Analysis of human male armpit sweat after fenugreek ingestion: Characterisation of odour active compounds by gas chromatography coupled to mass spectrometry and olfactometry

In this study, the strong “maple-syrup” odour which appears after fenugreek ingestion was investigated. Headspace solid-phase microextraction (HS-SPME) was applied to extract volatile odourant compounds from human male armpit sweat samples. Two male volunteers were considered who have similar diet; they had to ingest fenugreek infusion over the same period of time. The HS-SPME extracts obtained were then analysed by gas chromatography coupled either to mass spectrometry (GC–MS) or flame ionisation detection and olfactometry (GC–O). In that latter case, a panel of eight assessors was used, and the detection frequency methodology was applied. A total of 44 compounds could be identified in sweat samples, with a wide range of chemical structures, some of them being reported for the first time in human armpit sweat. Eight compounds appearing only after fenugreek ingestion could be identified: 2,5-dimethylpyrazine, β-pinene, 3-octen-2-one, camphor, terpinen-4-ol, 4-isopropyl-benzaldehyde, neryl acetate and β-caryophyllene. Due to their odourant notes, such compounds should be responsible for the strong “maple-syrup” odour present in sweat after fenugreek ingestion. GC–O confirmed the role of some odourant compounds in the “maple-syrup” odour of sweat, especially 2,5-dimethylpyrazine which was the best perceived odour. Among these eight compounds, some of them were previously reported in the fenugreek seeds, namely β-pinene, 3-octen-2-one and camphor.     

Comparison between three optimization methods for the minimization of maximum bending load and springback in wiping die bending obtained by an experimental approach

The sheet metal bending process is widely used in the automotive industries, and it is actually one of the most important manufacturing processes. The robustness and the reliability of the bending operation, like many other forming operations, depend of several parameters (geometry, material, and process). In this paper, the die radius and the clearance between the punch and the sheet are optimised in order to reduce the maximum bending load and the springback. Two optimization problems are formulated, and three optimization procedures based on the response surface method are proposed and used to find the optimum solutions. Global and local approximations are used to replace the initial optimization problem, which is implicit by an explicit problem, and the optimum is localised using two algorithms: a sequential quadratic programming and an evolution strategies. The objective functions are evaluated experimentally into a limited points number, which are defined using a design of experiments technique. Good results are obtained from the three optimization procedures. The ability of each technique to find the optimal solution is evaluated, and the results show a good agreement between those three methods.     

Viscoelastic behavior of HDPE polymer using tensile and compressive loading

Large containers for liquids, exposed to different static loadings, are mainly made of high-density polyethylene (HDPE). The viscoelastic response of HDPE under long-term tensile and compressive creep is investigated. Monotonic experiments under tension are performed over a wide range of strain rates. In these experiments, the transition in the damage mechanisms (development and propagation of contraction in the HDPE specimen) is analyzed. The monotonic tensile behavior of the HDPE is found to be nonlinear and depends on the strain rates. It is observed that both elastic modulus and plastic flow stress present an increase with displacement speed due to the viscoelastic behavior of HDPE. A similar observation can be made for monotonic compressive tests by developing a new experimental device that ensures accurate measurement of the strain. Such a device makes use of an extensometer of compressive displacement of the specimen. In addition, the long-term behavior of HDPE is evidenced through creep and relaxation tests at an imposed range respectively of lower stresses and strains. It is shown that the normalized curves, associated with these tests, can be represented by a single curve characterizing the compressive creep compliance or relaxation stresses versus time. The linearity of the viscoelastic behavior is confirmed within the linear domain of the monotonic compressive and tensile tests.     

An Evaluation of RetinaNet on Indoor Object Detection for Blind and Visually Impaired Persons Assistance Navigation

Neural Processing Letters - Indoor object detection presents a computer vision task that deals with the detection of specific indoor classes. This task attracts a lot of attention, especially in...     

Covariance-guided landmine detection and discrimination using ground-penetrating radar data

Ground penetrating Radar (GPR) can detect and deliver the response signal from any buried kind of object like plastic or metallic landmines, stones, and wood sticks. It delivers three kinds of data: Ascan, Bscan, and Cscan. However, it cannot discriminate between landmines and inoffensive objects or ‘clutter.’ One-class classification is an alternative to detect landmines, especially, as landmines features data are unbalanced. In this article, we investigate the effectiveness of the Covariance-guided One-Class Support Vector Machine (COSVM) to detect, discriminate, and locate landmines efficiently. In fact, compared to existing one-class classifiers, the COSVM has the advantage of emphasizing low variance directions. Moreover, we will compare the one-class classification to multiclass classification to tease out the advantage of the former over the latter as data are unbalanced. Our method consists of extracting Ascan GPR data. Extracted features are used as an input for COSVM to discriminate between landmines and clutter. We provide an extensive evaluation of our detection method compared to other methods based on relevant state of the art one-class and multiclass classifiers, on the well-known MACADAM database. Our experimental results show clearly the superiority of using COSVM in landmine detection and localization.     

Tomographic Reconstruction of 3D Objects Using Marked Point Process Framework

For reconstructing sparse volumes of 3D objects from projection images taken from different viewing directions, several volumetric reconstruction techniques are available. Most popular volume reconstruction methods are algebraic algorithms (e.g. the multiplicative algebraic reconstruction technique, MART). These methods which belong to voxel-oriented class allow volume to be reconstructed by computing each voxel intensity. A new class of tomographic reconstruction methods, called “object-oriented” approach, has recently emerged and was used in the Tomographic Particle Image Velocimetry technique (Tomo-PIV). In this paper, we propose an object-oriented approach, called Iterative Object Detection—Object Volume Reconstruction based on Marked Point Process (IOD-OVRMPP), to reconstruct the volume of 3D objects from projection images of 2D objects. Our approach allows the problem to be solved in a parsimonious way by minimizing an energy function based on a least squares criterion. Each object belonging to 2D or 3D space is identified by its continuous position and a set of features (marks). In order to optimize the population of objects, we use a simulated annealing algorithm which provides a “Maximum A Posteriori” estimation. To test our approach, we apply it to the field of Tomo-PIV where the volume reconstruction process is one of the most important steps in the analysis of volumetric flow. Finally, using synthetic data, we show that the proposed approach is able to reconstruct densely seeded flows.     

Constructability Concepts in West Port Highway in Malaysia

This paper presents a case study of the applications and nonapplications of constructability concepts to illustrate, in a practical way, the impact that these concepts can have on a project’s success. This case study, which conveys an important message with regard to the application of constructability concepts, was purposely chosen from among prestigious projects in peninsular Malaysia. The basic message, as viewed by the interviewees, is that applying these constructability concepts will enhance a project’s constructability, consequently optimizing the schedule, cost, and quality of the project for the benefit of all the parties involved. The interviewees for the case study agreed that the applied constructability concepts were derived from their own experience and not based on any existing formal program. The absence of a systematic technique for transferring construction experience and knowledge to all the participants in all phases of a construction project is the reason behind the lack of constructability in our construction industry today.     

SN-SVM: a sparse nonparametric support vector machine classifier

This paper introduces a novel sparse nonparametric support vector machine classifier (SN-SVM) which combines data distribution information from two state-of-the-art kernel-based classifiers, namely, the kernel support vector machine (KSVM) and the kernel nonparametric discriminant (KND). The proposed model incorporates some near-global variations of the data provided by the KND and, hence, may be viewed as an extension to the KSVM. Similarly, since the support vectors improve the choice of \(\kappa \) -nearest neighbors ( \(\kappa -NN\) ’s), it can also serve as an extension to the KND. The proposed model is capable of dealing with both heteroscedastic and non-normal data while avoiding the small sample size problem. The model is a convex quadratic optimization problem with one global optimal solution, so it can be estimated easily and efficiently using numerical methods. It can also be reduced to the classical KSVM model and as such existing SVM programs can be used for easy implementation. Through the Bayesian interpretation with the help of a Gaussian prior, we show that our method provides a sparse solution by assigning non-zero weights to only a fraction of the total number of training samples. This sparsity can be used by existing sparse classification algorithms to obtain better computational efficiency. The experimental results on real-world datasets and face recognition applications show that the proposed SN-SVM model improves the classification accuracy over contemporary classifiers and also provides sparser solution than the KSVM.     

受温度、湿度、机械应力循环变化的CFRP-混凝土界面有限元模型

环境对FRP与混凝土粘结界面影响的模型是预测加强构件寿命周期的关键因素。组合系统的使用性能取决于系统的物理和化学老化程度。物理老化是一个可逆过程,主要与温度变化密切相关;由于树脂或界面长期曝露在环境中,化学老化是纤维的永久性退化。由于老化导致的强度降低和系统性能变化是评估土木工程基础强度的重要因素。本文建立有限元模型对长期暴露在交替温度和恒定湿度状态下的界面性能进行预测,同时也考虑了持续荷载的作用。所预测的强度、裂缝模式、破坏模式以及沿着粘结方向的应变变化与试验结果吻合较好。