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Abstract Bilateral upper urinary tract stones are more likely to lead to impairment of renal function, but few biochemical metabolic studies of bilateral upper urinary tract stones have been reported. We collected clinical data from 555 patients with upper urinary tract stones admitted to Beijing Tsinghua Changgung Hospital from June 2020 to June 2024, and divided them into unilateral and bilateral stone groups by CT scans, analysed the metabolic differences between unilateral and bilateral stone groups by statistical methods, and used multifactorial logistic regression analysis to explore the risk factors that might affect the formation of bilateral stones. A total of 281 cases of unilateral and 274 cases of bilateral stones were identified. The proportion of male patients in the bilateral group was higher than that in the unilateral group (P

Abstract This study presents an advanced approach utilizing vacuum distillation (VD) to purify Al from Al–Mg alloys. The activity coefficients of Al-Mg binary alloy components were meticulously determined by applying the molecular interaction volume model (MIVM), non-random two-liquid (NRTL) model, and Wilson equation. Comparative analysis between experimental data and calculated activity coefficients enabled the selection of an optimal model exhibiting high computational accuracy and extensive applicability. The NRTL model, chosen for its precision, was then employed to compute the activity coefficients of Al-Mg alloy across various temperatures, leading to the development of a vapor-liquid equilibrium (VLE) diagram. This VLE diagram provides a robust quantitative foundation for assessing the purification limits of Al within the temperature range of 973–1373 K in the distillation process. Theoretical analyses underscore that VD offers a rapid and efficient method for Al purification. Experimental verification at a kilogram scale, conducted at 1373 K for 120 min, achieved an Al purity level of 99.9989% with a direct recovery efficiency of 97.98%. Notably, this VD process aligns with environmental standards for sustainable metallurgical practices, as it prevents the release of gaseous emissions or effluents, thereby meeting the industry’s growing demand for eco-friendly Al recycling methods.

Abstract Natural selection has driven arthropods to evolve fantastic natural compound eyes (NCEs) with a unique anatomical structure, providing a promising blueprint for artificial compound eyes (ACEs) to achieve static and dynamic perceptions in complex environments. Specifically, each NCE utilises an array of ommatidia, the imaging units, distributed on a curved surface to enable abundant merits. This has inspired the development of many ACEs using various microlens arrays, but the reported ACEs have limited performances in static imaging and motion detection. Particularly, it is challenging to mimic the apposition modality to effectively transmit light rays collected by many microlenses on a curved surface to a flat imaging sensor chip while preserving their spatial relationships without interference. In this study, we integrate 271 lensed polymer optical fibres into a dome-like structure to faithfully mimic the structure of NCE. Our ACE has several parameters comparable to the NCEs: 271 ommatidia versus 272 for bark beetles, and 180o field of view (FOV) versus 150–180o FOV for most arthropods. In addition, our ACE outperforms the typical NCEs by ~100 times in dynamic response: 31.3 kHz versus 205 Hz for Glossina morsitans. Compared with other reported ACEs, our ACE enables real-time, 180o panoramic direct imaging and depth estimation within its nearly infinite depth of field. Moreover, our ACE can respond to an angular motion up to 5.6×106 deg/s with the ability to identify translation and rotation, making it suitable for applications to capture high-speed objects, such as surveillance, unmanned aerial/ground vehicles, and virtual reality.

Regenerative fuel cells can operate alternately as an electrolyzer and as a fuel cell, frequently involving water as a reactant or product. Modifying the electrode surface to manipulate water can prevent electrode flooding and enhance the electrode's mass transfer efficiency by facilitating better contact with gaseous reactants. However, conventional electrodes face difficulties in allowing water droplets to penetrate in a single direction leaving electrodes. In this work to address this issue, a wettability gradient electrode is designed and fabricated for efficient water manipulation in regenerative fuel cells. The findings demonstrate that the water removal strategy in the electrolyzer mode yields the highest ammonia yield and Faradaic efficiency of 3.39 × 10-10 mol s-1 cm-2 and 0.49 %, respectively. Furthermore, in the fuel cell mode, the discharging process sustains for approximately 20.5 h, which is six times longer than the conventional strategy. The ability to sustain the discharging process for extended periods is particularly advantageous in regenerative fuel cells, as it enables the cells to operate for longer periods without the need for regeneration.

Diffractive optical imaging spectroscopy as a promising miniaturized and high throughput portable spectral imaging technique suffers from the problem of low precision and slow speed, which limits its wide use in various applications. To reconstruct the diffractive spectral image more accurately and fast, a three-dimensional spectrum recovery algorithm is proposed in this paper. The algorithm takes advantage of a neural network for image reconstruction which consists of a U-Net architecture with 3D convolutional layers to improve the processing precision and speed. Numerical experiments are conducted to prove its effectiveness. It is shown that the mean peak signal-to-noise ratio (MPSNR) of the recovered image relative to the original image is improved by 1.8 dB in comparison to other traditional methods. In addition, the obtained mean structural similarity (MSSIM) of 0.91 meets the standard of discrimination to human eyes. Moreover, the algorithm runs in just 0.36 s, which is faster than other traditional methods. 3D convolutional networks play a critical role in performance improvement. Improvements in processing speed and accuracy have greatly benefited the realization and application of diffractive optical imaging spectroscopy. The new algorithm with high accuracy and fast speed has a great potential application in diffraction lens spectroscopy and paves a new way for emerging more portable spectral imaging technique.

A flexible and stable optical fiber sensor based on the Sagnac loop is proposed and experimentally demonstrated for the measurement of salinity and temperature, simultaneously. The sensing unit consists of a tapered polarization maintaining fiber (tPMF) and a fiber Bragg grating (FBG) connected in series in the Sagnac loop. The temperature response comes from the high birefringence of PMF inside Sagnac loop, and the salinity response is enabled by the high-order modes excited in the tapered area of PMF. Besides, the FBG is for temperature compensation. We have succeeded in implementing dual-parameter measurements with the sensitivities of 0.356 nm/‰ for salinity and 0.616 nm/°C for temperature, respectively. The designed sensor has the potential for long-term monitoring of real ocean states.

Diffractive multispectral optical imaging plays an essential role in optical sensing, which typically suffers from the image blurring problem caused by the spatially variant point spread function. Here, we propose a novel high-quality and efficient hybrid space calibrated 3D network “HSC3D” for spatially variant diffractive multispectral imaging that utilizes the 3D U-Net structure combined with space calibration modules of magnification and rotation effects to achieve high-accuracy eight-channel multispectral restoration. The algorithm combines the advantages of the space calibrated module and U-Net architecture with 3D convolutional layers to improve the image quality of diffractive multispectral imaging without the requirements of complex equipment modifications and large amounts of data. A diffractive multispectral imaging system is established by designing and manufacturing one diffractive lens and four refractive lenses, whose monochromatic aberration is carefully corrected to improve imaging quality. The mean peak signal-to-noise ratio and mean structural similarity index of the reconstructed multispectral images are improved by 3.33 dB and 0.08, respectively, presenting obviously improved image quality compared with a typical Unrolled Network algorithm. The new algorithm with high space calibrated ability and imaging quality has great application potential in diffraction lens spectroscopy and paves a new method for complex practical diffractive multispectral image sensing.

Abstract Multi‐modal brain image registration has been widely applied to functional localisation, neurosurgery and computational anatomy. The existing registration methods based on the dense deformation fields involve too many parameters, which is not conducive to the exploration of correct spatial correspondence between the float and reference images. Meanwhile, the unidirectional registration may involve the deformation folding, which will result in the change of topology during registration. To address these issues, this work has presented an unsupervised image registration method using the free form deformation (FFD) and the symmetry constraint‐based generative adversarial networks (FSGAN). The FSGAN utilises the principle component analysis network‐based structural representations of the reference and float images as the inputs and uses the generator to learn the FFD model parameters, thereby producing two deformation fields. Meanwhile, the FSGAN uses two discriminators to decide whether the bilateral registration have been realised simultaneously. Besides, the symmetry constraint is utilised to construct the loss function, thereby avoiding the deformation folding. Experiments on BrainWeb, high grade gliomas, IXI and LPBA40 show that compared with state‐of‐the‐art methods, the FSGAN provides superior performance in terms of visual comparisons and such quantitative indexes as dice value, target registration error and computational efficiency.

Abstract Tumour segmentation in medical images (especially 3D tumour segmentation) is highly challenging due to the possible similarity between tumours and adjacent tissues, occurrence of multiple tumours and variable tumour shapes and sizes. The popular deep learning‐based segmentation algorithms generally rely on the convolutional neural network (CNN) and Transformer. The former cannot extract the global image features effectively while the latter lacks the inductive bias and involves the complicated computation for 3D volume data. The existing hybrid CNN‐Transformer network can only provide the limited performance improvement or even poorer segmentation performance than the pure CNN. To address these issues, a short‐term and long‐term memory self‐attention network is proposed. Firstly, a distinctive self‐attention block uses the Transformer to explore the correlation among the region features at different levels extracted by the CNN. Then, the memory structure filters and combines the above information to exclude the similar regions and detect the multiple tumours. Finally, the multi‐layer reconstruction blocks will predict the tumour boundaries. Experimental results demonstrate that our method outperforms other methods in terms of subjective visual and quantitative evaluation. Compared with the most competitive method, the proposed method provides Dice (82.4% vs. 76.6%) and Hausdorff distance 95% (HD95) (10.66 vs. 11.54 mm) on the KiTS19 as well as Dice (80.2% vs. 78.4%) and HD95 (9.632 vs. 12.17 mm) on the LiTS.

At present, the operating temperature of the fourth-generation of nickel-based powder superalloys has not reach the target of 815 °C, and the traditional superalloy design and optimization methods are costly and inefficient. To overcome these challenges, phase diagram is used to calculate the thermodynamic parameters of the typical first three generations of nickel-based powder superalloys and representative fourth-generation superalloys. From strengthening processes including solid solution strengthening and γ′ phase strengthening, the effects of alloy compositions on alloy characteristics are examined. Based on this analysis, the thermodynamic standards that satisfy the performance requirements of the fourth-generation nickel-based powder superalloys are established. Then, the standards are applied to JMatPro and machine learning techniques as the foundation for determining the composition selection range, and several new nickel-based superalloys are created. The machine learning aided alloy design concept and theoretical results will provide significant guidance for further study of new nickel-based superalloys.

Abstract Excessive CO2 and food shortage are two grand challenges of human society. Directly converting CO2 into food materials can simultaneously alleviate both, like what green crops do in nature. Nevertheless, natural photosynthesis has a limited energy efficiency due to low activity and specificity of key enzyme D‐ribulose‐1,5‐bisphosphate carboxylase/oxygenase (RuBisCO). To enhance the efficiency, many prior studies focused on engineering the enzymes, but this study chooses to learn from the nature to design more efficient reactors. This work is original in mimicking the stacked structure of thylakoids in chloroplasts to immobilize RuBisCO in a microreactor using the layer‐by‐layer strategy, obtaining the continuous conversion of CO2 into glucose precursor at 1.9 nmol min−1 with enhanced activity (1.5 times), stability (≈8 times), and reusability (96% after 10 reuses) relative to the free RuBisCO. The microreactors are further scaled out from one to six in parallel and achieve the production at 15.8 nmol min−1 with an energy conversion efficiency of 3.3 times of rice, showing better performance of this artificial synthesis than NPS in terms of energy conversion efficiency. The exploration of the potential of mass production would benefit both food supply and carbon neutralization.

Ethnopharmacological relevance: Shizhifang Decoction, a traditional Chinese medicine prescription formulated by Professor Zheng Pingdong of Shuguang Hospital, has been widely utilized in clinical settings for the treatment of hyperuricemia due to its proven safety and efficacy. Objective: In this study, we used network pharmacology, molecular docking technology, and experimental validation to elucidate the therapeutic effects and underlying mechanisms of Shizhifang Decoction in managing hyperuricemia. Methods: Quality control and component identification of the freeze-dried powder of Shizhifang Decoction were conducted using ultra-high performance liquid chromatography-tandem quadrupole time-of-flight mass spectrometry. Active ingredients and their corresponding targets were obtained from Traditional Chinese Medicine Systems Pharmacology, Traditional Chinese Medicine Information Database, The Encyclopedia of Traditional Chinese Medicine, and other databases. Disease-related targets for hyperuricemia were collected from GeneCards and DisGeNET databases. The Venny platform is used to screen common targets for drug active ingredients and diseases. Subsequently, we constructed an active component-target-disease interaction network using the Search Tool for the Retrieval of Interacting Genes/Proteins (STRING) database, create a component disease common target network using Cytoscape 3.9.1 software, from which core targets were selected. Import common targets into the Database for Annotation, Visualization and Integrated Discovery (DAVID) for Gene Ontology enrichment and Kyoto Encyclopedia of Genes and Genomes pathway analysis. Molecular docking was then conducted to validate the binding capacity of key active ingredients and their associated targets in Shizhifang Decoction. The theoretical predictions were further confirmed through in vitro and in vivo experiments. Result: A total of 35 active ingredients and 597 action targets were identified, resulting in 890 disease-related targets for hyperuricemia. After comprehensive analysis, 99 common targets were determined. Protein-protein interaction network analysis revealed crucial relationships between these targets and hyperuricemia. Among them, 12 core targets (CASP3, IL1B, IL6, TNF, TP53, GAPDH, PTGS2, MYC, INS, VEGFA, ESR1, PPARG) were identified. Gene Ontology enrichment analysis demonstrated significant associations with the regulation of inflammatory response, cell apoptosis, and the positive regulation of extracellular regulated protein kinases 1 and extracellular regulated protein kinases 2 cascades. Kyoto Encyclopedia of Genes and Genomes pathway analysis highlighted inflammation and apoptosis-related pathways as critical mediators of Shizhifang Decoction's effects on hyperuricemia. Molecular docking studies further supported the interactions between apoptosis-related proteins and active ingredients in the extracellular regulated protein kinases 1/2 signaling pathway. In vitro experiments confirmed the downregulation of apoptosis-related proteins (caspase-3, Bax, Bcl-2) and the inhibition of the extracellular regulated protein kinases 1/2 signaling pathway by Shizhifang Decoction. These findings were also validated in animal models, demonstrating the potential of Shizhifang Decoction to mitigate renal injury induced by hyperuricemia through extracellular regulated protein kinases 1/2-mediated inhibition of renal tubular epithelial cell apoptosis. Conclusion: Our study provides valuable insights into the main mechanism by which Shizhifang Decoction ameliorates hyperuricemia. By targeting the ERK1/2 signaling pathway and modulating cell apoptosis, Shizhifang Decoction exhibits promising therapeutic potential for the treatment of hyperuricemia. These findings support the continued exploration and development of Shizhifang Decoction as a potential herbal remedy for hyperuricemia management.

Natural compound eyes (NCEs) are the most abundant and successful eye designs in the animal kingdom. An NCE consists of a number of ommatidia, which are distributed along a curved surface to receive light. This curved feature is critical to the functions of NCE, and it ensures that different ommatidia point to slightly different directions and thus enables panoramic vision, depth perception, and efficient motion tracking while minimizing aberration. Consequently, biomimetic curved artificial compound eyes (BCACEs) have garnered substantial research attention in replicating the anatomical configuration of their natural counterparts by distributing ommatidia across a curved surface. The reported BCACEs could be briefly categorized into 2 groups: fixed focal lengths and tunable focal lengths. The former could be further subcategorized into simplified BCACEs, BCACEs with photodetector arrays within curved surfaces, and BCACEs with light guides. The latter encompasses other tuning techniques such as fluidic pressure modulation, thermal effects, and pH adjustments. This work starts with a simple classification of NCEs and then provides a comprehensive review of main parameters, operational mechanisms, recent advancements, fabrication methodologies, and potential applications of BCACEs. Finally, discussions are provided on future research and development. Compared with other available review articles on artificial compound eyes, our work is distinctive since we focus especially on the “curved” ones, which are difficult to fabricate but closely resemble the architecture and functions of NCEs, and could potentially revolutionize the imaging systems in surveillance, machine vision, and unmanned vehicles.

Multi-modal medical image fusion (MMIF) is crucial for disease diagnosis and treatment because the images reconstructed from signals collected by different sensors can provide complementary information. In recent years, deep learning (DL) based methods have been widely used in MMIF. However, these methods often adopt a serial fusion strategy without feature decomposition, causing error accumulation and confusion of characteristics across different scales. To address these issues, we have proposed the Coupled Image Reconstruction and Fusion (CIRF) strategy. Our method parallels the image fusion and reconstruction branches which are linked by a common encoder. Firstly, CIRF uses the lightweight encoder to extract base and detail features, respectively, through the Vision Transformer (ViT) and the Convolutional Neural Network (CNN) branches, where the two branches interact to supplement information. Then, two types of features are fused separately via different blocks and finally decoded into fusion results. In the loss function, both the supervised loss from the reconstruction branch and the unsupervised loss from the fusion branch are included. As a whole, CIRF increases its expressivity by adding multi-task learning and feature decomposition. Additionally, we have also explored the impact of image masking on the network’s feature extraction ability and validated the generalization capability of the model. Through experiments on three datasets, it has been demonstrated both subjectively and objectively, that the images fused by CIRF exhibit appropriate brightness and smooth edge transition with more competitive evaluation metrics than those fused by several other traditional and DL-based methods.

Deep learning techniques with remarkable performance have been successfully applied to hyperspectral image (HSI) classification. Due to the limited availability of training data, earlier studies primarily adopted the patch-based classification framework, which divides images into overlapping patches for training and testing. However, this framework results in redundant computations and possible information leakage. This study proposes an objective evaluation-based efficient learning framework for HSI classification. It consists of two main parts: (i) a leakage-free balanced sampling strategy and (ii) an efficient fully convolutional network (EfficientFCN) optimized for the accuracy-efficiency trade-off. The leakage-free balanced sampling strategy first generates balanced and non-overlapping training and test data by partitioning the HSI and its ground truth image into non-overlapping windows. Then, the generated training and test data are used to train and test the proposed EfficientFCN. EfficientFCN exhibits a pixel-to-pixel architecture with modifications for faster inference speed and improved parameter efficiency. Experimental results demonstrate that the proposed sampling strategy can provide objective performance evaluation. EfficientFCN outperforms many state-of-the-art approaches concerning the speed-accuracy trade-off. For instance, compared to the recent efficient models EfficientNetV2 and ConvNeXt, EfficientFCN achieves 0.92% and 3.42% superior accuracy and 0.19s and 0.16s faster inference time, respectively, on the Houston dataset. Code is available at https://github.com/xmzhang2018.

BackgroundDiabetic nephropathy (DN) is a prevalent and debilitating disease that represents the leading cause of chronic kidney disease which imposes public health challenges Tongmai Jiangtang capsule (TMJT) is commonly used for the treatment of DN, albeit its underlying mechanisms of action are still elusive.MethodsThis study retrieved databases to identify the components and collect the targets of TMJT and DN. Target networks were constructed to screen the core components and targets. Samples from the GEO database were utilized to perform analyses of targets and immune cells and obtain significantly differentially expressed core genes (SDECGs). We also selected a machine learning model to screen the feature genes and construct a nomogram. Furthermore, molecular docking, another GEO dataset, and Mendelian randomization (MR) were utilized for preliminary validation. We subsequently clustered the samples based on SDECG expression and consensus clustering and performed analyses between the clusters. Finally, we scored the SDECG score and analyzed the differences between clusters.ResultsThis study identified 13 SDECGs between DN and normal groups which positively regulated immune cells. We also identified five feature genes (CD40LG, EP300, IL1B, GAPDH, and EGF) which were used to construct a nomogram. MR analysis indicated a causal link between elevated IL1B levels and an increased risk of DN. Clustering analysis divided DN samples into four groups, among which, C1 and CI were mainly highly expressed and most immune cells were up-regulated. C2 and CII were the opposite. Finally, we found significant differences in SDECG scores between C1 and C2, CI and CII, respectively.ConclusionTMJT may alleviate DN via core components (e.g. Denudatin B, hancinol, hirudinoidine A) targeting SDECGs (e.g. SRC, EGF, GAPDH), with the involvement of feature genes and modulation of immune and inflammation-related pathways. These findings have potential implications for clinical practice and future investigations.

Cellulose is a potential alternative to petroleum-derived polymers, and cellulose membranes are used in many applications such as sensors, flexible materials, and functional membranes. Most bottom-up methods are energy-, chemical-, and time-intensive compared to top-down methods for preparing cellulose membranes. Additionally, most methods to improve the hydrophobicity of cellulose membranes to increase their lifetime are complex and require large amounts of organic solvents. To balance sustainability, degradability, and service life from a whole life cycle perspective, which is the biggest challenge for the industrialization of cellulose membranes, a top-down approach was used here to prepare cellulose membranes using camphor pine, walnut, and loofah materials. The effect of low temperature heat treatment on the prepared membranes’ wettability and water stability were investigated. The results showed that the water stability of loofah cellulose membrane was influenced by both surface and internal wettability due to the non-homogeneous structure. The heat treatment temperature of 200 °C for loofah cellulose membrane, 150 °C for camphor pine cellulose membrane, and 225 °C for walnut cellulose membrane was more effective.

Recent epidemiological studies suggest that individuals with Down syndrome are more susceptible to SARS-CoV-2 infection and have higher rates of hospitalization and mortality than the general population. However, the main drivers behind these disparate health outcomes remain unknown. Herein, we performed experimental infections with SARS-CoV-2 in a well-established mouse model of Down syndrome. We observed similar SARS-CoV-2 replication kinetics and dissemination in the primary and secondary organs between mice with and without Down syndrome, suggesting that both groups have similar susceptibilities to SARS-CoV-2 infection. However, Down syndrome mice exhibited more severe disease as defined by clinical features including symptoms, weight loss, pulmonary function, and survival of mice. We found that increased disease severity in Down syndrome mice could not be attributed solely to increased infectivity or a more dramatic pro-inflammatory response to infection. Rather, results from RNA sequencing suggested that differences in the expression of genes from other physiological pathways, such as deficient oxidative phosphorylation, cardiopulmonary dysfunction, and deficient mucociliary clearance in the lungs may also contribute to heightened disease severity and mortality in Down syndrome mice following SARS-CoV-2 infection.

Carcinoembryonic antigen (CEACAM5), as a broad-spectrum tumor biomarker, plays a crucial role in analyzing the therapeutic efficacy and progression of cancer. Herein, we propose a novel biosensor based on specklegrams of tapered multimode fiber (MMF) and two-dimensional convolutional neural networks (2D-CNNs) for the detection of CEACAM5. The microfiber is modified with CEA antibodies to specifically recognize antigens. The biosensor utilizes the interference effect of tapered MMF to generate highly sensitive specklegrams in response to different CEACAM5 concentrations. A zero mean normalized cross-correlation (ZNCC) function is explored to calculate the image matching degree of the specklegrams. Profiting from the extremely high detection limit of the speckle sensor, variations in the specklegrams of antibody concentrations from 1 to 1000 ng/mL are measured in the experiment. The surface sensitivity of the biosensor is 0.0012 (ng/mL)−1 within a range of 1 to 50 ng/mL. Moreover, a 2D-CNN was introduced to solve the problem of nonlinear detection surface sensitivity variation in a large dynamic range, and in the search for image features to improve evaluation accuracy, achieving more accurate CEACAM5 monitoring, with a maximum detection error of 0.358%. The proposed fiber specklegram biosensing scheme is easy to implement and has great potential in analyzing the postoperative condition of patients.

Economical and efficient electrocatalysts are crucial to the hydrogen evolution reaction (HER) in water splitting to produce hydrogen. Heterostructured electrocatalysts generally exhibit enhanced HER catalytic activity due to the strong electron coupling effects and synergistic optimization of hydrogen adsorption–desorption. Herein, in-plane heterostructured MoN/Mo2N nanosheets are fabricated as high-efficiency HER electrocatalysts in the alkaline medium from bulk MoS2 by molten salt-assisted synthesis. Density-functional theory calculations and experiments show that the in-plane heterostructured MoN/Mo2N nanosheets facilitate interfacial electron redistribution from Mo2N to MoN, giving rise to more negative H2O adsorption energy and optimal hydrogen adsorption free energy (ΔGH* = −0.017 eV). Consequently, a low overpotential of 126 mV at 10 mA cm−2 and a small Tafel slope of 69.5 mV dec−1 are achieved in the 1M KOH electrolyte, demonstrating excellent HER characteristics. Moreover, the overpotential shows negligible change after operating at 50 mA cm−2 for 12 h, confirming the excellent stability. The results reveal a novel and effective strategy to design highly efficient 2D in-plane heterostructured HER electrocatalysts for water splitting.

Aiming at the problem that the existing rope falling device can only detect the impact force and cannot synchronously detect the impact displacement, this paper introduces a large-range high-precision displacement sensor and constructs a rope impact force-displacement detection device. Taking the nylon kernmantle rope for high-altitude fall protection commonly used in aerial work and rock climbing as the research object, the impact response behavior of the rope when drop mass is dropped once and repeatedly is systematically studied, and the impact force and impact displacement are discussed. Further, the evolution of the elastic modulus of the rope is discussed and this could provide theoretical support for the design of the impact-resistant rope structure and the rope impact protection.

The reduction ability of NO to N2 and the oxidation performance of 1,2-dichloroethane (DCE) over α-MnO2 catalysts were investigated. The results show that α-MnO2-3 exhibited the highest catalytic activity in 63.5 % conversion of NOx reduction by C3H8 at 250 °C, and 80 % conversion of DCE combustion by O2 at 338 °C. It is revealed the active phase of α-MnO2-3 is tetragonal α-MnO2 with the selectively exposed plane of (211). It was proposed the high DCE decomposition of α-MnO2-3 was ascribed to the redox properties. The overall characterization results revealed that α-MnO2-3 catalyst preserves more active sites of low valence Mn and higher surface adsorbed oxygen (Oads) /lattice oxygen (Olatt) at the outermost layers, and lower reduction temperature in H2-TPR profiles than that of other catalysts. Meanwhile, NH3-TPD profile of α-MnO2-3 also shows a large number of acid sites promote NOx reduction.

In this study, we carried out a comparative study of two different numerical strategies for the modeling of the biogeochemical processes in microbially induced calcite precipitation (MICP) process. A simplified MICP model was used, which is based on the mass transport theory. Two numerical strategies, namely the operator splitting (OS) and the global implicit (GI) strategies, were adopted to solve the coupled reactive mass transport problems. These two strategies were compared in the aspects of numerical accuracy, convergence property and computational efficiency by solving the presented MICP model. To look more into the details of the model, sensitivity analysis of some important modeling parameters was also carried out in this paper.

The Vernier effect created using an incorporated Lyot–Sagnac loop is used to create an ultra-high sensitivity temperature sensor based on a ring laser cavity. Unlike standard double Sagnac loop systems, the proposed sensor is fused into a single Sagnac loop by adjusting the welding angle between two polarization-maintaining fibers (PMFs) to achieve effective temperature sensitivity amplification. The PMFs are separated into two arms of 0.8 m and 1 m in length, with a 45° angle difference between the fast axes. The sensor’s performance is examined both theoretically and experimentally. The experimental results reveal that the Vernier amplification effect can be achieved via PMF rotating shaft welding. The temperature sensitivity in the laser cavity can reach 2.391 nm/°C, which is increased by a factor of more than eight times compared with a single Sagnac loop structure (0.298 nm/°C) with a length of 0.8 m without the Vernier effect at temperatures ranging from 20 °C to 30 °C. Furthermore, unlike traditional optical fiber sensing that uses a broadband light source (BBS) for detection, which causes issues such as low signal-to-noise ratio and broad bandwidth, the Sagnac loop can be employed as a filter by inserting itself into the fiber ring laser (FRL) cavity. When the external parameters change, the laser is offset by the interference general modulation, allowing the external temperature to be monitored. The superior performance of signal-to-noise ratios of up to 50 dB and bandwidths of less than 0.2 nm is achieved. The proposed sensor has a simple structure and high sensitivity and is expected to play a role in biological cell activity monitoring.

An optical fiber ring laser (FRL) cavity-based sensitive temperature and salinity sensor is proposed and experimentally demonstrated. The sensor consists of a Sagnac loop with a waist of 15 µm and a total length of 30 cm made of tapered polarization-maintaining fiber (PMF). Sagnac loop dual parameter sensing was theoretically modeled and presented. The salinity sensitivity of 0.173 nm/‰ was made possible by the efficient interaction between the tapered PMF cladding mode and the external refractive index. In addition, temperature sensitivity of 0.306 nm/°C was achieved through ultrahigh birefringence of PMF. Apart from that, the previous sensing system used a broadband light source (BBS) as the input light, resulting in a wide bandwidth and a poor signal-to-noise ratio (SNR). The Sagnac loop integrated into the FRL system can achieve a high SNR of approximately 50 dB and a narrow bandwidth of 0.15 nm while serving as the filter and sensor head. Additionally, the developed sensor has the advantages of simple design, low cost, and easy fabrication. It can also extend sensing distance indefinitely within a given range, which is anticipated to have positive effects on the testing of marine environments in laboratories.

We have shown in a previous work that the combination of the emulsion solvent evaporation technique and droplet-based microfluidics allows for the synthesis of well-defined monodisperse mesoporous silica microcapsules (hollow microspheres), whose size, shape and composition may be finely and easily controlled. In this study, we focus on the crucial role played by the popular Pluronic® P123 surfactant, used for controlling the mesoporosity of synthesised silica microparticles. We show in particular, that although both types of initial precursor droplets, prepared with and without P123 meso-structuring agent, namely P123+ and P123− droplets, have a similar diameter (≃30 μm) and a similar TEOS silica precursor concentration (0.34 M), the resulting microparticles exhibit two noticeably different sizes and mass densities. Namely, 10 μm and 0.55 g/cm3 for P123+ microparticles, and 5.2 μm and 1.4 g/cm3 for P123− microparticles. To explain such differences, we used optical and scanning electron microscopies, small-angle X-ray diffraction and BET measurements to analyse structural properties of both types of microparticles and show that in the absence of Pluronic molecules, P123− microdroplets divide during their condensation process, on average, into three smaller droplets before condensing into silica solid microspheres with a smaller size and a higher mass density than those obtained in the presence of P123 surfactant molecules. Based on these results and on condensation kinetics analysis, we also propose an original mechanism for the formation of silica microspheres in the presence and in the absence of the meso-structuring and pore-forming P123 molecules.

Abstract This paper successfully prepared a unique titanium dioxide nanorod arrays nanocomposite doped with carbon quantum dots and gold nanoparticles as a high‐efficiency photocatalyst. Due to their enhanced electron–hole separation efficiency and strong light‐scattering effects, one‐dimensional nanostructured titanium dioxide nanorods were utilised as the basic material for photocatalysis. Furthermore, gold nanoparticles and carbon quantum dots modified on the surface of titanium dioxide nanorods were used as visible light absorbers and photosensitisers, respectively. As a result, a photocurrent of 14 mA·cm‐2 was achieved by Au/CQDs‐TiO2 nanorods composite in the photoelectrochemical measurements, which exceed five times that of pure titanium dioxide nanorods. The synergistic effect of gold nanoparticles and carbon quantum dots was demonstrated by several characterisation techniques. The proposed Au/CQDs‐TiO2 nanorods nanocomposite film developed by simple successive ionic layer adsorption and reaction and hydrothermal methods offers a practical solution for current environmental and energy issues.

ZnCo2O4 prepared via the coprecipitation method showed an efficient bifunctional activity for both NOx reduction and 1,2-dichloroethane (DCE) oxidation. Compared with Co3O4 and ZnO, ZnCo2O4 catalyst showed best catalytic activity and stability in both NOx reduction and DCE oxidation. A maximum NOx conversion of 68.9% and complete DCE combustion were obtained at 300 and 400 °C, respectively. ZnCo2O4 catalyst exhibited the highest specific surface area (m2 g−1) and concentration of surface Co3+, which were considered as the main factors that determined its catalytic performance.

Novel Co-based superalloys, as potentially ideal aero-engine hot section materials, have a higher temperature bearing capacity and better oxidation resistance than Ni-based superalloys. Coarsening evolution of γ′ phase and failure mechanism of Co-Ni-Al-Ti-based superalloys during the isothermal aging process at 1073 K were investigated using multiple characterizations and testing methods. The results show that γ′ phase is uniformly distributed on the γ phase matrix, and coarsening with the increase in isothermal aging time, which results in a decrease in maximum tensile strength. Furthermore, Mo element is preferred to distribute in γ′ phase and provides stronger solution strengthening effect than Cr element, which determines more excellent mechanical properties of 2Mo superalloy than that of 2Cr superalloy. The coarsening rate of γ′ phase in the 2Cr superalloy is significantly higher than that in the 2Mo superalloy. Grain boundary failure is dominant in isothermal aging, and the cracks nucleate and expand along the vertical direction of loading stress on the grain boundary. The current work suggests that the coarsening of the γ′ phase, reduction in the volume fraction of γ′ phase, and formation of ′-precipitate depleted zone (PDZ) near the grain boundary during aging controls the deterioration of mechanical properties in Co-Ni-Al-Ti-based superalloys.

Medical image classification plays an important role in disease diagnosis since it can provide important reference information for doctors. The supervised convolutional neural networks (CNNs) such as DenseNet provide the versatile and effective method for medical image classification tasks, but they require large amounts of data with labels and involve complex and time-consuming training process. The unsupervised CNNs such as principal component analysis network (PCANet) need no labels for training but cannot provide desirable classification accuracy. To realize the accurate medical image classification in the case of a small training dataset, we have proposed a light-weighted hybrid neural network which consists of a modified PCANet cascaded with a simplified DenseNet. The modified PCANet has two stages, in which the network produces the effective feature maps at each stage by convoluting inputs with various learned kernels. The following simplified DenseNet with a small number of weights will take all feature maps produced by the PCANet as inputs and employ the dense shortcut connections to realize accurate medical image classification. To appreciate the performance of the proposed method, some experiments have been done on mammography and osteosarcoma histology images. Experimental results show that the proposed hybrid neural network is easy to train and it outperforms such popular CNN models as PCANet, ResNet and DenseNet in terms of classification accuracy, sensitivity and specificity.

A novel mechanism of sensitivity enhancement of D-shaped fiber-based surface plasmon resonance (SPR) sensors for the optofluidic device has been proposed. The sandwiched structure optofluidic platform is developed with metal-coated D-shaped fiber as the sensing device, while another thin metal layer is situated under the inner wall of the substrate as a second metal layer to construct the sandwiched microchannel. It has been found that the sensitivity of the D-type fiber SPR sensor is enhanced significantly with the sandwiched metal-coated structure of microchannel. In the proposed structure, the measurand analyte is considered as a sandwich channel layer between two thin metal layers. The sensitivity of the proposed structure is dependent on the volume of the measurand and the thickness of the metal layers. The computed sensitivity with a double metal layer and sandwich measurand layer concept is 4085 nm/RIU in the region of 1.33 to 1.36. The sensitivity is enhanced by more than a factor of `2.3' in comparison with the sensitivity of the normal D-shape fiber SPR sensor. It can be enhanced further up to ~12,500 nm/RIU by the deposition of higher RI polymeric overlay just above the second metal layer. The computed resolution of the proposed sensor with standard interrogation technique is ~1 × 10-7 which is quite competitive within the optical fiber sensor domain. A detailed numerical analysis has been accomplished. This structure will be useful in distinct chemical and biological sensing applications where the volume of an analyte is critical. This new concept of enhancement of sensitivity with limited measurand volume will open a new designing methodology for optical fiber biosensors.

Ultrasound imaging has been widely used for clinical diagnosis. However, the inherent speckle noise will degrade the quality of ultrasound images. Existing despeckling methods cannot deliver sufficient speckle reduction and preserve image details well at high noise corruption and they cannot realize real-time ultrasound image denoising. With the popularity of deep learning, supervised learning for image denoising has recently attracted considerable attention. In this paper, we have proposed a novel residual UNet using mixed-attention mechanism (MARU) for real-time ultrasound image despeckling. In view of the signal-dependent characteristics of speckle noise, we have designed an encoder-decoder network to reconstruct the despeckled image by extracting features from the noisy image. Furthermore, a lightweight mixed-attention block is proposed to effectively enhance the image features and suppress some speckle noise during the encoding phase by using separation and re-fusion strategy for channel and spatial attention. Besides, we have graded the speckle noise levels with a certain interval and designed an algorithm to estimate the noise levels for despeckling real ultrasound images. Experiments have been done on the natural images, the synthetic image, the image simulated using Field II and the real ultrasound images. Compared with existing despeckling methods, the proposed network has achieved the state-of-the-art despeckling performance in terms of subjective human vision and such quantitative indexes as peak signal to noise ratio (PSNR), structural similarity (SSIM), equivalent number of looks (ENL) and contrast-to-noise ratio (CNR).

Optical fibers are typically used in telecommunications services for data transmission, where the use of fiber tags is essential to distinguish between the different transmission fibers or channels and thus ensure the working functionality of the communication system. Traditional physical entity marking methods for fiber labeling are bulky, easily confused, and, most importantly, the label information can be accessed easily by all potential users. This work proposes an encrypted optical fiber tag based on an encoded fiber Bragg grating (FBG) array that is fabricated using a point-by-point femtosecond laser pulse chain inscription method. Gratings with different resonant wavelengths and reflectivities are realized by adjusting the grating period and the refractive index modulations. It is demonstrated that a binary data sequence carried by a fiber tag can be inscribed into the fiber core in the form of an FBG array, and the tag data can be encrypted through appropriate design of the spatial distributions of the FBGs with various reflection wavelengths and reflectivities. The proposed fiber tag technology can be used for applications in port identification, encrypted data storage, and transmission in fiber networks.

Ultrasensitive nanomechanical instruments, e.g. atomic force microscopy (AFM), can be used to perform delicate biomechanical measurements and reveal the complex mechanical environment of biological processes. However, these instruments are limited because of their size and complex feedback system. In this study, we demonstrate a miniature fiber optical nanomechanical probe (FONP) that can be used to detect the mechanical properties of single cells and in vivo tissue measurements. A FONP that can operate in air and in liquids was developed by programming a microcantilever probe on the end face of a single-mode fiber using femtosecond laser two-photon polymerization nanolithography. To realize stiffness matching of the FONP and sample, a strategy of customizing the microcantilever’s spring constant according to the sample was proposed based on structure-correlated mechanics. As a proof-of concept, three FONPs with spring constants varying from 0.421 N m ^−1 to 52.6 N m ^−1 by more than two orders of magnitude were prepared. The highest microforce sensitivity was 54.5 nm μ N ^−1 and the detection limit was 2.1 nN. The Young’s modulus of heterogeneous soft materials, such as polydimethylsiloxane, muscle tissue of living mice, onion cells, and MCF-7 cells, were successfully measured, which validating the broad applicability of this method. Our strategy provides a universal protocol for directly programming fiber-optic AFMs. Moreover, this method has no special requirements for the size and shape of living biological samples, which is infeasible when using commercial AFMs. FONP has made substantial progress in realizing basic biological discoveries, which may create new biomedical applications that cannot be realized by current AFMs.

Registration between remote sensing images has been a research focus in the field of remote sensing image processing. Most of the existing image registration algorithms applied to feature point matching are derived from image feature extraction methods, such as scale-invariant feature transform (SIFT), speed-up robust features (SURF) and Siamese neural network. Such methods encounter difficulties in achieving accurate image registration where there is a large bias in the image features or no significant feature points. Aiming to solve this problem, this paper proposes an algorithm for multi-source image registration based on geographical location information (GLI). By calculating the geographic location information that corresponds to the pixel in the image, the ideal projected pixel position of the corresponding image is obtained using spatial coordinate transformation. Additionally, the corresponding relationship between the two images is calculated by combining multiple sets of registration points. The simulation experiment illustrates that, under selected common simulation parameters, the average value of the relative registration-point error between the two images is 12.64 pixels, and the registration accuracy of the corresponding ground registration point is higher than 6.5 m. In the registration experiment involving remote sensing images from different sources, the average registration pixel error of this algorithm is 20.92 pixels, and the registration error of the image center is 21.24 pixels. In comparison, the image center registration error given by the convolutional neural network (CNN) is 142.35 pixels after the registration error is manually eliminated. For the registration of homologous and featureless remote sensing images, the SIFT algorithm can only offer one set of registration points for the correct region, and the neural network cannot achieve accurate registration results. The registration accuracy of the presented algorithm is 7.2 pixels, corresponding to a ground registration accuracy of 4.32 m and achieving more accurate registration between featureless images.

In this work, the Co-Ni basic carbonate nanowires were in-situ grown on carbon nanotube (CNT) network through a facile chemical bath deposition method, which could be further converted into active hydroxide via cyclic voltammetry strategy. A series of carbonate nanowire/nanotube with different Co/Ni ratio revealed the different growth status of the nanowires on CNT network. The nanostructures of the as-synthesized samples were examined via powder X-ray diffraction (XRD), scanning electron microscopy (SEM), transmission electron microscopy (TEM), X-ray photoelectron spectroscopy (XPS) techniques. The Co/Ni ratio of the carbonate largely affected the size of the nanowires, that the low Co/Ni ratio was beneficial for thin nanowire formation and the nanowires loading on CNT network. Subsequently, the electrochemical performance of the Co-Ni basic hydroxides was studied in a three-electrode test system. The nanowires with low Co/Ni ratio 1/2 can form nanowire array on individual CNTs, which exhibited better electrochemical capacitive performance than the composite network with high Co/Ni ratio nanowires after electrochemical activation. The addition of Co enhanced the rate performance of the hydroxide/CNT, especially improved the long cycle stability largely compared to the rate performance of pure Ni converted hydroxide/CNT composite film reported by our previous research. This result is valuable for the design of inorganic electrochemical active composites based on conductive networks for energy conversion/storage applications.

In pursuit of Sustainable Development Goals (SDGs), land cover change (LCC) has been utilized to explore different dynamic processes such as farmland abandonment and urban expansion. The study proposed a multi-scale spatiotemporal pattern analysis and simulation (MSPAS) model with driving factors for SDGs. With population information from the census, multi-scale analysis criteria were designed using the combination of administrative and regional divisions, i.e., district, province, nation and ecological region. Contribution and correlation of LCC or population were quantified between multiple scales. Different kinds of driving factors were explored in the pattern analysis and then utilized for the definition of adaptive land suitability rules using the Cellular Automata-Markov (CA-Markov) simulation. As a case study of the MSPAS model, Nepal entered into a new era by the establishment of a Federal Republic in 2015. The model focused on four specific land cover classes of urban, farmland, forest and grassland to explore the pattern of Nepal’s LCC from 2016 to 2019. The result demonstrated the performance of the MSPAS model. The spatiotemporal pattern had consistency, and characteristics between multiple scales and population were related to LCC. Urban area nearly doubled while farmland decreased by 3% in these years. Urban areas expanded at the expense of farmland, especially in Kathmandu and some districts of the Terai region, which tended to occur on flat areas near the existing urban centers or along the roads. Farmland abandonment was relatively intense with scattered abandoned areas widely distributed in the Hill region under conditions of steep topography and sparse population. The MSPAS model can provide references for the development of sustainable urbanization and agriculture in SDGs.

Down syndrome (DS) is the most common genetic cause of Alzheimer’s disease (AD) due to trisomy for all or part of human chromosome 21 (Hsa21). It is also associated with other phenotypes including distinctive facial features, cardiac defects, growth delay, intellectual disability, immune system abnormalities, and hearing loss. All adults with DS demonstrate AD-like brain pathology, including amyloid plaques and neurofibrillary tangles, by age 40 and dementia typically by age 60. There is compelling evidence that increased APP gene dose is necessary for AD in DS, and the mechanism for this effect has begun to emerge, implicating the C-terminal APP fragment of 99 amino acid (β-CTF). The products of other triplicated genes on Hsa21 might act to modify the impact of APP triplication by altering the overall rate of biological aging. Another important age-related DS phenotype is hearing loss, and while its mechanism is unknown, we describe its characteristics here. Moreover, immune system abnormalities in DS, involving interferon pathway genes and aging, predispose to diverse infections and might modify the severity of COVID-19. All these considerations suggest human trisomy 21 impacts several diseases in an age-dependent manner. Thus, understanding the possible aging-related mechanisms associated with these clinical manifestations of DS will facilitate therapeutic interventions in mid-to-late adulthood, while at the same time shedding light on basic mechanisms of aging.

Ribulose-1,5-bisphosphate carboxylase/oxygenase (RuBisCO) is a difficult enzyme to work with. Here, the authors covalently immobilized it in a microfluidic reactor to enhance its storage/thermal stabilities and reusability, which enabled the continuous artificial synthesis of glucose precursor.

A unique type of refractive index (RI) sensor is proposed using a dual Mach-Zehnder interferometer (MZI) structure based on side-hole fibers (SHFs). The MZI structure contains two single-mode fibers (SMFs), two coreless fiber (CLF) sections and an SHF section, which are spliced together in the order of SMF-CLF-SHF-CLF-SMF. The SMFs and the CLFs enable light lead in/out and beam splitting/combining, respectively. As a special feature of the structure, one hole of SHF is exposed for liquid filling and to form two MZIs as well. Three types of sensors are fabricated, namely S1, S2 and S3. Numerical simulation and experimental studies have been conducted to characterize the sensing performance. The RI sensitivity of the S1 using the ~550 μm long SHF section reaches 14,000 nm/RIU. When a shorter SHF section is used in S2, the detectable RI range is broadened due to larger FSR. When the closed hole of SHF in S3 is filled with liquid to introduce the Vernier effect, the sensitivity can be further enhanced to over 44,000 nm/RIU (i.e., Refractive Index Unit), which corresponds to the detection limit at the level of 1.0 × 10-5 RIU. This sensor design is original and easy to package, which gives it potential for label-free biochemical analyses.

Nonlocal means (NLM), a patch-based nonlocal recovery paradigm, has attracted much attention in recent decades. The decay parameter will greatly affect restoration performance of the NLM method. However, the existing NLM methods with decay parameter adaptation cannot determine this parameter effectively. To address this problem, we have proposed the minimum mean square error (MSE) based decay parameter adaptation method for the NLM denoising. In the proposed method, the globally optimal decay parameter is determined to produce the pre-denoised image based on the derived relation between the global minimum MSE and the decay parameter. Then, the pixel-wise MSE is estimated based on the pre-denoised result and the corresponding method noise. Finally, the optimal pixel-wise decay parameter is obtained by minimizing the pixel-wise MSE to produce the estimated restored image and the boosting strategy is implemented on this image to generate the final denoised result. Extensive simulations on the standard test images and real images corrupted with Gaussian noise and speckle noise demonstrate that the proposed method significantly outperforms some compared NLM methods in noise reduction and detail preservation and can provide better restoration performance than other state-of-the-art denoising methods in most cases in terms of objective metrics and human vision.

Flexible, highly sensitive stain sensors prepared from polymer nanocomposites have attracted a great deal of interests, due to the rapid development of robotics, transportation, aerospace and health monitoring. However, these sensors are often limited by unideal mechanical properties, sensitivity and cycling performance. We herein report a facile approach to developing high-cycling performance strain sensors based on a nanocomposite film. The film was prepared by dispersing 3 nm-thick graphene platelets (GnPs) within an epoxy matrix. TEM micrographs confirmed that GnPs were uniformly dispersed in epoxy whilst some were connected to each other, which contributed to the mechanical properties and electrical conductivity of the resulting nanocomposites. At 2.0 vol% GnPs, Young's modulus, fracture toughness and energy release rate of neat epoxy were improved by 93%, 135% and 215%, respectively. Having gauge factors of 1–33, the film sensor demonstrated high sensitivity to tensile strains 0–0.67%. Meanwhile, the film sensor revealed excellent performance over 35,000 cycles due to high fracture toughness and effective dissipation of the heat accumulated during fatigue cycles. Practically, the film sensor also demonstrated effective response to temperature, humidity and damage evolution. This work provides an effective strategy for developing graphene-based strain sensors of excellent mechanical properties, sensitivity and reliability.

Background: Arteriosclerosis Obliterans (ASO) is an important cause for lower limb amputation. We aimed to compare the effects of interventional and conservative therapies on lower extremity ASO, and to analyze the risk factors for prognosis. Methods: Ninety-eight eligible patients were randomly divided into experimental (n = 50) and control groups (n = 48). Conservative therapy was conducted for control group, and experimental group was given conservative and interventional therapies. Their baseline clinical data, hospital stay length, claudication distance, Rutherford stage, diseased arteries, Ankle-Brachial Index (ABI), clinical outcomes and adverse events were recorded. Then they were divided into poor (n = 31) and good prognosis groups (n = 67), and risk factors were explored by univariate and multivariate analyses. Results: The hospital stay length of experimental group was significantly shorter than that of control group (p < 0.05). After treatment, Rutherford stage decreased, and vascular patency rate and total response rate rose in experimental group (p < 0.05). Claudication distance and ABI significantly increased in both groups compared with those before treatment, especially in experimental group (p < 0.05). The incidence rates of cardiovascular and cerebrovascular events, amputation and death were significantly lower in experimental group (p < 0.05). Age, conservative therapy, smoking, hypertension, diabetes mellitus, hyperlipidemia and fibrinogen were independent risk factors for poor prognosis (p < 0.05). Age of >70 years old and fibrinogen level of >4 g/L had greater effects on prognosis. Conclusion: Interventional therapy works well for lower extremity ASO, with high safety, easy recovery and mild complications. Poor prognosis is caused by old age, conservative therapy, smoking, hypertension, diabetes mellitus, hyperlipidemia and increased fibrinogen.

This study presents a deep extraction of localized spectral features and multi-scale spatial features convolution (LSMSC) framework for spectral-spatial fusion based classification of hyperspectral images (HSIs). First, adjacent spectral bands are grouped based on their similarity measurements, where the whole hypercube is partitioned into several sub-cubes, each corresponding to one band group. Then, the proposed localized spectral features extraction (LSF) strategy is used to extract localized spectral features, which are extracted from each band group using the 1D convolutional neural network (CNN). Meanwhile, the proposed HiASPP strategy is employed to extract the multi-scale features from the first several principal components of each sub-cube. Finally, the extracted spectral and spatial features are concatenated for spectral-spatial fusion based classification of HSI. Experiments conducted on three publicly available datasets have demonstrated that the proposed architecture outperforms several state-of-the-art approaches.

Development of efficient electrocatalysts combining the features of low cost and high performance for both the hydrogen evolution reaction (HER) and oxygen evolution reaction (OER) still remains a critical challenge. Here, we proposed a facile strategy to construct in situ a novel hierarchical heterostructure composed of 0D−2D CoSe2/MoSe2 by the selenization of CoMoO4 nanosheets grafted on a carbon cloth (CC). In such integrated structure, CoSe2 nanoparticles dispersed well and tightly bonded with MoSe2 nanosheets, which can not only enhance kinetics due to the synergetic effects, thus promoting the electrocatalytic activity, but also effectively improve the structural stability. Benefiting from its unique architecture, the designed CoSe2/MoSe2 catalyst exhibits superior OER and HER performance. Specifically, a small overpotential of 280 mV is acquired at a current density of 10 mA·cm−2 for OER with a small Tafel slope of 86.8 mV·dec−1, and the overpotential is 90 mV at a current density of 10 mA·cm−2 for HER with a Tafel slope of 84.8 mV·dec−1 in 1 M KOH. Furthermore, the symmetrical electrolyzer assembled with the CoSe2/MoSe2 catalysts depicts a small cell voltage of 1.63 V at 10 mA·cm−2 toward overall water splitting.

This paper proposes a compact planar optofluidic optical cross-connect (OXC) switch based on electro-optic (EO) waveguides in nematic liquid crystal (NLC). In the cross-bar fabric, each unit cell of the proposed device consists of two portions: the NLC EO waveguides for 1 × 2 switching and the fixed NLC-core waveguides to support subsequent beam paths of the lead-in/lead-out process or the bending process. With numerical simulations, a sample design of 8 × 8 OXC is analyzed and optimized, which provides the predicted broadband performance as approximately 8.0 dB maximum on-chip insertion loss, -45.9 dB cross-talk and 16 ms switching time in the footprint of 2 mm × 2 mm. The loss reduction and the switching speed-up issues are also discussed, with the expected 5.5 dB maximum on-chip insertion loss and 1 ms switching time. The proposed OXC switch is new in the working principle using both electric-field reconfigurable NLC waveguides and fixed NLC-core waveguides, and can leverage the liquid-crystal on silicon fabrication. It is potential for optical circuit switching in the small-scale data centers, or protection switching in optical communication networks.

Artificial photosynthesis (APS) mimics natural photosynthesis (NPS) to store solar energy in chemical compounds for applications such as water splitting, CO2 fixation and coenzyme regeneration. NPS is naturally an optofluidic system since the cells (typical size 10 to 100 µm) of green plants, algae, and cyanobacteria enable light capture, biochemical and enzymatic reactions and the related material transport in a microscale, aqueous environment. The long history of evolution has equipped NPS with the remarkable merits of a large surface-area-to-volume ratio, fast small molecule diffusion and precise control of mass transfer. APS is expected to share many of the same advantages of NPS and could even provide more functionality if optofluidic technology is introduced. Recently, many studies have reported on optofluidic APS systems, but there is still a lack of an in-depth review. This article will start with a brief introduction of the physical mechanisms and will then review recent progresses in water splitting, CO2 fixation and coenzyme regeneration in optofluidic APS systems, followed by discussions on pending problems for real applications.

This review aims to summarize the recent advances and progress of plasmonic biosensors based on patterned plasmonic nanostructure arrays that are integrated with microfluidic chips for various biomedical detection applications. The plasmonic biosensors have made rapid progress in miniaturization sensors with greatly enhanced performance through the continuous advances in plasmon resonance techniques such as surface plasmon resonance (SPR) and localized SPR (LSPR)-based refractive index sensing, SPR imaging (SPRi), and surface-enhanced Raman scattering (SERS). Meanwhile, microfluidic integration promotes multiplexing opportunities for the plasmonic biosensors in the simultaneous detection of multiple analytes. Particularly, different types of microfluidic-integrated plasmonic biosensor systems based on versatile patterned plasmonic nanostructured arrays were reviewed comprehensively, including their methods and relevant typical works. The microfluidics-based plasmonic biosensors provide a high-throughput platform for the biochemical molecular analysis with the advantages such as ultra-high sensitivity, label-free, and real time performance; thus, they continue to benefit the existing and emerging applications of biomedical studies, chemical analyses, and point-of-care diagnostics.