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This study aimed to assess the influence of cortical bone thickness, bone density, and residual ridge morphology in the posterior mandibular area on the accuracy of implant placement using tooth-supported digital guides. The research included 75 implants from 55 patients. Each patient underwent a cone-beam computed tomography (CBCT) scan for image analysis. Simplant® Pro 17 software (SIMPLANT Pro 17.01) was utilized to measure cortical bone thickness, bone density, and residual ridge morphology at the implant sites. Subsequently, 3Shape Trios software (3Shape TRIOS Design Studio 1.7.19.0) was applied to delineate optimal implant positions and design tooth-supported surgical guides. After implant treatment, the linear and angular deviations from the planned placement were quantified. Multiple linear regression, Kruskal–Wallis test, Conover–Iman test, and Bonferroni adjustment were conducted to investigate the impact of bone characteristics on implant placement precision. The tooth-supported digital guides used in this study were sufficient to fulfill the precision criteria for implant treatment. Bone density was found to significantly influence the buccal-lingual angular deviation, mesio-distal linear deviation, and mesio-distal angular deviation (p < 0.05). Additionally, significant variances were noted in the coronal deviation, apical deviation and depth deviation in buccal-lingual orientation, coronal deviation, and apical deviation in mesio-distal orientation across various residual ridge morphologies (p < 0.05). Low bone density and S-shape morphology may affect the accuracy of implant placement using tooth-supported surgical guides. [ABSTRACT FROM AUTHOR]

The saltwater‐freshwater mixing zones in coastal aquifers can host complex physical exchange and biogeochemical transformations. The land‐sourced dense contaminant plumes could be transferred into the mixing zone of the saltwater wedge due to the density effect prior to discharge to the sea. However, the mixing process between dense contaminants and the saltwater wedge has not received much attention, largely due to the lack of physical evidence. This study used laboratory experiments and numerical simulations to investigate the transport and discharge behaviors of variable‐density contaminant plumes in tidally influenced unconfined coastal aquifers. Results demonstrate that the highly dense contaminants mix with the underlying saltwater and finally merge with the saltwater wedge. This process significantly extends the contaminant discharge durations, thereby reducing the peak value of contaminant efflux. The dense contaminants are elongated along the landward margin of the saltwater wedge, leading to a larger spreading area (Ms) than that of constant‐density contaminants. The sensitivity analysis indicates that the high density of contaminants acts as a trigger to induce the mixing of them and wedges. The higher hydraulic conductivity, lower dispersivities and reduced inland freshwater flux significantly increase the residence times (Rt) and discharge duration (Dt) by enhancing the mixing of dense contaminants with seawater. In contrast, both Rt and Dt values are not only non‐monotonic functions of tidal amplitudes but also less sensitive to tidal effects. Compared with the non‐tidal condition, however, the addition of tides significantly increases both Rt and Ms values of dense contaminant plumes. The results presented herein provide valuable insights into the mechanisms of dense contaminants mixing into saltwater wedges, which could guide practitioners in designing effective strategies to protect coastal environments from land‐sourced contaminants. Key Points: The process of dense contaminant plumes mixing into the saltwater wedge is experimentally observed for the first timeThe high density of contaminants triggers their mixing with the saltwater wedge, thereby extending their residence timesThe gravity and modified Rayleigh numbers assess how hydrogeological parameters affect the mixing of dense contaminants into saltwater wedges [ABSTRACT FROM AUTHOR]

Geological formations with fractures are frequently encountered in various research fields. Accurately characterizing these fractured media is of paramount importance when it comes to tasks that demand precise predictions of liquid flow and solute transport within them. Since directly measuring fractured media poses inherent challenges, data assimilation (DA) techniques are typically employed to derive inverse estimates of media properties using observable state variables. Nonetheless, the considerable difficulties arising from the strong heterogeneity and non‐Gaussian nature of fractured media have diminished the effectiveness of existing DA methods. In this study, we formulate a novel DA approach known as parameter estimator with deep learning (PEDL) that harnesses the capabilities of DL to capture nonlinear relationships and extract non‐Gaussian features. To evaluate PEDL's performance, we conduct three case studies, comprising two numerical cases and one real‐world case. In these cases, we systematically compare PEDL with three widely used DA methods: ensemble smoother with multiple DA (ESMDA), iterative local updating ES (ILUES), and ES with DL‐based update (ESDL). Notably, in the problems characterized by highly non‐Gaussian features, ESMDA and ILUES produce significantly divergent results. Conversely, employing the DL‐based update, ESDL demonstrates improved performance. However, its estimation uncertainty remains high, potentially attributable to ESDL's updating mechanism. Comprehensive analyses confirm PEDL's validity and adaptability across various ensemble sizes and DL model architectures. Moreover, even in scenarios where structural difference exists between the accurate reference model and the simplified forecast model, PEDL adeptly identifies the primary characteristics of fracture networks. Key Points: A deep learning‐based data assimilation method, named parameter estimator with deep learning (PEDL), is proposed to characterize fractured media with highly non‐Gaussian featuresPEDL outperforms three popular data assimilation methods based on deep learning or the Kalman formulaComprehensive analyses confirm PEDL's validity and adaptability across various ensemble sizes and DL model architectures [ABSTRACT FROM AUTHOR]

Seawater intrusion (SI) poses a substantial threat to water security in coastal regions, where numerical models play a pivotal role in supporting groundwater management and protection. However, the inherent heterogeneity of coastal aquifers introduces significant uncertainties into SI predictions, potentially diminishing their effectiveness in management decisions. Data assimilation (DA) offers a solution by integrating various types of observational data with the model to characterize heterogeneous coastal aquifers. Traditional DA techniques, like ensemble smoother using the Kalman formula (ESK) and Markov chain Monte Carlo, face challenges when confronted with the non‐linearity, non‐Gaussianity, and high‐dimensionality issues commonly encountered in aquifer characterization. In this study, we introduce a novel DA approach rooted in deep learning (DL), referred to as ESDL, aimed at effectively characterizing coastal aquifers with varying levels of heterogeneity. We systematically investigate a range of factors that impact the performance of ESDL, including the number and types of observations, the degree of aquifer heterogeneity, the structure and training options of the DL models. Our findings reveal that ESDL excels in characterizing heterogeneous aquifers under non‐linear and non‐Gaussian conditions. Comparison between ESDL and ESK under different experimentation settings underscores the robustness of ESDL. Conversely, in certain scenarios, ESK displays noticeable biases in the characterization results, especially when measurement data from non‐linear and discontinuous processes are used. To optimize the efficacy of ESDL, attention must be given to the design of the DL model and the selection of observational data, which are crucial to ensure the universal applicability of this DA method. Key Points: Non‐linearity and non‐Gaussianity in coastal aquifer characterization problems pose challenges to traditional data assimilation (DA) methodsWe propose to address these issues with a deep learning‐based DA method called ESDLVarious factors influencing the performance of ESDL are systematically investigated [ABSTRACT FROM AUTHOR]

In view of the insufficient strength,vibration deformation and fracture of the sley system of the beating-up mechanism in the existing loom,the structure of the sley system is optimized. According to the finite element modal calculation theory,the vibration equation of the new multi-degree-of-freedom sley foot system is established. The modal and mode of the sley foot are analyzed in ANSYS. The rigid-flexible coupling model of the new beating-up mechanism is established in ADAMS,and the flexible dynamic simulation and stress analysis are carried out. The results show that the new sley system is easy to manufacture and install,and can meet the requirement of mechanical properties of beating-up motion. The possibility of resonance deformation of the new sley is very small. The beating-up acceleration curve of the flexible sley foot fluctuate more than that of the rigid body,and the change of the loom speed have a great influence on the acceleration of the beating-up mechanism. The stress distribution of the main body of the new sley is less than that of the original sley.

Introduction. A super cold surge during the winter of December 2015 to February 2016 was widely reported by Chinese media. This cold surge originated from the Siberian High and swept [...]

All-optical terahertz (THz) modulators have received tremendous attention due to their significant role in developing future sixth-generation technology and all-optical networks. Herein, the THz modulation performance of the Bi2Te3/Si heterostructure is investigated via THz time-domain spectroscopy under the control of continuous wave lasers at 532 nm and 405 nm. Broadband-sensitive modulation is observed at 532 nm and 405 nm within the experimental frequency range from 0.8 to 2.4 THz. The modulation depth reaches 80% under the 532 nm laser illumination with a maximum power of 250 mW and 96% under 405 nm illumination with a high power of 550 mW. The mechanism of the largely enhanced modulation depth is attributed to the construction of a type-II Bi2Te3/Si heterostructure, which could promote photogenerated electron and hole separation and increase carrier density dramatically. This work proves that a high photon energy laser can also achieve high-efficiency modulation based on the Bi2Te3/Si heterostructure, and the UV-Visible control laser may be more suitable for designing advanced all-optical THz modulators with micro-level sizes. [ABSTRACT FROM AUTHOR]

Large‐scale multi‐heterostructure and optimal band alignment are significantly challenging but vital for photoelectrochemical (PEC)‐type photodetector and water splitting. Herein, the centimeter‐scale bismuth chalcogenides‐based cascade heterostructure is successfully synthesized by a sequential vapor phase deposition method. The multi‐staggered band alignment of Bi2Te3/Bi2Se3/Bi2S3 is optimized and verified by X‐ray photoelectron spectroscopy. The PEC photodetectors based on these cascade heterostructures demonstrate the highest photoresponsivity (103 mA W−1 at −0.1 V and 3.5 mAW−1 at 0 V under 475 nm light excitation) among the previous reports based on two‐dimensional materials and related heterostructures. Furthermore, the photodetectors display a fast response (≈8 ms), a high detectivity (8.96 × 109 Jones), a high external quantum efficiency (26.17%), and a high incident photon‐to‐current efficiency (27.04%) at 475 nm. Due to the rapid charge transport and efficient light absorption, the Bi2Te3/Bi2Se3/Bi2S3 cascade heterostructure demonstrates a highly efficient hydrogen production rate (≈0.416 mmol cm−2 h−1 and ≈14.320 µmol cm−2 h−1 with or without sacrificial agent, respectively), which is far superior to those of pure bismuth chalcogenides and its type‐II heterostructures. The large‐scale cascade heterostructure offers an innovative method to improve the performance of optoelectronic devices in the future. [ABSTRACT FROM AUTHOR]