Effect of Mode of Drying on Microstructure of Potato

Potato v. Irga was subjected to blanching, and thereafter was dried by convection, puff-drying, and freeze-drying. Microstructure of raw, blanched, and dried tissue was analyzed under the light microscope using computer image analysis. It was found that the tissue of the investigated variety is built up from cells much smaller than those described in literature for other cultivars. Blanching caused starch gelatinization and increase of cross-sectional area of cells. There was no evidence of broken cell walls. Convective drying resulted in cell shrinkage and some breaking of cell walls. It was estimated that some 12% of cells lost integrity during drying. Limited disintegration of the tissue caused by convective drying is attributed to small size of cells, large contact area, and high cohesive forces between cells. Puff-drying damaged the tissue much more than the convective drying. The most devastating to tissue microstructure was freeze-drying, probably freezing per se.     

Hypermobile Robots – the Survey

This article presents a survey on hypermobile robots – a group of articulated mobile robots that typically comprise of several segments with powered wheels, tracks, or legs to propel the vehicle forward. Segments are connected by 2- or 3-degree-of-freedom (DOF) joints that may or may not be powered and provide better mobility as compared with regular mobile robots. The origins are analyzed and over 14 projects are compared in order to find the best methodology of designing and developing hypermobile robots.     

An approach to reduce energy consumption and performance losses on heterogeneous servers using power capping

Journal of Scheduling - Rapid growth of demand for remote computational power, along with high energy costs and infrastructure limits, has led to treating power usage as a primary constraint in...     

Mobile context-based framework for threat monitoring in urban environment with social threat monitor

Engaging users in threat reporting is important in order to improve threat monitoring in urban environments. Today, mobile applications are mostly used to provide basic reporting interfaces. With a rapid evolution of mobile devices, the idea of context awareness has gained a remarkable popularity in recent years. Modern smartphones and tablets are equipped with a variety of sensors including accelerometers, gyroscopes, pressure gauges, light and GPS sensors. Additionally, the devices become computationally powerful which allows for real-time processing of data gathered by their sensors. Universal access to the Internet via WiFi hot-spots and GSM network makes mobile devices perfect platforms for ubiquitous computing. Although there exist numerous frameworks for context-aware systems, they are usually dedicated to static, centralized, client-server architectures. There is still space for research in the field of context modeling and reasoning for mobile devices. In this paper, we propose a lightweight context-aware framework for mobile devices that uses data gathered by mobile device sensors and performs on-line reasoning about possible threats based on the information provided by the Social Threat Monitor system developed in the INDECT project.     

Modelling and solving grid resource allocation problem with network resources for workflow applications

A problem of allocating resources of a grid to workflow applications is considered. The problem consists, generally, in allocating distributed grid resources to tasks of a workflow in such a way that the resource demands of each task are satisfied. Grid resources are divided into computational resources and network resources. Computational tasks and transmission tasks of a workflow are distinguished. We present a model of the problem, and an algorithm for finding feasible resource allocations. A numerical example is included, showing the importance of the resource allocation phase on a grid. Some conclusions and directions for future research are given.     

Speeding up global optimization with the help of intelligent supervisors

It is shown in the paper that Developmental Genetic Programming is an efficient tool for evolutionary development of intelligent supervisors that solve an extension of Resource-Constrained Project Scheduling Problem. The extension assumes that resources are only partially available. It also assumes that renewable resources affect the project cost. The cost should be as low as possible and a deadline of the project must be met. This is apparent with regard to software houses and building enterprises. Computational experiments showed that supervisors find solutions of the problem much faster than other genetic approaches. A specific property of the supervisor is that it has various strategies of allocating the resources to the tasks. The supervisor uses the strategies in order to develop a procedure for producing the best schedule for the whole project. The analysis of the evolutionary process was performed and experimental results were compared with the optimal ones obtained by means of the exhaustive search method.     

3D‐vision based detection,localization, and sizing of broccoli heads in the field

This paper describes a 3D vision system for robotic harvesting of broccoli using low‐cost RGB‐D sensors, which was developed and evaluated using sensory data collected under real‐world field conditions in both the UK and Spain. The presented method addresses the tasks of detecting mature broccoli heads in the field and providing their 3D locations relative to the vehicle. The paper evaluates different 3D features, machine learning, and temporal filtering methods for detection of broccoli heads. Our experiments show that a combination of Viewpoint Feature Histograms, Support Vector Machine classifier, and a temporal filter to track the detected heads results in a system that detects broccoli heads with high precision. We also show that the temporal filtering can be used to generate a 3D map of the broccoli head positions in the field. Additionally, we present methods for automatically estimating the size of the broccoli heads, to determine when a head is ready for harvest. All of the methods were evaluated using ground‐truth data from both the UK and Spain, which we also make available to the research community for subsequent algorithm development and result comparison. Cross‐validation of the system trained on the UK dataset on the Spanish dataset, and vice versa, indicated good generalization capabilities of the system, confirming the strong potential of low‐cost 3D imaging for commercial broccoli harvesting.     

Using association rules to assess purchase probability in online stores

The paper addresses the problem of e-customer behavior characterization based on Web server log data. We describe user sessions with the number of session features and aim to identify the features indicating a high probability of making a purchase for two customer groups: traditional customers and innovative customers. We discuss our approach aimed at assessing a purchase probability in a user session depending on categories of viewed products and session features. We apply association rule mining to real online bookstore data. The results show differences in factors indicating a high purchase probability in session for both customer types. The discovered association rules allow us to formulate some predictions for the online store, e.g. that a logged user who has viewed only traditional, printed books, has been staying in the store from 10 to 25 min, and has opened between 30 and 75 pages, will decide to confirm a purchase with the probability of more than 92 %.

     

Effects of Subclinical Depression and Aging on Generative Reasoning About Linear Orders: Same or Different Processing Limitations?

The performance of older adults and depressed people on linear order reasoning is hypothesized to be best explained by different theoretical models. Whereas depressed younger adults are found to be impaired in generative inference making, older adults are well capable of making such inferences but exhibit problems with working memory (Experiments 1 and 2). Restriction of the available study time impairs reasoning by nondepressed control participants and. as such, proves to be a good model of older adults' but not depressed participants' limitations (Experiment 3). These results are replicated comparing depressed and older participants with a control group in the same study, providing increased power and linking the results to additional control measures of processing speed and working memory (Experiment 4). (PsycINFO Database Record (c) 2010 APA, all rights reserved)