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An improved method was developed for the detection of 16 polycyclic aromatic hydrocarbons (PAHs) in mainstream cigarette smoke during combustion. The methodological approach involved matrix matching standard solution preparation, direct injection of the extraction solution, and measurement of standard linearity curves to quantitatively analyze the release of target compounds. Considering the diverse physical and chemical properties as well as distribution patterns of the 16 PAHs in cigarette smoke, the impact of room temperature and high-temperature ion sources on the response of target compounds was investigated. Additionally, the preparation methods of matrix solvents and the matrix effects on the quantitative results of target compounds were also explored. Furthermore, a systematic study was conducted on the smoke release distribution of commercially available cigarettes with different circumferences. The results demonstrated that direct injection of sample extract eliminates the need for sample purification, concentration, and other processing steps employed in current detection methods. This simplifies sample pretreatment procedures while reducing organic solvent usage and consumables consumption. Moreover, it resolves issues related to low operational efficiency when dealing with large quantities of samples. The use of matrix solvent to prepare standard solution effectively reduces the influence of matrix effect, and solves the technical problem of low recovery of target substance in flue gas caused by no purification treatment when using gas chromatography-tandem mass spectrometry (GC-MS/MS) to analyze 16 kinds of PAHs. Under the normal working parameters of the instrument, the electron impact ion source (EI) was used under high temperature to improve the ionization efficiency of the targets in complex matrix and to ensure the accurate analysis of targets with low concentrations. The correlation coefficients (R2) of the calibration curves for 16 PAHs are 0.996 4-0.999 8, the limits of dectection and the limits of quantification are 0.29-1.48, 0.95-4.94 ng/cigarette, respectively. Under different concentration levels, the average recovery is 87.71%-123.26%, and the precision is within 1.58%-9.45%. By analyzing the reference cigarette 3R4F, there is no significant difference between this method and those reported in the literature. The total amount of PAHs released from 58 brands of cigarette samples are 742.76-1 505.99 ng/cigarette. At the same time, with the decrease of tar content in cigarette, the release of PAHs will be reduced accordingly. This method is simple, practical, stable, and accurate, providing more technical means for the detection of trace PAHs in cigarette smoke.

Summary: Mesoscale to nanoscale three-dimensional (3D) fabrication mostly requires complicated industry processing techniques. Here, we present a protocol for 3D shaping control by solidifying a water-based TiO2 nanofluid drop on a polygonal wettability-patterned surface. We detail the steps for preparing stable TiO2 nanofluid and wettability-patterned surfaces. We then describe the experimental procedure to obtain various and precise 3D morphologies by adjusting the deposited TiO2 nanofluid drop volume. This protocol provides a promising technique for future 3D manufacturing.For complete details on the use and execution of this protocol, please refer to Jiang et al.1 : Publisher’s note: Undertaking any experimental protocol requires adherence to local institutional guidelines for laboratory safety and ethics.

Ammonia protonic ceramic fuel cells (NH3-PCFCs) are highly appealing energy conversion technologies due to their high efficiency, environmental responsibility, and benign safety features. Nonetheless, progress in NH3-PCFCs is notably impeded by the restricted performance and insufficient lifespan of standard Ni-cermet anodes for ammonia cracking, especially at 550 °C or below. Herein, we report an efficient ammonia cracking layer with a formula of xCo3O4/100-xBaZr0.8Y0.2O3-δ (Co/BZY) (x=10, 20, 30), which is deposited onto the Ni-BaZr0.1Ce0.7Y0.1Yb0.1O3−δ (BZCYYb) anode to significantly enhance the NH3 decomposition catalytic activity, thereby improving the performance and durability of NH3-PCFCs at low temperatures. The cells with the addition of a 20Co/80BZY anode catalytic layer (ACL) exhibit low area-specific resistance (ASR) and promising operational longevity under NH3 conditions. At 550°C, the NH3-PCFCs with a 20Co/80BZY ACL exhibit a high peak power density of 0.626 W cm−2 and promising operation durability. This study provides important guidance for constructing high-performance and durable NH3-PCFCs.

Constructing tissue/organ analogs with natural structures and cell types in vitro offers a valuable strategy for the in situ repair of damaged tissues/organs. Three-dimensional (3D) bioprinting is a flexible method for fabricating these analogs. However, extrusion-based 3D bioprinting faces the challenge of balancing the use of soft bioinks with the need for high-fidelity geometric shapes. To address these challenges, recent advancements have introduced various suspension mediums based on gelatin, agarose, and gellan gum microgels. The emergence of these gel-based suspension mediums has significantly advanced the fabrication of tissue/organ constructs using 3D bioprinting. They effectively stabilize and support soft bioinks, enabling the formation of complex spatial geometries. Moreover, they provide a stable, cell-friendly environment that maximizes cell viability during the printing process. This minireview will summarize the properties, preparation methods, and potential applications of gel-based suspension mediums in constructing tissue/organ analogs, while also addressing current challenges and providing an outlook on the future of 3D bioprinting.

Excessive pilot workload is one of the significant causes of flight accidents. The detection of flight workload can help optimize aircraft crew operation procedures, improve cockpit human–machine interface (HMIs) design, and ultimately reduce the risk of flight accidents. However, traditional detection methods often employ invasive or patch-based devices that can interfere with the pilot’s control. In addition, they generally lack real-time capabilities, while the workload of pilots actually varies continuously. Moreover, most models do not take individual physiological differences into account, leading to the poor performance of new pilots. To address these issues, this study developed a real-time pilot workload detection model based on low-interference devices, including telemetry eye trackers and a pressure-sensing seat cushion. Specifically, the Adaptive KNN-Ensemble Pilot Workload Detection (AKE-PWD) model is proposed, combining KNN in the outer layer for identifying the physiological feature cluster with the ensemble classifier corresponding to this cluster in the inner layer. The ensemble model employs random forest, gradient boosting trees, and FCN–Transformer as base learners. It utilizes soft voting for predictions, integrating the strengths of various networks and effectively extracting the sequential features from complex data. Results show that the model achieves a detection accuracy of 82.6% on the cross-pilot testing set, with a runtime of 0.1 s, surpassing most studies that use invasive or patch-based detection devices. Additionally, the model demonstrates high accuracy across different individuals, indicating good generalization. The results are expected to improve flight safety.

In recent years, video data have increased in size, which results in enormous transmission pressure. Rate control plays an important role in stabilizing video stream transmissions by balancing the rate and distortion of video compression. To achieve high-quality videos through low-bandwidth transmission, video multi-scale-based end-to-end rate control is proposed. First, to reduce video data, the original video is processed using multi-scale bicubic downsampling as the input. Then, the end-to-end rate control model is implemented. By fully using the temporal coding correlation, a two-branch residual-based network and a two-branch regression-based network are designed to obtain the optimal bit rate ratio and Lagrange multiplier λ for rate control. For restoring high-resolution videos, a hybrid efficient distillation SISR network (HEDS-Net) is designed to build low-resolution and high-resolution feature dependencies, in which a multi-branch distillation network, a lightweight attention LCA block, and an upsampling network are used to transmit deep extracted frame features, enhance feature expression, and improve image detail restoration abilities, respectively. The experimental results show that the PSNR and SSIM BD rates of the proposed multi-scale-based end-to-end rate control are −1.24% and −0.50%, respectively, with 1.82% rate control accuracy.

This investigation involved the extraction of a novel polysaccharide from the spore fermentation broth of Tremella fuciformis using a method that combined ultrasonic and enzymatic assistance with ethanol precipitation. It was then compared with enzymatic and ultrasonic extraction methods. The objective of this research is to offer a reference point for expanding the application of ultrasonic-assisted enzymatic extraction technology in T. fuciformis polysaccharides (TFPs). Based on single-factor experiments, Box-Behnken was used to optimize the extraction conditions of TFPs by ultrasonic-enzymatic-assisted ethanol precipitation extraction. The results revealed an optimal combination of enzymes, with a cellulase-to-papain ratio of 2:1, an enzyme addition of 4000U/100 mL, an enzymolysis temperature of 49 °C, ultrasonic power at 3 W/mL and an ultrasonic time of 20 min. The extraction rate of TFPs and α- amylase inhibition rates were 23.94 % and 61.44 %, respectively. Comparing the physicochemical properties, structural characterization and in vitro activity of TFPs extracted through different methods, the results showed that ultrasonic treatment significantly influences the apparent morphology of polysaccharide and could enhance its in vitro biological activity. However, different extraction techniques exhibit insubstantial impact on the chemical composition, glycosidic bonds or glycosidic ring configurations within the polysaccharides. Among them, ultrasonic-enzymatic-assisted ethanol precipitation extraction of polysaccharide has the highest extraction rate and the lowest viscosity. It has significant effects on ABTS+ scavenging activity, α- amylase inhibition rate and glucose dialysis retardation index, polysaccharide treated with ultrasonic-enzymatic showed the best performance. These findings suggest that ultrasonic-enzymatic-assisted ethanol precipitation extraction can enhance the activities of TFPs, thereby providing a valuable insight for their future development and application.

Summary: Desiccant-coated heat exchangers provide a practical solution for the efficient removal of moisture from the air. Here, we present a protocol to synthesize an ultra-hygroscopic polymer to develop a LiCl loaded in curdlan hydrogel (LiCl@Cur)-coated heat exchanger for deep dehumidification. We describe steps for preparing the curdlan gel solution, hydrogel, LiCl solution, and LiCl@Cur. We then detail procedures for preparing curdlan-coated and LiCl@Cur-coated heat exchangers. The coated heat exchanger described in this protocol has a maximum dehumidification capacity of 12 g/kg.For complete details on the use and execution of this protocol, please refer to Pan et al. (2023).1 : Publisher’s note: Undertaking any experimental protocol requires adherence to local institutional guidelines for laboratory safety and ethics.

Ground Penetrating Radar (GPR) is a non-destructive geophysical technique utilizing electromagnetic pulses to detect subsurface material properties. The analysis of regions of interest (ROIs) in GPR images often entails the identification of hyperbolic reflection regions of underground targets through accurate segmentation, a crucial preprocessing step. Currently, this represents a research gap. In the hyperbolic reflection region, manual segmentation not only demands professional expertise but is also time-consuming and error-prone. Automatic segmentation can aid in accurately determining the location and depth of the reflection region, thereby enhancing data interpretation and analysis. This study presents a deep residual Convolutional Neural Network (Res-CNN) that integrates skip connections within an encoder-decoder framework for ROI-binarized hyperbolic segmentation. The proposed framework includes designed downsampling and upsampling modules that facilitate feature computation sharing between these two modules through skip connections within network blocks. In the evaluation of both simple and complex models, our method attained PSNR, SSIM, and FSIM values of 57.1894, 0.9933, and 0.9336, and 58.4759, 0.9958, and 0.9677, respectively. Compared to traditional segmentation methods, the proposed approach demonstrated clearer segmentation results, enabling intelligent and effective identification of the ROI region containing abnormal hyperbolic reflection waves in GPR images.

For an exhaustive removal of Cr(VI), three different types of biochar were prepared, modified and evaluated for Cr(VI) removal from wastewater. Magnetic eucalyptus biochar (MBC) was synthesized via a facile pyrolysis process of eucalyptus biochar pretreated with FeCl3 firstly coupled with K2CO3 activation. As a comparison, raw biochar (BC) and FeCl3 modified biochar (FBC) were prepared. The physicochemical property of adsorbent synthesis and removal mechanism was examined using BET, SEM with EDS, zeta potential, XRD, FTIR, XPS and Vibrating sample magnetometer (VSM), and the effect of various reaction conditions and parameters were evaluated for Cr(VI) removal. Results demonstrated that compared to BC and FBC, MBC has a larger specific surface area (870.3264 m2/g), higher content of Fe mainly existed as γ-Fe2O3 and Fe3O4, and higher zeta potential (10.66). This is due to the significant alleviation of pore blockage caused by iron oxides and more efficient conversion of iron oxides with the participation of K2CO3 during modification process. Upon batch tests, the removal efficiency could achieve more than 91% for 200 mg/L Cr(VI) solution with optimum adsorbent dosage (0.01 g) at desired acid condition (pH = 2) within 9 h. The separation behavior of MBC for Cr(VI) was highly identical with elovich and Freundlich model. MBC showed better adsorption performance up to three cycles and under various co-existing ions. Besides, the removal ability of MBC gained an obvious increase of 2–5 folds and 3–10 folds at different initial Cr(VI) concentrations, compared with BC and FBC. Characterization analyses unraveled that physicochemical removal process was responsible for the efficient elimination of Cr(VI) in an aqueous solution, resulting from adsorption, electrostatic attraction, pore filling, complexation, ion exchange, redox reaction and precipitation due to the fabrication of iron oxides into biochar.

Abstract A “turn‐on” fluorescent sensing strategy was developed for ultrasensitive detection of heavy metal mercury ion (Hg2+) directly in water. It utilized an aqueous soluble aggregation‐induced emission (AIE)‐active probe, named 1,1,2,2‐tetrakis(4‐(1H‐tetrazol‐5‐yl)phenyl)ethene (TPE‐4TA), composing of a luminescent tetraphenylene core and multiple anionic tetrazolate spawns. The probe retained dark when molecularly dissolved in water. However, mercury ions can ligate with multiple tetrazolate groups to form infinite coordination polymer particles spontaneously, thus inducing a fluorogenic AIE response for in situ analysis. Moreover, by embedding this AIE sensing molecules in a hydrophilic polyvinyl alcohol substrate, the resulting hydrogel film allowed in situ detection of Hg2+ on a laser‐induced fluorescence analysis setup in aqueous environment. This strategy worked effectively in common aqueous environments with pH from 4 to 7.5 and showed high sensitivity (limit of detection down to 0.38 ppb/1.9 nM), good selectivity and a wide linearity range for quantification. This work may lead to a reliable and promising platform for on‐site environmental water analysis.

A Si layer with a thickness of 100 nm and a 50 nm C layer were sputtered on copper-based graphene by magnetron sputtering to form a Graphene/Si/C composite nanolayer electrode. The lithium storage performance was studied by the constant current charge-discharge test, cyclic voltammetry test, cycle performance test, and AC impedance test. The results show that the reversible capacity of the Graphene/Si/C thin film electrode has a serious decay in the early cycle, with an average decay of 1.79 mAh/g per cycle within 200 cycles; the decay is relatively gentle in the latter stage, with an average decay of 0.723 mAh/g per cycle after 1000 cycles. By comparing the SEM and EDS before and after cycling, it was found that the film was broken after cycling, resulting in the loss of active substances.

Directional hydraulic fracturing (DHF) is popular with hydraulic fracturing operations in coal mining to create cave-hard roofs, in which radial initial notches are created around open borehole walls before injecting high-pressurized fluid. Despite extensive field application of DHF, the three-dimensional irregular hydraulic fracture (HF) geometry in DHF remains unclear, and the HF re-orientation mechanism requires comprehensive understanding. Here, we experimentally examined factors affecting HF re-orientation in DHF in transparent gelatin samples with a self-developed experimental device. We found that it is the ratio between the differential stress and gelatin elastic moduls that determines HF re-orientation rather than the absolute magnitudes of these two factors. Both shear failure and tensile failure occur during HF re-orientation. The HF tends to propagate asymmetrically, and the step-like HF geometry is likely to form in gelatin samples with low elastic moduli and under high differential stresses. HF re-orientation is not necessarily a near-borehole effect, and HFs can propagate along the notch direction for longer distances in stiffer gelatin samples under relatively low or moderate differential stresses. Finally, recommendations are provided for the effective utilization of DHF at coal mine sites.

The essence of Chinese calligraphy inheritance resides in calligraphy education. However, it encounters challenges such as a scarcity of calligraphy instructors, time-consuming and inefficient manual assessment methods, and inconsistent evaluation criteria. In response to these challenges, this paper introduces a deep learning-based automatic calligraphy evaluation model. Initially, hard-pen handwriting samples from 100 volunteers were collected and preprocessed to create a dataset consisting of 4800 samples, along with the corresponding label files for hard-pen calligraphy evaluation. Subsequently, YOLOv5 was utilized for region detection and character recognition on the evaluation samples to obtain the corresponding standard samples. Lastly, a Siamese metric model, with VGG16 as the primary feature extraction submodule, was developed for hard-pen calligraphy evaluation. To improve feature extraction and propagation, a transformer structure was introduced to extract global information from both the evaluated and standard samples, thereby optimizing the evaluation results. Experimental results demonstrate that the proposed model achieves a precision of 0.75, recall of 0.833, and mAP of 0.990 on the hard-pen calligraphy evaluation dataset, effectively realizing automatic calligraphy evaluation. This model presents a novel approach for intelligently assessing hard-pen calligraphy.

Timely detection of defects is essential for ensuring safe and stable operation of concrete buildings. Automatic segmentation of concrete buildings’ surfaces is challenging due to the high diversity of crack appearance, the detailed information, and the unbalanced proportion of crack pixels and background pixels. In this work, the Double Feature Pyramid Network is designed for high-precision crack segmentation. Our work reached the state-of-the-art level in crack segmentation, with key contributions outlined as follows: firstly, considering the diversity of crack shapes, the network constructs a feature pyramid containing three feature extraction backbones to extract the global feature map with three scale input images. In particular, due to the biggest challenge being too much single-pixel crack area, the targeted feature pyramid based on the high-resolution is added to extract adequate shallow semantic information. Lastly, designing a cascade feature fusion unit to aggregate the extracted multi-dimensional feature maps and obtain the final prediction. Compared with existing crack detection methods, the superior performance of this method has been verified based on extensive experiments, with Pixel Accuracy of 65.99%, Intersection over Union of 44.71%, and Recall of 62.95%, providing a reliable and efficient solution for the health monitoring and maintenance of concrete structures. This work contributes to the advancement of research and practical applications in related fields, offering robust support for the monitoring and maintenance of concrete structures.

Global photographic aesthetic image generation aims to ensure that images generated by generative adversarial networks (GANs) contain semantic information and have global aesthetic feelings. Existing image aesthetic generation algorithms are still in the exploratory stage, and images screened or generated by a computer have not yet achieved relatively ideal aesthetic quality. In this study, we use an existing generative model, StyleGAN, to build the height of image content and put forward a new method based on the GAN disentangled representation of a global aesthetic image generation algorithm by mining GANs’ latent space, potential global aesthetic feeling, and aesthetic editing of the original image to realize the aesthetic feeling and content of high-quality global aesthetic image generation. In contrast with the traditional aesthetic image generation methods, our method does not need to retrain GANs. Using the existing StyleGAN generation model, by learning a prediction model to score the generated image and the score as a label to learn a support vector machine decision surface, we use the learned decision to edit the original image to obtain an image with a global aesthetic feeling. This method solves the problems of poor content construction effect and poor global beauty of the aesthetic images generated by the existing methods. Experimental results show that the proposed method greatly increases the aesthetic score of the generated images and makes the generated images more in line with people’s aesthetic.

2-butanone has been certified to cause potential harm to the human body, environment, etc. Therefore, achieving a method for the high sensitivity and low limit detection of 2-butanone is of great significance. To achieve this goal, this article uses ZIF-67 prepared by a precipitation method as a cobalt source, and then prepares cobalt-modified zinc oxide nanosheets through a hydrothermal method. The microstructure of the materials was observed by SEM, EDS, TEM, HRTEM, XPS and XRD. The test data display that the sensor ZC2 can produce a high response (2540) to 100 ppm 2-butanone at 270 °C, which is 21 times higher than that of pure ZnO materials. Its detection limit is also optimized to 24 ppb. The sensor (ZC2) also excels in these properties: selectivity, repeatability and stability over 30 days. Further analysis indicates that the synergistic and catalytic effects of p-n heterojunction are the key sources for optimizing the performance of sensors for detecting 2-butanone.

Unsaturated soil exhibits extremely complex engineering mechanical properties under the coupling effect of moistening and stress fields. Firstly, the effective stress principle and limit equilibrium conditions of unsaturated soil under the coupling effect of moistening and stress fields were discussed based on the basic principles of unsaturated soil. Secondly, a fractional-order model considering the viscoelasticity and strain hardening of unsaturated soil was established based on the fractional calculus theory. Then, based on the principle of damage mechanics, the damage variable evolution equation under the coupling effect of moistening and stress fields was established, and the fractional calculus-based statistical constitutive damage model of unsaturated soil under the coupling effect of moistening and stress fields was developed. In turn, parameters of the developed model were solved using a triaxial test of unsaturated loess, and the calculated data using the developed model were compared with the experimental data, which demonstrated that the developed model in this paper performed well in describing the whole strain hardening process of unsaturated soil under the coupling effect of moistening and stress fields. Finally, the sensitivity of the main parameters of the developed model was discussed under the coupling effect of moistening and stress fields, which showed that the proposed model performed well in reflecting the main mechanical properties of unsaturated loess.

In response to the problem of no supporting equipment for the cultivation of Codonopsis in the hilly and mountainous areas of northwest China, a combined machine for transplanting outcrop of Codonopsis with micro ridges covered with film is designed. The key components of the prototype are analyzed and designed, and the structures and working parameters of the seedbed preparation device, seedling-casting device, rotary tillage soil-covering device, film-covering device, seedling head burial, and film edge soil-covering device are determined. The transmission system scheme is established, and the working mechanism of the core components is analyzed. Field experiments show that when the target seedling spacing is 4.4 cm and the machine moves forward at a speed of 0.1, 0.15, and 0.2 m/s, the variation coefficient of planting spacing and the qualification rate of planting depth meet the standard requirements. The qualified rate of planting posture and film side outcrop are greatly affected by the operating speed of the machine and decrease with the increase in operating speed. When the operating speed reaches 0.1 m/s, the average variation coefficient of planting spacing is 0.08% and the average qualified rate of planting depth, planting posture and film side outcrop is 95.83%, 94.17%, and 93.33%, respectively, which shows that the operating performance is better than that of the operating speeds of 0.15 m/s and 0.2 m/s. This study provides a new reference for the theoretical research and design of mechanized and automated transplanting machinery for Codonopsis seedlings.

According to the intelligent development needs of railway operation and maintenance, turnout maintenance also needs an efficient and intelligent means of detection. It is the main method used to measure the access depth of static contact manually. In order to change the disadvantages of the low efficiency and strong subjectivity of traditional schemes, a low-complexity accurate ranging algorithm of the Mask RCNN is proposed to measure the on–off working parts. Firstly, the Mask RCNN and an interactive iterative method are used to segment the region of interest accurately twice. Secondly, the graph distortion is corrected according to the vertex mapping principle. Finally, the accurate actual distance is calculated through fitting the linear distance transformation equation. Through the secondary segmentation and correction algorithm, the accurate calculation of a small target is completed. The experimental results show that the algorithm can accurately measure the distance of different working parts; the average processing time is 0.8 s/amplitude and the measurement error is ±1 mm.

Human emotion label prediction is crucial to Artificial Intelligent in the Internet of Things (IoT). Facial expression recognition is the main technique to predict human emotion labels. Existing facial expression recognition methods do not consider the compound emotion and the fuzziness of emotion labels. Fuzzy learning is a mathematical tool for dealing with fuzziness and uncertainty information. The advantage of using fuzzy learning for human emotion recognition is that multiple fuzzy sentiment labels can be processed simultaneously. This paper proposes a fuzzy learning-based expression recognition method for human emotion label prediction. First, a fuzzy label distribution system is constructed using fuzzy sets for representing facial expressions. Then, two fuzzy label distribution prediction methods based on fuzzy rough sets are proposed to solve the compound emotion prediction. The probability that a sample is likely and definitely belongs to an emotion is obtained by calculating the upper and lower approximations. Experiments show the proposed algorithm not only performs well on human emotion label prediction but can also be used for other label distribution prediction tasks. The proposed method is more accurate and more general than other methods. The improvement of the method on the effect of emotion recognition extends the application scope of artificial intelligence in IoT.

Ferroptosis is a type of iron-dependent programmed cell death. Once such kind of death occurs, an individual cell would undergo a series of changes related to reactive oxygen species (ROS) in mitochondria. A mitochondrial-targeted ratiometric fluorescent probe (MBI-OMe) was developed to specifically detect ferroptosis-induced ClO−, whose recognition group is p-methoxyphenol, and the mitochondrial-targeted group is benzimidazole. The fluorescence of MBI-OMe was first quenched by 30 μM of Fe3+, and then MBI-OMe appeared as a ratiometric signal at 477 nm and 392 nm in response to ferroptosis-induced ClO− in living cells. MBI-OMe was successfully used to evaluate changes in ClO− induced by ferroptosis.

X-ray generation via synchrotron radiation and bremsstrahlung in the interaction of short laser pulses with a solid target is of much current interest owing to its numerous applications. The efficiency of laser to X-ray energy conversion is thus a crucial factor. We found that the energy conversion efficiency of synchrotron radiation and bremsstrahlung is mainly governed by the ratio of the laser pulse width to the preplasma width, which is in turn governed by the laser profile, intensity, and spot size. Synchrotron radiation dominates when the ratio is less than unity, otherwise bremsstrahlung dominates. The type of radiation can thus be controlled by tailoring the laser parameters.

The poor cycle stability caused by the shuttle effect of polysulfides which have been key scientific issue in the development of high-efficiency lithium–sulfur (Li–S) batteries. In this work, the authors report a Fe-doped Co3O4 (named FCO) that was used as a sulfur-loaded host material for Li–S batteries. We demonstrate the important roles of well-designed Co3O4 particles and Fe atoms in regulating polysulfide conversion due to the strong adsorption of polysulfides by polar Co3O4, whereas Fe atoms and Co3O4 catalyze polysulfide conversion. Therefore, the LiS batteries with FCO-180 (When the hydrothermal temperature is 180 °C) sea urchinlike composites exhibited a high superior energy density (992.7 mAh g−1 at 0.2 C, after 100 cycles) and long-term cyclability (649.4 mAh g−1 at 1 C, 300 cycles) with high sulfur loading (75 wt%). This work confirms that the FCO-180 sea urchinlike increases not only the capacity of high-rate but also a generic and feasible strategy to construct practical Li–S batteries for emerging energy-storage applications.

The purpose of this study was to investigate the effect of ultrasound-assisted enzymolysis on modified solubilization of yeast β-glucan and its related mechanism. The depolymerization effects of this system on the physicochemical properties and structural features of the degraded fragments were studied systematically. The structure and physicochemical properties of the samples showed that the solubility of yeast β-glucan achieved 75.35 % after modification; and ultrasonic enzymatic enhanced the degradation efficiency. The yeast β-glucan obtained after solubilization and modification owned better antioxidant activities. The yeast β-glucan particles become obviously smaller, sparsely dispersed in the aqueous solution and the stability was improved. In addition, the hydrogen bonds in yeast β-glucan native triple helix structure were partially broken. Moreover, the disruption of yeast β-glucan's original structure made it decreased thermostability and easier to dissolve in water. The atomic force microscope (AFM) imaging directly verified the branched-chain morphology of yeast β-glucan and the small-strand degradation fragments. Therefore, this research can provide a feasible and effective approach for improving solubility of water-insoluble yeast β-glucan to enlarge its food and biomedical applications.

Abstract As an important category of porous crystalline materials, metal‐organic frameworks (MOFs) have attracted extensive research interests owing to their unique structural features such as tunable pore structure and enormous surface area. Besides controlling the size, dimensionality, and composition of MOFs, further exploring the crystal‐phase‐dependent physicochemical properties is essential to improve their performances in various applications. Recently, great progress has been achieved in the phase engineering of nanomaterials (PEN), which provides an effective strategy to tune the functional properties of nanomaterials by modulating the arrangement of atoms. In this review, we adopt “phase” instead of “topology” to describe the crystal structure of MOFs and summarize the recent advances in phase engineering of MOFs. The two main strategies used to control the phase of MOFs, that is, phase‐controlled synthesis and phase transformation of MOFs, will be highlighted. The roles of various reaction parameters in controlling the crystal phase of MOFs are discussed. Then, the phase dependence of MOFs in various applications including luminescence, adsorption, and catalysis are introduced. Finally, some personal perspectives about the challenges and opportunities in this emerging field are presented.

Tuberculosis (TB) is still a global disease threatening people’s lives. With the emergence of multi-drug-resistant Mycobacterium tuberculosis the prevention and control of tuberculosis faces new challenges, and the burden of tuberculosis treatment is increasing among the world. Ilamycins are novel cyclopeptides with potent anti-TB activities, which have a unique target protein against M. tuberculosis and drug-resistant strains. Herein, ilamycin F, a major secondary metabolite isolated from the marine-derived mutant strain Streptomyces atratus SCSIO ZH16 ΔilaR, is used as a scaffold to semi-synthesize eighteen new ilamycin derivatives (ilamycin NJL1–NJL18, 1–18). Our study reveals that four of ilamycin NJLs (1, 6, 8, and 10) have slightly stronger anti-TB activities against Mtb H37Rv (minimum inhibitory concentration, 1.6–1.7 μM) compared with that of ilamycin F on day 14th, but obviously display more potent activities than ilamycin F on day 3rd, indicating anti-TB activities of these derivatives with fast-onset effect. In addition, cytotoxic assays show most ilamycin NJLs with low cytotoxicity except ilamycin NJL1 (1). These findings will promote the further exploration of structure-activity relationships for ilamycins and the development of anti-TB drugs.

Environmental monitoring is a crucial part of environmental management on construction sites. With the increasing integration of environmental-monitoring systems and cyber-physical systems (CPS), the environmental-monitoring cyber-physical system (E-CPS) has been developed, but it still suffers from uncertainty problems and a lack of robustness. In this study, ontology is utilized to establish an E-CPS model that can realize the integration and interaction of physical space, cyberspace, and social space, and the E-CPS model contains perception, transportation, fusion, and decision-making layers. Three uncertainty scenarios are then identified in four layers of the E-CPS to address the current E-CPS shortcomings. The proposed E-CPS model is applied in a construction project, and simulation experiments are then conducted on construction sites. The results show that the abnormal-data-recognition algorithm based on spatiotemporal correlation, whose detection rate is stable around 96%, improves the system’s anti-interference ability against anomalous data entering the perception layer and the transportation layer. This algorithm ensures the accuracy of environmental monitoring for early warning. The sensory data-fusion results based on the belief function method vary from 52.16 to 52.50, with a decrease rate reduced to 0.65%. Finally, the decision-fusion algorithm based on the improved Dempster–Shafer (D-S) evidence theory achieves robust performance. This study could enhance the robustness of the E-CPS in uncertainty conditions and aid the project managers to make decisions and take targeted measures according to the environmental monitoring results and experts’ decisions.

It is important to create a comfortable wind environment around high-rise buildings for outdoor activities. To predict the wind environment, Computational Fluid Dynamics (CFD) has been widely used by designers and engineers. However, the simulation results of different CFD turbulence models might significantly vary. This paper researched the wind environment around a typical high-rise building and verified the accuracy of the CFD simulations based on polyhedral meshes. The differences between the simulation results of the k-ε turbulence models and those of the wind tunnel experiments were compared from the perspectives of wind speed and turbulence energy. The results show that the modified k-ε models could still not perfectly match the wind tunnel experiment results. Specifically, in the low-wind-speed areas, the simulation results of the Realizable Two-Layer K-Epsilon (RTLKE) model were the closest to the experimental results of the wind tunnels, while in the high-wind-speed areas the simulation results of the Standard Two-Layer K-Epsilon (STLKE) model were the closest to the experimental results of the wind tunnels. Therefore, it is recommended that these two k-ε turbulence models are applied under different conditions—the RTLKE model should be used to simulate low-wind areas around high-rise buildings (e.g., defining the size of the static-wind area around high-rise buildings, predicting the diffusion time of pollutants around high-rise buildings, etc.); STLKE should be used to simulate high-wind-speed areas around high-rise buildings (e.g., the high speed wind area around high-rise buildings during a typhoon, the maximum wind speed area around high-rise buildings, etc.). It is expected that findings from this research study supplement some existing high-rise building design guidance.

Building extraction from high spatial resolution imagery (HSRI) plays an important role in the remotely sensed imagery application fields. However, automatically extracting buildings from HSRI is still a challenging task due to such factors as large size variations of buildings, background complexity, variations in appearance, etc. Especially, it is difficult to extract both crowded small buildings and large buildings with accurate boundaries. To address these challenges, this paper presents an end-to-end encoder–decoder model to automatically extract buildings from HSRI. The designed network, called AEUNet++, is based on UNet++, attention mechanism and multi-task learning. Specifically, the AEUNet++ introduces the UNet++ as the backbone to extract multiscale features. Then, the attention block is used to effectively fuse different-layer feature maps instead of direct concatenation in the output of traditional UNet++, which can assign adaptive weights to different-layer feature maps as their relative importance to enhance the sensitivity of the mode and suppress the background influence of irrelevant features. To further improve the boundary accuracy of the extracted buildings, the boundary geometric information of buildings is integrated into the proposed model by a multi-task loss using a proposed distance class map during training of the network, which simultaneously learns the extraction of buildings and boundaries and only outputs extracted buildings while testing. Two different data sets are utilized for evaluating the performance of AEUNet++. The experimental results indicate that AEUNet++ produces greater accuracy than U-Net and the original UNet++ architectures and, hence, provides an effective method for building extraction from HSRI.

Acute myocardial infarction has a high clinical mortality rate. The initial exclusion or diagnosis is important for the timely treatment of patients with acute myocardial infarction. As a marker, cardiac troponin I (cTnI) has a high specificity, high sensitivity to myocardial injury and a long diagnostic window. Therefore, its diagnostic value is better than previous markers of myocardial injury. In this work, we propose a novel aptamer electrochemical sensor. This sensor consists of silver nanoparticles/MoS2/reduced graphene oxide. The combination of these three materials can provide a synergistic effect for the stable immobilization of aptamer. Our proposed aptamer electrochemical sensor can detect cTnl with high sensitivity. After optimizing the parameters, the sensor can provide linear detection of cTnl in the range of 0.3 pg/ml to 0.2 ng/ml. In addition, the sensor is resistant to multiple interferents including urea, glucose, myoglobin, dopamine and hemoglobin.

Interest in cancer immunotherapy has rapidly risen since it offers many advantages over traditional approaches, such as high efficiency and prevention of metastasis. Efforts have primarily focused on two major strategies for regulating the body's antitumor immune response mechanisms: “enhanced immunotherapy” that aims to amplify the immune activation, and “normalized immunotherapy” that corrects the defective immune mechanism in the tumor immune microenvironments (TIMEs), which returns to the normal immune trajectory. However, due to the complexity and heterogeneity of the TIMEs, and lack of visualization research on the immunotherapy process, cancer immunotherapy has not been widely used in clinical setting. Recently, through the design and modification of nanomaterials, intelligent TIME-responsive nanoplatforms were developed from which encouraging results in many aspects of immunotherapy have been achieved. In this mini review, the status of designed nanomaterials for nanoplatform-based immune regulation of TIMEs has been emphasized, particularly with respect to the aforementioned approaches. It is envisaged that future prospects will focus on a combination of multiple immunotherapies for more efficient cancer inhibition and elimination.

Software-defined networking (SDN) decouples the control plane and data plane through OpenFlow technology and allows flexible network control. It has been widely applied in different areas and has become a focus of attention in the future network. With SDN’s development, its security problem has become a necessary point of research to be solved urgently. In this paper, we propose a novel attack, namely, the packet injection exploiting attack. By maliciously injecting false hosts into SDN network topology, attackers can further use them to launch a denial of service (DoS) attack. The consequences affect the throughput and processing capabilities of the controller, severely consume data plane resources, and ultimately affect the entire network. To prevent the packet-injection exploiting attack, we designed PIEDefender, an efficient, protocol-independent component built on SDN controllers to detect and mitigate attacks effectively. We implement the PIEDefender prototype on the Floodlight controller and assess the effectiveness in the software environment. Experimental results show that PIEDefender achieves a 97.8% injection detection precision and a 97.96% DoS detection precision, incurring an average CPU consumption of 10%. The evaluation demonstrates that the PIEDefender can effectively mitigate the attack against SDN with limited overhead.

A novel magnetic solid phase extraction based on mercaptophenylboronic acid (MPBA)-functionalized Fe3O4-NH2@Au nanomaterial (Fe3O4-NH2@Au-MPBA) was developed for selective separation and enrichment of catecholamines (including dopamine, norepinephrine and adrenaline). Fe3O4-NH2@Au-MPBA nanoparticles were achieved by self-assembly-anchoring MPBA molecules on the surface of Fe3O4-NH2@Au nanocomposites, which were synthesized via a facial ultrasonic auxiliary in situ reduction process. The interaction between cis-diol from catecholamines and boronic acid was reversible and could be flexibly controlled by adjusting pH value. The catecholamines could be quickly adsorbed by Fe3O4-NH2@Au-MPBA in weak alkaline solution (pH 8.0–9.0) and subsequently released in acid solution (pH 1.0–2.0). The process of adsorption and dissociation was very fast. Furthermore, the three catecholamines could be detected in urine from children by high performance liquid chromatography (HPLC) with electrochemical detector. Under optimal conditions, norepinephrine (NE), epinephrine (EP) and dopamine (DA) were separated very well from internal standard and exhibited a good linearity in the range of 2.5–500.0 ng mL−1, with correlation coefficients of r2 > 0.9907. Limits of detection (LOD) (signal to noise = 3) were 0.39, 0.27 and 0.60 ng mL−1 for NE, EP and DA, respectively. Recoveries for the spiked catecholamines were in the range of 85.4–105.2% with the relative standard deviation (RSD) < 11.5%.

The uncertainty failure of the used part leads the complexity selection of remanufacturing processes. The different remanufacturing process combinations among used parts of used products also make the formulation of tolerance schemes more difficult. It is hard to guarantee the optimality of process-tolerance schemes by traditional serial production modes in general, in which tolerance design is followed by process formulation. In order to generate the optimal remanufacturing scheme of process and tolerance for used products, an optimization method to integrate designs of process-tolerance (IDP-T) based on fault features was presented. In this work, the failure description set of used parts was constructed by combining the attribute characteristics and failure characteristics. Case-based reasoning (CBR) was first utilized to generate the feasible remanufacturing process plans of used parts. Then, based on the feasible process plans, the factors of cost, quality loss, closed-loop accuracy and machining ability of remanufacturing were comprehensively considered to construct the optimization model of IDP-T. The Beetle Antennae Search algorithm (BAS) was used for the optimal alternative selection. Finally, a used gearbox was taken as an example to illustrate the validity and practicality of the proposed method. The results showed that the proposed method was effective in the optimization of IDP-T for remanufacturing.

Ransomware has become a serious threat on Android and new cases of ransomware are continuously growing. Most existing ransomware detectors use sensitive text or APIs to detect ransomware. Some goodware applications with the functionalities of locking screen and encrypting files have similar behaviors with ransomware. It is difficult for ransomware detectors to identity them. In this paper, we made detailed analyses of three kinds of active ransomware. We proposed a behavior-based ransomware detector on Android, called KRDroid. KRDroid deploys on servers or PCs, that is, ransomware cannot be activated and cause any loss during testing. Experiments showed that our ransomware-oriented detector can find 1809 of 1862 unseen ransomware. It can also distinguish goodware with similar ransom behaviors to ransomware with an accuracy of 97.5%.

Cu2O/CuO nanosheets in-situ grown on Cu-Carbon cloths (Cu-CCs), namely Cu2O/CuO@Cu-CCs, are constructed by a simple strategy with electroless copper plating, chemical etching, and thermal dehydration. The as-prepared material is directly used as binder-free electrodes for supercapacitors (SCs). CCs coated with Cu, as the current collector, can effectively promote the charge collection and electron transfer, while the hierarchical Cu2O/CuO nanosheets provide massive active sites for fast faradic reactions. The composite electrode exhibits high specific capacitance [1.71 F cm−2, equivalent to 835.2 F g−1, at the current density of 10 mA cm−2 (3.57 A g−1)]. The asymmetric supercapacitor device using Cu2O/CuO@Cu-CCs as the positive electrode and activated carbon as the negative electrode, achieves a superior energy density up to 60.26 Wh kg−1 at a power density of 299.73 W kg−1 and an excellent long-term cycling stability (9.65% loss of its initial capacitance after 5,000 cycles). The excellent electrochemical performance is mainly ascribed to the unique hierarchical structure of Cu2O/CuO@Cu-CCs, making it attractive as a potential electrode material for high performance SCs.

In order to further improve the photocatalytic performance of BiFeO3 (BFO), novel Au-induced hierarchical nanofibers/nanoflakes structured BiFeO3 homojunctions (Aux-BFO, x = 0, 0. 6, 1.2, 1.8, 2.4 wt%) were in situ synthesized through a simple reduction method with assist of sodium citrate under the analogous hydrothermal environment. The effect of loading amount of Au nanoparticles (NPs) on the physicochemical properties and photocatalytic activity was investigated in detail. The Au1.2-BFO NFs sample show the best photocatalytic activity (85.76%), much higher than that for pure BFO samples (49.49%), mainly due to the hierarchical nanofibers/nanoflakes structured homojunction, the surface plasmon resonance (SPR) effect of Au NPs, as well as the presence of defects (Fe2+/Fe3+ pairs and oxygen vacancy). Furthermore, the possible formation mechanism of the unique homojunction and the enhanced photocatalytic mechanism for the degradation of methylene blue (MB) dye are proposed. It is proven that holes (h+) play the decisive role in the photocatalytic process. The present work provides a fascinating way to synthesize efficient homojunctions for the degradation of organic pollutes.

In this paper, the effects of Bamboo shoot dietary fiber (BSDF) on the mechanical properties, moisture distribution, and microstructure of frozen dough were investigated. The state and distribution of water in frozen dough was determined by differential scanning calorimetry (DSC) and low-field nuclear magnetic resonance (LNMR) spectroscopy. The microstructure of frozen dough was studied. The structure of the gluten protein network found in wheat flour dough was studied by scanning electron microscopy (SEM). The result showed that the BSDF could significantly improve the viscoelasticity and extensibility of frozen dough after thawing in a dose-dependent manner. It was significantly improved with the increase in the addition amount of BSDF (P

This paper demonstrates a method for the development of stepped-metal coating on optical fiber Bragg grating. The paper also analyzes the dual-peak resonance restructured by the nickel/copper stepped-metal coating. According to the coefficients of linear thermal expansion of the coatings, the modeling and experimental analysis is categorized into three types: Type A, Type B, and Type C. We denote the temperature sensitivity difference between the two peaks as ΔKT for Type A, ΔK′T for Type B, and ΔK″T for Type C. The experimental results show that ΔKT, ΔK′T, and ΔK″T are 2.1 pm/°C, 6.5 pm/°C, and 0.8 pm/°C, respectively. The model analysis and the experimental results all show the Type B stepped-metal coating causes the most obvious temperature sensitivity difference between the two resonance peaks. The stepped-metal coating on the same one Bragg grating can restructure the single resonance into dual-peak resonance with different temperature sensing, and Type B can be used to develop a dual-parameter optical fiber Bragg grating sensor at one location which can measure two physical parameters simultaneously.

This paper develops a novel classification optimization approach integrating class adaptive Markov Random Field (MRF) and fuzzy local information (CAMRF-FLI) for high spatial resolution multispectral imagery (HSRMI). Firstly, the raw classification results, including initial fuzzy memberships and class labels of every pixel, are achieved by a pixel-wise classification method for a given image. Secondly, the class adaptive MRF-based data energy function is developed to integrate class spatial dependency information. Thirdly, a novel spatial energy function integrating fuzzy local information is constructed. Finally, based on the total of data and spatial energies, the raw classification map is regularized by a global minimization of the energy function using its iterated conditional modes (ICM). The effectiveness of CAMRF-FLI is performed by two data sets. The results indicate it can refine the classification map in homogeneous areas, meanwhile, reduce most of the edge blurring artifact, and improve the classification accuracy compared with some conventional approaches.