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Abstract Ketamine has recently become an anesthetic drug used in human and veterinary clinical medicine for illicit abuse worldwide, but the detection of illicit abuse and inference of time intervals following ketamine abuse are challenging issues in forensic toxicological investigations. Here, we developed methods to estimate time intervals since ketamine use is based on significant metabolite changes in rat serum over time after a single intraperitoneal injection of ketamine, and global metabolomics was quantified by ultra-performance liquid chromatography-quadrupole-time-of-flight mass spectrometry (UPLC-Q-TOF–MS). Thirty-five rats were treated with saline (control) or ketamine at 3 doses (30, 60, and 90 mg/kg), and the serum was collected at 21 time points (0 h to 29 d). Time-dependent rather than dose-dependent features were observed. Thirty-nine potential biomarkers were identified, including ketamine and its metabolites, lipids, serotonin and other molecules, which were used for building a random forest model to estimate time intervals up to 29 days after ketamine treatment. The accuracy of the model was 85.37% in the cross-validation set and 58.33% in the validation set. This study provides further understanding of the time-dependent changes in metabolites induced by ketamine abuse.

Given their dangerous effects on the nervous system, neurotoxins represent a significant threat to public health. Various therapeutic approaches, including chelating agents, receptor decoys, and toxin-neutralizing antibodies, have been explored. While prophylactic vaccines are desirable, it is oftentimes difficult to effectively balance their safety and efficacy given the highly dangerous nature of neurotoxins. To address this, we report here on a nanovaccine against neurotoxins that leverages the detoxifying properties of cell membrane-coated nanoparticles. A genetically modified cell line with constitutive overexpression of the α7 nicotinic acetylcholine receptor is developed as a membrane source to generate biomimetic nanoparticles that can effectively and irreversibly bind to α-bungarotoxin, a model neurotoxin. This abrogates the biological activity of the toxin, enabling the resulting nanotoxoid to be safely delivered into the body and processed by the immune system. When co-administered with an immunological adjuvant, a strong humoral response against α-bungarotoxin is generated that protects vaccinated mice against a lethal dose of the toxin. Overall, this work highlights the potential of using genetic modification strategies to develop nanotoxoid formulations against various biological threats.

Abstract C–H bond activation enables the facile synthesis of new chemicals. While C–H activation in short-chain alkanes has been widely investigated, it remains largely unexplored for long-chain organic molecules. Here, we report light-driven C–H activation in complex organic materials mediated by 2D transition metal dichalcogenides (TMDCs) and the resultant solid-state synthesis of luminescent carbon dots in a spatially-resolved fashion. We unravel the efficient H adsorption and a lowered energy barrier of C–C coupling mediated by 2D TMDCs to promote C–H activation and carbon dots synthesis. Our results shed light on 2D materials for C–H activation in organic compounds for applications in organic chemistry, environmental remediation, and photonic materials.

Ecological networks are important channels that describe ecological systems and thus facilitate interactions between communities and ecosystems. Building a reasonable and effective ecological network is imperative for maintaining a healthy ecosystem. Taking Baiyangdian Basin as an example, an ecological network was constructed through “source–resistance surface–corridor”. Based on complex network theory, an ecological network was abstracted into a topological structure. The characteristics of the ecological network were analyzed from robustness and redundancy indices. Simulation of nodes was conducted to evaluate the plasticity of the constructed ecological network. The results indicate that: (1) A total of 145 ecological source areas were identified in Baiyangdian Basin, with a total area of 6479.24 km2. In total, 338 ecological corridors with a total length of 3939.87 km were identified. Further, 104 ecological pinch points were identified. (2) The complex network in the research area had scale-free characteristics, with a topological structure of 8 communities, multiple closed loops, and good connectivity. (3) The distribution of nodes in the network was uneven, with node 52 being the most important node in the network. The analyzed characteristics of the ecological network in Baiyangdian Basin is imperative for optimizing the structure of the ecological network.

Carbon pooling and release occur all the time in all corners of the earth, where the land use factor is key to influencing the realization of carbon peaking and neutrality. Land use patterns and carbon emissions change under different scenarios and analyzing the correlation will help formulate scientific land use policies for the future. In this study, through remote sensing data, we investigated the changes in land use patterns and carbon emissions in the Baiyangdian basin in China from 2000 to 2020 and analyzed the carbon conduction effect with the help of a land transfer matrix. The geographical simulation and optimization system-future land use simulation (GeoSOS-FLUS) and Markov models were used to predict the land use changes and carbon emissions under the four different scenarios for the region in 2035. The results indicated that 1) the net land use carbon emissions increased from 52,163.03 × 103 to 260,754.91.28 × 103 t from 2000 to 2020, and the carbon source-sink ratio exhibited a general uptrend; 2) the net carbon emissions due to terrestrial transfers increased over time. The carbon conduction effects due to the transfer of forests, grasslands, water areas, and unused lands to built-up lands also showed a rising trend, albeit the latter two exhibited only small changes; 3) in 2035, the net carbon uptake under the four development scenarios was predicted to be 404,238.04 × 103, 402,009.45 × 103, 404,231.64 × 103, and 404,202.87×103 t, respectively, with all values much higher than that of the study area in 2020. The maximum carbon sink capacity was 817.88 × 103 t under the double-carbon target scenario, and the maximum carbon source emission was 405,033.61 × 103 t under the natural development scenario. The above results provide an essential reference for low carbon-based urban land use regulations for the Baiyangdian basin and other similar projects in the future.

Abstract A gas chromatography-mass spectrometry (GC–MS) method for the determination of difenidol hydrochloride in biological specimens has been developed. The method exhibited excellent recovery (> 90%) and precision (RSD

The structure and function of ecological spaces play a crucial role in resisting risk interference and providing ecological services. Conducting resilience assessments of ecological spaces is of great significance for maintaining ecological security. Taking the Baiyangdian Basin as the research object, based on the evaluation results of ecosystem service function importance and ecological environmental sensitivity, the ecological space importance level of the Baiyangdian Basin is divided. Based on the three basic characteristics of “resistance, adaptability, recoverability”, the resilience of ecological space model was constructed by selecting ecosystem service value, landscape structure stability, ecological resilience model and coefficient. The research results show that: (1) The overall importance index of ecological space in the Baiyangdian Basin is relatively low. The non-critical area accounted for 43.00% of the total area, the general area accounted for 30.78%, and the core area accounted for 26.22%, which mainly distributed in the northern part of the study area and at the confluence of rivers. (2) The proportion of areas with extremely important ecosystem service functions in Baiyangdian Basin is 31.73%, concentrated in the central and southwestern parts of the basin. The highly sensitive ecological environment areas accounted for 21.96%, mainly distributed in high-altitude areas in the northern part of the basin and river convergence areas. (3) On the whole, the overall resilience level of the study area is not high and there is a significant difference in resilience between the north and south, with 68.60% of the regions in the low level of resilience. The area with high resilience level accounted for only 1.51%, and the highest resilience level was found at river confluence. The research results provide a theoretical basis for maintaining and improving the ecological environment of the basin, and provide a basis for implementing precise policies to optimize the ecological space of the basin.

Abstract The determination of length of time from the last drinking is critical for cases like drunk driving, sexual assault victims, and also postmortem suspected poisoning cases. The study was aimed to established a method of estimating the time of last drinking through the pharmacokinetic study of conjugation metabolites of alcohol in blood after a single oral dose. Twenty-six volunteers (14 males) consumed alcohol with food at a fixed dose of 0.72 g/kg after fasting for 12 h. Five milliliters of blood were collected 120 h after the start of drinking, and all samples were analyzed with headspace-gas chromatography and high-performance liquid chromatography–tandem mass spectrometry. The time point of last drinking was estimated through the relationship between the concentration ratio of ethyl glucuronide to ethyl sulphate and the length of time after drinking. Pharmacokinetic parameters were analyzed by a pharmacokinetic software DAS according to the non-compartment model. A good correlation model was obtained from the relationship between concentration ratio of ethyl glucuronide to ethyl sulphate in blood and the time of alcohol use, and the margin of error was mostly lower than 10%. The time of maximum concentration, maximum concentration, and elimination half-life of ethyl glucuronide in blood were 4.12 ± 1.07 h, 0.31 ± 0.11 mg/L and 2.56 ± 0.89 h; the time of maximum concentration, maximum concentration, and elimination half-life of ethyl sulphate in blood were 3.02 ± 0.70 h, 0.17 ± 0.04 mg/L, and 2.04 ± 0.76 h. The study established a potential method to estimate the length of time after a moderate oral dose, and provided pharmacokinetic parameters of ethyl glucuronide and ethyl sulphate in Chinese population.

Vein segmentation and projection correction constitute the core algorithms of an auxiliary venipuncture device, responding to accurate venous positioning to assist puncture and reduce the number of punctures and pain of patients. This paper proposes an improved U-Net for segmenting veins and a coaxial correction for image alignment in the self-built vein projection system. The proposed U-Net is embedded by Gabor convolution kernels in the shallow layers to enhance segmentation accuracy. Additionally, to mitigate the semantic information loss caused by channel reduction, the network model is lightweighted by means of replacing conventional convolutions with inverted residual blocks. During the visualization process, a method that combines coaxial correction and a homography matrix is proposed to address the non-planarity of the dorsal hand in this paper. First, we used a hot mirror to adjust the light paths of both the projector and the camera to be coaxial, and then aligned the projected image with the dorsal hand using a homography matrix. Using this approach, the device requires only a single calibration before use. With the implementation of the improved segmentation method, an accuracy rate of 95.12% is achieved by the dataset. The intersection-over-union ratio between the segmented and original images is reached at 90.07%. The entire segmentation process is completed in 0.09 s, and the largest distance error of vein projection onto the dorsal hand is 0.53 mm. The experiments show that the device has reached practical accuracy and has values of research and application.

Pollen, as an important component of Eucommia ulmoides (EUP), is rich in nutrients and is receiving increasing attention. At present, there are no reports on research related to the chemical composition and quality standards of EUP, and there are significant quality differences and counterfeit phenomena in the market. This study used a UPLC-QTOF-MS system to identify 49 chemical components in EUP for the first time. In the second step, 2,2-diphenyl-1-picrylhydrazyl (DPPH)-HPLC antioxidant activity screening technology was used to identify the main active components of EUP, quercetin-3-O-sophoroside (QSH), quercetin-3-O-sambubioside (QSB), and quercetin 3-O-neohesperidoside (QNH), and their purification, preparation, and structure identification were carried out. Third, molecular docking was used to predict the activity of these components. Fourth, the intracellular ROS generation model of RAW264.7 induced by H2O2 was used to verify and evaluate the activity of candidate active ingredients to determine their feasibility as Q-markers. Finally, a quality control method for EUP was constructed using the three selected components as Q-markers. The identification of chemical components and the discovery, prediction, and confirmation of characteristic Q-markers in EUP provide important references for better research on EUP and the effective evaluation and control of its quality. This approach provides a new model for the quality control of novel foods or dietary supplements.

Qing-Jin-Hua-Tan-Decoction (QJHTD), a classic famous Chinese ancient prescription, has been used for treatment of pulmonary diseases since Ming Dynasty. A total of 22 prototype compounds of QJHTD absorbed into rat blood were chosen as candidates for the pharmacological network analysis and molecular docking. The targets from the intersection of compound target and ALI disease targets were used for GO and KEGG enrichment analyses. Molecular docking was adopted to further verify the interactions between 22 components and the top 20 targets with higher degree values in the component–target–pathway network. In vitro experiments were performed to verify the results of network pharmacology using SPR experiments, Western blot experiments, and the PMA-induced neutrophils to produce neutrophil extracellular trap (NET) model. The compound–target–pathway network includes 176 targets and 20 signaling pathways in which the degree of MAPK14, CDK2, EGFR, F2, SRC, and AKT1 is higher than that of other targets and which may be potential disease targets. The biological processes in QJHTD for ALI mainly included protein phosphorylation, response to wounding, response to bacterium, regulation of inflammatory response, and so on. KEGG enrichment analyses revealed multiple signaling pathways, including lipid and atherosclerosis, HIF-1 signaling pathway, renin–angiotensin system, and neutrophil extracellular trap formation. The molecular docking results showed that baicalin, oroxylin A-7-glucuronide, hispidulin-7-O-β-D-glucuronide, wogonoside, baicalein, wogonin, tianshic acid, and mangiferin can be combined with most of the targets, which might be the core components of QJHTD in treatment of ALI. Direct binding ability of baicalein, wogonin, and baicalin to thrombin protein was all micromolar, and their KD values were 11.92 μM, 1.303 μM, and 1.146 μM, respectively, revealed by SPR experiments, and QJHTD could inhibit Src phosphorylation in LPS-activated neutrophils by Western blot experiments. The experimental results of PMA-induced neutrophils to produce NETs indicated that QJHTD could inhibit the production of NETs. This study revealed the active compounds, effective targets, and potential pharmacological mechanisms of QJHTD acting on ALI.

Machining feature recognition is considered the key connecting technique to the integration of Computer-Aided Design (CAD) and Computer-Aided Process Planning (CAPP), and decision-making of the part processing scheme and the optimization of process route can effectively improve the processing efficiency and reduce the cost of product machining cost. At present, for the recognition of machining features in CAD models, there is a lack of a systematic method to consider process information (such as tolerance and roughness) and an effective process route optimization method to plan part processing procedures. Here we represent a novel model processing feature recognition method, and, on the basis of feature processing plan decision, realize the optimization of the process route. On the basis of a building model Attributed Adjacency Graph (AAG) based on model geometry, topology, and process information, we propose an AAG decomposition and reconstruction method based on Decomposed Base Surface (DBS) and Joint Base Surface (JBS) as well as the recognition of model machining features through Attributed Adjacency Matrix-based (AAM) feature matching. The feature machining scheme decision method based on fuzzy comprehensive evaluation is adopted, and the decision is realized by calculating the comprehensive evaluation index. Finally, the Machining Element Directed Graph (MEDGraph) is established based on the constraint relationship between Machining Elements (MEs). The improved topological sorting algorithm lists the topological sequences of all MEs. The evaluation function is constructed with the processing cost or efficiency as the optimization objective to obtain the optimal process route. Our research provides a new method for model machining feature recognition and process route optimization. Applications of the proposed approach are provided to validate the method by case study.

In cloud manufacturing systems, the multi-granularity of service resource and design task models leads to the complexity of cloud service matching. In order to satisfy the preference of resource requesters for large-granularity service resources, we propose a multistage cloud-service matching strategy to solve the problem of matching tasks and resources with different granularity sizes. First, a multistage cloud-service matching framework is proposed, and the basic strategy of matching tasks with cloud services is planned. Then, the context-aware task-ontology modeling method is studied, and a context-related task-ontology model is established. Thirdly, a process-decomposition method of design tasks is studied, and the product development process with small granularity tasks is established. Fourthly, a matching strategy of ontology tasks and cloud services is studied, and the preliminary matching is accomplished. Finally, intelligent optimization is carried out, and the optimal cloud service composition is found with the optimal design period as the objective function. With the help of the preceding method, the service matching of maximizing the task granularity is realized on the premise of ensuring the matching success rate, which meets the preference of resource requesters for large-granularity service resources.

The continuous phase modulation (CPM) technique is an excellent solution for underwater acoustic (UWA) channels with limited bandwidth and high propagation attenuation. However, the severe intersymbol interference is a big problem for the algorithm applying in shallow water. To solve this problem, an algorithm for prefiltered single-carrier frequency-domain equalization (PF-SCFDE) is presented in this paper. The regular whitening filter is replaced by a prefilter in the proposed algorithm. The output information sequence of this prefilter contains the forward information. To improve the performance, the output of the equalizer, combined with the forward information, is used to make the maximum likelihood estimation. The simulation results with minimum-shift keying and Gaussian-filtered minimum-shift keying signals over shallow water acoustic channels with low root mean square delay spread demonstrate that PF-SCFDE outperformed the traditional single-carrier frequency-domain equalization (SCFDE) by approximately 1 dB under a bit error rate (BER) of 10−4. A shallow sea trial has demonstrated the effectiveness of PF-SCFDE; PF-SCFDE had a reduction in BER of 18.35% as compared to the traditional SCFDE.

Print matter authentication based on anti-counterfeiting techniques has received continuously increasing concern from academia and industry. However, the existing printing anti-counterfeiting solutions often have the defects of poor identification experience, high cost, or weak anti-counterfeiting ability, and cannot achieve pre-sale anti-counterfeiting. Therefore, a novel steganography-based pattern for print matter anti-counterfeiting by smartphone cameras is proposed in this study. Firstly, every pixel in the original binary message image (such as QR code) is replaced by a square pixel block with the same binary gray value of 0 or 255 (the first-level expansion). Secondly, the obtained image is encrypted based on the logistic chaotic sequence, and then scrambled by Arnold transform. Lastly, once again every pixel in the generated image is replaced with a square pixel block (the second-level expansion), the size and gray value of which can be set to control the semi-fragile ability to distinguish an originally printed pattern from its illegitimate copy. If the message extracted from the printed pattern through the inverse procedure is complete enough to decode and read, the pattern is assumed to be an original print. Experimental results verify the advancement and effectiveness of the proposed scheme in distinguishing the copied pattern.

In order to simplify the complexity and reduce the cost of the microphone array, this paper proposes a dual-microphone based sound localization and speech enhancement algorithm. Based on the time delay estimation of the signal received by the dual microphones, this paper combines energy difference estimation and controllable beam response power to realize the 3D coordinate calculation of the acoustic source and dual-microphone sound localization. Based on the azimuth angle of the acoustic source and the analysis of the independent quantity of the speech signal, the separation of the speaker signal of the acoustic source is realized. On this basis, post-wiener filtering is used to amplify and suppress the voice signal of the speaker, which can help to achieve speech enhancement. Experimental results show that the dual-microphone sound localization algorithm proposed in this paper can accurately identify the sound location, and the speech enhancement algorithm is more robust and adaptable than the original algorithm.

Abstract The layered MoS2 has recently attracted significant attention for its excellent nonlinear optical properties. Here, the ultrafast nonlinear optical (NLO) absorption and excited carrier dynamics of layered MoS2 (monolayer, 3–4 layers, and 6–8 layers) are investigated via Z-scan and transient absorption spectra. Our experimental results reveal that NLO absorption coefficients of these MoS2 increase from–27 × 103 cm/GW to–11 × 103 cm/GW with more layers at 400-nm laser excitation, while the values decrease from 2.0 × 103 cm/GW to 0.8 × 103 cm/GW at 800 nm. In addition, at high pump fluence, when the NLO response occurs, the results show that not only the reformation of the excitonic bands, but also the recovery time of NLO response decreases from 150 ps to 100 ps with an increasing number of layers, while the reductive energy of A excitonic band decreases from 191.7 meV to 51.1 meV. The intriguing NLO response of MoS2 provides excellent potentials for the next-generation optoelectronic and photonic devices.

In recent years, source camera identification has become a research hotspot in the field of image forensics and has received increasing attention. It has high application value in combating the spread of pornographic photos, copyright authentication of art photos, image tampering forensics, and so on. Although the existing algorithms greatly promote the research progress of source camera identification, they still cannot effectively reduce the interference of image content with image forensics. To suppress the influence of image content on source camera identification, a multiscale content-independent feature fusion network (MCIFFN) is proposed to solve the problem of source camera identification. MCIFFN is composed of three parallel branch networks. Before the image is sent to the first two branch networks, an adaptive filtering module is needed to filter the image content and extract the noise features, and then the noise features are sent to the corresponding convolutional neural networks (CNN), respectively. In order to retain the information related to the image color, this paper does not preprocess the third branch network, but directly sends the image data to CNN. Finally, the content-independent features of different scales extracted from the three branch networks are fused, and the fused features are used for image source identification. The CNN feature extraction network in MCIFFN is a shallow network embedded with a squeeze and exception (SE) structure called SE-SCINet. The experimental results show that the proposed MCIFFN is effective and robust, and the classification accuracy is improved by approximately 2% compared with the SE-SCINet network.

In the field of surface defect detection, the scale difference of product surface defects is often huge. The existing defect detection methods based on Convolutional Neural Networks (CNNs) are more inclined to express macro and abstract features, and the ability to express local and small defects is insufficient, resulting in an imbalance of feature expression capabilities. In this paper, a Multi-Scale Feature Learning Network (MSF-Net) based on Dual Module Feature (DMF) extractor is proposed. DMF extractor is mainly composed of optimized Concatenated Rectified Linear Units (CReLUs) and optimized Inception feature extraction modules, which increases the diversity of feature receptive fields while reducing the amount of calculation; the feature maps of the middle layer with different sizes of receptive fields are merged to increase the richness of the receptive fields of the last layer of feature maps; the residual shortcut connections, batch normalization layer and average pooling layer are used to replace the fully connected layer to improve training efficiency, and make the multi-scale feature learning ability more balanced at the same time. Two representative multi-scale defect data sets are used for experiments, and the experimental results verify the advancement and effectiveness of the proposed MSF-Net in the detection of surface defects with multi-scale features.

Meconopsis Vig. is a genus possessing important medicinal and ornamental values in the Papaveraceae. Many species in this genus are commonly used in traditional Tibetan medicines over thousands of years. In this study, we reported the complete chloroplast genome sequence of Meconopsis punicea. Total lengths of the chloroplast genomes were 152,933 bp. The genome had typical quadripartite structure, LSC region (83,031 bp) and SSC region (17,920 bp) were separated by a pair of IRs (25,991 bp), respectively. Moreover, they were composed of 131 genes, including 86 protein coding genes, 37 tRNA genes, 8 rRNA genes and one pseudogene. Phylogenetic analysis based on complete chloroplast genomes showed that M. integrifolia had closer relationship with M. punicea; meanwhile, Meconopsis was closely related to Papaver in Papaveraceae.

In production, due to natural conditions or process peculiarities, a single product often may exhibit more than one type of defect. The accurate identification of all defects has an important guiding significance and practical value to improve the planting and production processes. Concerning the surface defect classification task, convolutional neural networks can be implemented as a powerful instrument. However, a typical convolutional neural network tends to consider an image as an inseparable entity and a single instance when extracting features; moreover, it may overlook semantic correlations between different labels. To address these limitations, in the present paper, we proposed a feature-wise attention-based relation network (FAR-Net) for multilabel jujube defect classification. The network included four different modules designed for (1) image feature extraction, (2) label-wise feature aggregation, (3) feature activation and deactivation, and (4) correlation learning among labels. To evaluate the proposed method, a unique multilabel jujube defect dataset was constructed as a benchmark for the multilabel classification task of the jujube defect images. The results of experiments show that owing to the relation learning mechanism, the average precision of the three main composite defects in the dataset increases by 5.77%, 4.07%, and 3.50%, respectively, compared to the backbone of our network, namely Inception v3, which indicated that the proposed FAR-Net effectively facilitated the learning of correlation between labels and eventually, improved the multilabel classification accuracy.

With the rapid development of information technology and the widespread use of the Internet, QR codes are widely used in all walks of life and have a profound impact on people’s work and life. However, the QR code itself is likely to be printed and forged, which will cause serious economic losses and criminal offenses. Therefore, it is of great significance to identify the printer source of QR code. A method of printer source identification for scanned QR Code image blocks based on convolutional neural network (PSINet) is proposed, which innovatively introduces a bottleneck residual block (BRB). We give a detailed theoretical discussion and experimental analysis of PSINet in terms of network input, the first convolution layer design based on residual structure, and the overall architecture of the proposed convolution neural network (CNN). Experimental results show that the proposed PSINet in this paper can obtain extremely excellent printer source identification performance, the accuracy of printer source identification of QR code on eight printers can reach 99.82%, which is not only better than LeNet and AlexNet widely used in the field of digital image forensics, but also exceeds state-of-the-art deep learning methods in the field of printer source identification.

Roller bearings are some of the most critical and widely used components in rotating machinery. Appearance defect inspection plays a key role in bearing quality control. However, in real industries, bearing defects are usually extremely subtle and have a low probability of occurrence. This leads to distribution discrepancies between the number of positive and negative samples, which makes intelligent data-driven inspection methods difficult to develop and deploy. This paper presents a small data-driven convolution neural network (SDD-CNN) for roller subtle defect inspection via an ensemble method for small data preprocessing. First, label dilation (LD) is applied to solve the problem of an imbalance in class distribution. Second, a semi-supervised data augmentation (SSDA) method is proposed to extend the dataset in a more efficient and controlled way. In this method, a coarse CNN model is trained to generate ground truth class activation and guide the random cropping of images. Third, four variants of the CNN model, namely, SqueezeNet v1.1, Inception v3, VGG-16, and ResNet-18, are introduced and employed to inspect and classify the surface defects of rollers. Finally, a rich set of experiments and assessments is conducted, indicating that these SDD-CNN models, particularly the SDD-Inception v3 model, perform exceedingly well in the roller defect classification task with a top-1 accuracy reaching 99.56%. In addition, the convergence time and classification accuracy for an SDD-CNN model achieve significant improvement compared to that for the original CNN. Overall, using an SDD-CNN architecture, this paper provides a clear path toward a higher precision and efficiency for roller defect inspection in smart manufacturing.

Lung metastasis poses a formidable challenge in the realm of cancer treatment, with conventional chemotherapy often falling short due to limited targeting and low accumulation in the lungs. Here, we show a microrobot approach using motile algae for localized delivery of drug-loaded nanoparticles to address lung metastasis challenges. The biohybrid microrobot [denoted "algae-NP(DOX)-robot"] combines green microalgae with red blood cell membrane-coated nanoparticles containing doxorubicin, a representative chemotherapeutic drug. Microalgae provide autonomous propulsion in the lungs, leveraging controlled drug release and enhanced drug dispersion to exert antimetastatic effects. Upon intratracheal administration, algae-NP(DOX)-robots efficiently transport their drug payload deep into the lungs while maintaining continuous motility. This strategy leads to rapid drug distribution, improved tissue accumulation, and prolonged retention compared to passive drug-loaded nanoparticles and free drug controls. In a melanoma lung metastasis model, algae-NP(DOX)-robots exhibit substantial improvement in therapeutic efficacy, reducing metastatic burden and extending survival compared to control groups. [ABSTRACT FROM AUTHOR]

The article explores the role of sulfur vacancies in enhancing the electrochemical nitrogen reduction reaction (ENRR) performance of transition metal disulfide (TMS2) catalysts. The study finds that the intentional engineering of sulfur vacancies during synthesis, as well as the in situ generation of sulfur vacancies, can improve the ENRR activity of TMS2 catalysts. The findings are supported by experimental observations on various TMS2 materials. The study highlights the importance of considering surface states and the capacity to form sulfur vacancies when designing efficient TMS2 catalysts for ENRR. [Extracted from the article]

Nonradicals are effective in selectively degrading electron-rich organic contaminants, which unfortunately suffer from unsatisfactory yield and uncontrollable composition due to the competitive generation of radicals. Herein, we precisely construct a local microenvironment of the carbon nitride--supported high-loading (~9 wt.%) Fe single-atom catalyst (Fe SAC) with sulfur via a facile supermolecular self-assembly strategy. Short-distance S coordination boosts the peroxymonosulfate (PMS) activation and selectively generates high-valent iron--oxo species (FeIV =O) along with singlet oxygen (¹O2), significantly increasing the ¹O2 yield, PMS utilization, and p-chlorophenol reactivity by 6.0, 3.0, and 8.4 times, respectively. The composition of nonradicals is controllable by simply changing the S content. In contrast, long-distance S coordination generates both radicals and nonradicals, and could not promote reactivity. Experimental and theoretical analyses suggest that the short-distance S upshifts the d-band center of the Fe atom, i.e., being close to the Fermi level, which changes the binding mode between the Fe atom and O site of PMS to selectively generate ¹O2 and FeIV =O with a high yield. The short-distance S-coordinated Fe SAC exhibits excellent application potential in various water matrices. These findings can guide the rational design of robust SACs toward a selective and controllable generation of nonradicals with high yield and PMS utilization. [ABSTRACT FROM AUTHOR]