The Dynamics in the Soft Numbers Coordinate System

"Soft Logic" extends the number 0 from a single point to a continuous line, which we term "The zero axis". One of the modern science challenges is finding a bridge between the real world outside the observer and the observer's inner world. In “Soft Logic” we suggested a constructive model of bridging the two worlds by defining, on the base of the zero axis, a new kind of numbers, which we called ‘Soft Numbers’. Inspired by the investigation and visualization of fractals by Mandelbrot, within the investigation of the dynamics of some special function of a complex variable on the complex plane, we investigate in this paper the dynamics of soft functions on the plane strip with a special coordinate system. The recursive process that creates this soft dynamics allows us to discover new dynamics sets in a plane

SIMILARITY ENHACEMENT IN TIME-AWARE RECOMMENDER SYSTEMS

Time-aware recommender systems (TARS) are systems that take into account a time factor - the age of the user data. There are three approaches for using a time factor: (1) the user data may be given different weights by their age, (2) it may be treated as a step in a biological process and (3) it may be compared in different time frames to find a significant pattern. This research deals with the latter approach. When dividing the data into several time frames, matching users becomes more difficult - similarity between users that was once identified in the total time frame may disappear when trying to match between them in smaller time frames. The user matching problem is largely affected by the sparsity problem, which is well known in the recommender system literature. Sparsity occurs where the actual interactions between users and data items is much smaller in comparison to the entire collection of possible interactions. The sparsity grows as the data is split into several time frames for comparison. As sparsity grows, matching similar users in different time frames becomes harder, increasing the need for finding relevant neighboring users. Our research suggests a flexible solution for dealing with the similarity limitation of current methods. To overcome the similarity problem, we suggest dividing items into multiple features. Using these features we extract several user interests, which can be compared among users. This comparison results in more user matches than in current TARS

Evaluation of gene-expression clustering via mutual information distance measure

BACKGROUND: The definition of a distance measure plays a key role in the evaluation of different clustering solutions of gene expression profiles. In this empirical study we compare different clustering solutions when using the Mutual Information (MI) measure versus the use of the well known Euclidean distance and Pearson correlation coefficient. RESULTS: Relying on several public gene expression datasets, we evaluate the homogeneity and separation scores of different clustering solutions. It was found that the use of the MI measure yields a more significant differentiation among erroneous clustering solutions. The proposed measure was also used to analyze the performance of several known clustering algorithms. A comparative study of these algorithms reveals that their "best solutions" are ranked almost oppositely when using different distance measures, despite the found correspondence between these measures when analysing the averaged scores of groups of solutions. CONCLUSION: In view of the results, further attention should be paid to the selection of a proper distance measure for analyzing the clustering of gene expression data

Set-up saving schemes for printed circuit boards assembly

Focusing on a basic printed circuit board (PCB) assembly line configuration characterized by very long set-up times, we examine two scheduling methods that can significantly reduce the set-up. Both methods -the Grouped Set-Up (GSU) method that has been recently introduced in the literature and the Sequence Dependent Scheduling (SDS) method, which has not been studied in this context -are based on component commonality among PCB types. Using the typical traditional scheduling method as a benchmark, the GSU and the SDS methods are compared in terms of three performance measures: line throughput, average work-in-process (WIP) inventory level, and implementation complexity. Guidelines for selecting the most appropriate method for a given production environment are proposed. The analysis is illustrated using real data from a typical production line

Group set-up for printed circuit board assembly

The current practice in the assembly of electronic components on printed circuit boards (PCBs) is serial production. a process characterized by very long set-up times. However, with the advent of efficient on-line process information .. new production control methods are now possible. This paper proposes a different production method, called the group set-up (GSU) method, which can significantly reduce set-up times. The traditional and the GSU production methods are compared, and it is shown that the GSU always performs better than the traditional method in terms of total production flow (throughput) and labour time However, the traditional method performs better than the GSU in terms of work in process (WIP) inventory; and in some cases, in terms of makespan (lead time). A detailed analysis for a small number of PCBs is presented

miR126-5p Downregulation Facilitates Axon Degeneration and NMJ Disruption via a Non-Cell-Autonomous Mechanism in ALS.

Axon degeneration and disruption of neuromuscular junctions (NMJs) are key events in amyotrophic lateral sclerosis (ALS) pathology. Although the disease\u27s etiology is not fully understood, it is thought to involve a non-cell-autonomous mechanism and alterations in RNA metabolism. Here, we identified reduced levels of miR126-5p in presymptomatic ALS male mice models, and an increase in its targets: axon destabilizing Type 3 Semaphorins and their coreceptor Neuropilins. Using compartmentalize

ACTIVITY CONTROLLER FOR A MULTIPLE ROBOT ASSEMBLY CELL

The research addresses the problems of the Activity Controller for a Multiple Robot Assembly Cell. It includes analysis and development of planning and control algorithms for operation of multi-robot assembly systems. The problem defined for the Activity Controller is which robot is to perform which operations; how, in terms of spatial routes and accessories; and when, in terms of timing and synchronization, in order to effectively achieve certain task objectives (e.g., minimize makespan time). General algorithms for the Activity Controller are presented emphasizing the real time operational control. Experimental work serves as a concept proof by designing and implementing the control algorithms on a computer controlled system. The system was used to simultaneously operate two robots that share tasks and auxiliary devices. The research is concluded with a scheme for evaluating the potential advantages of multi-robot assembly cell under an Activity Controller

A Compact and Accurate Model for Classification

We describe and evaluate an information-theoretic algorithm for datadriven induction of classification models based on a minimal subset of available features. The relationship between input (predictive) features and the target (classification) attribute is modeled by a tree-like structure termed an information network (IN). Unlike other decision-tree models, the information network uses the same input attribute across the nodes of a given layer (level). The input attributes are selected incrementally by the algorithm to maximize a global decrease in the conditional entropy of the target attribute. We are using the prepruning approach: when no attribute causes a statistically significant decrease in the entropy, the network construction is stopped. The algorithm is shown empirically to produce much more compact models than other methods of decision-tree learning, while preserving nearly the same level of classification accuracy