High-grade glioma diffusive modeling using statistical tissue information and diffusion tensors extracted from atlases

Glioma, especially glioblastoma, is a leading cause of brain cancer fatality involving highly invasive and neoplastic growth. Diffusive models of glioma growth use variations of the diffusion-reaction equation in order to simulate the invasive patterns of glioma cells by approximating the spatiotemporal change of glioma cell concentration. The most advanced diffusive models take into consideration the heterogeneous velocity of glioma in gray and white matter, by using two different discrete diffusion coefficients in these areas. Moreover, by using diffusion tensor imaging (DTI), they simulate the anisotropic migration of glioma cells, which is facilitated along white fibers, assuming diffusion tensors with different diffusion coefficients along each candidate direction of growth. Our study extends this concept by fully exploiting the proportions of white and gray matter extracted by normal brain atlases, rather than discretizing diffusion coefficients. Moreover, the proportions of white and gray matter, as well as the diffusion tensors, are extracted by the respective atlases; thus, no DTI processing is needed. Finally, we applied this novel glioma growth model on real data and the results indicate that prognostication rates can be improved.     

Numerical investigation of Solid Recovered Fuels’ co-firing with brown coal in large scale boilers – Evaluation of different co-combustion modes

In the current work the co-combustion of Solid Recovered Fuels’ (SRFs’) with brown coal in large scale pulverised coal boilers under different operational conditions is numerically investigated. In order to overcome the difficulty of the complex, inhomogeneous nature of waste recovered fuels, SRF is modelled as a mixture of two different fractions, the biogenic and the plastic one. For each fraction different combustion mechanisms are presented, whilst for the first time the proposed combustion mechanism of the plastic fraction is incorporated in a commercial CFD code and validated against available experimental data. A 600 MW_e brown coal boiler is simulated as a reference and its operational characteristics are compared with parameterised scenarios of SRF co-firing conditions. Based on the numerical results, the optimum co-firing concepts regarding the more efficient operation of the boiler (hot spots and fuel’s burnout) are identified, decreasing the environmental impact of the boiler’s emissions.     

Relative validity of a semiquantitative food frequency questionnaire designed for schoolchildren in western Greece

Water soluble β-carotene (WS-BC) is a carotenoid form that has been developed as a food colorant. WS-BC is known to contain 10% of all-trans β-carotene (AT-BC). The aim of the present study was to investigate in vivo tissue uptake of AT-BC after the administration of WS-BC into rats. Seven-week-old male rats were administered 20 mg of WS-BC dissolved in saline by intravenous injection into the tail vein. At 0, 6, 24, 72, 120 and 168 hours (n = 7/time), blood was drawn and liver, lungs, adrenal glands, kidneys and testes were dissected. The levels of AT-BC in the plasma and dissected tissues were quantified with HPLC. After intravenous administration, AT-BC level in plasma first increased up to 6 h and returned to normal at 72 h. In the testes, the AT-BC level first increased up to 24 h and then did not decrease but was retained up to 168 h. In the other tissues, the level first increased up to 6 h and then decreased from 6 to 120 or 168 h but did not return to normal. The accumulation of WS-BC in testes but not in the other 5 tissues examined may suggest that AT-BC was excreted or metabolized in these tissues but not in testes. Although WS-BC is commonly used as a food colorant, its effects on body tissues are still not clarified. Results of the present study suggest that further investigations are required to elucidate effects of WS-BC on various body tissues.     

Detection and classification of winding faults in windmill generators using Wavelet Transform and ANN

This paper exploits the Wavelet Transform (WT) analysis along with Artificial Neural Networks (ANN) for the diagnosis of electrical machines winding faults. A novel application is presented exploring the problem of automatically identifying short circuits of windings, which often appear during machine manufacturing and operation. Such faults are usually resulting from electrodynamics forces generated during the flow of large short circuit currents, as well as forces occurring when the machines are transported. The early detection and classification of winding failures is of particular importance, as these kinds of defects can lead to winding damage due to overheating, imbalance, etc. Application results and investigations of windmill generator winding turn-to-turn faults between adjacent winding wires are presented. The ANN approach is proven effective in detecting and classifying faults based on WT features extracted from high frequency measurements of the admittance, current, or voltage responses.     

Multiband Media Access Control in Impulse-Based UWB Ad Hoc Networks

We propose a MAC protocol for use in multihop wireless networks that deploy an underlying UWB (ultra wide band)-based physical layer. We consider a multiband approach to better utilize the available spectrum, where each transmitter sends longer pulses in one of many narrower frequency bands. The motivation comes from the observation that, in the absence of a sophisticated equalizer, the size of a slot for transmitting a UWB pulse is typically dictated by the delay spread of the channel. Therefore, using a wider frequency band to shorten the transmission time for each pulse does not increase the data rate in proportion to the available bandwidth. Our approach allows data transmissions to be contiguous and practically interference free, and, thus, highly efficient. For practicality, we ensure the conformance of our approach to FCC-imposed emission limits. We evaluate our approach via extensive simulations, and our results demonstrate the significant advantages of our approach over single-band solutions: the throughput increases significantly and the number of collisions decreases considerably. Finally, we analyze the behavior of our MAC protocol in a single-hop setting in terms of its efficiency in utilizing the multiple bands     

Modelling nitrogen in the Yeşilirmak River catchment in Northern Turkey: impacts of future climate and environmental change and implications for nutrient management

Recent research in catchments of rapidly developing countries such as Brazil and China suggests that many catchments of the developing world are already showing signs of nitrogen pollution reminiscent of past experiences in developed countries. This paper looks at both the individual and combined effects of future climate change and other likely environmental changes on in-stream nitrate concentrations in a catchment in Northern Turkey. A model chain comprised of simulated future temperature and precipitation from a Regional Circulation Model (RCM), a conceptual hydrological model (HBV) and a widely tested integrated catchment nitrogen model (INCA-N) is used to model future changes in nitrate concentrations. Two future periods (2021-2050 and 2069-2098) are compared to the 1961-1990 baseline period in order to assess the effectiveness of several possible interventions available to catchment authorities. The simulations show that in the urbanised part of the catchment, the effects of climate change and other environmental changes act in the same direction, leading to peak nitrate concentrations of 7.5 mg N/l for the 2069-2098 period, which corresponds to a doubling of the baseline values. Testing different available policy options reveals that the installation of wastewater treatment works (WWTWs) in all major settlements of the catchment could ensure nitrate levels are kept at near their baseline values for the 2021-2050 period. Nevertheless, a combination of measures including WWTWs, meadow creation, international agreements to reduce atmospheric N concentrations and controls on agricultural practises will be required for 2069-2098. The approach presented in this article could be employed in order to anticipate future pollution problems and to test appropriate solutions, some of which will necessitate international co-operation, in other catchments around the world.     

Gastrointestinal Stromal Tumors (GISTs): Novel Therapeutic Strategies with Immunotherapy and Small Molecules

Gastrointestinal stromal tumors (GISTs) are the most common types of malignant mesenchymal tumors in the gastrointestinal tract, with an estimated incidence of 1.5/100.000 per year and 1–2% of gastrointestinal neoplasms. About 75–80% of patients have mutations in the KIT gene in exons 9, 11, 13, 14, 17, and 5–10% of patients have mutations in the platelet-derived growth factor receptor a (PDGFRA) gene in exons 12, 14, 18. Moreover, 10–15% of patients have no mutations and are classified as wild type GIST. The treatment for metastatic or unresectable GISTs includes imatinib, sunitinib, and regorafenib. So far, GIST therapies have raised great expectations and offered patients a better quality of life, but increased pharmacological resistance to tyrosine kinase inhibitors is often observed. New treatment options have emerged, with ripretinib, avapritinib, and cabozantinib getting approvals for these tumors. Nowadays, immune checkpoint inhibitors form a new landscape in cancer therapeutics and have already shown remarkable responses in various tumors. Studies in melanoma, non-small-cell lung cancer, and renal cell carcinoma are very encouraging as these inhibitors have increased survival rates. The purpose of this review is to present alternative approaches for the treatment of the GIST patients, such as combinations of immunotherapy and novel inhibitors with traditional therapies (tyrosine kinase inhibitors).     

A 3D fibre beam-column element with shear modelling for the inelastic analysis of steel structures

A force-based fiber beam-column element is proposed for the capacity assessment of frame structures under high shear. The proposed element is suitable for the performance assessment of large scale steel structures, which are not flexure-dominated. The element formulation follows the assumptions of the Timoshenko beam theory, while its kinematics are obtained through the natural-mode method. The element state-determination phase, instead of uniaxial material laws, typically associated with fiber elements, is based on a three-dimensional law taking into consideration the interaction between axial, bending, shear and torsion. Numerical examples are presented confirming the accuracy and the computational efficiency of the proposed formulation under monotonic, cyclic and dynamic/seismic loading. Compared to experimental results and the results of detailed finite element models, excellent agreement is achieved.     

Statistical analysis of major accidents in petrochemical industry notified to the major accident reporting system (MARS)

The European major accident reporting system (MARS) was created within the framework of European Union (EU) directive 82/501, the so-called "SEVESO" directive, and in order to register all the major industrial accidents notified to the European Union authorities from the member states. Statistical analysis of these accidents offers significant data to the understanding and prevention of industrial accidents. This paper makes an analysis of some characteristics of major accidents in the petrochemical sector included in MARS. The statistical analysis focused on the main categorization fields of the MARS short reports and additionally a refinement of the immediate causes of major accidents with focus on the organizational factors was attempted through the details provided in the full reports of the database.     

An association rule mining-based methodology for automated detection of ischemic ECG beats

Currently, an automated methodology based on association rules is presented for the detection of ischemic beats in long duration electrocardiographic (ECG) recordings. The proposed approach consists of three stages. 1) Preprocessing: Noise is removed and all the necessary ECG features are extracted. 2) Discretization: The continuous valued features are transformed to categorical. 3) Classification: An association rule extraction algorithm is utilized and a rule-based classification model is created. According to the proposed methodology, electrocardiogram (ECG) features extracted from the ST segment and the T-wave, as well as the patient's age, were used as inputs. The output was the classification of the beat as ischemic or not. Various algorithms were tested both for discretization and for classification using association rules. To evaluate the methodology, a cardiac beat dataset was constructed using several recordings of the European Society of Cardiology ST-T database. The obtained sensitivity (Se) and specificity (Sp) was 87% and 93%, respectively. The proposed methodology combines high accuracy with the ability to provide interpretation for the decisions made, since it is based on a set of association rules.