Energy efficiency is of utmost importance in modern applications. Power consumption optimisation could be improved by a comprehensive analytical modeling of the characteristics of critical blocks in ...a system. Dynamic range (DR) has a strong effect on the power consumption of analog circuits, and is determined by circuit non-linearity and noise level. Noise is well modelled even in deep sub-micron technologies, yet there is a lack of analysis and modeling of the non-linearity. An analytical MOSFET differential pair non-linearity model is presented in this work. The proposed model is universal to a wide range of technologies from long to ultra-deep sub-micron devices, and is valid for all operating regions as it is based on the EKV MOSFET model. Furthermore, a model including drain-voltage-induced non-linearity is also developed, and a concise 3 dB input intercept point (IIP3) formula incorporating the drain induced non-linearity in terms of the voltage gain is presented. The proposed models are validated with DC and AC simulations and measurements.
Differential uniformity and non-linearity of functions are the properties of great interest in cryptographic world. In this article, both the properties have been explored when the function is a ...permutation over Zn and some interesting results have been obtained. Also, some differential n-uniform non-affine permutations have been constructed over Zn, n being composite.
Inversion permutation over GF(2n) is known to have good non-linearity and differential uniformity. Its behaviour with respect to these properties has been studied over another domain, the ring of integer modulo p, where p is a prime. Further, conditions for maximum possible value of differential uniformity and non-linearity have been derived for permutations obtained by swapping two positions in the inversion permutation.
Photovoltaic (PV) reliability and durability have seen increased interest in recent years. Historically, and as a preliminarily reasonable approximation, linear degradation rates have been used to ...quantify long‐term module and system performance. The underlying assumption of linearity can be violated at the beginning of the life, as has been well documented, especially for thin‐film technology. Additionally, non‐linearities in the wear‐out phase can have significant economic impact and appear to be linked to different failure modes. In addition, associating specific degradation and failure modes with specific time series behavior will aid in duplicating these degradation modes in accelerated tests and, eventually, in service life prediction. In this paper, we discuss different degradation modes and how some of these may cause approximately linear degradation within the measurement uncertainty (e.g., modules that were mainly affected by encapsulant discoloration) while other degradation modes lead to distinctly non‐linear degradation (e.g., hot spots caused by cracked cells or solder bond failures and corrosion). The various behaviors are summarized with the goal of aiding in predictions of what may be seen in other systems. Published 2016. This article is a U.S. Government work and is in the public domain in the USA.
Degradation rates are important for financial projections and may be indicative of different degradation modes, however, degradation rates based on the assumption of linearity may not be sufficiently accurate. Non‐linearities at the beginning and end of a PV system's life cycle can have significant economic and technical consequences. In this paper we discuss different degradation modes and how some of these cause roughly linear degradation while other degradation modes lead to distinctly non‐linear degradation.
To fulfill the 2030 Agenda, the complexity of sustainable development goal (SDG) interactions needs to be disentangled. However, this understanding is currently limited. We conduct a cross‐sectional ...correlational analysis for 2016 to understand SDG interactions under the entire development spectrum. We apply several correlation methods to classify the interaction as synergy or trade‐off and characterize them according to their monotony and linearity. Simultaneously, we analyze SDG interactions considering population, location, income, and regional groups. Our findings highlight that synergies always outweigh trade‐offs and linear outweigh non‐linear interactions. SDG 1, 5, and 6 are associated with linear synergies, SDG 3, and 7 with non‐linear synergies. SDG interactions vary according to a country's income and region along with the gender, age, and location of its population. In summary, to achieve the 2030 Agenda the detected interactions and inequalities across countries need be tracked and leveraged to “leave no one behind.”
•Effect of crystallite nano-size on the gap energy Eg of ZnO nanopowders.•Effect of crystallite nano-size on the varistor properties.•Effect of Ga-doping on the crystallite size of ZnO ...nanopowders.•Effect of Ga-doping on the linear and nonlinear regions of (I-V)of the ZnO varistor.•The high resistance region in the linear region,also known as the "Ohmic region'.•The non-linear region is expanded, and the ability to discharge impulsive currents is greatly enhanced.
Nanostructured ZnO has received a considerable amount of interest, owing to its unique physical and chemical characteristics, as well as its remarkable performance in the fields of electronics, optics, and photonics. This work aims to study the influence of Ga dopant on the structural, optical, and electrical properties of ZnO nanopowders. Undoped and Ga-doped ZnO (GZO) nanopowders were successfully synthesised with the soft chemical sol-gel method. The solution was prepared using zinc acetate dihydrate and gallium (III) nitrate hydrate as precursors. The ethylene glycol is used as solvent. The X-ray diffractometer, scanning electronic microscope (SEM), UV–Vis, FTIR, and four-point probe method are used to analyse the properties of the synthesised powders. XRD and FTIR measurements show the growth of pure and Ga-doped ZnO crystals, which have a hexagonal wurtzite structure, and the average crystallite size varies from 9.09 nm to 24.8 nm. The nanospherical morphology of the nanopowders synthesised can be seen in the SEM images. The UV–visible spectroscopy shows that the optical gap energy increases with Ga concentration, from 3.59 eV to 3.67 eV. The I-V electrical measurements indicate that the breakdown voltage and the non-linear coefficient increase with Ga doping. This study will open the way for the investigation of a relationship between the electrical and nanostructural features of ZnO-based varistors for future device applications.
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An automated experiment in multimodal imaging to probe structural, chemical, and functional behaviors in complex materials and elucidate the dominant physical mechanisms that control device function ...is developed and implemented. Here, the emergence of non‐linear electromechanical responses in piezoresponse force microscopy (PFM) is explored. Non‐linear responses in PFM can originate from multiple mechanisms, including intrinsic material responses often controlled by domain structure, surface topography that affects the mechanical phenomena at the tip‐surface junction, and the presence of surface contaminants. Using an automated experiment to probe the origins of non‐linear behavior in ferroelectric lead titanate (PTO) and ferroelectric Al0.93B0.07N films, it is found that PTO shows asymmetric nonlinear behavior across a/c domain walls and a broadened high nonlinear response region around c/c domain walls. In contrast, for Al0.93B0.07N, well‐poled regions show high linear piezoelectric responses, when paired with low non‐linear responses regions that are multidomain show low linear responses and high nonlinear responses. It is shown that formulating dissimilar exploration strategies in deep kernel learning as alternative hypotheses allows for establishing the preponderant physical mechanisms behind the non‐linear behaviors, suggesting that automated experiments can potentially discern between competing physical mechanisms. This technique can also be extended to electron, probe, and chemical imaging.
This work introduces an automated experiment for probing the origins of non‐linear behavior in ferroelectric materials, formulating dissimilar exploration strategies in deep kernel learning as alternative hypotheses to establish the preponderant physical mechanisms behind non‐linear behaviors. The approach is general and can be applied to structure‐property relationships via multimodal scanning probe, electron, and chemical imaging.
Roll-over stability of tall buildings under wind loads is considered. The nonlinear nature of the problem is taken into account, including geometric, physical, and structural non-linearity. The ...problem is solved on the base of a system of linearized incremental equations of structural mechanics that describes the behavior of a system tall building - foundation soil. Several methods are examined for solving nonlinear problems of roll-over stability, specifically: 1) deformation method of systems equilibrium states tracing; 2) method of linearization of nonlinear equations and systems equilibrium states tracing; 3) method of linearization of nonlinear physical relations of a systems with constructive, static, geometric nonlinearity; 4) method of linearization of nonlinear physical relations of a system with constructive nonlinearity based on nonlinear incremental structural mechanics; 5) method of the deformation process tracing for a physically nonlinear soil base, given the increase of discharge zones and constructive nonlinearity. Each of these methods is used to solve a model task. These tasks take into account roll-over stability of high structures under action of wind loads. In general, the problem of roll-over stability of a high object can be represented as repeatedly nonlinear one with various types of non-linearity. In this regard, in the practice of high-rise buildings designing, it is necessary to develop scientifically and methodically substantiated methods of assessing roll-over stability, considering non-linear factors. Taking these factors into account will make it possible to assess the roll-over stability of a high-rise object more accurate.
The paper systematically studies the impact of a range of recent advances in convolution neural network (CNN) architectures and learning methods on the object categorization (ILSVRC) problem. The ...evaluation tests the influence of the following choices of the architecture: non-linearity (ReLU, ELU, maxout, compatability with batch normalization), pooling variants (stochastic, max, average, mixed), network width, classifier design (convolutional, fully-connected, SPP), image pre-processing, and of learning parameters: learning rate, batch size, cleanliness of the data, etc.
The performance gains of the proposed modifications are first tested individually and then in combination. The sum of individual gains is greater than the observed improvement when all modifications are introduced, but the “deficit” is small suggesting independence of their benefits.
We show that the use of 128 × 128 pixel images is sufficient to make qualitative conclusions about optimal network structure that hold for the full size Caffe and VGG nets. The results are obtained an order of magnitude faster than with the standard 224 pixel images.
The tourism sector has been deeply ravaged by the COVID-19 pandemic as many individuals abstained entirely from travel. Thus, before contemplating the trajectory of the sector’s recovery, it is ...essential to understand individuals’ travel intentions both during and after the pandemic. The present study contributes in this regard by examining the impact of individuals’ personality traits categorised by the five-factor model, or the Big Five, on their leisure travel intentions during and after the pandemic. To this end, we utilised an artificial neural network (ANN) approach to analyse 500 responses from individuals residing in Japan. The results reveal that extraversion has the strongest relative influence on intentions to travel during the pandemic, whereas openness to experience has the strongest influence on travel intentions after the pandemic. This study is the first of its kind to examine the influence of the Big Five personality traits on travel intentions in the context of a pandemic.
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