Search Results (33)

Evaluating emotional understanding in Large Language Models (LLMs) is challenging because assessments are subjective, ambiguous, multidimensional, and sensitive to controllable generation parameters. We developed a unified mathematical framework for characterizing LLM “emotional individuality” that integrates softmax sampling–temperature control (the decoding-time temperature parameter exposed by the API and typically used to modulate output randomness during token generation), fuzzy set theory with Shannon-type fuzzy entropy, and persona-based cognitive diversity analysis. We evaluated 36 API-accessible LLMs from seven major vendors on Japanese literary texts, using four personas each assigned a sampling temperature ($T\in \{0.1,0.4,0.7,0.9\}$), yielding 4227 /4320 trial responses (97.8% coverage), of which 4067/4227 contained valid numeric emotion scores (96.2%). Temperature controllability varied approximately 25-fold (${\kappa }_M\in [0.039,0.982]$) with both positive and negative temperature–variance relationships across models. Because each sampling temperature is deterministically assigned to a persona in our design, ${\kappa }_M$ should be interpreted as an operational temperature–variance association across persona conditions rather than an isolated causal temperature effect. The model-level mean fuzzy entropy ranged from approximately 0.40 to 0.66, and the numerical stability consistency scores ranged from approximately 0.548 to 0.780. We also observed text-dependent structure, including genre-specific variation in the Interest–Sadness relationship. For practitioners, the framework is most directly useful as a benchmark-design and model-screening template for structured emotion-scoring tasks; its empirical conclusions remain limited to the present Japanese literary, text-only setting. Full article

The stability, elasticity, and thermoelectric property of ABX₃ (A = K, Ag, Cu; B = Si_xGe_ySn_zPb_{(1−x−y−z)}; X = Br, I) metal halide perovskites (MHPs) with B-type medium entropy sub-lattices (MESLs) are investigated by first principles calculations. The results show that the order of dissociation formation enthalpy ${\Delta H}_{\mathrm{f}}$ for conventional unit cell APbX₃ with changing atomic type in the A site is K < Ag < Cu, and for each case Br < I. The ${\Delta H}_{\mathrm{f}}$ values of (KBBr₃, KBI₃, AgBBr₃) and (CuBBr₃, CuBI₃, AgBI₃) with MESL in the B site slightly increase and decrease, respectively, with the exception of certain situations. By using Slack’s model, the lattice thermal conductivity (LTC) ${\kappa }_l$ at finite temperatures is obtained. It is found that the LTC ${\kappa }_l$ for all MHPs shows an extremely low value at room temperature, not exceeding 1.5 Wm⁻¹K⁻¹. Interestingly, it is also found that the B-type MESLs significantly increase the ZT_max values of KPbX₃, whereas they decrease the ZT_max values of CuPbX₃ and AgPbX₃, except for in some cases. All calculated parameters show obvious variation laws with the increase in atomic number of the high-content B-type atom in the ABX₃, and CuBX₃ and AgBX₃ materials exhibit an extremely low ZT value (ZT ≈ 0) due to their high σ accompanied by high ${\kappa }_e$ and low S. We believe that KSi_0.375Ge_0.25Sn_0.25Pb_0.125Br₃ with a ZT value of 3.012 can serve as an excellent thermoelectric material at room temperature. These findings make contributions to the design of high-quality thermoelectric MHP materials. Full article

Accurate mapping of wetland vegetation is essential for ecosystem monitoring and conservation planning. Traditional workflows combining Sentinel-1 SAR, Sentinel-2 optical imagery, and topographic data have advanced vegetation classification but require extensive preprocessing and often yield fragmented boundaries and “salt-and-pepper” noise. In this study, we compare a conventional multi-sensor classification framework with a novel embedding-based approach derived from the AlphaEarth foundation model, using a cluster-guided Random Forest classifier applied to the dynamic wetland system of Narran Lake, New South Wales. Both approaches achieved high accuracy ac with test performance typically in the ranges: OA = 0.985–0.991, Cohen’s κ = 0.977–0.990, weighted F1 = 0.986–0.991, and MCC = 0.977–0.990. Embedding based maps showed markedly improved spatial coherence (lower edge density, local entropy, and patch fragmentation), producing smoother, ecologically consistent boundaries while requiring minimal preprocessing. Differences in class delineation were most evident in fire-affected and agricultural areas, where embeddings demonstrated greater resilience to spectral disturbance and post-fire variability. Although overall accuracies exceeded 0.98, these high values reflect the use of spectrally pure, homogeneous training samples rather than overfitting. The results highlight that embedding-driven methods can deliver cleaner, more interpretable vegetation maps with far less data preparation, underscoring their potential to streamline large-scale ecological monitoring and enhance the spatial realism of wetland mapping. Full article

Berberine (BBR), a protoberberine alkaloid with a long history of medicinal use, has consistently demonstrated benefits in glucose–lipid metabolism and inflammatory balance across both preclinical and human studies. These diverse effects are not mediated by a single molecular target but by BBR’s capacity to restore network coordination among metabolic, immune, and microbial systems. At the core of this regulation is an AMP-activated Protein Kinase (AMPK)-centered mechanistic hub, integrating signals from insulin and nutrient sensing, Sirtuin 1/3 (SIRT1/3)-mediated mitochondrial adaptation, and inflammatory pathways such as nuclear Factor Kappa-light-chain-enhancer of Activated B cells (NF-κB) and NOD-, LRR- and Pyrin Domain-containing Protein 3 (NLRP3). This hub is dynamically regulated by system-level inputs from the gut, mitochondria, and epigenome, which in turn strengthen intestinal barrier function, reshape microbial and bile-acid metabolites, improve redox balance, and potentially reverse the epigenetic imprint of metabolic stress. These interactions propagate through multi-organ axes, linking the gut, liver, adipose, and vascular systems, thus aligning local metabolic adjustments with systemic homeostasis. Within this framework, BBR functions as a negentropic modulator, reducing metabolic entropy by fostering a coordinated balance among these interconnected systems, thereby restoring physiological order. Combination strategies, such as pairing BBR with metformin, Sodium-Glucose Cotransporter 2 (SGLT2) inhibitors, and agents targeting the microbiome or inflammation, have shown enhanced efficacy and substantial translational potential. Berberine ursodeoxycholate (HTD1801), an ionic-salt derivative of BBR currently in Phase III trials and directly compared with dapagliflozin, exemplifies the therapeutic promise of such approaches. Within the hub–axis paradigm, BBR emerges as a systems-level modulator that recouples energy, immune, and microbial circuits to drive multi-organ remodeling. Full article

Contemporary neural and generative architectures are deficient in self-preservation mechanisms and sustainable stability. In uncertain or noisy situations, they frequently demonstrate oscillatory learning, overconfidence, and structural deterioration, indicating a lack of biological regulatory principles in artificial systems. We present Bio-RegNet, a meta-homeostatic Bayesian neural network architecture that integrates T-regulatory-cell-inspired immunoregulation with autophagic structural optimization. The model integrates three synergistic subsystems: the Bayesian Effector Network (BEN) for uncertainty-aware inference, the Regulatory Immune Network (RIN) for Lyapunov-based inhibitory control, and the Autophagic Optimization Engine (AOE) for energy-efficient regeneration, thereby establishing a closed energy–entropy loop that attains adaptive equilibrium among cognition, regulation, and metabolism. This triadic feedback achieves meta-homeostasis, transforming learning into a process of ongoing self-stabilization instead of static optimization. Bio-RegNet routinely outperforms state-of-the-art dynamic GNNs across twelve neuronal, molecular, and macro-scale benchmarks, enhancing calibration and energy efficiency by over 20% and expediting recovery from perturbations by 14%. Its domain-invariant equilibrium facilitates seamless transfer between biological and manufactured systems, exemplifying a fundamental notion of bio-inspired, self-sustaining intelligence—connecting generative AI and biomimetic design for sustainable, living computation. Bio-RegNet consistently outperforms the strongest baseline HGNN-ODE, improving ARI from 0.77 to 0.81 and NMI from 0.84 to 0.87, while increasing equilibrium coherence $\kappa$ from 0.86 to 0.93. Full article

Kaniadakis deformed $\kappa$-mathematics is an area of mathematics that has found relevance in the analysis of complex systems. Specifically, the mathematical framework in the context of a first-order decay $\kappa$-differential equation is investigated, facilitating an in-depth examination of the $\kappa$-mathematical structure. This framework serves as a foundational platform, representing the simplest non-trivial setting for such inquiries which are demonstrated for the first time in the literature. Finally, additional avenues of study are discussed. Full article

This study examined branched-chain fatty acids (BCFAs)’ effects on oxidative stress, energy metabolism, inflammation, tight junction disruption, apoptosis, and Toll-like receptor 4/nuclear factor kappa-B (TLR4/NF-κB) signaling in lipopolysaccharide (LPS)-induced calf small intestinal epithelial cells (CSIECs). Eight groups were used: a control group, an LPS-induced group, and six BCFA treatment groups (12-methyltridecanoic acid (iso-C14:0), 13-methyltetradecanoic acid (iso-C15:0), 14-methylpentadecanoic acid (iso-C16:0), 15-methylhexadecanoic acid (iso-C17:0), 12-methyltetradecanoic acid (anteiso-C15:0), and 14-methylhexadecanoic acid (anteiso-C17:0)) with LPS. The BCFA pretreatments significantly increased CSIEC activity compared to the LPS-induced group, with iso-C14:0 showing the highest activity (89.73%). BCFA reduced Reactive Oxygen Species (ROS) generation and malondialdehyde (MDA) levels and improved the superoxide dismutase (SOD), glutathione peroxidase (GSH-Px), and catalase (CAT) activities and glutathione (GSH) levels. Iso-C16:0 optimized total antioxidant capacity (T-AOC). BCFA enhanced the mitochondrial membrane potential, Adenosine Triphosphate (ATP) enzyme activity, and ATP content, with iso-C14:0 increasing ATP by 27.01%. BCFA downregulated interleukin (IL)-1β, IL-8, tumor necrosis factor (TNF)-α, and interferon (INF)-γ gene expression, reduced IL-6 levels, and increased IL-10 expression. Myeloid differentiation factor 88 (MyD88) mRNA levels were reduced. BCFA alleviated Zonula Occludin (ZO-1), Claudin-1, and Claudin-4 decrease and increased Occludin levels. BCFA mitigated LPS-induced increases in Caspase-3 and BCL2-Associated X (BAX) mRNA levels, reduced Caspase-8 and Caspase-9 expression, and increased B-Cell Lymphoma-2 (BCL-2) mRNA levels. The Entropy Weight-TOPSIS method was adopted, and it was discovered that iso-C15:0 has the best effect. In summary, BCFA supplementation mitigated oxidative stress and enhanced mitochondrial function. BCFA inhibited TLR4/NF-κB signaling pathway overactivation, regulated inflammatory cytokine gene expression, reduced cellular apoptosis, preserved tight junction integrity, and supported barrier function. Full article

A novel approach to the quantum version of $\kappa$-entropy that incorporates it into the conceptual, mathematical and operational framework of quantum computation is put forward. Various alternative expressions stemming from its definition emphasizing computational and algorithmic aspects are worked out: First, for the case of canonical Gibbs states, it is shown that $\kappa$-entropy is cast in the form of an expectation value for an observable that is determined. Also, an operational method named “the two-temperatures protocol” is introduced that provides a way to obtain the $\kappa$-entropy in terms of the partition functions of two auxiliary Gibbs states with temperatures $\kappa$-shifted above, the hot-system, and $\kappa$-shifted below, the cold-system, with respect to the original system temperature. That protocol provides physical procedures for evaluating entropy for any $\kappa$. Second, two novel additional ways of expressing the $\kappa$-entropy are further introduced. One determined by a non-negativity definite quantum channel, with Kraus-like operator sum representation and its extension to a unitary dilation via a qubit ancilla. Another given as a simulation of the $\kappa$-entropy via the quantum circuit of a generalized version of the Hadamard test. Third, a simple inter-relation of the von Neumann entropy and the quantum $\kappa$-entropy is worked out and a bound of their difference is evaluated and interpreted. Also the effect on the $\kappa$-entropy of quantum noise, implemented as a random unitary quantum channel acting in the system’s density matrix, is addressed and a bound on the entropy, depending on the spectral properties of the noisy channel and the system’s density matrix, is evaluated. The results obtained amount to a quantum computational tool-box for the $\kappa$-entropy that enhances its applicability in practical problems. Full article

In this work, we have studied the thermodynamic properties of the Van der Waals black hole in the framework of the relativistic Kaniadakis entropy. We have shown that the black hole properties, such as the mass and temperature, differ from those obtained by using the the Boltzmann–Gibbs approach. Moreover, the deformation $\kappa$-parameter changes the behavior of the Gibbs free energy via introduced thermodynamic instabilities, whereas the emission rate is influenced by $\kappa$ only at low frequencies. Nonetheless, the pressure–volume ($P\left(V\right)$) characteristics are found independent of $\kappa$ and the entropy form, unlike in other anti-de Sitter (AdS) black hole models. In summary, the presented findings partially support the previous arguments of Gohar and Salzano that, under certain circumstances, all entropic models are equivalent and indistinguishable. Full article

The axiomatic structure of the $\kappa$-statistcal theory is proven. In addition to the first three standard Khinchin–Shannon axioms of continuity, maximality, and expansibility, two further axioms are identified, namely the self-duality axiom and the scaling axiom. It is shown that both the $\kappa$-entropy and its special limiting case, the classical Boltzmann–Gibbs–Shannon entropy, follow unambiguously from the above new set of five axioms. It has been emphasized that the statistical theory that can be built from $\kappa$-entropy has a validity that goes beyond physics and can be used to treat physical, natural, or artificial complex systems. The physical origin of the self-duality and scaling axioms has been investigated and traced back to the first principles of relativistic physics, i.e., the Galileo relativity principle and the Einstein principle of the constancy of the speed of light. It has been shown that the $\kappa$-formalism, which emerges from the $\kappa$-entropy, can treat both simple (few-body) and complex (statistical) systems in a unified way. Relativistic statistical mechanics based on $\kappa$-entropy is shown that preserves the main features of classical statistical mechanics (kinetic theory, molecular chaos hypothesis, maximum entropy principle, thermodynamic stability, H-theorem, and Lesche stability). The answers that the $\kappa$-statistical theory gives to the more-than-a-century-old open problems of relativistic physics, such as how thermodynamic quantities like temperature and entropy vary with the speed of the reference frame, have been emphasized. Full article

It is known that Kaniadakis entropy, a generalization of the Shannon–Boltzmann–Gibbs entropic form, is always super-additive for any bipartite statistically independent distributions. In this paper, we show that when imposing a suitable constraint, there exist classes of maximal entropy distributions labeled by a positive real number $\aleph >0$ that makes Kaniadakis entropy multi-additive, i.e., $S_{\kappa }\left[p^{\mathrm{A}\cup \mathrm{B}}\right]=(1+\aleph )\left(S_{\kappa }\left[p^{\mathrm{A}}\right]+S_{\kappa }\left[p^{\mathrm{B}}\right]\right)$, under the composition of two statistically independent and identically distributed distributions $p^{\mathrm{A}\cup \mathrm{B}}(x,y)=p^{\mathrm{A}}\left(x\right)p^{\mathrm{B}}\left(y\right)$, with reduced distributions $p^{\mathrm{A}}\left(x\right)$ and $p^{\mathrm{B}}\left(y\right)$ belonging to the same class. Full article

The rapid evolution in materials science has resulted in a significant interest in high-entropy alloys (HEAs) for their unique properties. This study focuses on understanding both quaternary and quinary body-centered cubic (BCC) of 12 refractory-based HEAs, and on analysis of their electronic structures, lattice distortions, mechanical, and thermal properties. A comprehensive assessment is undertaken by means of density functional theory (DFT)-based first principles calculations. It is well known that multiple constituents lead to notable lattice distortions, especially in quinary HEAs. This distortion, in turn, has significant implications on the electronic structure that ultimately affect mechanical and thermal behaviors of these alloys such as ductility, lattice thermal conductivity, and toughness. Our in-depth analysis of their electronic structures revealed the role of valence electron concentration and its correlation with bond order and mechanical properties. Local lattice distortion (LD) was investigated for these 12 HEA models. M1 (WTiVZrHf), M7 (TiZrHfW), and M12 (TiZrHfVNb) have the highest LD whereas the models M3 (MoTaTiV), M5 (WTaCrV), M6 (MoNbTaW), and M9 (NbTaTiV) have the less LD. Furthermore, we investigated the thermal properties focusing on Debye temperature (Θ_D), thermal conductivity (κ), Grüneisen parameter (γ_α), and dominant phonon wavelength (λ_dom). The NbTaTiV(M9) and TiVNbHf(M10) models have significantly reduced lattice thermal conductivities (κ_L). This reduction is due to the mass increase and strain fluctuations, which in turn signify lattice distortion. The findings not only provide an understanding of these promising materials but also offer guidance for the design of next-generation HEAs with properties tailored for potential specific applications. Full article

Kaniadakis statistics is a widespread paradigm to describe complex systems in the relativistic realm. Recently, gravitational and cosmological scenarios based on Kaniadakis ($\kappa$-deformed) entropy have been considered, leading to generalized models that predict a richer phenomenology comparing to their standard Maxwell–Boltzmann counterparts. The purpose of the present effort is to explore recent advances and future challenges of Gravity and Cosmology in Kaniadakis statistics. More specifically, the first part of the work contains a review of $\kappa$-entropy implications on Holographic Dark Energy, Entropic Gravity, Black hole thermodynamics and Loop Quantum Gravity, among others. In the second part, we focus on the study of Big Bang Nucleosynthesis in Kaniadakis Cosmology. By demanding consistency between theoretical predictions of our model and observational measurements of freeze-out temperature fluctuations and primordial abundances of ${}^{4}He$ and D, we constrain the free $\kappa$-parameter, discussing to what extent the Kaniadakis framework can provide a successful description of the observed Universe. Full article

The controlled deposition of CoCrFeNiMo${}_{0.2}$ high-entropy alloy (HEA) microparticles was achieved by using laser-induced forward transfer (LIFT). Ultra-short laser pulses of 230 fs of 515 nm wavelength were tightly focused into ∼2.4 μm focal spots on the ∼50-nm thick plasma-sputtered films of CoCrFeNiMo${}_{0.2}$. The morphology of HEA microparticles can be controlled at different fluences. The HEA films were transferred onto glass substrates by magnetron sputtering in a vacuum (${10}^{-8}$ atm) from the thermal spray-coated substrates. The absorption coefficient of CoCrFeNiMo${}_{0.2}$$\alpha \approx 6\times {10}^5$ cm${}^{-1}$ was determined at 600-nm wavelength. The real and imaginary parts of the refractive index $(n+i\kappa )$ of HEA were determined from reflectance and transmittance by using nanofilms. Full article

Human life necessitates high-quality sleep. However, humans suffer from a lower quality of life because of sleep disorders. The identification of sleep stages is necessary to predict the quality of sleep. Manual sleep-stage scoring is frequently conducted through sleep experts’ visually evaluations of a patient’s neurophysiological data, gathered in sleep laboratories. Manually scoring sleep is a tough, time-intensive, tiresome, and highly subjective activity. Hence, the need of creating automatic sleep-stage classification has risen due to the limitations imposed by manual sleep-stage scoring methods. In this study, a novel machine learning model is developed using dual-channel unipolar electroencephalogram (EEG), chin electromyogram (EMG), and dual-channel electrooculgram (EOG) signals. Using an optimum orthogonal filter bank, sub-bands are obtained by decomposing 30 s epochs of signals. Tsallis entropies are then calculated from the coefficients of these sub-bands. Then, these features are fed an ensemble bagged tree (EBT) classifier for automated sleep classification. We developed our automated sleep classification model using the Sleep Heart Health Study (SHHS) database, which contains two parts, SHHS-1 and SHHS-2, containing more than 8455 subjects with more than 75,000 h of recordings. The proposed model separated three classes if sleep: rapid eye movement (REM), non-REM, and wake, with a classification accuracy of 90.70% and 91.80% using the SHHS-1 and SHHS-2 datasets, respectively. For the five-class problem, the model produces a classification accuracy of 84.3% and 86.3%, corresponding to the SHHS-1 and SHHS-2 databases, respectively, to classify wake, N1, N2, N3, and REM sleep stages. The model acquired Cohen’s kappa ($\kappa$) coefficients as 0.838 with SHHS-1 and 0.86 with SHHS-2 for the three-class classification problem. Similarly, the model achieved Cohen’s $\kappa$ of 0.7746 for SHHS-1 and 0.8007 for SHHS-2 in five-class classification tasks. The model proposed in this study has achieved better performance than the best existing methods. Moreover, the model that has been proposed has been developed to classify sleep stages for both good sleepers as well as patients suffering from sleep disorders. Thus, the proposed wavelet Tsallis entropy-based model is robust and accurate and may help clinicians to comprehend and interpret sleep stages efficiently. Full article