Search Results (1,178)

Kaprekar’s routine, i.e., sorting the digits of an integer in ascending and descending order and subtracting the two, defines a finite deterministic map on the state space of fixed-length digit strings. While its attractors (such as 495 for $D=3$ and 6174 for $D=4$) are classical, the global information-theoretic structure of the induced dynamics and its dependence on the digit length D have received little attention. Here an exhaustive analysis is carried out for $D\in \{3,4,5,6\}$. For each D, all states are enumerated and the transition structure is computed numerically; attractors and convergence distances are obtained, and the induced distribution over attractors across iterations is used to construct “entropy funnels”. Despite the combinatorial growth of the state space, average distances remain small and entropy decays rapidly before entering a slow tail. Permutation symmetry is then exploited by grouping states into digit multisets and, in a further reduction, into low-dimensional digit-gap features. On this gap space, a first-order Markov approximation is empirically estimated by counting one-step transitions induced by the exhaustively enumerated deterministic map. From the resulting empirical transition matrix, drift fields and the stationary distribution are computed numerically. These quantities serve as descriptive summaries of the projected dynamics and are not derived in closed form. Full article

This study focuses on a bulb tubular pump to clarify the flow characteristics and energy loss laws of low-lift tubular pumps under variable speed regulation and addresses deviations from optimal operating conditions in complex scenarios. For two typical rotational speeds, a full-flow passage model was established; the SST k-ω turbulence model was used to solve 3D incompressible viscous flow, energy loss was analyzed via entropy production theory, and simulations were experimentally validated. The results showed the following: pump efficiency exhibited a “first rise then fall” trend, head decreased monotonically with flow rate, and the optimal operating point shifted to lower flow rates at slower speeds. Meanwhile, local entropy production rate effectively characterized loss location and intensity, with aggravated off-design loss concentrated near the hub and rim along the spanwise direction and within 30 mm of the near-wall region. This study clarifies core energy loss mechanisms, providing a quantitative basis for operation optimization and structural improvement to support the safe, economical operation of low-lift pump stations. Full article

To obtain an excellent electromagnetic wave (EMW) absorption material, a strategy was proposed in this study with the aid of in-situ growth of carbon nanotubes (CNTs) on the surface of a metal–organic framework (MOF)-derived FeCoNiMnMg high-entropy alloy (HEA). The HEA@CNT composite was successfully prepared via a solvothermal method combined with a one-step pyrolysis process. With the pyrolysis temperature increasing from 600 °C to 800 °C, the length of CNTs grew from 200 nm to about 600 nm approximately, while the defect density of CNTs was enhanced. This structural evolution significantly improved the dielectric properties and impedance matching. Consequently, the sample prepared at 800 °C (HEA@CNT-800) exhibited outstanding microwave absorption performances, achieving a minimum reflection loss (RL_min) of −57.52 dB at a matched thickness of 2.3 mm and an effective absorption bandwidth (EAB) of 4.4 GHz at a thinner thickness of 1.9 mm. This work provides a novel perspective for designing high-performance MOF-derived absorption materials. Full article

Accurate prediction of Blood Glucose (BG) levels is essential for effective diabetes management and the prevention of adverse glycemic events. This study introduces a novel designed hybrid Gated Recurrent Unit-Transformer (GRU-Transformer) model tailored to forecast BG levels at 15, 30, 45, and 60 min horizons using only Continuous Glucose Monitoring (CGM) data as input. The proposed approach integrates advanced CGM feature extraction step. The extracted features are statistically the mean, the median, the maximum, the entropy, the autocorrelation and the Detrended Fluctuation Analysis (DFA). In addition, in order to define more enhanced and specific features, the custom 3-points monotonicity score, the sinusoidal time encoding, and the workday/weekend binary features are proposed in this work. This approach enables the model to capture physiological dynamics and contextual temporal patterns of Type 1 Diabetes (T1D) with great accuracy. To thoroughly assess the performance of the proposed method, we relied on several well-established metrics, including Root Mean Squared Error (RMSE), Coefficient of Determination (R²), Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE), and Root Mean Squared Percentage Error (RMSPE). Experimental results demonstrate that the proposed method achieves superior predictive accuracy for both short-term (15–30 min) and long-term (45–60 min) forecasting. Specifically, the model attained the lowest average RMSE values, with 4.00 mg/dL, 6.65 mg/dL, 7.96 mg/dL, and 8.91 mg/dL and yielding consistently high R² scores for the respective prediction horizons. This new method distinguishes itself by continuously exceeding current prediction models, reinforcing its potential for real-time CGM and clinical decision support. Its high accuracy and adaptability make it a favorable tool for improving diabetes management and personalized glycemic control. Full article

We review aspects of entanglement entropy in the quantum mechanics of $N\times N$ matrices, i.e., matrix quantum mechanics (MQM), at large N. In doing so, we review standard models of MQM and their relation to string theory, D-brane physics, and emergent non-commutative geometries. We overview, in generality, definitions of subsystems and entanglement entropies in theories with gauge redundancy and discuss the additional structure required for definining subsystems in MQMs possessing a $U\left(N\right)$ gauge redundancy. In connecting these subsystems to non-commutative geometry, we review several works on ‘target space entanglement,’ and entanglement in non-commutative field theories, highlighting the conditions in which target space entanglement entropy displays an ‘area law’ at large N. We summarize several example calculations of entanglement entropy in non-commutative geometries and MQMs. We review recent work in connecting the area law entanglement of MQM to the Ryu–Takayanagi formula, highlighting the conditions in which $U\left(N\right)$ invariance implies a minimal area formula for the entanglement entropy at large N. Finally, we make comments on open questions and research directions. Full article

This article develops a hybrid neural network method for detecting UDP flooding in critical infrastructure microgrid protection systems. This method combines sequential statistics (CUSUM) and a multimodal convolutional 1D-CNN architecture with a composite scoring criterion. Input features are generated using packet-aggregated one-minute vectors with metrics for packet count, average size, source entropy, and HHI concentration index, as well as compact sketches of top sources. To ensure forensically relevant incident recording, a greedy artefact selection policy based on the knapsack problem with a limited forensic buffer is implemented. The developed method is theoretically justified using a likelihood ratio criterion and adaptive threshold tuning, which ensures control over the false alarm probability. Experimental validation on traffic datasets demonstrated high efficiency, with an overall accuracy of 98.7%, a sensitivity of 97.4%, an average model inference time of 5.3 ms (2.5 times faster than its LSTM counterpart), a controlled FPR of 0.96%, and a reduction in asymptotic detection latency with an increase in intensity from 35 to 12 s. Moreover, with a storage budget of 10 MB, 28 priority bins were selected (their total size was 7.39 MB), ensuring the approximate preservation of 85% of the most informative packets for subsequent examination. This research contribution involves the creation of a ready-to-deploy, resource-efficient detector with low latency, explainable statistical layers, and a built-in mechanism for generating a standardized evidence package to facilitate rapid law enforcement response. Full article

A conditional framework of Conditional Cosmological Recurrence (CCR) is introduced, as follows: if a causal patch admits a finite operational Hilbert space dimension D (as motivated by holographic and entropy bounds), then unitary quantum dynamics guarantee almost-periodic evolution, leading to recurrences. The central contribution is the explicit formulation of a micro-to-macro bridge, as follows: (i) finite regions discretize field modes; (ii) gravitational bounds cap entropy and energy; and (iii) the number of accessible states is finite, yielding CCR. The analysis differentiates global microstate recurrences (with double-exponential timescales in $S_{max}$) from operationally relevant coarse-grained returns (exponential in subsystem entropy), with conservative timescale estimates. For predictivity in eternally inflating settings, a causal-diamond measure with xerographic typicality and a single no-Boltzmann-brain constraint is employed, thereby avoiding volume-weighting pathologies. The scope is explicitly conditional: if future quantum gravity demonstrates $D=\infty$ for causal patches, CCR is falsified. Full article

The cosmological constant (CC, $\Lambda$) problem stands as one of the most profound puzzles in the theory of gravity, representing a remarkable discrepancy of about 120 orders of magnitude between the observed value of dark energy and its natural expectation from quantum field theory. This paper synthesizes two innovative paradigms—holographic naturalness (HN) and pre-geometric gravity (PGG)—to propose a unified and natural resolution to the problem. The HN framework posits that the stability of the CC is not a matter of radiative corrections but rather of quantum information and entropy. The large entropy $S_{\mathrm{dS}}\sim M_{\mathrm{P}}^2/\Lambda$ of the de Sitter (dS) vacuum (with $M_{\mathrm{P}}$ being the Planck mass) acts as an entropic barrier, exponentially suppressing any quantum transitions that would otherwise destabilize the vacuum. This explains why the universe remains in a state with high entropy and relatively low CC. We then embed this principle within a pre-geometric theory of gravity, where the spacetime geometry and the Einstein–Hilbert action are not fundamental, but emerge dynamically from the spontaneous symmetry breaking of a larger gauge group, SO($1,4$)→SO($1,3$), driven by a Higgs-like field ${\varphi }^A$. In this mechanism, both $M_{\mathrm{P}}$ and $\Lambda$ are generated from more fundamental parameters. Crucially, we establish a direct correspondence between the vacuum expectation value (VEV) v of the pre-geometric Higgs field and the de Sitter entropy: $S_{\mathrm{dS}}\sim v$ (or $v^3$). Thus, the field responsible for generating spacetime itself also encodes its information content. The smallness of $\Lambda$ is therefore a direct consequence of the largeness of the entropy $S_{\mathrm{dS}}$, which is itself a manifestation of a large Higgs VEV v. The CC is stable for the same reason a large-entropy state is stable: the decay of such state is exponentially suppressed. Our study shows that new semi-classical quantum gravity effects dynamically generate particles we call “hairons”, whose mass is tied to the CC. These particles interact with Standard Model matter and can form a cold condensate. The instability of the dS space, driven by the time evolution of a quantum condensate, points at a dynamical origin for dark energy. This paper provides a comprehensive framework where the emergence of geometry, the hierarchy of scales and the quantum-information structure of spacetime are inextricably linked, thereby providing a novel and compelling path toward solving the CC problem. Full article

This study evaluates energy conversion and heat transfer in a germicidal chamber employing gold nanorods (AuNRs) irradiated with an infrared laser (808 nm, 0.8 W) to generate heat via localized surface plasmon resonance. The investigation focused on the preliminary selection of chamber materials and the geometry of the bottom surface supporting the AuNRs as the heat source in a photothermoablation application. A one-way multiphysics and multiscale approach was applied, integrating nanoscale heating phenomena with a macroscale fluid and heat flow. The validated 2D numerical model shows satisfactory agreement with experimental data and is suitable for further design analyses. Computational Fluid Dynamics (CFD) simulations were conducted to determine temperature and entropy distributions, mean and maximum temperatures, and Nusselt numbers, allowing the assessment of the energy conversion process under different configurations and AuNR dimensions. The results indicate that a configuration with a gradually descending stepped structure enhances interactions between nanoparticles and the fluid, increasing the internal energy and producing elevated temperatures. Under optimal conditions, a temperature rise of approximately 75 °C was achieved. These findings demonstrate that integrating material selection, surface geometry, and nanoparticle absorbance optimization can significantly improve the efficiency of bacterial inactivation in germicidal chambers. This study provides a framework for future investigations on fully three-dimensional multiscale and multiphysical modeling, as well as a targeted AuNR design to maximize the thermal performance. Full article

AlCoCrFeNi high-entropy alloys (HEAs) are promising materials due to their exceptional mechanical properties and thermal stability. This study employs first-principles calculations based on density functional theory (DFT) to investigate the phase stability and electronic properties of AlCoCrFeNi HEA. The atomic size difference (δ) was determined to be 5.44%, while the mixing enthalpy (ΔH_mix) was found to be −14.24 kJ/mol, and the valence electron concentration (VEC) was measured at 7.2, indicating a dual-phase structure consisting of the BCC and B2 phases. The formation energies indicated that the BCC phase exhibits the highest stability under typical conditions. The elastic properties were assessed, revealing Young’s modulus of 250 GPa, a shear modulus of 100 GPa, and a bulk modulus of 169 GPa, which suggest high stiffness. The alloy demonstrated a Poisson’s ratio of 0.25 and a G/B ratio of 0.59, indicating relatively brittle behavior. Microhardness simulations predicted a value of 604 HV0.2, which closely aligns with experimental measurements of 602 HV0.2 at 1300 W laser power, 532 HV0.2 at 1450 W, and 544 HV0.2 at 1600 W. The electronic structure analysis revealed metallic behavior, with the d-orbitals of Co, Fe, and Ni contributing significantly to the electronic states near the Fermi level. These findings offer valuable insights into the phase behavior and mechanical properties of AlCoCrFeNi HEA, which are crucial for the design of high-performance materials suitable for extreme engineering applications. Full article

This study proposes 4D-MRSimNet, a framework that employs attention mechanisms to focus on distinct dimensions. The approach applies enhancements to key responses in the spatial and spectral domains and provides a characterization of dynamic evolution in temporal domain, which extracts and integrates complementary emotional features to facilitate final classification. At the feature level, differential entropy (DE) and power spectral density (PSD) are combined within four core frequency bands ($\theta$, $\alpha$, $\beta$, and $\gamma$). These bands are recognized as closely related to emotional processing. This integration constructs a complementary feature representation that preserves both energy distribution and entropy variability. These features are organized into a 4D representation that integrates electrode topology, frequency characteristics, and temporal dependencies inherent in EEG signals. At the network level, a multi-resolution convolutional module embedded with SimAM attention extracts spatial and spectral features at different scales and adaptively emphasizes key information. A bidirectional GRU (BiGRU) integrated with temporal attention further emphasizes critical time segments and strengthens the modeling of temporal dependencies. Experiments show that our method achieves an accuracy of 97.68% for valence and 97.61% for arousal on the DEAP dataset and 99.60% for valence and 99.46% for arousal on the DREAMER dataset. The results demonstrate the effectiveness of complementary feature fusion, multidimensional feature representation, and the complementary dual attention enhancement strategy for EEG emotion recognition. Full article

Accurate identification of harmonic components is a prerequisite for addressing resonance risks in new energy power stations. Traditional Variational Modal decomposition (VMD) is susceptible to the influence of the modal decomposition order K and the penalty factor α when decomposing harmonic signals. This paper proposes an adaptive parameter selection method for VMD based on an improved Triangular Topology Aggregation Optimization (TTAO) algorithm. Firstly, the pre-set parameters of variational modal decomposition—modal order K and penalty factor α—exhibit strong coupling. Conventional optimization algorithms cannot effectively coordinate adjustments to both parameters. This paper employs an enhanced TTAO algorithm, whose triangular topology unit structure and dual aggregation mechanism enable simultaneous adjustment of modal order K and penalty factor α, effectively resolving their coupled optimization challenge. Using minimum envelope entropy as the fitness function, the algorithm obtains an optimized parameter combination for VMD to decompose the signal. Subsequently, dominant modal components are selected based on Pearson’s correlation coefficients for reconstruction, with harmonic parameters precisely identified using the Prony algorithm. Simulation results demonstrate that under a 20 dB noise environment, the proposed method achieves a signal-to-noise ratio (SNR) of 25.6952 for steady-state harmonics, with a root mean square error (RMSE) of 0.4889. The mean errors for frequency and amplitude identification are 0.055% and 3.085%, respectively, significantly outperforming methods such as PSO-VMD and EMD. Moreover, the runtime of our model is markedly shorter than that of the PSO-VMD algorithm, effectively resolving the symmetric trade-off between recognition accuracy and runtime inherent in variational modal decomposition. Full article

In communication environments with limited computing resources, securely and efficiently transmitting image data has become a challenging problem. However, most existing image data protection schemes are based on high-dimensional chaotic systems as key generators, which suffer from issues such as high algorithmic complexity and large computational overhead. To address this, this paper presents new designs for a 1D Sine Fractional Chaotic Map (1D-SFCM) as a random sequence generator and provides mathematical proofs related to the boundedness and fixed points of this model. Furthermore, this paper improves the traditional 2D compressive sensing (2DCS) algorithm by using the newly designed 1D-SFCM map to generate a chaotic measurement matrix, which can effectively enhance the quality of image recovery and reconstruction. Moreover, referring to the principle of gene mutation in biogenetics, this paper designs an image encryption algorithm based on DNA base substitution. Finally, the security of the proposed encryption scheme and the quality of image compression and reconstruction are verified through indicators such as key space, information entropy, and Number of Pixel Change Rate (NPCR). Full article

The shipping service industry plays a pivotal role in enhancing port competitiveness and fostering urban economic growth, yet limited studies systematically integrate its spatial temporal dynamics with the processes driving port–city synergy. This study constructs a three-dimensional analytical framework encompassing port operations, urban economic development, and shipping service industry agglomeration. Using data from 13 port cities in Jiangsu Province (2015–2023), we apply the entropy weight method, coupling coordination degree model, relative development model, and panel Tobit regression to evaluate interaction intensity, coordination patterns, and influencing factors. Results reveal a clear spatial gradient in coupling coordination, higher in southern Jiangsu and lower in the north, driven by disparities in economic foundations, port capacities, and service industry structures. In most cities, port operations and urban economies lag behind shipping service industry agglomeration, reflecting the predominance of low- and mid-end services. Port construction level, cargo and container throughput, economic development, openness, fixed asset investment, and population density significantly promote coordination, whereas R&D capacity shows no significant effect. The findings advance understanding of port–city service interlinkages and provide targeted policy recommendations for differentiated regional development, infrastructure enhancement, and upgrading toward high-end shipping services, with implications for maritime regions worldwide. Full article

Dacarbazine (DTIC) is a clinically important anticancer drug whose photosensitivity poses challenges for its stability and interactions with supramolecular hosts. Here, we investigate its complexation with the host 1,10-N,N′-bis-(β-D-ureidocellobiosyl)-4,7,13,16-tetraoxa-1,10-diazacyclooctadecane (TN), a hybrid urea–carbohydrate–diazacrown system, using combined experimental and computational approaches. While TN has been studied as a host molecule, its specific interactions with DTIC and the associated thermodynamic characteristics had not been characterized. Computational results (obtained at the density functional theory level (DFT)) indicate that TN primarily forms non-inclusion complexes, with DTIC engaging in hydrogen bonding with sugar units, urea bridges, and diazacrown ether moieties. Experimental ¹H NMR studies in D₂O confirmed these interaction patterns, showing notable chemical shifts for sugar protons. Conductometric measurements between 293 and 313 K allowed for the determination of formation constants and thermodynamic parameters. The results demonstrate that TN:DTIC complexation is spontaneous, exothermic, and enthalpy-driven, accompanied by decreased system entropy. Comparison with previous studies on cyclodextrin complexes shows that TN forms strong associations with DTIC, owing to its abundant donor–acceptor groups, which facilitate extensive hydrogen-bonding networks. These findings provide new insights into DTIC stabilization and highlight TN’s potential as a multifunctional platform for drug delivery. Full article