Accuracy Improvement of In-line Near-Infrared Spectroscopic Moisture Monitoring in a Fluidized Bed Drying Process

An exploratory analysis of a large representative dataset obtained in a fluidized bed drying process of a pharmaceutical powder has revealed a significant correlation of spectral intensity with granulate humidity in the whole studied range of 1091.8–2106.5 nm. This effect was explained by the dependence of powder refractive properties, and hence light penetration depth, on the water content. The phenomenon exhibited a close spectral similarity to the well-known stochastic variation of spectral intensities caused by the process turbulence (the so-called “scatter effect”). Therefore, any traditional scatter-corrective preprocessing incidentally eliminates moisture-correlated variance from the data. To preserve this additional information for a more precise moisture calibration, a time-domain averaging of spectral variables has been suggested. Its application resulted in a distinct improvement of prediction accuracy, as compared to the scatter-corrected data. Further improvement of the model performance was achieved by the application of a dynamic focusing strategy when adjusting the model to a drying process stage. Probe fouling was shown to have a minor effect on prediction accuracy. The study resulted in a considerable reduction of the root-mean-square error of in-line moisture monitoring to 0.1%, which is close to the reference method's reproducibility and significantly better than previously reported results

Energy consumption prediction using people dynamics derived from cellular network data

Energy efficiency is a key challenge for building sustainable societies. Due to growing populations, increasing incomes and the industrialization of developing countries, the world primary energy consumption is expected to increase annually by 1.6%. This scenario raises issues related to the increasing scarcity of natural resources, the accelerating pollution of the environment, and the looming threat of global climate change. In this paper we introduce a new and original approach to predict next week energy consumption based on human dynamics analysis derived out of the anonymized and aggregated telecom data, which is processed from GSM network call data records (CDRs). We introduce an original problem statement, analyze regularities of the source data, provide insight on the original feature extraction method and discuss peculiarities of the regression models applicable for this big data problem. The proposed solution could act on energy producers/distributors as an essential aid to smart meters data for making better decisions in reducing total primary energy consumption by limiting energy production when the demand is not predicted, reducing energy distribution costs by efficient buy-side planning in time and providing insights for peak load planning in geographic space.Telecom Italia SpASET Distribuzione Sp

Upgrading the ECR ion source within FAMA

Recent upgrading of the Facility for Modification and Analysis of Materials with Ion Beams-FAMA, in the Laboratory of Physics of the Vinca Institute of Nuclear Sciences, included the modernization of its electron cyclotron resonance ion source. Since the old ion source was being extensively used for more than 15 years for production of multiply charged ions from gases and solid substances, its complete reconstruction was needed. The main goal was to reconstruct its plasma and injection chambers and magnetic structure, and thus intensify the production of multiply charged ions. Also, it was decided to refurbish its major subsystems the vacuum system, the microwave system, the gas inlet system, the solid substance inlet system, and the control system. All these improvements have resulted in a substantial increase of ion beam currents, especially in the case of high charge states, with the operation of the ion source proven to be stable and reproducible

Upgrading of the CAPRICE type ECR ion source

The CAPRICE-type ECR ion source mVINIS has been upgraded by increasing its magnetic field to improve a plasma confinement and thereby enhance the source performance. This modification made it also possible to increase the internal diameter of the plasma chamber and to replace the coaxial microwave input by a waveguide. Some major subsystems such as: the vacuum system, the microwave system, the gas inlet system, the solid substance inlet system, and the control system have been also refurbished. All these improvements have resulted in a substantial increase of ion beam currents, especially in the case of high charge states, with the operation of the ion source proven to be stable and reproducible. This modification can be applied to other CAPRICE-type ion sources. © 2018 Author(s).17th International Conference on Ion Sources 2018; Geneva's International Conference Centre Geneva; Switzerland; 15 September 2017 through 20 September 2017; Code 13992

Structural properties and energy spectrum of novel GaSb/AlP self-assembled quantum dots

In this work, the formation, structural properties, and energy spectrum of novel self-assembled GaSb/AlP quantum dots (SAQDs) were studied by experimental methods. The growth conditions for the SAQDs’ formation by molecular beam epitaxy on both matched GaP and artificial GaP/Si substrates were determined. An almost complete plastic relaxation of the elastic strain in SAQDs was reached. The strain relaxation in the SAQDs on the GaP/Si substrates does not lead to a reduction in the SAQDs luminescence efficiency, while the introduction of dislocations into SAQDs on the GaP substrates induced a strong quenching of SAQDs luminescence. Probably, this difference is caused by the introduction of Lomer 90°-dislocations without uncompensated atomic bonds in GaP/Si-based SAQDs, while threading 60°-dislocations are introduced into GaP-based SAQDs. It was shown that GaP/Si-based SAQDs have an energy spectrum of type II with an indirect bandgap and the ground electronic state belonging to the X-valley of the AlP conduction band. The hole localization energy in these SAQDs was estimated equal to 1.65–1.70 eV. This fact allows us to predict the charge storage time in the SAQDs to be as long as >>10 years, and it makes GaSb/AlP SAQDs promising objects for creating universal memory cells

Synergy effect of combined near and mid-infrared fibre spectroscopy for diagnostics of abdominal cancer

Cancers of the abdominal cavity comprise one of the most prevalent forms of cancers, with the highest contribution fromcolon and rectal cancers (12% of the human population), followed by stomach cancers (4%). Surgery, as the preferred choice of treatment, includes the selection of adequate resection margins to avoid local recurrences due to minimal residual disease. The presence of functionally vital structures can complicate the choice of resection margins. Spectral analysis of tissue samples in combination with chemometric models constitutes a promising approach for more e cient and precise tumour margin identification. Additionally, this technique provides a real-time tumour identification approach not only for intraoperative application but also during endoscopic diagnosis of tumours in hollow organs. The combination of near-infrared and mid-infrared spectroscopy has advantages compared to individual methods for the clinical implementation of this technique as a diagnostic tool

A framework for ensemble modelling of climate change impacts on lakes worldwide : the ISIMIP Lake Sector

Empirical evidence demonstrates that lakes and reservoirs are warming across the globe. Consequently, there is an increased need to project future changes in lake thermal structure and resulting changes in lake biogeochemistry in order to plan for the likely impacts. Previous studies of the impacts of climate change on lakes have often relied on a single model forced with limited scenario-driven projections of future climate for a relatively small number of lakes. As a result, our understanding of the effects of climate change on lakes is fragmentary, based on scattered studies using different data sources and modelling protocols, and mainly focused on individual lakes or lake regions. This has precluded identification of the main impacts of climate change on lakes at global and regional scales and has likely contributed to the lack of lake water quality considerations in policy-relevant documents, such as the Assessment Reports of the Intergovernmental Panel on Climate Change (IPCC). Here, we describe a simulation protocol developed by the Lake Sector of the Inter-Sectoral Impact Model Intercomparison Project (ISIMIP) for simulating climate change impacts on lakes using an ensemble of lake models and climate change scenarios for ISIMIP phases 2 and 3. The protocol prescribes lake simulations driven by climate forcing from gridded observations and different Earth system models under various representative greenhouse gas concentration pathways (RCPs), all consistently bias-corrected on a 0.5 degrees x 0.5 degrees global grid. In ISIMIP phase 2, 11 lake models were forced with these data to project the thermal structure of 62 well-studied lakes where data were available for calibration under historical conditions, and using uncalibrated models for 17 500 lakes defined for all global grid cells containing lakes. In ISIMIP phase 3, this approach was expanded to consider more lakes, more models, and more processes. The ISIMIP Lake Sector is the largest international effort to project future water temperature, thermal structure, and ice phenology of lakes at local and global scales and paves the way for future simulations of the impacts of climate change on water quality and biogeochemistry in lakes.Peer reviewe

Predictive Modeling of Human Behavior: Supervised Learning from Telecom Metadata

Big data, specifically Telecom Metadata, opens new opportunities for human behavior understanding, applying machine learning and big data processing computational methods combined with interdisciplinary knowledge of human behavior. In this thesis new methods are developed for human behavior predictive modeling based on anonymized telecom metadata on individual level and on large scale group level, which were studied during research projects held in 2012-2016 in collaboration with Telecom Italia, Telefonica Research, MIT Media Lab and University of Trento. It is shown that human dynamics patterns could be reliably recognized based on human behavior metrics derived from the mobile phone and cellular network activity (call log, sms log, bluetooth interactions, internet consumption). On individual level the results are validated on use cases of detecting daily stress and estimating subjective happiness. An original approach is introduced for feature extraction, selection, recognition model training and validation. Experimental results based on ensemble stochastic classification and regression tree models are discussed. On large group level, following big data for social good challenges, the problem of crime hotspot prediction is formulated and solved. In the proposed approach we use demographic information along with human mobility characteristics as derived from anonymized and aggregated mobile network data. The models, built on and evaluated against real crime data from London, obtain accuracy of almost 70% when classifying whether a specific area in the city will be a crime hotspot or not in the following month. Electric energy consumption patterns are correlated with human behavior patterns in highly nonlinear way. Second large scale group behavior prediction result is formulated as predicting next week energy consumption based on human dynamics analysis derived out of the anonymized and aggregated telecom data, processed from GSM network call detail records (CDRs). The proposed solution could act on energy producers/distributors as an essential aid to smart meters data for making better decisions in reducing total primary energy consumption by limiting energy production when the demand is not predicted, reducing energy distribution costs by efficient buy-side planning in time and providing insights for peak load planning in geographic space. All the studied experimental results combine the introduced methodology, which is efficient to implement for most of multimedia and real-time applications due to highly reduced low-dimensional feature space and reduced machine learning pipelines. Also the indicators which have strong predictive power are discussed opening new horizons for computational social science studies