Optimization and Deep Learning Modeling of the Yield and Properties of Baobab-Derived Biodiesel Catalyzed by Waste Banana Bunch Stalk Biochar.

The integration of optimization techniques and deep learning models, which offer a promising avenue for improving the efficiency and sustainability of biodiesel production processes from baobab seed oil (BSO), is rare. This study utilized a multi-input-multioutput (MIMO) deep learning technique and the most recent central composite design (CCD) optimization tool to model and optimize the yield and properties of biodiesel produced from BSO. First, the baobab seed oil was extracted using a solvent extraction method. BSO was subsequently analyzed and converted to biodiesel by reacting CH3OH catalyzed by waste banana bunch stalk biochar activated by KOH. Multiobjective optimization and prediction of the biodiesel yield (Y) and several key fuel properties, including the cetane number (CN), kinematic viscosity (VS), and purity (P), were achieved. With better correlation coefficients of 0.9709, 0.9464, and 0.9714 for response training, response testing, and response validation, respectively, and a root-mean-square error of 0.00755, the MIMO model on the logsig transfer function accurately predicted the biodiesel yield and properties more than did the MISO and response surface methodology models. The optimum Y (96 wt %), CN (48), VS (3.3 mm2/s), and P (98.3%) were concurrently accomplished at a reaction temperature of 56 °C, a reaction time of 115 min, a CH3OH/BSO molar ratio of 15:1, a catalyst dosage of 6 wt %, and a stirring speed of 400 rpm with 98% optimal validation accuracy. CCD sensitivity analysis revealed that the CH3OH/BSO ratio was the most sensitive (50.9%) input predictor among the other input variables studied.

Improving distance imaging accuracy through temporal position correction with phase difference compensation.

This study introduces a time-domain-based phase compensation method to address decoherence effects in optical heterodyne detection, which is critical for remote sensing and distance imaging. The numerical simulations demonstrate a substantial reduction in localization bias (6.56-2.85) and an increased probability of bias values below 2 (21.6%-70.5%). The experiments show significant improvement in whiteboard distance imaging accuracy at 10 m from the detector, with 91.7% of the data falling within 10-12 m, compared to a mere 2.3% accuracy before compensation. The method effectively enhances intensity image quality, mitigates decoherence phenomena, and improves detection accuracy and reliability without additional hardware.

Optical heterodyne detection system for a high-speed camera based on the HSA phase compensation method.

This study proposes an innovative active optical heterodyne detection system to address the limitations of existing swarm intelligence algorithms in resolving phase compensation issues within coherent detection systems. The design incorporates a high-speed camera array detector, offering improved practicality and a simplified structure. Employing a heuristic search algorithm (HSA) transforms the high-dimensional problem into multiple one-dimensional optimization problems, significantly enhancing algorithmic running speed. The HSA maintains excellent performance even with increased array elements, allowing for real-time phase correction in large arrays. Experimental results using shot peening comparison samples reveal a substantial amplification of the heterodyne signal spectrum amplitude peak, approximately 60 times greater than the original signal. This innovative approach holds great potential for active optical heterodyne detection of dim targets, paving the way for further research in the field.

Investigation polarimetric scattering of light from the randomly rough surface based on the calculation of the Mueller matrix.

As the transmission matrix of scattering and incident light, the Mueller matrix reflects the polarimetric scattering characteristics of the rough surface, providing a significant reference for the study of light scattering. Currently, few calculations of the Mueller matrix for a two-dimensional randomly rough surface have been carried out by numerical methods. In this paper, we use six polarization states of incident light and calculate their scattering polarization states numerically by finite-difference time-domain method and obtain the rough surface Mueller matrix by combination. To verify the accuracy of the calculated Mueller matrix, the polarization state of the scattering light obtained by simulation is compared with the predicted result, and the maximum relative error is 0.0635, yielding a good result. In addition, we use this method to obtain the Mueller matrix at different incidence angles and investigate the polarization scattering characteristics. The results show that the derived parameters of the Mueller matrix of different media at different incidence angles have distinct trends. This polarization scattering property obtained from the Mueller matrix can be effectively applied to target recognition, material detection, and other fields.

Patient-derived models: Promising tools for accelerating the clinical translation of breast cancer research findings.

Breast cancer has become the highest incidence of cancer in women. It was extensively and deeply studied by biologists and medical workers worldwide. However, the meaningful results in lab researches cannot be realized in clinical, and a part of new drugs in clinical experiments do not obtain as good results as the preclinical researches. It is urgently that promote a kind of breast cancer research models that can get study results closer to the physiological condition of the human body. Patient-derived models (PDMs) originating from clinical tumor, contain primary elements of tumor and maintain key clinical features of tumor. So they are promising research models to facilitate laboratory researches translate to clinical application, and predict the treatment outcome of patients. In this review, we summarize the establishment of PDMs of breast cancer, reviewed the application of PDMs in clinical translational researches and personalized precision medicine with breast cancer as an example, to improve the understanding of PDMs among researchers and clinician, facilitate them to use PDMs on a large scale of breast cancer researches and promote the clinical translation of laboratory research and new drug development.

Innovative OPA-based optical chip for enhanced digital holography.

Digital holographic imaging has emerged as a transformative technology with significant implications for AR/VR devices. However, existing techniques often suffer from limitations such as restricted field of view (FOV), high power consumption, and contrast distortion. This paper introduces an innovative optical phased array (OPA)-based chip, integrating polarization, amplitude, and phase multiplexing for enhanced complex amplitude holographic imaging. A checkerboard-style staggered array is employed in the control strategy, substantially reducing power consumption and enabling the potential for large-scale array integration. To further enhance imaging quality, we introduce what we believe are two novel calibration strategies: one is to achieve super-resolution through block imaging methods, and the other is to image using sparse aperture methods. These advancements not only provide a robust foundation for high-quality holographic imaging, but also present a new paradigm for overcoming the inherent limitations of current active holographic imaging devices. Due to challenges in chip fabrication, the research is primarily simulation-based. Nevertheless, this work presents meaningful advancements in digital holographic imaging for AR/VR applications and provides a foundation for future experimental validations.

Photonic integrated interferometric imaging based on main and auxiliary nested microlens arrays.

The traditional microlens array imager lacks content information and has low imaging quality when restoring the image of the target, which limits the development of photon integrated detection and imaging technology. Therefore, this paper proposes a new structure of the microlens array optical frequency detection imager, wherein the main microlens array is embedded into the auxiliary microlens array to achieve high-quality restoration of the target. The simulation results show that the proposed structure increased the peak signal-to-noise ratio (PSNR) of the restored image by 21.05% and reduced the mean square error (MSE) by 29.39%. The microlens array with the main and auxiliary nested structure can achieve high-quality imaging of the target and has great application potential in the field of target detection.

GDF11 knockdown downregulates SMURF1 to inhibit breast cancer progression by activation of p53 and inactivation of ERα signaling.

Breast cancer (BC) is a prevalent neoplasm that occurs in women all over the world. Growth and differentiation factor 11 (GDF11) plays an essential role in cancer progression. This study focused on investigating the biological role and underlying mechanisms of GDF11 in BC. We detected the expression of GDF11 in 27 patients with BC and BC cell lines. Kaplan-Meier plotter was employed to analyze the relationship between GDF11 expression and overall survival (OS) of BC patients. The proliferative, migratory, invasive, and apoptotic abilities of T47D cells were examined. Correlation analysis of GDF11 with Smad ubiquitination regulatory factor 1 (SMURF1) was conducted. The association between GDF11 and the p53 pathway was analyzed by western blot and PFT-α (a p53 inhibitor)-mediated rescue assays. A brief analysis of the role of estrogen receptor alpha (ERα) signaling in BC progression was performed. The results showed that GDF11 was increased in BC tissues and cell lines, and the high expression of GDF11 was associated with the poor OS of BC patients. GDF11 knockdown inhibited the proliferation, migration, and invasion of T47D cells, but promoted cell apoptosis. Meanwhile, the GDF11 knockdown reduced the SMURF1 expression and invoked the p53 pathway activation. SMURF1 overexpression and PFT-α partially blocked the effects of GDF11 knockdown. In addition, GDF11 knockdown and SMURF1 silencing inhibited the activation of the ERα signaling pathway. In summary, GDF11 was involved in the progression of BC by regulating SMURF1-mediated p53 and ERα pathways, opening up a new way for BC treatment.

Spatial decoherence compensation algorithm for a target speckle field in heterodyne detection based on frequency analysis and time translation.

The decoherence effect of a laser caused by a speckle field seriously restricts the development of heterodyne lidar. To address this problem, we proposed a spatial decoherence compensation algorithm, whose feasibility was proved by experiments with a system featuring simple structure and convenient operation. The results demonstrated that the speed of the proposed algorithm was several orders higher than that of other algorithms and the system SNR was increased by a maximum of 1464 times after the algorithm processing. The proposed algorithm can process the signal in real time and effectively, having great application potential in long-distance weak target detection.

High-resolution dynamic imaging system based on a 2D optical phased array.

We propose an imaging system with scanning feedback of an optical phased array (OPA) for moving targets with unknown speed. The system combines OPA scanning velocimetry capability with OPA-based ghost imaging to enable trajectory tracking of targets moving within the field-of-view of the system while accomplishing image reconstruction. The proposed system can perform image reconstruction for millimeter-scale moving targets placed up to 20 m away from the camera. The system can be applied in areas such as autonomous driving and high-resolution imaging.

Filterless radio-over-fiber system that generates 80 and 160 GHz millimeter waves based on two MZMs.

In this paper, we propose a radio-over-fiber system with no filters and generate 80 and 160 GHz millimeter (mm) waves via two Mach-Zehnder modulators (MZMs). The two MZMs, biased at the maximum transmission point, are used to suppress odd-order sidebands. By controlling the phase difference between the RF driving signal of the two MZMs, the $\pm({4}{n - 2})$-order is canceled. By adjusting the optical attenuator and phase shifter, the 0-order sideband is canceled, so only the $\pm 4{n}$-order sidebands are left. The simulation results show that using a 10 GHz RF signal to drive the MZMs, we obtain an 80 GHz mm wave signal with a 36.59 dB optical sideband suppression ratio (OSSR), a 30.27 dB radio frequency sideband suppression ratio (RFSSR), and a 160 GHz mm wave signal with a 30.34 dB OSSR and 24.77 dB RFSSR. The results are consistent with the theoretical analysis. Because no optical filter is employed and only two MZMs are used, the system exhibits a simple structure, good performance and is low cost.

Application of DBN and GWO-SVM in analog circuit fault diagnosis.

For large-scale integrated electronic equipment, the complex operating mechanisms make fault detection very difficult. Therefore, it is important to accurately identify analog circuit faults in a timely manner. To overcome this problem, this paper proposes a novel fault diagnosis method based on the deep belief network (DBN) and restricted Boltzmann machine (RBM) optimized by the gray wolf optimization (GWO) algorithm. First, DBN is used to extract the deep features of the analog circuit output signal. Then, GWO is used to optimize the penalty factor c and kernel parameter g of support vector machine (SVM). Finally, GWO-SVM is used to diagnose the signal features extracted by the DBN. Fault diagnosis simulation was conducted for the Sallen-Key band-pass filter and a four-opamp biquad highpass filter. The experimental results show that compared with the existing algorithms, the algorithm proposed in this paper improves the accuracy of Sallen-Key bandpass filter circuit to 100% and shortens the fault diagnosis time by about 90%; for four-opamp biquad highpass filter, the accuracy rate has increased to 99.68%, and the fault diagnosis time has been shortened by approximately 75%, and reduce hundreds of iterations. Moreover, the experimental results reveal that the proposed fault diagnosis method greatly improves the accuracy of analog circuit fault diagnosis, which solves a major problem in analog circuitry and has great significance for the future development of relevant applications.

Improved Mask R-CNN for Aircraft Detection in Remote Sensing Images.

In recent years, remote sensing images has become one of the most popular directions in image processing. A small feature gap exists between satellite and natural images. Therefore, deep learning algorithms could be applied to recognize remote sensing images. We propose an improved Mask R-CNN model, called SCMask R-CNN, to enhance the detection effect in the high-resolution remote sensing images which contain the dense targets and complex background. Our model can perform object recognition and segmentation in parallel. This model uses a modified SC-conv based on the ResNet101 backbone network to obtain more discriminative feature information and adds a set of dilated convolutions with a specific size to improve the instance segmentation effect. We construct WFA-1400 based on the DOTA dataset because of the shortage of remote sensing mask datasets. We compare the improved algorithm with other state-of-the-art algorithms. The object detection AP50 and AP increased by 1-2% and 1%, respectively, objectively proving the effectiveness and the feasibility of the improved model.

Efficacy and safety of OM-85 in paediatric recurrent respiratory tract infections which could have a possible protective effect on COVID-19 pandemic: A meta-analysis.

INTRODUCTION: The OM-85 (Broncho-Vaxom) consumption has drawn considerable attention in the prevention of recurrent respiratory tract infections. However, it has been reported that the relationship between OM-85 consumption and recurrent respiratory tract infections is variable. This meta-analysis was performed to evaluate this relationship. METHODS: A systematic literature search up-to May 2020 was performed and 14 studies were detected with 1859 paediatric subjects, of them 890 consumed OM-85. They were reporting relationships between OM-85 consumption and recurrent respiratory tract infections. Odds ratio (OR) or mean differences (MD) with 95% confidence intervals (CIs) was calculated to evaluate the prognostic role of OM-85 consumption and recurrent respiratory tract infections using the dichotomous or continuous method with a random or fixed-effect model. RESULTS: OM-85 consumption was significantly related to lower frequency of respiratory tract infections (MD, -1.16; 95% CI, -1.66 to -0.65, P < .001); lower total duration of respiratory tract infections (MD, -19.51; 95% CI, -23.00 to -16.01, P < .001); lower incidence of respiratory tract infections (OR, 0.40; 95% CI, 0.21-0.77, P = .006); lower number of antibiotic courses (MD, -1.40; 95% CI, -2.63 to 0.17, P = .03); and lower antibiotic use (OR, 0.38; 95% CI, 0.29-0.52, P < .001). However, OM-85 consumption was not significantly related to adverse event rate (OR, 1.02; 95% CI, 0.52-2.03, P = .94); or to wheezing attacks frequency (MD, -0.25; 95% CI, -0.59 to 0.08, P = .14). CONCLUSIONS: The impact of OM-85 consumption on recurrent respiratory tract infections may have a great effect as a tool to improve subjects' immunity against recurrent respiratory tract infections, which could be helpful in crucial situations, eg, COVID-19 pandemic. OM-85 non-consumers had an independent risk relationship with recurrent respiratory tract infections. This relationship forces us to recommend OM-85 consumption with those with a high risk of recurrent respiratory tract infections to avoid any possible complications.

Low-complexity carrier phase estimation for M-ary quadrature amplitude modulation optical communication based on dichotomy.

In high-speed optical communication, the blind phase search (BPS) algorithm performs carrier phase estimation better but with higher computational complexity (CC), bringing a larger computational burden to the digital signal processing unit. In this paper, a new low-complexity CPE algorithm (DBPS) is proposed for M-ary quadrature amplitude modulation (M-QAM) formats. It uses the BPS algorithm to estimate the compensation phase interval, before using dichotomy to quickly and accurately determine the compensation phase value. Simulation results show the CC (multiplication / addition) of DBPS is reduced by 2.79 / 2.84 (16-QAM), 5.35 / 5.45 (64-QAM), and 2.98 / 3.01 (128-QAM) than that of BPS, and DBPS has a smaller phase tracking error variance. DBPS can relax the limitation of optical communication rate caused by high-speed data operations.

Filterless radio-over-fiber system based on polarization multiplexing to generate an 80 GHz millimeter wave.

We propose a filterless full-duplex radio-over-fiber system based on polarization multiplexing and demonstrate the generation of an 80 GHz millimeter wave using two Mach-Zehnder modulators. By adjusting the polarization direction, we could generate an 80 GHz frequency millimeter-wave signal and restore the original pure light carrier, providing a light source for the uplink. The simulation results show that the 80 GHz millimeter-wave signal was obtained with a 23.48 dB radio-frequency sideband suppression ratio. Furthermore, we showed that the proposed scheme is relatively flexible and free from the limitation of filter fixed bandwidth in addition to being simple and economical.

Research on Airplane and Ship Detection of Aerial Remote Sensing Images Based on Convolutional Neural Network.

The wide range, complex background, and small target size of aerial remote sensing images results in the low detection accuracy of remote sensing target detection algorithms. Traditional detection algorithms have low accuracy and slow speed, making it difficult to achieve the precise positioning of small targets. This paper proposes an improved algorithm based on You Only Look Once (YOLO)-v3 for target detection of remote sensing images. Due to the difficulty in obtaining the datasets, research on small targets for complex images, such as airplanes and ships, is the focus of research. To make up for the problem of insufficient data, we screen specific types of training samples from the DOTA (Dataset of Object Detection in Aerial Images) dataset and select small targets in two different complex backgrounds of airplanes and ships to jointly evaluate the optimization degree of the improved network. We compare the improved algorithm with other state-of-the-art target detection algorithms. The results show that the performance indexes of both datasets are ameliorated by 1-3%, effectively verifying the superiority of the improved algorithm.

Compensation for the Decoherence Effect in Heterodyne Detection of Rough Targets and a Target Vibration Characteristic Measurement System.

In practical applications of signal detection, the roughness of a target surface significantly affects detection efficiency. In this paper, we propose a signal processing method that improves the sensitivity of a detection system by up to 100 times. In experiments, the target vibration measurement system successfully captured an automotive vibration power spectrum using the proposed signal processing method. This technology opens a new avenue for development in the field of rough surface target detection and recognition.