All crystal clear: 18th-century glass à la façon de Bohème from the cistercian nunnery of Clairefontaine, Belgium

Excavations at the Cistercian nunnery of Clairefontaine, located near Arlon in the south of Belgium, revealed an assemblage of 18th-century colorless glass. The morphology of the vessels and the engraved decoration suggest a central European origin or, at least, stylistic inspiration. The composition of the glass points to a recipe combining silica, lime, and potash: a colorless potash glass à la façon de Bohème. This article considers the technology, morphology, and origin of the vessels. The art-historical analysis is supported by chemical research (scanning electron microscopy–energy-dispersive X-ray spectroscopy [SEM-EDX]). The finds are also discussed in light of the emerging northwestern European glass industry, changing consumer practices during the 18th century, and their meaning for the inhabitants of the abbey

Analyzing the Real Time Factors: Which Causing the Traffic Congestions and Proposing the Solution for Pakistani City

AbstractVehicle ownerships integral part of modern life and traffic congestion an unavoidable inconvenience. The Western countries have a far better control on the pace of number of vehicles on a road matched with supporting infrastructure. In contrast, cash strapped underdeveloped countries have a poorly built and scarce number of main roads with problems compounded by soft car loans, leases and other discounts. As a result several developing countries have been inundated with peripheral complications such as pollution and congestion undermining their economy with enormous energy bills negatively impacting respective economy. Case in point is Pakistan, where depilating infrastructure or absence outright thereof and ever more number of vehicles on the road presents a unique and highly complicated problem. One can term traffic in Sub-Continent as controlled chaos and we plan to develop an organized solution from the chaos. This presents a unique challenge in traffic management. We have developed a smart phone application when the phone is placed in vehicles, provides data for the origin and destination routes. Taking 6 parameters, which we believe mostly impacts the destination arrival time for the driver in Pakistan we propose to develop a model supported by empirical data that will enable driver to select weather they are interested in economy of fuel or economy of time in reaching their destination. We propose to plot time it takes to reach destination versus the 6 factors that determines destination arrival time. The curve will be generated for each route and from the graph median time, standard deviation as well as confidence interval will be computed. Large data will be collected and statistical analysis will be performed to verify the integrity of the model

An estimation of total vehicle travel reduction in the case of telecommuting. Detailed analysis using an activity-based modeling approach

peer reviewedransportation Demand Management (TDM) is often referred to as a strategy adopted by transport planners with the goal to increase transport system efficiency. One of the possible measures that can be adopted in TDM is the implementation of telecommuting. A significant number of studies have been conducted in the past to evaluate the effect of telecommuting on peak-period trips. However it is less studied whether telecommuting also effectively and significantly reduces total vehicle travel. For this reason, a conventional modeling approach was adopted in this paper to calculate total kilometers of travel saved in the case telecommuting would materialize in the Flanders area. In a second part, the paper also introduces the use of an activity-based modeling approach to evaluate the effect of telecommuting. By doing so, an operational activity-based framework is externally validated by means of another completely different model, both calibrated for the same application and study area

Knowledge of the Concept Light Rail Transit: Determinants of the Cognitive Mismatch between Actual and Perceived Knowledge

peer reviewedThe Flemish public transport company “De Lijn” is planning the development of a new Light Rail network for medium range distance trips (10 to 40km). A challenge exists in the fact that the concept of Light Rail Transit (LRT) is relatively unknown in Flanders. Therefore this paper explores the knowledge of the concept ‘Light Rail Transit’ among the Flemish population. To investigate the knowledge, two separate binary logit models are estimated to explore the determinants of the overall actual knowledge and the determinants of a cognitive mismatch. The results show that age, sex, public transit use, household size, bicycle ownership and weekly number of shopping activities contribute significantly to the overall actual knowledge of the LRT-concept. Besides, cognitive mismatch is only significantly affected by age and gender. Moreover, the results reveal a serious lack of knowledge of the concept of LRT. Consequently, a successful implementation of the LRT-system in Flanders may be jeopardized and thus it is of crucial importance to raise the level of knowledge. A first option is knowledge acquisition based on experience of the transit network. In this view, it can be a good idea to develop “travel-one-day-for-free” marketing actions. Second, it is important to provide information to the travelers by contriving information campaigns based on the determinants identified by the models. How the campaigns should be constructed from an intrinsic and psychological point of view and deliberating between the methods of communication to reach the various target groups are some important considerations for further research

Surveying activity-travel behavior in Flanders: Assessing the impact of the survey design

peer reviewedEver since car ownership and car use started to increase in Western Europe and the USA, transportation planners attempted to model people’s travel behavior. In the context of the Feathers project a dynamic activity-based travel demand framework is developed for Flanders. In this paper, the complete survey design of the data collection effort required for such dynamic activity-based model is discussed. A mixed survey design of using a PDA application on the one hand, and using traditional paper and pencil diaries on the other hand, turns out to be a very suitable way of collecting detailed information about planned and executed activity-travel behavior of households. The results show that no attrition effects are present, not on the number of out-of-home activities reported, nor on the number of trips reported. Moreover the survey mode (PDA versus paper and pencil) has no direct impact on the quantities investigated. Notwithstanding, it is essential for further analysis on the Feathers data to explicitly take into account mode effects because of two reasons. First, the effect of explanatory variables can be influenced by the survey mode. Second, the variance in the estimation of the quantity investigated can differ significantly. Heteroscedatisc linear regression models provide the required framework to explicitly take into account these mode effects

Semantic Annotation of Mobile Phone Data Using Machine Learning Algorithms

Cell phone call location data has been utilized for the study of travel patterns, but the underlying activities that originate the movement are still at a less explored stage. Resulted from routine and automated features of decision-making processes, human activity and travel behaviour exhibit a high level of spatial-temporal periodicities as well as a certain order of the activities. In this chapter, a method has been developed based on these regularities, which predicts activities being conducted at call locations. The method includes four steps: a set of comprehensive variables is defined; feature selection techniques are applied; a group of state-of-the-art machine learning algorithms and an ensemble of the above algorithms are employed; an additional enhancement algorithm is designed. Using data gathered from natural communication of 80 users over a period of 1 year, the proposed method is evaluated. Based on the ensemble of the models, prediction accuracy of 69.7% was achieved. Using the enhancement algorithm, the performance obtained 7.6% improvement. The experimental results demonstrate the potential to annotate call locations based on the integration between machine learning algorithms and the characteristics of underlying activity and travel behaviour, contributing towards the semantic interpretation and application of the massive data