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Effective phytoremediation of Mn contaminated soil requires the selection of a species with good manganese tolerance. Broussonetia papyrifera is an important economic plant and pioneer species, it could be well adapted to drought and saline-alkali environment. In order to understand the effect of Mn stress on B. papyrifera, the effects of different concentrations of Mn (0–50 mmol/L) stress on the growth, morphology, Mn tolerance and physiological indexes of the plant were explored. The results showed that the biomass, surface area, length, root volume, tips, forks, and crossings of B. papyrifera reached the maximum at the Mn concentration of 1 mmol/L. Mn content in the tissue and TF in plants increased with the increase of concentration, while the BCF increased first and then decreased, and the maximum BCF was 0.154 at 10 mmol/L. The accumulation of Mn lead to cell membrane lipid peroxidation, which increased toxic substances in plants, resulting in the increase of MDA and PRO, and affected the synthesis of chlorophyll. However, B. papyrifera could effectively alleviate oxidative stress by increasing the activities of antioxidant enzymes (SOD, POD, CAT), protein and soluble sugar. The results suggested that B. papyrifera had a good oxidative stress mechanism to Mn stress and could be used as candidates for remediation of pollution in mining areas.

With the rapid progress of the Internet of Things (IoT) technology, human behavior recognition has become an important research topic in the field of ubiquitous computing and has obtained quite a number of research achievements. Accurate human behavior recognition can enhance the quality of human-computer interaction and facilitate the development of various sensing applications. With the popularity of smartphones and the improved performance of sensors such as speakers and microphones built in smartphones, the behavior recognition technique based on ultrasound signal of the smartphone has gained more attention and achieved several research results. In this paper, we first review the common behavior recognition techniques including light, video, sound, and frequency radio and outline their main characteristics. Then, we introduce the fundamental principle of human behavior recognition based on ultrasound signals. Specifically, these systems treat speakers and microphones embedded in smartphones as the transceiver and leverage the received signal changes caused by human movement including phase differences, frequency shift, and time of flight (ToF) to recognize human behavior. Next, we investigate the state-of-the-art studies and applications and analyze the signal processing techniques such as data collection, signal preprocessing, feature description, and behavior recognition approach. Afterward, according to the purpose of these applications, we classify them into five groups and compare them in detail including hand gesture recognition, activity recognition, hand trajectory tracking, vital sign monitoring, and lip reading. Finally, we conclude by discussing the limitations, challenges, and open issues involved in behavior recognition based on ultrasound signal of smartphone.

With the rapid development of Internet of Things, hand gesture recognition has drawn wide attention in the field of ubiquitous computing because it provides us with simple and natural human-computer interaction mode. Among these various implementations, hand gesture recognition using ultrasonic signals of smartphone has become a hot research topic due to its various advantages. In this paper, we consider the smartphone as an active sonar sensing system to identify hand movements. Specifically, the speakers emit ultrasonic signal and the microphone on the same phone receives the changed echo affected by hand movements. This paper investigates the state-of-the-art hand gesture applications and presents a comprehensive survey on the characteristics of studies using the active sonar sensing system. Firstly, we review the existing research of hand gesture recognition based on acoustic signals. After that, we introduce the characteristics of ultrasonic signal and describe the fundamental principle of hand gesture recognition. Then, we focus on the typical methods used in these studies and present a detailed analysis on signal generation, feature extraction, preprocessing, and recognition methods. Next, we investigate the state-of-the-art ultrasonic-based applications of hand gesture recognition using smartphone and analyze them in detail from dynamic gesture recognition and hand tracking. Afterwards, we make a discussion about these systems from signal acquisition, signal processing, and performance evaluation to obtain some insight into development of the ultrasonic hand gesture recognition system. Finally, we conclude by discussing the challenges, insight, and open issues involved in hand gesture recognition based on ultrasonic signal of the smartphone.

Recently, device-free human behavior recognition has become a hot research topic and has achieved significant progress in the field of ubiquitous computing. Among various implementation, behavior recognition based on WiFi CSI (channel state information) has drawn wide attention due to its major advantages. This paper investigates more than 100 latest CSI based behavior recognition applications within the last 6 years and presents a comprehensive survey from every aspect of human behavior recognition. Firstly, this paper reviews general behavior recognition applications using the WiFi signal and presents the basic concept of CSI and the fundamental principle of CSI-based behavior recognition. This paper analyzes the key components and core characteristics of the system architecture of human behavior recognition using CSI. Afterward, we divide the sensing procedures into many steps and summarize the typical studies from these steps, including base signal selection, signal preprocessing, and identification approaches. Next, based on the recognition technique, we classify the applications into three groups, including pattern-based, model-based, and deep learning-based approach. In every group, we categorize the state-of-the-art applications into three groups, including coarse-grained specific behavior recognition, fine-grained specific behavior recognition, and activity inference. It elaborates the typical behavior recognition applications from five aspects, including experimental equipment, experimental scenario, behavior, classifier, and system performance. Then, this paper presents comprehensive discussions about representative applications from the implementation view and outlines the major consideration when developing a recognition system. Finally, this article concludes by analyzing the open issues of CSI-based behavior recognition applications and pointing out future research directions.

Conventional activated carbon used in decolorization of citric acid fermentation broth has the disadvantage of high citric acid loss. In this study, a novel biomass-based carbon material, namely HBCM, was prepared via H3PO4 activation of hydrothermally treated corncob. The material was fully characterized by SEM, BET, TG, FTIR, XPS and pHpzc. The results showed that the material's SBET was as high as 1720 m2 g−1 and several weak-acid functional groups existed on its surface, which contributed to efficient decolorization with low citric acid loss. By adjusting the solution pH value to around 7, nearly no citric acid was lost. Furthermore, the adsorption behavior of pigments on HBCM was systematically investigated under optimized pH. The results indicated that the adsorption was spontaneous and endothermic. Intra-particle diffusion was the rate-limiting step. By comparing FTIR data before and after adsorption, it was found that oxygen-containing functional groups on the HBCM surface participated in pigment adsorption. Overall, the tailor-made HBCM performed excellently with a 99% decolorization ratio and nearly no citric acid loss under optimum operating conditions. It could be a potential adsorbent in the removal of pigments from citric acid fermentation broth.

In this work nanofiltration technology has been employed for removal of inhibitors and recovery of monosaccharides from dilute acid lignocellulose hydrolysates. The influences of feed solution pH, permeate flux, and Na2SO4 concentration on the rejection of monosaccharides and inhibitors were investigated. The results showed that the pH for the separation of carboxylic acids and furans from monosaccharides should be as low as possible. With increase of Na2SO4 concentration carboxylic acid and furan rejection decreased. Subsequently, the Donnan steric pore and dielectric exclusion model coupled with mass balance was used to predict the rejection of solutes at different permeate fluxes. In order to select a suitable permeate flux and operating time, multi-objective optimization was carried out to obtain the maximum total inhibitor removal efficiency, the maximum monosaccharide recovery rate, and the minimum water consumption. The optimal operating conditions were then verified using the real hydrolysate as feed solutions. More specifically, for the treatment of 6 L of a hydrolysate solution, 13 L of water and a treatment time of 35 min were required. This process allowed the removal of 90% inhibitors, while 93.55% glucose, 90.75% xylose, and 90.53% arabinose were recovered. Finally, a batch column equipped with a strong acid cation exchange resin was employed to recover the monosaccharides from the hydrolysate. Using water as an eluent, 95.37% of the sulfuric acid and 94.87% of the monosaccharides were recovered. In all, we demonstrated that the combination of nanofiltration with electrolyte exclusion chromatography is a promising integrated process for the recovery of monosaccharides and inorganic acids from dilute acid corncob hydrolysates.

A series of porous-carbon adsorbents termed as HDPC (hydrochar-derived pyrolysis char) were prepared from corncob and used for the 1-butanol recovery from aqueous solution. The influences of pyrolysis temperature on properties of the adsorbents were systematically investigated. The results showed that hydrophobicity, surface area, and pore volume of HDPC samples increased with an increase in pyrolysis temperature. Furthermore, the adsorption mechanism of 1-butanol on the adsorbents was explored based on correlation of the samples properties with adsorption parameters extracted from the 1-butanol adsorption isotherms (K F and Q e12 ). Overall, the 1-butanol adsorption capacity increased with a decrease in polarity and an increase in aromaticity, surface area and pore volume of HDPC samples. However, at different pyrolysis temperature, the factors causing the increase of 1-butanol adsorption on the adsorbents are variable. The kinetic experiments revealed that the pores played a vital role in the 1-butonal adsorption process. The intraparticle diffusion model was used to predict the adsorption kinetic process. The simulation results showed that intraparticle diffusion was the main rate-controlling step in the 1-butanol adsorption process.