Search Results (1,646)

This study compares Logit, Probit, Extreme Value, and Artificial Neural Network (ANN) models using data from 2012 to 2024 in the Taiwan electronics industry. ANN outperforms traditional models, achieving 98% accuracy in predicting financial distress. Two robust distress signals are identified: Return on Assets (threshold: 7.03%) and Total Asset Growth (threshold: −9.05%). The nonlinear impacts of financial distress on variables are analyzed, with a focus on contextual considerations in decision-making. These findings bring attention to the importance of utilizing advanced techniques like ANN for improved predictive accuracy, offering profound clarification for risk assessment and management. Full article

Taiwan’s rural regions face aging populations, digital divides, and fragmented heritage narratives that limit sustainable cultural revitalization. This study investigates how a community-based Alternate Reality Game (ARG) can integrate dispersed cultural assets in Shiding District into a coherent, immersive experience that supports intergenerational learning and community engagement. Drawing on ARG/transmedia narrative theory, scaffolding theory, intergenerational learning, and value co-creation, the research adopts an exploratory sequential mixed-methods design: qualitative interviews and co-design workshops inform ARG system development, followed by field implementation and pre–post evaluation with 78 participants across three age groups. The results show large improvements in user experience and immersion, while quantitative changes in cultural understanding, perceived learning support, and community engagement are modest and not consistently positive, despite rich qualitative accounts of heightened awareness of local history and community life. Participants’ narratives highlight a reciprocal scaffolding dynamic, in which younger visitors provide digital assistance and older residents contribute local knowledge, as well as strong perceptions of co-creation with community hosts. These findings suggest that a low-cost, participatory ARG can effectively reduce on-site narrative fragmentation and foster emotionally engaging, intergenerational experiences, but that deeper and more durable cultural learning effects likely require refined measurement and longer-term engagement. The study contributes an integrated design and evaluation framework for rural ARG applications and offers practical guidelines for communities and policymakers seeking inclusive, story-driven models of digital heritage revitalization. Full article

This paper presents an innovative concept for the musealization of everyday public space through the use of natural stone cladding as an in situ palaeontological exhibition. Polished slabs of Holy Cross Mts marble, widely used as flooring in public buildings, contain abundant and well-preserved Devonian marine fossils, offering a unique opportunity to revitalize public engagement with palaeontology and geoheritage. The proposed exhibition transforms passers-by into active observers by integrating authentic fossil material directly into daily circulation routes, thereby emphasizing the educational and geotouristic potential of ordinary architectural elements. The case study focuses on the main hall of the University of the National Education Commission (Kraków, Poland), where over 1000 m² of fossil-bearing limestone flooring is used as a continuous exhibition surface. The target audience includes students of Earth sciences, zoology, biological sciences, pedagogy, social sciences, and humanities, for whom the exhibition serves as both an educational supplement and a geotouristic experience. The scientific, educational, and touristic value of the proposed exhibition was assessed using a modified geoheritage valorization method and compared with established palaeontological collections in Kraków and Kielce. The expert valuation method used in the article enables a comparison of the described collection with other similar places on Earth, making its application universal and global. The results demonstrate that polished stone cladding can function as a valuable geoheritage asset of regional and global significance, offering an accessible, low-cost, and sustainable model for disseminating palaeontological knowledge within public space. Full article

The Haezendonck–Goovaerts (HG) risk measure defined on Orlicz spaces via the so-called normalised Young function is a direct generalisation of the Expected Shortfall risk measure. The HG measure is known to be a coherent one, thus making it more robust than some of the alternatives, such as Value-at-Risk, for aggregating and comparing risks, and at the same time more flexible for capital allocation problems, risk premium estimation, solvency assessment, and stress testing in insurance and finance. As random risk in practical applications is often assessed in a portfolio setting—a collection of insurance policies or financial assets, like stocks or bonds—we examine the situation in which the total portfolio risk is expressed as the sum of individual random risks. For this, we consider the sum $S_n^{\left(\xi \right)}={\xi }_1+\dots +{\xi }_n$ of possibly dependent and non-identically distributed real-valued random variables ${\xi }_1,\dots ,{\xi }_n$ with consistently varying distributions. Assuming that the collection $\{{\xi }_1,\dots ,{\xi }_n\}$ follows the dependence structure, similar to the asymptotic independence, we obtain the asymptotic estimations of the HG risk measure for the sum $S_n^{\left(\xi \right)}$ when the confidence level tends to 1. The formulas presented in our work show that in the case where a portfolio of random losses contains consistently varying losses and the others are asymptotically negligible, it is sufficient for risk assessment to consider only the tails of those dominant losses. Full article

Designing cost-effective, reliable diagnostic sensor suites for complex assets remains challenging due to conflicting objectives across stakeholders. A holistic framework that integrates the Normalised Diagnostic Contribution Index (NDCI)—which scores sensors by separation power, severity sensitivity, and uniqueness—with a Multi-Objective Sensor Optimisation Framework (MOSOF) is presented. Using a high-fidelity virtual aircraft model coupling engine, fuel, electrical power system (EPS), and environmental control system (ECS), NDCI against minimum Redundancy-maximum Relevance (mRMR) is benchmarked under a rigorous nested cross-validation protocol. Across subsystems, NDCI yields more compact suites and higher diagnostic accuracy, notably for engine (88.6% vs. 69.0%) and ECS (67.7% vs. 52.0%). Then, a multi-objective optimisation reflecting an airline use-case (diagnostic performance, cost, reliability, and benefit-to-cost) is executed, identifying a practical Pareto-optimal ‘knee’ solution comprising 12–14 sensors. The recommended suite delivers a normalised performance of ≈0.69 at ≈USD36k with ≈145 kh MTBF, balancing the cross-subsystem information value with implementation constraints. The NDCI-MOSOF workflow provides a transparent, reproducible pathway from raw multi-sensor data to stakeholder-aware design decisions, and constitutes transferable evidence for model-based safety and certification processes in Integrated Vehicle Health Management (IVHM). The limitations (simulation bias, cost/MTBF estimates), validation on rigs or in-service fleets, and extensions to prognostics objectives are discussed. Full article

Forest carbon sinks (FCS)—referring specifically to ecosystem-based carbon sequestration provided by forest ecosystems—are being increasingly recognized as a strategic form of natural capital under China’s “dual carbon” goals. While the ecological value of FCS is being translated into economic benefits through carbon markets, eco-compensation, and green finance, the extent to which ecosystem carbon sinks can continuously drive regional economic growth—and how such effects differ across regions—remains insufficiently understood. Using panel data for 294 Chinese prefecture-level cities from 2010 to 2022, this study employs dynamic panel methods to examine the dynamic, nonlinear, and heterogeneous impacts of ecosystem-based FCS on economic growth. The results show that (1) FCS significantly promote economic growth but follow an inverted U-shaped pattern, indicating diminishing marginal returns; (2) notable regional heterogeneity exists, with the strongest effects in central and western regions, while eastern cities exhibit weaker responses due to structural and spatial constraints; and (3) clear threshold effects are present, suggesting that industrial upgrading, urbanization, and moderate government intervention can amplify the economic contribution of FCS. These findings clarify the mechanism through which FCS transitions from ecological assets to economic capital, providing theoretical and empirical support for sustainable forest management, ecological-industrial integration, and carbon market optimization in the pursuit of carbon neutrality. Full article

As urbanisation accelerates, land-use planning has become a challenging factor in cities’ sustainable development. This process is based both on the historical heritage of Central and Eastern European cities and on concepts combining urban planning with ecology. Cemeteries, traditionally viewed as places of remembrance, hold significant potential as ecological assets within urban environments. On the other hand, they are problematic in relation to the location, neighbourhood, and form of past/present human activity. Many of these aspects are regulated in the investigated countries. This paper examines sustainable cemetery planning from legal, ecological, and spatial perspectives, highlighting their role in addressing environmental challenges while preserving cultural and social values. The research draws on comparative case studies from eight Central and Eastern European countries, combining an analysis of international and national legal frameworks with a functional–spatial assessment of cemetery surroundings. In addition, an environmental survey conducted among experts and residents highlights diverse perceptions regarding cemetery functions and their integration into urban landscapes. The findings demonstrate that policy-based management and multifunctional design are essential for enhancing the ecological, cultural, and social value of cemeteries. Reframing cemeteries as multifunctional green spaces offers a practical pathway toward more resilient and environmentally responsible urban development. Some important differences between the following countries have been observed. Full article

Urban rivers are critical ecological and cultural assets facing accelerating biodiversity loss. This study examines the integrated redevelopment of the Jadro River spring in Solin, Croatia, where a protected ichthyological reserve intersects layered heritage and urban edges to enhance conservation and public value. Using a single-case study design that combines archival project documentation, participant observation by the architect–authors, and a post-occupancy review three years after completion, the analysis synthesizes ecological, social, and design evidence across planning, delivery, and operation phases. The project delivered phased visitor and interpretation centers, accessible paths and bridges, habitat-compatible materials, and formalized access management that relocated parking from riverbanks, reduced episodic pollution sources, and prioritized inclusive, low-impact use. Governance and programming established a municipal management plan, curriculum-ready interpretation, and carrying capacity monitoring, transforming an underused picnic area into an educational, recreational, and conservation-oriented public landscape while safeguarding sensitive habitats. A transferable design protocol emerged, aligning blue green infrastructure, heritage conservation, adaptive reuse, and social–ecological system (SES)-informed placemaking to protect the endemic soft-mouth trout and strengthen a sense of place and community stewardship. The case supports SES-based riverpark renewal in which conservative interventions within protected cores are coupled with consolidated services on resilient ground, offering a replicable framework for ecologically constrained urban headwaters. Full article

As financial markets become increasingly complex, optimizing investment portfolios under multiple conflicting objectives has become a central challenge for decision-makers. This paper presents a comprehensive benchmarking framework for multi-objective portfolio optimization based on metaheuristics, designed to operate on real-world financial data. This framework integrates preprocessing, and optimization using four state-of-the-art algorithms: NSGA-II, MOEA/D, SMS-EMOA, and SMPSO. Using historical data from over 11,000 assets listed on U.S. exchanges, including ARCA, NYSE, NASDAQ, OTC, AMEX, and BATS, we define a suite of benchmark scenarios with increasing dimensionality and constraint complexity. Our results highlight algorithmic strengths and limitations, reveal significant trade-offs between return and risk, and demonstrate the effectiveness of multi-objective metaheuristics in constructing diversified, high-performance investment portfolios. Each portfolio is encoded as a real-valued vector combining asset selection and allocation, enabling fine-grained diversification control. All datasets and source code are publicly available to ensure reproducibility. Full article

This study introduces a unified and methodologically symmetric comparative framework for multivariate cryptocurrency forecasting, addressing long-standing inconsistencies in prior research where model families, feature sets, and preprocessing pipelines differ across studies. Under an identical and rigorously controlled experimental setup, we benchmark six deep learning architectures—LSTM, GPT-2, Informer, Autoformer, Temporal Fusion Transformer (TFT), and a Vanilla Transformer—together with four widely used econometric models (ARIMA, VAR, GARCH, and a Random Walk baseline). All models are evaluated using a shared multivariate feature space composed of more than forty technical indicators, identical normalization procedures, harmonized sliding-window formations, and aligned temporal splits across five high-liquidity assets (BTC, ETH, XRP, XLM, and SOL). The experimental results show that transformer-based architectures consistently outperform both the recurrent baseline and classical econometric models across all assets. This superiority arises from the ability of attention mechanisms to capture long-range temporal dependencies and adaptively weight informative time steps, whereas recurrent models suffer from vanishing-gradient limitations and restricted effective memory. The best-performing deep learning models achieve MAPE values of 0.0289 (BTC, GPT-2), 0.0198 (ETH, Autoformer), 0.0418 (XRP, Informer), 0.0469 (XLM, Informer), and 0.0578 (SOL, TFT), substantially improving upon the performance of both LSTM and all econometric baselines. These findings highlight the effectiveness of attention-based architectures in modeling volatility-driven nonlinear dynamics and establish a reproducible, symmetry-preserving benchmark for future research in deep-learning-based financial forecasting. Full article

Location-based services (LBSs) and positioning systems have spread worldwide due to the emergence of Internet of Things (IoT) and other application domains that require real-time estimation of the position of a person, tag, or asset in general in order to provide users with services and apps with added value. Whereas Global Navigation Satellite Systems (GNSSs) are well-established solutions outdoors, positioning is still an open challenge indoors, where different sensory technologies may be considered for that purpose, such as radio frequency, infrared, or ultrasounds, among others. With regard to ultrasonic systems, previous works have already developed indoor positioning systems capable of achieving accuracies in the range of centimeters but limited to a few square meters of coverage and severely affected by the Doppler effect coming from moving targets, which significantly degrades the overall positioning performance. Furthermore, the actual bandwidth available in commercial transducers often constrains the ultrasonic transmission, thus reducing the position accuracy as well. In this context, this work proposes a novel excitation and processing method for an ultrasonic positioning system, which significantly improves the transmission capabilities between an emitter and a receiver. The proposal employs a superheterodyne approach, enabling simultaneous transmission and reception of signals across multiple channels. It also adapts the bandwidths and central frequencies of the transmitted signals to the specific bandwidth characteristics of available transducers, thus optimizing the system performance. Binary spread spectrum sequences are utilized within a multicarrier modulation framework to ensure robust signal transmission. The ultrasonic signals received are then processed using filter banks and matched filtering techniques to determine the Time Differences of Arrival (TDoA) for every transmission, which are subsequently used to estimate the target position. The proposal has been modeled and successfully validated using a digital twin. Furthermore, experimental tests on the prototype have also been conducted to evaluate the system’s performance in real scenarios, comparing it against classical approaches in terms of ranging distance, signal-to-noise ratio (SNR), or multipath effects. Experimental validation demonstrates that the proposed narrowband scheme reliably operates at distances up to 40 m, compared to the 34 m limit of conventional wideband approaches. Ranging errors remain below 3 cm at 40 m, whereas the wideband scheme exhibits errors exceeding 8 cm. Furthermore, simulation results show that the narrowband scheme maintains stable operation at SNR as low as $-32$ dB, whereas the wideband one only achieves up to $-17$ dB, highlighting the significant performance advantages of the proposed approach in both experimental and simulated scenarios. Full article

This article examines how the recognition, measurement, and disclosure of biological assets (BAs) under IAS 41 affect value creation in Colombian agribusinesses following IFRS adoption. Using EMIS Benchmark data for Colombia, we construct a panel of 157 agro-industrial firms that are neither subsidiaries of multinationals nor listed on the stock exchange; the panel covers 2012–2022, spanning the period before and after IFRS adoption. The database combines accounting and financial indicators with categorical variables capturing the scope of activities, valuation methods (historical cost, realisable value, present value, fair value), and disclosure policies for BAs. Value creation is proxied by EBITDA, return on equity (ROE), and return on assets (ROA). We estimate fixed-effects panel models for three IFRS groups. Results show that, in Group 1, defining the accounting scope and using fair value and present value as measurement bases are associated with higher firm value, while Groups 2 and 3 display positive but statistically weaker effects. Explicit disclosure is also associated with higher profitability, particularly for SMEs. These findings are consistent with agency and firm theories: when entrepreneurial activities are recognised, measured, and disclosed consistently and transparently, information asymmetry and agency costs fall, and accounting policies become a driver of organisational performance in agribusinesses in emerging markets. The results also support the assumptions of institutional theory, as external regulatory pressures from IFRS and internal pressures arising from relationships among firms in the agro-industrial sector shape and reinforce information disclosure practices. Full article

This paper presents OpS-EWMA-LLM (Operational State Shifts Detection using Exponential Weighted Moving Average and Labeling using Large Language Model), a hybrid framework that combines fleet-normalized statistical shift detection with LLM-assisted diagnostics to identify and interpret operational state changes across heterogeneous fleets. First, we introduce a residual-based EWMA control chart methodology that uses deviations of each component’s sensor reading from its fleet-wide expected value to detect anomalies. This statistical approach yields near-zero false negatives and flags incipient faults earlier than conventional methods, without requiring component-specific tuning. Second, we implement a pipeline that integrates an LLM with retrieval-augmented generation (RAG) architecture. Through a three-phase prompting strategy, the LLM ingests time-series anomalies, domain knowledge, and contextual information to generate human-interpretable diagnostic insights. Finaly, unlike existing approaches that treat anomaly detection and diagnosis as separate steps, we assign to each detected event a criticality label based on both statistical score of the anomaly and semantic score from the LLM analysis. These labels are stored in the OpS-Vector to extend the knowledge base of cases for future retrieval. We demonstrate the framework on SCADA data from a fleet of wind turbines: OpS-EWMA successfully identifies critical temperature deviations in various components that standard alarms missed, and the LLM (augmented with relevant documents) provides rationalized explanations for each anomaly. The framework demonstrated robust performance and outperformed baseline methods in a realistic zero-tuning deployment across thousands of heterogeneous equipment units operating under diverse conditions, without component-specific calibration. By fusing lightweight statistical process control with generative AI, the proposed solution offers a scalable, interpretable tool for condition monitoring and asset management in Industry 4.0/5.0 settings. Beyond its technical contributions, the outcome of this research is aligned with the UN Sustainable Development Goals SDG 7, SDG 9, SDG 12, SDG 13. Full article

This study investigates how U.S. Federal Reserve interest rate cuts during the 2019–2020 easing cycle influenced the performance of equity mutual funds, with a particular emphasis on contrast between growth and value investment styles. Using an event study framework, we examine abnormal returns, cumulative abnormal returns, and risk-adjusted performance metrics, including those based on both 30 days static and rolling Jensen’s alpha and Sharpe ratios, across short-term (30-day) and long-term (6-month and 1-year) windows surrounding three major rate cut events. Our empirical results show that growth funds significantly outperform value funds following rate reductions, especially over longer horizons. This performance advantage is more pronounced in risk-adjusted measures and strengthens when incorporating rolling dynamics, indicating that and asymmetric sensitivity of fund styles to interest rate changes, shaped by differences in duration exposure and investor sentiment. Overall, this study offers novel insights into how monetary policy influences fund-level dynamics beyond broad market movements and deepens the understanding of monetary transmission in asset management by incorporating time-varying performance metrics. Full article

Among various statistical process-control (SPC) methods, control charts are widely employed as essential instruments for monitoring and improving process quality. This study focuses on a new modified exponentially weighted moving-average (New Modified EWMA) control chart that enhances detection capability under integrated and fractionally integrated time-series processes. Special attention is given to the effect of symmetry on the chart structure and performance. The proposed chart preserves a symmetric monitoring configuration, in which the two-sided design ($\mathrm{LCL}>0$) establishes control limits that are equally spaced around the center line, enabling balanced detection of both upward and downward shifts. Conversely, the one-sided version ($\mathrm{LCL}=0$) introduces a deliberate asymmetry to increase sensitivity to upward mean shifts, which is particularly useful when downward deviations are physically implausible or less critical. The efficacy of the control chart utilizing both models is assessed through Average Run Length (ARL). Herein, the explicit formula of ARL is derived and compared to the ARL obtained from the Numerical Integral Equation (NIE) in terms of both accuracy and computational time. The accuracy of the analytical ARL expression is validated by its negligible percentage difference ($\%diff$) in comparison to the results derived using the NIE approach, and the display processing time not exceeding 3 s. To confirm the highest capability, the suggested method is compared to both the classic EWMA and the modified EWMA charts using evaluation metrics such as ARL and SDRL (standard deviation run length), as well as RMI (relative mean index) and PCI (performance comparison index). Since asset values are volatile due to positive and negative market influences, symmetry is crucial in financial monitoring. Thus, symmetric control-chart structures reduce directional bias and better portray financial market activity by balancing upward and downward movements. Finally, examination of US stock prices illustrates performance, employing a symmetrical two-sided control chart for the rapid detection of changes through the new modified EWMA, in contrast to standard EWMA and modified EWMA charts. Full article