Search Results (1,312)

Quantum computing has emerged as a revolutionary technology in recent years, with wide-ranging applications across many fields. It provides a significant advantage in terms of exponential speedups, leading researchers to believe that classical computing cannot overcome this gap. However, its true potential has not yet been thoroughly investigated in statistics. In the present study, we incorporate quantum dynamics into the statistical estimation method and propose a quantum-based estimation approach, i.e., quantum maximum likelihood estimation. The proposed method leverages quantum principles and dynamics to estimate the unknown parameters of probability distributions. This study implements the proposed method to estimate the Laplace location parameter. Simulation studies and real-world analyses are performed to evaluate the performance of the QMLE estimate of the Laplace parameter compared to the MLE estimate. The validity of the QMLE estimate is also assessed through variance and convergence analyses. All the findings validate the potential computational advantages of the QMLE approach as a competitive and promising method for parameter estimation of the Laplace parameter. QMLE provides more accurate, precise, efficient, less uncertain, and better-fitting estimates than MLE. Overall, the results indicate that statistical estimation theory can be improved by incorporating quantum dynamics into the classical estimation process. Full article

Electromagnetic wave absorbing materials are important for electromagnetic protection, radar stealth, wireless communication, and advanced electronic systems. However, traditional design methods mainly rely on repeated experiments and full-wave simulations, which are time-consuming and inefficient when dealing with complex compositions, microstructures, and multilayer structures. Machine learning provides a new route to accelerate the design of high-performance absorbers by learning the relationship among material composition, structure, electromagnetic parameters, and absorption performance. This review summarizes recent progress in machine-learning-empowered electromagnetic wave absorbing materials. First, the basic physical principles of electromagnetic wave absorption are introduced, including reflection loss, impedance matching, attenuation, and physical limits such as the Rozanov and Snoek limits. Then, typical machine learning models are discussed, including classical machine learning, deep learning, generative models, physics-informed models, large language models, and artificial-intelligence (AI) Agents. Their applications are further summarized from forward property prediction, high-throughput screening, inverse design, electromagnetic parameter decoupling, physics-informed modeling, explainability, multi-objective optimization, and data augmentation. Finally, the main challenges and future directions are discussed, including data standardization, physics-guided learning, foundation models, autonomous laboratories, and engineering-scale validation. This review shows that machine learning is changing absorber research from experience-driven trial-and-error to data-driven and knowledge-driven design, and provides a useful reference for developing next-generation electromagnetic wave absorbing materials. Full article

The classical Grothendieck compactness principle states that every norm-compact subset of a Banach space lies in the closed convex hull of a norm null sequence. Replacing the norm topology by the weak topology yields a characterization of Banach spaces with the Schur property. In the present paper, we establish an extension of this principle to the framework of super weakly compact sets. Specifically, we prove that for a Banach space X with the weak Banach–Saks property, every super weakly compact subset of X is contained in the closed convex hull of a uniformly weakly null sequence if and only if X has the Schur property. In addition, we demonstrate that every Banach space failing the Schur property contains a weakly null sequence which is not uniformly weakly null. Full article

In this paper, we investigate the theory of nonexpansive mappings in the framework of fuzzy normed GE-algebras. After recalling the fundamental concepts of GE-algebras and fuzzy GE-norms, we introduce the notion of fuzzy nonexpansive mappings and examine their basic structural properties. We show that the composition of two fuzzy nonexpansive mappings remains fuzzy nonexpansive, and establish that every fuzzy nonexpansive mapping is sequentially continuous with respect to fuzzy convergence. Further, by employing the concept of a fuzzy GE-interpolation family, we define $\alpha$-averaged mappings in fuzzy normed GE-algebras and prove that such averaged operators preserve nonexpansiveness. We also develop a demiclosedness-type principle and provide fixed point equivalence results between a mapping and its associated averaged mapping under suitable assumptions. Finally, we prove that the fixed point set of a fuzzy nonexpansive mapping is sequentially closed. These results extend classical ideas from metric fixed-point theory and Banach space theory to the algebraic setting of fuzzy normed GE-algebras. Full article

This paper investigates two central themes of contemporary transhumanism—human enhancement and artificial intelligence—from the perspective of Classical Islamic Philosophical Ethics (CIPE). First, it reconstructs the metaphysical framework and ethical orientation of CIPE through an analysis of its major representatives. Second, it examines the concept of enhancement in transhumanist thought in light of the metaphysical assumptions and ethical principles of this tradition. The analysis argues that although transhumanist enhancement theory generates significant tensions with classical philosophical conceptions of human nature, it can nevertheless be interpreted as compatible with certain premises of CIPE when understood within a broader framework of human perfection, intellectual development, and the harmony of body and soul. Building on this discussion, the paper further argues that CIPE offers valuable insights into contemporary debates concerning the topics of enhancement and artificial intelligence. In particular, it highlights the importance of harmony, integrity, and the reinterpretation of traditional philosophical concepts in response to emerging technological challenges. Overall, the paper seeks to contribute to discussions on transhumanism from within the Islamic intellectual tradition. It also aims to demonstrate the possibility of a middle path between the rejection of transhumanism on the basis of classical philosophy and its uncritical acceptance, thereby opening new avenues for dialogue between ancient philosophical traditions and contemporary technological developments. Full article

Network intrusion detection systems (IDS) operate in high-dimensional feature spaces under evolving attack patterns and asymmetric misclassification costs, where false negatives represent a critical security risk. Reinforcement learning (RL) offers a natural mechanism for encoding domain-specific misclassification costs directly into the learning signal through reward shaping, enabling cost-sensitive policy optimization in adaptive streaming environments. However, the integration of variational quantum models into RL-based IDS remains insufficiently explored. This work investigates a variational quantum reinforcement learning (VQRL) framework for intrusion detection, in which parameterized quantum circuits are employed to model the policy function. We adopt an RL formulation primarily as a principled cost-sensitive optimization approach rather than to exploit sequential state dependencies, and we employ Instantaneous Quantum Polynomial (IQP) embedding as a quantum feature encoding strategy. The study analyzes how embedding expressivity interacts with varying levels of dimensionality reduction via principal component analysis (PCA) on the CICIDS2017 dataset. Experiments demonstrate that VQRL-IQP achieves high recall and reduces false negative rates in moderately high-dimensional feature spaces compared to a classical RL baseline. This improvement is accompanied by an increase in false positive rates, reflecting a trade-off shaped jointly by the reward structure and the structural properties of IQP encoding. Statistical validation across five independent runs confirms the consistency of these trends. Importantly, no general quantum advantage in accuracy or computational efficiency is claimed; rather, the results indicate that VQRL-IQP offers a distinct error trade-off that is operationally valuable in security-critical scenarios where minimizing missed attacks is the primary objective. Full article

Quantum computing presents a critical threat to the cryptographic basis of metaverse platforms, with Shor’s algorithm capable of breaking traditional public-key cryptography and Grover’s algorithm significantly weakening symmetric encryption. The present self-sovereign identity (SSI) ecosystems are built on classical cryptographic systems that are susceptible to quantum attacks; hence, there is an immediate need for quantum-secure identity management in persistent virtual environments. This article proposes a solution called Quantum-Resistant MetaSSI (QR-MetaSSI), which is a comprehensive model that integrates NIST-standardized post-quantum cryptography (PQC) with W3C-compliant SSI principles. We design lattice-based decentralized identifiers (PQ-DIDs), hash-based verifiable credentials (PQ-VCs), and a hybrid authentication protocol that meets the needs of the metaverse, such as latency, interoperability, and persistent identities. The framework is subjected to mathematical modeling and simulation studies. Our study indicates that QR-MetaSSI keeps the authentication delay below 150 ms, which is inside the VR comfort range with 128-bit quantum security. Besides that, a comparative evaluation reveals that the proposed solution drastically reduces the risk of a quantum attack compared with classical ECC-based SSI systems at a level of computational overhead that is completely reasonable. QR-MetaSSI is a major step forward in the security of the metaverse, providing not only theoretical bases but also practical implementation instructions for the migration to quantum-resistant identity management. This framework not only addresses the most important breaches in security but also keeps the performance standards that are necessary for the creation of virtual environments that are highly immersive. Full article

Online business reviews encode rich customer sentiment that is critical for commercial decision making, yet accurately predicting star ratings from free text remains a challenging five-class classification problem. Classical ensemble methods—Soft Voting, Weighted Voting, and Stacking—aggregate complementary base-model outputs to improve predictive performance, but they produce opaque decisions that are unintelligible to business stakeholders. This paper proposes using a large language model (LLM), specifically unsloth/LLaMA-3.3-70B-Instruct, as an Explainable AI (XAI) ensemble aggregator: the LLM receives the predictions and confidence scores of four heterogeneous base models (Logistic Regression, Support Vector Machine, Naïve Bayes, and BERT-base-uncased) and reasons over them to produce both a final star-rating prediction and a natural-language explanation. We evaluate the full pipeline on 10,000-sample balanced and natural-distribution test sets derived from the Yelp Academic Dataset, with additional cross-lingual validation on Spanish Amazon Reviews. The LLM aggregator (LLAMA_AGG) achieves the highest macro-F1 on both pipelines (0.6800 on balanced; 0.6720 on natural) and the best ordinal calibration (QWK = 0.9111 on balanced; 0.9337 on natural), outperforming all classical aggregators and base models. A detailed Explainable AI analysis reveals that the LLM revises 28.07% of its standalone predictions after observing the ensemble outputs, improving the accuracy by +22.2 percentage points on the revised cases. The aggregator corrects severe polar bias in the standalone LLM (±0.35 recall improvement on mid-range star classes) and produces longer explanations when evidence is conflicted—a quantitative signal of deliberative reasoning. A formal human evaluation with two judges confirms high explanation faithfulness (4.47/5) and readability (4.82/5). Model scale ablation shows an 8B parameter variant achieves 90.8% agreement with the 70B model, enabling practical deployment. These findings demonstrate that Explainable AI can be achieved through LLM-based ensemble aggregation, establishing a principled approach for business-review sentiment analysis. Full article

This study reconceptualizes self-understanding not as a reflective outcome but as a conditionally constituted process grounded in the Buddhist principle of dependent origination (pratītyasamutpāda). Adopting a comparative philosophical analysis, it examines how traditional meditation and AI-mediated meditation differently configure the conditions under which experience and self-understanding arise. Drawing on early Buddhist texts, Madhyamaka philosophy, and classical meditation theory, the study develops an analytical framework centered on conditions, interdependence, non-self, and the processes of arising, transformation, and cessation. The analysis shows that traditional meditation operates as a structure of conditional disclosure, in which practitioners observe the dynamic interplay of experiential conditions. By contrast, the AI-mediated systems examined in this study tend to pre-configure these conditions through algorithmic classification, procedural guidance, and interface design. In such contexts, self-understanding is increasingly shaped through technologically mediated interpretations. The findings suggest that the key distinction lies not in the presence of conditions themselves but in the visibility and configurational control of those conditions. This study contributes a theoretical framework for understanding how digital environments may reshape contemplative agency and the conditions under which self-understanding is formed. Full article

Optimal node deployment in Wireless Sensor Networks (WSNs) is crucial for maximizing monitoring coverage. However, traditional metaheuristics like the Sparrow Search Algorithm (SSA) often suffer from premature convergence and redundant clustering, creating severe coverage holes. To address this, we introduce the Density-Aware Repulsive Sparrow Search Algorithm (DAR-SSA). Integrating electrostatic principles, DAR-SSA calculates a local density-based repulsive force vector to actively disperse nodes from high-density clusters. This physics-guided approach, combined with a dynamic explorer-exploiter allocation rule, ensures a computationally efficient balance between global and local search phases. Evaluated via a probabilistic sensing model, DAR-SSA significantly outperforms standard SSA, its variants (EFSSA, EASOA), and classical algorithms (PSO, GWO). In high-density urban deployments, DAR-SSA achieves a 95.25% effective coverage rate, compared to SSA’s 76.74%. In low-density environments, coverage reaches 97.12%. Validated by Wilcoxon rank-sum tests, DAR-SSA proves to be a robust, efficient framework for mitigating spatial redundancy and maximizing WSN sensing coverage. Full article

Energy-efficient operation of industrial thermal systems is a key requirement for sustainable manufacturing and resource-aware process design, particularly under environmental constraints such as dew-point conditions. In this context, minimizing energy consumption while maintaining stable thermal regulation is essential to reduce operational costs and improve system sustainability. This work presents an energy-aware experimental comparison of three control strategies—classical PID, fractional-order PID (FOPID), and hysteresis control—applied to a real thermoelectric thermal regulation system operating under dynamic ambient conditions and dew-point constraints. Unlike conventional control studies focused primarily on tracking performance, this research adopts a sustainability-oriented multi-criteria evaluation framework that explicitly positions energy consumption as a first-order assessment dimension alongside thermal regulation quality and control effort. A set of physically consistent performance indicators is introduced, including total energy consumption, control effort, energy-per-regulation metrics, and a global energy efficiency index, enabling a comprehensive assessment of industrial thermal control strategies from a resource efficiency perspective. Experimental results demonstrate that controller evaluation strongly depends on the inclusion of energy-based metrics. While PID control achieves competitive tracking performance with low error, FOPID provides the best overall trade-off between thermal accuracy and energy consumption, resulting in the highest energy efficiency index. In contrast, hysteresis control, despite its structural simplicity and robustness, leads to higher energy usage due to frequent switching dynamics, reducing its suitability for energy-constrained sustainable applications. The results highlight that thermal regulation near dew-point constraints should be evaluated through an energy-aware multi-criteria framework rather than through pure tracking metrics, enabling a more complete characterization of controller performance for sustainable industrial applications. The proposed framework provides a scalable methodology for evaluating and designing energy-efficient control strategies, supporting sustainable industrial operation and contributing to resource optimization principles aligned with circular economy objectives. Full article

The creation of Basic Probability Assignment (BPA) still represents a basic problem in the Dempster-Shafer (D-S) theory of evidence especially when it comes to representing continuous uncertainty and class ambiguity. In order to overcome this problem, this paper suggests a BPA construction model depending on Gaussian overlap. The main principle behind the approach is the creation of focal elements based on the overlaps between conditional probability distributions of classes, allowing characterisation of uncertainty in a data driven manner. Namely, attribute level evidence is represented by Gaussian distributions, and singleton and composite focal elements are composite focal elements are generated through Gaussian product responses and normalized to obtain BPAs. Composite focal elements are further projected into singleton-level decision scores through proportional belief and plausibility transformations for decision-making and attribute-weight calculation. Moreover, to dynamically modify the role played by different attributes, a Kullback-Leibler (KL) divergence-based weighting scheme is used. These parts combine to form a full pipeline of continuous evidence modeling to BPA generation as proposed by the given method. The experimental results show that the proposed method achieves 98.00 ± 2.67% accuracy on the Iris dataset, 97.21 ± 1.76% accuracy on the Wine dataset, and 90.86 ± 1.20% accuracy on the Breast Cancer Wisconsin dataset. Compared with existing BPA generation methods, the proposed method obtains the best performance on the Iris and Wine datasets. Compared with classical machine learning models, the method also achieves the highest accuracy on the Iris dataset and remains competitive on the Wine and Breast Cancer Wisconsin datasets. Full article

High-throughput technologies such as RNA-seq and single-cell transcriptomics generate increasingly large and high-dimensional gene expression datasets in which nonlinear dependence structures are common. Because classical methods primarily capture linear associations, they may fail to characterize many biologically relevant patterns of dependence. To address this limitation, diverse nonlinear dependence measures—including information-theoretic, rank-based, kernel-based, distance-based, copula-based, and clustering-based approaches—have been developed. However, the field remains fragmented, and comparative evaluations are often inconsistent. This review organizes nonlinear methods into major methodological families and critically compares their statistical behavior, strengths, limitations, and characteristic modes of failure. We emphasize that method selection depends on matching inferential objectives to estimator assumptions, analytical constraints, and characteristic failure modes. By identifying recurring trade-offs among flexibility, robustness, interpretability, and computational scalability, we provide scenario-based guidance for method selection in transcriptomics, network inference, and functional genomics. In doing so, we aim to align inferential objectives with analytical requirements, supporting principled and application-specific use of nonlinear dependence methods in modern omics research. Full article

Fusing spatial observations from sensors with heterogeneous error characteristics is a persistent challenge in geostatistics. Classical kriging assumes a Gaussian likelihood for all observations, an assumption that fails when sensors exhibit one-sided or asymmetric noise. We present a Variable Rank Kriging (VRK) formulation that supports per-observation heterogeneous likelihoods where each observation may define its own likelihood function, thus enabling principled fusion of sensors whose noise structures are significantly different in terms of distribution family and magnitude. Within this framework, we use the exponential (one-sided) likelihood as a case study to demonstrate the method and compare it with sampling-based numerical alternatives for general likelihoods without closed forms. A non-collocated RTK calibration workflow uses kriging predictions from a sparse high-accuracy reference to characterise sensor-specific likelihood parameters without requiring co-located paired observations. Synthetic 1-D and 2-D experiments show that correct per-point likelihood specification reduces RMSE by up to 92% (1-D) and 57% (2-D) relative to a misspecified Gaussian model while also eliminating systematic positive bias. A demonstration using NEON Airborne Observation Platform lidar data at Harvard Forest confirms these findings in a practical, real-world scenario. Across multiple subsamples of the lidar dataset, the exponential likelihood reduces vegetated-zone RMSE by $20.6\%$ (open zone: $18.6\%$) and mean absolute bias by $26.5\%$ relative to a heteroscedastic Gaussian baseline. The open-source vrk Python ($>=$3.10) package provides a reproducible implementation that can be applied to any spatial domain that requires multi-sensor spatial fusion with heterogeneous error structures. Full article

We address the paradoxical transformation of a classical-mechanical particle motion when the space and time scales of observation pass below the uncertainty principle threshold. This is analyzed in the language of classical statistical mechanics, considering specifically many-particle systems inhomogeneous along one spatial direction. We employ the density functional formalism in its square-gradient form and find: (i) The macroscopic solution is analogous to the classical trajectory of a particle under a potential of force given by (minus) the free energy density. Whereas, (ii) fluctuations around the solution in (i) are equal to the quantum-mechanical wave functions of a particle under a potential given by the curvature of the free energy density. We illustrate this situation with three textbook examples: A particle in a box, the harmonic oscillator, and the hydrogen atom. We show that their time-independent Schrödinger equation wave functions describe, respectively, the fluctuations of a fluid interface, of critical point fluctuations, and of a confined ideal gas. At large scales, sharp probability distributions make fluctuations irrelevant; the vanishing of the first variation yields the macroscopically observable statistical-mechanical non-uniformity, equivalent to the classical particle trajectory. But at sufficiently small scales, with necessarily very few particles, distributions appear much wider, fluctuations dominate, and one obtains the Schrödinger equation (for the microscopic potential). Full article