Search Results (377)

Surgical smoke generated by energy-based instruments during minimally invasive surgery severely degrades intraoperative visibility in laparoscopic procedures, prolonging operation time and elevating surgical risk. Although deep-learning desmoking methods have improved spatial clarity, most operate frame-by-frame and produce temporal artifacts—flicker, brightness drift, and color instability—that hinder clinical adoption. To our knowledge, no prior framework has jointly addressed spatial restoration and temporal consistency within a unified surgical smoke removal pipeline. We proposed DCP-TS, a unified spatiotemporal framework that coupled a Dark Channel Prior (DCP)-guided conditional generative adversarial network (cGAN) with an inference-time module integrating optical flow alignment, exponential moving-average luminance smoothing, and adaptive gamma correction. A key novelty was that this stabilizer was smoke-aware and operated entirely at inference time, requiring no retraining or post-processing, which distinguished it from generic video temporal-consistency methods. On laparoscopic colorectal surgery videos, DCP-TS achieved a PSNR of 23.39 dB, SSIM of 0.62, NIQE of 4.17, and BRISQUE of 23.66, outperforming DehazeFormer and Colores et al. across all metrics. Temporal analysis showed an approximate 28% reduction in inter-frame luminance variation, and a double-blind reader study with five experienced laparoscopic surgeons confirmed substantial improvements in brightness stability (4.37 vs. 2.86) and overall perceptual quality (4.18 vs. 3.51 on a 5-point Likert scale). The system ran at 22 fps with ~3.9 GB GPU memory on standard operating-room hardware, supporting real-time intraoperative deployment. DCP-TS demonstrated that physics-guided spatiotemporal modeling could transform frame-by-frame desmoking into a clinically promising, perceptually more continuous video stream. Full article

This work deals with questions of enhancing the scalability and security of linear chain Bitcoin by introducing a D-BTC (Domino Bitcoin) protocol, supported by a simply connected two-dimensional structure. The paper seeks to answer the question: can the linear topology of Bitcoin be replaced by a richer geometric structure that simultaneously (i) enlarges the number of valid positions where parallel mining can occur, and (ii) strengthens the asymptotic decay of the double-spend reversal probability? In the D-BTC protocol, the blocks, called B-dominoes (Bitcoin dominoes) are organized as a finite connected region subset of ${\mathbb{Z}}^2$ without holes, also called a lattice. Simple connectivity plays a central role in D-BTC and to mine a (valid) B-domino, a miner has to compute four PoW (Proof of Work), corresponding to cardinal directions, allowing them to add it to the frontier of the lattice, under the constraint that the new lattice is simply connected. We introduce a new deterministic consensus based on maximization of the lattice surface. By using a simple version of the isoperimetric inequality, we see that the frontier size grows as $\Omega \left(\sqrt{n}\right)$, where n is the lattice size. Following the Nakamoto’s heuristic, and under the honest majority assumption, a double-spending attack is successful with probability decaying exponentially in $k^2$, where k is the minimum Manhattan distance of the concerned B-domino from the lattice frontier. Additionally, we set up implementations and experiments to demonstrate the practical viability of the protocol with authentic gossip-based message propagation and complete Merkle tree verification. Full article

Lumpy skin disease (LSD), a vector-borne viral disease of cattle, re-emerged in Italy in June 2025 after six years of absence in Europe, affecting the island of Sardinia, which had previously been disease-free. The insular setting, the predominance of extensive cattle farming systems, and the rapid implementation of control measures provided a unique opportunity to investigate epidemic dynamics and evaluate vaccination effectiveness under field conditions. This study aimed to describe the epidemiological pattern of the first epidemic season (June–October 2025), estimate key transmission parameters, and assess vaccination effectiveness at the farm level. Confirmed outbreaks consistent with local transmission and notified between 20 June and 26 October 2025 were analyzed to characterize epidemic transmission dynamics, while vaccination effectiveness was assessed over an extended follow-up period through 31 December 2025. The between-farm basic reproduction number (R₀) was estimated from the early exponential growth phase using log-linear regression and doubling time calculations. Spatio-temporal clustering was assessed using Kulldorff’s scan statistic under a Poisson model, accounting for the population at risk. Vaccination effectiveness was evaluated using a time-dependent Cox proportional hazards model with a 21-day post-vaccination lag. A total of 79 outbreaks were confirmed, of which 68 were consistent with local transmission. Affected farms included a total of 3443 cattle, with morbidity, mortality, and case fatality rates of 14.4%, 7.0%, and 31.1%, respectively. The exponential growth phase lasted four weeks, with an estimated growth rate of 0.366 per week and a doubling time of 1.89 weeks. The estimated R₀ ranged from 1.55 to 1.92, depending on the assumed generation time, indicating moderate but sustained transmission. The median apparent spatial spread velocity was 4.8 km/day. Spatio-temporal analysis identified a single highly significant cluster in the central-eastern area, accounting for approximately 27% of outbreaks (RR = 58.06; p < 0.001). Vaccination was associated with a substantial reduction in outbreak risk (HR = 0.18; 95% CI: 0.06–0.51; p = 0.001), corresponding to an estimated effectiveness of approximately 82% at the farm level. The 2025 Sardinian epidemic was characterized by moderate transmissibility and strong spatial clustering during the early phase. Rapid implementation of vaccination was associated with a significant reduction in outbreak risk, even under conditions of high infection pressure. The integration of spatio-temporal analyses and time-dependent modeling proved essential to support evidence-based control strategies in newly affected regions. Full article

Control charts are used in statistical process control (SPC) to keep track of processes and identify changes in the way that they work. The control limits around the center line are uniform, which means they react the same way to changes going in either direction. In contrast, linear charts use imbalance to make it easier to identify individual data points. Therefore, using imbalance in the creation of control charts helps keep track of and maintain consistency with data that has a big impact on results when it goes beyond predetermined limits. In this study, we look at both one-sided and two-sided control charts by getting an explicit closed-form formula for the double-modified EWMA control chart’s average run length (ARL). The study is mostly about developing better ways to spot things using autoregressive fractionally integrated models and external variables (ARX and ARFIX) in the presence of exponential white noise. The ARL is used to test how well the proposed chart works in both modeling systems. The NIE method is used to prove that the explicit closed-form ARL formula works. The closed-form ARL expression is shown to be valid under the given ARX and ARFIX model assumptions, exponential white noise errors, stationarity conditions, and fixed one-sided or two-sided control limits. The results show that %RPC has a value below 10⁻⁶, and the computation times for the ARX and ARFIX models remain below 1.6 s and 3 s, respectively, after that point. To show how much better it is, the suggestion is compared to Type-EWMA control charts, such as classical and modified EWMA charts, in terms of run-length efficiency using ARL and SDRL, as well as overall efficiency by the relative index and with mean and standard deviation. The simulation study checks how well the proposed chart works in both symmetric two-sided and asymmetric one-sided frameworks. For the crude oil application, the one-sided upper control chart is used to detect abrupt upward price shifts, which may indicate precautionary demand shocks, market uncertainty, and risk spillovers to financial markets. According to the findings, the suggested chart is able to identify shifts at a faster rate than both traditional EWMA charts and modified EWMA charts, which demonstrates that it is beneficial in a real setting. Full article

The presence of residual moisture in tetrahydrofuran (THF) greatly limits its suitability for moisture-sensitive processes, including polymerization, Grignard chemistry, and fine-chemical production, where the allowable water concentration is generally lower than 10 mg/kg. Here, a hierarchical microporous/mesoporous composite adsorbent was prepared via extrusion molding, combining an LTA-type zeolite microporous framework with an amorphous mesoporous matrix. Characterization by XRD, FTIR, SEM, and pore analysis confirmed that the LTA crystal structure was retained while mesopores provided channels for mass transport. Static dehydration tests showed that the composite reduced THF water content from 70 mg/kg to 8.3 mg/kg, compared to 23.4 mg/kg for commercial 3A molecular sieves. The enhanced performance arises from micropores supplying uniform adsorption sites for deep dehydration and mesopores accelerating diffusion. Water vapor adsorption, kinetic and isotherm analyzes, regeneration, and competitive adsorption experiments indicated improved water accessibility and high selectivity, with kinetics described by a double-exponential model. The adsorbent remained stable over six adsorption–regeneration cycles. These results demonstrate that hierarchical microporous/mesoporous structures effectively achieve deep THF dehydration. Full article

Combinatorial optimization problems (COPs) are challenging for conventional computers because their solution spaces grow exponentially. To reduce exhaustive-search burden, hardware approaches have explored stochastic traversal of energy landscapes, including quantum annealers, CMOS Ising solvers, and probabilistic computing systems. However, quantum annealers require cryogenic operation, while CMOS Ising solvers typically rely on pseudorandom bitstreams or shared random pulses. A CMOS-compatible probabilistic bit with a physical random source is attractive for scalable optimization hardware. We present a CMOS p-bit that generates stochastic states from transistor device noise. The p-bit combines a transistor-noise random source, a correlated double sampling circuit, a calibrated comparator, and a 5-bit probability controller to convert local-field inputs into digitally tunable output probabilities. Because the random source is local to each p-bit and does not require PRNG state or seed assignment, the local random-source circuit in each p-bit does not need to grow larger as the number of p-bits increases, while system-level scaling is still governed by the p-bit count, weighted-sum logic, and interconnects. Prototype p-bit chips fabricated in a 180 nm CMOS process show 32-level output-probability control, pass the NIST Statistical Test Suite, and achieve 50 MHz updates with 6.95 pJ/bit at 50% output probability under a 1.8 V supply. Interfaced with FPGA-based weighted-sum logic, the prototype probabilistic circuit demonstrates invertible Boolean operation using a clamped gate network and performs integer factorization. Full article

We develop a fast Chebyshev spectral collocation method for a coupled system of nonlinear Klein–Gordon equations augmented by Caputo-type fractional memory integrals. The governing equations retain the classical second-order time derivative as the leading operator and incorporate weakly singular convolution integrals modelling viscoelastic memory damping. The spatial discretisation employs Chebyshev–Gauss–Lobatto collocation, while the temporal integration uses a Newmark scheme (${\beta }_{\mathrm{NM}}=1/4$) combined with an implicit–explicit linearisation in which the linear spatial operator is treated implicitly and the nonlinear terms are treated explicitly through a second-order extrapolation. This linearisation eliminates the need for Newton–Raphson iterations at each time step. To overcome the dense memory bottleneck arising from two distinct fractional orders $\alpha \ne \beta$, the convolution memory kernels are compressed by independent sum-of-exponentials approximations obtained from a double-exponential quadrature of the kernel’s integral representation, which significantly reduces the computational complexity of the history term. A rigorous stability estimate and a global convergence bound are established using a discrete Grönwall inequality. Numerical experiments confirm the theoretical temporal and spatial convergence rates and demonstrate the practical speed-up afforded by the sum-of-exponentials acceleration. A solitary wave collision scenario illustrates the method’s capability to capture asymmetric dispersive wakes generated by the fractional memory. Full article

This study investigates the 2.0 μm luminescence efficiency of Tm³⁺-doped crystals under 793 nm excitation. An analytical model decomposing laser slope efficiency into the quantum defect, fluorescence quantum efficiency (${\eta }_q$), and a mode matching factor was established, highlighting ${\eta }_q$ optimization as key. Using a high-precision spectral system, the comparative study of Tm:YAG and Tm:YAP crystals revealed unprecedented ${\eta }_q$ values of 184.8% and 190.6%, respectively. This breakthrough, corroborated by double-exponential decay kinetics, verifies the cross-relaxation-dominated quantum cutting mechanism. Superior performance of Tm:YAP crystal is attributed to its lower phonon energy, effectively suppressing non-radiative losses, providing a foundation for high-performance 2.0 μm lasers. Full article

In a seepage control system composed of geomembranes, the mechanical properties of the geomembrane welds directly determine the overall safety and durability of the system. To clarify the influence of welding parameters on the mechanical properties of the welds, this paper prepares weld specimens using different welding processes and systematically investigates the effects of welding temperature, welding pressure, and welding speed on the mechanical properties of double wedge welds in HDPE geomembranes through peel tests, shear tests, and DIC deformation measurement technology. The results indicate that the peel strength of HDPE welds has no linear correlation with welding pressure but there exists a threshold effect. The peel strength exhibits an exponential relationship with welding temperature and a Gaussian relationship with welding speed. The shear strength of the welds can be fitted by an exponential function for all three welding parameters. The coefficient of determination (R²) for each of the above fitting equations is higher than 0.9. Under different welding parameters, the yield strength of the double welds is slightly lower than that of the base material (approximately 89–94% of the base material), while the yield strain decreases more significantly (to 62–81% of the base material). Observations of the weld deformation distribution using DIC show that when the specimen elongation is below 11%, strain is concentrated near the weld; after reaching the yield strain, necking occurs; and the strain concentration shifts to the necking region. As the elongation further increases, significant plastic yield deformation occurs in the necking region, with a maximum strain of 500%. Full article

An empirical model of the kinetics of Hydrogen-Induced Cracking (HIC) in API 5L steels was derived using the best-fit equation for experimental data obtained from cathodic charging tests. The model represents the growth of both individual and interconnecting cracks, using a double exponential equation known as the Gumbel distribution. Current density was the main independent input variable, as it is related to the hydrogen influx during the cathodic charging experiment. The results indicated that in the initial hours of cathodic charging most of the available HIC nucleation sites are activated, the growth of these individual cracks being the main contribution to the overall kinetics. Further crack growth is due to the interconnection of individual cracks, decreasing the growth rate until it becomes nearly zero. The proposed model is used in a simulation algorithm that accurately describes the complete HIC kinetics, for both short- and long-term hydrogen charging exposure, reproducing the effects of applied current density on the total cracked area and growth rates. Finally, the simulation algorithm adequately predicts the spatial distribution of HIC in a bidimensional plane that emulates the detection of HIC by C-scan ultrasonic inspection. Full article

Noble metal nanostructures provide versatile platforms for light manipulation through localized surface plasmon resonances (LSPRs). Among them, triangular silver nanoplates (AgNPTs) exhibit strong field-enhancement and spectral tunability, yet assembling them reproducibly on solids is challenging. We report a two-step functionalization strategy for constructing ordered AgNPT dimers on silica substrates, combining 3-aminopropyltriethoxysilane (APTES) anchoring with 1,4-butanedithiol bridging. AFM reveals face-to-face dimers with well-defined sub-nanometer gaps. Large-area AFM statistics collected over multiple regions (N = 80 nanoplates per condition) confirm reproducible and selective vertical dimerization. Extinction spectroscopy reveals sequential dielectric and coupling effects: thiol adsorption red-shifts the main resonance from 700 to 780 nm because of increased local refractive index and near-field damping, whereas dimerization partially restores it to ≈750 nm, consistent with plasmon hybridization within rigid ∼0.7 nm molecular gaps, where nonclassical moderation may occur but classical hybridization fully explains the observed shifts. Concomitantly, the extinction intensity doubles, following an exponential growth toward saturation during assembly. Surface-enhanced Raman scattering (SERS) measurements using 4-mercaptobenzoic acid (4-MBA) confirm a fourfold increase in the SERS enhancement factor from monolayer to bilayer, consistent with near-field coupling and hotspot formation at interplate junctions. Quantitative plasmon sensitivity analysis yields comparable results between experiments and finite-difference-time-domain simulations, confirming that the observed spectral shifts arise from near-field coupling and dielectric modulation rather than ensemble effects. This reproducible methodology enables precise tuning of NPT orientation, spacing, and optical response, providing a robust platform for enhanced sensing, SERS, and nanophotonic device engineering. Full article

Optical density (OD)-based cell growth measurement is commonly used in high-throughput screening (HTS) during drug discovery or when deciphering the pharmaceutical mechanism of action. While resuspending the cells via a mixing step is often assumed to be necessary prior to OD measurement, its essentiality in HTS workflows has not been systematically verified. Here, through the measurement of the growth of several strains of the microbial yeast Schizosaccharomyces pombe cells, we compared the overall growth dynamics between samples that have been mixed and not mixed. Using statistical quantification by a two-tailed paired t-test followed by multiple comparison corrections, we concluded from the comparison of the doubling time of cells growing in the exponential phase that mixing did not significantly affect the biological interpretation compared to unmixed samples. Doubling time quantification between mixed and unmixed samples showed a difference of approximately 10% on average based on the assessment of the growth of eight strains. As such, if the experimental outcome can accommodate this level of variability, incorporating a mixing step before OD determination would not be necessary. These observations support the simplification of HTS processes, improving the cost efficacy and process efficiency of readouts, yet maintaining the accuracy of data acquisition. Full article

Multiview video coding grows exponentially with the number of views, and VVC-based systems face particularly severe computational burdens from exhaustive inter-view prediction searches. We propose VVC-MV-CM, a complexity-managed multiview extension of VVC that combines rule-based pre-screening with CNN-based adaptive inter-view prediction bypassing within a two-stage decision engine. Performance trends are observed across 19 test sequences covering planar, arc, and spherical camera configurations under all-view and selected-view encoding modes. For planar all-view configurations, VVC-MV-CM-A achieves −52.7% BD-rate relative to MIV-A with 68% encoding time reduction. Arc arrangements yield competitive performance at −1.26% (all-view) and approximately −1% (selected-view) BD-rate. Spherical configurations demonstrate −19.8% (all-view) and −15.0% (selected-view) BD-rate gains, driven by multi-reference redundancy and temporal prediction prioritization. View density analysis reveals a 4.8 percentage-point compression difference between all-view and selected-view configurations, corresponding to approximately 2.4% efficiency gain per doubling of camera count. The proposed codec achieves 1.17–1.46× encoding time relative to MIV anchors with 18–36% decoding speedup, establishing configuration-adaptive prediction as an effective and deployable approach to multiview video coding across a wide range of geometric complexities and view-sampling densities. Full article

Accurate prediction of landslide displacement is crucial for hazard prevention. However, recurrent neural network (RNN) models have limitations in simultaneously capturing lag time and feature importance, and their black-box nature limits their interpretability. Moreover, the performance of single models varies across different deformation stages, especially during acceleration. To address these challenges, we propose an interpretable deep reinforcement learning-based adaptive ensemble (DRL-AE) framework. The method employs Seasonal and Trend decomposition using Loess to separate cumulative displacement into trend and periodic components. Trend and periodic sequences are predicted using double exponential smoothing and three RNN variants, respectively. An improved Convolutional Block Attention Module (ICBAM) enhances periodic feature extraction and provides temporal–spatial interpretability. The Deep Deterministic Policy Gradient algorithm adaptively integrates multi-model predictions in response to evolving environmental conditions. To validate the DRL-AE, a case study is conducted on the Baijiabao landslide in Zigui County, China. The results indicate that the DRL-AE substantially enhances prediction accuracy. For periodic displacement, it reduces MAE by 10.02% and RMSE by 6.65%, and increases R² by 4.27% compared with the ICBAM-GRU model. The results also confirm the effectiveness of ICBAM in feature extraction, and the generated heatmaps provide intuitive interpretability of the relevant triggering factors. Full article

Atrial septal defect (ASD) affects 1:11.3 children in some US states; however, the antecedents of these trends are yet to be identified. A total of 1882 ASD rates (ASDRs) for 2003–2020 were sourced from the National Birth Defects Prevention Network reports. A total of 406,893 ASDs are reported. Substance (cigarettes, binge alcohol, cannabis, cannabinoids, analgesics, cocaine) exposure data were taken from the National Survey of Drug Use and Health. Income and ethnicity data were derived from the US Census. Adjustment was performed by mixed effects, survey and generalized additive regression. Causal analysis was by inverse probability weighting and E-values. Data were analyzed in RStudio. The highest ASDR of 884/10,000 live births was amongst Non-Hispanic Asians and Pacific Islanders in Nevada in 2016–2020. The 2005–2018 median ASDR rose >12-fold in Nevada and New Mexico, >6-fold in New York, and 4.2-fold nationally 1989–2020; it doubled in NY from 2012–2016 to 2016–2020. The average state ASDR rose supra-exponentially (p = 0.0075) and was associated with higher cannabis use states (p = Zero, Cohen’s D = 1.24), apparently driven by cannabis legalization (p = Zero). Estimated exposures to Δ9THC, cannabidiol and cannabigerol were implicated (from p = 2.67 × 10^–68). Cannabis-legal states were compared with others (mean ASDR (C.I.) 178.15 (131.68, 224.62) vs. 74.28 (70.60, 77.96), p = Zero; O.R. 1.82 (1.81, 1.84), E-values 3.04 (lower C.I. 3.02), Cohen’s D 1.29 (0.96, 1.62)). Overall, 29/39 (74.4%) E-value estimates were >4; 39/39 (100%) were >1.25. Cannabis, cannabinoids and cannabis legalization are strong candidates for driving the US ASDR supra-exponentially. Estimates of many cannabinoids, including cannabidiol, Δ9THC, and cannabigerol, are implicated. The results are consistent with other large epidemiological studies. The importance of the results is magnified by the increasing legalization and penetration of cannabinoids into the US population. Since therapeutic abortion is not practiced for ASD, it may be used as a bellwether index of heritable transgenerational cannabinoid genotoxicity and epigenotoxicity associated with cannabinoid exposure. Full article