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This article presents an alternative approach of analyzing possibly multitype point patterns in space and space–time that occur on network structures, and it introduces several different graph-related intensity measures. The proposed formalism allows us to control for processes on undirected, directional, as well as partially directed network structures and is not restricted to linearity or circularity. Supplementary materials for this article are available online. See Supplements section at the end for a description and link.

This article introduces a relative study of linearity performance and harmonic distortion among Si junctionless (JL) FinFET, conventional inversion-mode (IM) FinFET, Tunnel FET, and InGaAs MOSFET. For this, a numerical device simulator has been applied. Our investigation discloses that JL device shows best performance for both cases.

X‐ray detection at X‐ray free‐electron lasers is challenging in part due to the XFEL's extremely short and intense X‐ray pulses. Experimental measurements are further complicated by the large fluctuations inherent to the self‐amplified spontaneous emission process producing the X‐rays. At the Linac Coherent Light Source the ePix10ka2M detector offers multiple gain modes, and auto‐ranging between these, to increase the dynamic range while retaining low noise. For diffuse scattering techniques, such as time‐resolved X‐ray solution scattering, where the shape of the scattering pattern largely does not change between exposures, a fixed mix of different gain modes offers many of the same advantages as auto‐ranging. We find that configuring individual ASICs in separate gain modes does not impact the intensity linearity of the gain response and has a limited effect on the effective dynamic range in regions with different gain mode settings while avoiding the complexities of auto‐ranging. Small (<5%) non‐linear gain contributions arise when pixels on the same ASIC are configured in different gain modes. We present a configuration scheme that is designed to select the optimal mixed gain configuration to minimize effects of saturation in the high‐/medium‐gain region, while maximizing the number of pixels with higher gain to improve the signal‐to‐noise ratio. The linearity of the intensity response of the ePix10k detector in mixed gain configurations is evaluated for solution phase scattering experiments at the LCLS.

Hierarchical occupancy and abundance models (HOAMs) have become a leading approach for inferring wildlife population dynamics because they explicitly account for imperfect detection. HOAMs are suitable for sampling approaches that produce detection histories from repeated visits to the same sites, including direct observations (e.g. bird point counts), indirect observations (e.g. tracks, dung) and remote and passive sensors (e.g. camera traps, acoustic recorders). Wildlife often exhibits non‐linear temporal trends or threshold‐like responses to environmental conditions. However, traditional HOAMs address non‐linearity crudely using global polynomial functions, despite well‐documented limitations. Generalised additive models (GAMs) provide a more flexible approach to non‐linearity, allowing smooth data‐driven estimation through basis functions and penalised splines. Yet, GAMs have remained sparsely adopted in hierarchical occupancy modelling, in part due to the need for custom code in Bayesian modelling languages. We demonstrate the applicability of GAMs within the occupancy and abundance modelling framework (hereafter ‘OccuGAMs’) by comparing traditional HOAMs with polynomials to OccuGAMs. In simulations, OccuGAMs recovered non‐linear relationships more accurately and more often, scoring better on energy and variogram metrics and produced more stable responses at smaller sample sizes. Polynomials performed well in some scenarios but were less generalisable, making OccuGAMs the more robust overall choice, especially when there is no a priori guidance about the functional form. Limitations of OccuGAMs include interpretability of model parameters and sensitivity to the choice and number of basis functions, which can be assessed with diagnostic tools. To promote wider accessibility, we provide code for OccuGAM implementation in JAGS and Stan as well as in the R packages flocker and mvgam.

Aim Population dynamics are usually assessed through linear trend analysis, quantifying their general direction. However, linear trends may hide substantial variations in population dynamics that could reconcile apparent discrepancies when quantifying the extent of the biodiversity crisis. We seek to determine whether the use of non‐linear methods and the quantification of temporal variability can offer a more complete representation of changes in global population dynamics than commonly‐used linear approaches. Methods We analysed 6437 population time series from 1257 vertebrate species from the Living Planet Database over the period 1950–2020. We modelled populations through the use of second‐order polynomials and classified trajectories according to their direction and acceleration. We modelled and classified these same populations using a more classical linear trend analysis. We quantified temporal variability using the mean squared error of the fitted polynomials. We then used generalised linear mixed models to test potential sources of heterogeneity in non‐linear trajectories and temporal variability. Results In all, 44.8% of the analysed population time series were non‐linear. Across all populations, 30% were declining, 30% were increasing, and 40% were with no linear trend. Among the population showing no linear trend, half were concave or convex. Non‐linearity was expressed differently between taxonomic groups, with mammals showing higher prevalence of non‐linearity. Marine and freshwater populations were more variable than terrestrial populations, and fish were more variable than other vertebrates. Differences between geographical regions were detected in both non‐linearity and temporal variability, but no straightforward pattern emerged. There were no differences in both components between IUCN categories. Main Conclusions Non‐linearity and temporal variability reveal usually overlooked dramatic declines or recovery signals in global population dynamics. Thus, moving beyond linearity can improve our understanding of complex population dynamics and better inform conservation decisions. In particular, populations usually classified as ‘stable’ can hide informative changes in non‐linear and variability patterns that need to be considered in global biodiversity assessments.

This letter reports an 85 GHz low noise amplifier (LNA) employing derivative superposition based non‐linearity cancellation and a current re‐use topology. The LNA employs a two‐stage stacked architecture, each featuring neutralized differential pairs utilizing the same DC current. In derivative superposition, an auxiliary branch consisting of neutralized differential pairs cells is added in the LNA in parallel to stage 1 to ensure non‐linearity cancellation. Layout‐based capacitive neutralization is implemented to improve GMAX, resulting in simplified routing, reduced parasitics, and a more compact layout. The proposed LNA is fabricated in TSMC 28 nm CMOS process and achieves a peak gain of 8.4 dB at 84.2 GHz with a measured 3 dB bandwidth (BW3dB $\\text{BW}_{3\\text{dB}}$ ) from 79.4 to 92 GHz. The minimum measured noise figure is 12.8 dB. The LNA draws 34 mA of DC current from a 1.2 V supply. The highly linear LNA with IIP3 +6.5 dBm is tailored for automotive RADAR and 6G receivers. A high‐performance 85 GHz stacked low noise amplifier is presented, featuring derivative superposition based non‐linearity cancellation. The two‐stage stacked architecture incorporates neutralized differential pairs with an auxiliary branch in parallel to stage 1 for non‐linearity cancellation. Implemented in TSMC 28 nm CMOS, the low noise amplifier achieves 8.4 dB peak gain at 84.2 GHz with 12.6 GHz as 3‐dB bandwidth, drawing 34 mA from a 1.2 V supply. With IIP3 of +6.5 dBm, it is tailored for automotive RADAR and 6G receivers.

The main focus of this research is on improving the performance of dynamic systems with actuator non‐linearities and time‐varying disturbances. To this end, using the concept of finite‐time stability, a novel observer is presented to estimate the disturbances in systems with input non‐linearities. Then, introducing an innovative sliding manifold, a robust observer‐based technique is derived to guarantee the finite‐time stability of the sliding surface. Hence, the closed‐loop system would be stable in the presence of hard non‐linearities and time‐varying disturbances. The performance of the proposed approach was assessed in using a simulation study of two numerical examples. The obtained results and comparison with a conventional method confirmed the benefits of the suggested approach.

For the first-ordered nonlinear ordinary differential equations, we explore several kinds of integral factors in this paper, where the concrete formulas of the integral factors are acquired, and some of them are applied to solve corresponding nonlinear ordinary differential equations. At first, the sufficient and necessary condition of linearity for ordinary differential equations is proved that the ordinary differential equations are linear if and only if their integral factors only have a unique independent variable. Secondly, for several types of nonlinear ordinary differential equations, their corresponding integral factors formulas are discussed with not the same methods according to their special characters and different structures. Finally, among some of the integral factors, they are applied to complete to solve their matching first-ordered nonlinear ordinary differential equations.

Optical imaging is commonly used for both scientific and technological applications across industry and academia. In image sensing, a measurement, such as of an object’s position or contour, is performed by computational analysis of a digitized image. An emerging image-sensing paradigm relies on optical systems that—instead of performing imaging—act as encoders that optically compress images into low-dimensional spaces by extracting salient features; however, the performance of these encoders is typically limited by their linearity. Here we report a nonlinear, multilayer optical neural network (ONN) encoder for image sensing based on a commercial image intensifier as an optical-to-optical nonlinear activation function. This nonlinear ONN outperforms similarly sized linear optical encoders across several representative tasks, including machine-vision benchmarks, flow-cytometry image classification and identification of objects in a three-dimensionally printed real scene. For machine-vision tasks, especially those featuring incoherent broadband illumination, our concept allows for a considerable reduction in the requirement of camera resolution and electronic post-processing complexity. In general, image pre-processing with ONNs should enable image-sensing applications that operate accurately with fewer pixels, fewer photons, higher throughput and lower latency.A nonlinear optical neural network image sensor based on an image intensifier enables efficient all-optical image encoding for a variety of machine-vision tasks.

In the present work, the phenol-sulfuric acid method and the 3,5-dinitrosalicylic acid (DNS) method were developed with the aim to quantitatively analyze total sugars and reducing sugars, respectively. In regard with the phenol-sulfuric acid assay, 1.0 mL of sample was treated with 1.0 mL of 5% phenol, 5.0 mL of concentrated H 2 SO 4 and measured at 485 nm, with the linearity range of 10–100 ppm for total sugars. The DNS method was performed on 2.0 mL of sample, using 1.5 mL of DNS at 80 °C for 10 minutes and measured at 510 nm, with the linearity range of 50–400 ppm for reducing sugars. The sugar contents of white dragon fruit-derived sugar-samples (extracted from species in Binh Thuan province, Vietnam) were also estimated by the above measured methods, exhibiting the total sugars of above 90% and the reducing sugars of above 5%. The methods were well-performed with the acceptable relative standard deviations of repeatability in accordance with the Association of Official Analytical Chemists (AOAC).