Building Virtual Age-Friendly Communities in Minecraft

A key theme of Age-Friendly Honolulu’s initiative is intergenerational exchange. The initiative believes that youth need to be aware of aging issues, demographic change, and think critically about the potential impact on their lives. The goal of the Age-Friendly Honolulu Youth Engagement Initiative is to teach youth about the role of the built environment and community features in promoting active aging and engagement among older adults. This article describes a new program in the field that teaches youth and older adults to assess the built environment and then use design thinking and Minecraft (a popular video game in which players use 3-D blocks to create virtual lands) to develop and envision ideas to improve the livability of their neighborhood.     

试论数字色彩应用

介绍色彩数字编程和色彩数字设计方法。将数字的可变性应用到色彩编程和色彩设计之中,以期对艺术设计中手工颜料调配、提高色彩的准确度及色彩的复制与研究提供参考。     

数控加工与编程数字化教学资源建设

为培养高职数控技术专业学生的数控加工与编程能力,通过调研典型制造型企业,以其岗位能力需求为基础,以企业的实践过程为任务流程,规划了数控加工与编程课程的教学体系,并构建了基于网络化教学平台的数字化教学资源库。     

Evaluation of multi-objective optimization approaches for solving green supply chain design problems

This paper evaluates the applicability of different multi-objective optimization methods for environmentally conscious supply chain design. We analyze a case study with three objectives: costs, CO₂ and fine dust (also known as PM – Particulate Matters) emissions. We approximate the Pareto front using the weighted sum and epsilon constraint scalarization methods with pre-defined or adaptively selected parameters, two popular evolutionary algorithms, SPEA2 and NSGA-II, with different selection strategies, and their interactive counterparts that incorporate Decision Maker׳s (DM׳s) indirect preferences into the search process. Within this case study, the CO₂ emissions could be lowered significantly by accepting a marginal increase of costs over their global minimum. NSGA-II and SPEA2 enabled faster estimation of the Pareto front, but produced significantly worse solutions than the exact optimization methods. The interactive methods outperformed their a posteriori counterparts, and could discover solutions corresponding better to the DM preferences. In addition, by adjusting appropriately the elicitation interval and starting generation of the elicitation, the number of pairwise comparisons needed by the interactive evolutionary methods to construct a satisfactory solution could be decreased.     

A Multi-Dimensional Conceptual Framework for Trauma-Informed Practice in Addictions Programming

Previous research has conceptualized trauma-informed practice in relation to five key values: safety, trust, choice, collaboration, and empowerment. This research identifies key organizational, programmatic, and interpersonal characteristics in community-based residential addictions treatment programming that exemplify each of these principles. Utilizing qualitative research methods, involving open-ended, one to one interviews with clients in residential substance misuse treatment (n = 41), respondents identified the importance of experiencing “safety” in relation to physical safety, confidentiality, reassurance, rule enforcement, and peer relationships. “Trust” was manifested in sharing, staff availability, nonjudgmental interactions, positive relationship dynamics, and caring. “Choice” was articulated in relation to individual needs, participation, opportunities, and focus of efforts. “Collaboration” was characterized in relation to opportunities for feedback, planning, goal setting, specificity, and support. Finally, “empowerment” was characterized by comfort in sharing, trigger management, trauma awareness, and understanding. The findings provide a conceptual framework for a trauma-informed social services organizational practice environment. Findings can inform adaptations to social service delivery processes and programs to become aligned with the values of trauma-informed practice. Future research can build on this framework by testing the study findings with quantitative methods along with replicating current methods in other social service delivery sectors.     

A counterexample to Beder's conjectures about Hadamard matrices

In this note we provide a counterexample which resolves conjectures about Hadamard matrices made in this journal. Beder [1998. Conjectures about Hadamard matrices. Journal of Statistical Planning and Inference 72, 7–14] conjectured that if H$\mathit{H}$ is a maximal m×n$m\times n$ row-Hadamard matrix then m is a multiple of 4; and that if n   is a power of 2 then every row-Hadamard matrix can be extended to a Hadamard matrix. Using binary integer programming we obtain a maximal 13×32$13\times 32$ row-Hadamard matrix, which disproves both conjectures. Additionally for n being a multiple of 4 up to 64, we tabulate values of m   for which we have found a maximal row-Hadamard matrix. Based on the tabulated results we conjecture that a m×n$m\times n$ row-Hadamard matrix with m?n-7$m?n-7$ can be extended to a Hadamard matrix.     

R-CMap—An open-source software for concept mapping

Planning and evaluating projects often involves input from many stakeholders. Fusing and organizing many different ideas, opinions, and interpretations into a coherent and acceptable plan or project evaluation is challenging. This is especially true when seeking contributions from a large number of participants, especially when not all can participate in group discussions, or when some prefer to contribute their perspectives anonymously. One of the major breakthroughs in the area of evaluation and program planning has been the use of graphical tools to represent the brainstorming process. This provides a quantitative framework for organizing ideas and general concepts into simple-to-interpret graphs. We developed a new, open-source concept mapping software called R-CMap, which is implemented in R. This software provides a graphical user interface to guide users through the analytical process of concept mapping. The R-CMap software allows users to generate a variety of plots, including cluster maps, point rating and cluster rating maps, as well as pattern matching and go-zone plots. Additionally, R-CMap is capable of generating detailed reports that contain useful statistical summaries of the data. The plots and reports can be embedded in Microsoft Office tools such as Word and PowerPoint, where users may manually adjust various plot and table features to achieve the best visual results in their presentations and official reports. The graphical user interface of R-CMap allows users to define cluster names, change the number of clusters, select rating variables for relevant plots, and importantly, select subsets of respondents by demographic criteria. The latter is particularly useful to project managers in order to identify different patterns of preferences by subpopulations. R-CMap is user-friendly, and does not require any programming experience. However, proficient R users can add to its functionality by directly accessing built-in functions in R and sharing new features with the concept mapping community.     

Semi-parametric segmentation of multiple series using a DP-Lasso strategy

We consider a semi-parametric approach to perform the joint segmentation of multiple series sharing a common functional part. We propose an iterative procedure based on Dynamic Programming for the segmentation part and Lasso estimators for the functional part. Our Lasso procedure, based on the dictionary approach, allows us to both estimate smooth functions and functions with local irregularity, which permits more flexibility than previous proposed methods. This yields to a better estimation of the functional part and improvements in the segmentation. The performance of our method is assessed using simulated data and real data from agriculture and geodetic studies. Our estimation procedure results to be a reliable tool to detect changes and to obtain an interpretable estimation of the functional part of the model in terms of known functions.     

Computing A-optimal and E-optimal designs for regression models via semidefinite programming

In semidefinite programming (SDP), we minimize a linear objective function subject to a linear matrix being positive semidefinite. A powerful program, SeDuMi, has been developed in MATLAB to solve SDP problems. In this article, we show in detail how to formulate A-optimal and E-optimal design problems as SDP problems and solve them by SeDuMi. This technique can be used to construct approximate A-optimal and E-optimal designs for all linear and nonlinear regression models with discrete design spaces. In addition, the results on discrete design spaces provide useful guidance for finding optimal designs on any continuous design space, and a convergence result is derived. Moreover, restrictions in the designs can be easily incorporated in the SDP problems and solved by SeDuMi. Several representative examples and one MATLAB program are given.     

Generalized minimum aberration mixed-level orthogonal arrays: A general approach based on sequential integer quadratically constrained quadratic programming

Orthogonal fractional factorial designs and in particular orthogonal arrays (OAs) are frequently used in many fields of application, including medicine, engineering, and agriculture. In this article, we present a methodology and an algorithm to find an OA, of given size and strength, which satisfies the generalized minimum aberration criterion. The methodology is based on the joint use of polynomial counting functions, complex coding of levels, and algorithms for quadratic optimization and puts no restriction on the number of levels of each factor.