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Acetone is a solvent used in many laboratories and factories. Serious problems will occur when it is exposed to the environment. Therefore, a new design hydrophobicity bimetallic metal material (10-Fe1Mn1-USY) was prepared for acetone adsorption under conditions of high humidity. Hydrophobic Y type zeolite was used to prepare bimetallic metal materials and the effect of different operating parameters (including different material, humidity, temperature, pollutant concentration, residence time, and regeneration) on the efficiency of acetone treatment was examined. Isothermal adsorption model, kinetics and thermodynamic model analysis were also used to establish the reaction mechanism. The 10-Fe1Mn1-USY material has good adsorption capacity (133 mg g−1) for acetone under a relative humidity of 50%. The main factors affecting the adsorption capacity are the contact angle, hydrophobicity, specific surface area, and Si/Al of the material. The isothermal adsorption and the kinetic adsorption behavior of 10-Fe1Mn1-USY material for acetone are more suitable for the Temkin isotherm adsorption model and the pseudo-first-order kinetic model. The adsorption of acetone by 10-Fe1Mn1-USY material is dominated by intra-particle diffusion. According to the thermodynamic analysis results, the adsorption behavior of 10-Fe1Mn1-USY material for acetone is a type of physical adsorption, and a spontaneous and non-sequential reaction.

Co-catalyst free SrTiO3 was prepared by hydrothermal method and it’s calcinated by different temperatures such as 300, 500, 700, and 900 °C to improve the photocatalytic hydrogen production. The SrTiO3 exhibits a cubic crystal structure which was revealed through X-ray diffraction (XRD) analysis. Scanning electron microscopy (SEM) images show the combination of nano spherical-needle morphology changed into nano-cubic morphology when the temperature varied from 300 to 900 °C. Further, transmission electron microscopy (TEM) result also confirms the nano-cubic crystal structure with well-resolved edges. The elemental mapping analysis shows the Sr, Ti, O elements were distributed uniformly in the prepared SrTiO3. The X-ray photoelectron spectroscopy (XPS) confirms the presence of chemical compositions and valance state of Sr, Ti, O elements in SrTiO3. The self-trapped excitons recombination of SrTiO3 was analyzed by photoluminescence (PL) analysis. The Electrochemical Impedance Spectroscopy (EIS) analysis of SrTiO3-900 °C shows the lowest electron transfer resistance which promotes effective charge separation. Among the different catalysts, SrTiO3-900 °C gives 6.8 mmol g−1 h−1 of hydrogen under Ultra-Violet irradiation.

While various studies have focused on the significance of computational thinking (CT) for the future career paths of individuals in science, technology, engineering, and mathematics (STEM), few studies have focused on computational thinking for non-STEM college students. This study explores the relationship between computational thinking and learning satisfaction for non-STEM-major college students. A conceptual model is proposed to examine the structural relationships among computational thinking, self-efficacy, self-exploration, enjoyment and learning satisfaction in an AppInventor-based liberal education course. Collecting data from 190 undergraduate students from Taiwan and analyzing the data by using partial least squares (PLS) methods, the research framework confirms the six proposed hypotheses. These results show that both computational thinking and enjoyment play significant roles in both self-exploration and digital self-efficacy. Moreover, digital self-efficacy and self-exploration also have a significant positive influence on learning satisfaction. These findings have implications for influencing the learning outcomes of non-STEM-major college students, computational thinking course instructors, and computational thinking relevant policies.

The use of nitrogen fertilizers in agriculture produces significant quantities of nitrogenous gases including ammonia, nitric oxide, and nitrous oxide. Through better farmland management practices, the emission of nitrogenous gases can be reduced while realizing clean water environment and climate-smart agriculture. In this article, we first provided an overview of the international movements on reducing nitrogenous gas emissions from farmlands. Then, we summarized the effect of agricultural management practices on nitrogen use efficiency for various crops, and evaluated their effect on nitrogenous gas emissions. The results indicated the importance of implementing site-specific sustainable management practices to enhance nitrogen use efficiency, and thus mitigate nitrogenous gas emissions. We also addressed the impact of agricultural activities on cropland nitrogen cycles, and highlighted the need to perform systematic trade-off evaluations with a well-defined scope to maximize environmental benefits and maintain ecosystem services. Lastly, we proposed three priority directions by moving toward a low-emission agriculture.

Background Transcranial direct current stimulation (tDCS) is a noninvasive brain stimulation to modulate cortical activity for improving motor function. However, the different tDCS applications for modulating cortical activity and dual task gait performance in chronic stroke have not yet been investigated. This study investigated the effects of different tDCS applications on dual task gait performance and contralesional M1 activation in chronic stroke. Methods Forty-eight participants were randomized to anodal, bilateral, cathodal, and sham tDCS groups. Each group received 20 min of tDCS stimulation, except the sham group. Gait performance was measured by GaitRite system during cognitive dual task (CDT) walking, motor dual task (MDT) walking, and single walking (SW). Contralesional M1 activity of unaffected tibialis anterior (TA) was measured using transcranial magnetic stimulation (TMS). Intragroup difference was analyzed by Wilconxon sign ranks test with Bonferroni correction, and Kruskal–Wallis one-way analysis of variance by ranks was used for intergroup comparisons, followed by post-hoc Mann–Whitney U tests with Bonferroni correction. Results The bilateral tDCS (p = 0.017) and cathodal tDCS (p = 0.010) improved the CDT walking speed more than sham group. The bilateral tDCS (p = 0.048) and cathodal tDCS (p = 0.048) also improved the MDT walking speed more than sham group. Furthermore, bilateral tDCS (p = 0.012) and cathodal tDCS (p = 0.040) increased the silent period (SP) more than the anodal and sham group. Thus, one-session of bilateral and cathodal tDCS improved dual task walking performance paralleled with increasing contralesional corticomotor inhibition in chronic stroke. Conclusions Our results indicate that one-session of bilateral and cathodal tDCS increased contralesional corticomotor inhibition and improved dual task gait performance in chronic stroke. Trial registration Thai Clinical Trials Registry (TCTR20180116001). Registered prospectively on 16th Jan, 2018 at http://www.thaiclinicaltrials.org.

Arsenic is a carcinogenic substance, with many cases of poisoning related to arsenic pollution in groundwater. In Taiwan arsenic in groundwater caused the notorious Blackfoot disease. Methods for arsenic removal from water include precipitation, membrane processes, ion exchange, and adsorption, but these processing technologies suffer from high investment costs and complex operations. The traditional adsorption method cannot be used for arsenic removal due to its high operating costs, difficulties in recovery, and low adsorption capacity. To address these issues, this study designed an adsorption material based on biochar for arsenic removal with higher adsorption properties and easy recovery. Biochar sources are readily available from waste wood as a cheap and environmentally friendly material. The efficiency of As (III) removal is also promoted by FeCl3 and KMnO4. The objectives of this research are to obtain optimum operation conditions by assessing the effects of different iron and manganese contents, different doses, different pH and different initial concentration. The adsorption mechanism between As (III) and biochar was studied by adsorption isotherms and the kinetic model. X-ray diffraction, energy-dispersive X-ray spectroscopy and elemental analyzer analysis results show that modified biochar has major elements of Fe and Mn. There is greater magnetism, 40 emu g− 1, in the modified biochar. The maximum adsorption efficiency of 81% and 0.72 mg g− 1 capacity occurs when the ratio of Mn, Fe and C is 4:1:1. The adsorption capacity is high under higher pH with pristine biochar and 1FeC under lower pH with 1Fe2MnC. The reaction mechanism is divided into four pathways. The first pathway is the attachment of As (III) ions into the pore of biochar via physical adsorption. In the second pathway, biochar can connect with As (III) through hydrogen bonding from the function group -OH in the biochar and the As (III) itself. In the third pathway, they can contact each other by electron force when the biochar surface is filled with a positive charge. In the fourth pathway, the compounds of manganese have strong oxidizability to oxidize As (III) to As(V). The iron ions then act as a bridge connecting the biochar and the As (III), resulting in the formation of new complex compounds.

With the explosion of unlabelled and high-dimensional data, unsupervised feature selection has become an critical and challenging problem in machine learning. Recently, data representation based model has been successfully deployed for unsupervised feature selection, which defines feature importance as the capability to represent original data via a reconstruction function. However, most existing algorithms conduct feature selection on original feature space, which will be affected by the noisy and redundant features of original feature space. In this paper, we investigate how to conduct feature selection on the dictionary basis space of the data, which can capture higher level and more abstract representation than original low-level representation. In addition, a similarity graph is learned simultaneously to preserve the local geometrical data structure which has been confirmed critical for unsupervised feature selection. In summary, we propose a model (referred to as DGL-UFS briefly) to integrate dictionary learning, similarity graph learning and feature selection into a uniform framework. Experiments on various types of real world datasets demonstrate the effectiveness of the proposed framework DGL-UFS.

Sb-based materials have been considered one of the most promising anode electrode materials for lithium-ion batteries, whereas they were commonly synthesized through time-consuming and costly processes. Here, Sb@Sb2O3/reduced graphene oxide (Sb@Sb2O3/rGO) composite was successfully synthesized by a facile one-pot chemical method at ambient temperature. Based on the XRD and TGA analysis, the mass fractions of Sb and Sb2O3 in the Sb@Sb2O3/rGO composite are ca. 34.05% and 26.6%, respectively. When used as an alternative electrode for lithium ion batteries, a high reversible capacity of 790.9 mA∙h/g could be delivered after 200 cycles with the capacity retention of 93.8% at a current density of 200 mA/g. And a capacity of 260 mA∙h/g could be maintained even at 2000 mA/g. These excellent electrochemical properties can be attributed to its well-constructed nanostructure. The Sb and Sb2O3 particles with size of 10 nm were tightly anchored on rGO sheets through electronic coupling, which could not only alleviate the stress induced by the volume expansion, suppress the aggregation of Sb and Sb2O3 particles, but also improve the electron transfer ability during cycling

The traffic congestion in the Hsuehshan tunnel and at the Toucheng interchange has led to traffic-related air pollution with increasing concern. To ensure the authenticity of our simulation, the concentration of the last 150 m in Hsuehshan tunnel was simulated using the computational fluid dynamics fluid model. The air quality at the Toucheng interchange along a 2 km length highway was simulated using the California Line Source Dispersion Model. The differences in air quality between rush hours and normal traffic conditions were also investigated. An unmanned aerial vehicle (UAV) with installed PM2.5 sensors was developed to obtain the three-dimensional distribution of pollutants. On different roads, during the weekend, the concentrations of pollutants such as SOx, CO, NO, and PM2.5 were observed to be in the range of 0.003–0.008, 7.5–15, 1.5–2.5 ppm, and 40–80 μg m− 3, respectively. On weekdays, the vehicle speed and the natural wind were 60 km h− 1 and 2.0 m s− 1, respectively. On weekdays, the SOx, CO, NO, and PM2.5 concentrations were found to be in the range of 0.002–0.003, 3–9, 0.7–1.8 ppm, and 35–50 μg m− 3, respectively. The UAV was used to verify that the PM2.5 concentrations of vertical changes at heights of 9.0, 7.0, 5.0, and 3.0 m were 45–48, 30–35, 25–30, and 50–52 μg m− 3, respectively. In addition, the predicted PM2.5 concentrations were 40–45, 25–30, 45–48, and 45–50 μg m− 3 on weekdays. These results provide a reference model for environmental impact assessments of long tunnels and traffic jam-prone areas. These models and data are useful for transportation planners in the context of creating traffic management plans.

Inter-comparison on the chemical characteristics, spatiotemporal variation, and source originality of atmosphere PM10 at the coastal regions and offshore islands of subtropical region in East Asia was investigated. A total of six sampling sites including three coastal and three island sites over the Minjiang estuary were selected for simultaneously manually sampling PM10 to investigate the differences of coastal and island PM10 concentration, chemical properties, and emission sources. After sampling, an ion chromatographer (IC) was used to analyze the concentration of major anions (F−, Cl−, SO42−, and NO3−) and cations (NH4+, K+, Na+, Ca2+and Mg2+). The metallic elements (Na, Ca, Al, Fe, Mg, K, Zn, Cr, Ti, Mn, Ni, Pb, and Cd) of PM10 were measured with an inductively coupled plasma-atomic emission spectrometer (ICP-AES). The carbonaceous contents, including elemental, organic, and total carbons (OC, EC, and TC), of PM10 were analyzed with an elemental analyzer (EA). The source apportionment of PM10 was further resolved by a receptor model based on chemical mass balance (CMB). Field measurement results showed that high concentrations of PM10 (102 ± 25 μg/m3) were commonly observed from late fall to early winter. The PM10 concentration showed a consistently descending trend from the west coastline to the far east islands, with the highest PM10 concentration at the coastal site (Site HQ) and the lowest at the far east island site (Site DY) in all seasons. Secondary inorganic aerosols (SIAs) and crustal materials were two most abundant compounds in PM10. Increasing anthropogenic trace elements was commonly observed in spring and winter, while northern monsoons were blown. High OC/EC ratios showed that secondary organic aerosols (SOAs) could be formed in the atmosphere of Minjiang estuary. Results from CMB receptor modeling indicated that the major sources of atmospheric PM10 at the coastal and offshore island regions of the Minjiang estuary were soil dusts (13–33%), secondary aerosols (15–30%), transport emissions (11–17%), sea salts (8–27%), and agricultural debris burning (2–8%). Particularly, during the poor air quality periods, a rising contribution of industrial emissions (1–16%) was resolved, suggesting that atmospheric PM10 over the Minjiang estuary was mainly attributed to cross-boundary transport rather than local emissions.

This paper proposes a new framework for RGB-D-based action recognition that takes advantages of hand-designed features from skeleton data and deeply learned features from depth maps, and exploits effectively both the local and global temporal information. Specifically, depth and skeleton data are firstly augmented for deep learning and making the recognition insensitive to view variance. Secondly, depth sequences are segmented using the handcrafted features based on skeleton joints motion histogram to exploit the local temporal information. All training segments are clustered using an Infinite Gaussian Mixture Model (IGMM) through Bayesian estimation and labelled for training Convolutional Neural Networks (ConvNets) on the depth maps. Thus, a depth sequence can be reliably encoded into a sequence of segment labels. Finally, the sequence of labels is fed into a joint Hidden Markov Model and Support Vector Machine (HMM-SVM) classifier to explore the global temporal information for final recognition. The proposed framework was evaluated on the widely used MSRAction-Pairs, MSRDailyActivity3D and UTD-MHAD datasets and achieved promising results.

A structured low-rank matrix recovery model for RGBD salient object detection is proposed. Firstly, the problem is described by a low-rank matrix recovery, and the hierarchical structure of RGB image is added to the sparsity term. Secondly, the depth information is fused into the model by a Laplacian regularization term to ensure that the image regions which share similar depth value will be allocated to similar saliency value. Thirdly, a variation of alternating direction method is proposed to solve the proposed model. Finally, both quantitative and qualitative experimental results on NLPR1000 and NJU400 show the advantage of the proposed RGBD salient object detection model.

This paper presents the first attempt to explore the potential of phosphorene as an adsorbent for arsenic [AS(III) and As(V)] removal under competition adsorption with citric acid (CA). In this study, the phosphorene was prepared by the exfoliation method. Results showed that CA reduced As(III) adsorption due to the lower stability constant of organic-As(III) complexes. The adsorption capacity with phosphorene was enhanced with a decrease in the initial concentration of CA. The adsorption rates for As(III) and As(V) were 0.033 and 0.069 min− 1, respectively, according to kinetics analysis. The maximum adsorption capacity obtained using the Langmuir model was 8.9 mg g− 1 for As(III) at pH 7 with 0.1 mM CA addition, and the As(V) adsorption capacity of phosphorene was 20 mg g− 1.

Polyimide/carbon black (PI/CB) nanocomposite films were fabricated via the direct ball-milling method with poly(amic acid) (PAA), the precursor of PI, as an in situ formed impurity-free dispersant. FTIR and Raman spectral results reveal that, besides physical adsorption, chemical grafting of PAA chains onto the CB surface occurs during the ball-milling process. Comparative studies show that introduction of various commercial dispersants improves the dispersion of CB. However, the mixtures exhibit poor reproducibility, unstable electrical properties, and decreased tensile strength; these issues may be attributed to interfacial pollution brought about by differences in the chemical structures of the dispersant and the matrix. The impurity-free dispersant is effective not only in ensuring the uniform dispersion of CB particles but also in enhancing filler-matrix interfacial adhesion. High-molecular weight PAA chains are effective reagents for impurity-free modification and can therefore be used to improve the electrical and mechanical properties of the resultant composite.

Salient object detection is very useful in many computer vision applications such as image segmentation, content-based image editing and object recognition. In this paper, we present a salient object detection algorithm by using color spatial distribution (CSD) and minimum spanning tree weight (MSTW). We first use a segmentation algorithm to decompose an image into superpixel-level elements, then use these elements as nodes to construct a minimum spanning tree (MST), each connected edge weight is the mean color difference between two nodes. CSD of each element can be computed by integrating color, spatial distance and MSTW. Note that if the color of one element is the most widely distributed over the entire image, it should have the biggest CSD value, we regard this element as a background node (BG Node). Then we use the MSTW between other element and BG node to generate a MSTW map. The superpixel-level saliency map can be obtained by combining the CSD map and MSTW map. Finally, we use a guided filter to get the pixel-level saliency map. Experimental results on two databases demonstrate that our proposed method outperforms other previous state-of-the-art approaches.

This study uncovered microbial characteristics of bioelectricity generation and dye decolorization in single-chamber microbial fuel cells (MFCs) using activated sludge for wine-containing wastewater treatment. Phylogenetic tree analysis on 16S rRNA gene fragments indicated that the predominant strains on anodic biofilm in acclimatized MFCs were Gamma-Proteobacteria Aeromonas punctata NIU-P9, Pseudomonas plecoglossicida NIU-Y3, Pseudomonas koreensis NIU-X8, Acinetobacter junii NIU-Y8, Stenotrophomonas maltophila NIU-X2. Our findings showed that the current production capabilities of these pure strains were only ca. 10% of those of their mother activated sludge, indicating that synergistic interactions among microbes might be the most influential factor to maximize power generation in MFCs. Plus, these electrochemically active strains also performed reductive decolorization of C.I. reactive blue 160, suggesting that bioelectricity generation might be directly associated to azo dye decolorization to deal with electron transfer on anodic biofilm in MFCs.

Titanium and its alloys are relatively new engineering metals because they have been used as structural materials only since 1952 [1]. Ti-6Al-4V is by far the most important and widely used titanium alloy, accounting for 60% of the titanium market in 1989. It can be readily welded, forged and machined [1]. However, the welding of Ti alloys may be not appropriate for all applications such as titanium honeycomb sandwich structure [2]. In such a case, brazing is a better way to join the titanium alloy. However, selection of filler metals used in brazing titanium alloys is critical to avoid the formation of undesirable intermetallics [2]. Ti-15Cu-15Ni in weight percent is a successful commercially available brazing filler metal which can braze titanium and its alloys [2–4]. Ti-15Cu15Ni can be used at temperatures up to 870 ◦C, and it is used in this study. Infrared brazing is featured with a very high heating rate of up to 3000◦C/min, and many successful joints have been made by infrared brazing [5–10]. Accordingly, it is a novel technique applied in brazing many structural alloys. The purpose of this research is focused on vacuum brazing including traditional furnace brazing and infrared brazing Ti-6Al-4V alloy using the Ti-15Cu-15Ni filler metal. The wetting angle measurement, microstructural evolution, and shear strength of the brazed joint are extensively assessed in the experiment. Ti-6Al-4V plate with the dimension of 10 × 10 × 3 mm was used in the experiment. The substrate was polished by SiC papers up to 1200 grits, and subsequently cleaned using an ultrasonic bath with acetone as the solvent prior to brazing. Ticuni R © foil purchased from Wesgo, Inc. was used as the brazing filler metal. Its nominal composition is 15%Cu, 15%Ni and Ti balance. The dimensions of Ticuni R © foil are 100 mm width and 50 μm thickness. The solidus and liquidus temperatures of the Ticuni R © foil are 910 and 960◦C, respectively.