Distinctive Microbial Signatures and Gut-Brain Crosstalk in Pediatric Patients with Coeliac Disease and Type 1 Diabetes Mellitus

Coeliac disease (CD) and Type 1 diabetes mellitus (T1DM) are immune-mediated diseases. Emerging evidence suggests that dysbiosis in the gut microbiome plays a role in the pathogenesis of both diseases and may also be associated with the development of neuropathy. The primary goal in this cross-sectional pilot study was to identify whether there are distinct gut microbiota alterations in children with CD (n = 19), T1DM (n = 18) and both CD and T1DM (n = 9) compared to healthy controls (n = 12). Our second goal was to explore the relationship between neuropathy (corneal nerve fiber damage) and the gut microbiome composition. Microbiota composition was determined by 16S rRNA gene sequencing. Corneal confocal microscopy was used to determine nerve fiber damage. There was a significant difference in the overall microbial diversity between the four groups with healthy controls having a greater microbial diversity as compared to the patients. The abundance of pathogenic proteobacteria Shigella and E. coli were significantly higher in CD patients. Differential abundance analysis showed that several bacterial amplicon sequence variants (ASVs) distinguished CD from T1DM. The tissue transglutaminase antibody correlated significantly with a decrease in gut microbial diversity. Furthermore, the Bacteroidetes phylum, specifically the genus Parabacteroides was significantly correlated with corneal nerve fiber loss in the subjects with neuropathic damage belonging to the diseased groups. We conclude that disease-specific gut microbial features traceable down to the ASV level distinguish children with CD from T1DM and specific gut microbial signatures may be associated with small fiber neuropathy. Further research on the mechanisms linking altered microbial diversity with neuropathy are warranted.     

Global and Local Mechanical Properties of Autogenously Laser Welded Ti-6Al-4V

Ti-6Al-4V sheets, 3.2-mm in thickness, were butt welded using a continuous wave 4 kW Nd:YAG laser welding system. The effect of two main process parameters, laser power and welding speed, on the joint integrity was characterized in terms of the joint geometry, defects, microstructure, hardness, and tensile properties. In particular, a digital image correlation technique was used to determine the local tensile properties of the welds. It was determined that a wide range of heat inputs can be used to fully penetrate the Ti-6Al-4V butt joints during laser welding. At high laser power levels, however, significant defects such as underfill and porosity, can occur and cause marked degradation in the joint integrity and performance. At low welding speeds, however, significant porosity occurs due to its growth and the potential collapse of instable keyholes. Intermediate to relatively high levels of heat input allow maximization of the joint integrity and performance by limiting the underfill and porosity defects. In considering the effect of the two main defects on the joint integrity, the underfill defect was found to be more damaging to the mechanical performance of the weldment than the porosity. Specifically, it was determined that the maximum tolerable underfill depth for Ti-6Al-4V is approximately 6 pct of the workpiece thickness, which is slightly stricter than the value of 7 pct specified in AWS D17.1 for fusion welding in aerospace applications. Hence, employing optimized laser process parameters allows the underfill depth to be maintained within the tolerable limit (6 pct), which in turn prevents degradation in both the weld strength and ductility. To this end, the ability to maintain weld ductility in Ti-6Al-4V by means of applying a high energy density laser welding process presents a significant advantage over conventional arc welding for the assembly of aerospace components.     

Effect of Postweld Heat Treatment on Microstructure, Hardness, and Tensile Properties of Laser-Welded Ti-6Al-4V

The effects of postweld heat treatment (PWHT) on 3.2-mm- and 5.1-mm-thick Ti-6Al-4V butt joints welded using a continuous wave (CW) 4-kW Nd:YAG laser welding machine were investigated in terms of microstructural transformations, welding defects, and hardness, as well as global and local tensile properties. Two postweld heat treatments, i.e., stress-relief annealing (SRA) and solution heat treatment followed by aging (STA), were performed and the weld qualities were compared with the as-welded condition. A digital image correlation technique was used to determine the global tensile behavior for the transverse welding samples. The local tensile properties including yield strength and maximum strain were determined, for the first time, for the laser-welded Ti-6Al-4V. The mechanical properties, including hardness and the global and local tensile properties, were correlated to the microstructure and defects in the as-welded, SRA, and STA conditions.     

EXTRACTION KINETICS OF COPPER BY TRILAURYLAMMONIUM CHLORIDE IN TOLUENE FROM AQUEOUS CHLORIDE MEDIA

Abstract

The rate of extraction of copper(II) from aqueous chloride solutions by tri-n-laurylammonium chloride (TLAHC1) dissolved in toluene has been investigated. The extraction rate was evaluated as function of the chemical composition of the system. The experiments were performed by using a stirred cell with constant inter-facial area. The experiments were performed at a stirring rate of the two phases where the influence of film diffusion is minimized. The data have been interpreted by a mechanism which assumes interfacial chemical reactions between the bulk species containing copper(II) and the molecules of TLAHC1 absorbed at the interface as rate determining.     

Thermal stability study of SDC/Na2CO3 nanocomposite electrolyte for low-temperature SOFCs

The novel core–shell nanostructured SDC/Na₂CO₃ composite has been demonstrated as a promising electrolyte material for low-temperature SOFCs. However, as a nanostructured material, stability might be doubted under elevated temperature due to their high surface energy. So in order to study the thermal stability of SDC/Na₂CO₃ nanocomposite, XRD, BET, SEM and TGA characterizations were carried on after annealing samples at various temperatures. Crystallite sizes, BET surface areas, and SEM results indicated that the SDC/Na₂CO₃ nanocomposite possesses better thermal stability on nanostructure than pure SDC till 700 °C. TGA analysis verified that Na₂CO₃ phase exists steadily in the SDC/Na₂CO₃ composite. The performance and durability of SOFCs based on SDC/Na₂CO₃ electrolyte were also investigated. The cell delivered a maximum power density of 0.78 W cm⁻² at 550 °C and a steady output of about 0.62 W cm⁻² over 12 h operation. The high performances together with notable thermal stability make the SDC/Na₂CO₃ nanocomposite as a potential electrolyte material for long-term SOFCs that operate at 500–600 °C.     

A new intrusion detection system using support vector machines and hierarchical clustering

Whenever an intrusion occurs, the security and value of a computer system is compromised. Network-based attacks make it difficult for legitimate users to access various network services by purposely occupying or sabotaging network resources and services. This can be done by sending large amounts of network traffic, exploiting well-known faults in networking services, and by overloading network hosts. Intrusion Detection attempts to detect computer attacks by examining various data records observed in processes on the network and it is split into two groups, anomaly detection systems and misuse detection systems. Anomaly detection is an attempt to search for malicious behavior that deviates from established normal patterns. Misuse detection is used to identify intrusions that match known attack scenarios. Our interest here is in anomaly detection and our proposed method is a scalable solution for detecting network-based anomalies. We use Support Vector Machines (SVM) for classification. The SVM is one of the most successful classification algorithms in the data mining area, but its long training time limits its use. This paper presents a study for enhancing the training time of SVM, specifically when dealing with large data sets, using hierarchical clustering analysis. We use the Dynamically Growing Self-Organizing Tree (DGSOT) algorithm for clustering because it has proved to overcome the drawbacks of traditional hierarchical clustering algorithms (e.g., hierarchical agglomerative clustering). Clustering analysis helps find the boundary points, which are the most qualified data points to train SVM, between two classes. We present a new approach of combination of SVM and DGSOT, which starts with an initial training set and expands it gradually using the clustering structure produced by the DGSOT algorithm. We compare our approach with the Rocchio Bundling technique and random selection in terms of accuracy loss and training time gain using a single benchmark real data set. We show that our proposed variations contribute significantly in improving the training process of SVM with high generalization accuracy and outperform the Rocchio Bundling technique.     

The effect of microstructure on the permeability of metallic foams

Pressure drop was measured across complex and simple structure metallic foams at different velocity ranges using air as working fluid. Darcian and non-Darcian permeability parameters, K and C, were determined by fitting experimental data with widely accepted quadratic model of Hazen-Dupuit-Darcy. Generally, the experimental results are in good agreement with the model. The differences in K and C values between the two types of metallic foams are due to the different microstructure. For the simple structure specimens, permeability K increased whereas non-Darcian permeability C decreased with increasing pore diameter. The effect of pore size on the permeability of complex structure metallic foams seems to be opposite to that observed with the simple structure specimens and to results reported by other researchers on other porous medium. This discrepancy mainly stems from the differences in window concentration in addition to some heterogeneity in the foam that impeded the gas flow on one side of the specimens. The difference in pressure drop observed in the different metallic foams is due to combined effect of K and C. However, for simple structure foams, K and C could be predicted by Ergun-like model using appropriate values for the empirical constants. The permeability K is significantly affected by pore size and porosity. The quadratic term of Hazen-Dupuit-Darcy equation is mainly due to the inertia of the flow and partially to the drag exerted by the microstructure of the metallic foam. For both foams, as the porosity increases, pressure drop decreases and permeability, K, increases. The introduction of the open cross sectional area term enabled better understanding of the permeability of metallic foams with intricate morphologies.     

一种同步语言多线程代码自动生成工具

随着安全关键系统对计算性能要求的日趋提高,能够提供更强计算能力而又减少电子设备的体积、重量和功耗的多核处理器将在安全关键领域得到广泛应用.同步语言能够表达确定性并发行为且具有精确时间语义等特性,适用于安全关键软件的建模和验证.目前,同步语言SIGNAL编译器主要支持串行代码生成,较少关注多线程代码生成.提出一种同步语言SIGNAL多线程代码生成工具.首先将SIGNAL程序转换为经过时钟演算的S-CGA中间程序;之后将S-CGA中间程序转换为时钟数据依赖图以分析依赖关系;然后对时钟数据依赖图进行拓扑排序划分,并针对划分结果提出优化算法和基于流水线方式的任务划分方法;最后将划分结果转换为虚拟多线程结构并进一步生成可执行多线程C/Java代码.通过在多核处理器上的实验,验证了所提方法的有效性.