Plane Waves of a Fiber-Reinforcement Magneto-thermoelastic Comparison of Three Different Theories

In this article, the coupled theory, Lord–Shulman theory, and Green–Lindsay (GL) theory are used to study the influence of a magnetic field on a fiber-reinforced thermoelastic half-space. Normal mode analysis is used to solve a thermal shock problem. Numerical results for the temperature, displacement components, and stress components are given and illustrated graphically. A comparison is made between the coupled and GL theories in the absence and presence of a magnetic field and reinforcement.     

Exploring the Biomaterial-Induced Secretome: Physical Bone Substitute Characteristics Influence the Cytokine Expression of Macrophages

In addition to their chemical composition various physical properties of synthetic bone substitute materials have been shown to influence their regenerative potential and to influence the expression of cytokines produced by monocytes, the key cell-type responsible for tissue reaction to biomaterials in vivo. In the present study both the regenerative potential and the inflammatory response to five bone substitute materials all based on β-tricalcium phosphate (β-TCP), but which differed in their physical characteristics (i.e., granule size, granule shape and porosity) were analyzed for their effects on monocyte cytokine expression. To determine the effects of the physical characteristics of the different materials, the proliferation of primary human osteoblasts growing on the materials was analyzed. To determine the immunogenic effects of the different materials on human peripheral blood monocytes, cells cultured on the materials were evaluated for the expression of 14 pro- and anti-inflammatory cytokines, i.e., IL-6, IL-10, IL-1β, VEGF, RANTES, IL-12p40, I-CAM, IL-4, V-CAM, TNF-α, GM-CSF, MIP-1α, Il-8 and MCP-1 using a Bio-Plex^® Multiplex System. The granular shape of bone substitutes showed a significant influence on the osteoblast proliferation. Moreover, smaller pore sizes, round granular shape and larger granule size increased the expression of GM-CSF, RANTES, IL-10 and IL-12 by monocytes, while polygonal shape and the larger pore sizes increased the expression of V-CAM. The physical characteristics of a bone biomaterial can influence the proliferation rate of osteoblasts and has an influence on the cytokine gene expression of monocytes in vitro. These results indicate that the physical structure of a biomaterial has a significant effect of how cells interact with the material. Thus, specific characteristics of a material may strongly affect the regenerative potential in vivo.     

Numerical and experimental investigation of the mesoscale fracture behaviour of quenched steels

During heat treatment processes, especially during quenching, cracks may form because of the presence of high thermal and mechanical stresses and strains. Notwithstanding the fact that increasingly detailed modelling for heat treatment is being performed (considering, i.a., grain size, creep and transformation plasticity), homogeneous microstructures are still normally assumed. Chemical and hence structural inhomogeneities are not commonly explicitly considered, which is especially accentuated in the case of real parts simulation because of the resulting numerical problem's size. Intensive quenching on a cylindrical specimen of 100Cr6 (SAE) steel is proposed here to experimentally investigate the microcrack generation. A finite element based model is proposed to numerically evaluate the fracture behaviour in a two‐step simulation. First, by solving the quenching problem in direct correspondance with the experimental test performed, and second, by studying the mesoscale response taking into account the influence of second phase particles in a representative volume element based approach. The maximum principal stress criterion is used to trigger the fracture by means of the extended finite element method at the mesoscale. The trend to form cracks in the surface region, experimentally observed, has been well captured by the model. The influence of carbides sizes and content on the mesoscale fracture response has been numerically analysed as well. A good agreement has been reached between the simulations and the experimental results, exhibiting the potential of the introduced approach to be used as a failure prediction methodology.     

Type 2 Diabetes Risk Prediction Using Deep Convolutional Neural Network Based-Bayesian Optimization

Diabetes mellitus is a long-term condition characterized by hyperglycemia. It could lead to plenty of difficulties. According to rising morbidity in recent years, the world’s diabetic patients will exceed 642 million by 2040, implying that one out of every ten persons will be diabetic. There is no doubt that this startling figure requires immediate attention from industry and academia to promote innovation and growth in diabetes risk prediction to save individuals’ lives. Due to its rapid development, deep learning (DL) was used to predict numerous diseases. However, DL methods still suffer from their limited prediction performance due to the hyperparameters selection and parameters optimization. Therefore, the selection of hyper-parameters is critical in improving classification performance. This study presents Convolutional Neural Network (CNN) that has achieved remarkable results in many medical domains where the Bayesian optimization algorithm (BOA) has been employed for hyperparameters selection and parameters optimization. Two issues have been investigated and solved during the experiment to enhance the results. The first is the dataset class imbalance, which is solved using Synthetic Minority Oversampling Technique (SMOTE) technique. The second issue is the model's poor performance, which has been solved using the Bayesian optimization algorithm. The findings indicate that the Bayesian based-CNN model superbases all the state-of-the-art models in the literature with an accuracy of 89.36%, F1-score of 0.88.6, and Matthews Correlation Coefficient (MCC) of 0.88.6.     

Carrier transport and related phenomena in MOS devices

Silicon-based devices are currently the most attractive group because they are functioning at room temperature and can be easily integrated into conventional silicon microelectronics. There are many models and simulation programs available to compute CV curves with quantum correction [Choi C-H, Wu Y, Goo JS, Yu Z, Dutton RW. IEEE Trans on Electron Devi 2000; 47(10): 1843; Croci S, Plossu C, Burignat S. J Mater Sci Mater Electron 2003; 14: 311; Soliman L, Duval E, Benzohra M, Lheurette E, Ketata K, Ketata M. Mater Sci Semicond Process 2001; 4: 163]. This work deals with the simulation of electron transfer through SiO₂ barrier of metal–oxide–semiconductor structure (MOS). The carrier density is given by a self consistent resolution of Schrödinger and Poisson equations and then the MOS capacitance is deduced and compared with results available in literature. As it is well known, the MOS capacitance–voltage profiling provides a simple determination of structure parameters. The extracted tunnel oxide thickness and substrate doping are compared with those used in the simulation. For the purpose to investigate the electron tunnelling through the barrier, we have used the transfer matrix approach. Using I–V simulations, we have shown that the traps in SiO₂ matrix have a drastic influence on electron tunnelling through the barrier. The trap-assisted contribution to the tunnelling current is included in many models [Maserjian J, Zamani N. J Appl Phys 1982; 53(1): 559; Houssa M, Stesmans A, Heyns MM. Semicond Sci Technol 2001; 16: 427; Aziz A, Kassmi K, Kassmi Ka, Olivie F. Semicond Sci Technol 2004; 19: 877; Wu You-Lin, Lin Shi-Tin. IEEE Trans Dev Mater Reliab 2006; 6(1): 75; Larcher L. IEEE Trans Electron Dev 2003; 50(5): 1246]; this is the basis for the interpretation of stress induced leakage current (SILC) and breakdown events. Memory effect becomes typical for this structure. We have studied the I–V dependence with trap parameters.     

Improvement of Aglycone π-Stacking Yields Nanomolar to Sub-nanomolar FimH Antagonists

Antimicrobial resistance has become a serious concern for the treatment of urinary tract infections. In this context, an anti-adhesive approach targeting FimH, a bacterial lectin enabling the attachment of E. coli to host cells, has attracted considerable interest. FimH can adopt a low/medium-affinity state in the absence and a high-affinity state in the presence of shear forces. Until recently, mostly the high-affinity state has been investigated, despite the fact that a therapeutic antagonist should bind predominantly to the low-affinity state. In this communication, we demonstrate that fluorination of biphenyl α-d -mannosides leads to compounds with perfect π–π stacking interactions with the tyrosine gate of FimH, yielding low nanomolar to sub-nanomolar K_D values for the low- and high-affinity states, respectively. The face-to-face alignment of the perfluorinated biphenyl group of FimH ligands and Tyr48 was confirmed by crystal structures as well as ¹H,¹⁵N-HSQC NMR analysis. Finally, fluorination improves pharmacokinetic parameters predictive for oral availability.     

Performance evaluation of OFDM and single‐carrier systems using frequency domain equalization and phase modulation

In this paper, we study the performance of the continuous phase modulation (CPM)‐based orthogonal frequency division multiplexing (CPM‐OFDM) system. Also, we propose a CPM‐based single‐carrier frequency domain equalization (CPM‐SC‐FDE) structure for broadband wireless communication systems. The proposed structure combines the advantages of the low complexity of SC‐FDE, in addition to exploiting the channel frequency diversity and the power efficiency of CPM. Both the CPM‐OFDM system and the proposed system are implemented with FDE to avoid the complexity of the equalization. Two types of frequency domain equalizers are considered and compared for performance evaluation of both systems; the zero forcing (ZF) equalizer and the minimum mean square error (MMSE) equalizer. Simulation experiments are performed for a variety of multipath fading channels. Simulation results show that the performance of the CPM‐based systems with multipath fading is better than their performance with single path fading. The performance over a multipath channel is at least 5 and 12 dB better than the performance over a single path channel, for the CPM‐OFDM system and the proposed CPM‐SC‐FDE system, respectively. The results also show that, when CPM is utilized in SC‐FDE systems, they can outperform CPM‐OFDM systems by about 5 dB. Copyright © 2010 John Wiley & Sons, Ltd.     

Evolution in the surface modification of textiles: a review

The development of technical textiles allows the introduction of new, interesting and original multi-functionalities in textiles through development of the architecture of fibres, yarns and fabrics, their morphology and surface functionalization without altering their physico-chemical proprieties. This issue of Textile Progress reports different techniques used to impart new functionalities to the surfaces of textiles during the last decade. Following a short, context-setting historical introduction, the preparatory processes which need to be applied to textile matrices to make them clean and ready for functionalization are examined prior to a comprehensive review of techniques and research related to the development of functional textiles ranging from the more-traditional techniques through to more-recent developments. The challenge now is to bring new performance features to bear whilst maintaining environmental sustainability, chemical toxicological acceptability, high performance and cost effectiveness. In this context, the review reports on developments in the use of polymerization, nanotechnologies, plasma treatment, electrospinning, microencapsulation and sol gel techniques to impart novel properties to a textile surface such as water-repellent, flame-retardant and antibacterial properties.     

A Multi-Expert System for chlorine electrolyzer monitoring

The Chlor-Alkali production is one of the largest industrial scale electro-synthesis in the world. Plants with more than 1000 individual reactors are common, where chlorine and hydrogen are only separated by 0.2 mm thin membranes. Wrong operating conditions can cause explosions and highly toxic gas releases, but also irreversible damages of very expensive cell components with dramatic maintenance costs and production loss. In this paper, a Multi-Expert System based on first-order logic rules and Decision Forests is proposed to detect any abnormal operating conditions of membrane cell electrolyzers and to advice the operator accordingly. Robustness to missing data – which represents an important issue in industrial applications in general – is achieved by means of a Dynamic Selection strategy. Experiments performed with real-world electrolyzer data indicate that the proposed system can significantly detect the different operating modes, even in the presence of high levels of missing data – or “wrong” data, as a consequence of maloperation –, which is essential for precise fault detection and advice generation.