Today administration and management of public services and infrastructure relies more and more on user data collected by many domestic and private devices including smartphones and Internet services. User data and user decision making has a large impact on public decision making processes, for example, plan-based traffic flow control. Furthermore, intelligent behaviour, i.e., cognitive, knowledge-based, adaptive, and self-organizing behaviour based on learning, emerges rapidly in today's machines and environments.

It is difficult to study such large-scale data collection, data mining, and their effect on public services like smart traffic control in field studies. Commonly, simulation in virtual reality worlds is used instead to derive an evaluation. In this work, a new concept and framework for augmented virtual reality simulation is introduced, suitable, but not limited to, to investigate large-scale socio-technical systems. Mobile agents are used to combine in-field ubiquitous Crowdsensing, e.g., performed by mobile devices, with simulation. The agents can operate both in real and virtual worlds including games and fusion both worlds be seamless migration. Agents are loosely coupled to their environment and platform and interact with each other via Tuple Spaces (generative communication) and via unicast or broadcast signals. Mobility is provided by agent process snapshot migration between platforms.

The agents are programmed in JavaScript and executed by the JavaScript Agent Machine (JAM) that can be deployed on a wide range of host platforms (mobile devices, servers, IoT devices, WEB browser). Simulation is performed by the Simulation Environment for JAM (SEJAM), extending JAM with a simulation layer providing visualization of 2D- and 3D worlds and an advanced simulation control with a wide range of integrated analysis tools. Each JAM node is capable to process thousands of agents concurrently. JAM and SEJAM nodes can be connected in clusters and in the Internet (of Things) enabling large-scale "real-world"-in-the-loop simulations with Millions of agents.

Such large-scale simulations producing Big Data and can offer an enhanced statistical strength beyond Big data analysis by understanding correlation between data and cause. Due to the loosely coupling of agents the emergence of strong heterogeneous Multiagentsystems and their cooperation can be investigated with one unified framework without disruption. Agents can represent different behaviour, goals, and individuals like chat bots, machines, robots, or artificial humans and their interaction with virtual and real individuals. We expect to simulate large-scale agent societies with agent population beyond one Million individual agents to get statistical strength. Agents are mobile with respect to networks of processing platform in the real world and with respect to the location and interaction domain in virtual worlds. The agent behaviour, planning, and decision making is based on an individual and global (domain specific) knowledge base.

Related video presentation at: http://www.sblab.de/assets/ecsa2018/out/ecsa2018.html

The carbon nanofilms on diamond were obtained by irreversible graphitization of diamond at above 1000 0C. The exposure to plasma reduces the surface conductance of the carbon nanofilms on diamond as result of a partial removal of carbon and the plasma-stimulated amorphization. The experimentally observed exponential dependence of conductance G of the carbon nanofilms on temperature T can be well represented by a relation G = A exp (BT). This behavior is explained by a model based on the thermally vibrating energy barriers formed between the carbon nanoclusters constituting the thin carbon nanofilms. The empirical constants A and B relate to the density of the carbon nanoclusters and the energy barrier height between them respectively.

The seismic monitoring of masonry structures is especially challenging due to their brittle resistance behavior. A tailored sensing system could, in principle, help to detect and locate cracks and anticipate the risks of local and global collapses, allowing prompt interventions and ensuring users’ safety. Unfortunately, off-the-shelf sensors do not meet the criteria that are needed for this purpose, due to their durability issues, costs and extensive maintenance requirements. As a possible solution for earthquake-induced damage detection and localization in masonry structures, the authors have recently introduced the novel sensing technology of “smart bricks”, that are clay bricks with self-sensing capabilities, whose electromechanical properties have been already characterized in previous work. The bricks are fabricated by doping traditional clay with conductive stainless steel microfibers, enhancing the electrical sensitivity of the material to strain. If placed at key locations within the structure, this technology permits to detect and locate permanent changes in deformation under dead loading conditions, associated to a change in structural conditions following an earthquake. In this way, a quick post-earthquake assessment of the monitored structure can be achieved, at lower costs and with lower maintenance requirements in comparison to traditional sensors.

In this paper, the authors further investigate the electro-mechanical behavior of smart bricks, with a specific attention to the fabrication of the electrodes, and exemplify their application for damage detection and localization in a full-scale shaking table test on a masonry building specimen. Experimental results show that smart bricks’ outputs can effectively allow the detection of local permanent changes in deformation following a progressive damage, as also confirmed by a 3D finite element simulation carried out for validation purposes.

Related video presentation available here.

A new frontier in clinical disease diagnostics was launched by a series of recent discoveries of a new phenomenon that makes important connections between the metabolic activities of resident microbes and human diseases. Numerous recent studies have demonstrated that the mechanisms leading to disease development involve not only pathogenesis, but also activities and interactions between the complex assemblage of microbes (microbiota) in the oral cavity, lungs, and gut, the microbial metabolites they produce, and the host immune system. These interactions may occur by either metabolism-dependent or metabolism-independent pathways. Consequently, dysbiosis or changes in microbiota composition, often modulate disease development by at least two different mechanisms, including disease-induced dysbiosis and alterations in dysbiotic patterns caused by abiotic, exogenous factors (diet, drug use, and various environmental factors). This paper summarizes recent evidence demonstrating how electronic-nose (e-nose) technologies with multisensor arrays could potentially be used to identify specific changes in microbiome composition, microbiome diversity, and related alterations in patterns of metabolic pathways at different locations in the body. This could be achieved through detections and discriminations between specific chemical VOC-biomarkers, products of microbial metabolism, identified in healthy patients and those related to dysbiosis associated with specific diseases. Recent advances in e-nose technologies, having capabilities of detecting complex mixtures of VOC-metabolites in the headspace of clinical samples, offer new tools for exploiting the common occurrence of microbial dysbiotic mechanisms for noninvasive early disease detection.

With over 6,000 rivers and 5358 lakes, surface water is one of the important resources in Nepal. However, their quantity and quality are decreasing due to human activities and climate change. Hence, the monitoring and estimation of surface water is an essential task. In Nepal, surface water has different characteristics such as varying temperature, turbidity, depth, and vegetation cover, for which remote sensing technology plays vital role in classification. In recent years machine learning methods with training dataset, have been outperforming different traditional methods. In this study, we tried to use satellite image from Landsat 8 to classify surface water in Nepal. Input of Landsat bands, their derived indices and ground truth from high resolution images available in Google Earth will be used. And their performance will be evaluated based on overall accuracy using cross-validation technique. The study will be will helpful to select optimum machine learning method for surface water classification and therefore, monitoring and management of the surface water in Nepal.

This study presents a new scheme for autonomous health monitoring of railroad infrastructure using a continuous stream of structural health monitoring data. The study utilized measured strains from an optimized sensor set deployed on a double track, steel, railway, truss bridge located in central Nebraska. The most common failure mode for the superstructure of this structural system is the stringer-to-floor beam connection failure, which was the focus of this study. However, the proposed methodology could be used to assess the condition of a wide range of structural elements and details. The damage feature adopted in this framework was the variations of Proper Orthogonal Modes (POMs) of the measured structural response. To automatically detect the occurrence, location, and intensity of deficiencies from the POMs, Artificial Neural Networks (ANN) was adopted. POM variations, which are traditionally input (load) dependent, were ultimately utilized as damage indicators. To alleviate the variability of POMs due to non-stationarity of the train loads, a preset windowing of measured output was completed in conjunction with automated peak-picking. Furthermore, input variability necessitated implementing ANNs to help decouple POM changes due to load variations from those caused by deficiencies, changes that would render the proposed framework input independent, a significant advancement. Damage “scenarios” were artificially introduced into select output (strain) datasets recorded while monitoring train passes across the selected bridge. This information, in turn, was used to train ANNs using MATLABs Neural Net Toolbox. Trained ANNs were tested against monitored loading events and artificial damage scenarios. Applicability of the proposed, output-only framework was investigated via studies of the bridge under operational conditions. To account for the effects of potential deficiencies at the stringer-to-floor beam connections, measured signal amplitudes were artificially decreased at select locations. Finally, to validate the applicability of the proposed method using low-cost measurement devices, the measured signals were corrupted by high levels of white, Gaussian noises featuring spatial correlations. It was concluded that the proposed framework could successfully identify 20 damage indices, which were artificially imposed on measured signals under operational conditions.

Since the 1970s, Korea has achieved exponential economic growth over a short period of time with huge number of infrastructures built. However, 30 years past these infrastructures are now deteriorating at rapid pace due to extensive use and climatic factors, raising safety issue in recent years. The current task force face limitations in monitoring and maintenance due to various reasons: insufficient budget, increasing number of infrastructure facilities requiring maintenance, shortage of manpower, and rapidly increasing number of aging infrastructure facilities. To overcome these limitations, a new approach is required that is different from manual inspection methods under the existing rules and regulations. In such context, this study aimed to explore the efficiency of bridge inspection for cracks by Unmanned Aerial Vehicle (UAV) that could observe inaccessible areas, could be conveniently and easily controlled, and could offer high economic benefits. A case study of UAV based crack detection of for high bridge in Wonjudaegyo Bridge, Korea was done. The result shows effective crack detection on the structure than traditional methods.

The drive towards miniaturization in polysilicon MEMS industry leads unavoidably to question the hypothesis of homogeneity commonly accepted for continuum mechanics. Silicon grain morphology and orientation eventually influences the mechanical response of MEMS devices, when critical structural components (such as e.g. suspension springs) shrink. Moreover, the deep reactive-ion etching process, leading to the so-called over-etch, whose relevance is more and more increasing when referred to dimensions comparable with the grain size, affects the accuracy of the geometrical layout. Under these conditions, a spread in the working operational behavior of the devices is expected, which is obviously a matter of concern both for MEMS design and reliability. While this consequence is well known and expected, the quantification of the aforementioned spread is far to be under control, both in design practice and theory.

In this work, through Monte Carlo analyses on statistical volume elements we show the effect of the grain morphology and orientation on the elastic effective properties of polysilicon beams constituting critical MEMS components. The extensive numerical investigation is summarized through statistical (lognormal) distributions for the elastic properties as a function of grain size morphology, quantifying therefore not only the expected mean values but also the also the spread around them. These (analytical) statistical distributions represent a simple while rigorous alternative to cumbersome numerical analyses. Their utility is testified through the analysis of a statically indeterminate MEMS structure, quantifying the possible initial offset away from the designed configuration due to residual stresses arising from the production process.

The major portion of existing infrastructure worldwide continues to be at a potential risk of failure on account of aging, corrosion and overloading principally during earthquakes. Their prolonged use beyond design service life proliferate damage that manifests itself in the form of cracks. The conventional safety evaluation techniques such as visual inspection and non-destructive test (NDT) entail time and effort and require that the vicinity of damage is known at priori and the portion of structure being inspected is readily accessible. This calls for an urgent need to facilitate real time structural assessment for early detection and diagnosis of cracks. Structural Health Monitoring (SHM) identifies damage by virtue of changes in the overall vibration response of the buildings. The paper focuses on real-time damage detection based on vibration studies accomplished by Structural Health Monitoring team of Central Building Research Institute (CBRI). The experiment was performed on the 1:3 scaled model of 6-story RC frame with masonry infill in the Building dynamics laboratory of CBRI. The forward problem is attended by inducing step-by-step damage in infill to investigate the changes in dynamic response as a result of change in physical properties of the structure. Recorded time histories are processed for Frequency Response Spectra (FRS) with Fast Fourier Transform (FFT) and mode shapes are obtained. Changes in natural frequency and modal curvature for each of the five damage cases are analyzed for damage detection and location in the structure. An Algorithm for damage identification viz. Curvature Damage Factor (CDF) approach is presented.

In the last decade, much attention has been paid to the development of devices for collecting and converting energy scattered in the environment - harvesters of energy. The most promising for this purpose are piezoelectric transducers. They make it possible to convert the mechanical energy of motion and scattered electromagnetic energy into an electrical signal. One of the main materials for such piezoelectric energy harvester is lead titanate zirconate (PbZrxTi1-xO3), which has high piezoelectric properties.

We developed and investigated the design and collected a laboratory energy harvester sample based on PZT thin films with a thickness of 1 - 1.5 μm. The films were formed by high-frequency reactive plasma sputtering in an oxygen atmosphere. The resulting sample was sensitive to mechanical acceleration and vibration. Sensor tests calibrated load on the electrochemical test stand showed that the sensor exhibits a power-law sensitivity dependence on the acceleration frequency, the highest sensitivity in the frequency range from 2 to 5 Hz with a sensitivity of up to 75 pC/g, which corresponds to a sensitivity of 1.2 -1.5 V/g.

The same sensor design was sensitive to scattered electromagnetic energy with a sensitivity of 6.8∙10^-4 V/(V/cm). At what, the maximum sensitivity reached at non-low frequencies of an electromagnetic field of 20-50 Hz. It is known that the intensity of the scattered energy in the atmosphere can reach hundreds of V / m. The developed laboratory sample had an area of about 1 cm². In the case of an area increase of 100 times, it is possible to obtain an output signal up to 1 V.

Thus, on the basis of small-size designs based on PZT films, it is possible to obtain autonomous energy sources.

The reported study was funded by RFBR according to the research project № 18-29-11019.