Tracking a Circular Economy Transition Through Jobs: Method Development and Application in Two Cities

The methodology presented in this paper produces a circular economy jobs (CE jobs) measure. Using jobs as a proxy indicator, these measure gives cities a robust number to indicate progress toward the circular economy and is designed to serve as a first step in developing a circular economy strategy. The CE jobs measure tracks the inputs and outputs of goods in a city's “boundaries” through the material import dependency of the city's economic sectors. At the same time, tracking and assessing the circularity of the local jobs in these economic sectors will also provide city leaders with an indication of which sectors circularity is happening and could potentially happen. This paper also concludes that the process of coming to the CE jobs has two parts, the first more relevant to the local government and the second better influenced by the national government. Both need to come together for a truly circular local economy to happen.

Neuro-Informed Learning

As the epicenter for learning activities, the brain is the coordinator of all actions associated with collecting information, organizing it, storing it, and eventually re-organizing it for application in the real world. And yet, to date, little has been known about what happens within the brain during learning activities. We have operated based on a black box set of assumptions that results in researchers testing inputs and outputs but lacking a true understanding of what happens between those two endpoints. However, the fields of neuroscience and cognitive science, along with neuro-technology engineers, have simultaneously been studying the brain and developing apparatus that allow us to understand what is happening in the brain in real-time during learning. The implications of these capabilities and a deeper understanding of learning are boundless. Accordingly, this chapter will delve into four key areas: (1) research and theories, (2) cognitive readiness and comprehension, (3) neuro-technology data, and (4) the necessary evolution of teachers to facilitators.

A Developed Data Envelopment Analysis Model for Efficient Sustainable Supply Chain Network Design

In recent years, various organizations have focused on considering the sustainability concept in the supply chain (SC) design. Managers try to increase the sustainability of SCs to achieve a competitive advantage in today’s growing market. Designing a sustainable supply chain (SSC) by integrating economic, social, and environmental dimensions affects the SC’s overall performance. To achieve the SSC, decision makers (DMs) are required to evaluate different strategies and then apply the most effective one to design SC networks. This study proposes an assessment approach based on the network data envelopment analysis (DEA) to choose an efficient strategy for each stage of an SSC network. This approach seeks to provide a sustainable design with DMs to avoid imposing additional costs on SCs that result from noncompliance with environmental and social issues. To this end, we consider sustainability-concept-related inputs and outputs in the network DEA model to choose the most efficient strategy for SSC design. The strategy selection process can become an important issue, especially when SCs active in a competitive environment. Accordingly, a crucial feature of the presented model is considering the issue of competition to choose the efficient strategy. Furthermore, undesirable outputs and feedbacks and independent inputs and outputs for intermediate stages in the network system are considered to create a structure compatible with the real world. The output of the proposed approach enables DMs to select the appropriate strategy for each stage of the SSC network to maximize the aggregate efficiency of the network.

Improvement of multi-digital multiplicating devices structures in different theoretical and numerical bases

The article proposes methods for improving the structures of multi-bit multipliers, which are characterized by increased speed, reduced structural complexity of the device and reduced structural complexity of inputs and outputs depending on the bit multipliers (512-2048 bits), respectively (1024- 4096) times, compared with known multipliers based on classic single-digit full adders. Optimization of structures of multi-bit multipliers is offered. Comparative estimates of structural, functional and relative functional and structural complexities of their circuit implementations are given. The use of optimized circuit solutions of multipliers allows to significantly improve the system characteristics of complex computing devices with a large number of such components in the crystals of microelectronic technologies.

Estimating dynamic cutting forces of machine tools from measured vibrations using sparse regression with nonlinear function basis

Estimating relationships between system inputs and outputs can provide insight to system characteristics. Furthermore, with an established input-output relationship and measured output, one can estimate the corresponding input to the system. Traditionally, the relationship between input and output can be represented with transfer functions or frequency response functions. However, those functions need to be built on physical parameters, which are hard to obtain in practical systems. Also, the reverse problem of solving for the input with a known/measured output is often more difficult to solve than the forward problem. This paper aims to explore the data-driven input-output relationship between system inputs and outputs for system diagnostics, prognostics, performance prediction, and control. A data-driven relationship can provide a new way for system input estimation or output prediction. In this paper, a sparse linear regression model with nonlinear function basis is proposed for input estimation with measured outputs. The proposed method explicitly creates a nonlinear function basis for the regression relationship. A threshold-based sparse linear regression is designed to ensure sparsity. The method is tested with experimental data from a spindle testbed that simulates cutting forces within machine tools. The results show that the proposed approach can predict the input force based on the measured vibration response with high accuracy. The current model is also compared with neural networks, which is another nonlinear regression method.

Measuring Avalanche Properties on RC4 Stream Cipher Variants

In the last three decades, the RC4 has been the most cited stream cipher, due to a large amount of research carried out on its operation. In this sense, dissimilar works have been presented on its performance, security, and usability. One of the distinguishing features that stand out the most is the sheer number of RC4 variants proposed. Recently, a weakness has been reported regarding the existence of statistical dependence between the inputs and outputs of the RC4, based on the use of the strict avalanche criterion and the bit independence criterion. This work analyzes the influence of this weakness in some of its variants concerning RC4. The five best-known variants of RC4 were compared experimentally and classified into two groups according to the presence or absence of such a weakness.

Functional Organisation of the Mouse Superior Colliculus

The superior colliculus (SC) is a highly conserved area of the mammalian midbrain that is widely implicated in the organisation and control of behaviour. SC receives input from a large number of brain areas, and provides outputs to a large number of areas. The convergence and divergence of anatomical connections with different areas and systems provides challenges for understanding how SC contributes to behaviours. Recent work in mouse has provided large anatomical datasets, and a wealth of new data from experiments that identify and manipulate different cells within SC, and its inputs and outputs. These data offer an opportunity to better understand the functional roles of SC. However, some of the observations appear, at first sight, to be contradictory. Here we review this recent work and suggest a simple framework which can capture the observations, and that requires only a small change to previous models. Specifically, the functional organisation of SC can be explained by supposing that three largely distinct circuits support three largely distinct classes of behaviour - arrest, turning towards, and the triggering of escape or pursuit. These behavioural classes are supported by the optic, intermediate and deep layers respectively.