HMDS: A Makespan Minimizing DAG Scheduler for Heterogeneous Distributed Systems

The problem of scheduling Directed Acyclic Graphs in order to minimize makespan ( schedule length ), is known to be a challenging and computationally hard problem. Therefore, researchers have endeavored towards the design of various heuristic solution generation techniques both for homogeneous as well as heterogeneous computing platforms. This work first presents HMDS-Bl , a list-based heuristic makespan minimization algorithm for task graphs on fully connected heterogeneous platforms. Subsequently, HMDS-Bl has been enhanced by empowering it with a low-overhead depth-first branch and bound based search approach, resulting in a new algorithm called HMDS . HMDS has been equipped with a set of novel tunable pruning mechanisms, which allow the designer to obtain a judicious balance between performance ( makespan ) and solution generation times, depending on the specific scenario at hand. Experimental analyses using randomly generated DAGs as well as benchmark task graphs, have shown that HMDS is able to comprehensively outperform state-of-the-art algorithms such as HEFT , PEFT , PPTS , etc., in terms of archived makespans while incurring bounded additional computation time overhead.

A modified cut-set method for mechanical subassembly identification

Purpose The purpose of this paper is to perform a comparative assessment on working of the existed subassembly identification (SI) methods, which are widely practiced during the product development stage and to propose an improved method for solving the SI problem in assembly sequence planning (ASP). Design/methodology/approach The cut-set method is found as a suitable method among various knowledge-based methods such as the theory of loops, theory of connectors and theory of clusters for the workability enhancement to meet the current requirements. Necessary product information is represented in the matrix format by replacing the traditional AND/OR graphs and the advanced predicates are included in the evaluation criteria. Findings The prominent methods in SI are followed a few of the predicates to avoid complexity in solution generation. The predicate consideration is found as the most influencing factor in eliminating the infeasible part combinations at SI. However, the quality of identified subassemblies without advanced predicates is not influencing the solution generation phase but practical applicability is affecting adversely. Originality/value The capability of performing SI by the cut-set method is improved to deal with the complex assembly configurations. The improved method is tested by applying on different assembly configurations and the effectiveness is compared with other existent methods of ASP along with the conventional method.

Exploration of the Dynamics of Neuro-Cognition During TRIZ

The Theory of Inventive Problem Solving (TRIZ) method and toolkit provides a well-structured approach to support engineering design with pre-defined steps: interpret and define the problem, search for standard engineering parameters, search for inventive principles to adapt, and generate final solutions. The research presented in this paper explores the neuro-cognitive differences of each of these steps. We measured the neuro-cognitive activation in the prefrontal cortex (PFC) of 30 engineering students. Neuro-cognitive activation was recorded while students completed an engineering design task. The results show a varying activation pattern. When interpreting and defining the problem, higher activation is found in the left PFC, generally associated with goal directed planning and making analytical. Neuro-cognitive activation shifts to the right PFC during the search process, a region usually involved in exploring the problem space. During solution generation more activation occurs in the medial PFC, a region generally related to making associations. The findings offer new insights and evidence explaining the dynamic neuro-cognitive activations when using TRIZ in engineering design.

Interactive Graph Stream Analytics in Arkouda

Data from emerging applications, such as cybersecurity and social networking, can be abstracted as graphs whose edges are updated sequentially in the form of a stream. The challenging problem of interactive graph stream analytics is the quick response of the queries on terabyte and beyond graph stream data from end users. In this paper, a succinct and efficient double index data structure is designed to build the sketch of a graph stream to meet general queries. A single pass stream model, which includes general sketch building, distributed sketch based analysis algorithms and regression based approximation solution generation, is developed, and a typical graph algorithm—triangle counting—is implemented to evaluate the proposed method. Experimental results on power law and normal distribution graph streams show that our method can generate accurate results (mean relative error less than 4%) with a high performance. All our methods and code have been implemented in an open source framework, Arkouda, and are available from our GitHub repository, Bader-Research. This work provides the large and rapidly growing Python community with a powerful way to handle terabyte and beyond graph stream data using their laptops.

Optimal Reactive Power Generation for Transmission Power Systems Considering Discrete Values of Capacitors and Tap Changers

In this paper, an improved coyote optimization algorithm (ICOA) is developed for determining control parameters of transmission power networks to deal with an optimal reactive power dispatch (ORPD) problem. The performance of ICOA method is superior to its conventional coyote optimization algorithm (COA) thanks to modifications of two new solution generations of COA. COA uses a center solution to generate an update step size in the first solution generation and produced one new solution by using random factors to diversify the search space in the second solution generation. By tackling the drawbacks of COA, ICOA can reduce control parameters and computation steps, shorten execution time, and provide better results. ICOA is compared to its conventional COA for three standard IEEE systems of 30-, 57-, and 118-buses with continuous and discrete control variables. Moreover, three other algorithms such as water cycle algorithm (WCA), salp swarm algorithm (SSA), and sunflower optimization algorithm (SFOA) have been also implemented for further investigation of the real performance of the proposed method. All the applied methods are metaheuristic algorithms based on population and randomization. The result comparison from the test systems has indicated that ICOA can provide higher solution quality than other methods with reasonable execution time. Therefore, ICOA is a reliable tool for finding optimal solutions of the ORPD problem.

Alleviating Environmental Health Disparities Through Community Science and Data Integration

Environmental contamination is a fundamental determinant of health and well-being, and when the environment is compromised, vulnerabilities are generated. The complex challenges associated with environmental health and food security are influenced by current and emerging political, social, economic, and environmental contexts. To solve these “wicked” dilemmas, disparate public health surveillance efforts are conducted by local, state, and federal agencies. More recently, citizen/community science (CS) monitoring efforts are providing site-specific data. One of the biggest challenges in using these government datasets, let alone incorporating CS data, for a holistic assessment of environmental exposure is data management and interoperability. To facilitate a more holistic perspective and approach to solution generation, we have developed a method to provide a common data model that will allow environmental health researchers working at different scales and research domains to exchange data and ask new questions. We anticipate that this method will help to address environmental health disparities, which are unjust and avoidable, while ensuring CS datasets are ethically integrated to achieve environmental justice. Specifically, we used a transdisciplinary research framework to develop a methodology to integrate CS data with existing governmental environmental monitoring and social attribute data (vulnerability and resilience variables) that span across 10 different federal and state agencies. A key challenge in integrating such different datasets is the lack of widely adopted ontologies for vulnerability and resiliency factors. In addition to following the best practice of submitting new term requests to existing ontologies to fill gaps, we have also created an application ontology, the Superfund Research Project Data Interface Ontology (SRPDIO).

Collaborative idea exchange and material tinkering influence families’ creative engineering practices and products during engineering programs in informal learning environments

Purpose This paper aims to investigate how families’ sociomaterial experiences in engineering programs held in libraries and a museum influence their creative engineering practices and the creativity expressed in their products derived from their inquiry-driven engineering activities. Design/methodology/approach This research project takes a naturalistic inquiry using qualitative and quantitative analyses based on video records from activities of 31 parent–child pairs and on creativity assessment of products that used littleBits as prototyping tools. Findings Families engaged in two sociomaterial experiences related to engineering – collaborative idea exchange and ongoing generative tinkering with materials – which supported the emergence of novel ideas and feasible solutions during the informal engineering programs. Families in the high novelty score group experienced multiple instances of collaborative idea exchange and ongoing generative tinkering with materials, co-constructed through parent-child collaboration, that were expansive toward further idea and solution generation. Families in the low novelty score group experienced brief collaborative idea exchange and material tinkering with specific idea suggestions and high involvement from the parent. An in-depth case study of one family further illustrated that equal engagement by the parent and child as they tinkered with the technology supported families’ creative engineering practices. Originality/value This analysis adds to the information sciences and learning sciences literatures with an account that integrates methodologies from sociocultural and engineering design research to understand the relationship between families’ engagement in creative engineering practices and their products. Implications for practitioners include suggestions for designing spaces to support families’ collaborative idea exchange and ongoing generative tinkering to facilitate the development of creative engineering practices during short-term engineering programs.

Cavin1 intrinsically disordered domains are essential for fuzzy electrostatic interactions and caveola formation

Caveolae are spherically shaped nanodomains of the plasma membrane, generated by cooperative assembly of caveolin and cavin proteins. Cavins are cytosolic peripheral membrane proteins with negatively charged intrinsically disordered regions that flank positively charged α-helical regions. Here, we show that the three disordered domains of Cavin1 are essential for caveola formation and dynamic trafficking of caveolae. Electrostatic interactions between disordered regions and α-helical regions promote liquid-liquid phase separation behaviour of Cavin1 in vitro, assembly of Cavin1 oligomers in solution, generation of membrane curvature, association with caveolin-1, and Cavin1 recruitment to caveolae in cells. Removal of the first disordered region causes irreversible gel formation in vitro and results in aberrant caveola trafficking through the endosomal system. We propose a model for caveola assembly whereby fuzzy electrostatic interactions between Cavin1 and caveolin-1 proteins, combined with membrane lipid interactions, are required to generate membrane curvature and a metastable caveola coat.

An Elaborate Preprocessing Phase (p3) in Composition and Optimization of Business Process Models

Business process optimization (BPO) has become an increasingly attractive subject in the wider area of business process intelligence and is considered as the problem of composing feasible business process designs with optimal attribute values, such as execution time and cost. Despite the fact that many approaches have produced promising results regarding the enhancement of attribute performance, little has been done to reduce the computational complexity due to the size of the problem. The proposed approach introduces an elaborate preprocessing phase as a component to an established optimization framework (bpoF) that applies evolutionary multi-objective optimization algorithms (EMOAs) to generate a series of diverse optimized business process designs based on specific process requirements. The preprocessing phase follows a systematic rule-based algorithmic procedure for reducing the library size of candidate tasks. The experimental results on synthetic data demonstrate a considerable reduction of the library size and a positive influence on the performance of EMOAs, which is expressed with the generation of an increasing number of nondominated solutions. An important feature of the proposed phase is that the preprocessing effects are explicitly measured before the EMOAs application; thus, the effects on the library reduction size are directly correlated with the improved performance of the EMOAs in terms of average time of execution and nondominated solution generation. The work presented in this paper intends to pave the way for addressing the abiding optimization challenges related to the computational complexity of the search space of the optimization problem by working on the problem specification at an earlier stage.