Search Results (541)

Sweet cherry (Prunus avium L.) is a non-climacteric fruit that reaches its optimal organoleptic and nutraceutical quality only when fully ripened on the tree. However, growers often harvest prematurely to avoid weather-related losses or to capture early-market premiums. This study evaluated the impact of harvest timing on the pomological and nutraceutical profiles of nine red-skinned sweet cherry cultivars (Black Star, Burlat, Cashmere, Ferrovia, Giorgia, Grace Star, Lapins, Regina, Sandra Rose) grown in the Puglia region of Southeastern Italy, a major Mediterranean production area. Fruits were collected at two maturity stages: light-red skin (Stage 1) and dark-red skin (Stage 2). Pomological parameters, i.e., fruit weight, equatorial diameter, CIELAB color parameters, soluble solids content (SSC), titratable acidity (TA) and nutraceutical metrics (total polyphenols, total anthocyanins, FRAP antioxidant activity) were measured. Delaying harvest significantly increased fruit weight (5.7–24.5%), equatorial diameter (up to 9.8%), and SSC (14.0% average, up to 26.4% in early ripening cultivars), while decreasing TA in most cultivars (non-significant in some). The SSC/TA ratio, a key driver of consumer preference, improved substantially across all genotypes. CIELAB parameters h° and C* decreased sharply, and L* generally decreased, reflecting intense anthocyanin accumulation. Total polyphenols increased by 20.7–68.8%, total anthocyanin content (TAC) by 203–1053% and FRAP antioxidant capacity by 20.7–111.7%. Two-way ANOVA revealed significant (p < 0.01), cultivar × stage, interactions for all parameters, confirming genotype-dependent ripening kinetics. Principal component analysis using all quality parameters clearly separated Stage 1 from Stage 2 fruits, with SSC, TAC, and FRAP as primary discriminators. These results demonstrate that delaying harvest until a uniform dark-red skin consistently enhanced fruit size, SSC, and health-promoting bioactive compounds across all evaluated genotypes. We provide cultivar-specific harvest calendars, colorimetric targets (hue angle ≤ 10° for optimal maturity), and a quantitative framework to help growers balance quality improvement against agronomic risks. Full article

The standard models of stock-price dynamics and option valuation rest on stochastic processes postulated at the outset; here, we lay down an entropic-inference framework that derives these processes rather than assuming them, by making explicit the information each one encodes. A symmetry comes first: markets reward returns rather than price levels, which selects the logarithm of price as the dynamical variable. The price then evolves through two channels, a continuous one carrying the constraints of continuity and directionality, and a jump channel carrying the arrival rate and the first two moments of the jump size. Because these constraints act on disjoint parts of the microstate, the channels factorize as a theorem, and the dynamics is the Merton jump-diffusion, with Geometric Brownian Motion as its no-jump limit; the log-price density obeys a Kolmogorov–Feller equation, of which the Fokker–Planck equation is the no-jump limit. The same principle, now imposing no-arbitrage through the mean log-return, selects the Esscher transform from among the many martingale measures an incomplete market admits, here derived rather than borrowed; the premium then satisfies Merton’s partial integro-differential equation, and the risk-neutral mixture of lognormals generates the implied-volatility smile, the Black–Scholes results returning when jumps vanish. What changes from one model to the next is never the inference but the information supplied to it. Full article

Portfolio optimization remains a challenging problem due to the dynamic, nonlinear, and uncertain characteristics of financial markets. Traditional portfolio construction approaches, including mean variance optimization and conventional Black–Litterman models, often suffer from inaccurate estimation of expected returns and unstable allocation caused by parameter uncertainty. These limitations become more significant under structural breaks, regime transitions, volatility clustering, and extreme market events. This study proposes a Transformer-GAN enhanced Black–Litterman framework (TG-BL) that integrates temporal representation learning, uncertainty-aware scenario generation, and Bayesian portfolio optimization. The proposed framework consists of three complementary components. First, a Transformer-based encoder is employed to extract long- range temporal dependencies and latent market representations from historical financial sequences. Second, a conditional Generative Adversarial Network (GAN) is introduced to generate diverse future return scenarios conditioned on Transformer- derived market representations, enabling probabilistic modeling of future uncertainty rather than deterministic prediction. Third, the generated return distributions are incorporated into the Black–Litterman framework through dynamically calibrated views and confidence estimation. Unlike conventional approaches that directly replace equilibrium returns with machine-generated predictions, the proposed method preserves the Bayesian structure of Black–Litterman by adjusting the influence of model- generated views according to predictive uncertainty. This mechanism allows AI- based forecasts to complement rather than dominate market equilibrium information. Extensive experiments are conducted using historical financial data under multiple market conditions. The evaluation framework includes portfolio performance comparison, GAN-generated scenario validation, robustness analysis under volatility and liquidity stress, and component- wise ablation experiments. The results demonstrate that the proposed TG-BL framework improves risk-adjusted portfolio performance while maintaining robustness against market uncertainty. The findings indicate that the integration of temporal feature extraction, uncertainty modeling, and Bayesian portfolio allocation provides an effective decision-support framework for quantitative investment management. Full article

The current reuse of blast furnace slag is limited, and the products made from it have low added value and minimal pricing potential. The primary objective of this research is to develop new eco-friendly 3D printing materials using blast furnace slag as the main raw material, simultaneously achieving combined optimization of color appearance and material performance, to increase the reutilization value of slag and address environmental problems caused by slag. Existing studies on slag-based 3D printing materials mainly focus on improving material performance, often neglecting the combined optimization of color and material performance. This study proposes a solution to create colored slag-based 3D printing materials, aiming to break the conventional view of slag waste as simply “black or gray.” This study optimized the particle size distribution of slag-based 3D printing materials using the Andreasen model. The CIELAB color difference formula was applied to reveal how color difference values varied under different mix ratios. Digital image analysis was conducted to evaluate the color characteristics of the specimens and the uniformity of the pigmentation. After 28 days of natural air curing, the color difference ΔE at various measurement points on each colored specimen remained below 3.0, indicating that iron oxide pigments exhibit satisfactory color stability within the slag matrix. To ensure high-quality 3D printing, this study examined the effect of water temperature on the curing time of colored slag-based 3D printing materials. Range analysis results showed that water temperature exerted the most significant influence on setting time (range = 255 s), substantially greater than that of pigment dosage (range = 15 s) and pigment type (range = 5 s). The Herschel–Bulkley constitutive model was used to calculate the flow index of the material. Printing tests confirmed that colored slag 3D printing materials are suitable for extrusion-based 3D printing. The 28-day compressive test results showed that the average fracture load of the three pigmented specimen groups ranged from 23.30 to 24.58 N. Cost analysis further indicated that the comprehensive material cost is approximately 467 RMB/ton, which is lower than that of commercially available colored cement, demonstrating favorable economic competitiveness. The development of colored materials for 3D printing based on blast furnace slag can expand their applications and market potential. It also improves material performance and market acceptance, and its cost advantage over commercial colored cement further enhances its viability for practical applications, promoting high-value recycling and reuse of slag waste. Full article

The deployment of grid-side independent energy storage stations (IESSs) is critical for managing the volatility introduced by high renewable energy penetration. However, investment in IESSs faces significant uncertainties, including fluctuating spot prices, policy changes, and equipment degradation, which traditional static valuation methods fail to address adequately. To bridge the gap between operational optimization and investment decision-making, this study proposes a novel framework integrating a mixed-integer linear programming (MILP) operational optimization model with the Black-Scholes-Merton Model (BSM). The MILP model explicitly incorporates capacity degradation, multi-market revenue structures and comprehensive cost expenditures. The BSM, with volatility estimated via Monte Carlo simulation, quantifies the value of delaying investment under different policy scenarios. Results indicate that capacity price subsidies provide superior early-stage cash flow relief compared to tax incentives, and their combination yields a synergistic effect, increasing the maximum tolerable electricity price decline rate from 4.08% to 8.88%. Furthermore, in pessimistic scenarios, the real options approach identifies positive returns (up to 8.02 million CNY) from delayed investment, whereas the net present value method would suggest immediate rejection. Sensitivity analysis reveals that construction cost and frequency control mileage are the most influential factors. This framework offers a robust quantitative tool for IESS investment timing and regional policy design. Full article

Machine learning (ML) models like XGBoost have gained traction in housing valuation as they outperform classical hedonic models in predictive accuracy. However, their black box nature has limited their interpretability, until recent advances like SHAP values enabled deeper insights into variable contributions. This study explores whether XGBoost not only predicts housing prices more accurately but can also help identify latent submarket structures, without imposing predefined spatial or socioeconomic boundaries. Using a raw dataset of 7092 housing listings from Barcelona (reduced to 6527 unique listings and finally to 6112 observations after outlier removal), we employ a sequential methodological framework: first, OLS and Spatial Durbin Models identify statistically significant predictors while accounting for spatial dependence; second, an XGBoost model is trained with these validated variables to extract SHAP values quantifying each attribute’s model-attributed contribution to individual predicted prices; third, clustering these SHAP values reveals three distinct submarkets with systematic differences in model-attributed valuation patterns. We validate the resulting segments through five complementary procedures: out-of-sample cluster assignment verifying segment generalizability; nested OLS interaction models that formally test differences in model-attributed valuation patterns across clusters; spatial block bootstrap resampling that assesses cluster stability under geographic subsampling; non-parametric tests against the 2017 cadastral value—an independent administrative proxy for spatial value stratification; and spatial autocorrelation statistics confirming non-random geographic structure. Together, this multi-pronged validation provides convergent evidence that the identified segments capture stable and externally coherent patterns in the model’s learned valuation structure. Unlike prior studies focused solely on prediction, our approach bridges ML’s technical rigor with econometric interpretability. We argue that this capability may stem from XGBoost’s tree-based architecture, which naturally partitions the feature space to accommodate heterogeneous valuation patterns. Future research could extend this framework to more recent tree-based models, further advancing interpretable ML for housing market analysis. Full article

Research on age segregation, defined as the spatial separation between older adults (65+) and younger populations, has lagged behind demographic shifts in the United States (US). Existing studies are often outdated, cross-sectional, and narrowly focused on metropolitan areas, with few examining race-specific patterns or structural drivers over time at the national scale. This exploratory study addresses these gaps by analyzing longitudinal trends and determinants of age segregation in US counties from 2005 to 2024, with attention to racial variation (White, Black, and Other). Using county-level data from the American Community Survey and fixed-effects regression models, we estimate how demographic, socioeconomic, industrial, and housing factors relate to changes in age segregation. Results indicate that overall age segregation declined in the mid-2010s but increased in recent years, which may reflect the responses to the economic recession in the late 2000s and baby boomers’ aging. Race-specific analyses show that age segregation is highest among Black populations, with contributing factors largely similar across groups but more pronounced for Black communities. Racial/ethnic diversity is consistently associated with lower age segregation, suggesting that demographic heterogeneity fosters intergenerational integration. Employment in secondary industries (e.g., construction and manufacturing) is positively associated with age segregation, particularly among Black populations, whereas housing market characteristics emerge as strong predictors of both overall and race-specific age segregation. These findings underscore the importance of considering race-specific and structural factors in understanding age segregation as the US population continues to age. Full article

Digital transformation is reshaping industries, business models, and investment opportunities, creating new challenges for international portfolio management. The European communication services sector has become a strategic component of the digital economy, driven by advances in artificial intelligence (AI), digital platforms, 5G infrastructure, cloud computing, cybersecurity, and data-driven business models. Despite its importance, limited evidence exists regarding the effectiveness of portfolio optimisation strategies within digitally transforming sectors. This study investigates international portfolio optimisation using constituent firms of the MSCI Europe Communication Services 35/20 Capped Index. Drawing upon Modern Portfolio Theory and the Treynor–Black framework, an actively managed portfolio is constructed and evaluated against the SPDR^® MSCI Europe Communication Services UCITS ETF and an equal-weight portfolio. Using daily market data, the analysis estimates asset returns, alpha and beta coefficients, portfolio weights, and risk-adjusted performance measures, including the Sharpe and Treynor ratios. Paired-samples t-tests are employed to assess the statistical significance of performance differences among investment strategies. The findings show that the Treynor–Black portfolio generated the highest annual return (27.32%), outperforming both the benchmark and equal-weight portfolios, and the highest percentage of Sharpe ratios (1.2159), suggesting that diversification benefits outweighed the advantages of active security selection. Hypothesis testing indicates no statistically significant difference between the Treynor–Black and equal-weight portfolios, and no statistically significant difference exists between the proposed and benchmark portfolios. The study extends the international portfolio management literature by applying the Treynor–Black model to a digitally transforming sector. The findings suggest that portfolio performance is influenced not only by firm-level financial characteristics but also by broader digital and institutional environments. Firms operating within digitally advanced and well-governed economies appear better positioned to exploit technological innovation and generate sustainable long-term value. Overall, the results demonstrate that successful international portfolio optimization requires balancing active security selection with diversification while recognizing the role of digital transformation, governance quality, and innovation ecosystems in shaping investment performance within the digital economy. Full article

The rapid miniaturization and increasing complexity of Printed Circuit Boards (PCBs) have rendered traditional automated optical inspection (AOI) systems inadequate for high-precision defect detection. While deep learning approaches have shown promise, they often struggle to balance the need for high-resolution feature extraction with the computational efficiency required for real-time industrial deployment. Furthermore, the “black-box” nature of most convolutional neural networks (CNNs) limits their adoption in stringent quality assurance environments where interpretability is paramount. To address these challenges, this paper proposes PCBVisionNet, a novel, lightweight deep learning architecture specifically engineered for automated multi-class PCB defect classification. The framework integrates a Dual-Domain Attention Mechanism (DDAM) and a Multi-Scale Feature Extractor (MSFE) with residual learning to effectively capture both microscopic anomalies and complex structural defects, enabling robust image-level classification of PCB defect categories. We evaluate the proposed model on three publicly available datasets: DeepPCB, PKU-Market-PCB, and HRIPCB. Experimental results demonstrate that PCBVisionNet achieves superior classification performance with a mean Average Precision (mAP) of 99.4% across defect categories, outperforming state-of-the-art architectures such as ResNet50, EfficientNet-B0, and Vision Transformers, while requiring 45% fewer parameters and reducing inference time by 23 ms per image. The integration of Gradient-weighted Class Activation Mapping (Grad-CAM) provides post hoc visual explainability, highlighting image regions that influence the classification decision to support interpretability and root-cause analysis. The proposed framework offers a highly accurate, efficient, and interpretable solution for modern smart manufacturing systems. Full article

Financial statement fraud inflicts large and recurring losses on capital markets, yet the dominant detection paradigm still relies on single, black-box classifiers (e.g., RUSBoost) trained on structured accounting ratios alone. Two limitations follow: (i) the rich, unstructured Management Discussion and Analysis (MD&A) narrative of the 10-K filing is discarded, and (ii) the resulting scores are difficult for auditors to trust because they carry no transparent, standards-aligned rationale. Recent large language model (LLM) systems have shown that multi-agent collaboration is more robust than a single LLM for anomaly detection, but no study has systematically transferred this paradigm to listed-company statement fraud. We propose FraudDebate-Agent, a four-role multi-agent system in which a Quantitative Analyst agent scores 28 raw accounting items and 14 ratios with gradient-boosted and tabular attention models, a Narrative Auditor agent quantifies tone, linguistic uncertainty, and year-over-year textual novelty of the MD&A with FinBERT, and an Industry Peer agent uses retrieval-augmented generation to measure industry-relative anomaly. A Critic–Debate agent then orchestrates a pair-wise Evidence-based Multi-Agent Debate (EMAD) that reconciles disagreement across modalities and arbitrates a reconciled fraud-risk assessment, which is aggregated over a tri-modal evidence graph. Our contributions are as follows: (1) the first use of an evidence-grounded debate mechanism for accounting fraud, which materially reduces LLM hallucination; (2) a numerical–textual–peer evidence graph that fuses heterogeneous signals; and (3) an explainable report aligned with the PCAOB AS 2401 fraud-risk taxonomy. On AAER-labelled firm-years linked across a SEC financial dataset and EDGAR-CORPUS, FraudDebate-Agent improves the area under the ROC curve and the rare-event ranking metric NDCG@k over the strongest single-modality and single-LLM baselines while producing substantially more faithful explanations. We frame the system as a fraud-risk screening and risk-ranking tool for AAER-labelled misstatement risk rather than a determination of fraudulent intent. We report results over multiple seeds to reflect real-world stochasticity and discuss limitations and cross-domain applications. Full article

This paper develops a model-driven analytical framework for portfolio optimization under a multidimensional Black–Scholes model with time-varying parameters, where both the drift and volatility functions evolve linearly over time. Within this framework, explicit closed-form expressions are derived for the covariance matrix of normalized asset prices and subsequently incorporated into the classical Markowitz mean–variance framework to obtain analytical representations of the global minimum-variance portfolio, the mean–variance efficient portfolio, and the corresponding efficient frontier. The proposed methodology establishes a direct connection between continuous-time stochastic asset-price modeling and portfolio optimization through a model-implied covariance structure. Its practical implementation is investigated through both numerical experiments and an empirical study using daily stock price data from 20 constituents of the $S\&P$ 500 index over the period 2020–2024. Monte Carlo simulations demonstrate the finite-sample sensitivity of portfolio optimization to covariance estimation, while the empirical analysis illustrates how the estimated model parameters, obtained using the maximum likelihood framework of Aït-Sahalia for discretely sampled diffusion processes, can be incorporated into the analytical covariance matrix for constructing efficient frontiers under realistic market conditions. Overall, the proposed framework provides an analytically tractable methodology that integrates continuous-time asset pricing models with classical mean–variance portfolio optimization, offering a coherent model-based covariance representation for portfolio selection under time-varying market environments. Full article

Fractional Brownian motion has been widely used in financial modeling to capture long-range dependence and persistent behavior in asset dynamics. In the fractional Black–Scholes framework, accurate estimation of the Hurst parameter is essential because estimation uncertainty can directly affect option pricing. In this paper, we propose a Bayesian framework for joint inference on the Hurst parameter and volatility in fractional stochastic differential equation models. Unlike approaches based solely on point estimation, the proposed framework propagates posterior uncertainty directly into option pricing distributions under the fractional Black–Scholes model. Simulation studies are conducted across multiple values of the Hurst parameter and sample sizes to evaluate estimation accuracy, posterior coverage, and pricing uncertainty. The results demonstrate stable posterior inference and coherent uncertainty quantification for both model parameters and option prices. The methodology is further illustrated using WTI crude oil and natural gas data under different market regimes. The empirical analysis indicates that differences in market behavior are driven primarily by changes in volatility rather than strong long-range dependence, while posterior option price distributions exhibit substantial variation in pricing uncertainty across regimes. These findings highlight the importance of incorporating joint parameter uncertainty into fractional financial models and demonstrate the practical value of Bayesian methods for option pricing. Full article

Microplastic (MP) contamination in foods has emerged as an increasing food safety concern; however, information regarding ice cream products remains limited. This study comparatively investigated MP contamination in locally produced unpackaged and industrially packaged ice creams marketed in Türkiye. A total of 24 samples (19 industrially packaged and 5 locally produced unpackaged) were analyzed using microscopic examination followed by ATR-FTIR polymer verification. Detected MPs were characterized according to polymer type, morphology, size, and color. MPs were detected in 100% of locally produced unpackaged samples and 42.1% of industrially packaged samples. EVA and ABS–EVA were the predominant polymer types, whereas fibers and black particles were the dominant morphology and color, respectively. A polymer-weighted pollution risk index (pRi) and a deterministic exposure assessment were also applied. Both the mean pRi values and estimated daily intake (EDI) were higher in locally produced unpackaged ice creams than in industrially packaged products. These findings suggest that differences in handling practices, environmental exposure, and food-contact materials may influence MP contamination. Overall, the results indicate that ice cream may represent a potential source of dietary MP exposure and highlight the importance of implementing effective contamination-control measures throughout production, packaging, and retail handling. This study provides valuable comparative baseline data for future food safety and dietary exposure assessments. Full article

This article presents the Asymmetric Cross-Market Dynamics Network (ACMD-Net), a forecasting framework built on the premise that commodity markets are fundamentally asymmetric. Three symmetry assumptions are statistically tested and rejected: volatility symmetry via the GJR-GARCH leverage test $\gamma >0$, $p<0.001$, coupling symmetry via the directional Granger causality DM statistic, 3.18–3.67, $p<0.001$, and cointegration symmetry via a likelihood ratio test, $p<0.01$. Each rejected hypothesis motivates a corresponding architectural component, yielding causally interpretable forecasts unavailable in black-box alternatives. The model is evaluated on daily CBOT futures for corn, wheat, and soybeans from January 2010 to December 2023, $T=3508$. ACMD-Net achieves RMSE reductions of 37–42% over ARIMA and 15–17% over standard LSTM. At short horizons ($h=1$), TFT achieves marginally lower point RMSE (3–4%, not statistically significant; DM $p>0.05$); at long horizons ($h=22$), TFT continues to report the lowest point RMSE across all commodities; differences versus ACMD-Net are not statistically significant DM $<1.96$ for all commodities. The architecture’s predictive value lies in economically grounded interpretability and superior directional accuracy rather than universal RMSE dominance. Directional accuracy ranges from 60 to 62% $p<0.001$, and net-positive trading returns are obtained for wheat and soybeans at 8–12-basis-point transaction costs. Ablation analysis identifies temporal attention as the primary performance driver, RMSE $+22.2\%$, upon removal, with econometric features contributing an additional 24.9% gain. Full article

Previous research has shown sector-conditional asymmetry in implied volatility levels and in option returns. However, no prior work has parameterised that asymmetry at the effective-theta layer in a form that fires a non-discretionary rule trigger. This study supplies the parameterisation, its formulation, the first observation, and the data evidence. An effective theta is defined as ${\Theta }_e=\alpha \left(s,r\right)\cdot {\Theta }_{BS}$, where ${\Theta }_{BS}$ is the standard Black–Scholes (BS) theta and $\alpha \left(s,r\right)$ is a sector- and regime-conditional scaling factor. A SIMDEC decomposition is used to filter the input space and to determine the corner where $\alpha$ matters most. The framework is a bounded retrieval-and-deterministic compute system. The instruments are retrieved from cached market data and the learned layer’s outputs are constrained to that admissible set. Therefore, by construction, it cannot confabulate a fictitious or out-of-bounds instrument and the generative-class hallucination failure mode cannot occur. This concerns the groundedness and bounds of every output and is distinct from the accuracy of the regime and quality labels. SIMDEC supplies the joint-state filtering partition and, together with the Sobol variance decomposition, an explainability and attribution layer in which every position-level evaluation maps to an interpretable joint-state bin and a variance-share attribution. A “first observation” arising from a three-position long-call cohort traversing terminal decay is deployed using eight intraday states tracked on the trajectory at primary-source resolution and illustrates the relationship of the $\alpha$ parameterisation to existing market conditions. To examine the effectiveness of the approach, a SIMDEC dataset from the same deployment supplies population-level support across 12 sectors and a three-tier quality stratification. The dataset is the output of the THETA AI/ML pipeline—a multi-architecture deep-learning inference system that treats SIMDEC joint-state partitioning and Sobol variance decomposition as complementary interpretability inputs, with the regime classifier carrying the labels and the composite quality scorer carrying the stratification. The PC-based, token-free analytical procedure for regulated decision-making settings, together with an illustrative example of the asymmetry in the effective-theta provide a “next level” contribution to traditional option methodology. Full article