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Physically secure and fog-enabled lightweight authentication scheme for WBAN
Abstract Wireless Body Area Networks (WBANs) are vital for healthcare, fitness monitoring, and remote patient care by means of combining sensors and wearable technologies for data collection and transmission. However, ensuring secure communication in WBANs remains a critical challenge and is generally insecure against the manipulation of data, breaches of privacy, and unauthorized access. Current authentication methods are vulnerable to security risks and have a significant computational burden. The above-said shortcomings are addressed by proposing a lightweight, physically secure, fog-enabled authentication scheme that guarantees data privacy and system resilience by integrating Physically Unclonable Functions ( $$\:PUFs$$ ) and Fog Computing. This approach involves two phases: WBAN node registration and secure anonymous authentication. The proposed system incurs a reduction in computational overhead of 64.33% and communication overhead of 29.58% compared to existing protocols. Short-life session keys are used to achieve mutual authentication between WBAN sensors and monitoring devices. The proposed scheme is analyzed using BAN logic against attacks on impersonation, replay, and unauthorized access using BAN logic. Its practical effectiveness is confirmed via informal analysis, which shows that it is a scalable and efficient solution for practical WBAN environments.
Battery prices are falling, so why are electric cars still so expensive?
A convergence metric for counting statistics in time-resolved small angle neutron scattering
This work introduces a model-independent, dimensionless metric for predicting optimal measurement duration in time-resolved small-angle neutron scattering using early-time data. Built on a Gaussian process regression framework, the method reconstructs scattering profiles with quantified uncertainty, even from sparse or noisy measurements. Demonstrated on the EQ-SANS instrument at the Spallation Neutron Source, the approach generalizes to general SANS instruments with a two-dimensional detector. A key result is the discovery of a dimensionless convergence metric revealing a universal power-law scaling in profile evolution across soft matter systems. When time is normalized by a system-specific characteristic time t*, the variation in inferred profiles collapses onto a single curve with an exponent between −2 and −1. This trend emerges within the first ten time steps, enabling early prediction of measurement sufficiency. The method supports real-time experimental optimization and is especially valuable for maximizing efficiency in low-flux environments such as compact accelerator-based neutron sources.
Hybrid enrichment of Ti13Nb13Zr alloy with zinc ions and silver nanoparticles using a combination of micro-arc oxidation and electrophoretic deposition
A sit in the sauna can save endangered frogs
Time-dependent density-functional study of intermolecular Coulombic decay for 2a1 ionized water dimer
A real-space, real-time time-dependent density functional theory with Ehrenfest dynamics is used to simulate intermolecular Coulombic decay (ICD) processes following the ionization of an inner-valence electron. The approach has the advantage of treating both nuclear and electronic motions simultaneously, allowing for the study of electronic excitation, charge transfer, ionization, and nuclear motion. Using this approach, we investigate the decay process of the 2a1 ionized state of the water dimer. For the 2a1 vacancy in the proton donor water molecule, ICD is observed in our simulations. In addition, we have identified a novel dynamical process: at the initial stage, the proton generally undergoes a back-and-forth motion. Subsequently, the system may evolve along two distinct pathways: in one, no proton transfer occurs; in the other, the proton departs again from its original position and ultimately completes the transfer process. In contrast, when the vacancy resides in the proton acceptor water molecule, no proton transfer occurs and ICD remains the sole decay channel.
B1 corrected T1 mapping in the differentiation and prediction of breast cancer
Teacher defies ban on evolution education in the 1920s
Temperature impact on thermo-electrochemical behavior of silicon-based photoelectrochemical flow cells
Increased attention has been focused on photoelectrochemical redox flow cell systems as a potential integrated technology for simultaneously converting and storing intermittent solar energy. Photoelectrochemical voltammetry and impedance spectroscopy tests were conducted using a single-junction c-Si photoelectrode immersed in Fe(CN)63−/4− under thermal load to evaluate the temperature effect on the thermo-electrochemical performance of silicon-based photoelectrochemical cells. It was observed that the current density significantly increased with temperature as a consequence of enhanced kinetics and electrolyte characteristics, while a detriment to the potential output was identified and predominantly attributed to variations of photovoltaic characteristics. Moreover, it was demonstrated that mass transport enhancement reaches its maximum contribution at 45 °C, followed by a slowdown in the observed trends at higher temperatures, which may lead to improved design development and optimized working conditions.
Optimization of TCN-BiLSTM for dissolved oxygen prediction based on improved sparrow search algorithm
African countries must rethink health-care financing
On the practical applicability of DM21 neural-network DFT functional for chemical calculations: Focus on geometry optimization
Density functional theory is the workhorse of present-day quantum chemistry thanks to its good balance between calculation accuracy and speed. In recent years, several neural network-based exchange–correlation functionals have been developed, with DM21, developed by Google DeepMind, being the most recognizable among them. In this study, we focus on evaluating the efficiency of DM21 functional on the task of optimizing molecular geometries and investigate how the non-smooth behavior of neural network-predicted exchange–correlation energy and potential affects the final geometry precision. We implement geometry optimization for the DM21 functional in PySCF and compare its performance with traditional functionals on various benchmarks. Our findings reveal that numerical noise coming from the neural network outputs contaminates numerical nuclear gradients required for geometry optimization. We also found that a numerical differentiation step in the range of 0.0001–0.001 Å is required to obtain sufficiently smooth nuclear gradients. Furthermore, we show that the non-smoothness of DM21 can be reproduced by adding random normally distributed noise to local energies of an analytical SCAN functional, allowing one to efficiently estimate the optimal numerical differentiation step for geometry optimization of a given molecule. Our findings show that DM21 does not outperform analytical functionals in the accuracy of optimized molecular geometries and is significantly slower, which limits its practical applicability to chemical calculations.
Optimization and predictive performance of fly ash-based sustainable concrete using integrated multitask deep learning framework with interpretable machine learning techniques
People are having fewer babies: Is it really the end of the world?
Bandgap opening induced by electron localization in graphene antidot lattices
Graphene antidot lattices (GALs) have garnered significant attention for their potential in semiconductor applications, yet the origin of bandgap opening remains controversial. Combining the octet rule, we propose a low-parameter physical model with weighted information entropy to quantitatively determine the electron density distribution, and the tight-binding parameters are obtained from the occupancy numbers based on the maximum entropy method. The results from our model reveal a complex bandgap opening mechanism in zigzag-edged hexagonal GALs (ZH-GALs), where specific inter-ribbon connections and quantum confinement cause the localization of π-electrons between antidots, leading to the elimination of energy levels degeneracy. We also observe that the anisotropy of rectangular ZH-GALs is enhanced as the defect radius increases, indicating a transition from GALs-like to graphene nanoribbons-like bandgap behavior. This study tells us that more than 1/9 ZH-GALs have considerable bandgaps, addressing the deficiency in band structure engineering between regimes dominated by defect scattering and quantum confinement.
Risk factors for invasive fungal infections in adult patients with hematological malignancies and/or stem cell transplant: a systematic review and meta-analysis
Kinetic analysis of phase transformations during continuous heating: Crystallization of glass-forming liquids
Phase transformations are widely studied using continuous-heating experiments. In isothermal studies, their kinetics are often described using the Johnson–Mehl–Avrami–Kolmogorov (JMAK) rate equation. For continuous-heating studies, the same analysis has only been applied numerically. Here, a JMAK rate equation for phase transformations during continuous heating is derived. The equation is applied to the crystallization of glass-forming liquids with different kinetic behaviors and validated by comparison to experimental data and numerical simulations for the crystallization of glassy Fe80B20 (at. %). The application of Kissinger’s method to analyze crystallization kinetics is subsequently justified for well-defined conditions. However, it is shown that the non-Arrhenius temperature dependence of crystal growth rates in glass-forming liquids can be more precisely determined by using the present model to fit the peak position, shape, and height for a series of crystallization exotherms. The implications of these analytical expressions for the design and development of glass-forming systems for a broad range of applications are considered, and the application of this JMAK rate equation to other transformations during continuous heating is explored.
Artificial intelligence model for predicting early biochemical recurrence of prostate cancer after robotic-assisted radical prostatectomy
Long-range proton transfer mechanism and fluorescence properties of HQBT chromophore: TD-DFT/CASSCF study
Herein, the intramolecular long-range proton transfer reaction mechanism of the HQBT chromophore in different solvents is investigated employing density functional theory and complete active space self-consistent field methods. The results show that HQBT successfully undergoes the first excited state intramolecular proton transfer (ESIPT) under photoexcitation. Subsequently, through the first cis–trans isomerization process of dihedral angle torsion, a minimum energy conical intersection (MECI) is formed between the ground state and the first excited state. The MECI continues the second cis–trans isomerization to generate the trans-keto structure. At this point, a new intramolecular hydrogen bond is formed and experiences the second ESIPT, thus achieving long-range transport of hydrogen protons. This careful theoretical research has significant guiding significance for the design of intelligent and efficient tautomeric molecular switches in the future.