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The synergistic effects of carboxymethylcellulose (CMC) combined with Cryptococcus laurentii FRUC DJ1 were studied on controlling green mould resulting from Penicillium digitatum in grapefruit fruit. The results indicate that both C. laurentii and the CMC treatment suppressed P. digitatum conidia germination. In addition, C. laurentii growth in vitro was not affected by low CMC concentrations, nevertheless, the biofilm of C. laurentii was enhanced. Compared with the control fruit, the grapefruit had a lower green mould in all treatments. Significantly synergistic effects were caused by combining C. laurentii and CMC on minimum decay incidence and lesion diameter. Combined treatment induced defence enzyme activities, including chitinase, β-1,3-glucanase, peroxidase, polyphenol oxidase, and phenylalanine ammonia-lyase, together with disease tolerance-associated total phenol. Also, this combination inhibited the pathogen growth by adhered to the hyphae and reduced its infection in fruit wounds. Moreover, the commercial quality parameters in the combined treatment of C. laurentii and CMC, including weight loss, total soluble solids, ascorbic acid, and titratable acidity, were superior to single treatment. The combination of C. laurentii and CMC can not only control postharvest decay but also maintain fruit qualities. Thus, it can be used in grapefruit for commercial purposes.

Soft lithography is one of the most promising nanofabrication technologies for scalable nanomanufacturing. It uses flexible molds or stamps to fabricate extremely high-resolution nanopatterns in a low cost and rapid fashion on both flat and curved surfaces. Although two-dimensional soft lithography has been widely investigated and applied for some applications, there is a lack of quantitative research on using nanometer scale 3D patterns fabricated by atomic force microscope (AFM) for 3D soft lithography. In this paper, we demonstrated the effectiveness of ultrasonic vibration assisted AFM-based nanomachining in fabricating 3D master nanotemplates for 3D soft lithography. We successfully applied Polydimethylsiloxane (PDMS) daughter molds, casted from a nanometer scale 3D master template, using a soft lithography technique solvent-assisted micromolding. In addition, we investigated the quality and reusability of the master nanotemplates, and the quality of the PDMS patterns by quantifying the volume differences using an automatic imaging registration algorithm. Results show that there are zero or little PDMS residuals in the polymethyl methacrylate (PMMA) master nanotemplate with a simple contour for the first and second daughter mold casting, but 7% residuals for the third casting. And a more complex master nanotemplate shows slightly higher residual levels.

Configuration evaluation is a key technology to be considered in the design of multiple aircrafts formation (MAF) configurations with high dynamic properties in engineering applications. This paper deduces the relationship between relative velocity, dynamic safety distance and dynamic adjacent distance of formation members, then divides the formation states into collision-state and matching-state. Meanwhile, probability models are constructed based on the binary normal distribution of relative distance and relative velocity. Moreover, configuration evaluation strategies are studied by quantitatively analyzing the denseness and the basic capabilities according to the MAF collision-state probability and the MAF matching-state probability, respectively. The scale of MAF is grouped into 5 levels, and previous lattice-type structures are extended into four degrees by taking the relative velocities into account to instruct the configuration design under complex task conditions. Finally, hardware-in-loop (HIL) simulation and outfield flight test results are presented to verify the feasibility of these evaluation strategies.

The local microenvironment may influence the success of stem cell therapy. Iron overload occurs in many hemorrhagic injuries due to hemolysis and hemoglobin degradation, which not only mediates local cell injury, but also induces damage to the transplanted cells. Here, an injectable nanoparticle encapsulated core-shell hydrogel was fabricated for simultaneous iron overload clearance and bone marrow mesenchymal stem cells (BMSCs) transplantation following intracerebral hemorrhage (ICH). The iron chelator-loaded low-molecular-weight keratin hydrogel with quick degradation property was selected as the outer shell to eliminate iron overload, and BMSCs implantation with high-molecular-weight keratin hydrogel was selected as the inner core. The epidermal growth factor and the basic fibroblast growth factor were entrapped within the poly (lactic-co-glycolic acid) (PLGA) nanoparticle, which was then encapsulated into the core hydrogel to support the BMSC growth and differentiation. The core-shell hydrogel can be easily formed by programmed injections, and the core-shell hydrogel displayed a strong protective effect against the toxicity of hemoglobin in cell experiments. Furthermore, more BMSCs survived in the core-shell hydrogel group in vivo as compared to that in the core hydrogel group and the vehicle group. Less iron deposition and ventricular enlargement, lower brain water content, and faster neurological recovery were also observed. The data demonstrated that this core-shell hydrogel is an effective strategy for promoting transplanted cell survival under the condition of an iron overload.

Ultrasonic-assisted ball burnishing (UABB) is a promising surface strengthening technology for improving the properties of components. This method superimposes ultrasonic vibration to the traditional deep rolling; it can improve the surface roughness of a material, enhance its surface hardness, and introduce a high compressive residual stress, thereby improving the fatigue performance of components. A mathematical model should be established to predict and analyze the relationship between the experimental process parameters and results and to predict the effect of UABB on surface roughness. In the present work, a mathematical model of surface roughness after UABB was established on the basis of Hertz contact theory, comprehensively considering the process parameters, dynamic vibration forces, and elastic rebound of the targets. UABB experiments were carried out to verify the correctness of the mathematical model. Results showed that UABB can significantly reduce the surface roughness of the material, and the values calculated from the derived mathematical model showed good agreement with the experimental findings. The surface roughness decreased with the burnishing force (50–250 N) and amplitude (4–10 μm) increased, whereas the feed rate decreased. The increases of radii of component and ball were detrimental to the improvement of the surface roughness. And elastic rebound was not conducive to roughness reduction and its influence was negligible. Finally, the derived surface roughness model can be used to predict the component values after UABB and understand the effects of machining parameters on the shaft targets during processing.

The ultrasonic-assisted burnishing process (UABP) is an effective surface finishing technology that obtains compressive residual stress and surface work hardening and decreases surface roughness. A three-dimensional explicit nonlinear finite element model (FEM) of the UABP on a shaft specimen was established and calibrated in this paper. A comparison of finite element (FE) simulation results with experimental data showed good agreement in terms of the predicted residual stress in both tangential and axial directions. The established FEM explores the influence of treatment parameters, such as ball diameters, static forces, spindle speeds, ultrasonic frequencies, vibration amplitudes, and friction coefficients, on the resultant profile of residual stress and equivalent plastic deformation. This dynamic explicit FE method is an effective approach to investigate the UABP, to relate the processing parameters with surface integrity, including the depth of residual stress and work hardening of objective surfaces, and to guide the design of the UABP parameters.

Thermal atomic force microscope (AFM) nanolithography enables many nanofabrication applications, but it requires special thermal probes. In this paper, we developed a vibration-assisted sample heating approach to implement the nanomachining of poly(methyl methacrylate) (PMMA) with a regular AFM probe with low stiffness (~0.16 N/m). The joule heating softens the PMMA, which lowers the normal force. The in-plane vibration with high frequency was applied to reduce the adhesion force between the AFM tip and the PMMA with elevated temperature, which enhances nanomachining performances. By using this approach, uniform nanopatterns with controllable dimensions were fabricated on PMMA films with small setpoint force.

Atomic force microscope (AFM)-based nanomanufacturing, as a low-cost and easy-to-setup technique, enables high-resolution maskless nanofabrication processes for arbitrary nanopatterns, but there are strong needs to advance the nanopatterning processes. Thermal-mechanical AFM-based nanomachining process lower the normal forces to few hundreds of nanonewtons, but it requires special thermal probes. Vibration-assisted nanomachining process increases the tip lifetime but forces are still in the range of few hundreds of nanonewtons. We have innovated a system with both vibration and heated sample to achieve nanomachining process with lower normal forces. To provide cantilever selection guidance for this novel nanomachining system and to understand the effect of cantilever stiffness and sample temperature on feature dimensions, we used both soft and stiff probes for the nanomachining processes in this paper. We fabricated nanostructures with controllable shape dimensions were fabricated on 200 nm depth polymethyl methacrylate (PMMA) films by using both soft and stiff AFM probes with a few tens of nanonewtons normal forces in this novel AFM-based nanomachining system. Besides, we analyzed the effects of different machining parameters on the lithography performance, which helps to understand the machining parameters for fabricating nanopatterns of different dimensions. We also found that other than the setpoint force, the probe category and sample heating temperature significantly affect the dimensions of fabricated nanopatterns. And there is a statistically significant interaction effect between the spring constants of probes and the sample temperatures.

This paper is concerned with the time-varying formation tracking issue for high-order multi-agent systems based on event-triggered mechanism and adaptive control strategy. The time-varying formation configuration involved in our work is expressed as a bounded piecewise continuously differentiable vector function. The follower agents are required to achieve a pre-specified time-varying formation while tracking the state trajectory of the leader. First of all, in the light of event-triggered schema, we constructed an adaptive control protocol with adjustable time-variant parameters for achieving time-varying formation tracking. Meanwhile, the corresponding triggering conditions were designed to avoid continuous communication. Then, a four-step algorithm and feasible conditions have been provided to determine protocol parameters. Additionally, theoretically proof reveals that the specified formation tracking could be realized while excluding Zeno behavior. Two simulation examples were finally provided to illustrate the effectiveness of the obtained results.

Compressive residual stress fields induced by ultrasonic surface rolling process play a key role in determining the fatigue performance of machine parts. The present work is an analytical approach to conducting optimum design of this field to obtain an optimum fatigue resistance. Thus, a mathematical model was presented to predict residual stresses based on circular and elliptical Hertz contact areas. Moreover, to validate the proposed analytical approach, experimental verification was carried out on 18CrNiMo7-6 steel. Analytic solutions were derived from the mathematical model and optimum characteristic parameters were obtained by investigating the characteristic parameters in this field, such as surface residual stress, maximum residual stress and its depths. Results showed that increasing the total force, Hertz contact area and ratio of the radius of tool tip to that of target body could significantly enhance the peak of compressive residual stress and its depth.

Porous grafted copolymer with excellent thermal stability and swelling capacity was synthesized from water soluble Prunus cerasifera gum polysaccharide (PG) and acrylamide (AM). The monosaccharide compositions and the structure of Prunus cerasifera tree gum were detected by a high-performance anion exchange chromatography (HPAEC) system and 1H NMR and 13C NMR, and the obtained PG-AM copolymer was characterized by Fourier transform infrared (FT-IR), scanning electron microscope (SEM), thermogravimetric analysis (TGA) and differential scanning calorimetry (DSC), respectively. The results indicated that the water soluble polysaccharides obtained from Prunus cerasifera tree gum were mainly composed of l-arabinose (39.78%) and d-galactose (40.59%) with minor amount of xylose, mannose and uronic acids. The maximum percent and the grafting efficiency of grafting acrylamide (AM) onto PG to form PG-AM were obtained by copolymerization between polysaccharide and 3 times (weight) acrylamide with 3 mmol/L potassium persulfate initiator at 50 °C for 1 h. In addition, lots of isolated and conjoint pores were observed in the prepared PG-AM materials, with a diameters distribution between 2 and 10 μm. Compared with PG, the synthesized copolymer PG-AM showed an excellent performance in thermal stability and swelling capacity. The detailed structural characteristic together with excellent thermal stability and swelling properties will benefit efficient utilization of the synthesized copolymer as a precursor for preparation of large-scale environmentally friendly advanced materials with various potential applications.

Intranasal drug delivery provided an alternative and effective approach for the intervention of an intracerebral hemorrhage (ICH). However, the short retention time at the absorption site and slow drug transport in intranasal gel influence the drug bioavailability and outcome of ICH. Herein, we fabricated a novel intranasal gel with oriented drug migration utilizing a charge-driven strategy to attenuate brain injury after ICH. Nicardipine hydrochloride (NCD) was entrapped in chitosan nanoparticles (CS NPs) and dispersed in an HAMC gel. Subsequently, one side of the gel was coated with a positively charged film. The oriented migration of CS NPs in the HAMC gel was determined, and the drug bioavailability was also enhanced. Furthermore, a blood-induced ICH rat model was established to evaluate the therapeutic effect of CS NPs + HAMC composites. Intranasal administration of the CS NPs + HAMC (+) composite showed a stronger neuroprotective effect in terms of brain edema reduction and neural apoptosis inhibition compared to the CS NPs + HAMC composite. These results suggested that the oriented and rapid drug transport from nose to brain can be achieved using the charge-driven strategy, and this intranasal drug delivery system has the potential to provide a new therapeutic strategy for the treatment of ICH.

Milled wood lignin (MWL) was isolated from Dendrocalamus sinicus, an abundant bamboo variety in the earth, using Bjorkman method. Elucidation and quantification of the chemical structures for the isolated MWL have been facilitated by employing FT-IR and NMR techniques. The obtained results showed that the MWL consists of syringyl (S), guaiacyl (G), and p-hydroxyphenyl (H) units, indicating it as grass type (HGS) lignin. There is no significant change in structure (i.e. cleavage at α-O-4′ and β-O-4′ linkage) was observed. NMR techniques indicated that the isolated lignin was rich in β-O-4′ aryl ether substructures and syringyl (S) units. Furthermore, the sufficient understanding of the chemical structure of the lignin benefits their effective utilization towards the production of renewable biomass and biofuels.

Accelerating and regulating collagen formation during wound healing repair is key issues for skin regeneration. Insulin can promote the healing of damaged skin by stimulating cellular migration and angiogenesis. Here, human hair keratin-conjugated insulin was synthesized to enhance full-thickness skin regeneration based on the excellent wound healing and hemostatic effects of keratin and the collagen deposition regulation ability of insulin. The insulin-conjugated keratin (Ins-K) was synthesized through the EDC/NHS reaction, which can supply a sustained release of insulin. The Ins-K hydrogel displayed similar water absorption, porosity and rheology properties to those of the keratin hydrogel. However, the Ins-K hydrogel shows a stronger hemostatic ability than the keratin hydrogel group, with a stronger wound healing effect found for the Ins-K hydrogel in the early regeneration stage (first 2 weeks) than for the keratin hydrogel treatment, resulting in smoother skin tissues at an excision section realized by regulating transforming growth factor β1 (TGF-β1) and hydroxyproline (HYP) expression. The results demonstrate that keratin promotes hemostasis and wound healing after insulin conjugation, which highlights the potential of keratin-based materials in tissue regeneration applications.

Ciprofloxacin (CIP) is a widely used third generation fluoroquinolone antibiotics, and has been often detected in wastewater treatment plants. Finding an effective way to remove them from wastewater is of great concern. Ultraviolet (UV)/chlorine advanced oxidation process (AOP) has many advantages in micropollutant removal. In this study, CIP degradation in UV/chlorine process was investigated. Only 41.2% of CIP was degraded by UV photolysis and 30.5% by dark chlorination in 30 min, while 98.5% of CIP was degraded by UV/chlorine process in 9 min. HCO3− had markedly inhibition, NO3− and SO42- had slight inhibition, and Cl− had a marginal inhibition on CIP degradation in UV/chlorine system. The degradation of CIP in UV/chlorine process was mainly attributed to the attack of reactive species. The relative contributions of hydrated electrons (eaq ), hydroxyl radicals (HO ), chlorine atoms (Cl ), and UV photolysis were investigated. Under neutral condition in aqueous solution, CIP degradation had highest pseudo first-order reaction rate constant, in which eaq had the highest contribution, followed by Cl , HO , and UV photolysis. The intermediates and byproducts were identified and the degradation pathway was proposed. The total organic chlorine (TOCl) and biotoxicity were further assessed. CIP and natural organic matters (NOMs) were removed efficiently in real water. UV/chlorine showed the potential for the wastewater treatment containing CIP.

Surgery is an important therapeutic strategy for intracerebral hemorrhage (ICH) in the clinic and is theoretically beneficial for the outcome of ICH by decreasing hematoma, reducing nervous tissue damage, and removing harmful chemicals. However, the outcome of ICH surgery is always unsatisfactory due to postoperative rebleeding. We hypothesized that the injection of hemostatic agents in situ after aspiration surgery could immediately activate hemostasis once rebleeding occurs. Therefore, keratin hydrogels (K-gels) were easily prepared as a hemostatic material via a rehydration method and had a porous structure. Collagenase was injected into the basal lamina to mimic ICH rebleeding, and the K-gels were then injected into the same injured site after 2 h for hemostatic therapy. The hematoma volume was significantly reduced by K-gel treatment, indicating that in situ infusion of the K-gels inhibited hematoma enlargement when rebleeding occurred. Moreover, brain damage, including cell apoptosis, neuroinflammatory reactions, and neurological deficits, was also relieved after K-gel treatment. These results suggested that in situ injection of the K-gels into the hematoma area after ICH surgery improves the therapeutic outcome by stopping postoperative rebleeding. K-gels have great potential for clinical hemostatic application because of their excellent hemostatic properties and biocompatibility.

As an emerging research topic for proximity service (ProSe), automatic emotion recognition enables the machines to understand the emotional changes of human beings which can not only facilitate natural, effective, seamless, and advanced human-robot interaction or human-computer interface but also promote emotional health. Facial expression recognition (FER) is a vital task for emotion recognition. However, significant gap between human and machine exists in FER task. In this paper, we present a conditional generative adversarial network-based approach to alleviate the intra-class variations by individually controlling the facial expressions and learning the generative and discriminative representations simultaneously. The proposed framework consists of a generator G and three discriminators (Di, Da, and Dexp). The generator G transforms any query face image into another prototypic facial expression image with other factors preserved. Armed with action units condition, the generator G pays more attention to information relevant to facial expression. Three loss functions ($L_{I}$ , La, and Lexp) corresponding to the three discriminators (Di, Da, and Dexp) were designed to learn generative and discriminative representations. Moreover, after rendering the generated expression back to its original facial expression, cycle consistency loss is also applied to guarantee the identity and produce more constrained visual representations. Optimized by combining both synthesis and classification loss functions, the learnt representation is explicitly disentangled from other variations such as identity, head pose, and illumination. Qualitative and quantitative experimental results demonstrate the proposed FER system is effective for expression recognition.

Bio-mimic wings are crucial constituents of Flapping-Wing Micro Air Vehicles, and the quality of such products plays a vital role in missions involving area exploration, aerial imaging and monitoring. Fused filament fabrication, as a versatile manufacturing approach, can be adopted to print single-layer artificial wings in an additive way. In such processes, quality of the printed wings is determined by the percentage of covered area, contingent on the vertical distance between the printing nozzle and the substrate. Even when properly calibrated, this distance is subject to variations due to the exceeding sensitivity to process drift, including vibration of the platform. In situ image analysis provides rapid guidance for process drift detection and assessment of the printed wing structures. While dynamic network analysis has been applied to quality assurance of steaming images, the computational overhead has made it unrealistic for monitoring high-definition images, particularly in the detection of images with incipient or tiny defects. In this study, we investigate image decomposition techniques, including wavelet transformation and bi-dimensional empirical mode decomposition (BEMD), to dissolve the original images into a series of basis images, which significantly reduces the number of communities in the network representation of images and diminishes the computational cost. Lastly, network generalized likelihood ratio control chart is developed to characterize anomalous variations in structures of the images. Our investigation shows that wavelet and EMD elevate the detection accuracy by 36.7% and 63.3%, while reducing the processing time by 43.5% and 73.9%, respectively, compared to the dynamic network approach without decomposition.

Nanoimprint lithography is a fabrication method by applying nanomolds on resists to form inversed patterns. It can be utilized to fabricate high-resolution nanopatterns in a low cost and rapid fashion on both flat and curved surfaces. The fidelity of fabricated master nanopatterns and elastomer nanomolds are essential to the quality of the nanoimprinted features. Although two-dimensional nanoimprint lithography has been widely investigated, there are limited researches about three-dimensional master nanopatterns and nanomolds. Even less of them discussed the fidelity of entire procedures throughout the initial master fabrication to the final nanoimprinting. In this paper, we demonstrated the effectiveness of our approach, atomic force microscope (AFM) based ultrasonic vibration assisted nanomachining, in fabricating three-dimensional master nanopatterns with complex contours and continuously height changes. Nanomolds for nanoimprint lithography were fabricated subsequently based on the master nanopatterns with a positive draft, which is naturally generated from the AFM tip. We investigated the fidelity of the nanomachining results and the residual effect of reusing mater patterns. Besides, we demonstrated the 3D nanoimprinting capability of the solvent-assisted microcontact molding process, discovering the possibility of tuning the layer-thickness of imprinted 3D nanopatterns.

Microlens arrays (MLAs), as one of the key features in the optoelectronics field, have attracted a lot of attention recently. Unfortunately, existing fabrication methods of MLAs have many disadvantages, such as complex fabrication procedures and difficult morphology control. This paper presents a low-cost and tunable fabrication approach for concave MLAs on ultraviolet (UV) polymer surfaces. We used condensed water droplets, formed by initiative cooling, as the template for the formation of concave MLAs in this approach. A sacrificial layer of polymer material was introduced to fabricate concave MLAs with different morphology parameters. The three most important parameters of MLAs, i.e. diameter, cross-sectional profile, and packing distance, can be tuned by the proposed novel fabrication approach. By controlling the condensation time of water droplets, we can control the diameter of the concave MLAs. Moreover, the cross-sectional profile can be controlled by replacing the sacrificial layer material to change the interaction among the water droplets, sacrificial polymers, and UV polymers. In addition, the packing distance of the MLAs can also be adjusted by introducing the lateral flow of the sacrificial polymer, which was driven by the additional sacrificial polymer dispensed on the substrate. Furthermore, the UV polymer film with MLAs on its surface was applied for the packaging of high power green LEDs. Consequently, the optical output power of green LED modules is enhanced by 11.4% when using the MLAs at the driving current of 500 mA.

To reveal the structural characteristics and physicochemical properties of perennial ryegrass lignin, sequential alkali extractions or double ball-milling and enzymatic hydrolysis on the basis of ultrasonic and hydrothermal pretreatments were proposed in this study. Results revealed that sequential alkali extractions released 89.4% of original lignin from the ryegrass cell walls and 0.75-4.16% of associated carbohydrates as compared to the double ball-milling and enzymatic hydrolysis (96.0% and 18.39%). It was observed that the two types of lignin prepared were SGH-type and had different amounts of p-coumarates and ferulates, and primarily consisted of β-O-4' linkages combined with minor amounts of β-β' and β-5' linkages. Besides, alkali-soluble lignins exhibited relatively fewer β-O-4' linkages, higher S/G ratios and H-type units, and abundant phenolic OH groups as compared to the double enzymatic lignin. Overall, the deeper investigation of the lignin structure of ryegrass will provide useful information for the efficient utilization of lignin macromolecules in biorefineries.

Synthetic images rendered by graphics engines are a promising source for training deep networks. However, it is challenging to ensure that they can help train a network to perform well on real images, because a graphics-based generation pipeline requires numerous design decisions such as the selection of 3D shapes and the placement of the camera. In this work, we propose a new method that optimizes the generation of 3D training data based on what we call "hybrid gradient". We parametrize the design decisions as a real vector, and combine the approximate gradient and the analytical gradient to obtain the hybrid gradient of the network performance with respect to this vector. We evaluate our approach on the task of estimating surface normal, depth or intrinsic decomposition from a single image. Experiments on standard benchmarks show that our approach can outperform the prior state of the art on optimizing the generation of 3D training data, particularly in terms of computational efficiency.

Single-view 3D is the task of recovering 3D properties such as depth and surface normals from a single image. We hypothesize that a major obstacle to single-image 3D is data. We address this issue by presenting Open Annotations of Single Image Surfaces (OASIS), a dataset for single-image 3D in the wild consisting of annotations of detailed 3D geometry for 140,000 images. We train and evaluate leading models on a variety of single-image 3D tasks. We expect OASIS to be a useful resource for 3D vision research. Project site: https://pvl.cs.princeton.edu/OASIS., Accepted to CVPR 2020

OBJECTIVE The aim of the study was to examine whether an electrical impedance tomography (EIT) electrode belt changed the lung function in healthy volunteers and patients with respiratory muscle weakness (RMW) and chronic obstructive pulmonary disease (COPD). APPROACH In total, thirty subjects were included (10 healthy volunteers, 10 subjects with RMW, maximum inspiratory pressure < 40 cmH2O, and 10 COPD, grade I-IV). Spirometry measurements were conducted in a sitting position once a day at similar times on two consecutive days. Slow expiratory vital capacity (VC), forced vital capacity (FVC) and maximum voluntary ventilation (MVV) manoeuvres were performed. On day 1, spirometry was performed without the EIT electrode belt, and on day 2, the belt was attached to the thorax. MAIN RESULTS Lung function was not influenced by the electrode belt in healthy subjects. The test-retest reliability in the healthy group was 0.89, 0.89 and 0.85 for VC, FVC and MVV, respectively. On the other hand, all investigated parameters were significantly decreased in the RMW group (VC, 51.3 ± 18.0 versus 46.5 ± 18.0% predicted, without versus with EIT belt, p< 0.01; FVC, 51.7 ± 19.0 versus 45.8 ± 18.1% predicted, p< 0.01; MVV, 41.0 ± 20.0 versus 38.8 ± 19.6% predicted, p< 0.01). VC and MVV also decreased significantly in the COPD group (VC, 77.4 ± 20.5 versus 74.6 ± 18.8% predicted, p< 0.05; MVV, 57.4 ± 15.7 versus 54.4 ± 12.5% predicted, p< 0.05). SIGNIFICANCE An EIT electrode belt could reduce lung volumes in subjects with pre-existing lung diseases. Comparing lung function acquired with an electrode belt to corresponding values obtained without the belt should be avoided.

Computer vision technology is being used by many but remains representative of only a few. People have reported misbehavior of computer vision models, including offensive prediction results and lower performance for underrepresented groups. Current computer vision models are typically developed using datasets consisting of manually annotated images or videos; the data and label distributions in these datasets are critical to the models' behavior. In this paper, we examine ImageNet, a large-scale ontology of images that has spurred the development of many modern computer vision methods. We consider three key factors within the "person" subtree of ImageNet that may lead to problematic behavior in downstream computer vision technology: (1) the stagnant concept vocabulary of WordNet, (2) the attempt at exhaustive illustration of all categories with images, and (3) the inequality of representation in the images within concepts. We seek to illuminate the root causes of these concerns and take the first steps to mitigate them constructively., Accepted to FAT* 2020

We introduce Recurrent All-Pairs Field Transforms (RAFT), a new deep network architecture for optical flow. RAFT extracts per-pixel features, builds multi-scale 4D correlation volumes for all pairs of pixels, and iteratively updates a flow field through a recurrent unit that performs lookups on the correlation volumes. RAFT achieves state-of-the-art performance. On KITTI, RAFT achieves an F1-all error of 5.10%, a 16% error reduction from the best published result (6.10%). On Sintel (final pass), RAFT obtains an end-point-error of 2.855 pixels, a 30% error reduction from the best published result (4.098 pixels). In addition, RAFT has strong cross-dataset generalization as well as high efficiency in inference time, training speed, and parameter count. Code is available at https://github.com/princeton-vl/RAFT.

Recent advances have spurred incredible progress in self-supervised pretraining for vision. We investigate what factors may play a role in the utility of these pretraining methods for practitioners. To do this, we evaluate various self-supervised algorithms across a comprehensive array of synthetic datasets and downstream tasks. We prepare a suite of synthetic data that enables an endless supply of annotated images as well as full control over dataset difficulty. Our experiments offer insights into how the utility of self-supervision changes as the number of available labels grows as well as how the utility changes as a function of the downstream task and the properties of the training data. We also find that linear evaluation does not correlate with finetuning performance. Code and data is available at \href{https://www.github.com/princeton-vl/selfstudy}{github.com/princeton-vl/selfstudy}., Comment: To appear in CVPR 2020

This paper develops and characterizes a three-dimensional (3-D) nanofabrication process using ultrasonic vibration assisted nanomachining based on an atomic force microscope (AFM). The superiorities of height control over force control in the process are explained and are demonstrated by the fabrication results. Three factors impacting actual feature depths are investigated, including the ultrasonic z -vibrational amplitude, the assigned base feature depth, and the machining speed. 3-D nanostructures with continuous height variations were successfully fabricated on polymethyl methacrylate (PMMA) films with the feature height manipulated through controlling the absolute height of the cantilever tip in AFM. By selecting machining parameters based on characterizations, feature dimensions can be controlled as desired values within small variances. The capability of transferring 3-D nanostructures from PMMA films to silicon substrates is further explored in this paper. After selecting recipes of the reactive ion etching process, 3-D nanostructures are successfully transferred to silicon substrates with controllable selectivity. The reported ultrasonic vibration assisted nanomachining process in height control provides a robust approach of fabricating 3-D nanostructures.

Maneuverability is one of the most important performances of underwater vehicles. To improve the maneuverability of underwater vehicles, a high-pressure waterjet propulsion system combined with a Thrust Vector Control System (TVCS) was presented, which controlled the sailing trajectory through adjusting the waterjet deflection angle and thrust vector instead of rudders and sterns. The TVCS was established in the form of 3RPS Parallel Manipulator and the position feedback was provided by measuring the displacements of three actuator cylinders. The relationship between the thrust direction and the displacements of the actuator cylinders was established through the procedure of solving an inverse kinematics problem. Simultaneously, the motion transformation function of the system was derived, and the Fuzzy-PID strategy was adopted. Accordingly, the Simulink model was built, and the simulation indicated that the system had good performance. In addition, the sailing trajectory was obtained, and the results showed that the turning diameter was 78 m, and it was independent of thrust and sailing speed. The underwater vehicle utilizing high-pressure water jet propulsion with TVCS had good maneuverability at either high or low speed conditions.

Surface enhanced Raman spectroscopy (SERS) is a surface-sensitive detection technique that dramatically increases the scattering signals of the analytes compared to traditional Raman spectroscopy. Rapid and low-cost fabrication of SERS substrates with easily tunable features remains a challenge, although many SERS substrates with high enhancement factors (EF) were investigated. Here, we report a novel and rapid approach of fabricating SERS substrates using ultrasonic vibration assisted nanomachining. Grids patterns with easily tuned dimensions were fabricated on PMMA surfaces. SERS substrates were fabricated after coating an 80 nm gold layer on the patterned silicon after reactive ion etching (RIE) using the patterned PMMA as the mask. Probing molecules of R6G with the area density of 2.8 × 10-12 M/mm2 can be detected, achieving an EF of 3.11 × 103. Results show a pattern with higher grids density tends to achieve a higher EF.

The stabilization, innocuity, and reutilization of sludge can be obtained via the co-combustion of sewage sludge in cement producing kilns. Off-gases containing odours and volatile organic compounds (VOCs) are generated during the process of sewage sludge drying. A two-stage thermophilic biofilter was constructed in a cement plant to remove SO2, NH3, and total VOCs (TVOCs) from the plant’s exhaust. The average concentrations of TVOCs, NH3, and SO2 in the inlet stream were 164.33, 38.46, and 51.12 mg/m3, and in the outlet stream were 44.54, 9.18, and 3.91 mg/m3, respectively. During biofilter operation, nutrient solution was circularly pumped onto the packing material to ensure the packing material remained moist. The moisture content of the packing material increased from 40 to 85% as the irrigation rate varied from 0.05 to 0.42 m3/h. The removal efficiencies of NH3 and TVOCs changed accordingly. When the irrigation rate was 0.3 m3/h, removal efficiencies were 88.71% for NH3 and 81.38% for TVOCs. More than 90% SO2 removal occurred throughout the operation. The polymerase chain reaction-denaturing gradient gel electrophoresis (PCR-DGGE) method was used to assay the microbial population. Both microbial counts and diversity increased when the irrigation rate increased.

Surface electromyography signal plays an important role in hand function recovery training. In this paper, an IoT-enabled stroke rehabilitation system was introduced which was based on a smart wearable armband (SWA), machine learning (ML) algorithms, and a 3-D printed dexterous robot hand. User comfort is one of the key issues which should be addressed for wearable devices. The SWA was developed by integrating a low-power and tiny-sized IoT sensing device with textile electrodes, which can measure, pre-process, and wirelessly transmit bio-potential signals. By evenly distributing surface electrodes over user’s forearm, drawbacks of classification accuracy poor performance can be mitigated. A new method was put forward to find the optimal feature set. ML algorithms were leveraged to analyze and discriminate features of different hand movements, and their performances were appraised by classification complexity estimating algorithms and principal components analysis. According to the verification results, all nine gestures can be successfully identified with an average accuracy up to 96.20%. In addition, a 3-D printed five-finger robot hand was implemented for hand rehabilitation training purpose. Correspondingly, user’s hand movement intentions were extracted and converted into a series of commands which were used to drive motors assembled inside the dexterous robot hand. As a result, the dexterous robot hand can mimic the user’s gesture in a real-time manner, which shows the proposed system can be used as a training tool to facilitate rehabilitation process for the patients after stroke., An IoT-enabled stroke rehabilitation system was implemented which was based on a smart wearable armband, machine learning algorithms, and a 3D printed dexterous robot hand. The smart wearable armband was developed by integrating a low-power and tiny-sized IoT sensing device with textile electrodes, which can measure, pre-process, and wirelessly transmit bio-potential signals. Machine learning (ML) algorithms were leveraged to analyze and discriminate features of different hand movements. The patient's hand movement intentions were further extracted to control the dexterous robot hand and the robot hand can mimic the patient's gesture in a real-time manner which enables active response to patient's volition.

Electrochemical dechlorination is a prospective strategy to remediate trichloroethylene (TCE)-contaminated groundwater. In this work, iron-nitrogen-doped carbon (FeNC) mimicking microbiological dechlorination coenzymes was developed for TCE removal under environmentally related conditions. The biomimetic FeNC-900, FeNC-1000, and FeNC-1100 materials were synthesized via pyrolysis at different temperatures (900, 1000, and 1100 °C). Due to the synergistic effect of Fe–N4 active sites and graphitic N sites, FeNC-1000 had the highest electron transfer efficiency and the largest electrochemical active surface area among the as-synthesized FeNC catalysts. The pseudo-first-order rate constants for TCE reduction using FeNC-1000 catalyst are 0.19, 0.28 and 0.36 h−1 at potentials of −0.8 V, −1.0 V and −1.2 V, respectively. Active hydrogen and direct electrons transfer both contribute to the dechlorination from TCE to C2H4 and C2H6. FeNC maintain a high reactivity after five reuse cycles. Our study provides a novel approach for the dechlorination of chlorinated organic contaminants in groundwater.

Excessive discharge of metal ions, such as Fe3+ and Cr2O72–/CrO42– may bring about potential threats to human health and the ecological environment. Consequently, the assembly of function-oriented materials as effective chemosensors has the vital practical significance. In this article, four metal-organic frameworks (MOFs), namely, {[Co(bptb)0.5(bibp)(H2O)2]·H2O}n (Ι), [Mn4(bptb)2(bibp)3(H2O)4]n (Ⅱ), [Ni2(bptb)(bipy)2(H2O)3]n (Ⅲ) and [Cd3(bptb)2(bipb)3(H2O)4]n (Ⅳ) (H4bptb = 2,4,4′,6-biphenyl tetracarboxylic acid, bibp = 4,4′-bis (imidazolyl) biphenyl, and bipy = 4,4′-bipyridine) have been hydrothermally synthesized, and their structures and physicochemical properties have been characterized and investigated. Single-crystal X-ray diffraction analysis reveals that they present fascinating three-dimensional configurations. Powder X-ray diffraction as well with thermogravimetric analysis demonstrates that Cd-MOF (Ⅳ) exhibits a high chemical and thermal stability. The studies of fluorescence properties show that Ⅳ may serve as a multi-responsive sensor for Fe3+ and Cr2O72–/CrO42– ions with high sensitivity, selectivity and efficiency. Moreover, the fluorescence quenching mechanisms have been studied systematically depend on the inductively coupled plasma-atomic emission spectrometer (ICP-AES), X-ray photoelectron spectroscopy (XPS) analyses, PXRD analysis, FT-IR spectra, UV–vis spectra and SEM mapping (scanning electron microscope). In addition, magnetic and cyclic voltammetry (CV) properties of Ι−Ⅲ have also been investigated.

The spray evolution and thermal pyrolysis of the heavy fuel oil (HFO) and its blends with #2 diesel were firstly investigated. First, the gas chromatography was performed to analyze their molecular distributions. The low sulfur HFOs with different viscosities show similar molecular distributions, and the content of light-end C 9 – C 17 alkanes and polyaromatics is reduced compared with the high sulfur HFOs. Then, the spray experiments were conducted. The increase of ambient temperature has little effects on the HFO sprays. However, the blends spray of HFOs and #2 diesel penetrates faster and produces some opaque products under high-temperature conditions, probably due to the enhanced turbulence intensity, potential thermal pyrolysis and polymerization. Finally, the thermogravimetric analysis coupled with the isothermal gas chromatography and mass spectrometry were conducted, and the apparent activation energy of thermal pyrolysis for various HFOs were obtained. The results revealed that the HFO undergoes four stages in the inert gas, namely the endothermic evaporation, mixed evaporation-pyrolysis, violent exothermic pyrolysis, and high-temperature polymerization, producing large amounts of residues. However, in the oxygen-containing condition, the combustion occurs instead of the high-temperature polymerization, and the residual quality is prominently reduced. The high ambient pressure is proved to have a significant effect on the pyrolysis and polymerization processes for the low sulfur HFO, the residual mass significantly increases under high pressures. For the blended HFO, due to the volatile components trapped inside the viscous shell generated by polymerization or asphaltene fractions, the residual weight of the blended HFOs increases.

Significance We show that socioeconomic attributes such as income, race, education, and voting patterns can be inferred from cars detected in Google Street View images using deep learning. Our model works by discovering associations between cars and people. For example, if the number of sedans in a city is higher than the number of pickup trucks, that city is likely to vote for a Democrat in the next presidential election (88% chance); if not, then the city is likely to vote for a Republican (82% chance)., The United States spends more than $250 million each year on the American Community Survey (ACS), a labor-intensive door-to-door study that measures statistics relating to race, gender, education, occupation, unemployment, and other demographic factors. Although a comprehensive source of data, the lag between demographic changes and their appearance in the ACS can exceed several years. As digital imagery becomes ubiquitous and machine vision techniques improve, automated data analysis may become an increasingly practical supplement to the ACS. Here, we present a method that estimates socioeconomic characteristics of regions spanning 200 US cities by using 50 million images of street scenes gathered with Google Street View cars. Using deep learning-based computer vision techniques, we determined the make, model, and year of all motor vehicles encountered in particular neighborhoods. Data from this census of motor vehicles, which enumerated 22 million automobiles in total (8% of all automobiles in the United States), were used to accurately estimate income, race, education, and voting patterns at the zip code and precinct level. (The average US precinct contains ∼1,000 people.) The resulting associations are surprisingly simple and powerful. For instance, if the number of sedans encountered during a drive through a city is higher than the number of pickup trucks, the city is likely to vote for a Democrat during the next presidential election (88% chance); otherwise, it is likely to vote Republican (82%). Our results suggest that automated systems for monitoring demographics may effectively complement labor-intensive approaches, with the potential to measure demographics with fine spatial resolution, in close to real time.

The purpose of this study was to discriminate the release behavior from three differently formulated racecadotril (BCS II) granules and to establish an in vitro-in vivo correlation. Three granule formulations of the lipophilic drug were prepared with equivalent composition but prepared with different manufacturing processes (dry granulation, wet granulation with or without binder). In vitro release of the three granules was investigated using a biphasic dissolution system (phosphate buffer pH6.8 and octanol) and compared to the conventional single phase USP II dissolution test performed under sink and non-sink conditions. In vivo studies with each granule formulation were performed in rats. Interestingly, the granule formulations exhibited pronouncedly different behavior in the different dissolution systems depending on different wetting and dissolution conditions. Single phase USP II dissolution tests lacked discrimination. In contrast, remarkable discrimination between the granule formulations was observed in the octanol phase of biphasic dissolution system with a rank order of release from granules prepared by wet granulation with binder>wet granulation without binder>dry granulation. This release order correlated well with the wettability of these granules. An excellent correlation was also established between in vitro release in the octanol phase of the biphasic test and in vivo data (R2=0.999). Compared to conventional dissolution methods, the biphasic method provides great potential to discriminate between only minor formulation and process changes within the same dosage form for poorly soluble drugs.

In this paper, a formation collision forecast and coordination algorithm (FCFCA) is proposed to avoid collisions in the configuration control of missile autonomous formation (MAF). First, the conditions are derived to forecast collisions of the MAF. Second, the transition target configuration is designed to coordinate collisions. Third, by minimizing the formation group cost, we obtain the optimal transition vector, which can avoid collisions and guarantee the efficiency, safety, and stability of the MAF. Finally, simulation and experiment results are presented to verify the effectiveness of the FCFCA.

This paper explores object detection in the small data regime, where only a limited number of annotated bounding boxes are available due to data rarity and annotation expense. This is a common challenge today with machine learning being applied to many new tasks where obtaining training data is more challenging, e.g. in medical images with rare diseases that doctors sometimes only see once in their life-time. In this work we explore this problem from a generative modeling perspective by learning to generate new images with associated bounding boxes, and using these for training an object detector. We show that simply training previously proposed generative models does not yield satisfactory performance due to them optimizing for image realism rather than object detection accuracy. To this end we develop a new model with a novel unrolling mechanism that jointly optimizes the generative model and a detector such that the generated images improve the performance of the detector. We show this method outperforms the state of the art on two challenging datasets, disease detection and small data pedestrian detection, improving the average precision on NIH Chest X-ray by a relative 20% and localization accuracy by a relative 50%., Comment: Published in ICCV 2019

Understanding the spatial relations between objects in images is a surprisingly challenging task. A chair may be "behind" a person even if it appears to the left of the person in the image (depending on which way the person is facing). Two students that appear close to each other in the image may not in fact be "next to" each other if there is a third student between them. We introduce SpatialSense, a dataset specializing in spatial relation recognition which captures a broad spectrum of such challenges, allowing for proper benchmarking of computer vision techniques. SpatialSense is constructed through adversarial crowdsourcing, in which human annotators are tasked with finding spatial relations that are difficult to predict using simple cues such as 2D spatial configuration or language priors. Adversarial crowdsourcing significantly reduces dataset bias and samples more interesting relations in the long tail compared to existing datasets. On SpatialSense, state-of-the-art recognition models perform comparably to simple baselines, suggesting that they rely on straightforward cues instead of fully reasoning about this complex task. The SpatialSense benchmark provides a path forward to advancing the spatial reasoning capabilities of computer vision systems. The dataset and code are available at https://github.com/princeton-vl/SpatialSense., Accepted to ICCV 2019

Highly expressive models such as deep neural networks (DNNs) have been widely applied to various applications. However, recent studies show that DNNs are vulnerable to adversarial examples, which are carefully crafted inputs aiming to mislead the predictions. Currently, the majority of these studies have focused on perturbation added to image pixels, while such manipulation is not physically realistic. Some works have tried to overcome this limitation by attaching printable 2D patches or painting patterns onto surfaces, but can be potentially defended because 3D shape features are intact. In this paper, we propose meshAdv to generate "adversarial 3D meshes" from objects that have rich shape features but minimal textural variation. To manipulate the shape or texture of the objects, we make use of a differentiable renderer to compute accurate shading on the shape and propagate the gradient. Extensive experiments show that the generated 3D meshes are effective in attacking both classifiers and object detectors. We evaluate the attack under different viewpoints. In addition, we design a pipeline to perform black-box attack on a photorealistic renderer with unknown rendering parameters., Comment: Published in IEEE CVPR2019

In recent years, favorable enhanced wound-healing properties and excellent biocompatibility of keratin derived from human hair have attracted considerable attention. Recombinant keratin proteins can be produced by recombinant DNA technology and have higher purity than extracted keratin. However, the wound-healing properties of recombinant keratin proteins remain unclear. Herein, two recombinant trichocyte keratins including human type I hair keratin 37 and human type II hair keratin 81 were expressed using a bacterial expression system, and recombinant keratin nanoparticles (RKNPs) were prepared via an ultrasonic dispersion method. The molecular weight, purity, and physicochemical properties of the recombinant keratin proteins and nanoparticles were assessed using gel electrophoresis, circular dichroism, mass spectrometry, and scanning electron microscope analyses. The RKNPs significantly enhanced cell proliferation and migration in vitro, and the treatment of dermal wounds in vivo with RKNPs resulted in improved wound healing associated with improved epithelialization, vascularization, and collagen deposition and remodeling. In addition, the in vivo biocompatibility test revealed no systemic toxicity. Overall, this work demonstrates that RKNPs are a promising candidate for enhanced wound healing, and this study opens up new prospects for the development of keratin biomaterials.

In situ keratin hydrogel offer a promising strategy to relieve the brain injury after intracerebral hemorrhage (ICH) by delivering the iron chelator directly to the stroke site. However, the injectable property of traditional keratin hydrogel is unsatisfactory, which can’t provide adaptable filling of lesion defects with irregular shapes. Herein, the thermo sensitive keratin-g-PNIPAM polymers with different graft ratios were synthesized, and deferoxamine mesylate (DFO) loaded thermo sensitive keratin hydrogels (TKGs) were prepared using the oxidative crosslinking method. The lower critical solution temperature of TKGs can be tailored from 28.5 to 31.8 °C by varying the graft ratios of keratin to NIPAM, and TKG can fill up the complex shapes of lesion cavities easily due to the characteristic of sol-gel transition. In addition, TKGs exhibit stronger adsorption and clearance capacities for the Fe2+ than keratin gel. Meanwhile, in situ injection of TKG with different DFO loadings (0.1, 1.0, and 10 mg/mL) into the hematoma region after ICH surgery showed a stronger effect on the reduction of ICH-induced iron deposits, brain non-heme iron content, brain edema and ROS level compared to the DFO treatment by intraperitoneal administration. Thus, the developed TKG can be potentially exploited for iron-induced brain injury after ICH.

We introduce a new embedding model to represent movie characters and their interactions in a dialogue by encoding in the same representation the language used by these characters as well as information about the other participants in the dialogue. We evaluate the performance of these new character embeddings on two tasks: (1) character relatedness, using a dataset we introduce consisting of a dense character interaction matrix for 4,378 unique character pairs over 22 hours of dialogue from eighteen movies; and (2) character relation classification, for fine- and coarse-grained relations, as well as sentiment relations. Our experiments show that our model significantly outperforms the traditional Word2Vec continuous bag-of-words and skip-gram models, demonstrating the effectiveness of the character embeddings we introduce. We further show how these embeddings can be used in conjunction with a visual question answering system to improve over previous results.

We consider the task of identifying human actions visible in online videos. We focus on the widely spread genre of lifestyle vlogs, which consist of videos of people performing actions while verbally describing them. Our goal is to identify if actions mentioned in the speech description of a video are visually present. We construct a dataset with crowdsourced manual annotations of visible actions, and introduce a multimodal algorithm that leverages information derived from visual and linguistic clues to automatically infer which actions are visible in a video. We demonstrate that our multimodal algorithm outperforms algorithms based only on one modality at a time., Comment: Accepted at ACL 2019

The transformation of green rust (GR) to form ferric oxyhydroxide at the oxic/anoxic interface, is an important process of iron cycles in subsurface environments. However, little is known in its electron transfer process and environmental impacts. Herein, we demonstrate that the GR oxygenation can generate hydroxyl radical (•OH; 1.6 μM) with a low rate of 0.012 μM/min. Interestingly, a strong correlation between the adsorbed phosphate on GR and the •OH generation is found; the presence of 20 mM phosphate significantly enhance the generation rate of •OH up to 0.13 μM/min, and a high cumulative concentration up to 20.9 μM. Our results demonstrate that the formation of iron-phosphate complexes is likely a key species determining •OH generation. Electrochemical analysis and DFT calculations indicate that phosphate can decrease the electron utilization ratio of GR but notably increase the electron selectivity for •OH production under oxic conditions. Subsequently, the generation of •OH radicals plays a role in oxidatively detoxifying diverse organic pollutants (e.g. chlorophenol and pharmaceuticals). Our findings provide new insights into the important role of oxyanions such as phosphate in determining electron transfer during iron cycles and consequently the fate of pollutants in subsurface environments.

In this report, three metal-organic frameworks (MOFs) were synthesized by hydrothermal reactions with 4,4′,4″-nitrilotribenzoic acid (H3NTB), 1,10-phenanthroline (phen) and water by introducing Zn(Ⅱ), Mn(Ⅱ) and Cu(Ⅱ) center ions, namely, [Zn(HNTB)(phen)]n (Ⅰ), [Mn(HNTB)(phen)•H2O]n (Ⅱ) and {[Cu(HNTB)(phen)•H2O]•H2O}n (Ⅲ). Single-crystal X-ray diffraction data analyses demonstrate that Ⅰ-Ⅲ are assembled into three-dimensional frameworks based on the building blocks of {ZnO4N2}, {MnO3N2} and {CuO4N2} through the connections of HNTB2−, intermolecular hydrogen bonds and the π … π interactions between pyridine rings, respectively. The fluorescent properties of Ⅰ-Ⅲ have been systematically studied in different solutions containing certain metal ions and organic molecules. As a result, Zn-MOF (I) exhibits a dual-responsive fluorescent sensor toward nitrobenzene (NB) and Fe3+ with high selectivity, stability and anti-interference ability at the existence of other corresponding analytes originating from its excellent fluorescent quenching efficiency. Moreover, the possible sensing mechanisms for the above analytes have also been elucidated, which are testified by PXRD analysis, ICP-AES, UV–vis spectra, XPS and the density functional theory (DFT).

Layered double hydroxide (LDH) was a kind of layer materials with multi-metal elements and good catalytic performance, but the metals can be dissolved during catalysis. Graphene oxide (GO) was a two-dimensional structure with abundant anionic groups with the potential of immobilizing metals. However, LDH combined with GO (LDH/GO) was few reported in the treatment of pharmaceuticals and personal care products (PPCPs). In this work, CoFeNi LDH/GO composites were designed and synthesized for activating peroxymonosulfate (PMS). Meanwhile, gatifloxacin (GAT), a typical antibiotic, was used to assess the performance of LDH/GO + PMS system. The characterization of LDH/GO using various measures showed that the adding GO was inserted the layer structure of LDH and the surface functional group of GO was immobilized in LDH well. GAT could be degraded completely after 45 min under the reaction conditions of pH 7.0, 38 mg L−1 GAT, 40 mg L−1 LDH/GO and 400 mg L−1 PMS. The removal of total organic carbon and defluorination reached 55.5% and 78.0% after 5 h reaction, respectively. Quenching tests suggested that the main radicals responsible for GAT degradation and mineralization was sulfate radical (SO4− ). The redox cycle made the catalysis of the LDH/GO composites work continuously. Compared with LDH + PMS system, the concentration of dissolved metals decreased 60.6%-74.3% at pH 3.0–11.0 in LDH/GO + PMS system. The reusability test indicates that LDH/GO has stable catalytic performance in removing PPCPs from wastewater.

Flapping wing deformation influences the aerodynamics of insect flight. This deformation is dictated by the dynamical properties of the insect wing, particularly its vibration spectra and mode shapes. However, researchers have not yet developed artificial insect wings with vibration spectra and mode shapes that are identical to their biological counterparts. The goal of the present work is to develop artificial insect wings that are both isospectral and isomodal with respect to real insect wings. To do so, we characterized hawkmoth Manduca sexta wings using experimental modal analyses. From these results, we created artificial wings using additive manufacturing and heat molding. Between artificial and real wings, the first two natural frequencies differ by 7% and 16% respectively, with differences of 16% and 131% in gains evaluated at those natural frequencies. Vibration modes are similar as well. This work provides a foundation for more advanced wing design moving forward.

The adsorption of iodine anions (iodide and iodate) on the sulfur terminated (001) chalcopyrite surface has been systematically investigated combining first-principles calculations based on density functional theory (DFT) with X-ray photoelectron spectroscopy (XPS) measurements. Based on the total energy calculations and geometric optimization, the thermodynamically preferred site was copper atom for iodide adsorption and iron atom for iodate adsorption, respectively. In the case of Cu site mode, the iodate underwent a dissociative adsorption, where one I O bond of iodate ion was broken and the dissociative oxygen atom adsorbed on the adjacent sulphur site. Projected density of states (PDOS) analysis further clarified the interaction mechanism between active sites of chalcopyrite surface and adsorbates. In addition, full-range XPS spectra qualitatively revealed the presence of iodine on chalcopyrite surface. High resolution XPS spectra of the I 3d peaks after adsorption verified the chemical environment of iodine. The binding energies of 618.8 eV and 623.5 eV for I 3d5/2 peaks unveiled that the adsorption of iodide and iodate ions on copper-iron sulfide minerals was the result of formation of low solubility metal iodides precipitate. Also two I 3d peaks with low intensity around 618 eV and 630 eV might be related to the inorganic reduction of iodate to iodide by reducing S2− ion of chalcopyrite.