Traditional cell culture and animal models utilized for preclinical drug screening have led to high attrition rates of drug candidates in clinical trials due to their low predictive power for human response. Alternative models using human cells to build in vitro biomimetics of the human body with physiologically relevant organ-organ interactions hold great potential to act as "human surrogates" and provide more accurate prediction of drug effects in humans. This review is a comprehensive investigation into the development of tissue-engineered human cell-based microscale multiorgan models, or multiorgan microphysiological systems for drug testing. The evolution from traditional models to macro- and microscale multiorgan systems is discussed in regards to the rationale for recent global efforts in multiorgan microphysiological systems. Current advances in integrating cell culture and on-chip analytical technologies, as well as proof-of-concept applications for these multiorgan microsystems are discussed. Major challenges for the field, such as reproducibility and physiological relevance, are discussed with comparisons of the strengths and weaknesses of various systems to solve these challenges. Conclusions focus on the current development stage of multiorgan microphysiological systems and new trends in the field.

From the point of view of the information supplied by an ATIS to the motorists entering a freeway of one of the most relevant is the Forecasted Travel Time, that is the expected travel time that they will experience when traverse a freeway segment. From the point of view of ATMS the dynamic estimates of time dependencies in OD matrices is a major input to dynamic traffic models used for estimating the current traffic state and forecasting its short term evolution. Travel Time Forecasting and Dynamic OD Estimation are two of the key components of ATIS/ATMS and the quality of the results that they could provide depend not only on the quality of the models but also on the accuracy and reliability of the measurements of traffic variables supplied by the detection technology.The quality and reliability of the measurements produced by traditional technologies, as inductive loop detectors, is not usually the required by real-time applications, therefore one wonders what could be expected from the new ICT technologies as for example Automatic Vehicle Location, License Plate Recognition, detection of mobile devices and so on. The main objectives of this paper are: to explore the quality of the data produced by the Bluetooth detection of mobile devices equipping vehicles for Travel Time Forecasting and its use to estimate time dependent OD matrices. Ad hoc procedures based on Kalman Filtering have been designed and implemented successfully and the numerical results of the computational experiments are presented and discussed.

No abstract

Biofunctional textiles with integrated drug-delivery systems can help in the fight against vector-borne diseases. The use of repellent agents derived from plants and oils is an alternative to DEET (N,N-diethyl-m-methylbenzamide), which has disadvantages that include toxic reactions and skin damage. However, some researchers report that oils can be ineffective due to reasons related to uncontrolled release. In this work, the mechanism of control of citronella oil (OC) complexed with β-cyclodextrin (βCD) on cotton (COT) and polyester (PES) textiles was investigated. The results obtained reveal that finishing cotton and polyester with β-cyclodextrin complexes allows for control of the release mechanism of the drug from the fabric. To assess the complexes formed, optical microscopy, SEM, and FTIR were carried out; the yield of complex formation was obtained by spectroscopy in the ultraviolet region; and controlled release was performed in vitro. Oil complexation with βCD had a yield of 63.79%, and it was observed that the release, which was in seconds, moved to hours when applied to fabrics. The results show that complexes seem to be a promising basis when it comes to immobilizing oils and controlling their release when modified with chemical crosslinking agents.

Time-Dependent Origin-Destination (OD) matrices are essential input for Dynamic Traffic Models such as microscopic and mesoscopic traffic simulators. Dynamic traffic models also support real-time traffic management decisions and they are traditionally used in the design and evaluation of Traffic Management and Information Systems (ATMS/ATIS). Timedependent OD estimations are typically based either on Kalman-Filtering or on bi-level mathematical programming, which can be considered in most cases as ad hoc heuristics. The advent of the new Information and Communication Technologies (ICT) provides new types of traffic data with higher quality and accuracy, which in turn allows new modeling hypotheses that lead to more computationally efficient algorithms. This paper presents ad hoc, Kalman Filtering procedures that explicitly exploit Bluetooth sensor traffic data , and it reports the numerical results from computational experiments performed at a network test site.