High-order QAM NANF transmission utilizing MIMO equalizer integrated with low-complexity decision-directed carrier phase estimation.

We experimentally realized a high-speed nested anti-resonant nodeless fiber (NANF) transmission with the assistance of the polarization division multiplexing (PDM) and probabilistic shaping (PS) technology. In this system, a low-complexity multiple-input multiple-output (MIMO) real-valued equalizer (RVE) is integrated with decision-directed carrier phase estimation (DDCPE), which is robust against the IQ cross talk and a tiny phase disturbance between PS symbols. By using the proposed MIMO-RVEDDCPE, the 60-Gbaud PDM-PS-256QAM signal has been delivered through 2-km NANF satisfying the soft-decision forward error correction (SD-FEC) threshold.

Development of a Strategy for the Total Synthesis of Aspidosperma Alkaloids via the Cyclobutenone-Based PET-Initiated Cationic Radical-Driven [2+2]/Retro-Mannich Reaction.

A novel strategy for the synthesis of Aspidosperma alkaloids has been achieved via a photoredox-initiated [2+2]/retro-Mannich reaction of tryptamine-substituted enaminones as a key step. The developed chemistry has been applied to the construction of the core tetracycle of Aspidosperma alkaloids (±)-aspidospermidine and (±)-limaspermidine.

SNR-enhanced signal delivery scheme with delta-sigma modulation in a four-mode fiber system.

This Letter proposes a scheme for optimizing the signal-to-noise ratio (SNR) of signal to improve the system performance by a 1 bit delta-sigma modulation (DSM) in a four-mode MDM system for mobile fronthaul. A 1 bit digitalized signal with an SNR of 60 dB from transmitter digital signal processing (Tx DSP) can be achieved. Based on this system, an experimental demonstration of the ultrahigh-order 1048576-QAM signal transmission over a 50 km strong-coupling few-mode fiber (FMF) is successfully realized. With DSP, the bit error rate (BER) of the received 1048576-QAM signals over four modes transmission is below the 20% soft-decision forward error correction (20% SD-FEC) threshold of 2.4 × 10-2. To the best of our knowledge, this is the first time that the combination of DSM technology and strong-coupling MDM system is achieved and that the highest-modulation order with DSM reported in MDM system is reached. This experimental demonstration of the proposed novel scheme in MDM system can provide an effective solution for ultra-large-capacity mobile fronthaul in the future.

A Highly Sensitive Model Based on Graph Neural Networks for Enzyme Key Catalytic Residue Prediction.

Determining the catalytic site of enzymes is a great help for understanding the relationship between protein sequence, structure, and function, which provides the basis and targets for designing, modifying, and enhancing enzyme activity. The unique local spatial configuration bound to the substrate at the active center of the enzyme determines the catalytic ability of enzymes and plays an important role in the catalytic site prediction. As a suitable tool, the graph neural network can better understand and identify the residue sites with unique local spatial configurations due to its remarkable ability to characterize the three-dimensional structural features of proteins. Consequently, a novel model for predicting enzyme catalytic sites has been developed, which incorporates a uniquely designed adaptive edge-gated graph attention neural network (AEGAN). This model is capable of effectively handling sequential and structural characteristics of proteins at various levels, and the extracted features enable an accurate description of the local spatial configuration of the enzyme active site by sampling the local space around candidate residues and special design of amino acid physical and chemical properties. To evaluate its performance, the model was compared with existing catalytic site prediction models using different benchmark datasets and achieved the best results on each benchmark dataset. The model exhibited a sensitivity of 0.9659, accuracy of 0.9226, and area under the precision-recall curve (AUPRC) of 0.9241 on the independent test set constructed for evaluation. Furthermore, the F1-score of this model is nearly four times higher than that of the best-performing similar model in previous studies. This research can serve as a valuable tool to help researchers understand protein sequence-structure-function relationships while facilitating the characterization of novel enzymes of unknown function.

Probabilistic shaping-based delta sigma modulation.

This Letter proposes a probabilistic shaping delta-sigma modulation technique. By performing delta-sigma modulation on the probability shaping signal at the transmitting end, under the same transmit power and the same net bit rate, the delta-sigma modulation signal based on probability shaping can obtain better anti-noise capability than the delta-sigma modulation signal only. Under the same information entropy conditions, the bit error rate performance of the PS-based 131072QAM signal modulated by delta-sigma modulation is better than that of the ordinary 65536QAM signal using the delta-sigma modulation scheme, and lower than that of soft decision-forward error correction (4 × 10-2).

Temperature errors in two-color pyrometry simultaneously considering reflection and combustion gas radiation.

Temperature measurements are ubiquitous in combustion systems. However, the accuracy of surface temperature measurements of critical components operating in a harsh combustion gases environment is greatly affected by reflection and combustion gas radiation. In this paper, an analytical two-color pyrometry model was used to quantitatively analyze the temperature errors caused by the combination of reflection and H2O-CO2-CO-N2 mixture radiation. As the results indicate, the most significant contributors to the measurement errors are found to be the error arising from H2O-CO2-CO-N2 mixture absorption and emission for two-color pyrometer operating at long wavebands. The errors due to reflection predominate over the measurement errors measured at short wavebands. In a combustor where reflected radiation from high-temperature surrounding and hot/cool combustion gas is present, two-color pyrometry is practically inoperative as a consequence of its unacceptably large measurement error and high measurement sensitivity. When the intervening gas is isothermal and the optical distance from surface to detector is considered optically thin, the temperature error has linear growth with both the H2O-CO2-CO-N2 mixture concentration and viewing path length increasing. This linear change provides us a method of linear extrapolation to eliminate the effect of uncertain gaseous absorption and emission. The results of this work can be used as a theoretical support for the design and application of a two-color pyrometer in a gas-fired furnace.

Effect of graphene on the mechanical and anisotropic thermal properties of Cu-Ta composites.

Strong interfacial bonding is the basic requirement for metal-graphene composites for higher thermo-mechanical properties. In the present work, a novel metal tantalum is introduced in the metal-graphene composites prepared by (ball-milling + molecular level mixing) followed by hot press sintering. SEM, transmission electron microscopy and high transmission electron microscopy are observed to check the interface area which shows the presence of tantalum carbide on the interface area which is formed during the sintering process. The formation of the carbide element significantly enhances the mechanical properties of composites. The addition of a very low amount of 0.1 vol% of rGO give the very high yield strength 200 MPa and ultimate tensile strength value 375 MPa with the good agreement of ductility, Vickers hardness 95 HV and bending strength 617 MPa which are much higher than unreinforced copper-tantalum composites and even from pure copper. The anisotropic thermal conductivity values are also significantly improving due to the better interfacial bonding and the ratio was 5 which is just 1.01 for pure copper. The formation of carbide elements and extraordinary high mechanical values with good ductility and anisotropic thermal conductivity ratio can lead to these materials used in thermal packaging systems and the electronic industry.

Stereoselective Synthesis of Cyclohepta[b]indoles by Visible-Light-Induced [2+2]-Cycloaddition/retro-Mannich-type Reactions.

A novel method for the concise synthesis of cyclohepta[b]indoles in high yields was developed. The method involves a visible-light-induced, photocatalyzed [2+2]-cycloaddition/ retro-Mannich-type reaction of enaminones. Experimental and computational studies suggested that the reaction is a photoredox process initiated by single-electron oxidation of an enaminone moiety, which undergoes subsequent cyclobutane formation and rapidly fragmentation in a radical-cation state to form cyclohepta[b]indoles.

A novel doublet extreme learning machines for Delta 3D printer fault diagnosis using attitude sensor.

Extreme learning machine (ELM) has better operation efficiency in fault diagnosis. However, the recognition accuracy of ELM algorithm is actually affected by the activation function. Moreover, most of the testing dataset are coming from high precision and expensive sensors. In this paper, raw data are collected by a low-cost attitude sensor, which is installed on the mobile platform of a delta 3D printer. A doublet activation function is proposed to improve the performance of ELM, named doublet ELM (DELM). The proposed method is evaluated using experimental data collected from the 3D printer, and its advantages are demonstrated by comparing with other activation functions. The experimental results indicate that the proposed method leads to the highest accuracy in different hidden nodes and the testing classification rate achieves 93% and 96% using only 8.33% of the dataset for model training, for R75 and R90 sub-datasets, respectively. Moreover, compared with peer methods, such as random forest, echo state network, and so on, the results show that the present DELM exhibits the best performance in small-sample and improves the accuracy of the 3D printer fault diagnosis.

Generative Transfer Learning for Intelligent Fault Diagnosis of the Wind Turbine Gearbox.

Intelligent fault diagnosis algorithms based on machine learning and deep learning techniques have been widely used in industrial applications and have obtained much attention as well as achievements. In real industrial applications, working loads of machines are always changing. Hence, directly applying the traditional algorithms will cause significant degradation of performance with changing conditions. In this paper, a novel domain adaptation method, named generative transfer learning (GTL), is proposed to tackle this problem. First, raw datasets were transformed to time-frequency domain based on short-time Fourier transformation. A domain discriminator was then built to distinguish whether the data came from the source or the target domain. A target domain classification model was finally acquired by the feature extractor and the classifier. Experiments were carried out for the fault diagnosis of a wind turbine gearbox. The t-distributed stochastic neighbor embedding technique was used to visualize the output features for checking the effectiveness of the proposed algorithm in feature extraction. The results showed that the proposed GTL could improve classification rates under various working loads. Compared with other domain adaptation algorithms, the proposed method exhibited not only higher accuracy but faster convergence speed as well.

Intelligent Fault Diagnosis of Delta 3D Printers Using Attitude Sensors Based on Support Vector Machines.

Health condition is a vital factor affecting printing quality for a 3D printer. In this work, an attitude monitoring approach is proposed to diagnose the fault of the delta 3D printer using support vector machines (SVM). An attitude sensor was mounted on the moving platform of the printer to monitor its 3-axial attitude angle, angular velocity, vibratory acceleration and magnetic field intensity. The attitude data of the working printer were collected under different conditions involving 12 fault types and a normal condition. The collected data were analyzed for diagnosing the health condition. To this end, the combination of binary classification, one-against-one with least-square SVM, was adopted for fault diagnosis modelling by using all channels of attitude monitoring data in the experiment. For comparison, each one channel of the attitude monitoring data was employed for model training and testing. On the other hand, a back propagation neural network (BPNN) was also applied to diagnose fault using the same data. The best fault diagnosis accuracy (94.44%) was obtained when all channels of the attitude monitoring data were used with SVM modelling. The results indicate that the attitude monitoring with SVM is an effective method for the fault diagnosis of delta 3D printers.