A Bayesian Network Based Adaptability Design of Product Structures for Function Evolution

Structure adaptability design is critical for function evolution in product families, in which many structural and functional design factors are intertwined together with manufacturing cost, customer satisfaction, and final market sales. How to achieve a delicate balance among all of these factors to maximize the market performance of the product is too complicated to address based on traditional domain experts’ knowledge or some ad hoc heuristics. Here, we propose a quantitative product evolution design model that is based on Bayesian networks to model the dynamic relationship between customer needs and product structure design. In our model, all of the structural or functional features along with customer satisfaction, manufacturing cost, sale price, market sales, and indirect factors are modeled as random variables denoted as nodes in the Bayesian networks. The structure of the Bayesian model is then determined based on the historical data, which captures the dynamic sophisticated relationship of customer demands of a product, structural design, and market performance. Application of our approach to an electric toothbrush product family evolution design problem shows that our model allows for designers to interrogate with the model and obtain theoretical and decision support for dynamic product feature design process

Optimizing energy efficiency of CNN-based object detection with dynamic voltage and frequency scaling

On the one hand, accelerating convolution neural networks (CNNs) on FPGAs requires ever increasing high energy efficiency in the edge computing paradigm. On the other hand, unlike normal digital algorithms, CNNs maintain their high robustness even with limited timing errors. By taking advantage of this unique feature, we propose to use dynamic voltage and frequency scaling (DVFS) to further optimize the energy efficiency for CNNs. First, we have developed a DVFS framework on FPGAs. Second, we apply the DVFS to SkyNet, a state-of-the-art neural network targeting on object detection. Third, we analyze the impact of DVFS on CNNs in terms of performance, power, energy efficiency and accuracy. Compared to the state-of-the-art, experimental results show that we have achieved 38% improvement in energy efficiency without any loss in accuracy. Results also show that we can achieve 47% improvement in energy efficiency if we allow 0.11% relaxation in accuracy

A new characterization methodology for starch gelatinization

A gelatinization degree control system, with a combination of Artificial Neural Networks (ANNs) and computer vision, was successfully developed. An intelligent measurement framework was purposely designed to achieve a precise investigation on phase transition and morphology change of starch in real time, as well as a process control during gelatinization. Base on a variation of birefringence number, the degree of gelatinization (DG) control system provided a direct and fast methodology without subjective uncertainty in studying starch gelatinization. In the course, the whole system was a cascade structure with the hot-stage temperature chosen as the inner-loop parameter, thus the granule morphology and birefringence at different DG could be easily observed and compared in real time, and the relative transition temperature was simultaneously calculated

Recovering lossless propagation of polaritons with synthesized complex frequency excitation

Surface plasmon polaritons and phonon polaritons offer a means of surpassing the diffraction limit of conventional optics and facilitate efficient energy storage, local field enhancement, high sensitivities, benefitting from their subwavelength confinement of light. Unfortunately, losses severely limit the propagation decay length, thus restricting the practical use of polaritons. While optimizing the fabrication technique can help circumvent the scattering loss of imperfect structures, the intrinsic absorption channel leading to heat production cannot be eliminated. Here, we utilize synthetic optical excitation of complex frequency with virtual gain, synthesized by combining the measurements taken at multiple real frequencies, to restore the lossless propagations of phonon polaritons with significantly reduced intrinsic losses. The concept of synthetic complex frequency excitation represents a viable solution to compensate for loss and would benefit applications including photonic circuits, waveguiding and plasmonic/phononic structured illumination microscopy.Comment: 20 pages, 4 figure

Purification and identification of novel xanthine oxidase inhibitory peptides derived from round scad (Decapterus maruadsi) protein hydrolysates

The objective of the present study was to investigate the xanthine oxidase (XO) inhibitory effects of peptides purified and identified from round scad (Decapterus maruadsi) hydrolysates (RSHs). In this study, RSHs were obtained by using three proteases (neutrase, protamex and alcalase). Among them, the RSHs of 6-h hydrolysis by neutrase displayed the strongest XO inhibitory activity and had an abundance of small peptides (<500 Da). Four novel peptides were purified by immobilized metal affinity chromatography and identified by nano-high-performance liquid chromatography mass/mass spectrometry. Their amino acid sequences were KGFP (447.53 Da), FPSV (448.51 Da), FPFP (506.59 Da) and WPDGR (629.66 Da), respectively. Then the peptides were synthesized to evaluate their XO inhibitory activity. The results indicated that the peptides of both FPSV (5 mM) and FPFP (5 mM) exhibited higher XO inhibitory activity (22.61 ± 1.81% and 20.09 ± 2.41% respectively). Fluorescence spectra assay demonstrated that the fluorescence quenching mechanism of XO by these inhibitors (FPSV and FPFP) was a static quenching procedure. The study of inhibition kinetics suggested that the inhibition of both FPSV and FPFP was reversible, and the type of their inhibition was a mixed one. Molecular docking revealed the importance of π-π stacking between Phe residue (contained in peptides) and Phe914 (contained in the XO) in the XO inhibitory activity of the peptides

Discovery and identification of potential biomarkers of papillary thyroid carcinoma

<p>Abstract</p> <p>Background</p> <p>Thyroid carcinoma is the most common endocrine malignancy and a common cancer among the malignancies of head and neck. Noninvasive and convenient biomarkers for diagnosis of papillary thyroid carcinoma (PTC) as early as possible remain an urgent need. The aim of this study was to discover and identify potential protein biomarkers for PTC specifically.</p> <p>Methods</p> <p>Two hundred and twenty four (224) serum samples with 108 PTC and 116 controls were randomly divided into a training set and a blind testing set. Serum proteomic profiles were analyzed using SELDI-TOF-MS. Candidate biomarkers were purified by HPLC, identified by LC-MS/MS and validated using ProteinChip immunoassays.</p> <p>Results</p> <p>A total of 3 peaks (<it>m/z </it>with 9190, 6631 and 8697 Da) were screened out by support vector machine (SVM) to construct the classification model with high discriminatory power in the training set. The sensitivity and specificity of the model were 95.15% and 93.97% respectively in the blind testing set. The candidate biomarker with <it>m/z </it>of 9190 Da was found to be up-regulated in PTC patients, and was identified as haptoglobin alpha-1 chain. Another two candidate biomarkers (6631, 8697 Da) were found down-regulated in PTC and identified as apolipoprotein C-I and apolipoprotein C-III, respectively. In addition, the level of haptoglobin alpha-1 chain (9190 Da) progressively increased with the clinical stage I, II, III and IV, and the expression of apolipoprotein C-I and apolipoprotein C-III (6631, 8697 Da) gradually decreased in higher stages.</p> <p>Conclusion</p> <p>We have identified a set of biomarkers that could discriminate PTC from non-cancer controls. An efficient strategy, including SELDI-TOF-MS analysis, HPLC purification, MALDI-TOF-MS trace and LC-MS/MS identification, has been proved successful.</p

Analysis on the Settlement of Adjacent Buildings Caused by the Underpassing Construction of the Biased Tunnel

Through the simulation analysis of the settlement and deformation law of the surface buildings caused by the construction of the biased tunnel, the following points are obtained: (1) The Peak formula is revised, and the influence range of the biased tunnel is predicted based on the formula. (2) It is concluded that when the tunnel is biased, the position of maximum deformation caused by ground settlement is generally in a parallel area 0.5 times the buried depth from the center line of the tunnel. (3) Through the double-layer verification of simulation analysis and monitoring measurement data, prior to the construction of buildings with similar weak foundations, their foundations should be reinforced in advance. (4) In the process of this simulation, the complicated influence of water pressure on tunnel excavation was not considered, which can be further optimized in the later stage