Methodology for predicting sequences of mean monthly clearness index and daily solar radiation data in remote areas: Application for sizing a stand-alone PV system

In this paper, a suitable adaptive neuro-fuzzy inference system (ANFIS) model is presented for estimating sequences of mean monthly clearness index () and total solar radiation data in isolated sites based on geographical coordinates. The magnitude of solar radiation is the most important parameter for sizing photovoltaic (PV) systems. The ANFIS model is trained by using a multi-layer perceptron (MLP) based on fuzzy logic (FL) rules. The inputs of the ANFIS are the latitude, longitude, and altitude, while the outputs are the 12-values of mean monthly clearness index . These data have been collected from 60 locations in Algeria. The results show that the performance of the proposed approach in the prediction of mean monthly clearness index is favorably compared to the measured values. The root mean square error (RMSE) between measured and estimated values varies between 0.0215 and 0.0235 and the mean absolute percentage error (MAPE) is less than 2.2%. In addition, a comparison between the results obtained by the ANFIS model and artificial neural network (ANN) models, is presented in order to show the advantage of the proposed method. An example for sizing a stand-alone PV system is also presented. This technique has been applied to Algerian locations, but it can be generalized for any geographical position. It can also be used for estimating other meteorological parameters such as temperature, humidity and wind speed.     

Detection of terahertz radiation with diode lasers

A standard commercial semiconductor is shown to be able to detect terahertz (THz) radiation at room temperature. A voltage variation across the active region of the device upon incident THz radiation is measured. The detected voltage signal scales linearly with the THz intensity measured with a Golay cell. A detailed analysis shows that thermal effects following the THz absorption by the carrier plasma play an important role in this detection process.     

A hybrid cascade method for EEG classification

Pattern Analysis and Applications - The classification of EEG signals is an essential step in the design of brain–computer interface. In order to optimize this step, a method for EEG...     

5G Smart Mobility Management Based Fuzzy Logic Controller Unit

In the paper, we propose a fuzzy logic controller system to be implemented for smart mobility management in the 5G wireless communication network. Mobility management is considered as a main issue for all-IP mobile networks future generation. As a network-based mobility management protocol, Internet Engineering Task Force developed the Proxy Mobile IPv6 (PMIPv6) in order to support the mobility of IP devices, and many other results were presented to reduce latency handover and the amount of PMIPv6 signaling, but it is not enough for the application needs in real-time. The present paper describes an approach based on the IEEE 802.21 Media Independent Handover (MIH) standard and PMIPv6, so we present a new vertical handover algorithm for anticipating handover process efficiently. Our object is to propose a smart mobility management that contribute in 5G wireless communication system network operating functions. Two proposed dynamic thresholds were successfully made to guaranty process triggering, and a new primitive MIH is proposed for signaling a needed handover to be done. Simulation results demonstrate a significant reduction of the handover delay, packet loss, handover blocking probability and signaling overhead. Simulation results and tests are accomplished.     

ABCG2 Is Overexpressed on Red Blood Cells in Ph-Negative Myeloproliferative Neoplasms and Potentiates Ruxolitinib-Induced Apoptosis

Myeloproliferative neoplasms (MPNs) are a group of disorders characterized by clonal expansion of abnormal hematopoietic stem cells leading to hyperproliferation of one or more myeloid lineages. The main complications in MPNs are high risk of thrombosis and progression to myelofibrosis and leukemia. MPN patients with high risk scores are treated by hydroxyurea (HU), interferon-α, or ruxolitinib, a tyrosine kinase inhibitor. Polycythemia vera (PV) is an MPN characterized by overproduction of red blood cells (RBCs). ABCG2 is a member of the ATP-binding cassette superfamily transporters known to play a crucial role in multidrug resistance development. Proteome analysis showed higher ABCG2 levels in PV RBCs compared to RBCs from healthy controls and an additional increase of these levels in PV patients treated with HU, suggesting that ABCG2 might play a role in multidrug resistance in MPNs. In this work, we explored the role of ABCG2 in the transport of ruxolitinib and HU using human cell lines, RBCs, and in vitro differentiated erythroid progenitors. Using stopped-flow analysis, we showed that HU is not a substrate for ABCG2. Using transfected K562 cells expressing three different levels of recombinant ABCG2, MPN RBCs, and cultured erythroblasts, we showed that ABCG2 potentiates ruxolitinib-induced cytotoxicity that was blocked by the ABCG2-specific inhibitor KO143 suggesting ruxolitinib intracellular import by ABCG2. In silico modeling analysis identified possible ruxolitinib-binding site locations within the cavities of ABCG2. Our study opens new perspectives in ruxolitinib efficacy research targeting cell types depending on ABCG2 expression and polymorphisms among patients.     

Transboundary pollution,R&;D spillovers and international trade

We consider a non-cooperative and symmetric three-stage game played by two regulator-firm hierarchies. We show that R&D spillovers and the competition of firms on the common market help non-cooperating countries to better internalize transboundary pollution. Surprisingly, international competition increases the per-unit emission-tax and decreases the per-unit R&D subsidy. It also leads to a higher investment in R&D and production, and to a lower emission ratio. In most cases, pollution in common market is lower than in autarky, implying a greater social welfare, and the contrary occurs in some other cases.     

Feature construction as a bi-level optimization problem

Neural Computing and Applications - Feature selection and construction are important preprocessing techniques in data mining. They allow not only dimensionality reduction but also classification...     

Deriving business processes with service level agreements from early requirements

When designing a service-based business process employing loosely coupled services, one is not only interested in guaranteeing a certain flow of work, but also in how the work will be performed. This involves the consideration of non-functional properties which go from execution time and costs, to trust and security. Ideally, a designer would like to have guarantees over the behavior of the services involved in the process. These guarantees are the object of Service Level Agreements.We propose a methodology to design service-based business processes together with Service Level Agreements that guarantee a certain quality of execution, with particular emphasis on security. Starting from an early requirements analysis modeled in the Secure Tropos formalism, we provide a set of user-guided transformations and reasoning tools the final output of which is a set of processes in the form of Secure BPELs together with a set of Service Level Agreements to be signed by participating services. To show the potential impact of the approach, we illustrate the functioning of the methodology on a collaborative procurement scenario derived from the application domain of a research project.     

Neoadjuvant chemotherapy response evaluation in breast cancer based on mammogram registration and tumor segmentation

The standard approach for assessing the response of breast tumors to neoadjuvant chemotherapy is to monitor gross changes in tumor size as measured by physical exam and/or conventional imaging such as mammography. However, the deformable nature of the breast and variation in the imaging procedure, make it difficult to match the shape of breasts between serials of temporal mammograms, particularly when the tumor is shrinking due to the treatment. In this paper we propose a method for assessing residual tumor size following neoadjuvant chemotherapy by analyzing changes between pre-treatment and post-treatment mammograms. Our method consists of three steps: (1) pre-treatment and post-treatment mammograms were first registered in order to circumvent the problem of patient repositioning and breast deformation. (2) Tumors at corresponding locations were segmented using region growing segmentation, (3) and based on changes in tumor sizes the response rate is quantified. The proposed method has been tested on 6 breast cancer patients undergoing neoadjuvant chemotherapy, and the experimental results demonstrate that our approach may improve the ability of mammography to evaluate breast tumor response.