Despite the great efforts to assure quality in higher education institutions, the ambiguity of its related concepts and requirements constitute a big challenge when trying to implement it as an automated information system. The present work introduces a framework for an automated information system that manages the quality assurance in higher educations institutions. The aim of designing such a system is to provide an automation tool that avoids unnecessary and redundant tasks associated to quality in higher education institutions. In addition, the proposed system helps all higher education stockholders to handle and monitor their tasks. Moreover, it aims to help the quality assurance center in a higher education institution to apply its qualitys standards, and to make sure that they are being maintained and enhanced. This information system contains a core module and 17 sub-modules, which are described in this paper.

n/a

n/a

AbstractConstruction logistics planning entails the coordination of supply and site activities by integrating their decisions and recognizing existing interdependencies to minimize the total material management cost. Despite the preliminary estimates of its benefits to the construction industry, few contractors adopted logistics management because of its demand for detailed data and decision of material supply and site operations. This paper presents the development of a new automated multi-objective construction logistics optimization system (AMCLOS) that would support the contractors in optimally planning material supply and storage. AMCLOS provides a holistic framework of automatically retrieving project spatial and temporal data from existing scheduling and BIM electronic files, seamlessly integrating relevant contractor and suppliers' data, and optimizing material supply and site decisions to minimize total logistics costs. The performance of AMCLOS was validated against a previous construction logistics planning model, which provided useful insights on material supply and storage logistics in congested and spacious sites. The developed system is envisioned to increase the implementation of logistics management practices and early integration and coordination of construction supply and site processes.

n/a

One.of.the.prime.factors.in.ensuring.a.consistent.marketing.of.crops.is.product.quality,.and.the.process.of.
determining.ripeness.stages.is.a.very.important.issue.in.the.industry.of.(fruits.and.vegetables).production,.
since.ripeness.is.the.main.quality.indicator.from.the.customers’.perspective..To.ensure.optimum.yield.of.
high.quality.products,.an.objective.and.accurate.ripeness.assessment.of.agricultural.crops.is.important..
This.chapter.discusses.the.problem.of.determining.different.ripeness.stages.of.tomato.and.presents.a.
content-based.image.classification.approach.to.automate.the.ripeness.assessment.process.of.tomato.via.
examining.and.classifying.the.different.ripeness.stages.as.a.solution.for.this.problem..It.introduces.a.
survey.about.resent.research.work.related.to.monitoring.and.classification.of.maturity.stages.for.fruits/
vegetables.and.provides.the.core.concepts.of.color.features,.SVM,.and.PCA.algorithms..Then.it.describes.
the.proposed.approach.for.solving.the.problem.of.determining.different.ripeness.stages.of.tomatoes..The.
proposed.approach.consists.of.three.phases,.namely.pre-processing,.feature.extraction,.and.classification.
phase..The.classification.process.depends.totally.on.color.features.(colored.histogram.and.color.moments),.
since.the.surface.color.of.a.tomato.is.the.most.important.characteristic.to.observe.ripeness..This.approach.
uses.Principal.Components.Analysis.(PCA).and.Support.Vector.Machine.(SVM).algorithms.for.feature.
extraction.and.classification,.respectively

One of the prime factors in ensuring a consistent marketing of crops is product quality, and the process of
determining ripeness stages is a very important issue in the industry of (fruits and vegetables) production,
since ripeness is the main quality indicator from the customers’ perspective. To ensure optimum yield of
high quality products, an objective and accurate ripeness assessment of agricultural crops is important. This
chapter discusses the problem of determining different ripeness stages of tomato and presents a content-based
image classification approach to automate the ripeness assessment process of tomato via examining and
classifying the different ripeness stages as a solution for this problem. It introduces a survey about resent
research work related to monitoring and classification of maturity stages for fruits/vegetables and provides
the core concepts of color features, SVM, and PCA algorithms. Then it describes the proposed approach for
solving the problem of determining different ripeness stages of tomatoes. The proposed approach consists of
three phases, namely pre-processing, feature extraction, and classification phase. The classification process
depends totally on color features (colored histogram and color moments), since the surface color of a tomato
is the most important characteristic to observe ripeness. This approach uses Principal Components Analysis
(PCA) and Support Vector Machine (SVM) algorithms for feature extraction and classification, respectively.