Reducing Occupational Noise Propagated from Centrifugal Fan through Dissipative Silencers: A Field Study

Acoustic performance of dissipative silencer was evaluated to determine the effectiveness of perforated duct porosity and absorbent material density in reducing occupational noise exposure propagated from centrifugal fan. Design charts were applied to predict noise reduction and length of a dissipative silencer. Dissipative silencers with various punched duct porosity (14%, 30% and 40%) and sound absorbent density (80 Kg/m³, 120 Kg/m³, and 140 Kg/m³) were designed and fabricated. According to ISO9612 and ISO11820, noise level was measured before and after installing all nine test silencers at fixed workstations around the discharge side of a centrifugal fan in a manufacturing plant. On average, the noise level at the discharge side of a fan without silencer was measured to be 93.6 dBA, whereas it was significantly mitigated by 67.4 dBA to 70.1 dBA after installing all silencers. Dynamic insertion loss for a dissipative silencer with 100 cm length was predicted to be 27.9 dB, which was in agreement with experimental ones. Although, there was no significant differences between insertion loss of silencers, the one with 30% porosity and 120 Kg/m³ rock wool density had the highest insertion loss of 26.2 dBA. Dissipative silencers noticeably reduced centrifugal fan noise exposures. Increasing sound absorbent density and duct porosity up to a certain limit could probably be effective in noise reduction of dissipative silencers.     

Optimisation of the ultrasound-assisted extraction of betalains and polyphenols from Amaranthus hypochondriacus var. Nutrisol

The present study optimised the ultrasound-assisted extraction (UAE) of bioactive compounds from Amaranthus hypochondriacus var. Nutrisol. Influence of temperature (25.86–54.14 °C) and ultrasonic power densities (UPD) (76.01–273.99 mW/mL) on total betalains (BT), betacyanins (BC), betaxanthins (BX), total polyphenols (TP), antioxidant activity (AA), colour parameters (L*, a*, and b*), amaranthine (A), and isoamaranthine (IA) were evaluated using response surface methodology. Moreover, betalain extraction kinetics and mass transfer coefficients (K_La) were determined for each experimental condition. BT, BC, BX, TP, AA, b*, K_La, and A were significantly affected (p < 0.05) by temperature extraction and UPD, whereas L*, a*, and IA were only affected (p < 0.05) by temperature. All response models were significantly validated with regression coefficients (R²) ranging from 87.46 to 99.29%. BT, A, IA, and K_La in UAE were 1.38, 1.65, 1.50, and 29.93 times higher than determined using conventional extraction, respectively. Optimal UAE conditions were obtained at 41.80 °C and 188.84 mW/mL using the desired function methodology. Under these conditions, the experimental values for BC, BX, BT, TP, AA, L*, a*, b*, K_La, A, and IA were closely related to the predicted values, indicating the suitability of the developed quadratic models. This study proposes a simple and efficient UAE method to obtain betalains and polyphenols with high antioxidant activity, which can be used in several applications within the food industry.     

RNAGCN: RNA tertiary structure assessment with a graph convolutional network

RNAs play crucial and versatile roles in cellular biochemical reactions. Since experimental approaches of determining their three-dimensional (3D) structures are costly and less efficient, it is greatly advantageous to develop computational methods to predict RNA 3D structures. For these methods, designing a model or scoring function for structure quality assessment is an essential step but this step poses challenges. In this study, we designed and trained a deep learning model to tackle this problem. The model was based on a graph convolutional network (GCN) and named RNAGCN. The model provided a natural way of representing RNA structures, avoided complex algorithms to preserve atomic rotational equivalence, and was capable of extracting features automatically out of structural patterns. Testing results on two datasets convincingly demonstrated that RNAGCN performs similarly to or better than four leading scoring functions. Our approach provides an alternative way of RNA tertiary structure assessment and may facilitate RNA structure predictions. RNAGCN can be downloaded from https://gitee.com/dcw-RNAGCN/rnagcn.     

p-Adic函数空间上Hardy型算子的多线性交换子估计

在p-adic域上研究分数次Hardy型算子与CMO(Q_p~n)函数生成的多线性交换子,建立了交换子在Lebesgue空间和Herz空间上的有界性.对Hardy算子的多线性交换子也得到了相应的结果.     

Strong Pancake 2e/12c Bond in π-Stacking Phenalenyl Derivatives Avoiding Bond Conversion

The nature of the 2e/12c bond and its conversion to a carbon-carbon single bond in phenalenyl dimers have prompted a great deal of interests recently. In this work, we theoretically investigated a series of π-stacking phenalenyl derivatives with 2e/12c bonding character by density functional theory (DFT) calculations to elucidate origin of this unusual bond conversion. Results show that bond-conversion of the phenalenyl dimer easily occurs at room-temperature both dynamically and thermodynamically. However, bond-conversion of hetero π-stacking adducts, in which the two center carbon atoms were substituted by boron and nitrogen atoms, respectively, is much more difficult, because the 2e/12c bond is stabilized by its charge transfer character. Consequently, the bond-conversion is an endothermic process, albeit with a low conversion barrier. Interestingly, Lewis acid-base interactions would be induced by substitution of the center nitrogen atom to phosphorus atom. The 2e/12c bond is further stabilized by 5.9 kcal mol⁻¹ and its conversion is also thermodynamically unfavorable.