Search Results (130)

The Harry Potter fandom community around the world prefers a universe of wizards and witches that includes all people, but also has concerns about the author’s perspective regarding gender identity. This disjunction paralyzes the cultural reader with moral confusion, which is a danger to their emotional investment in the text. Although scholars have analyzed this phenomenon using fragmented prisms, such as social media activism, cognitive engagement, translation, pedagogy, and fan creativity, there is no unifying model that can be used to understand why reading pleasure endures. This article aims to fill this gap by examining these strands of research in a divergent manner, adopting a convergent mixed-methods study approach. Based on neurocognitive (EEG) values, cross-cultural focus groups, social media analysis, and corpus linguistics, we outline the terrain of reader coping mechanisms. We identify separate fan fractions and examine the corresponding practices. The results are summarized by proposing a model called the MDRL (Moral dissonance repair loop) which is a theoretical model that shows how translation smoothing, pedagogical reframing and fan-based re-moralization interact with one another in creating a system that enables the reader to be collectively able to obtain their relations with the text back to a manageable point and continue being engaged. This model makes a theoretical contribution to new areas in the study of fans, moral psychology, and cognitive literature. Full article

Accurate short-term passenger forecasts help regional airports align capacity with demand and plan investments effectively. Drawing on quarterly traffic data for 2010–2024 supplied by the Polish Civil Aviation Authority, this study employs Holt–Winters exponential smoothing to predict passenger volumes at Szczecin–Goleniów Airport for 2025. Additive and multiplicative formulations were parameterized with Excel Solver, using the mean absolute percentage error to identify the better-fitting model. The additive version captured both the steady post-pandemic recovery and pronounced seasonal peaks, indicating that passenger throughput is likely to rise modestly year on year, with the highest loads expected in the summer quarter and the lowest in early spring. These findings suggest the airport should anticipate continued growth and consider adjustments to terminal capacity, apron allocation, and staffing schedules to maintain service quality. Because the Holt–Winters method extrapolates historical patterns and does not incorporate external shocks—such as economic downturns, policy changes, or public health crises—its projections are most reliable over the short horizon examined and should be complemented by scenario-based analyses in future work. This study contributes to sustainable airport management by providing a reproducible, data-driven forecasting framework that can optimize resource allocation with minimal environmental impact. Full article

Market-oriented ecological restoration is vital for advancing ecological civilization and promoting harmonious human–nature relationships. However, the precise implementation pathway remains unclear. Few studies specifically address challenges that arise during ecological restoration implementation. Ensuring the smooth and effective implementation and landing of ecological restoration projects harmonizes ecological and economic objectives at the regional scale and fosters sustainable development in the region. Based on the policies of market-oriented ecological restoration collected from various Chinese provinces, and through multi-level institutional analysis, the policy measures are categorized into three phases: early, middle, and late. For each phase, we summarize the challenges encountered in implementing market-oriented ecological restoration projects. Finally, by the method of constructing theoretical models, we propose sustainable countermeasures based on multiple theoretical models. The results show (1) China’s ecological restoration sector is experiencing rapid growth, and market-oriented policies in China, multiple Chinese provinces, and municipalities have enacted successive market-oriented ecological restoration policies, and the outlook for ecological restoration marketization in China remains highly promising. (2) The implementation process of current market-oriented ecological restoration projects confronts and encounters several challenges. These include the absence of project screening and evaluation mechanisms, limited investment and financing channels, ill-defined approval processes, ambiguous delineation of departmental responsibilities, insufficient industry incentives, and the absence of effective operational and management mechanisms. (3) To address the identified challenges, taking forest ecological restoration as an example, theoretical models should be developed encompassing six critical dimensions: the aspects of the mechanism, mode, approval process, management system, industrial chain, and platform. This aims to provide sustainable pathways for the effective implementation of market-oriented forest ecological restoration projects. Full article

This paper proposes hybrid LSTM-DES models that combine traditional forecasting methods with recurrent neural network techniques. We experimented with these proposed models using four passenger datasets from different regions of Thailand. Additionally, we compared their performance with several individual forecasting models, including the Double Moving Average (DMA), Double Exponential Smoothing (DES), and Holt–Winters methods (both additive and multiplicative trends), as well as long short-term memory (LSTM) recurrent neural networks. Our proposed hybrid model builds upon previous work with improvements in hyperparameter tuning using the GRG nonlinear optimization method. The results demonstrate that the hybrid LSTM-DES models outperformed all individual models in terms of both accuracy and demand variation. The reason behind the success of the hybrid model is that it works well with both linear and nonlinear trends, as well as the seasonality of certain periods. Furthermore, the forecast results for train passengers will serve as input variables to estimate the future revenue of train travel programs in various regions, including rail tourism. This information will help identify which regions should receive increased focus and investment by the train tourism program. For example, if the forecasted number of passengers in the northern region is high, the State Railway of Thailand will promote and improve infrastructure at the train station and nearby tourist attractions. Full article

Nowadays, financial institutions increasingly leverage artificial intelligence to enhance decision-making and optimize investment strategies. A specific application is the automatic analysis of large volumes of unstructured textual data to extract relevant information through deep learning (DL) methods. However, the effectiveness of these methods is often limited by the scarcity of high-quality labeled data. To address this, we propose a new data augmentation technique, Concatenation Augmentation (CA). This is designed to overcome the challenges of processing unstructured text, particularly in analyzing professional profiles from corporate governance reports. Based on Mixup and Label Smoothing Regularization principles, CA generates new text samples by concatenating inputs and applying a convex additive operator, preserving its spatial and semantic coherence. Our proposal achieved hit rates between 92.4% and 99.7%, significantly outperforming other data augmentation techniques. CA improved the precision and robustness of the DL models used for extracting critical information from corporate reports. This technique offers easy integration into existing models and incurs low computational costs. Its efficiency facilitates rapid model adaptation to new data and enhances overall precision. Hence, CA would be a potential and valuable data augmentation tool for boosting DL model performance and efficiency in analyzing financial and governance textual data. Full article

This paper introduces a two-phase decision support system based on information theory and financial practices to assist investors in solving cardinality-constrained portfolio optimization problems. Firstly, the approach employs a stock-picking procedure based on an interactive multi-criteria decision-making method (the so-called TODIM method). More precisely, the best-performing assets from the investable universe are identified using three financial criteria. The first criterion is based on mutual information, and it is employed to capture the microstructure of the stock market. The second one is the momentum, and the third is the upside-to-downside beta ratio. To calculate the preference weights used in the chosen multi-criteria decision-making procedure, two methods are compared, namely equal and entropy weighting. In the second stage, this work considers a portfolio optimization model where the objective function is a modified version of the Sharpe ratio, consistent with the choices of a rational agent even when faced with negative risk premiums. Additionally, the portfolio design incorporates a set of bound, budget, and cardinality constraints, together with a set of risk budgeting restrictions. To solve the resulting non-smooth programming problem with non-convex constraints, this paper proposes a variant of the distance-based parameter adaptation for success-history-based differential evolution with double crossover (DISH-XX) algorithm equipped with a hybrid constraint-handling approach. Numerical experiments on the US and European stock markets over the past ten years are conducted, and the results show that the flexibility of the proposed portfolio model allows the better control of losses, particularly during market downturns, thereby providing superior or at least comparable ex post performance with respect to several benchmark investment strategies. Full article

Compartment boundaries divide the embryo into segments with distinct fates and functions. In the vascular system, compartment boundaries organize endothelial cells into arteries, capillaries, and veins that are the fundamental units of a circulatory network. For vascular smooth muscle cells (SMCs), such boundaries produce mosaic patterns of investment based on embryonic origins with important implications for the non-uniform distribution of vascular disease later in life. The morphogenesis of blood vessels requires vascular cell movements within compartments as highly-sensitive responses to changes in fluid flow shear stress and wall strain. These movements underline the remodeling of primitive plexuses, expansion of lumen diameters, regression of unused vessels, and building of multilayered artery walls. Although the loss of endothelial compartment boundaries can produce arterial–venous malformations, little is known about the consequences of mislocalization or the failure to form SMC-origin-specific boundaries during vascular development. We propose that the failure to establish a normal compartment boundary between cardiac neural-crest-derived SMCs of the 6th pharyngeal arch artery (future ductus arteriosus) and paraxial-mesoderm-derived SMCs of the dorsal aorta in mid-gestation embryos leads to aortic coarctation observed at birth. This model raises new questions about the effects of fluid flow dynamics on SMC investment and the formation of SMC compartment borders during pharyngeal arch artery remodeling and vascular development. Full article

Battery energy storage systems (BESSs) can play a significant role in overcoming the challenges in Distribution Systems (DSs) with a high level of penetration from renewable energy sources (RESs). In this paper, the goal is to determine the optimal location, size, and charging/discharging dispatches of BESSs in DSs with a high level of photovoltaic (PV) installations. The problem of the location and size of BESSs is solved with multi-criteria optimization using Non-dominated Sorting Genetic Algorithm-II (NSGA-II). The criteria of the multi-criteria optimization are minimal investment costs for BESS and improvement of the network performance index. The network performance index includes the reduction in annual losses of active energy in DSs and the minimization of voltage deviations. The dispatch of a BESS is determined using auxiliary optimization. Linear Programming (LP) is used for auxiliary optimization, with the aim of dispatching the BESS to smooth the load profile in DS. The proposed optimization method differs from previous studies because it takes in its calculations all days of the year. This was performed using the K-means clustering technique. The days of one year are classified by the level of consumption and PV production. The optimization was performed for five different levels of PV penetration (60%, 70%, 80%, 90%, and 100%) and for two scenarios: the first with one BESS and the second with two BESSs. The proposed methodology is applied to the IEEE 33 bus balanced radial distribution system. The results demonstrate that with an optimal choice of location and parameters of the BESS, significant improvement in network performance is achieved. This refers to a reduction in losses of active power, improvement of voltage profile, smoothing the load diagram, and reducing the peak load. For the scenario with one BESS and PV penetration of 100%, the reduction in daily energy losses reaches a value of up to 10% compared to the base case (case without a BESS). The reduction in peak load goes to 20%. Further, the highest voltage during the day is significantly lower in all buses compared to the base case. Similarly, the lowest voltage during the day is considerably higher. The methodology from this paper can be applied to any radial distribution network with a variable number of BESSs. The testing results confirm the effectiveness of the proposed method. Full article

The purpose of this study is to examine the effect of the two determinants of the technology acceptance model (TAM3), perceived ease of use (PEOU), and perceived usefulness (PU) on auditors’ intention to adopt and use blockchain technology (BT) in Lebanon. This study also aims to investigate the moderating role of age on these associations to determine the antecedents of PU and PEOU. A sample of 332 auditors working in Lebanon was used to collect data and the analysis was conducted using the third version of partial least squares structural equation modeling (PLS3-SEM). Results show that perception of external control and computer self-efficacy significantly affect the PEOU. Job relevance and output quality are PU antecedents and positively influence the variable. In addition, PEOU and PU have a significant positive impact on auditors’ intention to adopt BT. This shows that auditors in Lebanon are more inclined to adopt BT if they feel that BT does not require substantial effort and that BT provides tangible benefits to their work. According to the researchers’ knowledge, this study is the first to examine auditors’ perception of using BT in one of the Middle Eastern countries, Lebanon, and the first to investigate the moderating role of age on the relationship between TAM3 determinants and auditors’ intention to adopt BT. In addition, this study highlights the practical implications of adopting BT in auditing in Lebanon by pinpointing the need for training programs, collaboration between auditors and other departments, developing regulatory frameworks to enhance efficiency, and organizing awareness and educational campaigns. Additionally, investments in infrastructure are critical to facilitate the smooth implementation and adoption of BT. Furthermore, audit firms should organize workshops to educate auditors on the application and the benefits of BT, invest in upgrading the IT systems to be compatible with BT platforms, and provide case studies and pilot projects to promote confidence in BT adoption. Full article

Background/Objectives: The COVID-19 pandemic has significantly altered healthcare systems worldwide, highlighting healthcare expenditure’s critical role in fostering population resilience and wellness. This extraordinary situation has brought to light the delicate balance that governments must maintain between the need to protect public health and budgetary restraints. The relationship between healthcare expenditure and outcomes, such as healthy life years, health expectancy, and standardized death rate, has become a central point in understanding the dynamics of healthcare systems and their capacity to adapt to emerging challenges. Methods: Using extensive datasets and predictive approaches such as artificial neural networks, exponential smoothing models, and ARIMA techniques, this study explores these connections in the context of the European Union. Results: The study better explains how healthcare financing schemes influence important health outcomes by examining past trends and forecasting future developments. The results show that household healthcare expenditures correlate negatively with standardized death rates and substantially benefit healthy life years and health expectancy. These findings underline the significance of household contributions in influencing health outcomes across various healthcare systems. Long-term and strategic investments in health services are essential, as the pandemic has demonstrated the proactive capacity of well-designed healthcare systems to reduce risks and enhance overall resilience. The results suggest that focused investments can raise life expectancy and lower death rates, supporting the development of robust, adaptable healthcare systems in the post-pandemic era. Conclusions: The main contribution of this research is demonstrating the significant role of healthcare expenditure, particularly household contributions, in improving key health outcomes and fostering healthcare system resilience in the EU context. Full article

Background/Objectives: Youthful, smooth skin is highly desired in modern society. Individuals invest in cosmetics, plastic surgeons, and dermatologists in pursuit of perfect skin. However, many do not seek out dietary changes to improve skin health. Although research has been conducted on the role of nutrition and select nutrients and phytonutrients on skin health, there is a lack of healthy food recommendations for clear skin. Methods: The literature was assessed to determine which nutrients and phytonutrients play a significant role in the protection and maintenance of skin health. Key compounds were highlighted as there is evidence to suggest they have a significant role in skin health: vitamin A, vitamin C, vitamin D, vitamin E, zinc, omega-6 and omega-3 fatty acids, polyphenols/flavonoids, copper, selenium, and silicon. USDA FoodData Central and FooDB (food database), were utilized to select foods and food groups containing the key nutrients and phytonutrients. Results: A skin-healthy dietary pattern is proposed in addition to a scoring system to assess diet. A sample skin-healthy daily diet was designed, using only whole foods, that met the Daily Values for vitamins and minerals and contained key compounds for skin health. Conclusions: There is a clear link between nutrition and skin health, or nutritional dermatology; however, more research needs to be done to find the intersection between both disciplines. Full article

The World Bank (WB) has described the European Union (EU) as a convergence machine, and the real and institutional convergence has been achieved for a long period of time, and EU’s cohesion policy, alongside the Recovery and Resilience Facility (RRF), remains crucial for driving reforms and fostering investments that promote growth. But, in the last two decades this convergence machine has stopped working, and the convergence process has turned in the divergence. The divergence process poses a great risk for the smooth functioning of the EU, and it increases vulnerability of the EU to negative economic shocks. Productivity and institutional convergence are a necessary precondition for the smooth functioning of the EU, reducing differences in standards of living, increasing resilience, and achieving environmental sustainability. In the present paper, we will apply log t-test over the period 2003–2023 to investigate the formation of productivity and institutional convergence clusters. Our goal is to identify which countries belong to the poor productivity/institutional clubs, and to provide the necessary policy implications. Results indicate the existence of multiple steady states, which means that EU is vulnerable to external economic shocks Full article

Aneurysm modeling and simulation play an important role in many specialist areas in the field of medicine such as surgical education and training, clinical diagnosis and prediction, and treatment planning. Despite the considerable effort invested in developing computational fluid dynamics so far, visual simulation of blood flow dynamics in aneurysms, especially the under-explored aspect of bifurcation aneurysms, remains a challenging issue. To alleviate the situation, this study introduces a novel Smoothed Particle Hydrodynamics (SPH)-based method to model and visually simulate blood flow, bifurcation progression, and fluid–structure interaction. Firstly, this research consider blood in a vessel as a kind of incompressible fluid and model its flow dynamics using SPH; and secondly, to simulate bifurcation aneurysms at different progression stages including formation, growth, and rupture, this research models fluid particles by using aneurysm growth mechanism simulation in combination with vascular geometry simulation. The geometry incorporates an adjustable bifurcation structure based on Murray’s Law, and considers the interaction between blood flow, tissue fluid, and arterial wall resistance. Finally, this research discretizes the computation of wall shear stress using SPH and visualizes it in a novel particle-based representation. To examine the feasibility and validity of the proposed method, this research designed a series of numerical experiments and validation scenarios under varying test conditions and parameters. The experimental results based on numerical simulations demonstrate the effectiveness and efficiency of proposed method in modeling and simulating bifurcation aneurysm formation and growth. In addition, the results also indicate the feasibility of the proposed wall shear stress simulation and visualization scheme, which enriches the means of blood analysis. Full article

The rapid investment casting (RIC) process requires a 3D-printed pattern to create a ceramic mold. Stereolithography (SLA) is a commonly used 3D printing method for pattern creation due to its ability to print complex shapes with smooth surfaces. The printing parameters can significantly affect the dimensional accuracy of the pattern. This study examines how different build orientations (0°, 45°, and 90°) affect the dimensional accuracy of parts produced using SLA. The specimens were printed using castable wax resin. They were measured to investigate the dimensional deviations using 3D scanning technology to understand the correlation between orientation and accuracy better. It was found that the orientation of the print affects the overall accuracy significantly. Parts printed at a 45° angle generally showed the smallest deviations from their nominal dimensions, except for certain features. For instance, cylindrical features showed deviations improving from −7.28% at 0° to −4.81% at 90°, while spherical features had deviations decreasing from −5.01% at 0° to −2.46% at 90°. Simple features, such as holes, exhibited minimal deviation across orientations, with the smallest error observed at 45° (1.98%). These results demonstrate different features and build orientations can affect the accuracy of the printed part differently. To ensure better accuracy, parts printed in different build orientations will require varying amounts of compensation during the design stage. By managing build orientations and controlling the inherent limitations of SLA, users can improve the print’s accuracy and meet quality standards more effectively. Research results can help industries optimize print settings and reduce dimensional errors. Full article

Bitcoin (BTC-USD) is a virtual currency that has grown in popularity after its inception in 2008. BTC-USD is an internet communication network that makes using digital money, including digital payments, easy. It offers decentralized clearing of transactions and money supply. This study attempts to accurately anticipate the BTC-USD prices (Close) using data from September 2023 to September 2024, comprising 390 observations. Four machine learning models—Multi-layer Perceptron, Extreme Learning Machine, Neural Network AutoRegression, and Extreme-Gradient Boost—as well as four time series models—Auto-Regressive Integrated Moving Average, Auto-Regressive, Non-Parametric Auto-Regressive, and Simple Exponential Smoothing models—are used to achieve this end. Various hybrid models are then proposed utilizing these models, which are based on simple averaging of these models. The data-splitting technique, commonly used in comparative analysis, splits the data into training and testing data sets. Through comparison testing with training data sets consisting of 30%, 20%, and 10%, the present work demonstrated that the suggested hybrid model outperforms the individual approaches in terms of error metrics, such as the MAE, RMSE, MAPE, SMAPE, and direction accuracy, such as correlation and the MDA of BTC. Furthermore, the DM test is utilized in this study to measure the differences in model performance, and a graphical evaluation of the models is also provided. The practical implication of this study is that financial analysts have a tool (the proposed model) that can yield insightful information about potential investments. Full article