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.     

Structural,dielectric and electrical properties of Sol–gel auto-combustion technic of CuFeCr0.5Ni0.5O4 ferrite

Journal of Materials Science - The CuFeCr0.5Ni0.5O4 (CFO) compound was synthesized using sol–gel reaction combustion technic. The structural analysis showed that the obtained composites have...     

A Hybrid Model Using Bio-Inspired Metaheuristic Algorithms for Network Intrusion Detection System

Network Intrusion Detection System (IDS) aims to maintain computer network security by detecting several forms of attacks and unauthorized uses of applications which often can not be detected by firewalls. The features selection approach plays an important role in constructing effective network IDS. Various bio-inspired metaheuristic algorithms used to reduce features to classify network traffic as abnormal or normal traffic within a shorter duration and showing more accuracy. Therefore, this paper aims to propose a hybrid model for network IDS based on hybridization bio-inspired metaheuristic algorithms to detect the generic attack. The proposed model has two objectives; The first one is to reduce the number of selected features for Network IDS. This objective was met through the hybridization of bio-inspired metaheuristic algorithms with each other in a hybrid model. The algorithms used in this paper are particle swarm optimization (PSO), multi-verse optimizer (MVO), grey wolf optimizer (GWO), moth-flame optimization (MFO), whale optimization algorithm (WOA), firefly algorithm (FFA), and bat algorithm (BAT). The second objective is to detect the generic attack using machine learning classifiers. This objective was met through employing the support vector machine (SVM), C4.5 (J48) decision tree, and random forest (RF) classifiers. UNSW-NB15 dataset used for assessing the effectiveness of the proposed hybrid model. UNSW-NB15 dataset has nine attacks type. The generic attack is the highest among them. Therefore, the proposed model aims to identify generic attacks. My data showed that J48 is the best classifier compared to SVM and RF for the time needed to build the model. In terms of features reduction for the classification, my data show that the MFO-WOA and FFA-GWO models reduce the features to 15 features with close accuracy, sensitivity and F-measure of all features, whereas MVO-BAT model reduces features to 24 features with the same accuracy, sensitivity and F-measure of all features for all classifiers.     

Ti3AlC2 coated D-shaped fiber saturable absorber for Q-switched pulse generation

In this letter, a titanium aluminum carbide (Ti3AlC2) coated D-shaped fiber is proposed and demonstrated as a new saturable absorber (SA) for Q-switched laser pulse generation. In preparing the SA, the Ti3AlC2 powder is dispersed in liquid polyvinyl alcohol (PVA) before the solution is dropped and left to dry onto a polished surface of D-shape fiber. The SA is added to an erbium-doped fiber laser (EDFL) cavity to modulate the cavity loss for Q-switching. The Q-switched laser is obtained at 1 561 nm. The pulse width of the pulses can be varied between 7.4 µs and 5.1 µs with a corresponding repetition rate range from 41.26 kHz to 54.35 kHz, when the pump power is increased from 42.2 mW to 71.5 mW. At 71.5 mW pump, the pulse energy is obtained at 70.3 nJ. The signal-to-noise ratio (SNR) of the fundamental frequency is recorded at 67 dB, which indicates the stability of the laser.     

Evaluating sustainable energy harvesting systems for human implantable sensors

Achieving most appropriate energy-harvesting technique for human implantable sensors is still challenging for the industry where keen decisions have to be performed. Moreover, the available polymeric-based composite materials are offering plentiful renewable applications that can help sustainable development as being useful for the energy-harvesting systems such as photovoltaic, piezoelectric, thermoelectric devices as well as other energy storage systems. This work presents an expert-based model capable of better evaluating and examining various available renewable energy-harvesting techniques in urban surroundings subject to various technical and economic, often conflicting, criteria. Wide evaluation criteria have been adopted in the proposed model after examining their suitability as well as ensuring the expediency and reliability of the model by worldwide experts’ feedback. The model includes establishing an analytic hierarchy structure with simultaneous 12 conflicting factors to establish a systematic road map for designers to better assess such techniques for human implantable medical sensors. The energy-harvesting techniques considered were limited to Wireless, Thermoelectric, Infrared Radiator, Piezoelectric, Magnetic Induction and Electrostatic Energy Harvesters. Results have demonstrated that the best decision was in favour of wireless-harvesting technology for the medical sensors as it is preferable by most of the considered evaluation criteria in the model.     

Solid solution phases from thermally crystallized Li2O-MgO-Al2O3-SiO2 glasses

Phase transformations have been studied in glasses related in composition to Li₂O-MgO-SiO₂ (930° C) eutectic glass modified by $\text{Al}{}_2\text{O}{}_3\text{Li}{}_2\text{O}$ replacement on a cation per cation basis. Crystallization processes and changes in the solid solutions of the mineral phases developed during the controlled heating of the glasses have been followed using X-ray and microscopic techniques.Solid solutions of lithia aluminosilicate phases (B-eucryptite and/or B-spodumene s.s.) are the essential minerals that crystallize in glasses through most of the heat treatment applied. Lithium metasilicate, disilicate, clinoenstatite and cordierite are also encountered.B-eucryptite s.s. is formed in early stage of crystallization of the glass which transforms into B-spodumene s.s. at higher temperature (800° C). However, in some cases, it persists at temperatures as high as 1000° C.Metastable B-eucryptite-B-quartz s.s. can accomodate considerable amounts of Mg²⁺ and Al³⁺ ions in its lattice especially at lower temperature. At higher temperature for long duration, a redistribution of the elements takes place, and the cordierite phase then developed.     

Nouvelle classe de simulateurs permettant la caractérisation de dispositifs embarqués sur des structures de grandes dimensions

This paper presents the qualification of an iemn exploitation original system, intended to create a realistic electromagnetic environment on large structures (planes, missiles, land vehicles). It consists in a horizontal polarization transmission line simulator which is supplied by a high voltage generator. The transient currents, induced on the wires, are solved by using integral equations in the space-time domain. The electromagnetic fields evaluated inside the working volumes are determined in each point and at each time. We include numerous comparisons with the experiments carried out by the Centre d’études de Gramat.     

Enhanced the structure and optical properties for ZnO/PVP nanofibers fabricated via electrospinning technique

Zinc oxide/polyvinylpyrrolidone (ZnO/PVP) nanocomposite fibers with enhanced structural, morphological and optical properties were purposefully tailored using electrospinning technique. Meanwhile, ZnO nanoparticles (NPs),with particle size of ~50 nm, were synthesized using a co-precipitation method. The nanocomposite fibers were prepared by an electrospun solution of PVP containing ZnO NPs of 2, 4, 6 and 8 wt%. Evidently, the morphological, thermal and optical properties of the ZnO/PVP nanocomposite fibers were enhanced by dispersing ZnO NPs into PVP fibers. Typically, controlling the ZnO NPs content and their dispersibility (0–8 wt%) into PVP fibers result in improved the thermal stability (an increase of onset decomposition temperature by ~120 °C above pure PVP fibers) as well as the UV–Vis protection (reduction in UV transmission by 70%) and the photoluminescence properties (a sharp UV emission around 380 nm) Overall, based on the enhanced properties, the PVP/ZnO nanocomposite fibers can be considered a promise material in optoelectronic sensors and UV photoconductor.     

Thermal Aging Effects on Cu Ball Shear Strength and Cu/Al Intermetallic Growth

Intermetallic growth and ball shear behavior of annealed Cu wire bonds on Al have been studied. The shear strength of Cu ball bonds decreased with time, and ductile fracture was the dominant failure mode from 125°C to 150°C. Al pad peel-off occurred as the aging temperature was increased above 150°C. The overall Cu/Al intermetallic thickness exhibited parabolic behavior as a function of time. A linear correlation was established between ball shear strength, metal peel-off occurrence, and intermetallic growth. The Cu/Al intermetallic growth activation energy was 0.23 eV, and the intermetallics identified in the experiment were CuAl₂ and CuAl.