Solid biofuels in Mexico: a sustainable alternative to satisfy the increasing demand for heat and power

Bioenergy is the largest renewable energy source in Mexico with an estimated 4–9% of total current energy demand. There are large uncertainties and contrasting estimates regarding its current extent and end-uses, particularly with traditional uses. However, a large potential exists to improve the efficiency of existing uses and, at the same time, to diversify the use of SBF in the industrial and power sectors. This paper aims at: providing the first updated and comprehensive estimate of current SBF demand in Mexico including traditional and modern uses; providing a consistent estimate of actual SBF supply potential; estimating the total potential substitution of fossil fuels that could be achieved by SBF considering an integrated “modernization scenario”; and finally describing the main barriers limiting SBF to fully triggering its potential. Results show that current SBF consumption reached 481 PJ/yr in 2015; SBF supply potential reaches 3622 PJ/yr, out of which 883 PJ/yr could be used to substitute up to 29% of current demand of FF, mitigating 66 MtCO_2e/yr of greenhouse gas (GHG) emissions, or near 88 MtCO_2e/yr if mitigation from traditional uses is added.     

Effectiveness of Deep Learning Models for Brain Tumor Classification and Segmentation

A brain tumor is a mass or growth of abnormal cells in the brain. In children and adults, brain tumor is considered one of the leading causes of death. There are several types of brain tumors, including benign (non-cancerous) and malignant (cancerous) tumors. Diagnosing brain tumors as early as possible is essential, as this can improve the chances of successful treatment and survival. Considering this problem, we bring forth a hybrid intelligent deep learning technique that uses several pre-trained models (Resnet50, Vgg16, Vgg19, U-Net) and their integration for computer-aided detection and localization systems in brain tumors. These pre-trained and integrated deep learning models have been used on the publicly available dataset from The Cancer Genome Atlas. The dataset consists of 120 patients. The pre-trained models have been used to classify tumor or no tumor images, while integrated models are applied to segment the tumor region correctly. We have evaluated their performance in terms of loss, accuracy, intersection over union, Jaccard distance, dice coefficient, and dice coefficient loss. From pre-trained models, the U-Net model achieves higher performance than other models by obtaining 95% accuracy. In contrast, U-Net with ResNet-50 outperforms all other models from integrated pre-trained models and correctly classified and segmented the tumor region.     

Adaptive predistortion using threshold decomposition‐based piecewise linear modeling

A new adaptive predistortion approach for nonlinear power amplifiers based on the piecewise linear (PWL) approximation of nonlinear functions is presented. The PWL model is obtained using threshold decomposition (TD) of the input‐output characteristics. The TD based PWL model is used to develop a baseband predistorter using both the direct and the indirect learning architectures. The proposed predistorter is proved to provide a better performance in suppressing nonlinear distortion than conventional polynomial predistorters and a reduced hardware complexity as compared to lookup table (LUT) approach. © 2011 Wiley Periodicals, Inc. Int J RF and Microwave CAE, 2011.     

The Digital Learning Classroom: Improving English Language Learners’ academic success in mathematics and reading using interactive whiteboard technology

This study presents the findings from the first-year evaluation of the Round Rock Independent School District’s (ISD) Digital Learning Classroom project, an initiative focused on the improvement of English Language Learners’ (ELL) learning using interactive whiteboard (IWB) technology. An objective of the evaluation was to determine the extent IWB technology could foster performance parity in academic achievement between ELL and regular students, that is, reduce the student achievement gap between these two student groups in 3rd and 5th grade mathematics and reading. These grade levels and subjects were the primary focus of the project because students in grades 3 and 5 that do not pass the state’s standardized assessments in mathematics and reading cannot be promoted to the next grade level and therefore, these are “high stakes” tests for students. A second evaluation objective was to determine whether and the extent to which the Digital Learning Classroom could increase ELL students’ academic learning relative to that of ELL students in traditional classrooms (i.e., without IWBs). Using a quasi-experimental design, the results strongly indicate that IWBs can foster performance parity thereby closing the achievement gap between ELL and regular students while increasing ELL student achievement. Pedagogical implications for teachers of ELL students within the context of Digital Learning Classroom project implementation are presented, as well as recommendations for future study of the Digital Learning Classroom in ELL classroom settings.     

On visual servoing based on efficient second order minimization

This paper deals with the Efficient Second order Minimization (ESM) and the image-based visual servoing schemes. In other words, it deals with the minimization based on the pseudo-inverse of the mean of the Jacobians or on the mean of Jacobian Pseudo-inverses. Chronologically, it has been noticed in Tahri and Chaumette (2003) [22] that ESM generally improves the system behavior when compared with the system in which only the simple Jacobian Pseudo-inverses are used. Subsequently, a mathematical explanation has been given in Malis (2004) [12]. In this paper, the proofs given by Malis are discussed and it will be shown that there is a limitation to the validity of the ESM. We will also show that the use of ESM does not necessarily ensure a better system behavior, especially in the situations where large rotational motions are involved. Further, a new appropriate formula of the ESM is proposed and validated using several kinds of features.     

Enhanced AODV routing protocol for Bluetooth scatternet

Bluetooth is one of the most widespread technologies for personal area networks that allow portable devices to form multi-hop Bluetooth ad hoc networks, so called scatternets. Routing is one of the challenges in scatternets because of its impact on the performance of the network. It should focus on reducing the power consumption in the network because most of the nodes are battery-operated portable devices. In this paper, we propose a routing protocol for Bluetooth scatternets that customizes the Ad hoc On-Demand Distance Vector (AODV) routing protocol by making it power-aware and suitable for scatternets. It enhances the AODV flooding mechanism by excluding all non-bridge slaves from taking apart in the AODV route discovery process. In addition, it improves the AODV route discovery phase by considering the hop count, the predicated node’s power, and the average traffic intensity for each node as metrics for best route selection. By removing HELLO packets, our protocol reduces the control packets overhead and the power consumption in network devices. Simulation results show that the proposed protocol achieved considerable improvements over other enhanced AODV protocols by increasing the data delivery ratio by 10.78%, reducing the average end-to-end delay by 8.11%, and reducing the average energy consumption by 7.92%.     

A Fuzzy Collaborative Assessment Methodology for Partner Trust Evaluation

Trust level assessment within collaborative networks is an interesting issue in the partner evaluation and partner selection literature. This paper proposes a fuzzy collaborative assessment methodology for partner trust evaluation within horizontal collaborative networks. The proposed approach concerns a group evaluation context where a decision‐making comity associated with a manufacturer needs to evaluate its company's partners for their ranking purposes. Different expertise levels are attributed to the comity members. In this paper, trust level is evaluated based on information‐sharing attributes considered in the literature as critical influencing factors. Different weights are associated with these attributes with respect to their corresponding influence on trust. The semantic fuzzy partitioning method is considered for the collaborative trust assessment based on unbalanced linguistic term sets representing information‐sharing attributes. The developed approach is applied to a real case showing its effectiveness and its objectivity.