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Operators in the main control room of a nuclear power plant have a crucial role in supervising all operations, and any human error can be fatal. By providing operators with information regarding the future trends of plant safety-critical parameters based on their actions, human errors can be detected and prevented in a timely manner. This paper proposed a Sequence-to-Sequence (Seq2Seq)-based Long Short-Term Memory (LSTM) model to predict safety-critical parameters and their future trends. The PCTran was used to extract data for four typical faults and fault levels, and eighty-six parameters were selected as characteristic quantities. The training, validation, and testing sets were collected in a ratio of 13:3:1, and appropriate hyperparameters were used to construct the Seq2Seq neural network. Compared with conventional deep learning models, the results indicated that the proposed model could successfully solve the complex problem of the trend estimation of key system parameters under the influence of operator action factors in multiple abnormal operating conditions. It is believed that the proposed model can help operators reduce the risk of human-caused errors and diagnose potential accidents.

In electromagnetic (EM) optimization of microwave design, a computationally bad starting point usually leads the local optimization process to be stuck into local optimum, which does not satisfy the design specifications. In this situation, global optimization methods can be an alternative to achieve the final optimal solution. However, global optimization methods always suffer from a relatively low convergence rate. To address this problem, we propose an efficient EM optimization technique with a novel parallel local sampling strategy and Bayesian optimization (BO) for microwave applications in this article. We develop a new parallel local sampling strategy to increase the exploitation ability near the potential optimal solution in each optimization iteration and improve the convergence of the entire optimization process. The local sampling range in each iteration is determined based on the derivative information of the current potential optimal solution. While conventional BO only uses the information of potential optimal solutions in each iteration during optimization, we propose to exploit both the generated local samples and the potential optimal solutions together to build a surrogate model and guide the optimization. Therefore, the exploration and exploitation during the proposed EM optimization are well balanced, and the entire EM optimization process is effectively accelerated in comparison to other existing global methods. Examples of EM optimizations of microwave components are used to demonstrate the proposed technique.

As nutrients, secondary metabolites, essential signal molecules and energy sources, fatty acids play a vital role in biomedicine, pharmacokinetics and human metabolism. The reduction of fatty acids is one of the main ways to control the synthesis of fatty aldehydes and fatty alcohols. The resulting fatty aldehydes and fatty alcohols play a key role in many fields. Therefore, the fatty acid reduction reaction is an important type of chemical reaction, and one of the thermodynamic properties of adiabatic electron affinity (AEA), which describes the ability of fatty acids to obtain electrons, has become particularly important. In our research, we first calculated the AEA of 26 carboxylic acid compounds by using theoretical methods including 5 composite ab initio methods (G3, G4, G3B3, CBS-QB3 and CBS-Q) and 34 density functional theory (DFT) methods. It is found that ωB97X provides the most accurate result with a minimum root mean square error (RMSE) of 1.35 kcal mol−1. Therefore, the ωB97X method was used to study saturated and unsaturated fatty acids with different kinds of substituents. The results show that electron withdrawing groups (EWGs), electron donating groups (EDGs) and conjugated groups (CEG) have great and different effects on the AEA. In addition, natural bond orbital (NBO) analysis and geometric structural analysis and frontier orbital energy analysis was investigated to further reveal the change pattern of AEA values of fatty acids.

The paper presents the simple and efficient analysis method of frequency-domain to control the core power and coolant temperature of Nuclear Power Plants (NPPs). In order to properly describe the neutron kinetic and thermal transmission of NPPs, a one-dimensional dynamic model has been accomplished by using the linearizing in time domain and discretizing in spatial domain, and then the transfer function matrix can be obtained through Laplace transform. Based on the transfer function matrix of NPPs, the effective of different system inputs: reactivity and feed water mass flow, can be analyzed by Nyquist method. On this basis, the power and temperature control system are presented, and the stability is proved in frequency domain. The proposed method is verified by RELAP5 that confirm its performance.

Native chemical ligation (NCL) and its modified versions (ligation-desulfurization chemistry) have revolutionized the way that large peptides and proteins are obtained by chemical synthesis. Ligation-desulfurization chemistry can generate thiol-derived peptide models, which can generate proteins through reductive desulfurization. The C-S bond cleavages are involved in the process. It is important for us to comprehend the strength of the C-S bond estimated by the homolytic bond dissociation enthalpies (BDEs). Therefore, 25 DFT methods were chosen to calculate the C-S BDEs of the 55 organic sulfides. It is reliable to predict C-S BDEs by M05-2X method with the corresponding root mean square error value of 6.2 kJ/mol. Then, the C-S BDEs of the thiol-derived peptide model and the structure-activity relationships were systematically researched by using this method. Besides, so as to comprehend the nature of the C-S BDE change mode, the analysis comprised of the natural bond orbit and the energies of frontier orbitals were studied.

The importance of selenium compounds in chemistry and biochemistry has become apparent. The pKa values are important for understanding the reactivity and applicability of selenium compounds. To this end, we calculated the the pKa values of 16 selenols in aqueous solution by using two kinds of DFT methods and PCM-Bondi, SMD and SMDsSAS models. It is observed that the highest precision with a lowest RMSE value of 0.38 pK units was provided by using the ωB97XD method with the SMD model. Therefore, this theoretical protocol was employed to calculate the pKa values of selenol compounds and selenoxo forms of selenocarboxylic acid compounds including R = alkyl, cycloalkyl, benzyl, allyl and aryl as well as the substituent effects. The results suggest that selenols can give higher pKa values than selenocarboxylic acids. Generally, for both substituted selenols and selenocarboxylic acids, EWGs can decrease the pKa values and EDGs can increase the pKa values, and the pKa of the EWG of –COOH is larger and the pKa values of the EDGs of –NH2 and –N(CH3)2 are smaller. In addition, linear relationships between the pKa values with the natural charges of Se atoms, EHOMO, etc. were obtained in different types of selenols and selenocarboxylic acids.

The purpose of this letter is to report a new optoelectronic device which consists of a wirelessly controllable driver circuitry and a slender implantable optical probe. Instead of the conventional silicon substrate, the probe is based on GaN material growing on a sapphire substrate. Thin-film blue micro light-emitting diodes ( $\mu $ LEDs) with a total optical power density of 176.9 mW/mm2 at 1 mA are directly fabricated at the probe by semiconductor device fabrication technologies. In a further research, we integrate a yellow $\mu $ LED on the probe to enable optical stimulation of both excitatory and inhibitory neurons. Besides, we adopt Bluetooth 4.0 wireless communication protocol; therefore, light emitting from the $\mu $ LEDs can be remotely controlled by a terminal with a distance as far as 20 m. The driver is also equipped with an accelerometer, a power management module, and a system-on-chip (SoC), and they are energized by a rechargeable lithium battery that has up to 10-h battery life. The whole device is a little bit bigger than a coin and weighs approximately 6 g. The probe has a length of 1.65 cm and a thickness of $150~\mu \text{m}$ . These features make the device suitable for a broad variety of optogenetic experiments.

Nuclear reactors may suffer from various disturbances during operation. These disturbances can cause core power deviates from the set parameters, and affect the power level of reactors. Due to the limited internal space of the reactor, the number of control rods is small. It is difficult to set up control rod groups dedicated to reactive compensation for modular reactor of medium or small size. Therefore, it is necessary to study a set of reactivity compensation measures that do not rely on control rods according to the actual needs of modular reactors to compensate for the power deviation caused by reactivity disturbances. A disturbance suppression method based on coolant flow control is proposed in the study. This method takes advantage of the Doppler effect of the coolant temperature, and changes the coolant flow rate to affect its temperature when reactive disturbances occur, thereby compensating for fluctuations of reactivity. Numerical experiments show that this method can effectively suppress the power deviation caused by reactive disturbances, and has engineering application value.

Arbuscular mycorrhizal (AM) fungi form a mutualistic symbiosis with a majority of terrestrial vascular plants. To achieve an efficient nutrient trade with their hosts, AM fungi sense external and internal nutrients, and integrate different hierarchic regulations to optimize nutrient acquisition and homeostasis during mycorrhization. However, the underlying molecular networks in AM fungi orchestrating the nutrient sensing and signaling remain elusive. Based on homology search, we here found that at least 72 gene components involved in four nutrient sensing and signaling pathways, including cAMP-dependent protein kinase A (cAMP-PKA), sucrose non-fermenting 1 (SNF1) protein kinase, target of rapamycin kinase (TOR) and phosphate (PHO) signaling cascades, are well conserved in AM fungi. Based on the knowledge known in model yeast and filamentous fungi, we outlined the possible gene networks functioning in AM fungi. These pathways may regulate the expression of downstream genes involved in nutrient transport, lipid metabolism, trehalase activity, stress resistance and autophagy. The RNA-seq analysis and qRT-PCR results of some core genes further indicate that these pathways may play important roles in spore germination, appressorium formation, arbuscule longevity and sporulation of AM fungi. We hope to inspire further studies on the roles of these candidate genes involved in these nutrient sensing and signaling pathways in AM fungi and AM symbiosis.

Optical camera communication (OCC) traditionally uses the bright and dark stripes of a rolling shutter pattern captured by the complementary metal–oxide–semiconductor image sensor to realize communication. We assign a unique identification [LED’s unique identification (LED-ID)] to each LED and introduce an LED-ID detection and recognition method of OCC using discrete Fourier transform (DFT). It is different from the traditional LED modulation and demodulation method. In this approach, LED-IDs are modulated to possess different frequency characteristics instead of transmitting data. Taking the DFT of the rolling shutter pattern to obtain a spectrum image, we can recognize the LED-ID according to the characteristics of the spectrum image. The experiment results show that the proposed method has good performance, with at least 426 recognizable LED-IDs and a recognition distance reaching 3 m. Therefore, the proposed method can be considered as one of the effective methods for OCC.

Deep neural network techniques are recently recognized as powerful tools in solving complex and challenging modeling problems of microwave components. However, direct training of a fully connected deep neural network with sigmoid functions using the backpropagation (BP) algorithm is difficult because of the vanishing gradient problem. In this paper, we propose a novel deep neural network modeling technique with batch normalization (BN) to address the vanishing gradient problem. BN layers are added before every sigmoid hidden layer of the deep neural network to normalize the inputs of each sigmoid hidden layer with additional scaling and shifting, thus overcoming the vanishing gradient problem. Automated model generation (AMG) algorithm is also utilized to automatically determine the suitable number of BN layers and sigmoid hidden layers during deep neural network training process. This proposed technique is illustrated by two microwave examples.

A kind of SiGe heterojunction phototransistor with gradual coupled ridge waveguide based on silicon-on-insulator (SOI SiGe GRC HPT) is designed and optimized to improve its optical responsivity and working speed. When the width and length of the ridge waveguide are optimized as 3um and 20um separately, the ridge waveguide based on SOI provide the same limitation for TE mode and TM mode and the suitable propagation path for incident light. The maximum characteristic frequency of SOI SiGe GRC HPT is 102GHz, its saturation current is 20mA and its optical responsivity is 0.5 A/W.

In the dual catalytic Suzuki–Miyaura reactions, the cross-coupling of Csp3-hybridized center organotrifluoroborate salts has been implemented successfully by single-electron transfer (SET), where the redox potentials are important for understanding the reactivities of organotrifluoroborate salts. To this end, the ΔGgas values of 27 organotrifluoroborate anions were calculated by using different composite high-level ab initio and DFT methods. Among different DFT methods, the M05-2X method gave the highest precision with a smallest RMSE of 1.58 kcal mol−1. Then, the 24 E° values of organotrifluoroborate anions were calculated by using the M05-2X method with two types of solvation models including PCM and SMD. It was found that the M05-2X method with a PCM–UAHF model can provide the most accurate results with the lowest RMSE value of 0.16 V. Therefore, the E° values of Csp3-hybridized center organotrifluoroborate anions (R–CH2–BF3−) including R = alkenyl, alkynyl, aryl and heterocyclyl and the Csp2-hybridized center vinyltrifluoroborate anions were systematically investigated with this theoretical protocol. The results indicated that for the Csp3-hybridized center organotrifluoroborate anions, the E° values range from 0.36 V to 1.57 V, and the electron-donating groups (EDG) of –N(CH3)2, –NHCH3etc. can significantly lower the E° values of organotrifluoroborate anions, which are favorable for reactions, whereas the electron-withdrawing groups (EWG) of –NO2, –CN etc. make the organotrifluoroborate anions unfavorable for reactions. For the Csp2-hybridized center organotrifluoroborate anions, the E° values range from 1.38 V to 2.43 V. In addition, the analysis of the natural bond orbital (NBO), the ground-state effect (GE), the radical-state effect (RE) and the energies of frontier orbitals was performed in order to further disclose the essence of the E° value change patterns. The linear relationships between the E° values with substituent constants σ+p and the natural charges of the corresponding C atoms of radicals, etc. were found in different types of organotrifluoroborate anions.

In nuclear power plants (NPPs), sensors provide real-time monitoring for nuclear power plant operators and assist operators in decision-making. In transient or accident conditions, sensors may be unable to transfer data due to various failures. This could lead to incorrect operation by the operator and cause serious consequences. The authors proposed a signal reconstruction method based on a one-dimensional convolutional neural network (1D CNN). The sensor data set was collected from NPP, which represented a power-down process. In this study, the authors built, trained and tested the CNN’s reconstruction performance by reconstructing different signals in NPP. An experiment on steam generator water level was conducted to validate the robustness of the proposed model when multiple signals were missing. The authors used the error and standard deviation to evaluate the experimental results. The results showed that the proposed model has a good performance on NPP’s sensors.

This paper presents a Simulink model of a dynamic zoom analog to digital converter (ADC) for use in the field of high resolution sensor readout circuit. The zoom ADC employs a 5-bit asynchronous SAR ADC in the front-end, which dynamically updates the 5-bit DAC references for the following 1-bit second order Sigma-Delta modulator (SDM). Data-weighted averaging (DWA) logic is adopted to alleviate the capacitance mismatch in the 5-bit DAC. The performance of the model is verified by using MATLAB Simulink. The result shows that when the input signal frequency is 150 Hz and the oversampling rate is 1000, the signal-to-noise ratio (SNR) is 127.8 dB, the effective resolution (ENOB) of the system can reach 20.94 bits.

Based on gyrator-C principle, a novel active inductor with high self-resonance frequency, high Q factor and independent adjustment of inductance is proposed in this paper. A varactor is added in parallel with the positive transconductor to improve the tunable range of inductance. A tunable resistor between positive transconductor and negative transconductor is used to generate negative resistance, thus enhance the Q factor. Furthermore, a feedback capacitor is also employed not only to generate negative conductance for enhancing the Q factor, but also to provide negative capacitance for achieving high self- resonance frequency. The results show that the self-resonance frequency is up to 12GHz, the peak Q factor is more than 1300. The peak inductance can be adjusted significantly between 25.1nH-84.8nH with the tuning range of 108.6%, whereas the variation of the peak Q factor is only 1.6%.