A Scalable Algorithm for Sparse Portfolio Selection

The sparse portfolio selection problem is one of the most famous and frequently studied problems in the optimization and financial economics literatures. In a universe of risky assets, the goal is to construct a portfolio with maximal expected return and minimum variance, subject to an upper bound on the number of positions, linear inequalities, and minimum investment constraints. Existing certifiably optimal approaches to this problem have not been shown to converge within a practical amount of time at real-world problem sizes with more than 400 securities. In this paper, we propose a more scalable approach. By imposing a ridge regularization term, we reformulate the problem as a convex binary optimization problem, which is solvable via an efficient outer-approximation procedure. We propose various techniques for improving the performance of the procedure, including a heuristic that supplies high-quality warm-starts, and a second heuristic for generating additional cuts that strengthens the root relaxation. We also study the problem’s continuous relaxation, establish that it is second-order cone representable, and supply a sufficient condition for its tightness. In numerical experiments, we establish that a conjunction of the imposition of ridge regularization and the use of the outer-approximation procedure gives rise to dramatic speedups for sparse portfolio selection problems.

A Real-World Exploration of Green Human Resources and Sustainability Education in Hyper-Connected and Technology-Driven Organizations

The early concept of corporate social responsibility (CSR), also frequently described as corporate citizenship or sustainability, grew from the seminal 1987 Brundtland Report, commissioned by the United Nations. CSR has progressed to the standpoint that in organizations necessitates the synchronized fulfillment of the firm's economic, legal, ethical, and philanthropic responsibilities in ways that focus strategy, operations, and behaviors towards the promotion of sustainability from a construct where organizational strategy is concerned with the care of the planet, people, and profit. This paper explores the role of green human resources interventions focused on creating organizational cultures that support sustainability in technical and hyper-connected organizations. The paper is not intended to reconstitute theory. The paper is highly theoretical and practical with the intention of influencing the world practice from practical real-world problem approaches and theories from the literature.

Developing Pre-Service Teachers' STEM Skills With Raspberry Pi Activities

The aim of this study was to explore the development of pre-service primary school teachers' STEM skills with Raspberry Pi activities. Data were collected from 16 pre-service teachers through semi-constructed interviews, reports, and a questionnaire. The results of the qualitative analysis showed that the participants developed the STEM skills mentioned in the literature such as confidence, computing, problem-solving, creativity, technological skills, and enhanced the learning potential of robotics. Moreover, the ready-to-use Python codes on Raspberry Pi platform could be an effective strategy for pre-service teachers with lack of programming to provide solutions on real-world problems. In addition, the participants successfully connected the Raspberry Pi, sensor kits, and Python scripts with real-world problems. This equipment motivated them to transpose a real-world problem to school knowledge. According to the results the combination of Raspberry Pi, sensors, and Python helped the participants upskill in computing.

Teaching Graduate Technology Management Students With Innovative Learning Approaches Around Cybersecurity

Every year in the U.S., 40,000 jobs for information security analysts go unfilled, and employers are struggling to fill 200,000 other cybersecurity related roles. Colleges and universities have created certificates, undergraduate, and graduate programs to train professionals in these job roles. This issue becomes more complicated when you explore the that competent workers in this field need more than just book knowledge to be effective. Engaged and experiential learning approaches encourages experimentation and expanding teaching cybersecurity beyond the use of just classroom lectures, textbooks, and PowerPoint slides. The use of experiential and scenario-based learning approaches helps students to develop real-world problem solving and critical thinking skills that demonstrate expertise beyond course grades and degrees. Developing the ability to strategic and adaptive is vital to be effective. This case study research intends not to reconstitute theory but to influence the practice of cybersecurity education through the use of innovative applied and engaged learning approaches.

A Modified Jellyfish Search Optimizer with Opposition Based Learning and Biased Passive Swarm Motion

Jellyfish Search Optimizer (JSO) is one of the latest nature inspired optimization algorithms. This paper aims to improve the convergence speed of the algorithm. For the purpose, it identifies two modifications to form a proposed variant. First, it proposes improvement of initial population using Opposition based Learning (OBL). Then it introduces a probability-based replacement of passive swarm motion into moves biased towards the global best. OBL enables the algorithm to start with an improved set of population. Biased moves towards global best improve the exploitation capability of the algorithm. The proposed variant has been tested over 30 benchmark functions and the real world problem of 10-bar truss structure design optimization. The proposed variant has also been compared with other algorithms from the literature for the 10-bar truss structure design. The results show that the proposed variant provides fast convergence for benchmark functions and accuracy better than many algorithms for truss structure design.

A Fair Allocation Algorithm for Predictive Police Patrolling

We address the tradeoff of developing good predictive models for police allocation vs. optimally deploying police officers over a city in a way that does not imply an unfair allocation of resources. We modify the fair allocation algorithm of [1] to tackle a real world problem: crime in the city of Bogota, Colombia. Our approach allows for more sophisticated prediction models and we ´ show that the whole methodology outperforms the current police allocating mechanism in the city. Results show that even with a simple model such as a Kernel Density Estimation of crime, one can have much better prediction than the current police model and, at the same time, mitigate fairness concerns. Although we can not provide general performance guarantees, our results apply to a real life problem and should be seriously considered by policy makers.

Parallel Algorithms of Well-Balanced and Weighted Average Flux for Shallow Water Model Using CUDA

Compute Unified Device Architecture (CUDA) implementations are presented of a well-balanced finite volume method for solving a shallow water model. The CUDA platform allows programs to run parallel on GPU. Four versions of the CUDA algorithm are presented in addition to a CPU implementation. Each version is improved from the previous one. We present the following techniques for optimizing a CUDA program: limiting register usage, changing the global memory access pattern, and using loop unroll. The accuracy of all programs is investigated in 3 test cases: a circular dam break on a dry bed, a circular dam break on a wet bed, and a dam break flow over three humps. The last parallel version shows 3.84x speedup over the first CUDA implementation. We use our program to simulate a real-world problem based on an assumed partial breakage of the Srinakarin Dam located in Kanchanaburi province, Thailand. The simulation shows that the strong interaction between massive water flows and bottom elevations under wet and dry conditions is well captured by the well-balanced scheme, while the optimized parallel program produces a 57.32x speedup over the serial version.

A Novel Description on Vague Graph with Application in Transportation Systems

Fuzzy graph (FG) models embrace the ubiquity of existing in natural and man-made structures, specifically dynamic processes in physical, biological, and social systems. It is exceedingly difficult for an expert to model those problems based on a FG because of the inconsistent and indeterminate information inherent in real-life problems being often uncertain. Vague graph (VG) can deal with the uncertainty associated with the inconsistent and determinate information of any real-world problem, where FGs many fail to reveal satisfactory results. Regularity definitions have been of high significance in the network heterogeneity study, which have implications in networks found across biology, ecology, and economy; so, adjacency sequence (AS) and fundamental sequences (FS) of regular vague graphs (RVGs) are defined with examples. One essential and adequate prerequisite has been ascribed to a VG with maximum four vertices is that it should be regular based on the adjacency sequences concept. Likewise, it is described that if ζ and its principal crisp graph (CG) are regular, then all the nodes do not have to have the similar AS. In the following, we obtain a characterization of vague detour (VD) g-eccentric node, and the concepts of vague detour g-boundary nodes and vague detour g-interior nodes in a VG are examined. Finally, an application of vague detour g-distance in transportation systems is given.

Involving im/migrant community members for knowledge co-creation: the greater the desired involvement, the greater the need for capacity building

Researchers need to observe complex problems from various angles and contexts to create workable, effective and sustainable solutions. For complex societal problems, including health and socioeconomic disparities, cross-sectoral collaborative research is crucial. It allows for meaningful interaction between various actors around a particular real-world problem through a process of mutual learning. This collaboration builds a sustainable, trust-based partnership among the stakeholders and allows for a thorough understanding of the problem through a solution-oriented lens. While the created knowledge benefits the community, the community is generally less involved in the research process. Often, community members are engaged to collect data or for consultancy and knowledge dissemination; however, they are not involved in the actual research process, for example, developing a research question and using research tools such as conducting focus groups, analysis and interpretation. To be involved on these levels, there is a need for building community capacity for research. However, due to a lack of funds, resources and interest in building capacity on the part of both researchers and the community, deeper and meaningful involvement of community members in research becomes less viable. In this article, we reflect on how we have designed our programme of research—from involving community members at different levels of the research process to building capacity with them. We describe the activities community members participated in based on their needs and capacity. Capacity-building strategies for each level of involvement with the community members are also outlined.

NC CAN COVID-19 Action Network: Project ECHO in North Carolina

Experts in geriatrics, infection control and nursing home administration joined the ECHO Hub team led by The Carolina Geriatrics Workforce Enhancement Program (CGWEP) at the University of North Carolina at Chapel Hill (UNC). Ninety-two of North Carolina’s 423 nursing homes enrolled in a 16-week videoconference series designed to address clinical, logistical, and leadership issues related to COVID-19. The CGWEP coordinated recruitment with two other Training Centers at UNC Family Medicine and the Mountain Area Health Education Center, reaching 58% of all NC nursing homes (N=245). Faculty used curriculum and pre-recorded videos provided by the Institute for Healthcare Improvement (IHI). Discussions demonstrated real-world problem solving as participants applied what they learned to local conditions. Quality Improvement (QI) experts from IHI mentored participants in gathering data and completing Plan, Do, Study, Act cycles to better respond to the challenges of COVID-19 among a critically vulnerable population.