Towards the Establishment of a Continuous Nondestructive Monitoring Technique for Fresh Concrete

Despite the fact that concrete is the most widely used construction material, conventional techniques for the estimation of the mechanical properties are only applicable to the hardened material. Moreover, concerning the fresh concrete examination, the already existing techniques provide only qualitative information, are not representative over the full time period of curing, setting and hardening or have limited accuracy and repeatability. In this study, the acoustic emission (AE) technique is employed in order to investigate the activity within fresh concrete as early as a few minutes after mixing. Several processes like settlement, segregation, formation and migration of air bubbles, as well as formation of hydrates produce AEs which so far have not been properly examined in literature. The first and foremost goal is to check the capacity of the new AE setup to record emissions earlier than previous studies (just after casting). The next goal concerns the possibility to characterize the different types of emissions relatively to their original source mechanism while the final goal would be the investigation of possible correlations between the early age acoustic activity and the final mechanical properties. For this reason, an AE setup is applied utilizing the favorable wave transmission properties of the metal casting molds, which act as a sort of waveguide. After validating its sensitivity, several laboratory tests are performed in concrete with varying water and aggregate content. It is found that the rate of AE activity follows trends similar to the typical hydration curves known from literature and is also indicative of the mix parameters. Finally, it should be mentioned that it is the first study that exploits the acoustic activity as early as the moment of mixing while apart from the number of emissions which is usually examined, the monitoring includes waveform parameters that show strong characterization potential.     

Statistical analysis of achievable resolution limit in the near field source localization context

In this fast communication, we derive the statistical resolution limit (SRL), characterizing the minimal parameter separation, to resolve two closely spaced known near-field sources impinging on a linear array. Toward this goal, we conduct on the first-order Taylor expansion of the observation model a Generalized Likelihood Ratio Test (GLRT) based on a Constrained Maximum Likelihood Estimator (CMLE) of the SRL. More precisely, the minimum separation between two near-field sources, that is detectable for a given probability of false alarm and a given probability of detection, is derived herein. Finally, numerical simulations are done to quantify the impact of the array geometry of the signal sources power distribution and of the array aperture on the statistical resolution limit.     

Mapping concentrations of surface water quality parameters using a novel remote sensing and artificial intelligence framework

The deterioration of surface water quality occurs due to the presence of various types of pollutants generated from human, agricultural, and industrial activities. Thus, mapping concentrations of different surface water quality parameters (SWQPs), such as turbidity, total suspended solids (TSS), chemical oxygen demand (COD), biological oxygen demand (BOD), and dissolved oxygen (DO), is indeed critical for providing the appropriate treatment to the affected waterbodies. Traditionally, concentrations of SWQPs have been measured through intensive field work. Additionally, quite a lot of studies have attempted to retrieve concentrations of SWQPs from satellite images using regression-based methods. However, the relationship between SWQPs and satellite data is complex to be modelled accurately by using regression-based methods. Therefore, our study attempts to develop an artificial intelligence modelling method for mapping concentrations of both optical and non-optical SWQPs. In this context, a remote-sensing framework based on the back-propagation neural network (BPNN) is developed for the first time to quantify concentrations of different SWQPs from the Landsat8 satellite imagery. Compared to other methods, such as Support Vector Machine, significant coefficients of determination (R²) between the Landsat8 surface reflectance and concentrations of SWQPs were obtained using the developed Landsat8-based-BPNN models. The resulting R² values were 0.991, 0.933, 0.937, 0.930, and 0.934 for turbidity, TSS, COD, BOD, and DO, respectively. Indeed, these findings indicate that the developed Landsat8-based-BPNN framework is capable of developing highly accurate models for retrieving concentrations of different SWQPs from the Landsat8 imagery.     

A Multichannel QoS MAC with Dynamic Transmit Opportunity for VANets

One of the most challenging issues facing vehicular networks lies in the design of an efficient MAC protocol due to the mobile nature of nodes and the interference associated with the dynamic environment. Moreover delay constraints for safety applications add complexity and latency requirements to the design. Existing MAC protocols overcome some challenges however don’t provide an integrated solution. Hence, the merit if this work lies in designing an efficient MAC protocol that incorporates various VANet’s challenges in a complete end-to-end solution. In this work, we propose an efficient Multichannel QoS Cognitive MAC (MQOG). MQOG assesses the quality of channel prior to transmission employing dynamic channel allocation and negotiation algorithms to achieve significant increase in channel reliability, throughput and delay constraints while simultaneously addressing Quality of Service. The uniqueness of MQOG lies in making use of the free unlicensed bands. The proposed protocols were implemented in OMNET++ 4.1, and extensive experiments demonstrated a faster and more efficient reception of safety messages compared to existing VANet MAC Protocols. Finally, improvements in delay, packet delivery ratios and throughput were captured.     

Comparison of SPOT cloud cover quality index against meteorological surface observations

A quality index, based on cloud contamination within the image, is assigned to each SPOT scene. In this study, the information content, of such a quality index, in terms of cloud cover, was tested by comparing it with coincident meteorological surface observation. Global observations during the period June-October 1995 were analysed, starting with the investigation of the spatial and temporal variability of cloud covers from both kinds of observations. Comparisons for a series of 'dependent' datasets generated by varying the time (from 0.5 to 1.5 h) and space (from 5 to 100 km) matching criteria were performed successively. Results were reported as 5 x 9 cloud-class contingency tables and relevant statistics evaluating the correlation between the two observations. The analyses showed an overall good agreement between the two cloud-cover estimations, the correlation was slightly lower (10%) than that obtained by comparing SYNOP against SYNOP. Compared with SYNOP observations, SPOT tended to slightly underestimate cases of broken cloud-cover. One of the most important sources of disagreement was the lack of quantitative information in the three-class cloud quality index code found in 30% of the SPOT images used in this study. When processed as described here, the information contained in the SPOT cloud cover quality index is consistent with the surface observation of cloud cover.     

Influence of Unmodified and β-Glycerophosphate Cross-Linked Chitosan on Anti-Candida Activity of Clotrimazole in Semi-Solid Delivery Systems

The combination of an antifungal agent and drug carrier with adjunctive antimicrobial properties represents novel strategy of complex therapy in pharmaceutical technology. The goal of this study was to investigate the unmodified and ion cross-linked chitosan’s influence on anti-Candida activity of clotrimazole used as a model drug in hydrogels. It was particularly crucial to explore whether the chitosans’ structure modification by β-glycerophosphate altered its antifungal properties. Antifungal studies (performed by plate diffusion method according to CLSI reference protocol) revealed that hydrogels obtained with chitosan/β-glycerophosphate displayed lower anti-Candida effect, probably as a result of weakened polycationic properties of chitosan in the presence of ion cross-linker. Designed chitosan hydrogels with clotrimazole were found to be more efficient against tested Candida strains and showed more favorable drug release profile compared to commercially available product. These observations indicate that novel chitosan formulations may be considered as promising semi-solid delivery system of clotrimazole.