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Purpose of Review In this article, we examine the intersection of human migration and climate change. Growing evidence that changing environmental and climate conditions are triggers for displacement, whether voluntary or forced, adds a powerful argument for profound anticipatory engagement. Recent Findings Climate change is expected to displace vast populations from rural to urban areas, and when life in the urban centers becomes untenable, many will continue their onward migration elsewhere (Wennersten and Robbins 2017 ; Rigaud et al. 2018 ). It is now accepted that the changing climate will be a threat multiplier, will exacerbate the need or decision to migrate, and will disproportionately affect large already vulnerable sections of humanity. Worst-case scenario models that assume business-as-usual approaches to climate change predict that nearly one-third of the global population will live in extremely hot (uninhabitable) climates, currently found in less than 1% of the earth’s surface mainly in the Sahara. Summary We find that the post–World War II regime designed to receive European migrants has failed to address population movement in the latter half of the twentieth century fueled by economic want, globalization, opening (and then closing) borders, civil strife, and war. Key stakeholders are in favor of using existing instruments to support a series of local, regional, and international arrangements to protect environmental migrants, most of whom will not cross international borders. The proposal for a dedicated UN agency and a new Convention has largely come from academia and NGOs. Migration is now recognized not only as a consequence of instability but as an adaptation strategy to the changing climate. Migration must be anticipated as a certainty, and thereby planned for and supported.

Optimization problems face random constraint violations when uncertainty arises in constraint parameters. Effective ways of controlling such violations include risk constraints, e.g., chance constraints and conditional Value-at-Risk constraints. This paper studies these two types of risk constraints when the probability distribution of the uncertain parameters is ambiguous. In particular, we assume that the distributional information consists of the first two moments of the uncertainty and a generalized notion of unimodality. We find that the ambiguous risk constraints in this setting can be recast as a set of second-order cone (SOC) constraints. In order to facilitate the algorithmic implementation, we also derive efficient ways of finding violated SOC constraints. Finally, we demonstrate the theoretical results via computational case studies on power system operations.

To improve the performance of Radio Frequency Identification (RFID) multi-label systems, the multi-label network structure needs to be quickly located and optimized. A multi-label location measurement method based on the NLM–Harris algorithm is proposed in this paper. Firstly, multi-label geometric distribution images are obtained through a label image acquisition system of a multi-label semi-physical simulation platform with two vertical Charge-Coupled Device (CCD) cameras, and Gaussian noise is added to the image to simulate thermoelectric interference. Then, a fast NLM algorithm that optimizes the kernel coefficient acquisition speed is used for image denoising. Finally, the Harris corner algorithm is used to obtain the corner points of the images. After screening the diagonal points, the pixel coordinates of the preset origin and the four corners of the labels are obtained. Furthermore, the actual coordinates of the labels are obtained according to the pixel relationship. The results show that the average absolute errors of x, y, and z coordinates are 0.773 mm, 0.782 mm, and 0.807 mm, respectively. In addition, the relative errors are 1.659%, 2.260%, and 0.258%, which shows the high location accuracy of the multi-label network. It is of great significance to measure and optimize the performance of multi-label systems.

The bat algorithm (BA) is a novel evolutionary optimization algorithm, most studies of which have been performed with low-dimensional problems. To test and improve the global search ability of BA with large-scale problems, two new variants using principal component analysis (PCA_BA and PCA_LBA) are designed in this paper. A correlation threshold and generation threshold are determined using the golden section method to enhance the effectiveness of this new strategy. To test performance, CEC’2008 large-scale benchmark functions are utilized and compared with other algorithms; simulation results indicate the validity of this modification.

With the continuous progress of The Times, the traditional paper graphic design has been unable to meet the needs of graphic design, through the use of computer aided technology in graphic design visual aesthetics, can effectively improve the aesthetic sense of the work. In this paper, first of all, the visual aesthetics of graphic design and graphic design of the construction type and computer aided technology analysis, at the same time, the specific application of computer aided technology in graphic design, for the reader’s reference.

Outlier mining constitutes an essential aspect of modern data analytics, focusing on the identification and interpretation of anomalous observations. Conventional density-based local outlier detection methodologies frequently exhibit limitations due to their inherent lack of data preprocessing capabilities, consequently demonstrating degraded performance when applied to novel or heterogeneous datasets. Moreover, the computation of the outlier factor for each sample in these algorithms results in considerably higher computational cost, especially in the case of large datasets. This paper introduces a local outlier detection method named FOLOF (FCM Objective Function-based LOF) through an examination of existing algorithms. The approach starts by applying the elbow rule to determine the optimal number of clusters in the dataset. Subsequently, the FCM objective function is employed to prune the dataset to extract a candidate set of outliers. Finally, a weighted local outlier factor detection algorithm computes the degree of anomaly for each sample in the candidate set. For the analysis, the Golden Section method was used to classify the outliers. The underlying causes of these outliers can be revealed by exploring the anomalous properties of each outlier data point through the outlier factors of each dimension property. This approach has been validated on artificial datasets, the UCI dataset, and an NBA player dataset to demonstrate its effectiveness.

There is a huge requirement for power systems to reduce power losses. Adding distributed generators (DGs) is the most common approach to achieving lower power losses. However, several challenges arise, such as determining the ideal size as well as location of the utilized distributed generators. Most of the existing methods do not consider the variety of load types, the variety and size of the utilized DGs besides reducing the convergence time and enhancing the optimization results. The paper performed an optimization algorithm that integrated a golden search-based flower pollination algorithm and fitness-distance balance (FDB) to find out the optimal size as well as the location of the distributed generators. It was then compared with different optimization methods to determine the best optimization technique, and it was determined to be the best technique. In addition, different types of DGs are considered, including solar energy, wind energy, and biogas, along with optimizing the size of the utilized DGs to reduce the system cost. Testing with different types of bus systems, and different types of DGs in a radial distribution system was done to reveal that the modified flower pollination with golden section search was superior in comparison to others with regards to convergence and power loss reduction.

Industrial robots find widespread application in machining, part assembly, and welding processes. This paper focuses on the precise control of industrial robots, using the permanent magnet synchronous motor (PMSM) as the driving device of the manipulator. By constructing a mathematical model of the PMSM and implementing a vector control strategy, we design an adaptive control algorithm based on the feature model. We also introduce the golden section adaptive control and the position outer-loop control, which are based on the nonlinear proportional regulator. This approach enables the creation of industrial robots that utilize high-precision servo control technology. After analyzing the response speed and positional accuracy of the servo control technology through testing experiments, we integrated this technology into the design of the automatic control system for industrial robots and conducted simulation tests to explore its performance in various applications. The results demonstrate the superior speed response speed and position control accuracy of the servo control technology; the response times for speed and position are approximately 0.2 seconds and 1 second, respectively, with a maximum deviation of 31.75 rpm when the speed remains constant. The industrial robot control system’s structural displacement and tool switching time are both within 2.88 mm and 0.65 s, and it has a large torque. The study confirms that servo control technology and automatic control of industrial robots have superior performance.

In this work, we generalize and describe the golden ratio in multi-dimensional vector spaces. We also introduce the concept of the law of similarity for multidimensional vectors. Initially, the law of similarity was derived for one-dimensional vectors. Although it operated with the values of the ratio of the parts of the whole, it created linear dimensions (a line is one-dimensional). The presented concept of the general golden ratio (GGR) for the vectors in a multidimensional space is described in detail with equations. It is shown that the GGR is a function of one or more angles, which is the solution to the golden equation described in this work. The main properties of the GGR are described, with illustrative examples. We introduce and discuss the concept of the golden pair of vectors, as well as the concept of a set of similarities for a given vector. We present our vision on the theory of the golden ratio for triangles and describe similarity triangles in detail and with illustrative examples.

The traditional remote graphic design system cannot provide users with the basis of design geometry theory, which leads to the disharmony of the design works. For this reason, a computer-aided remote graphic design system is proposed. Determine the image import direction, obtain the contour ratio, compare it with different dynamic rectangle ratios, and give the difference between the image and the standard value and the recommended adjustment value. The experimental results show that the works designed by the design system conform to geometry, are visually harmonious, and the evaluation results are good.