vast compositional design space, lack of systematic design principles, and the slow pace of traditional material development. This review emphasizes the potential of high-throughput and combinatorial techniques, such as composition-spread alloy films and the statistical design of experiments (DoE), combined with machine learning and materials informatics, to accelerate the discovery, optimization, and characterization of palladium-based membranes. These approaches reduce development time and costs while improving efficiency. Focusing on critical properties such as surface catalytic activity, resistance to chemical and physical stresses, and the incorporation of low-cost base metals, this study introduces domain-specific descriptors to address data scarcity and improve material screening. By integrating computational and experimental methods, future research can identify hidden material correlations and expedite the rational design of next-generation hydrogen separation membranes. Full article

This study investigates how an increase in the quality of business ventures, measured as their success probability, affects trust and return on investment (ROI) in situations where the investor–entrepreneur interaction is affected by moral hazard and asymmetric information. We model a repeated trust problem between investors and entrepreneurs, featuring moral hazard and adverse selection. Hidden Markov techniques and computer simulations are used to derive the main results. We find that trust and ROI may decline as quality improves. Although lenders tend to reduce the requirements for granting initial credit, they nevertheless become less tolerant of current borrowers who fail to pay back. Additionally, we demonstrate a novel substitution effect, where lenders prefer new borrowers over existing borrowers that experienced early failures. The main conclusions of our study are that while impressing early on is effective in gaining first access to credit, it may nevertheless hurt the cause of getting credit in subsequent periods, following an early failure. In business environments plagued with ex post moral hazard, entrepreneurs might do better by gaining trust first and impressing later. Furthermore, our results imply that in a thriving economy, not only are bad loans made, but good loans are lost as well. Full article

The knowledge economy is becoming a key factor in the sustainable development of various sectors, including energy. One of the central elements in the energy of the future is renewable energy, which is becoming increasingly important in the global economy, especially in the context of achieving Sustainable Development Goal 7.a (SDG 7.a). In the last decade, Poland, like many other countries, has faced energy security challenges; a strong dependence on fossil energy sources, including imported ones; and the need to modernize its energy infrastructure. The development of renewable energy sources in Poland is becoming a priority in the state energy policy, facilitated by global trends and international commitments, including participation in achieving the SDGs. The knowledge economy is based on the efficient use of intellectual resources, innovative technologies, and scientific data. This article analyzes the role of scientific publications in forming innovative solutions for the energy sector, including renewable energy. The authors used modern research methods: scientometric, bibliometric, and correlation analyses of publications in the Scopus database and a specially created prompt for the processing of an array of 1,731,987 information units and z-statistics. The authors found six hidden reasons limiting the publication activity of Polish energy scientists. These reasons led Poland to leave the top 20 leading countries in the world market for scientific products in the energy sector. As a result, the authors rejected three research hypotheses and formulated management recommendations. This study emphasizes the importance of the knowledge economy in developing sustainable energy in Poland and the world. Full article