Search

In this paper, methodological approaches to improving the accuracy of calculating the complex little-studied technological process of torsion forging based on computer simulation are provided. A method for setting the inverse problem for one of the known types of compression tests for aluminum alloy samples is presented. The results of simulation tests and their comparison with the data obtained during the experiments are presented. A compression test of solid cylindrical samples is considered. The scheme of realization of the inverse problem (inverse method) is presented. The results can be applied when preparing initial data at the stage of development or optimizing the technological process of torsion forging based on computer simulation in specialized programs, for example, the QFORM program.

The application of additive manufacturing (AM) has increased exponentially in recent years. Industries are keen to explore this innovative technology but are apprehensive about the high processing cost of the process. Hence, it is crucial to carry out a cost analysis of the process. This paper presents an approach to compare the costs of an AM process (selective laser sintering (SLS)) and a traditional manufacturing process (injection molding (IM)) in the presence of uncertainties. Initially, the deterministic cost models comprising necessary cost variables for SLS and IM are described. The deterministic models are converted to fuzzy set-based models for tackling uncertainties. For this purpose, important uncertain variables are treated as fuzzy members and fuzzy arithmetic is employed. Only linear triangular fuzzy numbers are used in this work. Fuzzy cost estimates produce three values (low, most likely and high estimates) of cost corresponding to membership grades. A methodology to compare two fuzzy costs of the processes is proposed for a variable demand scenario. Concept of fuzzy reliability is suitably utilized and variability in demand is tackled from probability theory. Variable demand is assumed to follow uniform as well as normal probability distributions. The methodology is illustrated with the help of two examples.

Although the applications of additive manufacturing are continuously increasing, but unless used judiciously, it cannot compete with traditional manufacturing processes. This article presents an analytical method for assessing the utility of additive manufacturing. For this, the concept of utilization factor is introduced and its effect on the production planning and associated costs is discussed. The uncertainty in the estimation of utilization factor and costs is addressed by the fuzzy set-based theory. For applying fuzzy arithmetic, linear triangular fuzzy numbers are used based on expert’s most likely, lower and upper estimates. Consideration of uncertainty with the help of fuzzy arithmetic provides a better picture for management to take decision. Further, a fuzzy set-based technique has been employed to compare an additive manufacturing process viz., selective laser sintering (SLS) with another process viz., injection moulding. The overall methodology is illustrated through case studies. It is demonstrated that even for SLS process, the unit cost of a part decreases with the quantities of production, which is reflected in the enhancement of the utilization factor. However, for very high volume of production, the SLS process cannot compete with injection moulding in terms of cost as well as production time.

In response to increasing supply chain costs, hospitals are investing in new technology to improve inventory visibility and control at the point of use. This poses new inventory optimization opportunities and challenges for hospital management. The technology-enabled increased inventory visibility allows hospitals to implement more sophisticated inventory policies to reduce costs and improve availability. One such policy, implemented at a large Midwest hospital, is a dual-source hybrid inventory system that combines periodic and continuous order-placement epochs under a single-item setting, in which different items are treated independently. In this paper, we extend the dual-source hybrid policy to a two-item setting. We propose different joint replenishment policies that take advantage of increased inventory visibility to coordinate periodic and continuous replenishments across items. We develop a simulation-based search engine and test the benefits of these different policies under different parameter settings. We find that more benefits can be obtained using simple joint replenishment policies than are obtained using just the hybrid policy without coordinated replenishments.

Cutting force is one of the most important parameters for assessing power consumption and tool wear. This work attempts to utilise the concept of data analytics for collecting and building a data warehouse in a cloud-based platform called Central Database Repository (CDR), which can share the information about the machining forces in turning operations directly to the process planner or shop floor operator. The estimation of cutting force is accomplished based on the input of process parameters. CDR uses multiple-linear regression on the data collected and stored in the data bank. Uncertainties in machining operations are taken care of in a separate cloud-based platform known as mini-repository. This study estimates the interval of cutting force with a certain level of confidence and a new concept of dynamic reliability is attached to it. The information from the CDR can be accessed from anywhere across the globe. There is a provision to update the database based on the feedback and filter out unnecessary data by evaluating Cook's distance. The proposed framework is explained through illustrative case studies.

We explore the use of superconducting quantum phase slip junctions (QPSJs), an electromagnetic dual to Josephson Junctions (JJs), in neuromorphic circuits. These small circuits could serve as the building blocks of neuromorphic circuits for machine learning applications because they exhibit desirable properties such as inherent ultra-low energy per operation, high speed, dense integration, negligible loss, and natural spiking responses. In addition, they have a relatively straight-forward micro/nano fabrication, which shows promise for implementation of an enormous number of lossless interconnections that are required to realize complex neuromorphic systems. We simulate QPSJ-only, as well as hybrid QPSJ + JJ circuits for application in neuromorphic circuits including artificial synapses and neurons, as well as fan-in and fan-out circuits. We also design and simulate learning circuits, where a simplified spike timing dependent plasticity rule is realized to provide potential learning mechanisms. We also take an alternative approach, which shows potential to overcome some of the expected challenges of QPSJ-based neuromorphic circuits, via QPSJ-based charge islands coupled together to generate non-linear charge dynamics that result in a large number of programmable weights or non-volatile memory states. Notably, we show that these weights are a function of the timing and frequency of the input spiking signals and can be programmed using a small number of DC voltage bias signals, therefore exhibiting spike-timing and rate dependent plasticity, which are mechanisms to realize learning in neuromorphic circuits.

Neuromorphic computing which aims to mimic the collective and emergent behavior of the brain's neurons, synapses, axons, dendrites offers an intriguing, potentially disruptive solution to society's ever-growing computational needs. Although much progress has been made in designing circuit elements that mimic the behavior of neurons and synapses, challenges remain in designing networks of elements that feature a collective response behavior. We present simulations of networks of circuits and devices based on superconducting and Mott-insulating oxides that display a multiplicity of emergent states that depend on the spatial configuration of the network. Our proposed network designs are based on experimentally known ways of tuning the properties of these oxides using light ions. We show how neuronal and synaptic behavior can be achieved with arrays of superconducting Josephson junction loops, all within the same device. We also show how a multiplicity of synaptic states could be achieved by designing arrays of devices based on hydrogenated rare-earth nickelates. Together, our results demonstrate a new research platform that utilizes the collective macroscopic properties of quantum materials to mimic the emergent behavior found in biological systems., Comment: 11 pages, 8 figures

This paper extends the results of Hermida's thesis about logical predicates to more general logical relations and a wider collection of types. The extension of type constructors from types to logical relations is derived from an interpretation of those constructors on a model of predicate logic. This is then further extended to n-ary relations by pullback. Hermida's theory shows how right adjoints in the category of fibrations are composed from a combination of Cartesian lifting and a local adjunction. This result is generalised to make it more applicable to left adjoints, and then shown to be stable under pullback, deriving an account of n-ary relations from standard predicate logic. A brief discussion of lifting monads to predicates includes the existence of an initial such lifting, generalising existing results.

A fuzzy set based cost model for additive manufacturing is proposed. The model is illustrated through an example of selective laser sintering (SLS), a commonly used additive manufacturing (AM) process. Initially, a deterministic cost model is developed. It is then converted to a fuzzy set based model by considering uncertainty in build time estimation and cost components. For the specific case of SLS, a build time estimation procedure is developed as a part of the cost model. A method to evaluate the reliability of the cost estimate in AM and its significance is also introduced. The cost model is illustrated through two examples of products manufactured by SLS.

Fused deposition modelling (FDM) is a popular additive manufacturing technology. This chapter discusses FDM process from the perspective of sustainability. Sustainability is one of the main concerns in the present era of manufacturing. The assessment of sustainability is important for comparison of the productivity and efficiency of FDM with other manufacturing processes. All the three facets of sustainability, i.e., economic, environmental and societal need to be addressed. In this article, the cost-analysis of FDM is carried considering it to be the first pillar of sustainability-analysis. The necessary cost variables and their importance are discussed for developing the cost model. For environmental sustainability, an analytical approach for the estimation of energy consumption, highlighting various energy-consuming elements, is discussed. Initially, deterministic cost and energy consumption models are presented, which are later converted to fuzzy set based models for handling uncertainties. The main uncertain variables are considered as fuzzy and the output values are obtained by carrying out fuzzy arithmetic operations. The values obtained from the fuzzy set based models are in the form of triplets (low, most likely and high), from which linear triangular membership functions can be constructed. Amongst the three aspects, the societal aspect is the least explored by researchers. This article makes an attempt to study the impact of FDM based additive manufacturing processes on the society. The overall sustainability analysis of two parts, manufactured by FDM, is presented.

Genome in a Bottle (GIAB) benchmarks have been widely used to help validate clinical sequencing pipelines and develop new variant calling and sequencing methods. Here we use accurate long and linked reads to expand the prior benchmark to include difficult-to-map regions and segmental duplications that are not readily accessible to short reads. Our new benchmark adds more than 300,000 SNVs, 50,000 indels, and 16 % new exonic variants, many in challenging, clinically relevant genes not previously covered (e.g., PMS2). We increase coverage of the autosomal GRCh38 assembly from 85 % to 92 %, while excluding problematic regions for benchmarking small variants (e.g., copy number variants and assembly errors) that should not have been in the previous version. Our new benchmark reliably identifies both false positives and false negatives across multiple short-, linked-, and long-read based variant calling methods. As an example of its utility, this benchmark identifies eight times more false negatives in a short read variant call set relative to our previous benchmark, mostly in difficult-to-map regions. To enable robust small variant benchmarking, we still exclude 3.6% of GRCh37 and 5.0% of GRCh38 in (1) highly repetitive regions such as large, highly similar segmental duplications and the centromere not accessible to our data and (2) regions where our sample is highly divergent from the reference due to large indels, structural variation, copy number variation, and/or errors in the reference (e.g., some KIR genes that have duplications in HG002). We have demonstrated the utility of this benchmark to assess performance in more challenging regions, which enables benchmarking in more difficult genes and continued technology and bioinformatics development. The v4.2.1 benchmarks are available under ftp://ftp-trace.ncbi.nlm.nih.gov/ReferenceSamples/giab/release/AshkenazimTrio/.

Modelling of micromachining is a challenging but much-needed task of modern science and technology era. In this chapter, salient issues related to modelling are illustrated through some case studies of machining of single-crystal material. Two micro-scale machining processes of a single-crystal metal—conventional machining (CM) and ultrasonically assisted machining (UAM)—are investigated using a crystal-plasticity-based approach. Differences between CM and UAM are elucidated employing three case studies with different cutting velocities. The emphasis of this research is placed on a comparison of the levels of cutting force, chip morphology and dissipated plastic energy between CM and UAM. The simulation results indicate that respective improvements of UAM compared to CM depend significantly on a ratio of the nominal velocity to a critical oscillatory speed. This study ultimately enhanced the understanding of micro-mechanisms, inherent in UAM at the micro-scale.

Interface stiffness is an important factor influencing the performance of mechanical products. Uncertain factors affect the interface stiffness and stability in the process of product design, manufacture, and operation. How to reduce the impact of uncertain factors on the interface stiffness is a vital problem in interface design. In this paper, a robust optimal design method is proposed for mechanical interfaces considering uncertain factors, which combines the finite element simulation, experiment, and optimization to reduce the sensitivity of interface stiffness to uncertain factors. The proposed interface design method provides an effective way to improve the interface stiffness under uncertain conditions. In order to validate the proposed method, the bolted connection structure of a flange is applied as an example. The interface stiffness of the flange is selected as an optimization target, and the Gaussian process regression is used to construct a two-layer optimal model of the objective function for the design and uncertain parameters. When experimental and optimization results differ significantly, the Kalman filter is used to provide the feedback for the optimization results until the results meet requirements. The final results show that the optimized mechanical interface stiffness is increased by 15.5%, and the error between the optimized prediction and experimental results is within 1% after three times experimental validation and feedback adjustment.

Metal forming is one of the important manufacturing processes. There are three types of metal forming processes: bulk metal forming, sheet metal forming, and sheet-bulk metal forming. Some popular metal forming processes are forging, rolling, wire drawing, extrusion, deep drawing, and bending. Engineers are interested in the estimation of forming load and product quality in metal forming. Various modeling techniques have been employed for this purpose, for example, slab method, slip line field method, visioplasticity, upper bound method, finite difference method, and finite element method. Among these, the finite element method is the most preferred method as it is capable of carrying out fairly realistic simulation of the process, although it is a computationally intensive method. Recently, a meshless method, molecular dynamics simulations, and soft computing methods have also got prominence. A realistic simulation requires a large number of material and process parameters as input, some of which may not be easily determinable; hence, researchers try to develop inverse modeling techniques for the online estimation of the necessary input parameters. This chapter summarizes the developments in the modeling of metal forming and highlights challenging issues along with providing directions for future research. Important tasks needing the immediate attention of researchers are reduction in computational time, consideration of microstructural aspects including surface integrity, and modeling of friction that plays a very significant role in microforming.

Emergency Departments (EDs) commonly face capacity imbalances and long wait times in a service system handling patients with different priorities. These problems are particularly important for low-priority patients who often remain in the queue for extended periods. We investigate two distinct approaches to address these challenges: fast track (FT) and dynamic priority queue (DPQ). Traditionally, EDs have prioritized patients using an Emergency Severity Index (ESI), in conjunction with FT, to strictly or partially dedicate resources to different ESI patient classes. With our proposed DPQ, patients are prioritized using ESI and additional real-time operational information about the patient, specifically the amount of accumulated wait time and flow time. Using an empirical simulation, we compare the impact of different resource allocation and prioritization approaches on patient length of stay (LOS), including the existing system at the ED, FT with strict and partial dedication and the possibility of shorter and less variable service times, and versions of the proposed DPQ using simple dynamic prioritization. Our main results are that: (i) the DPQ approach dominates the other approaches tested; (ii) for various ED sizes, FT with strict and partial dedication do not reduce average LOS of low-priority patients without significantly increasing average LOS of high-priority patients, unless service time mean and variance are reduced; (iii) DPQ using accumulated wait time or accumulated flow time improves performance. The results are robust to changes in the proportion of patients in each priority level. Overall, expanding decision making about patient prioritization from only considering the patient's clinical condition to also including operational data can improve performance dramatically, even without improved service times.

In this article, a strategy for the optimum selection of laser process parameters, namely, scan speed, power and beam diameter, is presented that considers not only the accuracy of laser bending, but also the residual stresses, the maximum temperature along with temperature-affected zone, radius of bend, energy consumption and production rate. Performance is predicted by means of a three-dimensional thermoelastic–plastic finite element model, incorporating certain features to make it computationally efficient. In addition, an empirical model is used for the quick prediction of the maximum temperature, which helps in reducing the number of time-consuming runs of the finite element model. As the optimization of the process is multi-objective in nature, a fuzzy-set based strategy is suggested for a higher level decision. The efficacy of the procedure is demonstrated through examples and in-house experiments.

The term “Big Data” refers to massive volumes of data that grow at an increasing rate and encompass complex data types such as audio and video. While the applications of Big Data and analytic techniques for business purposes have received considerable attention, it is less clear how external sources of Big Data relate to the transaction processing-oriented world of accounting information systems. This paper uses the Resource-Event-Agent Enterprise Ontology (REA) (McCarthy 1982; International Standards Organization [ISO] 2007) to model the implications of external Big Data sources on business transactions. The five-phase REA-based specification of a business transaction as defined in ISO (2007) is used to formally define associations between specific Big Data elements and business transactions. Using Big Data technologies such as Apache Hadoop and MapReduce, a number of information extraction patterns are specified for extracting business transaction-related information from Big Data. We also present a number of analytics patterns to demonstrate how decision making in accounting can benefit from integrating specific external Big Data sources and conventional transactional data. The model and techniques presented in this paper can be used by organizations to formalize the associations between external Big Data elements in their environment and their accounting information artifacts, to build architectures that extract information from external Big Data sources for use in an accounting context, and to leverage the power of analytics for more effective decision making.

Many knowledge-intensive projects such as new product and software design, research, and high technology development have flexible scope and involve co-production between a client and a vendor. In such projects, it is often challenging to estimate how much progress can be achieved within a certain time window or how much time may be needed to achieve a certain degree of progress, especially because the client and vendor often adjust their efforts as a function of the project's progress, the time until the deadline, and the incentives in place. Effective contracts should therefore be flexible in scope and foster collaboration. In this study, we characterize the collaborative work dynamics of a client and a vendor who are engaged in a multi-state, multi-period stochastic project with a finite deadline. We show that when the client can verify the vendor's effort, it is optimal that they both exert high effort in one of two situations: when either not enough progress has been made and the deadline is close (deadline effect), or conversely, when so much progress has been made that the project state is close to a completion state set by the client (milestone effect). Hence, in this case, progress will typically be faster when the project is about to be stopped, due to either reaching the deadline or reaching the client's desired completion state. However, when the client cannot verify the vendor's effort, the vendor is prone to free-riding. Considering a time-based contract that pays the vendor a per-period fee and a fixed completion bonus, we show that the equilibrium completion state is decreasing in the per-period fee and increasing in the bonus, justifying the use of both incentive mechanisms in practice. Moreover, we show that, under such contracts, some form of milestone effect arises in equilibrium, but the deadline effect does not. Hence, in those cases, early progress will typically lead to early project conclusion at a high state; whereas, slow progress will typically make the project drag until the deadline while still at a low state.

Satisfaction of personalized customers’ requirements of battery packs determines the competitiveness of electric vehicles. To meet the personalized requirements of battery packs, this paper develops a method for the design of an open interface for the connections and disconnections of battery pack modules to the electric vehicles. First, location and initial design scheme of the open interface are proposed based on relation analysis between customer demands and the electric vehicle module. To better facilitate the functional interactions between battery pack modules and electric vehicles, adaptability of open interface is then enhanced through design evaluation and modification. Influence of uncertainties due to unknown battery pack modules to the performance of electric vehicle is minimized through structure optimization of the open battery pack interface. The design can support electric vehicle personalization, better lifecycle flexibility, open technology innovation, and open business model.

In the stage of product design, it is very helpful to understand customer needs accurately to improve product performance. The current customer needs identification scheme cannot meet the dynamic and precise characteristics of customer needs. A precise identification architecture of customer demand based on online sales data and probability theory is proposed. Online sales data provide potential customer demand information, the data is used to build the relationship between customer satisfaction and product function. Vector similarity is used to match user needs and product functions. At the same time, a ranking method of product satisfaction recommended to users is proposed by using the concepts of probability and statistics. The characteristic of this method is that it can feedback user’s needs in real time. The customer eventually gets a product that meets the demand and the customer is most satisfied with the product. Finally, this paper demonstrates the implementation process of this method by taking the multi-functional desk as an example.

Molecular dynamics (MD) and single-crystal plasticity finite-element method (CP FEM) are approaches used to simulate the micro-machining process. At such small-length scales, anisotropic behaviour of material becomes important; the two methods can essentially capture it. Therefore, it is important to understand the fundamental principle behind these methods as well as their capabilities and limitations in order to select the scheme to simulate the micro-machining process. In this paper, the fundamentals of MD and CP FEM are introduced in brief. The applicability of the respective method is further illustrated with the help of examples from the literature. Thereafter, the two methods are compared and discussed in terms of their various aspects and capabilities. This discussion should enrich the reader and assist their choice of an appropriate simulation method.

Fully coupled randomly disordered recurrent superconducting networks with additional open-ended channels for inputs and outputs are considered the basis to introduce a new architecture to neuromorphic computing in this work. Various building blocks of such a network are designed around disordered array synaptic networks using superconducting devices and circuits as an example, while emphasizing that a similar architectural approach may be compatible with several other materials and devices. A multiply coupled (interconnected) disordered array of superconducting loops containing Josephson junctions [equivalent to superconducting quantum interference devices (SQUIDs)] forms the aforementioned collective synaptic network that forms a fully recurrent network together with compatible neuron-like elements and feedback loops, enabling unsupervised learning. This approach aims to take advantage of superior power efficiency, propagation speed, and synchronizability of a small world or a random network over an ordered/regular network. Additionally, it offers a significant factor of increase in scalability. A compatible leaky integrate-and-fire neuron made of superconducting loops with Josephson junctions is presented, along with circuit components for feedback loops as needed to complete the recurrent network. Several of these individual disordered array neural networks can further be coupled together in a similarly disordered way to form a hierarchical architecture of recurrent neural networks that is often suggested as similar to a biological brain.

An artificial neural network (ANN) model of laser-assisted bending is developed based on the data obtained from a finite element method (FEM) model. FEM model is validated with the experiments. In the experimental setup, the sheet is clamped like a cantilever beam with mechanical load on the free end. A laser beam scans the bend line for reducing the flow stress of the material locally. ANN considers four process parameters viz., laser power, mechanical load, distance of the scan-line from the free end, and scan speed. The ANN predicts the most likely, upper, and lower estimates of the bend angle. Commercial package MATLAB® is used for developing the ANN model. First, a multilayer perceptron (MLP) neural network is trained and tested using the data from the FEM model. Using the best fit model of the MLP, additional data are generated for training a radial basis function (RBF) neural network. The RBF neural network is tested and validated against experimental data and subsequently used for obtaining the most likely, lower, and upper estimates of bend angle as a function of the considered process parameters. A parametric study is also carried out based on the RBF neural network model. Finally, it is shown that the neural network can be used for the inverse prediction using the bisection method.

This paper demonstrates an approach to address the unique control and security concerns in database environments by using audit modules embedded into application programs. Embedded audit modules (EAM) are sections of code built into application programs that capture information of audit significance on a continuous basis. The implementation of EAMs is presented using INGRESS a relational database management system. An interface which enables the auditor to access audit-related information stored in the database is also presented. The use of EAMs as an audit tool for compliance and substantive testing is discussed. Advantages and disadvantages of employing EAMs in database environments and future directions in this line of research are discussed.

In this paper with the help of Simulink the Self Excited Induction Generator is made also Laboratory hardware implementation of the different arrangement scheme of Capacitor for the generation of Voltage in IG is discussed. The voltage is developed in Induction Generator which is Self Excited due to the residual magnetism present in the rotor circuit. To build-up and maintains the voltage in SEIG requires the minimum Capacitor. The Induction Motor starts working as an Induction Generator when actual speed of an Induction Motor larger than the Synchronous Speed (i.e. the slip is negative) by means of an outside prime mover, the direction of induced torque is inverted and it draws the current way it flows real power a way of machine but machine wants reactive power compensation and this work is done by minimum value of Capacitor.

Superconducting digital computing systems, primarily involving Josephson junctions are actively being pursued as high performance and low energy dissipating alternatives to CMOS-based technologies for petascale and exascale computers, although several challenges still exist in overcoming barriers to practically implement these technologies. In this paper, we present an alternative superconducting logic structure: quantized charge-based logic circuits using quantum phase-slip junctions, which have been identified as dual devices to Josephson junctions. Basic principles of logic implementation using quantum phase-slips are presented in simulations with the help of a SPICE model that has been developed for the quantum phase-slip structures. Circuit elements that form the building blocks for complex logic circuit design are introduced. Two different logic gate designs: OR gate and XOR gate are presented to demonstrate the usage of the building blocks introduced., 4 pages, 8 figures, EuCAS 2017

This study investigates whether using hyperlinks that connect summarized financial graphs with detailed financial statement information reduces the effect of graphical distortions on nonprofessional investors’ perceptions of firm performance. Using 385 participants from Amazon Mechanical Turk (M-Turk), we find that while distorted graphs do bias nonprofessional investors’ perceptions of firm performance, the provision and use of hyperlinks to the underlying source information eliminate those effects (i.e., de-bias). Using the dual-process theory of cognitive processing (Kahneman and Frederick 2002; Evans 2006, 2008), we find that hyperlinks enhance the overriding effect of System 2 processing (i.e., analytical processing) on System 1 processing (i.e., intuitive processing) and indirectly reduce the decision-biasing effect of distorted graphs on nonprofessional investors’ perceptions. The study contributes to standard setting as well as financial reporting practice by providing empirical evidence that the SEC’s policy guidance on implementing hyperlinks has benefits to nonprofessional investors. Second, it contributes to both the literature on distorted graphs and hyperlinks by suggesting hyperlinking to source data mitigates the effects of graphical distortions.

Although services are often defined as co-productive of value, the concept of value is often difficult to measure. Yet measuring value is not necessarily a prerequisite for service process improvement. In this paper, we propose a general framework for the modeling and analysis of services with co-production. The framework identifies three major process stages: (i) the production stage, which involves co-production, (ii) the output sharing stage, and (iii) the consumption stage. Value realization and distribution depends on all three stages. Using our framework, we argue that process improvement efforts may often shift away from value measurement to focus on more actionable levers such as the co-production process, the rules of operation, and sharing rules. Furthermore, we use our framework to discuss a broader perspective on value measurement, the application of the gap quality model to co-productive process management, the distinction between products and services, and the impact of information and communication technologies on service processes.

International audience; This paper presents a joint effort between a group of computer scientists and bioinformaticians to take an important step towards a general big data platform for genome analysis pipelines. The key goals of this study are to develop a thorough understanding of the strengths and limitations of big data technology for genomic data analysis, and to identify the key questions that the research community could address to realize the vision of personalized genomic medicine. Our platform, called Gesall, is based on the new " Wrapper Technology " that supports existing genomic data analysis programs in their native forms, without having to rewrite them. To do so, our system provides several layers of software , including a new Genome Data Parallel Toolkit (GDPT), which can be used to " wrap " existing data analysis programs. This platform offers a concrete context for evaluating big data technology for genomics: we report on super-linear speedup and sublinear speedup for various tasks, as well as the reasons why a parallel program could produce different results from those of a serial program. These results lead to key research questions that require a synergy between ge-nomics scientists and computer scientists to find solutions.

In hospitals, the management of operating rooms faces a trade-off between the need to be responsive to emergency surgeries and to conduct scheduled elective surgeries efficiently. Operating rooms can be configured as flexible and handle both electives and emergencies, or as dedicated to focus on either electives or emergencies. With flexible rooms, the prioritization of emergencies over scheduled electives can lead to schedule disruptions. Focused rooms can lead to imbalances between capacity and surgery workload. Whereas hospital administrators typically handle this trade-off by employing either flexible rooms (complete flexibility) or dedicated rooms (complete focus), we investigate whether a combination of flexible and dedicated rooms (partial flexibility) could be a preferable alternative. The ensuing question is what is the right combination of flexible and dedicated rooms? A versatile simulation model is developed to evaluate different resource allocation policies under various environmental parameters and performance metrics, including patient wait time, staff overtime, and operating room utilization. The main result is that partial flexibility configurations outperform both complete flexibility and complete focus policies by providing solutions with improved values of expected wait time for both emergency and elective patients.

In his seminal paper on “Types, Abstraction and Parametric Polymorphism,” John Reynolds called for homomorphisms to be generalized from functions to relations. He reasoned that such a generalization would allow type-based “abstraction” (representation independence, information hiding, naturality or parametricity) to be captured in a mathematical theory, while accounting for higher-order types. However, after 30 years of research, we do not yet know fully how to do such a generalization. In this article, we explain the problems in doing so, summarize the work carried out so far, and call for a renewed attempt at addressing the problem.

Passive expressions in Algol-like languages represent computations that read the state but do not modify it. The need for such read-only computations arises in programming logics as well as in concurrent programming. It is also a central facet in Reynoldsʼs Syntactic Control of Interference. Despite its importance and essentially basic character, capturing the notion of passivity in semantic models has proved to be difficult. In this paper, we provide a new model of passive expressions using an automata-theoretic framework recently proposed by the author. The central idea is that the store of a program is viewed as an abstract form of an automaton, with a representation of its states as well as state transitions. The framework allows us to combine the strengths of conventional state-based models and the more recent event-based models to synthesize new “automata-based” models. Once this basic framework is set up, relational parametricity does the job of identifying passive computations.

We consider a supply chain structure with shipments from an external warehouse directly to retailers and compare two enhancement options: costly transshipment among retailers after demand has been realized vs. cost-free allocation to the retailers from the development of a centralized depot. Stochastic programming models are developed for both the transshipment and allocation structures. We study the impact of cost parameters and demand coefficient of variation on both system structures. Our results show an increasing convex relationship between average costs and demand coefficient of variation, and furthermore that this increase is more pronounced for the allocation structure. We employ simulation and nonlinear search techniques to computationally compare the cost performance of allocation and transshipment structures under a wide range of system parameters such as demand uncertainty and correlation; lead times from the external warehouse to retailers, from warehouse to central depot, and from depot to retailers; and transshipment, holding, and penalty costs. The transshipment approach is found to outperform allocation for a broad range of parameter inputs including many situations for which transshipment is not an economically sound decision for a single period. The insights provided enable the manager to choose whether to invest in reducing lead times or demand uncertainty and assist in the selection of investments across identical and nonidentical retailers.

We extend continuous assurance research by proposing a novel continuous assurance architecture grounded in information fusion research. Existing continuous assurance architectures focus primarily on methods of monitoring assurance clients' systems to detect anomalous activities and have not addressed the question of how to process the detected anomalies. Consequently, actual implementations of these systems typically detect a large number of anomalies, with the resulting information overload leading to suboptimal decision making due to human information processing limitations. The proposed architecture addresses these issues by performing anomaly detection, aggregation, and evaluation. Within the proposed architecture, artifacts developed in prior continuous assurance, ontology, and artificial intelligence research are used to perform the detection, aggregation, and evaluation information fusion tasks. The architecture contributes to the academic continuous assurance literature and has implications for practitioners involved in the development of more robust and useful continuous assurance systems.

Congestion leads to bandwidth degradation and, packet losses thereby causing retransmissions and higher energy drain rate by the nodes in Mobile Ad hoc Networks(MANET). So providing Quality of Service(QoS) and reducing congestion levels are some of the major issues to be addressed in these networks. However , use of appropriate congestion control measures is complicated due to the interdependence of various parameters that cause congestion. Applying cost based metrics for attaining QoS is prevalently used in MANET’s but they are inefficient because of the uncertainities involved in the channel characteristics. Hence routing solutions based on the principles of entropy and fuzzy logic would be appropriate for MANET’s. We are proposing a novel method based on entropy and fuzzy logic (Dual-Fuzzy-Entropy-AODV) to assess the quality of a path discovered by a routing protocol. Simulation studies and evaluation of our proposed mechanism shows improvement in the performance results.

Separation Logic has witnessed tremendous success in recent years in reasoning about programs that deal with heap storage. Its success owes to the fundamental principle that one should keep separate areas of the heap storage separate in program reasoning. However, the way Separation Logic deals with program variables continues to be based on traditional Hoare Logic without taking any benefit of the separation principle. This has led to unwieldy proof rules suffering from lack of clarity as well as questions surrounding their soundness. In this paper, we extend the separation idea to the treatment of variables in Separation Logic, especially Concurrent Separation Logic, using the system of Syntactic Control of Interference proposed by Reynolds in 1978. We extend the original system with permission algebras, making it more powerful and able to deal with the issues of concurrent programs. The result is a streamined presentation of Concurrent Separation Logic, whose rules are memorable and soundness obvious. We also include a discussion of how the new rules impact the semantics and devise static analysis techniques to infer the required permissions automatically.

Physicians at a branch of the emergency department at Cincinnati Children's Hospital Medical Center complained that their schedules were too erratic because of the multitude of operating requirements, regulatory constraints, physician preferences, and holiday requests. We addressed this issue by using integer programming to build cyclic schedules that can be repeated throughout the year. These schedules are flexible enough to handle incorporating holidays, work assignments, and vacation requests ex post. After we rolled out the calendar-year-based cyclic schedule, we captured statistics to assess the viability and the quality of the yearly schedule generated. Surveys of the physicians and the scheduler after implementation showed that the new schedule provides predictability and well-balanced work patterns.

Reasoning about concurrent programs involves representing the information that concurrent processes manipulate disjoint portions of memory. In sophisticated applications, the division of memory between processes is not static. Through operations, processes can exchange the implied ownership of memory cells. In addition, processes can also share ownership of cells in a controlled fashion as long as they perform operations that do not interfere, e.g., they can concurrently read shared cells. Thus the traditional paradigm of distributed computing based on locations is replaced by a paradigm of concurrent computing which is more tightly based on program structure. Concurrent Separation Logic with Permissions, developed by O’Hearn, Bornat et al., is able to represent sophisticated transfer of ownership and permissions between processes. We demonstrate how these ideas can be used to reason about fine-grained concurrent programs which do not employ explicit synchronization operations to control interference but cooperatively manipulate memory cells so that interference is avoided. Reasoning about such programs is challenging and appropriate logical tools are necessary to carry out the reasoning in a reliable fashion. We argue that Concurrent Separation Logic with Permissions provides such tools. We illustrate the logical techniques by presenting the proof of a concurrent garbage collector originally studied by Dijkstra et al., and extended by Lamport to handle multiple user processes.

This case is designed to impart practical skills in data analysis techniques aimed at fraud examination. Instructors could employ any one of widely available tools such as ACL, IDEA, Microsoft Access, or Picalo, which is an open-source data analysis tool. Couched in the context of a manufacturer of electronic components in the southeastern United States, the case involves the identification of potentially fraudulent travel expense reimbursements. In the case scenario, traveling salespersons submit expense reimbursement claims, which are subject to a number of business rules. Using data analysis techniques, students are required to identify potentially fraudulent travel expense reimbursements. The data analysis techniques covered in the case include basic features such as identifying duplicates and gaps to more advanced features like joining tables, finding unmatched records, filtering data based on various criteria, and classifying and summarizing data. The degree of structure provided to students is within the control of the instructor, with less structure making for a more realistic and challenging assignment. Spreadsheet files containing the travel expense data are designed to facilitate easy changing of numbers between semesters.

Data models are frequently created with little thought about their layout. Our research examined how data models organized in a columnar fashion compared to equivalent data models which did not, by improving novice designer's performance in error detection, comprehension, and query construction. Experimental results showed that the columnar organized data model improved performance in error detection and querying, but not in comprehension. Also, the columnar organized data model was perceived as being more useful. The results of our research have important implications for data model design, especially for novices who must interpret and use them.

Although a considerable body of research in information systems has established that computer-mediated communication (CMC) is beneficial for brainstorming (idea generation) tasks, less is known about its effectiveness for more complex decision-making tasks. This paper reports the results of two experiments comparing the performance of face-to-face and CMC teams in decision-making tasks that move beyond brainstorming. In the first experiment, the performance of face-to-face and computer-mediated teams was compared in two tasks: one requiring participants to engage in convergent thinking and a second brainstorming task requiring divergent thinking. Consistent with predictions derived from McGrath's task circumplex model, the results of experiment one reveal that participants using computer-mediated communication perform significantly better than those interacting face-to-face on the divergent (brainstorming) task. On the convergent task, computer-mediated and face-to-face teams performed equally well; i.e., there was not a significant difference in their performance. In the second experiment, the performance of face-to-face and computer-mediated teams was again compared in two tasks: an integrative negotiation task and an idea-generation task. The results of the second experiment were similar to those of experiment one, in that computer-mediated teams significantly outperformed face-to-face teams in the idea-generation task, while computer-mediated and face-to-face teams performed equally well on the integrative negotiation task. These experiments contribute to the literature by shedding additional light on the conditions under which computer-mediated communication is as effective as, and in some cases more effective than, face-to-face interaction.

We experimentally compared the effectiveness of face-to-face (FTF) and synchronous computer-mediated communication when using a chat tool in solving hidden-profile business problems. In such problems, information critical to its solution is dispersed among team members and they must share it to solve the problem. Unlike prior research using hidden-profile tasks, our study used a real-world business-oriented task, established real rather than ad hoc teams, and imposed a time constraint on them. Hypotheses derived from media richness theory were found to be supported, with the results revealing that computer-mediated teams using the chat tool were less successful in exchanging and processing information than FTF teams and were thus less successful at solving the hidden-profile problem. The results also showed that, when operating under a time constraint, FTF was preferred over computer-mediated communication due to the relative immediacy of feedback and multiplicity of cues available in the FTF setting, as media richness theory predicted.

The accounting information systems (AIS) discipline lacks a sense of identity relative to the closely related field of management information systems (MIS). By identifying how AIS is evolving and what makes AIS unique from MIS, the field can gain a better understanding of its importance. We analyze a sample of syllabi for the introductory AIS and MIS courses, focusing on similarities and differences between the AIS and MIS fields as reflected in the topical coverage choices made by AIS and MIS educators. We also investigate the effects of the passage of the Sarbanes-Oxley Act on topical coverage in the introductory AIS course. The findings reveal that while AIS courses have a unique focus on transaction processing and internal controls relative to MIS courses, more technical topics such as hardware and software issues and IS solutions are covered more commonly in MIS courses rather than AIS courses. We also find that AIS topical coverage has changed since the passage of the Sarbanes-Oxley Act, with a more pronounced focus on internal control and security compared to the pre-Sarbanes Oxley era. The key contribution of this paper is in providing empirical evidence in support of the “intersection view” of AIS, which asserts that while AIS and MIS have some topics in common there are also many issues unique to each field.

Currently information used to describe sea-level stations (such as location, collection and transmission capabilities, operator identification, etc.) is distributed among databases held by multiple agencies, institutions and organizations. Such information could be used to support detection and warning. However, the Indian Ocean Tsunami of 26 December, 2004 made it clear that such information is not readily accessible, is difficult to use, and is often incomplete. In addressing this issue, agencies within the Pacific region are collaborating to develop a web service to expose station metadata enabling various types of real-time data mining client applications that support decision-making and strategic planning at Tsunami Warning Centers. Because information about sea levels has a broad range of applications, integration of this information in a way that is comprehensive, and enhances its access and use, would have a tremendous impact on lives and livelihoods.

The finite element analysis of the cold flat rolling process is well established. However, the requirement of large computational time makes it unsuitable for online applications. Recently, there have been some applications of modeling the rolling process by means of neural networks. In most of the previous works, trained networks predict only roll force and roll torque. The input data for training the neural network have been obtained either through experiments or from finite element method (FEM) code. In this work, the neural networks have been used for predicting the velocity field and location of neutral point. The training data are obtained from a rigid-plastic finite element code. The trained network provides a suitable guess for the velocity field and location of the neutral point, that is further refined by the finite element code. The post-processor of the FEM code computes roll force, roll torque, strain distribution, etc. This procedure provides highly accurate solution with reduced computational time and is suitable for on line control or optimization.

Many knowledge-intensive projects such as research and development, high technology, and management and information technology consulting involve co-production between a client and a vendor. Yet, the decisions on "who" should work and "when" can often be made independently by the client or the vendor, and revised on the fly. In this paper, we consider a project with co-production that has a finite deadline and benefits for early completion. We characterize the collaborative work dynamics between a vendor and a client and show how they are affected by contractual arrangements. We show that, when efforts are verifiable, the client and the vendor should both work when not enough progress has been made and the deadline is close, or conversely, when so much progress has been made and there is still plenty of time left until the deadline; otherwise, only one of them should work. Moreover, when efforts are not observable, only the latter collaboration phase can be sustained in equilibrium under simple contracts such as fixed fee or time-and-materials, because the deadline has a negative impact on the vendor's work incentives. Overall, our analysis suggests that, although most project work planning tools assume a constant work rate, actual work dynamics evolve in a predictable fashion as the project unfolds and can easily be accounted for at the project planning stage.

This paper presents a quantitative model for telecommunication network installation by companies in the broadband-access business, specialized to the fixed-wireless case. Under stochastic demand modeled using scenarios, we maximize the expected demand coverage subject to a budget constraint on hub installation, and technological constraints on demand coverage by installed hubs. There are multiple hub types, differing in costs and capacities. We present a practical greedy heuristic based on the budgeted maximum-coverage problem and analyze its worst-case performance. For special cases with a single hub type or a single demand scenario, we show that a guarantee of 1 − 1/e or 63.2% applies to our greedy heuristic. For the general case we develop a data-dependent performance guarantee. Through computational experiments, we show that the greedy heuristic’s empirical performance is, on average, within 2% of the optimal expected demand coverage.

We study a two-phase, budget-constrained, network-planning problem with multiple hub types and demand scenarios. In each phase, we install (or move) capacitated hubs on selected buildings. We allocate hubs to realized demands, under technological constraints. We present a greedy algorithm to maximize expected demand covered and computationally study its performance.