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Bisphenol A epoxy (BAEP) is a widely-used material in daily life, and increasing BAEP wastes are being generated. Pyrolysis is a potential technology for BAEP recovery, but conventional processes are inefficient and high energy-consuming. Herein, Fe-catalyzed pyrolysis process was proposed, and comprehensive comparisons were performed on characteristics of products obtained from BAEP pyrolysis in the presence and absence of iron. The results demonstrated that the addition of iron reduced the decomposition temperature of BAEP by approximately 10–20 °C and promoted the generation of gaseous products, in addition, the appropriate conditions were determined as the iron content of 4%, pyrolysis temperature of 400 °C, holding time of 90 min, and heating rate of 5 °C/min. Moreover, based on the analyses of Py-GCMS for volatiles and XPS for residues, the catalytic mechanism was revealed as the facilitation effect of iron on the conversion of lattice oxygen to adsorbed oxygen. This work would be conducive to energy saving and light products production during BAEP pyrolysis and offer valuable references for studies on the recovery of some other organic materials/wastes. [Display omitted] [ABSTRACT FROM AUTHOR]

The in-ground-effect (IGE) aircraft wake vortex poses great hazards to airplanes. Especially when the vortex column is deformed with a long wavelength, the vortex core position is harder to determine, and thus, the IGE wake vortex is essential to air traffic safety and requires more detailed research. In this study, to account for the effects of roll-up-induced structures, the wake vortex pair was initialized using the vortex sheet model proposed by Lin et al. [1]. The asymmetrical wing loading effect on IGE wake evolution was considered for the first time. The Large Eddy Simulation results showed that the crosswind strengthened the upwind vortex, while the long-term wake vortex evolution was not significantly influenced by the wind. Most importantly, in certain crosswind conditions, the wake vortex deformed sinusoidally and linked with its mirror counterpart. From a linear stability analysis, it was found that this long-wave deformation could only occur on the upwind side owing to the redistribution of the strain field caused by the near-ground wind shear. Due to secondary vortex generation, the deformation was suppressed and a low growth rate of α ≈ 0.41 was recorded for the long-wave instability. [ABSTRACT FROM AUTHOR]

• The method for detecting pedestrian height and distance information based on monocular cameras are reviewed. • The process of using the background difference method and the contour detection method to optimize the detection effect is explored. • The method to simplify application deployment in the form of configuration parameters are discussed. • The effect of this method on measuring other objects is tested. With the rapid development of smart cities, pedestrian detection information has expanded from human faces to human body features such as height due to the needs of urban security and urban management. In this paper, we propose a parametric method to detect the height and distance of pedestrians based on monocular cameras. First, we use target detection to detect the position of pedestrians in the figure. Then, the pedestrian's position in the frame is calibrated using a combination of an improved block differencing method and global contour detection to obtain the actual contour of the pedestrian with the complex background removed. After that, we use the perspective principle combined with the reference information to obtain the measurement height and distance formulas. Finally, we calculate the height of the pedestrian and the distance from the camera after checking the calibration. The experimental results show that the distance measurement accuracy is above 97%, and the height is above 95% a range of 25 m. The application of this method can evolve face detection to human body detection, extend the feature capture of the human face to the capture of human body height and other features, and improve the accuracy of face detection through the assistance of these features. [ABSTRACT FROM AUTHOR]

It is well-known that the long-lasted aircraft wake vortex above the airport runway jeopardizes flight safety. Therefore, the research on the enhancement of wake vortex decay is pretty much essential. The previous studies suggest that the wake decay process can be improved by introducing artificial perturbations [1]. For example, the plate lines on the ground can trigger the helical secondary vortex structures (SVS), propagating along with the primary vortices. As a result, the vortex decay will be enhanced. In the present research, active flow control methods on wake vortex decay, like air blowing or suction, are studied. Blowing/suction zones are embedded on the ground to generate airflow below the wake vortex tubes. Different sizes and shapes of the blowing/suction zones are tested. The large eddy simulation (LES) method is used to quantitatively investigate the blowing/suction effects on wake vortex decay. Furthermore, the coherent vortex structures observed and the acceleration mechanism on wake decay caused by active flow control are studied carefully. According to the present research, it is found that with a small blowing velocity (1.79 m/s) and a short blowing duration (13.15 s), the aircraft wake strength can be roughly reduced by 50% at t ⁎ = 1. Based on the simulation results, it is revealed that the apparent effect on the wake vortex development is due to the detached vortex generated near the blowing zone rather than the blowing flow itself. Besides, a detached vortex with a scale of 0.2 b 0 is needed to achieve a better wake decay enhancement. [ABSTRACT FROM AUTHOR]

• Gelatin projectile were prepared using polyethylene foam as inner support. • Projectile's impact load-time curves at different impact velocities were obtained. • Relationship between the projectile deformation and impact load curve was studied. • Unsupported projectiles present a "peanut-tadpole-jet flow" shapes during flight. • Gelatin projectile with inner support could obtain a more ideal impact curve. In accordance with the requirement to use gelatin bird projectiles instead of real birds for impact tests, four kinds of gelatin bird projectiles with length-to-diameter ratios of 2 were prepared. In addition to one unsupported projectile, three of the projectiles were strengthened using polyethylene foamed plastic as an inner support(cruciform internally supported, fishbone internally supported, and cylindrical internally supported projectiles). Impact tests were conducted using a rigid target, and the impact load–time curves at different impact velocities were obtained for each projectile. The relationship between the shape of the gelatin bird before impact and the curve of the impact load was studied, and the variation in the impact load–time curve of the gelatin bird projectiles under the same impact velocity was analyzed. The results show that the unsupported gelatin bird projectile produces "peanut–tadpole–jet flow" shapes during the deformation process, and produces a highly distorted impact load–time curve. The polyethylene foamed plastic support structures maintained the length-to-diameter ratio of the gelatin bird projectiles to a certain extent, and produced better impact curves. [ABSTRACT FROM AUTHOR]

Erianin is an important bibenzyl compound in dendrobium and has a wide spectrum of pharmacological properties. Since Erianin was discovered, abundant results have been achieved in the in vitro synthesis, structural modification, and pharmacological mechanism research. Researchers have developed a series of simple and efficient in vitro synthesis methods to improve the shortcomings of poor water solubility by replacing the chemical structure or coating it in nanomaterials. Erianin has a broad anti-tumor spectrum and significant anti-tumor effects. In addition, Erianin also has pharmacological actions like immune regulation, anti-inflammatory, and anti-angiogenesis. A comprehensive understanding of the synthesis, metabolism, structural modification, and pharmacological action pathways of Erianin is of great value for the utilization of Erianin. Therefore, this review conducts a relatively systematic look back at Erianin from the above four aspects, to give a reference for the evolvement and further appliance of Erianin. [Display omitted] • Erianin, an extract of Dendrobium officinale, has anti-tumor effects on multiple common tumors of the body. • The anti-cancer effect of Erianin is mainly through promoting tumor cell apoptosis and inhibiting proliferation in many ways. • Erianin has poor water solubility, its drug delivery can be achieved by encapsulating or conjugating it with nanomaterials. [ABSTRACT FROM AUTHOR]

• This vertical alignment and porous structure of V-MOF derived V 2 O 5 @C can promote rapid electron transfer Zn2+ transport. • The derivatives inherit the high specific surface area and porosity of MOFs , which is conducive to enrich active sites. • The V 2 O 5 @C NBAs/CNTF//Zn battery demonstrated an ultrahigh capacity of 0.71 mAh cm−2 at 2 mA cm−2 and excellent rate capability. • The as-assembled FARZIBs exhibited an ultrahigh energy density of 215 mWh cm−3 at a power density of 600 mW cm−3. Fiber-shaped energy storage devices with light weight, superflexibility, and weavability demonstrate promising prospects for application in wearable and portable electronics. Especially, fiber-shaped aqueous rechargeable zinc ion batteries (FARZIBs), which can facilitate the development of wearable electronic products owing to their high safety, low cost, environmental friendliness. Nevertheless, it is very challenging to achieve high rate capability, energy density, and cycling performance simultaneously for the FARZIBs. Herein, a high-performance FARZIB is created from vanadium-based metal-organic frameworks (MOFs) derived vanadic oxide (V 2 O 5) nanowire-bundle arrays (NBAs) grown on highly conductive carbon nanotube fibers (CNTFs) directly as the binder-free cathode. Profiting from high specific area and porous structure of MOFs, as well as arrays structure and binder-free features, our as-assembled FARZIBs exhibited a high capacity of 0.71 mAh cm−2 at a current density of 2 mA cm−2, and demonstrated outstanding rate capability and prominent cycling performance. Moreover, the FARZIBs delivered an extremely high energy density of 215 mWh cm−3 at a power density of 600 mW cm−3. Therefore, our work brings new prospects for the next generation of wearable electronics. The fiber-shaped aqueous rechargeable Zn-ion batteries take advantage of the high capacity of the positive electrode (V-MOF-derived carbon incorporated V 2 O 5 @C NBAs/CNTF) to yield excellent electrochemical performance, therfore demonstrating great potential for next-generation portable wearable energy storage devices. [Display omitted] [ABSTRACT FROM AUTHOR]

[Display omitted] • Machine learning was used to model T α in pyrolysis from preliminary analyses of biomass. • NN and RF algorithms were explored, and crucial hyperparameters were optimized. • With R2 of 0.9462 and RMES of 14.63 K, RF model presented high prediction accuracy. • α was the dominant factor on T α and the compositional properties contributed 1/3. • The results helped in reducing repetitive experiments and controlling the process. Biomass pyrolysis presents a viable solution to address the increasing global energy demand, and substantial efforts have been devoted worldwide to monitoring the conversion process through thermogravimetric experiments. In this study, the machine learning (ML) algorithm was employed to simulate and predict the temperature corresponding to specific conversion degrees (T α) by utilizing the compositional properties of the feedstock and heating rate as input variables. A dataset consisting of 1750 entries was collected from literature and deployed to construct neural network (NN) and random forest (RF) algorithms. After hyperparameter optimization, the RF model, equipped with 13 estimators and a maximum tree depth of 4, exhibited impressive accuracy in predicting T α , yielding an R2 value of 0.9462 and an RMES of 14.63 K. The subsequent feature importance analysis revealed that the conversion degree was the predominant influencing factor for T α , while the compositional properties of biomass accounted for 32.3 % of the overall influence. Validation experiments conducted using unexploited biomass further confirmed the robustness of the developed model. This research successfully demonstrated the feasibility of an ML approach in predicting reaction temperature based on preliminary feedstock analysis and desired pyrolysis progress, effectively eliminating the necessity for repetitive experiments. Furthermore, it provided valuable references for future ML research and applications in related fields. [ABSTRACT FROM AUTHOR]

• c-di-GMP was spontaneously reinforced under inhibited electrical signaling. • Exogenous HSL benefited the biofilm restoration, but weakened c-di-GMP dominance. • Both TEA and exogenous HSL induced the selective enrichment of Geobacter. • Thauera played significant roles in c-di-GMP signaling under TEA stress. • A new avenue for biofilm regulation and enrichment of Geobacter was proposed. Electrogenic biofilms in microbial electrochemical systems have played significant roles in simultaneous wastewater treatment and energy recovery owing to their unique extracellular electron transfer. Their formation has been shown to be regulated by electrical and chemical communication, but the interaction between these signal communication pathways has not been studied. This research investigated the coordination between intracellular c-di-GMP signaling and reinforced quorum sensing with or without exogenous HSL (a common quorum sensing molecule), on the formation of mixed-cultured electrogenic biofilm under electrical signaling disruption by tetraethylammonium (TEA, a broad-range potassium channel blocker). Intracellular c-di-GMP was spontaneously reinforced in response to TEA stress, and metagenomic analysis revealed that the dominant DGC (the genes for producing c-di-GMP) induced the eventual biofilm formation by mediating exopolysaccharide synthesis. Meanwhile, reinforced quorum sensing by exogenous HSL could also benefit the biofilm restoration, however, it alleviated the TEA-induced communication stress, resulting in the weakening of c-di-GMP dominance. Interestingly, suppressing electrical communication with or without HSL addition both induced selective enrichment of Geobacter of 85.5% or 30.1% respectively. Functional contribution analysis revealed the significant roles of Geobacter and Thauera in c-di-GMP signaling, especially Thauera in resistance to TEA stress. This study proposed a potential strategy for electrogenic biofilm regulation from the perspectives of cell-to-cell communication. [Display omitted] [ABSTRACT FROM AUTHOR]

[Display omitted] • Machine learning was used to model the activation energy of biomass pyrolysis. • ANN, RF, and SVM models were applied using biomass properties and reaction degree. • With R2 of 0.911, the optimized RF model presented high accuracy for E α prediction. • The contributions of variables were determined as α > C > ash > N > H > S > method. • The constructed model could help reduce repetitive experiments and save resources. The investigations of biomass pyrolysis kinetics are traditionally accomplished through experiments at the expense of extensive resources worldwide. The estimation of activation energy (E α) of biomass pyrolysis is a crucial and essential aspect of process optimization and control. In this study, machine learning methods were applied for the first time to predict the E α values of the pyrolysis process based on the preliminary analysis of the biomass feedstocks and conversion degree (α). A total of 1523 sets of E α values calculated via three frequently-used model-free kinetic methods were collected from literature and modeled by using artificial neural network (ANN), random forest (RF), and support vector machine (SVM) algorithms. The training and optimization of the models showed that the RF algorithm exhibited satisfactory accuracy, making it a promising tool for a quick prediction of E α values. Moreover, the feature importance analysis indicated that E α mainly depended on the α, C content, and ash content of the biomass. This work suggested the effective application of machine learning in determining E α accurately, which could help in understanding the pyrolysis mechanisms, reducing the experimental workload, and improving the optimization of the pyrolysis process. [ABSTRACT FROM AUTHOR]