Vibration characteristics and environmental responses of different vehicle-track-ballast coupling systems in subway operation

The vibration characteristics of two ballast beds are analyzed in this study from five aspects including the amplitude-frequency characteristic curve of foundation reaction. This study also shows that the maximum ground Z vibration level caused by a normal monolithic ballast bed structure is 75 dB. The range of its vibration influence during daytime is approximately 30 m. The maximum ground Z vibration level caused by a rubber floating slab track structure is 52 dB, whereas that caused by a steel spring floating slab track structure is 57 dB. The maximum damping amount in horizontal speed of a rubber floating slab track structure is 74 %, whereas the reduction of vertical ground vibration speed and acceleration is 92 % and 93 %, respectively. The reduction in Z level is 37 %. The horizontal speed reduction in a steel spring floating slab track structure is 71 %, whereas the reduction of ground vertical vibration speed and acceleration is 83 % and 84 %, respectively. The reduction in Z level is 29 %

Effect of electronic stimulation at Neiguan (PC 6) acupoint on gene expression of adenosine triphosphate-sensitive potassium channel and protein kinases in rats with myocardial ischemia

AbstractObjectiveTo investigate the effects of electronic stimulation at acupoints Neiguan (PC 6) and Lieque (LU 7) on the gene expression of the adenosine triphosphate (ATP)-Sensitive potassium channel (KATP: Kir6.1, Kir6.2, SUR2A, and SUR2B) and protein kinases (PKA, PKG, and PKCβ2) in myocardial cells of rats with myocardial ischemia (MI) induced by isoproterenol (ISO).MethodsRats were randomly divided into a control, model, Neiguan (PC 6), Lieque (LU 7), and non-acupoint groups. The MI model was established by injecting rats with ISO. Electro-acupuncture treatment was given to the acupuncture groups, once a day for 7 days. Gene expression was analyzed with real-time PCR.ResultsThe gene expression of KATP and protein kinases in the model group was higher than those in the control group (P < 0.05). After acupuncture treatment, the KATP and protein kinase expression levels were significantly lower in the Neiguan (PC 6) and Lieque (LU 7) groups compared with the model group (P < 0.05). The Neiguan (PC 6) group lowered these levels significantly more than that of the Lieque (LU 7) group (P < 0.05). No significant differences were observed between the model and non-acupoint groups (P > 0.05).ConclusionOur findings suggest that electronic needling of Neiguan (PC 6) can both reduce the gene expression of KATP and protein kinases in rats with ISO-induced MI

Flexible operation of large-scale coal-fired power plant integrated with solvent-based post-combustion CO2 capture based on neural network inverse control

Post-combustion carbon capture (PCC) with chemical absorption has strong interactions with coal-fired power plant (CFPP). It is necessary to investigate dynamic characteristics of the integrated CFPP-PCC system to gain knowledge for flexible operation. It has been demonstrated that the integrated system exhibits large time inertial and this will incur additional challenge for controller design. Conventional PID controller cannot effectively control CFPP-PCC process. To overcome these barriers, this paper presents an improved neural network inverse control (NNIC) which can quickly operate the integrated system and handle with large time constant. Neural network (NN) is used to approximate inverse dynamic relationships of integrated CFPP-PCC system. The NN inverse model uses setpoints as model inputs and gets predictions of manipulated variables. The predicted manipulated variables are then introduced as feed-forward signals. In order to eliminate steady-state bias and to operate the integrated CFPP-PCC under different working conditions, improvements have been achieved with the addition of PID compensator. The improved NNIC is evaluated in a large-scale supercritical CFPP-PCC plant which is implemented in gCCS toolkit. Case studies are carried out considering variations in power setpoint and capture level setpoint. Simulation results reveal that proposed NNIC can track setpoints quickly and exhibit satisfactory control performances

Fast, High-Quality Hierarchical Depth-Map Super-Resolution

The low spatial resolution of acquired depth maps is a major drawback of most RGBD sensors. However, there are many scenarios in which fast acquisition of high-resolution and high-quality depth maps would be desirable. One approach to achieve higher quality depth maps is through super-resolution. However, edge preservation is challenging, and artifacts such as depth confusion and blurring are easily introduced near boundaries. In view of this, we propose a method for fast, high-quality hierarchical depth-map super-resolution (HDS). In our method, a high-resolution RGB image is degraded layer by layer to guide the bilateral filtering of the depth map. To improve the upsampled depth map quality, we construct a feature-based bilateral filter (FBF) for the interpolation, by using the extracted RGB shallow and multi-layer features. To accelerate the process, we perform filtering only near depth boundaries and through matrix operations. We also propose an extension of our HDS model to a Classification-based Hierarchical Depth-map Super-resolution (C-HDS) model, where a context-aware trilateral filter reduces the contributions of unreliable neighbors to the current missing depth location. Experimental results show that the proposed method is significantly faster than existing methods for generating high-resolution depth maps, while also significantly improving depth quality compared to the current state-of-the-art approaches, especially for large-scale 16x super-resolution.</p

A tomato HD-Zip homeobox protein, LeHB-1, plays an important role in floral organogenesis and ripening

Ethylene is required for climacteric fruit ripening. Inhibition of ethylene biosynthesis genes, 1-aminocyclopropane-1-carboxylate (ACC) synthase and ACC oxidase, prevents or delays ripening, but it is not known how these genes are modulated during normal development. LeHB-1, a previously uncharacterized tomato homeobox protein, was shown by gel retardation assay to interact with the promoter of LeACO1, an ACC oxidase gene expressed during ripening. Inhibition of LeHB-1 mRNA accumulation in tomato fruit, using virus-induced gene silencing, greatly reduced LeACO1 mRNA levels, and inhibited ripening. Conversely, ectopic overexpression of LeHB-1 by viral delivery to developing flowers elsewhere on injected plants triggered altered floral organ morphology, including production of multiple flowers within one sepal whorl, fusion of sepals and petals, and conversion of sepals into carpel-like structures that grew into fruits and ripened. Our findings suggest that LeHB-1 is not only involved in the control of ripening but also plays a critical role in floral organogenesis

Оцінка впливу замісної гормоно-терапії гіпотиреозу на стан вагітності та виношування плоду

Гіпотиреоз – захворювання щитовидної залози, при якому знижується її продуктивність, тиреоїдних гормонів виробляється менше чим необхідно організму для нормальної життєдіяльності. За результатами популяційних досліджень, поширеність гіпотиреозу серед вагітних становить 2-3 %. Серед них близько двох третин мають субклінічний та 0,5 % − маніфестний гіпотиреоз. За даними багатьох дослідників, тільки 20-30% жінок з гіпотиреозом мають клінічні прояви гіпотиреозу, у інших, як правило, захворювання протікає без симптомів. Патологія ЩЗ негативно впливає на перебіг вагітності, розвиток плода й адаптацію новонародженого. Тиреоїдна дисфункція загрожує викиднями, передчасними пологами, відшаруванням плаценти, прееклампсією, післяпологовим тиреоїдитом у матері, а також зниженням інтелектуального потенціалу народжених дітей

A deep learning diagnostic platform for diffuse large B-cell lymphoma with high accuracy across multiple hospitals

Diagnostic histopathology is a gold standard for diagnosing hematopoietic malignancies. Pathologic diagnosis requires labor-intensive reading of a large number of tissue slides with high diagnostic accuracy equal or close to 100 percent to guide treatment options, but this requirement is difficult to meet. Although artificial intelligence (AI) helps to reduce the labor of reading pathologic slides, diagnostic accuracy has not reached a clinically usable level. Establishment of an AI model often demands big datasets and an ability to handle large variations in sample preparation and image collection. Here, we establish a highly accurate deep learning platform, consisting of multiple convolutional neural networks, to classify pathologic images by using smaller datasets. We analyze human diffuse large B-cell lymphoma (DLBCL) and non-DLBCL pathologic images from three hospitals separately using AI models, and obtain a diagnostic rate of close to 100 percent (100% for hospital A, 99.71% for hospital B and 100% for hospital C). The technical variability introduced by slide preparation and image collection reduces AI model performance in cross-hospital tests, but the 100% diagnostic accuracy is maintained after its elimination. It is now clinically practical to utilize deep learning models for diagnosis of DLBCL and ultimately other human hematopoietic malignancies