Search Results (58)

Background and Objectives: Childhood obesity is a major global health concern and is increasingly recognized as a factor influencing skeletal development. Emerging evidence suggests that excess adiposity may alter craniofacial growth patterns, with potential implications for orthodontic diagnosis and treatment planning. However, the extent to which obesity affects craniofacial morphology in growing individuals remains incompletely understood. This study aims to evaluate the association between body mass index (BMI) and craniofacial morphology in children and adolescents using selected sagittal and linear parameters, and to assess the independent effects of age and sex. Materials and Methods: This cross-sectional orthodontic study included 130 subjects aged ≤ 19 years. Anthropometric measurements were recorded, and BMI was used to classify participants as normal weight, overweight, or obese. Standardized lateral cephalometric radiographs were analyzed using skeletal and soft-tissue parameters. Statistical analyses included normality testing, one-way ANOVA with post hoc comparisons, and multivariate modeling. Results: Obesity was significantly associated with increased sagittal skeletal dimensions. Mandibular body length, mandibular unit length, SNB angle, and maxillary unit length demonstrated progressive increases across BMI categories (p < 0.05). In contrast, vertical growth parameters showed no significant differences. Soft-tissue analysis revealed reduced facial convexity and lower facial height ratios in obese subjects. Age was strongly associated with increases in linear jaw dimensions, whereas sex differences were limited primarily to skeletal size rather than morphological relationships. Conclusions: Childhood obesity is associated with enhanced sagittal craniofacial growth, particularly involving mandibular structures, while vertical skeletal patterns remain largely unaffected. These findings highlight the importance of incorporating BMI assessment into orthodontic evaluation and suggest that obesity may influence growth timing, facial morphology, and airway-related risk factors. Full article

Portfolio optimization is one of the most difficult sequential decision problems, as uncertainty and the non-stationary nature of financial markets hinder the development of robust strategies. Reinforcement learning is an attractive framework for addressing this problem, as it allows agents to learn market-adaptive strategies through data-driven interactions. However, existing risk-neutral reinforcement learning solutions for portfolio management are oblivious to downside risk and are mainly concerned with maximizing returns. To address this limitation, this paper proposes a novel risk-sensitive reinforcement learning framework for risk-aware portfolio optimization based on a conditional value-at-risk-based learning objective that explicitly controls extreme loss events. It formulates the portfolio optimization problem as a Markov decision process and solves it using a linearized actor–critic architecture. It also develops theoretical results to analyze important aspects of the learning process, specifically proving that the convexity of the conditional value-at-risk-based formulation and convergence of learning hold under standard assumptions. The proposed algorithm is applied in a realistic investment setting using NIFTY 50 market data. Quantitative results from a rolling window backtesting methodology show that the proposed model achieves the best risk-adjusted portfolio performance, i.e., a Sharpe ratio (0.610), while significantly reducing tail risk, as measured by the conditional value-at-risk (−0.121) and maximum drawdown (−0.198), compared to classical strategies and risk-neutral reinforcement learning solutions. Overall, the results demonstrate that integrating coherent risk measures into reinforcement learning provides an effective approach for developing robust and risk-aware portfolio optimization strategies in dynamic financial environments. Full article

Background: Pulmonary infarction (PI) is the result of the occlusion of distal pulmonary arteries resulting in damage to downstream lung areas that become ischemic, hemorrhagic, or necrotic, and it is often a complication of an underlying condition such as pulmonary embolism (PE). Since in most of cases it is located peripherally, lung ultrasound (LUS) can be a good evaluation tool. The typical radiological features of PI are well-known; however, there are limited data on its sonographic characteristics and its evolution. Methods: The aim of this study is to evaluate, using LUS, a convenience sample of patients with acute PE with computed tomography (CT) consolidation findings consistent with PI. Patients’ clinical characteristics were collected and LUS findings at baseline and their short-term progression was assessed. LUS was performed within 72 h of PE diagnosis (T0) and repeated after one (T1) and four weeks (T2). Each procedure started with a focused examination of the areas of lesions based on CT findings, followed by an exploration of the other posterior and lateral lung fields. The convex probe was used for initial evaluation integrating LUS evaluation with the linear one was employed for smaller and more superficial lesions and when appropriate. Color Doppler mode was added to study vascularization. Results: From June to October 2023, 14 consecutive patients were enrolled at the Respiratory Unit of the University Hospital of Pisa. The main population characteristics included the absence of respiratory failure and prognostic high-risk PE (100%), the absence of significant comorbidities (79%), and the presence of typical symptoms, such as chest pain (57%) and dyspnea (50%). The average number of consolidations per patient was 1.4 ± 0.6. Follow-up LUS showed the disappearance of some consolidations and some morphological changes in the remaining lesions: the presence of hypoechoic consolidation with a central hyperechoic area (“bubbly consolidation”) was more typical at T1 while the presence of a small pleural effusion often persisted both at T1 and T2. A decrease in wedge/triangular-shaped consolidations was observed (82% at T0, 67% at T1, 24% at T2), as was an increase in elongated shapes, representing a residual pleural thickening over time (9% at T0, 13% at T1, 44% at T2). A reduction in size was also observed by comparing the mean diameter, long axis, and short axis measurements of each consolidation at the three different studied time points: the average of the short axes and the median of the mean diameters showed a statistically significant reduction after four weeks. Additionally, a correlation between lesion size and pleuritic pain was described, although it did not achieve statistical significance. Conclusions: Patients’ clinical characteristics and ultrasound features are consistent with previous studies studying PI at PE diagnosis. Most consolidations detected by LUS change over time regarding size and form, but a minority of them do not differ. LUS is a safe and non-invasive exam that could help to improve patients’ clinical approach in emergency rooms as well as medical and pulmonology settings, clinically contextualized for cases of chest pain and dyspnea. Future studies could expand the morphological study of PI. Full article

Uncertainty-aware decision-making increasingly relies on multimodal sensing pipelines that must fuse correlated measurements, propagate uncertainty, and trigger reliable control actions. This study develops a unified mathematical framework for multimodal data fusion and Bayesian decision-making under uncertainty. The approach integrates adaptive Covariance Intersection (aCI) for correlation-robust sensor fusion, a Gaussian state–space backbone with Kalman filtering, heteroskedastic Bayesian regression with full posterior sampling via an affine-invariant MCMC sampler, and a Bayesian likelihood-ratio test (LRT) coupled to a risk-sensitive proportional–derivative (PD) control law. Theoretical guarantees are provided by bounding the state covariance under stability conditions, establishing convexity of the aCI weight optimization on the simplex, and deriving a Bayes-risk-optimal decision threshold for the LRT under symmetric Gaussian likelihoods. A proof-of-concept agro-environmental decision-support application is considered, where heterogeneous data streams (IoT soil sensors, meteorological stations, and drone-derived vegetation indices) are fused to generate early-warning alarms for crop stress and to adapt irrigation and fertilization inputs. The proposed pipeline reduces predictive variance and sharpens posterior credible intervals (up to 34% narrower 95% intervals and 44% lower NLL/Brier score under heteroskedastic modeling), while a Bayesian uncertainty-aware controller achieves 14.2% lower water usage and 35.5% fewer false stress alarms compared to a rule-based strategy. The framework is mathematically grounded yet domain-independent, providing a probabilistic pipeline that propagates uncertainty from raw multimodal data to operational control actions, and can be transferred beyond agriculture to robotics, signal processing, and environmental monitoring applications. Full article

Background: In adult spinal deformity (ASD) surgery, appropriate rod length determination is crucial, as excessive cranial rod length can lead to skin problems, especially in thin elderly patients if proximal junctional kyphosis (PJK) develops. In adolescent idiopathic scoliosis (AIS), correction is primarily performed in the coronal plane, and rod length changes are relatively predictable. Moreover, PJK is uncommon in AIS, making excess rod length rarely a clinical concern. In contrast, ASD correction involves more complex three-dimensional realignment, including restoration of lumbar lordosis (LL), which makes it challenging to predict postoperative changes in rod trace length (RTL). Furthermore, because PJK occurs more frequently in ASD surgery, appropriate rod length selection becomes clinically important. This study aimed to quantitatively evaluate changes in RTL before and after posterior correction. Method: Thirty patients with ASD who underwent staged lateral lumbar interbody fusion (LLIF) followed by posterior corrective fusion from T9 to the pelvis were retrospectively analyzed. RTL before posterior correction (Pre-RTL) was estimated from the planned screw insertional point on axial CT after LLIF, and postoperative RTL (Post-RTL) was measured from screw head centers on post-operative CT. LL and Cobb angle were assessed before and after posterior correction. Correlations between RTL change and alignment change were evaluated. Results: Postoperative RTL was shortened in all patients, with an average reduction of approximately 16–17 mm. RTL shortening demonstrated significant correlations with LL correction (R = 0.51, p = 0.003) and Cobb angle correction (R = 0.70, p = 0.00001). Greater shortening of RTL was observed on the convex side in patients with preoperative Cobb angle ≥ 10° (p = 0.04). Conclusions: Greater coronal deformity, particularly on the convex side, was associated with increased RTL shortening. These findings suggest that routine preparation of excessively long rods may be unnecessary. Consideration of anticipated RTL shortening may help avoid excessive cranial rod length and potentially reduce the risk of skin complications associated with PJK, particularly in thin elderly patients. Full article

Market equilibrium is characterized by a state wherein aggregate demand equals aggregate supply for all assets, a condition arising from consumers maximizing utility within budget constraints and producers maximizing profits. This paper investigates a financial market populated by non-homogeneous investors who may employ heterogeneous deviation measures to formulate their risk functions. By integrating the minimax risk diversification principle with the framework of individual utility maximization, we analytically derive the master fund for each investor. Furthermore, we establish the necessary and sufficient conditions for the existence of a unique non-negative equilibrium price system for risky assets and provide its explicit formula. A key finding is that the market portfolio is a convex combination of all investors’ master funds. Full article

In this paper, we propose a sparse distributionally robust optimization (DRO) model incorporating the Conditional Value-at-Risk (CVaR) measure to control tail risks in uncertain environments. The model utilizes sparsity to reduce transaction costs and enhance operational efficiency. We reformulate the problem as a Min-Max-Min optimization and convert it into an equivalent non-smooth minimization problem. To address this computational challenge, we develop an approximate discretization (AD) scheme for the underlying continuous random vector and prove its convergence to the original non-smooth formulation under mild conditions. The resulting problem can be efficiently solved using a subgradient method. While our analysis focuses on CVaR penalty, this approach is applicable to a broader class of non-smooth convex regularizers. The experimental results on the portfolio selection problem confirm the effectiveness and scalability of the proposed AD algorithm. Full article

We introduce a novel portfolio optimization framework—Distributionally Robust Multivariate Stochastic Cone Order (DR-MSCO)—which integrates partial orders on random vectors with Wasserstein-metric ambiguity sets and adaptive cone structures to model multivariate investor preferences under distributional uncertainty. Grounded in measure theory and convex analysis, DR-MSCO employs data-driven cone selection calibrated to market regimes, along with coherent tail-risk operators that generalize Conditional Value-at-Risk to the multivariate setting. We derive a tractable second-order cone programming reformulation and demonstrate statistical consistency under empirical ambiguity sets. Empirically, we apply DR-MSCO to 23 Borsa Istanbul equities from 2021–2024, using a rolling estimation window and realistic transaction costs. Compared to classical mean–variance and standard distributionally robust benchmarks, DR-MSCO achieves higher overall and crisis-period Sharpe ratios (2.18 vs. 2.09 full sample; 0.95 vs. 0.69 during crises), reduces maximum drawdown by 10%, and yields endogenous diversification without exogenous constraints. Our results underscore the practical benefits of combining multivariate preference modeling with distributional robustness, offering institutional investors a tractable tool for resilient portfolio construction in volatile emerging markets. Full article

This paper introduces a novel multi-objective optimization framework for sustainable portfolio rebalancing under uncertainty. The model simultaneously targets return maximization, downside risk control, and liquidity preservation, addressing the complex trade-offs faced by investors in volatile markets. Unlike traditional static approaches, the framework allows for dynamic asset reallocation and explicitly incorporates nonlinear transaction costs, offering a more realistic representation of trading frictions. Key financial parameters—including expected returns, volatility, and liquidity—are modeled using interval arithmetic, enabling a flexible, distribution-free depiction of uncertainty. Risk is measured through semi-absolute deviation, providing a more intuitive and robust assessment of downside exposure compared to classical variance. A core innovation lies in the behavioral modeling of investor preferences, operationalized through three strategic configurations, pessimistic, optimistic, and mixed, implemented via convex combinations of interval bounds. The framework is empirically validated using a diversified cryptocurrency portfolio consisting of Bitcoin, Ethereum, Solana, and Binance Coin, observed over a six-month period. The simulation results confirm the model’s adaptability to shifting market conditions and investor sentiment, consistently generating stable and diversified allocations. Beyond its technical rigor, the proposed framework aligns with sustainability principles by enhancing portfolio resilience, minimizing systemic concentration risks, and supporting long-term decision-making in uncertain financial environments. Its integrated design makes it particularly suitable for modern asset management contexts that require flexibility, robustness, and alignment with responsible investment practices. Full article

In direct-current gas-insulated transmission lines (DC GIL), complex heat transfer processes accelerate surface charge accumulation on insulators, causing local electric field distortion and elevating the risk of surface flashover. This study develops a three-dimensional multi-physics coupled mathematical model for ±200 kV DC GIL basin-type insulators. The bulk and surface conductivity of insulator materials were experimentally measured under varying temperature and electric field conditions, with fitting equations derived to describe their behavior. The model investigates surface charge accumulation and electric field distribution under DC voltage and polarity-reversal conditions, incorporating multi-field coupling effects. Results show that, at a 3150 A current in a horizontally arranged DC GIL, insulator temperatures reach approximately 62.8 °C near the conductor and 32 °C near the enclosure, with the convex surface exhibiting higher temperatures than the concave surface and distinct radial variations. Under DC voltage, surface charge accumulates faster in high-temperature regions, with both charge and electric field distributions stabilizing after approximately 300 h, following significant changes within the first 40 h. Following stabilization, the distribution of surface charge and electric field varies across different radial directions. During polarity reversal, residual surface charges cause electric field distortion, increasing maximum field strength by 13.6% and 47.2% on the convex and concave surfaces, respectively, with greater distortion on the concave surface, as calculated from finite element simulations with a numerical accuracy of ±0.5% based on mesh convergence and solver tolerance. These findings offer valuable insights for enhancing DC GIL insulation performance. Full article

This paper demonstrates that perfectly calibrating a multi-asset model to observed market prices of all basket call options is insufficient to uniquely determine the price of a best-of call option. Previous research on multi-asset option pricing has primarily focused on complete market settings or assumed specific parametric models, leaving fundamental questions about model risk and pricing uniqueness in incomplete markets inadequately addressed. This limitation has critical practical implications: derivatives practitioners who hedge best-of options using basket-equivalent instruments face fundamental distributional uncertainty that compounds the well-recognized non-linearity challenges. We establish this non-uniqueness using convex analysis (extreme ray characterization demonstrating geometric incompatibility between payoff structures), measure theory (explicit construction of distinct equivalent probability measures), and geometric analysis (payoff structure comparison). Specifically, we prove that the set of equivalent probability measures consistent with observed basket prices contains distinct measures yielding different best-of option prices, with explicit no-arbitrage bounds $[a_K,b_K]$ quantifying this uncertainty. Our theoretical contribution provides the first rigorous mathematical foundation for several empirically observed market phenomena: wide bid-ask spreads on extremal options, practitioners’ preference for over-hedging strategies, and substantial model reserves for exotic derivatives. We demonstrate through concrete examples that substantial model risk persists even with perfect basket calibration and equivalent measure constraints. For risk-neutral pricing applications, equivalent martingale measure constraints can be imposed using optimal transport theory, though this requires additional mathematical complexity via Schrödinger bridge techniques while preserving our fundamental non-uniqueness results. The findings establish that additional market instruments beyond basket options are mathematically necessary for robust exotic derivative pricing. Full article

The risk measures such as value at risk, and conditional values at risk do not always account for the sensitivity of large losses with certainty, as large losses often break the homogeneity especially seen in an illiquidity risk. In this study, we examine the characteristics of large-loss sensitivity more holistically, including small probability, within the framework of risk measures. The analysis incorporates the certainty equivalent, generation of the optimal certainty equivalent formulation, divergence utility, and general utility functions in their original form, and their relationship with expectiles and elicitability. The discussion provides a summary in the understanding of risk measure status and sensitivity involving small probably cases. Additionally, we evaluate large-loss sensitivity in risk-sharing scenarios using the convex conjugation of the divergence utility. By clarifying the conditions affecting large-loss sensitivity, the findings highlight the limitations of existing risk measures and suggest directions for future improvement. Furthermore, these insights contribute to enhancing the stability of risk-sharing business models. Full article

This paper examines the extent of stock-returns’ co-movements among firms in different countries and explores how various measures of closeness affect those co-movements by estimating a spatial autoregressive (SAR) convex combination model that merges four weight matrices—geographical distance, bilateral trade, sector similarity, and company size—into one global matrix. Our results reveal strong spatial stock-market dependence, show that spatial proximity is better captured by financial-distance measures than by pure geographical distance, and indicate that the weight matrix based on sector similarities outperforms the other linkages in explaining firms’ co-movements. Extending the traditional SAR model, the study simultaneously evaluated cross-country and within-country dependencies, providing insights valuable to investors building optimal portfolios and to policymakers monitoring contagion and systemic risk. Full article

In this paper, we take a new perspective to describe the model uncertainty, and thus propose two new classes of risk measures under model uncertainty. To be precise, we use an auxiliary random variable to describe model uncertainty. By proposing new sets of axioms under model uncertainty, we axiomatically introduce and characterize conditional coherent and convex risk measures under a random environment, respectively. As examples, we also discuss the connections of the introduced conditional coherent risk measures under random environments with two existing risk measures. This paper mainly gives some theoretical results, and it is expected to make meaningful complement to the study of coherent and convex risk measures under model uncertainty. Full article

We introduce a new coherent risk measure, the minimal-entropy risk measure, which is built on the minimal-entropy $\sigma$-martingale measure—a concept inspired by the well-known minimal-entropy martingale measure used in option pricing. While the minimal-entropy martingale measure is commonly used for pricing and hedging, the minimal-entropy $\sigma$-martingale measure has not previously been studied, nor has it been analyzed as a traditional risk measure. We address this gap by clearly defining this new risk measure and examining its fundamental properties. In addition, we revisit the entropic risk measure, typically expressed through an exponential formula. We provide an alternative definition using a supremum over Kullback–Leibler divergences, making its connection to entropy clearer. We verify important properties of both risk measures, such as convexity and coherence, and extend these concepts to dynamic situations. We also illustrate their behavior in scenarios involving optimal risk transfer. Our results link entropic concepts with incomplete-market pricing and demonstrate how both risk measures share a unified entropy-based foundation. Full article