This study aims to provide a comprehensive model to assist investors in strategic decision-making amid market uncertainty. Global economic uncertainty characterized by cycles of stagflation and recession has recurred in history and is expected to recur until 2025. This condition encourages the importance of investment strategies that can protect asset values from economic pressures. This study uses a quantitative approach with forecasting methods and risk analysis based on time series data. The data used are daily gold and silver prices from the London Bullion Market Association (LBMA) in USD, collected over a two-year period, namely from January 3, 2023 to January 4, 2025. The data is secondary and obtained from the official LBMA website. The research stages begin with a literature study to understand relevant concepts and methods, followed by data collection, and continued with data preprocessing. The preprocessing stages include checking for outliers, handling missing values using the series mean method, and merging data for temporal consistency. For the forecasting process, the Fuzzy Time Series–Markov Chain method is used, which consists of several steps: the formation of universe and interval sets using the Sturges formula, the definition of fuzzy sets, the fuzzification process, the formation of Fuzzy Logical Relationships (FLR) and Fuzzy Logical Relationship Groups (FLRG), and the preparation of transition probability matrices. The forecasting results are obtained through the defuzzification process, which are then evaluated using the Mean Absolute Percentage Error (MAPE) indicator to assess the accuracy of the model. Risk analysis is carried out using the Value at Risk (VaR) approach using the Extreme Value Theory (EVT) method and the Generalized Pareto Distribution (GPD). The entire analysis process is carried out using Microsoft Excel and RStudio software to ensure accuracy and efficiency in data processing and statistical modeling. This study has succeeded in developing a hybrid Fuzzy Time Series–Markov Chain model to forecast precious metal prices, especially gold and silver, with a very high level of accuracy. Based on an evaluation of various training and testing data proportions, the best model was obtained at a 95:5 ratio, with MAPE values of 0.66% for gold and 1.18% for silver in the training data, and 0.55% and 0.94% in the testing data. These results indicate that the model is able to effectively capture historical price patterns and provide predictions close to the actual value.

Open-set pedestrian identification via transformer-based neural architecture with extreme value theory

Television networks depend heavily on advertising revenue, yet the placement of in-game commercials during football matches often disrupts the viewing experience and provokes negative audience reactions. This study investigates football viewers’ attitudes, perceptions, and retention of in-game commercials across selected viewing centers in Kano metropolis, Nigeria, anchored in expectancy–value theory. The research adopts a descriptive survey design with a sample of 269 respondents proportionally drawn from a population of 900 viewers. Data were collected through structured questionnaires and analyzed using descriptive statistics. The findings reveal low exposure to and weak recall of halftime and floating commercials, with the majority of viewers perceiving advertisements as excessive, intrusive, and irritating. Only a minority of respondents reported purchasing advertised products, indicating limited influence of in-game commercials on consumer behavior. Although some viewers acknowledged the relevance of certain advertisements, overall attitudes were predominantly negative, manifested in frequent channel switching, disengagement, and expressions of manipulation. The study concludes that in-game commercials are largely ineffective in sustaining audience attention or driving consumer action and recommends that broadcasters and advertisers adopt less obtrusive, more engaging, and contextually relevant advertising strategies to enhance message retention and mitigate viewer dissatisfaction, thereby improving advertising effectiveness in sports broadcasting.

This study investigates the public transport capacity shortage between the Hengqin Port and the University of Macau (UM), situating the issue within the distinctive governance context of the Macau SAR. Drawing on stakeholder theory and public value theory, we analyze how cross-border administrative fragmentation and competing local interests shape public service. Using the surveys of 150 UM students and in-depth interviews, we found that the rising demand for cross-border commuter service is only part of the capacity shortfall. Cross-border transport capacity is constrained by jurisdictional divides between Macau and Guangdong authorities, fiscal incentives, and protectionist practices that prioritize local operators over regional efficiency. These dynamics reflect broader compromises of the “One Country, Two Systems,” where coordination between governments and accountability remain limited. The study concludes with policy recommendations aimed at alleviating immediate transport shortage while fostering cooperative mechanisms to strengthen integrated public service across Macau and Hengqin.

This study delivers a robust earthquake risk analysis for the highly seismically active Pazarcık region in Kahramanmaraş, Turkey. By examining a substantial dataset of 2,148 earthquake occurrences spanning from 1114 to 2023, including three significant historical records, we meticulously calculated earthquake probabilities using a diverse array of seven cutting-edge statistical and machine learning methodologies. Furthermore, we effectively estimated the location and depth of the impactful earthquake that struck on February 6, 2023. The analytical methods employed—ranging from the Poisson process, Gutenberg-Richter law, Weibull distribution, Gamma distribution, and the ETAS model to Extreme Value Theory and the sophisticated XGBoost algorithm—enabled us to rigorously evaluate the probabilities of earthquakes with magnitudes M≥3, M≥4, M≥5, M≥6, and M≥7 over timeframes extending from 1 to 250 years. Our findings convey a striking probability estimate: the likelihood of an M≥7 earthquake occurring in the region within the next 50 years is between 45.6% and 51.2% (averaging at 45.9%), escalating to 70.5% within 100 years and a staggering 94.9% within 250 years. In our pursuit of precise location and depth estimations, the Kernel Density Estimation (KDE) method emerged as the most reliable tool. It achieved an impressively low margin of error of (−0.0004°, −0.0092°) for the coordinates of the February 6, 2023 earthquake, alongside approximately a 0.5 km margin of error in depth estimation. Overall, the depth estimation accuracy reached an astounding 98.5%. This comprehensive study powerfully demonstrates that employing a multi-method approach not only enhances the reliability of results but is essential for accurate earthquake hazard risk analyses.

Technology has profoundly transformed corporate training practices within organizations. A significant impact was seen in training practices managed by Human Resources (HR), leading to altered organizational training cultures and emphasizing the need for new approaches. The transition from traditional to e-training methods has yielded considerable financial and non-financial benefits for organizations. However, it is of utmost importance to ensure the sustainability of corporate training programs alongside these benefits. This conceptual review examines the impact of e-training quality dimensions and the sustainability of corporate e-training programs, highlighting key factors influencing their continued adoption and effectiveness. The study synthesizes existing literature, drawing on frameworks such as Information Systems Success (ISS) Model and Perceived Value Theory, Stakeholder Theory and the Social Cognitive Theory, alongside concepts from sustainability science, to evaluate relevant quality indicators within the sustainable e-training programmes. The review identifies core e-training quality factors, including learner quality, instructor quality, information quality, system quality, and institutional quality as crucial to employee engagement and knowledge retention. It also reveals a significant gap in the integration of e-training quality models with corporate sustainability in the current literature. The sustainability of corporate e-training programmes core factors including equal training opportunities, long-term oriented training, an integrated approach to training, a focus on well-being, environmentally-oriented training, and team building . Key academic databases were used, including; JSTOR, Taylor & Francis, Emeralds, Oxford Journals, Science Direct, Google Scholar and reviewed 100 paper from 2005 onwards. The paper proposes a conceptual framework linking e-training quality dimensions to the sustainability of e-training programme outcomes. In this framework, e-training quality is identified as the independent variable, while the sustainability of e-training programmes serves as the dependent variable. Learning organisations and technology self-efficacy function as moderating variables, and perceived value of e-training act as the mediating variable. The literature suggests that the proposed variables are interrelated; however, these relationships have not yet been empirically tested. The study calls for empirical validation and further research to explore how these factors interact across diverse corporate contexts.

While green purchasing behavior is central to the development of sustainable tourism, the specific roles that social media exposure and digital peer networks play in impacting such behavior require further elucidation. Drawing on customer value theory, this paper analyzes how social media and peer networks, through shaping the emotional, economic, functional, and relational values customers hold toward green tourism products, influence their green purchasing attitude and intention. Analyzing the survey data gathered from 259 Chinese travelers with Partial Least Squares Structural Equation Modeling, we identify both statistically significant and non-significant correlations, accompanied by their respective standardized estimates and confidence intervals. Firstly, compared with economic value, relational and functional value play a bigger role in strengthening green purchasing attitude, which, in turn, positively impact purchasing intention. Secondly, while personal norms intensify the linkage between green purchasing attitude and intention, digital trust is shown to amplify the emotional value. With the linkage between economic value and green purchase attitude being statistically non-significant, we concludes that relational and functional values dominate attitudinal formation in the context of green tourism purchasing. The study offers theoretical and practical implications for tourism businesses, policymakers, and digital marketers committed to supporting sustainability in tourism. By drawing on the findings presented, stakeholders can enhance their strategies to promote more responsible consumer behaviors, thus aligning with both economic and environmental sustainability goals.

The hybrid framework of extreme value theory (EVT) models and machine-learning-based anomaly detection methods has emerged as an effective approach for traffic-conflict-based crash estimation. This study investigates the generalizability of the hybrid framework across prevalent safety hierarchy models, namely pyramid-shaped and diamond-shaped hierarchies. Two anomaly detection methods—isolation forest (IForest) and minimum covariance determinant (MCD)—were integrated into the EVT modeling framework and compared with the conventional peak over threshold (POT) method. The methods were applied to two distinct datasets. One is the highD dataset corresponding to pyramid-shaped safety hierarchy, and the other is an intersection dataset corresponding to diamond-shaped hierarchy. The results reveal that IForest and MCD exhibit performance comparable to POT when applied to pyramid-shaped data with large sample sizes. However, these methods underperformed with diamond-shaped data because of its double-peak distribution, with performance improving when the data was reshaped to a pyramid shape. Furthermore, in the context of smaller datasets, an increase in the contamination rate for IForest and MCD was found to improve performance, surpassing POT in certain cases. Additionally, IForest displays sensitivity to the order of data, whereas MCD yields consistent outcomes regardless of the data arrangement.

In the article I reconstruct the essential terms of the Stoic semiotics. I believe a careful philological analysis seems to be a key for elucidating statements appearing in sources which commonly are regarded as unclear or self-contradictory, and for bringing to light a highly original theory of reference, with special regard to the notion of lekton (meaning of a sentence). Since the Stoics quite consistently differentiate between two synonymous verbs hyphestanai and hyparkhein, I propose to introduce two modes of existence: “dependent on phantasia” and “dependent on the somaton”, respectively. Given the analysis of axiomata (proposition) and some other detailed concepts, it becomes possible to reconstruct an interesting, ontologically economic theory of a great explanatory value, whereby true propositions remain tightly connected to the real world, whereas false propositions entail a looser relationship, but the existence of such propositions in the world is sufficiently corroborated.

The construction of a digital government is a significant initiative to modernize the national governance system and enhance governance capabilities, with the creation of public value focused on optimizing the supply level of basic public services within improved governance efficiency. This research begins with public value theory and technology empowerment theory to explain the theoretical mechanisms by which digital government can promote the supply level of basic public services. Utilizing panel data from 31 provinces in China from 2017 to 2021, this study investigates the impact of digital government construction on the supply level of basic public services and further examines the moderating effects of government support, the level of digital technology development, and regional heterogeneity. The research findings indicate that digital government construction can significantly enhance the supply level of basic public services, with more pronounced effects in regions where government support is strong or the level of digital technology development is high. Analysis of regional heterogeneity shows that the improvement in the supply level of basic public services due to digital government construction is more significant in the eastern region compared to the central and western regions. This study, based on the practice of digital government construction, provides a theoretical basis and decision-making reference for optimizing the top-level design of digital government, improving the supply level of public services, and achieving the integration of “digital + public services.”

The time derivative of a physical property often gives rise to another meaningful property. Since weak values provide empirical insights that cannot be derived from expectation values, this paper explores what physical properties can be obtained from the time derivative of weak values. It demonstrates that, in general, the time derivative of a weak value that is invariant under an electromagnetic gauge transformation is neither a weak value nor a gauge-invariant quantity. Left- and right-hand time derivatives of a weak value are defined, and two necessary and sufficient conditions are presented to ensure that they are also gauge-invariant weak values. Finite-difference approximations of the left- and right-hand time derivatives of weak values are also presented, yielding results that match the theoretical expressions when these are gauge invariant. With these definitions, a local Ehrenfest-like theorem can be derived, giving a natural interpretation for the time derivative of weak values. Notably, a single measured weak value of the system's position provides information about two additional unmeasured weak values: the system's local velocity and acceleration, through the first- and second-order time derivatives of the initial weak value, respectively. These findings also offer guidelines for experimentalists to translate the weak value theory into practical laboratory setups, paving the way for innovative quantum technologies. An example illustrates how the electromagnetic field can be determined at specific positions and times from the first- and second-order time derivatives of a weak value of position.

Subject. The article addresses factors of the growing cost of flying hour, which is an integral indicator of helicopter efficiency at the stage of its operation. Objectives. The purpose is to develop an econometric model of dependence of cost per flying hour on the main relative aircraft performance characteristics that determine the effectiveness of design and technological solutions for civil and multi-purpose helicopters, to make a decision at the preliminary design stage regarding the efficiency of designed product during the operational phase and the feasibility of R&D. Methods. The study rests on basic provisions of the theory of the firm, the theory of fundamental value of assets, international standards of the aviation equipment operation manual. Results. Based on econometric analysis, the main determinants of cost per flight hour are identified from the set of relative flight characteristics of the helicopter. The paper demonstrates that the number of products in operation, the cost of flight hour, the efficiency and transparency of logistics infrastructure in the provision of maintenance services represent a tool for managing the cost of product life cycle during its operation. Conclusions. The main factors having the greatest impact on the efficiency of after-sales service of products in the aviation industry are fuel efficiency, specific fuel consumption (fuel consumption per unit of power). The cost per flight hour is an integral indicator of the effectiveness of after-sales service and depends on relative flight characteristics of aircraft in operation.

In this article, I address the fragmented and often siloed understanding of value, value creation, and innovation that pervades both academic literature and business practice. My primary objective is to synthesize a comprehensive theoretical framework that integrates the economic, technological, cultural, and behavioral dimensions of how value is created and perceived in products. My core contribution is a unified model that connects a “Holistic Value Construct” - which maps the domains of value from tangible to intangible - with dual, interconnected innovation paths: a “Technological Innovation Path” focused on production and a “Cultural Innovation Path” centered on the construction of meaning. This framework reveals that sustainable competitive advantage lies in mastering the interplay between these two fundamental forces. I conclude by highlighting the profound implications of this integrated perspective for business strategy, product development, and the direction of future academic research.

During the global transition of energy structures toward renewable sources, offshore wind power has experienced rapid advancement, coinciding with increasingly complex wave environments. This study focuses on the wave conditions of an offshore wind farm project in Vietnam. A dual-nested numerical framework (WAVEWATCH III + SWAN) is established, integrated with 32-year (1988–2019) high-resolution WRF wind fields and fused bathymetry data (GEBCO + in situ measurements). This framework overcomes the limitations of short-term datasets (10–22 years) in prior studies and achieves 1′ × 1′ (≈1.8 km) intra-farm resolution—critical for capturing topographic modulation of waves. A systematic analysis of the regional wave climate characteristics is performed, encompassing wave roses, joint distributions of significant wave height and spectral peak period, wave–wind direction correlations, and significant wave height–wind speed relationships. Extreme value theory, specifically the Pearson Type-III distribution, is applied to estimate extreme wave heights and corresponding periods for return periods ranging from 1 to 100 years, yielding critical design wave parameters for wind turbine foundations and support structures. Key findings reveal that the wave climate is dominated by E–SE (90°–120°) monsoon-driven waves (60% of Hs = 0.5–1.5 m), while extreme waves are uniquely concentrated at 120°—attributed to westward Pacific typhoon track alignment and long fetch. For the outmost site (A55, 7.18 m water depth), the 100-year return period significant wave height (Hs100 = 4.66 m, Tp100 = 13.05 s) is 38% higher than sheltered shallow-water sites (A28, Hs100 = 2.7 m), reflecting strong bathymetric control on wave energy. This study makes twofold contributions: (1) Methodologically, it validates a robust framework for long-term wave simulation in tropical monsoon–typhoon regions, combining 32-year high-resolution data with dual-nested models. (2) Scientifically, it reveals the directional dominance and spatial variability of waves in the Mekong estuary, advancing understanding of typhoon–wave–topography interactions. Practically, it provides standardized design parameters (compliant with DNV-OS-J101/IEC 61400-3) for offshore wind projects in Southeast Asia.

Work values play a crucial role in understanding individual behavior in the workplace as it is serving as the foundation for one’s decision-making at work. However, cross-cultural research indicates that the structure of work values may vary depending on the socio-cultural context of the worker lives in. Therefore, this study aims to explore the structure of work values among Indonesian employees based on Schwartz’s theory of universal human values through an empirical examination using Exploratory Factor Analysis (EFA) on the dataset collected from 519 employees aged 27-55 years old residing on Java Island using Portrait Value Questionnaire combined with PVQ-RR. Based on the analysis, the data met the assumptions for EFA (KMO = 0.911; Bartlett’s χ²(78) = 3353.020, p < .001). PCA with Varimax rotation identified a more interpretable three-factor solution (EV = 6.184, 1.547, 0.808), explaining 65.69% of the total variance. We then construct three new higher-order factors were identified as (1) relational harmony and social security, (2) self-determination grounded in socio-cultural values, and (3) achievement and status as work identity. Adapting terminology and clustering value indicators into higher-order factors can yield a blueprint that is more sensitive to Indonesian culture without obscuring the meaning of the constructs, thereby providing an initial blueprint for developing a more context-sensitive Indonesian-version of PVQ (Portrait Values Questionnaire).

The rapid digital transformation of China’s vocational education system has introduced extensive opportunities for flexible, technology-enhanced learning. However, sustaining long-term engagement in online learning environments remains an unresolved challenge, particularly among students enrolled in application-oriented majors such as Intelligent Transportation. The present study investigates the key factors influencing the sustainability of online learning and proposes practical optimization strategies to enhance students’ continued participation and learning quality. Drawing upon self-regulation theory, expectancy–value theory, and the community of inquiry framework, this research conceptualizes online learning sustainability as the persistent, self-motivated, and value-driven engagement of learners in digital learning activities over time. Empirical data were collected through a structured questionnaire survey of 200 Chinese vocational college students majoring in Intelligent Transportation. The study utilized a five-point Likert scale to assess seven latent constructs—self-regulated learning (SRL), digital literacy (DL), institutional support (IS), teacher presence (TP), peer interaction (PI), motivation (MO), and perceived usefulness (PU)—as predictors of sustainability intention (SI). Statistical analyses included descriptive statistics, reliability testing, Pearson correlations, and standardized multiple regression. All measurement scales demonstrated satisfactory internal consistency (Cronbach’s α = 0.84–0.87). The regression model revealed that SRL, MO, and PU exerted the strongest direct effects on SI, while IS, DL, and TP played important enabling roles. PI exhibited a smaller but significant positive influence, emphasizing the value of peer networks in maintaining engagement. Based on the empirical findings, the paper proposes twelve evidence-based strategies addressing institutional policy, digital infrastructure, instructional design, and learner support systems. These include developing analytics-based early-warning systems, enhancing teacher feedback cycles, integrating certification-oriented learning pathways, and strengthening self-regulation and motivation training. The study contributes both theoretically and practically to the growing discourse on sustainable online learning in vocational education, offering a replicable model and strategic roadmap for educators and policymakers seeking to align digital pedagogy with the objectives of China’s high-quality vocational education reform.

The rapid integration of Generative Artificial Intelligence into public sector administration presents a dualistic reality of enhanced efficiency and significant threats to administrative integrity, a tension acutely felt in emerging digital economies. This study addresses the critical absence of an evidence-based framework for leveraging prompt engineering to ensure Generative AI use by administrative and technical staff at the University of Cape Coast reinforces, rather than undermines, administrative integrity. Employing an exploratory sequential mixed-methods design, the research combined qualitative interviews and a quantitative survey to first explore staff practices and then measure prevalent risks. The study achieved its purpose by diagnosing a bifurcated risk structure—comprising substantive Administrative Integrity and procedural Transparency & Accountability concerns—and subsequently developing a novel, theoretically-informed framework for prompt engineering. This framework fills a crucial gap in the literature, which has largely overlooked micro-practices of AI use in African administrative contexts, by providing a multi-layered governance model anchored in Public Value, Institutional, and Technology Acceptance theories. The new knowledge created demonstrates that integrity-preserving AI use requires an integrated system of institutional governance, iterative practice, and robust oversight, moving beyond technical skill-building. The study positions prompt engineering as a vital mechanism for upholding due process and due diligence. Key policy implications include the need to mandate structured prompt engineering literacy, institutionalise curated prompt libraries, and formally integrate AI accountability matrices into administrative procedures to guide effective resource allocation and decision-making for sustainable digital transformation.

Niche parties have emerged in many democracies worldwide. Various aspects of these parties have been studied, including the role of mainstream parties in their electoral success. Key to that success, arguably, is news media attention. Is the media attention that niche parties receive affected by mainstream parties as well? In this paper, we combine news value theory with party competition theory to argue that other parties influence niche party visibility. Focusing on green parties in 11 countries between 1992 and 2021, we find evidence that the salience of green policies in mainstream parties’ manifestoes enhance green party visibility in newspapers if these parties take an adversarial position. This positive effect turns negative as the mainstream party becomes greener, which suggests that it steals a niche party’s thunder. The insight that mainstream parties can influence media attention to niche parties opens up new lines of research on the emergence of niche parties.

Purpose This study examines the use of artificial intelligence (AI) in consumer-facing food systems to support sustainable consumption. It aims to synthesize empirical insights, identify dominant types of AI interventions and propose a process-based theoretical model that captures how AI solutions facilitate sustainable consumer behavior. Design/methodology/approach A systematic literature review was conducted in accordance with the SPAR-4-SLR protocol. 66 peer-reviewed articles published between 2010 and 2025 were included. Results were thematically organized using Transformative Consumer Research (TCR) and Technology Affordances to explore how AI-enabled solutions facilitate value creation and foster behavior change in sustainable food consumption contexts. Findings Five key AI domains were identified: product discovery, sustainability signaling, dietary guidance, household food management and social engagement. While personalization and health nudging dominate current applications, the potential of AI to drive long-term sustainable behavior remains underexploited. Existing research is fragmented and often lacks an integrative theoretical foundation. Originality/value This paper introduces the AI-Enabled Behavioral Activation Model for Sustainable Consumption, a novel theoretical framework grounded in transformative consumer research (TCR), transformative value theory (TVT), technology affordances and the COM-B model of behavior change. The model conceptualizes how AI solutions catalyze sustainable behavior by enabling consumer capabilities, shaping motivational processes, and expanding action opportunities, all while generating experiential and ethical forms of value. This integrative, process-based approach advances theoretical understanding of AI’s transformative potential in food-related consumer behavior.

Using Extreme Value Theory with a peaks-over-threshold method, we modelled the top 2% of sports-injury losses from 200,000 simulated claims. A generalized Pareto fit via MLE yielded a positive shape (? = 0.783), indicating a fat tail where rare injuries dominate severity. Q–Q and P–P diagnostics show good agreement between model and data. The implied 100-year loss is round 3.31 billion (currency units), and TVaR confirms that conditional on approaching the tail, predicted losses increase quickly. These findings support need for capital buffer to mitigate costly injuries, severe-scenario stress testing, and pricing loadings that specifically consider for costly but rare injuries.