Search Results (1,016)

In this paper, we introduce a new extension of the Topp–Leone-G family, called the generalized Marshall–Olkin Topp–Leone-G (GMOTL-G) family of distributions. The proposed family is obtained by combining the generalized Marshall–Olkin and Topp–Leone-G generators, leading to a more flexible class of models for lifetime data. We study several of its mathematical and statistical properties and focus in particular on the generalized Marshall–Olkin Topp–Leone exponential (GMOTL-E) distribution as an important special case. For this model, we derive and discuss a number of useful characteristics, including the moment generating function, moments, order statistics, residual and reversed residual life functions, mean deviations, asymptotic behavior, and stochastic ordering. We also develop maximum likelihood estimation for the model parameters under both complete and right-censored samples. In addition, we construct a goodness-of-fit test for the proposed model under independent right censoring using a chi-square type approach. The performance of the estimation and testing procedures is investigated through simulation, and the results show good behavior of the estimators and satisfactory agreement between empirical and theoretical significance levels. Finally, two real data applications, one with complete data and one with right-censored data, are presented to illustrate the flexibility and practical usefulness of the proposed model. These results show that the new family provides an effective tool for modeling lifetime data and for assessing model adequacy in the presence of right censoring. Full article

Background/Objectives: Inborn errors of immunity (IEI) are rare and complex pediatric disorders that create significant information gaps for families and non-specialist healthcare professionals. Large language models (LLMs) such as ChatGPT are increasingly used as on-demand health information resources; however, evidence on their performance in rare pediatric diseases remains limited. This study aimed to evaluate the reliability, quality, readability, understandability, reproducibility, and safety-related concerns of ChatGPT-4o responses to frequently searched questions about pediatric IEI posed by healthcare professionals and patients/caregivers. Methods: This cross-sectional evaluation used the publicly accessible ChatGPT-4o interface to generate responses to 20 frequently searched questions about pediatric IEI, equally distributed between healthcare professional (n = 10) and patient/caregiver queries (n = 10). Three pediatric allergy-immunology specialists independently evaluated response quality using the modified DISCERN (mDISCERN) and Global Quality Scale (GQS) tools, supplemented by a structured expert-based assessment of misinformation, safety-related concerns, suspected factual issues, missing disclaimers, and clinically meaningful inter-iteration inconsistency. Text readability was assessed using four validated indices (ARI, FRES, FKGL, GFR), comprehensibility using the Patient Education Materials Assessment Tool (PEMAT), and reproducibility using natural language processing methods. Results: ChatGPT-4o demonstrated strong overall performance, with median mDISCERN and GQS scores of 4 (IQR: 3–5) for both query types. Readability scores substantially exceeded recommended thresholds, with FKGL scores of 12.96 ± 0.69 and 10.83 ± 0.67 for professional and patient/caregiver queries, respectively. Mean PEMAT understandability scores were 71.80 ± 5.75% for professional queries and 80.80 ± 4.73% for patient/caregiver queries (p = 0.001). Reproducibility was high, with semantic similarity rates of 86.10 ± 3.84% and 87.30 ± 3.68%, respectively. Suspected factual issues were identified in 4 of 20 responses (20%), safety-related concerns in 3 (15%), clinically meaningful inter-iteration inconsistencies in 3 (15%), and missing medical disclaimers in all 20 responses (100%). Conclusions: ChatGPT-4o showed strong performance across validated quality metrics for pediatric IEI information support; however, its high reading level, universal absence of medical disclaimers, and occasional clinically meaningful inconsistencies limit its suitability as a standalone source for clinically sensitive guidance. These findings underscore the need for AI-driven patient education tools with improved readability, adaptive complexity adjustment, and safety-oriented communication. Full article

Behavioral ransomware detection often achieves high accuracy under standard evaluation settings, but such results may not generalize under distribution shift or when previously unseen ransomware families are encountered. This study evaluates detection performance on the MLRan dataset, which contains 4880 samples from 64 ransomware families, using four evaluation protocols: stratified, temporal, family-disjoint, and open-set. The family-disjoint and open-set protocols were constructed at the family level to limit overlap between learned and held-out ransomware families. The study proposes the Hierarchical Sparse Neural Network (HSNN), a taxonomy-aligned model that uses group-level and branch-level gating to support structured interpretability and modality-level analysis. Compared with the FlatMLP baseline, HSNN achieved a slightly lower average macro-F1 score (0.9839 vs. 0.9860) but showed better calibration and lower model complexity. Specifically, HSNN reduced expected calibration error by 34.1% and parameter count by 42%. HSNN also showed slightly lower variability across random seeds and stable gate patterns. Under the open-set family protocol, HSNN achieved one of the strongest macro-F1 scores (0.9930 vs. 0.9913 for FlatMLP) using a maximum-softmax novelty baseline. Feature analysis indicates that string-based artifacts remain strong predictors, while the hierarchical structure distributes importance across multiple behavioral modalities. These results position HSNN as a competitive alternative to dense neural baselines when calibration, compactness, and structured interpretability are considered alongside macro-F1 performance. Full article

The sixth generation of mobile networks (6G) represents a paradigmatic shift in the conception of wireless communication systems, where Artificial Intelligence (AI) is not integrated as an additional feature but is conceived as a native and fundamental component of the physical layer (PHY). This paper presents a comprehensive survey of the state of the art in AI-native physical layer for 6G, synthesizing approximately 100 references from the period 1948–2025. The survey systematically covers 5 main PHY components (channel coding, channel estimation, signal detection, beamforming, and semantic communications) and analyzes 8 AI architectural families (autoencoders, CNN, RNN/LSTM, Transformers, GNN, GAN, Diffusion Models, and Foundation Models), addressing theoretical foundations, proposed architectures, learning algorithms, implementation challenges, and future research directions. A rigorous mathematical framework underpinning these developments is presented, including optimization formulations, convergence analysis, and theoretical performance characterization. Published results from the literature demonstrate that AI-native physical layer can improve conventional performance metrics and enable emerging capabilities essential to 6G, such as semantic communications, predictive environmental adaptation, and operation in previously inaccessible computational complexity regimes. However, such gains are conditional on adequate training resources, robust channel-matched data, and careful consideration of known limitations including generalization across channel distributions, sample inefficiency, model interpretability, and hardware implementation constraints—all of which are critically analyzed in this survey. A reproducible proof-of-concept benchmark further confirms that, under severe resource constraints, autoencoder-based codes currently underperform conventional schemes, highlighting the gap between theoretical potential and practical deployment readiness. Full article

Heterogeneity in survival data poses substantial challenges for identifying appropriate mixture structures. The $\alpha$-mixture family provides a flexible class of survival models that generalizes standard mixture formulations through a continuous weighting parameter, allowing it to balance failure rates and distributional shapes. Despite its theoretical appeal, the Bayesian inference for $\alpha$-mixture survival models has received limited attention. In this paper, we develop a Bayesian framework for inference for $\alpha$-mixture survival models, with a particular emphasis on estimation and structural identification. The posterior inference is conducted using Markov chain Monte Carlo methods, and simulation studies demonstrate accurate recovery of model parameters across a range of heterogeneous survival settings. The posterior distribution of the mixing parameter $\alpha$ offers a principled mechanism for model selection by identifying the mixture structure most consistent with the observed data. Applications to real-world datasets illustrate the interpretability and practical utility of the proposed approach in survival analysis. Full article

Objectives: Despite the critical role fathers play in family health decisions, most research on HPV vaccination focuses predominantly on mothers. This study examines gender differences in HPV knowledge and vaccination attitudes among Greek parents, addressing a significant gap in the literature. Methods: A cross-sectional study using convenience sampling was conducted in waiting rooms of public primary healthcare settings in the Larissa prefecture of central Greece, between September and December 2024. Of 250 distributed questionnaires, 208 were returned (response rate: 83%), of which 192 were eligible for analysis. The analysis compares responses from fathers (n = 42) and mothers (n = 150) regarding HPV knowledge, intentions to vaccinate their sons, and general vaccine attitudes; no explicit restriction to one respondent per family was applied. Statistical comparisons employed chi-square tests, Fisher’s exact test, and binary logistic regression. Results: Fathers demonstrated significantly lower HPV awareness compared to mothers (42.9% vs. 64.0%, χ² = 10.907, p = 0.004). Vaccination intentions for sons were similar between groups (fathers: 85.7%, mothers: 85.3%, p = 0.540). No statistically robust association between HPV awareness and vaccination intention was identified in either group, likely reflecting the high overall intention rates and limited outcome variability. Binary logistic regression identified female sex as the only significant independent predictor of HPV awareness (OR = 2.26, 95% CI: 1.12–4.58, p = 0.024). Conclusions: While fathers exhibit significantly lower HPV knowledge than mothers, they demonstrate equal willingness to vaccinate their sons. These findings suggest that knowledge gaps do not necessarily translate to vaccine hesitancy, but highlight the need for targeted, father-inclusive health education interventions. Public health programs should actively engage fathers in HPV vaccination discussions to capitalize on their positive vaccination intentions while addressing their information needs. Full article

Industrial control systems (ICSs) increasingly rely on legacy communication protocols such as ModbusTCP, which lack built-in security mechanisms and remain widely exposed to network-based attacks. This paper investigates the security limitations of authentication mechanisms in ModbusTCP-enabled programmable logic controllers (PLCs) and demonstrates how plaintext credential transmission and limited connection handling capabilities can be exploited to perform brute-force and denial-of-service (DoS) attacks. An experimental testbed based on two industrial Delta PLC families (DVP-13SE and DVP-311SV3) was developed to systematically evaluate these vulnerabilities under realistic conditions. The results show that authentication credentials can be easily captured through network sniffing, while the PLC communication stack supports a maximum of 16 concurrent connections and can process up to approximately 8600 Modbus operations per second, making it susceptible to resource exhaustion and performance degradation under distributed attack scenarios. To address these limitations, this paper proposes a lightweight deception-based protection mechanism, termed the PLC misleading algorithm (PMA), which is implemented directly within the PLC ladder logic. Unlike traditional network-level defenses, PMA operates at the device level and dynamically misleads attackers by generating controlled randomized responses while preserving consistent behavior for legitimate clients. Experimental results demonstrate that PMA significantly mitigates brute-force effectiveness by preventing reliable password extraction while introducing minimal overhead (2.2% memory usage) and maintaining acceptable communication latency. Additionally, the proposed approach significantly reduces observable attack traffic, with only 0.246 Modbus operations per second observed during the attack phase, thereby limiting the effectiveness of automated exploitation tools. These findings highlight the potential of in-device deception mechanisms as a practical and deployable security layer for legacy industrial systems, and provide new insights into the resilience of PLC-based infrastructures against network-level attacks. This work bridges the gap between lightweight PLC-level protections and the growing need for robust cybersecurity mechanisms in industrial IoT environments. Full article

In the spirit of the solution of for modeling price changes in high-volatility markets for univariate commodities, here we generalize an approach to the case of modeling price changes jointly for two related commodities. Often, conditional distributions are more easily understood in financial markets, where the fluctuations in one commodity can shed significant light on the behavior of a related commodity. With this observation, we enhance and characterize the entire family of bivariate joint densities for which both the conditional distributions are specified to be of the Laplace form. Such bivariate distributions will be referred to as bivariate Laplace conditional (BLC) distributions. We study the marginals of the BLC distributions and establish that they are not only sub-Gaussian but also super-Laplacian and, hence, super-Cauchy, i.e., they have heavier tails than Gaussian distributions but lighter tails than the usual Laplace and Cauchy distributions. Distance correlation is suggested as a measure of the association between the two marginal variables, as their product moment correlation is zero but they may be non-linearly dependent. A real-life data set is analyzed to illustrate the use of BCL distributions in practice. We believe that this is the first work using conditional specifications in bivariate financial data analysis. Full article

This study presents the development of a comprehensive framework wherein probabilistic and analytical techniques collaboratively produce identities pertinent to special functions. The fundamental premise is that ratios of random variables, particularly within the family of Gamma and Beta distributions, inherently generate integral representations and hypergeometric structures that can be harnessed to derive non-trivial summation formulas. A pivotal outcome of this investigation is that, in the context of independent and identically distributed random variables, symmetry plays a crucial role. A key property of ratios of i.i.d. random variables translates into straightforward probabilistic assertions that directly lead to precise analytic identities, thereby enabling the recovery of classical results such as Kummer’s and Watson’s summation theorems as consequences of distributional symmetry. When the assumption of identical distribution is relaxed, hypergeometric representations continue to exist; however, the absence of symmetry impedes closed-form evaluations of the same nature. This contrast underscores symmetry as the fundamental mechanism underlying exact summation formulas and elucidates why such identities are contingent upon specific parameter configurations. More generally, this methodology offers a probabilistic interpretation of hypergeometric identities and establishes a conceptual connection between probability theory and the theory of special functions. Furthermore, it suggests that more expansive constructions, based on non-identically distributed variables or iterated processes, could be fruitfully explored. In particular, examining ratios may lead to new identities or alternative derivations of classical results. Full article

The objective of this work is to investigate the $\mathbb{T}=(s,t)$-deformed free convolution ${⊞}_{\mathbb{T}}$ for $s>0$ and $t\in \mathbb{R}$ and to clarify its structural and asymptotic properties within the framework of Cauchy–Stieltjes kernel (CSK) families. The methodology is based on the analysis of the associated variance functions (VFs), which provide an effective analytic tool for describing deformation mechanisms, invariance properties, and convolution structures. In particular, we derive an explicit formula for the VF of convolution powers and exploit this representation to develop approximation procedures for distributions in CSK families generated by the $(s,t)$-deformed free Gaussian and free Poisson laws. We also establish several limit theorems describing the asymptotic behavior of the deformation. These findings highlight intrinsic symmetry and scaling properties and reveal connections with free additive, Boolean additive, and free multiplicative convolutions, thereby placing the $(s,t)$-deformation within a unified probabilistic framework governed by transformation, invariance, and structural regularity. Full article

This work presents a new family of distributions (FoDs) called the exponentiated half logistic-generalized-Topp-Leone-G (EHL-GEN-TL-G) family. This family can be expressed as an infinite linear combination of exponentiated-G densities, which facilitates the derivation of its important statistical properties. The shapes of the density and hazard rate functions were investigated for special cases. The model parameters were estimated using six different methods, with the maximum likelihood technique emerging as the best approach. The consistency of the parameter estimates was then validated through Monte Carlo simulations. The exponentiated half logistic-generalized-Topp-Leone-Weibull (EHL-GEN-TL-W) distribution, a sub-model of the EHL-GEN-TL-G family, was applied to three sets of engineering failure time data. The results indicated that, based on in-sample goodness-of-fit criteria, the EHL-GEN-TL-W model provided the best fit among the several established models considered. Additionally, the EHL-GEN-TL-W regression model was developed, and its practical utility in modeling failure data was demonstrated. Full article

Compressed sensing (CS) applied in the ambiguity domain offers an effective approach for recovering time–frequency distributions (TFDs) of non-stationary signals from sparse representations. Existing methods predominantly rely on the Wigner–Ville distribution (WVD) as the initial representation due to its simplicity and high auto-term concentration. However, the WVD performs poorly for signals with higher-order frequency-modulated (FM) components and is highly sensitive to interference and noise, which then propagate into the reconstruction. This paper introduces the systematic use of the L-Wigner distribution (LWD) as the initial representation for CS-based reconstruction, providing front-end adaptability to signal characteristics. By generating a controllable family of TFDs ranging from the spectrogram to cross-term-free polynomial WVDs, the LWD enables effective suppression of interference and noise while simultaneously enhancing auto-term localization for nonlinear FM components. The proposed LWD-based reconstruction framework is evaluated against the conventional WVD-based method using several state-of-the-art reconstruction algorithms, whose parameters are jointly optimized through a multi-objective meta-heuristic framework to ensure a fair comparison. Experiments on noisy synthetic signals and real-world gravitational-wave data demonstrate consistent performance gains. On synthetic signals, the proposed approach reduces the average reconstruction error index by up to 36.6%, improves the ${\ell }_1$-reconstruction error by up to 75.8%, and achieves complete elimination of cross-term energy. In addition, robustness analysis under additive white Gaussian noise shows up to a 75% improvement in ${\ell }_1$ performance. For real gravitational-wave data, the method reduces the mean reconstruction index by up to 5.8% while maintaining auto-term preservation and eliminating cross-term artifacts. These results establish the LWD-based initialization as an effective and general strategy for TFD reconstruction in complex signal environments. Full article

The COVID-19 pandemic accelerated the global shift to e-learning, raising concerns about its sustainability, student well-being, and educational inequality. This study evaluates e-learning in higher education during crises by examining its psychological, behavioral, social, and academic impacts on university students in the United Arab Emirates over two academic years of remote learning. Using a mixed-methods approach, data were collected from two cohorts (n = 228): Group 1 (G1, n = 76; 2020–2021) and Group 2 (G2, n = 152; 2021–2022). Analysis included descriptive statistics, independent sample t-tests, and thematic analysis. The results revealed significant differences between groups across most domains (p < 0.001). G2 reported higher psychological distress, including increased depression, stress, and reduced focus, while G1 demonstrated stronger behavioral and social adaptation, such as better self-care, family communication, and engagement in hobbies and sports. Regression analysis showed a strong linear relationship between online and campus grade distributions (R² = 0.7862), indicating academic consistency across learning modes. However, the findings highlight a sustainability paradox: although e-learning enhances flexibility and access and reduces environmental impact, prolonged reliance is linked to psychological strain, behavioral risks, and widening social inequality. The study underscores the need for a resilient and sustainable education model that supports students academically, psychologically, and socially to ensure the well-being and public health of all. These insights are particularly relevant amid ongoing regional crises and the continued expansion of e-learning and generative AI in education. Aligning with sustainable education goals, such approaches contribute to SDGs 3, 4, and 10, and support broader progress toward the 2030 Agenda. Full article

Intergenerational equity has become central to contemporary sustainability discourse and climate litigation, as courts increasingly confront whether present generations may legitimately deplete ecological resources in ways that impose irreversible burdens on those yet to come. This article argues that the normative structure underlying contemporary intergenerational climate claims reflects a recurring institutional logic identifiable much earlier in legal history. Focusing on the Gortyn Code (5th century BCE), one of the earliest and most extensive surviving Greek law codes, the analysis reveals how rules governing property, inheritance, guardianship, and family relations constructed an architecture of intergenerational continuity through enforceable constraints on present authority over inherited assets. The Code restricted alienation of inherited assets, structured succession through fixed distributive formulas, and imposed mechanisms designed to preserve the material foundations of future social existence. These provisions are then interpreted in relation to contemporary sustainability frameworks, emphasizing trusteeship, burden inheritance, and ecological thresholds. The article considers recent climate litigation to illustrate how modern courts increasingly translate intergenerational commitments into enforceable duties through functionally equivalent reasoning. The findings suggest that climate adjudication represents a modern manifestation of a deeper logic already visible in the Gortyn Code, one that emerges regardless of whether the resource at stake is owned or unowned, and that this parallel carries implications for the design and institutional anchoring of intergenerational obligations in contemporary climate governance. Full article

Although mass-spawning pooling systems are widely used for small yellow croaker (Larimichthys polyactis) aquaculture, they often induce severe genetic bottlenecks driven by reproductive skew. This study evaluated cross-generational genetic diversity and spawning patterns to propose an optimal genetic management strategy. We analyzed 1049 adult broodstock and 950 juvenile offspring using nine microsatellite markers. To mitigate reproductive skew, fertilized eggs were collected via multi-time sampling (19 times) over a two-month spawning season and reared to the juvenile stage. Genetic diversity was highly conserved across generations, with expected heterozygosity maintained at 0.860 in the offspring. Parentage assignment succeeded for 96.2% of the offspring (914 individuals), revealing 802 unique families, of which 89.9% (721 families) were singletons. Also, 60.9% of the broodstock contributed to reproduction, exhibiting a right-skewed participation distribution. Importantly, comparisons with a short-term single-event collection control group demonstrated that our multi-time strategy effectively prevented drastic reductions in effective population size (N_e). These patterns highlight the species asynchronous spawning physiology and confirm that the strategy approximates random mating with minimal genetic drift. We suggest this long-term, multi-time egg collection method as an effective protocol for the sustainable genetic management of multiple-spawning marine fish. Full article