Synthesis of a Fe3O4-rGO-ZnO-catalyzed photo-Fenton system with enhanced photocatalytic performance

In this work, we designed a magnetically-separable Fe₃O₄-rGO-ZnO ternary catalyst, ZnO anchored on the surface of reduced graphene oxide (rGO)-wrapped Fe₃O₄ magnetic nanoparticles, where rGO, as an effective interlayer, can enhance the synergistic effect between ZnO and Fe₃O₄. The effects of three operational parameters, namely irradiation time, hydrogen peroxide dosage, and the catalyst dosage, on the photo-Fenton degradation of methylene blue and methyl orange were investigated. The results showed that the Fe₃O₄-rGO-ZnO had great potential for the destruction of organic compounds from wastewater using the Fenton chemical oxidation method at neutral pH. Repeatability of the photocatalytic activity after 5 cycles showed only a tiny drop in the catalytic efficiency.     

Exploration of GH94 Sequence Space for Enzyme Discovery Reveals a Novel Glucosylgalactose Phosphorylase Specificity

The substantial increase in DNA sequencing efforts has led to a rapid expansion of available sequences in glycoside hydrolase families. The ever-increasing sequence space presents considerable opportunities for the search for enzymes with novel functionalities. In this work, the sequence-function space of glycoside hydrolase family 94 (GH94) was explored in detail, using a combined approach of phylogenetic analysis and sequence similarity networks. The identification and experimental screening of unknown clusters led to the discovery of an enzyme from the soil bacterium Paenibacillus polymyxa that acts as a 4-O-β-d -glucosyl-d -galactose phosphorylase (GGalP), a specificity that has not been reported to date. Detailed characterization of GGalP revealed that its kinetic parameters were consistent with those of other known phosphorylases. Furthermore, the enzyme could be used for production of the rare disaccharides 4-O-β-d -glucosyl-d -galactose and 4-O-β-d -glucosyl-l -arabinose. Our current work highlights the power of rational sequence space exploration in the search for novel enzyme specificities, as well as the potential of phosphorylases for rare disaccharide synthesis.     

Transmission line detection in aerial images: An instance segmentation approach based on multitask neural networks

Camera-based transmission line detection (TLD) is a fundamental and crucial task for automatically patrolling powerlines by aircraft. Motivated by instance segmentation, a TLD algorithm is proposed in this paper with a novel deep neural network, i.e., CableNet. The network structure is designed based on fully convolutional networks (FCNs) with two major improvements, considering the specific appearance characteristics of transmission lines. First, overlaying dilated convolutional layers and spatial convolutional layers are configured to better represent continuous long and thin cable shapes. Second, two branches of outputs are arranged to generate multidimensional feature maps for instance segmentation. Thus, cable pixels can be detected and assigned cable IDs simultaneously. Multiple experiments are conducted on aerial images, and the results show that the proposed algorithm obtains reliable detection performance and is superior to traditional TLD methods. Meanwhile, segmented pixels can be accurately identified as cable instances, contributing to line fitting for further applications.     

Heterogeneously Catalyzed Methanolysis of Gmelina Seed Oil to Biodiesel

The heterogeneous catalysis of transesterification of gmelina seed oil to biodiesel is evaluated. The oil was extracted from the seeds with n‐hexane by solvent extraction and characterized to determine its physiochemical properties. Response surface methodology was applied to optimize the effect of process variables on the biodiesel yield. The base‐activated clay catalyst performed as montmorillonite clay with the characteristic property of a Brønsted acid. It has an improved surface area after activation that enhanced its catalytic activity on transesterification reaction. Under optimal conditions, the biodiesel yield was 70.1 %, thus demonstrating that the model predicted well the biodiesel production.     

Constructing magnetically recoverable CdS/CaFe2O4 P–N heterojunctions for enhanced photoelectrochemical properties towards H2 evolution reaction

A novel CdS/CaFe₂O₄ (CS/CFO) heterogeneous p-n junction was created by thermal deposition of CaFe₂O₄ nanoparticles on CdS rods. The CS/CFO hetero-structured photocatalysts exhibited increasingly efficient visible light harvesting compared to the bare CdS. The CS/CFO composites also presented higher photocurrent and slower decay of photoluminescence, suggesting a better separation of the photo-generated electrons and holes. The photocatalytic H₂ evolution quantity on the optimized CS/CFO composite from water in the presence of ethanol was up to 2200 μmol after 3-h visible light illumination, which is more than twice that of the pristine CdS. The chemical interaction between CdS and CaFe₂O₄ was confirmed by the shifts in the XPS peaks, which made it possible for the charge carriers to transfer across the p-n junction interface. This research highlights the importance of forming an interfacial p-n heterojunction between two semiconductors for efficient charge separation and improved photocatalytic performance.     

Nitrogen-phosphorus co-functionalized reduced graphene oxide supported NiCoPd-CeOx nanoparticles as a highly efficient and stable catalyst for formic acid dehydrogenation

Formic acid (HCOOH, FA), a common liquid hydrogen storage material, has attracted tremendous research interest. However, the development of efficient, low-cost and high-stable heterogeneous catalyst for selective dehydrogenation of FA remains a major challenge. In this paper, a simple co-reduction method is proposed to synthesize nitrogen-phosphorus co-functionalized rGO (NPG) supported ultrafine NiCoPd-CeO_x nanoparticles (NPs) with a mean size of 1.2 nm. Remarkably, the as-prepared Ni_0.2Co_0.2Pd_0.6-CeO_x/NPG shows outstanding catalytic activity for FA dehydrogenation, affording a high TOF value of 6506.8 mol H₂ mol Pd^?1 h^?1 at 303 K and a low activation energy of 17.7 kJ mol^?1, which is better than most of the reported heterogeneous catalysts, and can be ascribed to the combined effect of well-dispersed ultrafine NiCoPd-CeO_x NPs, modified Pd electronic structure, and abundant active sites. The reaction mechanism of dehydrogenation of FA is also discussed. Furthermore, the optimized Ni_0.2Co_0.2Pd_0.6-CeO_x/NPG shows excellent stability over 10^th run with 100% conversion and 100% H₂ selectivity, which may provide more possibilities for practical application of FA system on fuel cells.     

Neural network-based flight control systems: Present and future

As the first review in this field, this paper presents an in-depth mathematical view of Intelligent Flight Control Systems (IFCSs), particularly those based on artificial neural networks. The rapid evolution of IFCSs in the last two decades in both the methodological and technical aspects necessitates a comprehensive view of them to better demonstrate the current stage and the crucial remaining steps towards developing a truly intelligent flight management unit. To this end, in this paper, we will provide a detailed mathematical view of Neural Network (NN)-based flight control systems and the challenging problems that still remain. The paper will cover both the model-based and model-free IFCSs. The model-based methods consist of the basic feedback error learning scheme, the pseudocontrol strategy, and the neural backstepping method. Besides, different approaches to analyze the closed-loop stability in IFCSs, their requirements, and their limitations will be discussed in detail. Various supplementary features, which can be integrated with a basic IFCS such as the fault-tolerance capability, the consideration of system constraints, and the combination of NNs with other robust and adaptive elements like disturbance observers, would be covered, as well. On the other hand, concerning model-free flight controllers, both the indirect and direct adaptive control systems including indirect adaptive control using NN-based system identification, the approximate dynamic programming using NN, and the reinforcement learning-based adaptive optimal control will be carefully addressed. Finally, by demonstrating a well-organized view of the current stage in the development of IFCSs, the challenging issues, which are critical to be addressed in the future, are thoroughly identified. As a result, this paper can be considered as a comprehensive road map for all researchers interested in the design and development of intelligent control systems, particularly in the field of aerospace applications.     

Retinal microaneurysms detection using adversarial pre-training with unlabeled multimodal images

The detection of retinal microaneurysms is crucial for the early detection of important diseases such as diabetic retinopathy. However, the detection of these lesions in retinography, the most widely available retinal imaging modality, remains a very challenging task. This is mainly due to the tiny size and low contrast of the microaneurysms in the images. Consequently, the automated detection of microaneurysms usually relies on extensive ad-hoc processing. In this regard, although microaneurysms can be more easily detected using fluorescein angiography, this alternative imaging modality is invasive and not adequate for regular preventive screening.In this work, we propose a novel deep learning methodology that takes advantage of unlabeled multimodal image pairs for improving the detection of microaneurysms in retinography. In particular, we propose a novel adversarial multimodal pre-training consisting in the prediction of fluorescein angiography from retinography using generative adversarial networks. This pre-training allows learning about the retina and the microaneurysms without any manually annotated data. Additionally, we also propose to approach the microaneurysms detection as a heatmap regression, which allows an efficient detection and precise localization of multiple microaneurysms. To validate and analyze the proposed methodology, we perform an exhaustive experimentation on different public datasets. Additionally, we provide relevant comparisons against different state-of-the-art approaches. The results show a satisfactory performance of the proposal, achieving an Average Precision of 64.90%, 31.36%, and 33.55% in the E-Ophtha, ROC, and DDR public datasets. Overall, the proposed approach outperforms existing deep learning alternatives while providing a more straightforward detection method that can be effectively applied to raw unprocessed retinal images.     

Fe3O4-carbon spheres core–shell supported palladium nanoparticles: A robust and recyclable catalyst for suzuki coupling reaction

Suzuki-Miyaura (S-M) is regarded the most powerful way for synthesis biaryls, triaryls, or incorporating of substituted aryl moieties in organic preparation by the cross-coupling of aryl boronic acid with aryl halides using the Pd catalyst. This work reports the combining of the hydrothermal and microwave-assisted protocol to convert the glucose to magnetic carbon spheres (Fe₃O₄-CSPs) decorated with Pd nanoparticles (NPs) as the catalyst for Suzuki-Miyaura cross-coupling reactions. The physicochemical properties in the produced composite were examined using FESEM, HRTEM, nitrogen isotherms, Raman spectroscopy, FTIR, XPS, and XRD. The as-fabricated composite Pd/Fe₃O₄-CSPs is mostly spherical with a core–shell structure and possesses a great surface area of 253.2 m²·g^-1. Its catalytic performance demonstrates that the composite has excellent stability and high tolerance Suzuki-Miyaura cross-coupling reactions in 30 min at 80 ℃. Both activated and deactivated aryl halides provided excellent yield. The as-fabricated catalyst was recycled for up to four catalytic cycles without a substantial decline in performance. Moreover, this research offers a facile roadmap for synthesizing Pd/Fe₃O₄-CSPs composites and promoting the practical implementation of Pd/Fe₃O₄-CSPs catalysts for organic transformation processes.     

Tabular data: Deep learning is not all you need

A key element in solving real-life data science problems is selecting the types of models to use. Tree ensemble models (such as XGBoost) are usually recommended for classification and regression problems with tabular data. However, several deep learning models for tabular data have recently been proposed, claiming to outperform XGBoost for some use cases. This paper explores whether these deep models should be a recommended option for tabular data by rigorously comparing the new deep models to XGBoost on various datasets. In addition to systematically comparing their performance, we consider the tuning and computation they require. Our study shows that XGBoost outperforms these deep models across the datasets, including the datasets used in the papers that proposed the deep models. We also demonstrate that XGBoost requires much less tuning. On the positive side, we show that an ensemble of deep models and XGBoost performs better on these datasets than XGBoost alone.