Width Limit Research Articles

Realizing submeter spatial resolution for Raman distributed fiber-optic sensing using a chaotic asymmetric paired-pulse correlation-enhanced scheme

The sensing spatial resolution and signal-to-noise ratio (SNR) of Raman distributed optical fiber sensors are limited by the pulse width and weak Raman scattering signals. Notably, the sensing spatial resolution cannot exceed the order of meters at several kilometers sensing distances. To break through this physical bottleneck, a novel, to our knowledge, Raman scattering model based on paired-pulse sensing is constructed. The fundamental origins of the observed limited spatial resolution of conventional schemes are analyzed, and a chaotic asymmetric paired-pulse correlation-enhanced scheme for Raman distributed fiber-optic sensing is proposed and experimentally demonstrated. The proposed scheme uses a chaotic asymmetric paired-pulse as the sensing signal and extracts the light intensity information of each data point of the sensing fiber, which carries the random undulation characteristics of chaotic time series, based on the time-domain differential reconstruction method. This scheme overcomes the pulse width limitation of spatial resolution via correlation and demodulation, enhances the correlation characteristics between the temperature-modulated Raman scattered light field and detection signal, and improves the SNR. Finally, a sensing performance of 10 km, a spatial resolution of 30 cm, and an SNR of 6.67 dB are realized in the experiment. This scheme provides a new research idea for a high-performance Raman distributed optical fiber sensing system.

New Guidelines for the Treatment of the Alveolar Septum in Immediate Dentoalveolar Restoration Technique Associated with Osseodensification: A Case Series.

Achieving initial implant stability at the molar extraction site can be challenging due to bone width, quality, and anatomical limitations like the maxillary sinus and inferior alveolar nerve. The implant placement should achieve precise centralization with the interradicular septum to facilitate implant stabilization and preservation/regeneration of the alveolar ridge post-extraction with bone grafting. Immediate non-occlusal crown placement aids peri-implant tissue maturation for the desired outcome. This retrospective series introduces guidelines for treating sockets with alveolar septum types. The approach involves immediate dentoalveolar restoration (IDR) and osseodensification (OD) with an autogenous graft for bone preservation. A new protocol for the treatment of the molar interradicular septum during immediate implant placement and/or alveolar ridge preservation/reconstruction was applied in 12 cases. Preoperative and postoperative cone-beam computed tomographic examinations were performed. Socket width was measured and compared between timepoints. The mean preoperative and postoperative (mean, 23.58 ± 9.70 months) socket widths were 9.51 ± 0.40 and 11.16 ± 0.30 mm, respectively (17.35% increase; p <0.05). IDR with OD is a predictable approach to the treatment of molar sockets during implant placement.

Explaining neural scaling laws

The population loss of trained deep neural networks often follows precise power-law scaling relations with either the size of the training dataset or the number of parameters in the network. We propose a theory that explains the origins of and connects these scaling laws. We identify variance-limited and resolution-limited scaling behavior for both dataset and model size, for a total of four scaling regimes. The variance-limited scaling follows simply from the existence of a well-behaved infinite data or infinite width limit, while the resolution-limited regime can be explained by positing that models are effectively resolving a smooth data manifold. In the large width limit, this can be equivalently obtained from the spectrum of certain kernels, and we present evidence that large width and large dataset resolution-limited scaling exponents are related by a duality. We exhibit all four scaling regimes in the controlled setting of large random feature and pretrained models and test the predictions empirically on a range of standard architectures and datasets. We also observe several empirical relationships between datasets and scaling exponents under modifications of task and architecture aspect ratio. Our work provides a taxonomy for classifying different scaling regimes, underscores that there can be different mechanisms driving improvements in loss, and lends insight into the microscopic origin and relationships between scaling exponents.

A case study on early-age cracking of high-strength concrete construction by coupled thermal-mechanical analysis and field monitoring

The use of high-strength concrete (HSC) in constructing high-rise reinforced concrete buildings and their deep foundations is becoming more popular because of the significant reduction in member size and, thus, self-weight in structural members. However, the higher cement content in HSC may result in increased heat of hydration and autogenous shrinkage, growing concerns about forming early-age cracks in HSC structural members. This case study assessed the early-age cracking of HSC in bored pile construction of high-rise buildings. A nonlinear transient coupled thermal-mechanical response analysis was performed to evaluate the potential of early-age cracking caused by the heat of hydration generated in the HSC using ABAQUS and two subprograms, USDFLD and UEXPAN. A 3-D finite element model was developed considering thermal effects, time-dependent evolution of early-age strength and stiffness of HSC, crack strain development, autogenous shrinkage, and stress-relaxation in the analysis. Field measurements were carried out on a 3-m diameter bored pile constructed using Grade C50/60 HSC (fck,cube = 60 MPa) for a high-rise building project in Hong Kong to evaluate the validity of the numerical model. Optical fiber sensors were used to monitor the temperature changes of the pile for approximately 380 hours after casting. The temporal and spatial temperature distributions measured in the field measurements agree well with the simulations. Large thermal gradients and high tensile stress zones were observed across the horizontal cross-sections of the pile, resulting in the formation of vertical annular cracks. Based on the results of the analysis, the maximum crack widths were predicted and compared with the durability crack width limits.

Analysis and Enlightenment on the Relationships between Two Kinds of Cutter Life Evaluation Indexes and Installation Radius: A Case Study

Accurate evaluation of cutter life at different cutter positions on the cutter head is helpful to determine the time of cutter change and reduce the time of cutter wear measurement, which is of great significance to improve the tunneling efficiency of tunnel boring machine (TBM) projects. Unfortunately, there is no unified cutter life evaluation index now. The field data of cutter wear are collected from a section of a long TBM tunneling water conveyance tunnel in China. Two kinds of cutter life evaluation indexes (based on the radial wear extent of cutter rings and replacement number of cutter rings) are selected and the variation rule between these two kinds of indexes with cutter installation radius is statistically analyzed. The results show that the regression relationships between the two kinds of cutter life evaluation indexes and installation radius mainly present linear functions and quadratic functions. Those regression relationships are affected by factors such as wear type, installation angle, cutter spacing, influence width, and allowable limit wear extent of cutter rings. Considering the calculation accuracy of the evaluation index, the actual working conditions of the disc cutter, and ignoring the influence of tunnel diameter, it is recommended to preferentially choose the radial wear extent of cutter rings per unit rolling distance as the evaluation index of cutter life. The research results can provide a reference for the selection of cutter life evaluation index, prediction of disc cutter life at different cutter positions, and establishment of cutter life prediction mode.

New Empirical Source-Scaling Laws for Crustal Earthquakes Incorporating Fault Dip and Seismogenic-Thickness Effects

Abstract New global source-scaling relations for the aspect ratio and rupture area for crustal earthquakes that include the width-limited effect and a possible free-surface effect are derived using a global dataset of finite-fault rupture models. In contrast to the commonly used scaling relations between moment magnitude (M), fault length (L), width (W), and area, we built self-consistent scaling relations by relating M to the aspect ratio (L/W) and to the fault area to model the change in the aspect ratio once the rupture width reaches the down-dip width limit of the fault. The width-limited effect of large-magnitude earthquakes depends on the fault dip and a regional term for the seismogenic thickness. The magnitude scaling of the aspect ratio includes a break in the magnitude scaling that is dip angle dependent. This dip angle-dependent magnitude scaling in the magnitude–area relation is modeled by a trilinear relation incorporating a dip-related transition range. The effect of the free surface was observed using a normalized depth term and parameterizing the source by the depth of the top of the fault rupture; it is more apparent in the area scaling relation. The scaling differences are related to the fault geometry, not to the rake angle, as commonly assumed. Finally, the corresponding L and W scaling relations obtained by converting the area and aspect ratio models to L and W models not only show good agreement with the previous regional scaling laws on average but also provide better fault-specific application due to the inclusion of a fault-specific dip angle and seismogenic thickness.

Sub-filter-scale shear stress analysis in hypersonic turbulent Couette flow

Direct numerical simulations of hypersonic turbulent Couette flows are performed for top-wall Mach numbers of 6, 7 and 8, inspired by non-reactive high-enthalpy wind tunnel free-stream conditions, with the goal of analysing the physical processes driving the sub-filter-scale (SFS) stresses to inform development of large-eddy simulation techniques for hypersonic wall-bounded flows. Semi-local scaling laws collapse mean profiles and second-order turbulent statistics well, in spite of the strong wall-normal gradients of temperature and density. On the other hand, the SFS shear stresses exhibit an unexpected profile characterized by a region of pronounced shear stress deficit, which becomes more pronounced for higher Mach numbers. Instantaneous visualizations suggest that the SFS shear stress deficit is induced by the counter-gradient resolved momentum transport driven by residual velocity motions at the interface between high-density low-speed streaks being ejected away from the wall, and low-density high-speed ones replacing the displaced fluid, qualifying this as a compressibility effect. It is shown that the SFS shear stresses are primarily driven by second-order interactions between residual velocities, in spite of their triply nonlinear nature. This, in turn, motivated a statistical quadrant analysis revealing the presence of a SFS shear stress deficit, that is, SFS processes driving momentum transport of the resolved field towards the top wall. Additionally, higher-Reynolds-number simulations reveal that there is an upper limit of spatial filter widths to resolve large-scale structures, and such deficit is observable at any Reynolds number when a reasonable spatial filter is applied.

Optimizing Sulfur Emission Control Areas for Shipping

The design of emission control areas (ECAs), including ECA width and sulfur limits, plays a central role in reducing sulfur emissions from shipping. To promote sustainable shipping, we investigate an ECA design problem that considers the response of liner shipping companies to ECA designs. We propose a mathematical programming model from the regulator’s perspective to optimize the ECA width and sulfur limit, with the aim of minimizing the total sulfur emissions. Embedded within this regulator’s model, we develop an internal model from the shipping liner’s perspective to determine the detoured voyage, sailing speed, and cargo transport volume with the aim of maximizing the liner’s profit. Then, we develop a tailored hybrid algorithm to solve the proposed models based on the variable neighborhood search meta-heuristic and a proposition. We validate the effectiveness of the proposed methodology through extensive numerical experiments and conduct sensitivity analyses to investigate the effect of important ECA design parameters on the final performance. The proposed methodology is then extended to incorporate heterogeneous settings for sulfur limits, which can help regulators to improve ECA design in the future. Funding: This work was supported by the National Natural Science Foundation of China [Grants 72025103, 71831008, 72201163, 72071173, 72371221, 72394360, 72394362, 72361137001 and HKSAR RGC TRS T32-707/22-N]. Supplemental Material: The online appendix is available at https://doi.org/10.1287/trsc.2023.0278 .

Compact dual‐frequency impedance transformer for frequency‐dependent complex source and load

SummaryThis study presents a compact hybrid dual‐frequency impedance transformer that is suitable for sources and loads having frequency‐dependent complex impedances. The basic transformer comprises two transmission lines and two shunt stubs, and this configuration is adequate for the design of a dual‐frequency impedance transformer for a few sources and loads. Compared with other studies, this work presents a distinct derivation concept to obtain the circuit parameters, thus enabling the achievement of a compact circuit. For some particular sources and loads that the basic structure is difficult to match because of line width limitations, lumped elements are added to overcome the limitation and further reduce the circuit size. The derived design formulas avoid errors caused by frequency‐dependent deviations in the component values due to parasitic effects, which constitute a critical problem in Radio Frequency (RF) circuits that employ lumped elements. The simulated results are in good agreement with the measured results, and the frequency responses obtained herein validate the proposed structure and design procedure.

Model for rapid evaluation of corner crack widths in reinforced concrete dapped-end conections

Dapped-end connections are widely used in concrete infrastructure, and in particular in Gerber-type bridges and precast concrete buildings. The main vulnerability of such connections is the early opening of inclined cracks at the re-entrant corner of the dapped end. Such cracks are difficult to control as they propagate at low loads and open significantly before the occurrence of secondary cracks. The goal of this paper is to propose a rational model for rapid evaluation of the width of the corner cracks, including the effects of secondary cracking and, indirectly, the effect of restrained shrinkage at low loads. The model assumptions stem from detailed test observations of specimens with light and heavy reinforcement, arranged in orthogonal or diagonal layouts. The model is developed in two steps: 1) predicting the crack width at yielding of the connection, and 2) capturing the shape of the response from zero load up to yielding. A database of 42 tests with a broad range of variables is used to validate the model. It is shown that the main properties influencing the width of the corner crack are the amount and detailing of the reinforcement, and their effect is well captured by the model. It is also illustrated how the model can be used to design the reinforcement of dapped ends to meet crack width limits at service conditions.

The Improved U-STFM: A Deep Learning-Based Nonlinear Spatial-Temporal Fusion Model for Land Surface Temperature Downscaling

The thermal band of a satellite platform enables the measurement of land surface temperature (LST), which captures the spatial-temporal distribution of energy exchange between the Earth and the atmosphere. LST plays a critical role in simulation models, enhancing our understanding of physical and biochemical processes in nature. However, the limitations in swath width and orbit altitude prevent a single sensor from providing LST data with both high spatial and high temporal resolution. To tackle this challenge, the unmixing-based spatiotemporal fusion model (STFM) offers a promising solution by integrating data from multiple sensors. In these models, the surface reflectance is decomposed from coarse pixels to fine pixels using the linear unmixing function combined with fractional coverage. However, when downsizing LST through STFM, the linear mixing hypothesis fails to adequately represent the nonlinear energy mixing process of LST. Additionally, the original weighting function is sensitive to noise, leading to unreliable predictions of the final LST due to small errors in the unmixing function. To overcome these issues, we selected the U-STFM as the baseline model and introduced an updated version called the nonlinear U-STFM. This new model incorporates two deep learning components: the Dynamic Net (DyNet) and the Chang Ratio Net (RatioNet). The utilization of these components enables easy training with a small dataset while maintaining a high generalization capability over time. The MODIS Terra daytime LST products were employed to downscale from 1000 m to 30 m, in comparison with the Landsat7 LST products. Our results demonstrate that the new model surpasses STARFM, ESTARFM, and the original U-STFM in terms of prediction accuracy and anti-noise capability. To further enhance other STFMs, these two deep-learning components can replace the linear unmixing and weighting functions with minor modifications. As a deep learning-based model, it can be pretrained and deployed for online prediction.

FM-FCN: A Neural Network with Filtering Modules for Accurate Vital Signs Extraction.

Neural networks excel at capturing local spatial patterns through convolutional modules, but they may struggle to identify and effectively utilize the morphological and amplitude periodic nature of physiological signals. In this work, we propose a novel network named filtering module fully convolutional network (FM-FCN), which fuses traditional filtering techniques with neural networks to amplify physiological signals and suppress noise. First, instead of using a fully connected layer, we use an FCN to preserve the time-dimensional correlation information of physiological signals, enabling multiple cycles of signals in the network and providing a basis for signal processing. Second, we introduce the FM as a network module that adapts to eliminate unwanted interference, leveraging the structure of the filter. This approach builds a bridge between deep learning and signal processing methodologies. Finally, we evaluate the performance of FM-FCN using remote photoplethysmography. Experimental results demonstrate that FM-FCN outperforms the second-ranked method in terms of both blood volume pulse (BVP) signal and heart rate (HR) accuracy. It substantially improves the quality of BVP waveform reconstruction, with a decrease of 20.23% in mean absolute error (MAE) and an increase of 79.95% in signal-to-noise ratio (SNR). Regarding HR estimation accuracy, FM-FCN achieves a decrease of 35.85% in MAE, 29.65% in error standard deviation, and 32.88% decrease in 95% limits of agreement width, meeting clinical standards for HR accuracy requirements. The results highlight its potential in improving the accuracy and reliability of vital sign measurement through high-quality BVP signal extraction. The codes and datasets are available online at https://github.com/zhaoqi106/FM-FCN.

Diagnostic Efficacy and Safety of Low-Contrast-Dose Dual-Energy CT in Patients With Renal Impairment Undergoing Transcatheter Aortic Valve Replacement.

This study aimed to evaluate the diagnostic efficacy and safety of low-contrast-dose, dual-source dual-energy CT before transcatheter aortic valve replacement (TAVR) in patients with compromised renal function. A total of 54 consecutive patients (female:male, 26:38; 81.9 ± 7.3 years) with reduced renal function underwent pre-TAVR dual-energy CT with a 30-mL contrast agent between June 2022 and March 2023. Monochromatic (40- and 50-keV) and conventional (120-kVp) images were reconstructed and analyzed. The subjective quality score, vascular attenuation, contrast-to-noise ratio (CNR), and signal-to-noise ratio (SNR) were compared among the imaging techniques using the Friedman test and post-hoc analysis. Interobserver reliability for aortic annular measurement was assessed using the intraclass correlation coefficient (ICC) and Bland-Altman analysis. The procedural outcomes and incidence of post-contrast acute kidney injury (AKI) were assessed. Monochromatic images achieved diagnostic quality in all patients. The 50-keV images achieved superior vascular attenuation and CNR (P < 0.001 in all) while maintaining a similar SNR compared to conventional CT. For aortic annular measurement, the 50-keV images showed higher interobserver reliability compared to conventional CT: ICC, 0.98 vs. 0.90 for area and 0.97 vs. 0.95 for perimeter; 95% limits of agreement width, 0.63 cm² vs. 0.92 cm² for area and 5.78 mm vs. 8.50 mm for perimeter. The size of the implanted device matched CT-measured values in all patients, achieving a procedural success rate of 92.6%. No patient experienced a serum creatinine increase of ≥ 1.5 times baseline in the 48-72 hours following CT. However, one patient had a procedural delay due to gradual renal function deterioration. Low-contrast-dose imaging with 50-keV reconstruction enables precise pre-TAVR evaluation with improved image quality and minimal risk of post-contrast AKI. This approach may be an effective and safe option for pre-TAVR evaluation in patients with compromised renal function.

LIOVIX® Printable Lithium Technology for Lithium Anode Manufacturing at Scale

The commercialization of lithium metal batteries requires the mass production of cost-effective thin lithium metal foil with thickness less than 30 µm. However, today’s known technologies are not yet commercially available. For example, thin lithium metal foil can be produced by hydraulic extrusion followed by a rolling process. This process usually requires a delicate balance between the pressure and tension created by both extruder and winder, so that the resulting foil is not torn or stretched, retaining all the dimensional characteristics required. This process can be extremely challenging when making lithium metal films with thickness less 20 µm due to the poor mechanical properties of lithium metal. Lithium metal foil can also be produced by physical vapor deposition (PVD). Lithium metal foil produced by PVD process are generally homogenous and have good control in thickness. However, PVD technology requires high vacuum chamber and is slow in the deposition rate, typically in the range of 0.1 to 100 nm s−1, making industrial scale of 20 µm lithium foil very challenging1.Livent has developed LIOVIX®printable lithium technology, a proprietary formulation that combines Stabilized Lithium Metal Powder (SLMP®), rheology modifiers and solvent to produce a stable suspension that can be printed on most common substrates. LIOVIX®printable lithium technology is an enabling technology that can produce thin lithium foil with thickness of 5 µm to 50 µm with no limitation in width, length and at high deposition speed. LIOVIX®enables pattern printing and can be applied to any substrate, for example a prefabricated anode, cathode, current collector, separator, and solid or polymer electrolyte. This technology enables 3D battery printing in a custom form factor shape.In this presentation, we will cover thin lithium foil manufacturing processes using standard industrial equipment and electrochemical performance results.References Acebedo, Begoña, et al. "Current Status and Future Perspective on Lithium Metal Anode Production Methods." Advanced Energy Materials (2023): 2203744.

A pre-failure narrow concrete cracks dataset for engineering structures damage classification and segmentation

Monitoring of structures’ condition plays a fundamental role in providing safety for users and extending the structures’ lifespan. The monitoring is conducted through on-site inspections by engineers thus this process is time-consuming, labor-intensive and prone to subjective engineering opinions. Detecting damage using machine learning algorithms on images can support engineers’ work, especially for early damages which are difficult to see with the human eye. This article is focused on the concrete crack detection problem in engineering structural elements. Despite the availability of several concrete crack detection datasets, no dataset allows semantic segmentation of cracks narrower than 0.3 mm (the crack width limit for typical engineering structures elements and environmental conditions according to EC 1992-1-1) and the ability for crack classification is limited. The provided open dataset represents only cracks below the crack width limit of 0.3mm, which do not yet indicate concrete elements failure. It is dedicated for early crack classification and segmentation, so that damage protection can be taken at an early stage to prevent structural element damages.

Random neural networks in the infinite width limit as Gaussian processes

Random neural networks in the infinite width limit as Gaussian processes

Experimental study of mode control in rotating detonation combustor using Tesla valve mode control configuration fueled by kerosene

The detonation mode has a significant impact on the operational characteristics and propulsion performance of the rotating detonation combustor (RDC). Controlling the combustion mode effectively is advantageous for improving the propulsion performance of the pressure gain combustor. A Tesla valve mode control combustion chamber (TVMC) was used for experimental research in this study. An equal-width annular combustion chamber (EWA) was used as a control test, with room temperature kerosene as the fuel, and oxygen-enriched air as an oxidizer. The experiment successfully validated the feasibility of mode control in TVMC along with its operational characteristics. Monitoring the pressure pulsation of detonation and guide channels in TVMC led to the unfolding of possible mode control mechanism. The results indicated that the rotating detonation wave (RDW) propagates clockwise in TVMC. After RDW propagating counterclockwise enters the guide channel, the width limitation of guide channel causes RDW decoupling. It changes the propagation direction of the leading shock wave, resulting in mode control. Because of the presence of a guide channel, the RDW is unstable at low equivalence ratios, and the operation range is also narrower.

DM-SM interactions mediated by spin-one particles: EFT study

Standard model-dark matter particles, mediated by spin one fields, are analyzed within the effective field theory framework. We [1, 2] consider dark particles masses from few MeV to 6.4 TeV. We restrict the EFT using bounds from relic density, Z invisible decay width, direct and indirect detection limits and collider constraints. Solutions below mZ are found for two operators. Others, around the electroweak scale or slightly above, are also compatible with all present limits.

Accelerated solutions of convection‐dominated partial differential equations using implicit feature tracking and empirical quadrature

SummaryThis work introduces an empirical quadrature‐based hyperreduction procedure and greedy training algorithm to effectively reduce the computational cost of solving convection‐dominated problems with limited training. The proposed approach circumvents the slowly decaying ‐width limitation of linear model reduction techniques applied to convection‐dominated problems by using a nonlinear approximation manifold systematically defined by composing a low‐dimensional affine space with bijections of the underlying domain. The reduced‐order model is defined as the solution of a residual minimization problem over the nonlinear manifold. An online‐efficient method is obtained by using empirical quadrature to approximate the optimality system such that it can be solved with mesh‐independent operations. The proposed reduced‐order model is trained using a greedy procedure to systematically sample the parameter domain. The effectiveness of the proposed approach is demonstrated on two shock‐dominated computational fluid dynamics benchmarks.

Adverse Events Associated With Disparity Between Patients' BMI and Operating Table Size-A Need for Improved Surgical Innovations.

The lack of proper equipment to accommodate patients with high BMI can jeopardize the safety of the patients and medical staff. In this review, we aim to discuss the availability of obesity accommodations in the operating room, along with its impact, implications, and future recommendations. Four databases were searched for articles pertaining to surgical table dimensions and the implications for safety, with a special focus on patients with larger BMIs. Articles were separated into 4 categories: Existing OR Table Options, Safety Implications for Patients, Reported Adverse Events Associated with Operating Table Inadequacy, and Safety Implications for Medical Staff. A total of 18 articles and documents were included in this review. Most of the literature that discusses surgical tables with higher weight capacity is specific only to weight loss surgeries. Operating table dimensions have changed little in the past 100years and standard operating tables have weight limits of 500 pounds. Several case reports underline the hazards of inadequately sized surgical tables. This review demonstrates that a lack of proper equipment, such as surgical tables with adequate width and weight limits, can be a major contributor to the endangerment of bariatric surgical patients and the medical professionals who care for them. Further research and surgical innovation may be required to develop superior operating tables to address the unique concerns of this patient populations.