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For decades, optical fiber interferometers have been extensively studied and applied for their inherent advantages. With the rapid development of science and technology, fiber sensors with higher detection sensitivity are needed on many occasions. As an effective way to improve measurement sensitivity, Vernier effect fiber sensors have drawn great attention during the last decade. Similar to the Vernier caliper, the optical Vernier effect uses one interferometer as a fixed part of the Vernier scale and the other as a sliding part of the Vernier scale. This paper first illustrates the principle of the optical Vernier effect, then different configurations used to produce the Vernier effect are classified and discussed. Finally, the outlook for Vernier effect fiber sensors is presented.

Human visual cortex does not represent the whole visual field with the same detail. Changes in receptive field size, population receptive field (pRF) size and cortical magnification factor (CMF) with eccentricity are well established, and associated with changes in visual acuity with eccentricity. Visual acuity also changes across polar angle. However, it remains unclear how RF size, pRF size and CMF change across polar angle. Here, we examine differences in pRF size and CMF across polar angle in V1, V2 and V3 using pRF modeling of human fMRI data. In these visual field maps, we find smaller pRFs and larger CMFs in horizontal (left and right) than vertical (upper and lower) visual field quadrants. Differences increase with eccentricity, approximately in proportion to average pRF size and CMF. Similarly, we find larger CMFs in the lower than upper quadrant, and again differences increase with eccentricity. However, pRF size differences between lower and upper quadrants change direction with eccentricity. Finally, we find slightly smaller pRFs in the left than right quadrants of V2 and V3, though this difference is very small, and we find no differences in V1 and no differences in CMF. Moreover, differences in pRF size and CMF vary gradually with polar angle and are not limited to the meridians or visual field map discontinuities. PRF size and CMF differences do not consistently follow patterns of cortical curvature, despite the link between cortical curvature and polar angle in V1. Thus, the early human visual cortex has a radially asymmetric representation of the visual field. These asymmetries may underlie consistent reports of asymmetries in perceptual abilities. •PRFs are smaller in horizontal than vertical visual field quadrants of V1, V2 & V3.•Cortical magnification is correspondingly larger in these horizontal quadrants.•Cortical magnification is larger in the lower than the upper quadrant.•The size of these differences typically increases with visual field eccentricity.•Upper versus lower quadrant PRF size differences change direction with eccentricity.

The Antarctic ecosystem is characterized by few consumer species and simple trophic levels (TLs), rendering it an ideal setting to investigate the environmental behavior of contaminants. The paper aims to assess the presence, sources and biomagnification behavior of polycyclic aromatic hydrocarbons (PAHs) of the Antarctic food web and is the first study of biomagnifications of PAHs in the Fildes Peninsula in Antarctica. Nine representative species from the Fildes Peninsula in Antarctica were sampled and evaluated for PAH presence. PAH concentrations ranged from 477.41 to 1237.54 ng/g lipid weight (lw) in the sampled Antarctic biota, with low molecular weight PAHs (naphthalene, acenaphthylene, acenaphthene, and fluorene) comprising the majority of the PAHs. PAHs concentrations were negatively correlated with TLs. Further, the food web magnification factor (FWMF) of ∑PAHs was 0.63, suggesting biodilution of PAHs along the TLs. Source analyses revealed that the PAHs mainly originated from petroleum contamination and the combustion of fossil fuels.

A large and balanced training data are the foremost requirement in proper convergence of a deep convolutional neural network (CNN). Medical data always suffer from the problem of unbalancing and inadequacy that makes it difficult to train CNN from scratch. It is known that the transfer learning approach provides great potential to deal with inadequate dataset besides the benefit of faster training. The efficient transfer of knowledge from natural images to histopathological images has yet to be achieved. In view of the foregoing, an attempt has been made toward the classification of BreakHis dataset using pre-trained ‘AlexNet’ model with a suitable fine-tuning approach. The effective depth of fine-tuning is also determined at different levels of magnification (40×, 100×, 200× and 400 ×). The experimental trials conform that the moderate level of fine-tuning is an optimum choice for the classification of magnification-dependent histology images in contrast to the shallow and deep tuning of the pre-trained network which in turn depends on the size and relative distribution of a dataset. Additionally, the layer-wise fine-tuning approach provides a neck-to-neck performance with the latest state-of-the-art developments.

Forced and free vibrational analyses of viscoelastic nanotubes containing fluid under a moving load in complex environments incorporating surface effects are conducted based on the nonlocal strain gradient theory and the Rayleigh beam model. To model the internal nanoflow, the slip boundary condition is employed. Adopting the Galerkin discretization approach, the reduced-order dynamic model of the system is acquired. Analytical and numerical methods are exploited to determine the dynamic response of the system. The impacts of geometry, scale parameter ratio, Knudsen number, fluid velocity, rotary inertia parameter, viscoelastic parameter, surface residual stress, surface elastic modulus, and hygro-thermo-magnetic environments on the dynamic magnification factor, critical moving load speed, cancellation, and maximum free vibration of the system are evaluated. The results indicate that the effects of the viscoelastic parameter on the dynamic behavior of the system differ significantly from those of other parameters. It is indicated that the dynamic magnification factor and critical moving load speed are enhanced by increasing the surface residual stress and the surface elastic modulus. The model and results of the current investigation can serve as a comprehensive benchmark for the optimum design of nanoflow sensors and targeted drug delivery systems.

The biomagnification of mercury, polychlorobiphenyls (PCBs), dichlorodiphenyltrichloroethane and its metabolites (DDTs) and perfluoroalkyl acids substances (PFASs) was evaluated in the trophic web of Lake Mergozzo, a small and deep Italian subalpine lake, which has been chosen because it is a protected environment, and discharges into the lake are mostly avoided. Carbon source and relative trophic levels were calculated by using 13C and 15N stable isotopes, respectively, and trophic magnification factors (TMFs) were derived. Zooplankton and thirteen species of fish were collected and analyzed, and the results showed the elevated level of biota contamination from both legacy and emerging pollutants, even if direct discharges were avoided. Concentrations in biota, expressed as sums of compounds, ranged from 0.4 to 60 µg kg−1 wet weight (ww) for PFASs, from 16 to 1.3 104 µg kg−1 lipid content (lw) for DDTs, from 17 to 1.5 104 µg kg−1 lw for PCBs and from 20.0 to 501 µg kg−1 ww for mercury (Hg). TMFs of this deep, cold lake, with a prevalent pelagic trophic chain, were high and clearly indicated fish biomagnification, except for PFAS. The biomagnification capability of PFAS in a fish-only food web was discussed by using the biomagnification of Hg as a benchmark for assessing their bioaccumulation potential.

In the seismic design of acceleration-sensitive nonstructural components, floor acceleration response spectra are commonly selected for analysis, which has proven to be effective in practice. To accurately study the floor acceleration response spectrum of a reinforced concrete structure under earthquakes, a 3-story reinforced concrete frame structure designed based on Chinese codes was built and placed on a shaking table for testing to obtain actual floor acceleration response for investigation of spectral characteristics. In addition, a set of finite element models of reinforced concrete frame buildings were analyzed to better study the variation of floor acceleration peaks and response spectra with different modal periods. The results show that floor dynamic magnification is highly related to structural dynamic characteristics and building’s relative height. Obvious peaks are observed in the floor response spectrum, which correspond to the structural modal periods. The values of the spectra, particularly the peaks, show a strong correlation with the floor level and the damping ratios of nonstructural components. Based on the observations gained from shaking table tests and numerical study, a function for predicting the floor dynamic magnification factor and a method for generating the spectral amplification factor of the floor are proposed. Then the findings acquired from the test, numerical study, and existing methods were applied for the validation of the proposed methods. It is shown that the proposed floor dynamic magnification factor prediction function and spectral amplification factor prediction method are useful for the seismic design of nonstructural components in various reinforced concrete structures, taking into account the structural dynamic characteristics, the floor level, and the damping ratio of nonstructural components.

Summary It is widely acknowledged that the tuned mass damper (TMD) is one of the most effective and simplest passive control devices, but its limited control performance is still a troubling problem. In order to surmount the shortage of TMD, the tuned tandem mass dampers (referred herein to as TTMD) have been proposed for mitigating the undesirable oscillation of structures under the ground acceleration. Based on the formulation of the mode‐generalized system in the specific vibration mode being controlled, the analytical expression is then derived for the dynamic magnification factor of the structure furnished with a TTMD. The optimum criterion can thereby be defined as minimization of the minimum values of the maximum dynamic magnification factor with a set of optimization variables embedded so as to give full play to the control device potential. The optimization implementation of TTMD is carried out by the MATLAB‐based coding and debugging. For the purpose of a mutual authentication to the optimization results, three metaheuristic algorithms, namely, genetic algorithm, particle swarm optimization, and simulated annealing, are concurrently taken into consideration. Results demonstrate that the proposed TTMD endows with the superior stroke performance with respect to TMD.

Open Ground-Storey (OGS) buildings, widely adopted for functional openness, are highly vulnerable to seismic collapse due to stiffness irregularity at the ground storey (GS). The magnification factor (MF), defined as the amplification applied to GS column design forces, acts as a practical strengthening measure to enhance GS stiffness and thereby mitigate the soft storey failure mechanism. While earlier studies recommended fixed MF values, their lack of adaptability often left stiffness deficiencies unresolved. This study develops a rational framework to quantify and predict the required MF for OGS columns, enabling safe yet functionally efficient design. A comprehensive set of three-dimensional reinforced concrete OGS models was analyzed under seismic loads, covering variations in plan geometry, ground-to-upper-storey height ratio (Hr), and GS infill percentage. Iterative stiffness-based evaluations established the MF demand needed to overcome stiffness deficiencies. To streamline prediction, advanced machine learning (ML) models were applied. Among these, black-box models achieved high predictive accuracy, but Symbolic Regression (SR) offered an interpretable closed-form equation that balances accuracy with transparency, making it suitable for design practice. A sensitivity analysis confirmed the Hr as the most influential parameter, with additional contributions from other variables. Validation on additional OGS configurations confirmed the reliability of the SR model, while seismic response comparisons showed that Modified OGS (MOGS) frames with the proposed MF achieved improved stiffness, reduced lateral displacements, uniform drift distribution, and shorter fundamental periods. The study highlights the novelty of integrating interpretable ML into structural design, providing a codifiable and practical tool for resilient OGS construction.

Ultra-high performance geopolymer concrete (UHPGC) has emerged as a low-carbon cementitious material with high mechanical performance and thus offers potential as a substitute for Portland cement-based ultra-high-performance concrete (UHPC). Experimental evidence on the eccentric compression response of reinforced UHPGC (R-UHPGC) columns, however, remains limited. In this study, six reinforced columns were tested under eccentric compression, with concrete type and eccentricity ratio taken as the main variables. The structural response was examined in terms of failure pattern, peak resistance, axial load–deflection behavior, and ductility. The results showed that at the same eccentricity ratio, the peak resistance of the R-UHPGC columns was approximately 20% lower than that of the corresponding R-UHPC columns. As eccentricity increased, the axial load resistance decreased, whereas the mid-height deflection and ductility increased. On the basis of the test results, available prediction methods for moment magnification factor and ultimate resistance originally developed for R-UHPC columns were assessed for their suitability for R-UHPGC members. A preliminary analytical approach was then established for estimating the second-order effect and load-carrying capacity of R-UHPGC columns.