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Digital computers were invented in the 1940s. They are sophisticated and versatile machines whose functioning is grounded in elaborate theory. Advances in theory and the availability of computers helped computer science to develop as an academic discipline, and university departments for the same started coming up in the 1960s. Computer science covers all phenomenon related to computers and consists primarily of man-made laws governing building, programming, and using computers. Computational thinking is a way of thinking influenced by computers and computer science. There are two schools of thought on computational thinking. The first school sees computational thinking as the use of computers to explore the world, while the other sees computational thinking as the application of concepts from computer science to solve real-world problems. Scholars typically agree that computational thinking has four essential components, viz., abstraction, decomposition, algorithm design, and generalization. Computational thinking is often feted by computer scientists as a useful skill that can be used by anybody anywhere. However, it is necessary to find out ways for successfully using computational thinking in domains other than computer science before it can be declared a universal skill. [ABSTRACT FROM AUTHOR]

Graph theory is considered one of the major subjects, and it also plays a significant role in the digital world. It has numerous uses in computer science, robot navigation, and chemistry. Graph theory is employed in forming the structures of different chemical networks, locating robots on a network, and troubleshooting computer networks. Additionally, it finds applications in scheduling airplanes and studying diffusion mechanisms. The current work investigates the metric dimension of the line graphs of the Bakelite and subdivided Bakelite networks. The results prove that these families of graphs do not have a constant metric dimension. The invention of Bakelite was influential in the development of modern plastics, and it has various applications in fields such as jewelry, clocks, toys, kitchenware, electrical, and sports industries. [ABSTRACT FROM AUTHOR]

It is particularly important to plan and design the rural landscape of the Qiang nationality, but the regional characteristics of the rural landscape of the Qiang nationality in the upper reaches of the Minjiang River must be maintained at the same time. On the basis of the regional culture formed by the regional geography and cultural environment of the Qiang ethnic group in the upper reaches of the Minjiang River, combined with the investigation of the Qiang ethnic group in the upper reaches of the Minjiang River, this study explores the formal characteristics and formation reasons of the rural landscape of the Qiang ethnic group in the upper reaches of the Minjiang River from the perspective of rural landscape planning and design. It summarizes the formal features of the Qiang rural landscape in the upper reaches of the Minjiang River in a graphical language. It analyzes the problems faced by traditional rural landscapes. It then summarizes the experience and deficiencies in practice through the research on the existing Qiang rural landscape planning and design. It analyzes the influence of the regional culture of the Qiang nationality on the planning and design of the rural landscape of the Qiang nationality and proposes a strategy for the planning and design of the rural landscape of the Qiang nationality. This study hopes that this can provide theoretical and practical reference for the planning and design methods of the Qiang rural landscape in the upper reaches of the Minjiang River and the protection, development, and dissemination of the Qiang culture in the upper reaches of the Minjiang River. Experiments show that there are 764 settlements when the radius of the river buffer is within 500 meters. [ABSTRACT FROM AUTHOR]

In the current era of increasingly complex social life, as people's demand quality is getting higher and higher, the solution of the problem often has multiple indicators to achieve the optimum. This forces the graph coloring problem (GCP) to become complicated, and it is difficult to directly obtain the optimal solution, which brings new challenges to the solution of the problem. In response to this problem, the GCP field is of great research significance. With the in-depth study of GCP, the research on multiobjective optimization (MOO) in graph coloring algorithm (GCA) is gradually carried out. Its performance advantage is of great significance for solving multicondition constraint problems. This paper aims to study the application of Genetic Algorithm (GA) in GCA. Through the analysis and research of GA, and the fusion of numerical analysis and scientific computing, it can be applied to the construction of the neighbor distinguishable uniform V-full coloring algorithm (AVDEVTCA) to solve the AVDEVTC problem. This paper describes the basic theory of MOO and graph coloring. It conducts an experimental analysis of the algorithm performance and uses the relevant theoretical formulas to explain it. The results show that the algorithm takes 5754.142 S seconds to test 21325415 images and can color a large number of results that cannot be done manually in less time. It has greatly improved in terms of time and manpower saving and greatly improved practicability and work efficiency. [ABSTRACT FROM AUTHOR]

Fatal diseases like cancer, dementia, and diabetes are very dangerous. This leads to fear of death if these are not diagnosed at early stages. Computer science uses biomedical studies to diagnose cancer, dementia, and diabetes. With the advancement of machine learning, there are various techniques which are accessible to predict and prognosis these diseases based on different datasets. These datasets varied (image datasets and CSV datasets) around the world. So, there is a need for some machine learning classifiers to predict cancer, dementia, and diabetes in a human. In this paper, we used a multifactorial genetic inheritance disorder dataset to predict cancer, dementia, and diabetes. Several studies used different machine learning classifiers to predict cancer, dementia, and diabetes separately with the help of different types of datasets. So, in this paper, multiclass classification proposed methodology used support vector machine (SVM) and K-nearest neighbor (KNN) machine learning techniques to predict three diseases and compared these techniques based on accuracy. Simulation results have shown that the proposed model of SVM and KNN for prediction of dementia, cancer, and diabetes from multifactorial genetic inheritance disorder achieved 92.8% and 92.5%, 92.8% and 91.2% accuracy during training and testing, respectively. So, it is observed that proposed SVM-based dementia, cancer, and diabetes from multifactorial genetic inheritance disorder prediction (MGIDP) give attractive results as compared with the proposed model of KNN. The application of the proposed model helps to prognosis and prediction of cancer, dementia, and diabetes before time and plays a vital role to minimize the death ratio around the world. [ABSTRACT FROM AUTHOR]

In the past ten years, the number of newly added labor forces in China has continued to grow. At the same time, with the increase in the number of university candidates, the number of university employments is also increasing. Due to employment pressure from society and alumni, the government promotes innovation and entrepreneurship and promotes innovative slogans through entrepreneurship. Universities have become the people who are concerned about this slogan. As an important tool for solving large-scale computer problems, scientific computing is increasingly used in various fields of science and engineering. With the development of high-performance computing technology, various parallel computers have appeared and are widely used. This article uses a scientific computing system to conduct research on innovation and entrepreneurship. The entire scientific computing system is a typical three-tier architecture, and the small service program is responsible for analyzing the scientific computing request in the request and generating related calculation expressions. Get the comprehensive evaluation level of innovation and entrepreneurship risk: excellent score is 3.45, good score is 2.56, general score is 1.68, and difference is 0.68. [ABSTRACT FROM AUTHOR]

In the current music education, the music teaching method is too simple, boring, relatively backward, unable to attract the attention of the students, and the students gradually lose their interest in music courses. In basic education, music, as a highly practical subject, must create a good classroom atmosphere, make the classroom active, and enable students to better experience music, enjoy music, and like music. Therefore, in order to change this situation, this paper, based on scientific computing visualization, studies the application of computer music technology in basic education music teaching, and proposes a related computing method based on scientific computing visualization-image segmentation method. This review compares the spectrum extraction accuracy between the improved algorithm and the two traditional algorithms and finds that the average accuracy of the improved algorithm is 90.88%. It can be seen that the method proposed in this review is more suitable for the study of computer music technology in basic education music teaching. Applied research and most students are more interested in music teaching combined with computer music technology, so learning music teaching combined with computer music technology is very necessary. The development of computer music technology is closely related to the development of modern information technology. [ABSTRACT FROM AUTHOR]

With the continuous development of the social economy, people pay more and more attention to their own protection and maintenance, especially the quality of commodities in terms of usage of the commodity. As commonly used commodities, the quality of their new products in cosmetics is extremely important. In the rapidly developing cosmetics industry, in view of these needs and limitations, the data management method of visualization in scientific computing is introduced in this paper. Multiple risk factors in cosmetics development are established and sorted from the three perspectives of cosmetics development, production, and sales, by combining the new cosmetic product development process. The biggest risks are clarified, the corresponding cosmetic product risk assessment model is constructed, and the new cosmetic product development risk strategy and related management are realized. The simulation experiment results show that the data management method of visualization in scientific computing is effective and can support the risk management of new cosmetic product development. [ABSTRACT FROM AUTHOR]

The classification inaccuracy occurs to the evaluation calculation of the total factor production impact in the traditional digital economy development area. This paper applies the fuzzy hierarchical (Visualization in Scientific Computing, VISC) algorithm to the calculation method of the total factor production impact evaluation in the digital economy development area. The quantitative recursive method is used to evaluate the ability of the data information model, to achieve the ability of controlling the acquisition of characteristic resources, and complete the classification and summary of the index parameters of the total factor production impact of the digital economy development area. Finally, the experiment proves that this calculation method is used to develop the evaluation of the impact of total factor production digital economy development area and improve the information integration and analysis capabilities, the accuracy of the evaluation of the impact of total factor production, and the efficiency of the use of digital economy development resources. [ABSTRACT FROM AUTHOR]

Patients are required to be observed and treated continually in some emergency situations. However, due to time constraints, visiting the hospital to execute such tasks is challenging. This can be achieved using a remote healthcare monitoring system. The proposed system introduces an effective data science technique for IoT supported healthcare monitoring system with the rapid adoption of cloud computing that enhances the efficiency of data processing and the accessibility of data in the cloud. Many IoT sensors are employed, which collect real healthcare data. These data are retained in the cloud for the processing of data science. In the Healthcare Monitoring-Data Science Technique (HM-DST), initially, an altered data science technique is introduced. This algorithm is known as the Improved Pigeon Optimization (IPO) algorithm, which is employed for grouping the stored data in the cloud, which helps in improving the prediction rate. Next, the optimum feature selection technique for extraction and selection of features is illustrated. A Backtracking Search-Based Deep Neural Network (BS-DNN) is utilized for classifying human healthcare. The proposed system's performance is finally examined with various healthcare datasets of real time and the variations are observed with the available smart healthcare systems for monitoring. [ABSTRACT FROM AUTHOR]

Chinese ceramics have a long history and have been deeply recognized by the world after centuries of changes. The current world ceramic industry is diversified. Under this background, there is a huge challenge to the development of Chinese ceramics. The update of materials, the participation of computers in ceramic design, and the demonstration of ingredients all constitute technological factors that cannot be ignored in the development of the ceramic industry. Therefore, this study uses the SWOT analysis method to analyze the development of Chinese ceramics, puts forward its advantages and disadvantages, opportunities, and threats, and under the background of advanced technology, and how to use computer software to demonstrate raw materials and color matching to make Chinese ceramics. The development achieves the goal of self-improvement and then completes the upgrade of its production process and production. Through the research of this study, it is found that the current development of China's ceramic industry should be good at using SWOT analysis, face up to its shortcomings and pressure from other ceramic production areas in the world, give full play to its advantages, maximize strengths and avoid weaknesses, and use technological innovation, combined with technical factors from other disciplines, to promote ceramic industry design and production capabilities and provide a reference for the current ceramic industry development. [ABSTRACT FROM AUTHOR]

Energy consumption is one of the major issues in today's computer science, and an increasing number of scientific communities are interested in evaluating the tradeoff between time-to-solution and energy-to-solution. Despite, in the last two decades, computing which revolved around centralized computing infrastructures, such as supercomputing and data centers, the wide adoption of the Internet of Things (IoT) paradigm is currently inverting this trend due to the huge amount of data it generates, pushing computing power back to places where the data are generated—the so-called fog/edge computing. This shift towards a decentralized model requires an equivalent change in the software engineering paradigms, development environments, hardware tools, languages, and computation models for scientific programming because the local computational capabilities are typically limited and require a careful evaluation of power consumption. This paper aims to present how these concepts can be actually implemented in scientific software by presenting the state of the art of powerful, less power-hungry processors from one side and energy-aware tools and techniques from the other one. [ABSTRACT FROM AUTHOR]

Loop selection for multilevel nested loops is a very difficult problem, for which solutions through the underlying hardware-based loop selection techniques and the traditional software-based static compilation techniques are ineffective. A genetic algorithm- (GA-) based method is proposed in this study to solve this problem. First, the formal specification and mathematical model of the loop selection problem are presented; then, the overall framework for the GA to solve the problem is designed based on the mathematical model; finally, we provide the chromosome representation method and fitness function calculation method, the initial population generation algorithm and chromosome improvement methods, the specific implementation methods of genetic operators (crossover, mutation, and selection), the offspring population generation method, and the GA stopping criterion during the GA operation process. Experimental tests with the SPEC2006 and NPB3.3.1 standard test sets were performed on the Sunway TaihuLight supercomputer. The test results indicated that the proposed method can achieve a speedup improvement that is superior to that by the current mainstream methods, which confirm the effectiveness of the proposed method. Solving the loop selection problem of multilevel nested loops is of great practical significance for exploiting the parallelism of general scientific computing programs and for giving full play to the performance of multicore processors. [ABSTRACT FROM AUTHOR]

Radial basis function (RBF) has been widely used in many scientific computing and engineering applications, for instance, multidimensional scattered data interpolation and solving partial differential equations. However, the accuracy and stability of the RBF methods often strongly depend on the shape parameter. A coupled RBF (CRBF) method was proposed recently and successfully applied to solve the Poisson equation and the heat transfer equation (Appl. Math. Lett., 2019, 97: 93–98). Numerical results show that the CRBF method completely overcomes the troublesome issue of the optimal shape parameter that is a formidable obstacle to global schemes. In this paper, we further extend the CRBF method to solve the elastostatic problems. Discretization schemes are present in detail. With two elastostatic numerical examples, it is found that both numerical solutions of the CRBF method and the condition numbers of the discretized matrices are almost independent of the shape parameter. In addition, even if the traditional RBF methods take the optimal shape parameter, the CRBF method achieves better accuracy. [ABSTRACT FROM AUTHOR]

The main focus of this investigation is the applications of modified sigmoid functions. Due to its various uses in physics, engineering, and computer science, we discuss several geometric properties like necessary and sufficient conditions in the form of convolutions for functions to be in the special class S S G ∗ earlier introduced by Goel and Kumar and obtaining third-order Hankel determinant for this class using modified sigmoid functions. Also, the third-order Hankel determinant for 2- and 3-fold symmetric functions of this class is evaluated. [ABSTRACT FROM AUTHOR]

On the basis of studying datasets of students' course scores, we constructed a Bayesian network and undertook probabilistic inference analysis. We selected six requisite courses in computer science as Bayesian network nodes. We determined the order of the nodes based on expert knowledge. Using 356 datasets, the K2 algorithm learned the Bayesian network structure. Then, we used maximum a posteriori probability estimation to learn the parameters. After constructing the Bayesian network, we used the message-passing algorithm to predict and infer the results. Finally, the results of dynamic knowledge inference were presented through a detailed inference process. In the absence of any evidence node information, the probability of passing other courses was calculated. A mathematics course (a basic professional course) was chosen as the evidence node to dynamically infer the probability of passing other courses. Over time, the probability of passing other courses greatly improved, and the inference results were consistent with the actual values and can thus be visualized and applied to an actual school management system. [ABSTRACT FROM AUTHOR]

The main focus of this investigation is the applications of modified sigmoid functions. Due to its various uses in physics, engineering, and computer science, we discuss several geometric properties like necessary and sufficient conditions in the form of convolutions for functions to be in the special class S S G ∗ earlier introduced by Goel and Kumar and obtaining third-order Hankel determinant for this class using modified sigmoid functions. Also, the third-order Hankel determinant for 2- and 3-fold symmetric functions of this class is evaluated. [ABSTRACT FROM AUTHOR]

The paper introduces DeepForge, a gateway to deep learning for scientific computing. DeepForge provides an easy to use, yet powerful visual/textual interface to facilitate the rapid development of deep learning models by novices as well as experts. Utilizing a cloud-based infrastructure, built-in version control, and multiuser collaboration support, DeepForge promotes reproducibility and ease of access and enables remote execution of machine learning pipelines. The tool currently supports TensorFlow/Keras, but its extensible architecture enables easy integration of additional platforms. [ABSTRACT FROM AUTHOR]

Implementing artificial neural networks is commonly achieved via high-level programming languages such as Python and easy-to-use deep learning libraries such as Keras. These software libraries come preloaded with a variety of network architectures, provide autodifferentiation, and support GPUs for fast and efficient computation. As a result, a deep learning practitioner will favor training a neural network model in Python, where these tools are readily available. However, many large-scale scientific computation projects are written in Fortran, making it difficult to integrate with modern deep learning methods. To alleviate this problem, we introduce a software library, the Fortran-Keras Bridge (FKB). This two-way bridge connects environments where deep learning resources are plentiful with those where they are scarce. The paper describes several unique features offered by FKB, such as customizable layers, loss functions, and network ensembles. The paper concludes with a case study that applies FKB to address open questions about the robustness of an experimental approach to global climate simulation, in which subgrid physics are outsourced to deep neural network emulators. In this context, FKB enables a hyperparameter search of one hundred plus candidate models of subgrid cloud and radiation physics, initially implemented in Keras, to be transferred and used in Fortran. Such a process allows the model's emergent behavior to be assessed, i.e., when fit imperfections are coupled to explicit planetary-scale fluid dynamics. The results reveal a previously unrecognized strong relationship between offline validation error and online performance, in which the choice of the optimizer proves unexpectedly critical. This in turn reveals many new neural network architectures that produce considerable improvements in climate model stability including some with reduced error, for an especially challenging training dataset. [ABSTRACT FROM AUTHOR]

The Cloud Computing paradigm is focused on the provisioning of reliable and scalable virtual infrastructures that deliver execution and storage services. This paradigm is particularly suitable to solve resource-greedy scientific computing applications such as parameter sweep experiments (PSEs). Through the implementation of autoscalers, the virtual infrastructure can be scaled up and down by acquiring or terminating instances of virtual machines (VMs) at the time that application tasks are being scheduled. In this paper, we extend an existing study centered in a state-of-the-art autoscaler called multiobjective evolutionary autoscaler (MOEA). MOEA uses a multiobjective optimization algorithm to determine the set of possible virtual infrastructure settings. In this context, the performance of MOEA is greatly influenced by the underlying optimization algorithm used and its tuning. Therefore, we analyze two well-known multiobjective evolutionary algorithms (NSGA-II and NSGA-III) and how they impact on the performance of the MOEA autoscaler. Simulated experiments with three real-world PSEs show that MOEA gets significantly improved when using NSGA-III instead of NSGA-II due to the former provides a better exploitation versus exploration trade-off. [ABSTRACT FROM AUTHOR]

The motivation for the research is the need to develop an integrated and holistic approach to fostering students' scientific inquiry based on scientific programming education by conducting computational experiments and simulations. At the same time, the implementation of the learner-centred approaches to scientific programming education and the related development of science, technology, engineering, and mathematics (STEM) learner-centred educational environment are of primary importance for K-16 education. The key interest is how to design and integrate learning resources which include software learning objects for making simulations. The research investigates educational aspects of the technological, pedagogical, and content knowledge (TPACK) framework applied to scientific computing and scientific programming educational domain and provides methodological guidelines and design principles of practical implementation of educational resources. These include design principles for the development of the model-based scientific inquiry-centred learning resources, generic design templates for designing educational aspects of scientific programming education, generic use case models for learning resources for scientific programming education, and supportive methodological considerations. [ABSTRACT FROM AUTHOR]

In recent years, the increasing disparity between the data access speed of cache and processing speeds of processors has caused a major bottleneck in achieving high-performance 2-dimensional (2D) data processing, such as that in scientific computing and image processing. To solve this problem, this paper proposes new dual unit tile/line access cache memory based on a hierarchical hybrid Z-ordering data layout and multibank cache organization supporting skewed storage schemes. The proposed layout improves 2D data locality and reduces L1 cache misses and Translation Lookaside Buffer (TLB) misses efficiently and it is transformed from conventional raster layout by a simple hardware-based address translation unit. In addition, we proposed an aligned tile set replacement algorithm (ATSRA) for reduction of the hardware overhead in the tag memory of the proposed cache. Simulation results using Matrix Multiplication (MM) illustrated that the proposed cache with parallel unit tile/line accessibility can reduce both the L1 cache and TLB misses considerably as compared with conventional raster layout and Z-Morton order layout. The number of parallel load instructions for parallel unit tile/line access was reduced to only about one-fourth of the conventional load instruction. The execution time for parallel load instruction was reduced to about one-third of that required for conventional load instruction. By using 40 nm Complementary Metal-Oxide-Semiconductor (CMOS) technology, we combined the proposed cache with a SIMD-based data path and designed a 5 × 5 mm2 Large-Scale Integration (LSI) chip. The entire hardware overhead of the proposed ATSRA-cache was reduced to only 105% of that required for a conventional cache by using the ATSRA method. [ABSTRACT FROM AUTHOR]

Partitioning and load balancing are important problems in scientific computing that can be modeled as combinatorial problems using graphs or hypergraphs. The Zoltan toolkit was developed primarily for partitioning and load balancing to support dynamic parallel applications, but has expanded to support other problems in combinatorial scientific computing, including matrix ordering and graph coloring. Zoltan is based on abstract user interfaces and uses callback functions. To simplify the use and integration of Zoltan with other matrix-based frameworks, such as the ones in Trilinos, we developed Isorropia as a Trilinos package, which supports most of Zoltan's features via a matrix-based interface. In addition to providing an easy-to-use matrix-based interface to Zoltan, Isorropia also serves as a platform for additional matrix algorithms. In this paper, we give an overview of the Zoltan and Isorropia toolkits, their design, capabilities and use. We also show how Zoltan and Isorropia enable large-scale, parallel scientific simulations, and describe current and future development in the next-generation package Zoltan2. [ABSTRACT FROM AUTHOR]

We present Tpetra, a Trilinos package for parallel linear algebra primitives implementing the Petra object model. We describe Tpetra's design, based on generic programming via C++ templated types and template metaprogramming. We discuss some benefits of this approach in the context of scientific computing, with illustrations consisting of code and notable empirical results. [ABSTRACT FROM AUTHOR]

The security problems that outbreak network services today are increasing at a dramatic pace especially with the unceasing improvement of network transmission rates and the sheer amount of data exchanged. This translates not only more incidents but also new types of attacks with network incidents becoming more and more frequent. A significant part of the attacks occurs at Top Level Domains (TLDs) who have the assignment of ensuring the correct functioning of Domain Name System (DNS) zones. The proposed solution has been developed and tested at FCCN (Foundation for National Scientific Computing), the TLD manager for the.PT domain. The system consists of network sensors that monitor the network in real-time and can dynamically detect, prevent, or limit the scope of the attempted intrusions or other types of attacks to the DNS service, thus improving its global availability. [ABSTRACT FROM AUTHOR]

A number of scientific applications are performance-limited by expressions that repeatedly call costly elementary functions. Lookup table (LUT) optimization accelerates the evaluation of such functions by reusing previously computed results. LUT methods can speed up applications that tolerate an approximation of function results, thereby achieving a high level of fuzzy reuse. One problem with LUT optimization is the difficulty of controlling the tradeoff between performance and accuracy. The current practice of manual LUT optimization adds programming effort by requiring extensive experimentation to make this tradeoff, and such hand tuning can obfuscate algorithms. In this paper we describe a methodology and tool implementation to improve the application of software LUT optimization. Our Mesa tool implements source-to-source transformations for C or C++ code to automate the tedious and error-prone aspects of LUT generation such as domain profiling, error analysis, and code generation. We evaluate Mesa with five scientific applications. Our results show a performance improvement of 3.0× and 6.9× for two molecular biology algorithms, 1.4× for a molecular dynamics program, 2.1× to 2.8× for a neural network application, and 4.6× for a hydrology calculation. We find that Mesa enables LUT optimization with more control over accuracy and less effort than manual approaches. [ABSTRACT FROM AUTHOR]

Cloud computing, with its promise of virtually infinite resources, seems to suit well in solving resource greedy scientific computing problems. To study the effects of moving parallel scientific applications onto the cloud, we deployed several benchmark applications like matrix-vector operations and NAS parallel benchmarks, and DOUG (Domain decomposition On Unstructured Grids) on the cloud. DOUG is an open source software package for parallel iterative solution of very large sparse systems of linear equations. The detailed analysis of DOUG on the cloud showed that parallel applications benefit a lot and scale reasonable on the cloud. We could also observe the limitations of the cloud and its comparison with cluster in terms of performance. However, for efficiently running the scientific applications on the cloud infrastructure, the applications must be reduced to frameworks that can successfully exploit the cloud resources, like the MapReduce framework. Several iterative and embarrassingly parallel algorithms are reduced to the MapReduce model and their performance is measured and analyzed. The analysis showed that Hadoop MapReduce has significant problems with iterative methods, while it suits well for embarrassingly parallel algorithms. Scientific computing often uses iterative methods to solve large problems. Thus, for scientific computing on the cloud, this paper raises the necessity for better frameworks or optimizations for MapReduce. [ABSTRACT FROM AUTHOR]

Scientific computing has entered a new era of scale and sharing with the arrival of cyberinfrastructure facilities for computational experimentation. A key emerging concept is scientific workflows, which provide a declarative representation of complex scientific applications that can be automatically managed and executed in distributed shared resources. In the coming decades, computational experimentation will push the boundaries of current cyberinfrastructure in terms of inter-disciplinary scope and integrative models of scientific phenomena under study. This paper argues that knowledge-rich workflow environments will provide necessary capabilities for that vision by assisting scientists to validate and vet complex analysis processes and by automating important aspects of scientific exploration and discovery. [ABSTRACT FROM AUTHOR]

An author index for the Volume 22, 2014 issue of the journal "Scientific Programming" is presented.