Iterative Computer Model Research Articles

Gene selection in a single cell gene decision space based on class-consistent technology and fuzzy rough iterative computation model

Gene selection in a single cell gene decision space based on class-consistent technology and fuzzy rough iterative computation model

Attribute reduction for hybrid data based on fuzzy rough iterative computation model

A hybrid information system (HIS) means an information system (IS) with categorical attributes, real-valued attributes and missing values. It is more difficult to deal with an HIS than an IS with a type of attributes. This paper studies attribute reduction in an HIS by means of fuzzy rough iterative computation model (FRIC-model). To more accurately express the difference between objects in an HIS, a novel distance function on the object set is first constructed. Then, fuzzy symmetric relations on the object set based on this function are established and fuzzy rough approximations are defined. Next, some evaluation functions that represent the classification ability of attribute subsets, such as fuzzy positive regions, dependency functions and attribute importance functions are introduced. After then, FRIC-model is given and an attribute reduction algorithm for hybrid data based on this model is designed. At last, numerical experiments are carried out to estimate the performance of the designed algorithm. The experimental results show that the designed algorithm is more effective than some existing algorithms.

Feature selection for interval-valued data via FRIC-model

Feature selection is one basic technology for data mining. This paper investigates feature selection for interval-valued data via fuzzy rough iterative computation model (FRIC-model). To depict the similarity between samples in an interval-valued decision information system (IVDIS), the fuzzy symmetry relation in an IVDIS is first introduced from the perspective of “The similarity between information values is fed back to the feature set”. After that, several attribute evaluation functions, such as fuzzy positive regions, dependency functions and attribute importance functions are defined. Subsequently, FRIC-model for interval-valued data is established by using the iterations of these functions. Next, An feature selection algorithm in an IVDIS based on this model is presented. Lastly, numerical experiments and statistics tests are carried out to estimate the performance of the presented algorithm. The experimental results illustrate that the presented algorithm maintains high classification accuracy, and does not occupy too much memory. These findings will provide new perspective for feature selection in an IVDIS.

Feature selection for a set-valued decision information system based on fuzzy rough iterative computation model

To deal with set-valued data, one usually exploits the information value similarity, which is fed back to the object set. Alternatively, it is feasible but scarcely considered to handle set-valued data from the perspective of the information-value similarity which is fed back to the feature set. Based on this perspective, this paper studies feature selection for a set-valued decision information system (SVDIS) by means of fuzzy rough iterative computation model. In order to describe the similarity between objects, the fuzzy symmetric relations in an SVDIS are first defined, and a variable parameter to control the similarity is introduced. Then, new fuzzy rough set model for set valued data is proposed, and this model employs the iterative computation strategy to define fuzzy rough approximations and dependency functions. Next, fuzzy rough iterative computation model is established by using the iteration of fuzzy positive region and fuzzy dependency. Moreover, fuzzy rough iterative computation model is applied to the feature selection for set-valued data, and the corresponding algorithm is given. Finally, experiments are carried out to evaluate the performance of the given algorithm. The experimental results indicate that the given algorithm is more effective than some existing algorithms.

Dynamic distributed iterative computational model for payment information management in shared logistics using blockchain-assisted Internet of Things approach

Dynamic distributed iterative computational model for payment information management in shared logistics using blockchain-assisted Internet of Things approach

Programmable Transformation and Controllable Locomotion of Magnetoactive Soft Materials with 3D-Patterned Magnetization.

Magnetoactive soft material (MASM) is distinguished for multifunctional shape manipulations under magnetic actuation, thereby holding a great promise in soft robotics, actuators, electronics, and metamaterials. However, the current research of MASM with continuum hard-magnetic profiles focuses little on the transformation mechanism, high dimensional shape transformation, and multistable locomotion. Herein, we developed a systematic methodology for programmable transformation and controllable locomotion of MASM with 3D-patterned continuum magnetization. An iterative computational model based on the equilibrium between magnetic torque and deformation-induced elastic torque was developed for precise prediction of MASM transformation. Multidimensional and complex shape manipulation ability of MASM was demonstrated by magnetically actuated transformations, including 1D to 2D, 2D to 3D, and 3D to 4D transformations of solid MASM, 2D to 3D pattern transformation of MASM-based elastin-like mesh, and 3D to 4D transformation of MASM-based cuboidal lattice. Multistable and controllable locomotion of MASM was verified by multimodal locomotion behaviors of a scallop-inspired robot for wall climbing in a dry frame and drug delivery in wet stomach, including roll, open, and close under self-locked and unlocked states.

IncGraph: An Improved Distributed Incremental Graph Computing Model and Framework based on Spark GraphX

The excavated information will become obsolete when the data changes in dynamic graphs. To compute the up-to-date results, the graph algorithm has to re-compute the entire data from scratch, which will consume huge computation time and resources. To reduce the cost of such calculations, this paper proposes a model called IncGraph to support incremental iterative computation over dynamic graphs. Different from the way of traditional iteration, IncGraph executes the graph algorithm through reusing the results of the previous graph and performs computation on the part of the graph that has changed. IncGraph has two critical components: (1) an incremental iterative computation model that consists of two steps: an incremental step to calculate the results on the changed vertices of the graph, and a merge step to calculate the results on the entire graph by using the results of the previous graph and the incremental step; and (2) an incremental update method to accelerate the iterative process within the iterative graph algorithm. We implemented IncGraph on GraphX and evaluate its performance and accuracy on a practical cluster. Experiment results verify the performance advantages of IncGraph model when performing the iterative graph algorithms on the dynamic graph, compared with the traditional iteration.

Mapreduce Iterative Computation Model Based on Non-Global Parallel and Heartbeat Synchronization

As the core computing mode of cloud computing, MapReduce is limited by the non-global parallel computing model that it is not easy to perform iterative synchronization algorithm. So it is impossible to perform fine-grained task adjustment based on semantics. This paper breaks through the original computational model of MapReduce, ensures that the existing computational model is compatible with the old MapReduce jobs, and introduces the heartbeat synchronization mechanism that allows the changed state data to interact between Parallel Layers of parallel tasks. This system provides a highly flexible message custom interface, and an adaptive message passing mechanism is designed for different application requirements, which supports algorithms with iterative processing and requirements of data interaction more efficiently. The experimental results on the real large-scale graph dataset showed that compared to the original MapReduce job external processing, the internal Parallel Layer iterative computation model proposed in this paper greatly reduces the processing time of the Mapreduce processing iterative algorithm.

Fuzzy rough set-based attribute reduction using distance measures

Attribute reduction is one of the most important applications of fuzzy rough sets in machine learning and pattern recognition. Most existing methods employ the intersection operation of fuzzy relations to construct the dependency function of attribute reduction. However, the intersection operation may lead to low discrimination of fuzzy decision in high-dimensional data space. In this study, we introduce distance measures into fuzzy rough sets and propose a novel method for attribute reduction. We first construct a fuzzy rough set model based on distance measure with a fixed parameter. Then, the fixed distance parameter is replaced by a variable one to better characterize attribute reduction with fuzzy rough sets. Some iterative formulas for computing fuzzy rough dependency and attribute significance are presented, and an iterative computation model based on a variable distance parameter is proposed. Based on this, a greedy convergent algorithm for attribute reduction is designed. The experimental comparison demonstrates that the proposed reduction algorithm is effective and performs better than some of the other existing algorithms.

A Design of the Evaluation and Prediction of Power Energy Consumption of Office Buildings based on Multivariate Linear Regression

In recent years, with the rapid increase of office buildings and the requirements to improve the office environment, office building energy consumption has increased significantly. That has brought great pressure to the user, environment and city power supply and demand. The electric energy consumption of office building conservation is the most important way to reduce the energy consumption. Simulation is a major method to understand about office building power consumption. At present domestic and foreign computer software simulation technology cannot work without a lot of professional parameters. And the obtained results is so professional that cannot give the management decision auxiliary. This paper presents a holistic statistical distribution instead of complex thermodynamic iterative computation model, and the analysis method. Using the mode facing to the whole for analysis of power energy consumption of office buildings, without more intricate parameters, and can be iterative adjusted according to the difference between measured data and the prediction.

A Novel Iterative and Dynamic Trust Computing Model for Large Scaled P2P Networks

Trust management has been emerging as an essential complementary part to security mechanisms of P2P systems, and trustworthiness is one of the most important concepts driving decision making and establishing reliable relationships. Collusion attack is a main challenge to distributed P2P trust model. Large scaled P2P systems have typical features, such as large scaled data with rapid speed, and this paper presented an iterative and dynamic trust computation model named IDTrust (Iterative and Dynamic Trust model) according to these properties. First of all, a three-layered distributed trust communication architecture was presented in IDTrust so as to separate evidence collector and trust decision from P2P service. Then an iterative and dynamic trust computation method was presented to improve efficiency, where only latest evidences were enrolled during one iterative computation. On the basis of these, direct trust model, indirect trust model, and global trust model were presented with both explicit and implicit evidences. We consider multifactors in IDTrust model according to different malicious behaviors, such as similarity, successful transaction rate, and time decay factors. Simulations and analysis proved the rightness and efficiency of IDTrust against attacks with quick respond and sensitiveness during trust decision.

An adaptive switching scheme for iterative computing in the cloud

Delta-based accumulative iterative computation (DAIC) model is currently proposed to support iterative algorithms in a synchronous or an asynchronous way. However, both the synchronous DAIC model and the asynchronous DAIC model only satisfy some given conditions, respectively, and perform poorly under other conditions either for high synchronization cost or for many redundant activations. As a result, the whole performance of both DAIC models suffers fromthe serious network jitter and load jitter caused bymultitenancy in the cloud. In this paper, we develop a system, namely HybIter, to guarantee the performance of iterative algorithms under different conditions. Through an adaptive execution model selection scheme, it can efficiently switch between synchronous and asynchronous DAIC model in order to be adapted to different conditions, always getting the best performance in the cloud. Experimental results show that our approach can improve the performance of current solutions up to 39.0%.

Structure–Activity Relationship for FDA Approved Drugs As Inhibitors of the Human Sodium Taurocholate Cotransporting Polypeptide (NTCP)

The hepatic bile acid uptake transporter sodium taurocholate cotransporting polypeptide (NTCP) is less well characterized than its ileal paralog, the apical sodium dependent bile acid transporter (ASBT), in terms of drug inhibition requirements. The objectives of this study were (a) to identify FDA approved drugs that inhibit human NTCP, (b) to develop pharmacophore and Bayesian computational models for NTCP inhibition, and (c) to compare NTCP and ASBT transport inhibition requirements. A series of NTCP inhibition studies were performed using FDA approved drugs, in concert with iterative computational model development. Screening studies identified 27 drugs as novel NTCP inhibitors, including irbesartan (Ki = 11.9 μM) and ezetimibe (Ki = 25.0 μM). The common feature pharmacophore indicated that two hydrophobes and one hydrogen bond acceptor were important for inhibition of NTCP. From 72 drugs screened in vitro, a total of 31 drugs inhibited NTCP, while 51 drugs (i.e., more than half) inhibited ASBT. Hence, while there was inhibitor overlap, ASBT unexpectedly was more permissive to drug inhibition than was NTCP, and this may be related to NTCP possessing fewer pharmacophore features. Findings reflected that a combination of computational and in vitro approaches enriched the understanding of these poorly characterized transporters and yielded additional chemical probes for possible drug-transporter interaction determinations.

Identify Online Store Review Spammers via Social Review Graph

Online shopping reviews provide valuable information for customers to compare the quality of products, store services, and many other aspects of future purchases. However, spammers are joining this community trying to mislead consumers by writing fake or unfair reviews to confuse the consumers. Previous attempts have used reviewers’ behaviors such as text similarity and rating patterns, to detect spammers. These studies are able to identify certain types of spammers, for instance, those who post many similar reviews about one target. However, in reality, there are other kinds of spammers who can manipulate their behaviors to act just like normal reviewers, and thus cannot be detected by the available techniques. In this article, we propose a novel concept of review graph to capture the relationships among all reviewers, reviews and stores that the reviewers have reviewed as a heterogeneous graph. We explore how interactions between nodes in this graph could reveal the cause of spam and propose an iterative computation model to identify suspicious reviewers. In the review graph, we have three kinds of nodes, namely, reviewer, review, and store. We capture their relationships by introducing three fundamental concepts, the trustiness of reviewers, the honesty of reviews, and the reliability of stores, and identifying their interrelationships: a reviewer is more trustworthy if the person has written more honesty reviews; a store is more reliable if it has more positive reviews from trustworthy reviewers; and a review is more honest if many other honest reviews support it. This is the first time such intricate relationships have been identified for spam detection and captured in a graph model. We further develop an effective computation method based on the proposed graph model. Different from any existing approaches, we do not use an review text information. Our model is thus complementary to existing approaches and able to find more difficult and subtle spamming activities, which are agreed upon by human judges after they evaluate our results.

Predicting Knee Replacement Damage in a Simulator Machine Using a Computational Model With a Consistent Wear Factor

Wear of ultrahigh molecular weight polyethylene remains a primary factor limiting the longevity of total knee replacements (TKRs). However, wear testing on a simulator machine is time consuming and expensive, making it impractical for iterative design purposes. The objectives of this paper were first, to evaluate whether a computational model using a wear factor consistent with the TKR material pair can predict accurate TKR damage measured in a simulator machine, and second, to investigate how choice of surface evolution method (fixed or variable step) and material model (linear or nonlinear) affect the prediction. An iterative computational damage model was constructed for a commercial knee implant in an AMTI simulator machine. The damage model combined a dynamic contact model with a surface evolution model to predict how wear plus creep progressively alter tibial insert geometry over multiple simulations. The computational framework was validated by predicting wear in a cylinder-on-plate system for which an analytical solution was derived. The implant damage model was evaluated for 5 million cycles of simulated gait using damage measurements made on the same implant in an AMTI machine. Using a pin-on-plate wear factor for the same material pair as the implant, the model predicted tibial insert wear volume to within 2% error and damage depths and areas to within 18% and 10% error, respectively. Choice of material model had little influence, while inclusion of surface evolution affected damage depth and area but not wear volume predictions. Surface evolution method was important only during the initial cycles, where variable step was needed to capture rapid geometry changes due to the creep. Overall, our results indicate that accurate TKR damage predictions can be made with a computational model using a constant wear factor obtained from pin-on-plate tests for the same material pair, and furthermore, that surface evolution method matters only during the initial "break in" period of the simulation.

Development of a Computer Model to Simulate the Injection Process of a Diesel Engine

The diesel engine injection system is greatly responsible for the emission of pollutants to the atmosphere, noise generation, and engine performance. Proper synchronization of the exact amount of fuel that can be burnt according to each crankshaft position is essential to achieve maximum power, consumption economy, and combustion without residues. In this sense, when designing an injection system, all of these factors must be taken into account. During the optimization, however, the improvement of some of them may result in detriment to others. For this reason, to achieve the system optimization, the relationship between the aforementioned variables and the parameters that characterize the injection must be established. According to this, it is important to design a strategy that allows the named “reverse modeling of the injection system”, that is to say, to determine the injection system dimensions according to the system requirements. In the present work, an iterative computer model based on the continuity and energy equations, solved by finite differences algorithms, has been proposed. This model has been designed for a Bosch rotary pump and makes it possible to plot the performance of the pressure lines in the different combustion chambers, the valve lift curves, and the fuel flow injected per cylinder and cycle. In this sense, we can conclude that the proposed computer model predicts accurately the opening and closing processes in the injector needle, as well as the pressure curves in the last division of the high-pressure tube.

Development of an in-vivo method of wrist joint motion analysis

Background. A clinically applicable method of plotting wrist joint motion in three-dimensions has not been described. Computer modelling has been used to improve joint arthroplasty elsewhere in the body. We aimed to develop a method of measuring, and modelling, wrist joint motion that could potentially be used to improve the kinematic performance of wrist arthroplasty designs. Methods. An electromagnetic system was used to record wrist motion in three-dimensions. A small pilot study attempted to assess repeatability. A larger group of volunteers with normal wrists was also studied. An iterative computer model, using a two-axis hinge, was developed. One output from this model, the offset of the two axes of motion, is presented as an example of the possible applications of this method of analysis. Findings. For any one individual, in the pilot study, the offset of the axes calculated was relatively reproducible. Between individuals the difference in the offset of the axes was more marked. In 99 normal sets of data the mean axis offset was 6.8 mm (range 28 mm to −21 mm) A positive value represented the radio-ulnar deviation axis placed distal to the flexion-extension axis. Interpretation. The three-dimensional motion plots generated using this method could be used clinically to follow disease progression or recovery following surgery. The computer modelling method described has potential applications, if further refined, to wrist joint arthroplasty design.

Spiral artery associated restricted growth (SPAARG): a computer model of pathophysiology resulting from low intervillous pressure having fetal programming implications

Failure of adequate trophoblastic conversion of maternal spiral arteries is associated with intrauterine growth restriction (IUGR). In addition to poor oxygen delivery, raised spiral artery resistance reduces placental intervillous pressure. An iterative type computer model was formed by linking an existing model of the fetus and a new nine cotyledon placental model. Simulation of compression cuffing of the spiral arteries to progressively restrict uteroplacental flow was performed, while observing various fetal and placental variables. Water moved to the fetus in the cotyledonary core villi, and to the mother in the outer villous layers. While the fetus could match villous capillary pressure to changes in intervillous pressure, net transplacental water movement was minimal, but when spiral artery resistance was increased sufficiently to cause mean intervillous pressure to fall below that which the fetus could match, a net flow to the mother appeared. That continued until the resulting fetal blood hemoconcentration produced a sufficient increase in colloid osmotic pressure to restrict further loss. All cells within the fetal-placental unit are then required to operate in an abnormal ionic environment, which may significantly affect systems such as the renin–angiotensin set-point, with implications for post-natal homeostasis such as control of adult blood pressure. Furthermore, in vivo, cells of the feto-placental unit respond to the increased intravascular osmotic pressure by production of intracellular osmolytes in order to match intracellular and vascular/interstitial osmotic pressures. This may explain the observed effects on postnatal water balance in growth restricted infants and could also provide a possible mechanism for the association of the systemic maternal complications associated with impaired placentation and reduced intervillus flow.

Preliminary evaluation of an iterative computer model of the cerebral circulation

Preliminary evaluation of an iterative computer model of the cerebral circulation

Immersion chilling of trays of cooked products

Cooking systems producing complete, or ingredients for, chilled meals are designed to achieve temperatures of 70°C for 2 min in the centre of the food, which should cause a 10 6-fold destruction of Listeria monocytogenes. However, spore-forming micro-organisms will survive this process and multiply if the food is not rapidly reduced to a temperature below 10°C. Experimental investigations have been carried out to study the cooling of different thicknesses of cooked solid–liquid mixtures under a range of immersion chilling conditions. An iterative computer model of the process has been developed and used to extend these data to cover alternative processing conditions and a range of cooked meals. If surface freezing was to be avoided then it was found that only the shallowest (10 mm) tray could be chilled within the limits of the most severe European Guidelines. However, the model also predicted that the 40 mm trays of food would cool within the limits of the Guidelines if oriented differently.