查看更多>>摘要：Recently, the performance of aptasensors has been significantly improved when combined with new nano materials. Herein, the amorphous Ni(OH)(2) nano-boxes with intact shell structure were synthesized and, then, were used as an efficient substrate for covalent immobilization of the NH2-functionalized aptamer to develop label-free electrochemical aptasensor for ultrasensitive analysis of trypsin. This multidimensional hollow structure places a large amount of surface area in a small space of electrode surface and, thus, increasing the loading of the aptamer strings which significantly increases the sensitivity of measurement. A good linear relationship was found between logarithmic value of trypsin and Rct from 1 fg mL(-1) to 500 ng mL(-1) with a low detection limit of 0.3 fg mL(-1). Furthermore, the proposed aptasensor showed good reproducibility, high stability and excellent feasibility in biological samples. This study showed that the Ni(OH)(2) nano-boxes nanocomposite have various advanced electrochemical properties making them suitable electrode materials for aptasensor application.
Tavares, Vitor SilvaCalliari, FelipeMonteiro, Elisabeth CostaTemporao, Guilherme Penello...
9页
查看更多>>摘要：In this paper, we propose a method for evaluating the impact of the detection gate width of an InGaAs/InP singlephoton avalanche diode detector by analyzing the probability of detecting 0 or 1 photon as a function of the optical attenuation and the consequent variation of the average number of photons per gate measured by the detector ((mu) over bar). With a detection efficiency eta of 15 % and a dead time of 1 mu s, considering gate widths of 4 ns, 8 ns, 12 ns, 16 ns, and 20 ns, an adequate optical power range for calibrating a single-photon avalanche diode detector was obtained in the range of 0.15 nW to 10 nW, with linear behavior and low measurement uncertainty of calibration curve fittings for 4 ns and 8 ns gate widths, respectively, associated with products (mu) over bar eta of 0.32 x 10(-4) to 190 x 10(-4) and 2.90 x 10(-4) to 140 x 10(-4).
查看更多>>摘要：This paper presents a high sensitivity displacement sensor that applies magnetic force to a suspended fiber Bragg grating (FBG), which can be used to measure micron displacement and reconstruct the morphology of damaged surface. In this paper, the sensor model is established, and a series of static and dynamic experiments are carried out. The static results show that the sensor has a sensitivity of 0.47 pm/mu m in the range of 0-1 mm, and the maximum relative error is less than 5.2%. In the response experiment of typical signal, the adjustment time of step response is less than 1.5 s, and steady-state error is less than 1.2 pm. The scanning experiment can reconstruct the image perfectly. All the results mean that the sensor provides a new solution to surface damage detection.
查看更多>>摘要：The gas sensing technique based on the differential pressure principle is quite suitable to evaluate the effectiveness of gas fire extinguishing system. This paper is focused on the study of the change rule of its response fluctuation that is critical to the concentration measuring accuracy. The variation of response fluctuation and sensing performance with sensing structure and constant temperature was explored. Results show that it will often enlarge the response fluctuation when promoting the response/sensitivity or response rate by changing the sensing structure, which can cover the improvement in sensitivity causing the decrease in measuring precision, especially for high concentration. However, the increase of temperature will reduce the response fluctuation while improving the response/sensitivity and response rate, which is a good way to promote sensing performance. The research in the paper can provide suggestions for the optimization design of the sensor.
查看更多>>摘要：The propagation characteristics of leaky Lamb wave (LLW) when the rigid mold is loaded with water or viscous resin by one side are calculated numerically and verified experimentally, and the amplitude of A(0) mode decreases linearly with the increase of liquid layer thickness. Then the valid applications of LLW in two-and threedimensional resin flow monitoring of hand lay-up and VARI processes are carried out. The amplitude of A(0) mode shows a downward trend in resin impregnation of both in-plane and through-thick directions, and the dry spots of different sizes could be identified. The amplitude and energy of received signals would decrease with the increase of resin coverage and the benchmark model of A(0) mode is established and validated. The new wide sensing range measurement methods are proposed to effectively monitor the two-and three-dimensional resin flow front by the PZT sensor network in both noninvasive and integrated ways.
查看更多>>摘要：The feasibility of introducing soil bulk density as a factor to distinguish different levels of soil compactness for the predictive modeling of soil moisture content was verified. Outdoor rainfall was simulated to set different initial soil moisture contents, after which the soil was allowed to air dry in a natural environment. Four drying and wetting (D-W) cycles were simulated in the experiment. An ultrasonic detection device was employed to measure the soil ultrasonic velocity and calculate the corresponding soil bulk density. An adaptive weighted data fusion algorithm was adopted to integrate the soil ultrasonic velocity and bulk density results, and a bivariate linear regression model was established to describe the relationships of soil ul-trasonic velocity and bulk density with soil moisture content over D-W cycles. The model suitably described the soil moisture content in a given cultivated layer as a function of soil ultrasonic velocity and bulk density.
Woodruff, ConnorDean, Steven W.Cagle, ColtonCroessmann, Charles Luke...
9页
查看更多>>摘要：Thermal analyses of projectile impact and subsequent combustion are investigated for aluminum projectiles using a high-velocity impact ignition system. Temperature measurements are compared using pyrometry and thermography. The implementation of these techniques is discussed, as well as their benefits and limitations in ballistic experiments. Results show pyrometry is best for measuring temperatures in the immediate vicinity surrounding the impact location, while thermography better quantifies temperature dissipation downstream from impact as the combusting debris cloud disperses. Temperatures comparable to the predicted adiabatic flame temperature are observed with the pyrometer. For thermography, emphasis is placed on the treatment of emissivity in temperature calculations. Three combustion stages are identified in the thermography data and attributed to 1) ignition and growth of the combustion front, 2) thermal dissipation due to initial particle burnout, and 3) a slower dissipation stage caused by reduced heat exchange between the burning debris cloud and surroundings.
查看更多>>摘要：Microseismic (MS) or acoustic emission (AE) system has been used for rock burst warning in heading face for a period of time, but the main problem focus on how to accurately obtain the target coal fracture event (CFE) or how to remove interference signals. In the study, the location and characteristics of CFEs in a heading face are determined, and the commonly STA/LTA (short-term to long-term average) method is used to distinguish CFEs from interference signals, results show that STA/LTA method cannot substantially remove the large number of interference signals in heading face. Based on this fact, a multi-channel verification index is proposed to pick up CFEs in heading face, the index can preliminarily provide a significant accuracy rate for CFEs and reflect the abnormal stress field of heading face, which lays an important reference for monitoring and warning index selection of rock burst in heading face.
查看更多>>摘要：Artificial intelligence has been widely used in the biomedical engineering field, which can assist the clinicians in disease diagnoses, help the engineers in physiological signal processing, or serve the people with chronic diseases in the homecare management. Blood clots in the deep veins of human body is called the deep vein thromboses (DVT). If the embolus passes through the lung, patient will have a life-threatening risk. Therefore, how to use the artificial intelligence to serve daily monitoring of the DVT condition is a valuable exploration. The light reflection rheography (LRR) has been used to detect the DVT of low limbs. In the previous study, the wearable device using LRR technique has been developed. But, this examination system could not be used by nonphysician because the signal-quality evaluation of LRR and the classification of positive or negative DVT using the LRR signal all need the manual process. The goal of this study is to use a two-dimension convolutional neural network (2D CNN) to evaluate the qualities of LRR signals and classify the positive or negative DVT from the LRR signal with high reliability. The LRR signal and the smoothed signal were combined together to form a 450x450 image as the input pattern. In this study, twenty subjects were recruited to perform four-time experiments. A cuff pressured to 100 mmHg and 150 mmHg occluded the veins of low limbs to simulate the slight and serious DVT scenarios, and which was placed at the top and bottom of the knee of left leg to simulate the distal and proximal embolization. In the signal-quality evaluation, there were 700 samples including 476 high qualities and 224 low qualities, which were marked by the experts according to the vein emptying phenomenon. In the DVT classification, there were 476 samples including 167 negative samples, 158 slight positive samples, and 151 serious positive samples. A 19-layer CNN model proposed by Visual Geometry Group (VGG-19) was used in the two experiments. We performed the inter-group and intra-group analysis. Both results were better than the previous study. The accuracies of signal-quality evaluation and DVT classification were 0.92 and 0.75, respectively. Thus, the proposed method could support people with the high risk for DVT examination at non-medical settings.
Karimi, MohsenAlibak, Ali HosinAlizadeh, Seyed Mehdi SeyedSharif, Mehdi...
11页
查看更多>>摘要：Biomass has attracted significant interest as a renewable energy source recently. This study employs different artificial intelligence (AI) scenarios to determine biomass heat capacity (Cp). Cumulatively, 1025 experimental measurements for 25 different biomass types (block and powder forms) were utilized to design/identify the most accurate AI model. Validity checking approves that more than 98% of the data are valid. The Cp is estimated as a function of biomass source, appearance shape, and temperature. The cascade feedforward (CFF) neural network appears as the most precise tool for the concerned matter. This CFF predicts the biomass Cp by absolute average relative deviation (AARD%) and regression coefficient (R-2) of 0.42% and 0.99347, respectively. Despite the empirical correlation that only considers the temperature effect on the biomass heat capacity, the CFF paradigm also incorporates the effect of biomass source and appearance shape. The powder form of biomasses has higher Cp than their block form.
NSTL
Elsevier