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We report the absorption and photoluminescence spectra of GaSe single crystals in the near-edge region. The temperatures explored the range from 17 to 300 K. Specifically, at a temperature of 17 K, the photoluminescence spectrum reveals an interesting phenomenon: the Wannier-Mott exciton separates into two states. These states are a triplet state with an energy of 2.103 eV and a singlet state with an energy of 2.109 eV. The energy difference between these two states is 6 meV. Furthermore, the bound exciton (BX) can be localized at an energy of 2.093 eV. It is worth noting that its phonon replicas (BX-nLO) can be clearly distinguished up to the fourth order. Interestingly, the energy gaps between these replicas exhibit a consistent spacing of 7 ± 0.5 meV. This intriguing finding suggests a high-quality crystalline structure as well as a strong coupling between the phonon and BX-nLO. Additionally, at low temperatures, both the ground state (n = 1) at 2.11 eV and the excited state (n = 2) at 2.127 eV of free excitons can be observed.

We report the temperature dependences of the dielectric function ε = ε1 + iε2 and critical point (CP) energies of the uniaxial crystal GaSe in the spectral energy region from 0.74 to 6.42 eV and at temperatures from 27 to 300 K using spectroscopic ellipsometry. The fundamental bandgap and strong exciton effect near 2.1 eV are detected only in the c-direction, which is perpendicular to the cleavage plane of the crystal. The temperature dependences of the CP energies were determined by fitting the data to the phenomenological expression that incorporates the Bose–Einstein statistical factor and the temperature coefficient to describe the electron–phonon interaction. To determine the origin of this anisotropy, we perform first-principles calculations using the mBJ method for bandgap correction. The results clearly demonstrate that the anisotropic dielectric characteristics can be directly attributed to the inherent anisotropy of p orbitals. More specifically, this prominent excitonic feature and fundamental bandgap are derived from the band-to-band transition between s and pz orbitals at the Γ-point.

In this paper, we present an unified framework for understanding hand action from the first-person video. The proposed framework composes two main components. The first component estimates three-dimensional (3D) hand joints from RGB images. Two network structures derived from the baseline HopeNet network are proposed: convolutional neural networks (CNNs) which are traditional multi-layer CNN and CNN combining with GraphCNN to perform 3D hand pose estimation, without the use of GraphUNet as in baseline HopeNet method. The second component of the framework recognizes hand action from skeleton stream. We first deploy two recent advanced neuronal networks that are PA-ResGCN and Double-feature Double-motion (DDNet). To focus more on the hand pose changes, we improve DDNet with two normalization strategies of the hand joints. Finally, we fuse PA-ResGCN with our improved DDNet to still boost the recognition performance. We evaluate our proposed methods on First-Person Hand Action Benchmark dataset. Experiments show that our model for 3D hand joints estimation achieves the best precision (36.6 mm). Our hand joint normalization strategies improve the original DDNet from 0.71% to 4.05% of accuracy with the ground-truth hand pose while the improvement is significantly larger (from 2.96% to 10.98%) with the estimated hand pose. The late fusion schemes outperform different state-of-the-art methods for the hand action recognition with the highest accuracy of 86.67%. These experimental results show potential and extendable possibilities for developing practical first-person vision applications.

Abstract Skeleton‐based human action recognition has emerged recently thanks to its compactness and robustness to appearance variations. Although impressive results have been obtained in recent years, the performance of skeleton‐based action recognition methods has to be improved to be deployed in real‐time applications. Recently, a lightweight network structure named Double‐feature Double‐motion Network (DD‐Net) has been proposed for the skeleton‐based human action recognition. With high speed, the DD‐Net achieves state‐of‐the‐art performance on hand and body actions. The DD‐Net could not distinguish actions if they have a weak connection with the global trajectories. However, the DD‐Net is suitable for human action recognition where actions strongly correlate to the global trajectories. In this paper, the authors propose TD‐Net, an improved version of the DD‐Net in which a new branch is added. The new branch takes the normalised coordinates of joints (NCJ) to enrich the spatial information. On five datasets for skeleton‐based human activity recognition that are MSR‐Action3D, CMDFall, JHMDB, FPHAB, and NTU RGB + D, the TD‐Net consistently obtains superior performance compared with the baseline model DD‐Net. The proposed method outperforms different state‐of‐the‐art methods, including both hand‐designed and deep learning‐based methods on four datasets (MSR‐Action3D, CMDFall, JHMDB, and FPHAB). Furthermore, the generalisation of the proposed method is confirmed through cross‐dataset evaluation. To illustrate the potential use of the model for real‐time human action recognition, the authors have deployed an application on an edge device. The experimental result shows that the application can process up to 40 fps for pose estimation using MediaPipe. It takes only 0.04 ms to recognise an action from skeleton sequences.

This work offered insights into the characteristics and performance of nickel nanocatalyst in situ synthesized through CeNiO3 perovskite for the bireforming of methane (BRM). Effects of calcination conditions on the characteristics of CeNiO3 structure and on the reduction regime of the as-prepared perovskite on the physicochemical properties and performance of nickel catalyst in BRM were assessed. The formation of high-crystallinity CeNiO3 at temperatures above 700 °C was confirmed. The synthesized CeNiO3 existed in the form of spherical particles 20–40 nm in size and uniformly distributed. Among the samples, one calcined at 700 °C for 2 h (C-700-2.0) had the smallest and the most uniform particle and the highest reducibility. This sample was found to be the optimum precursor for the in situ synthesis of a highly effective nano-Ni catalyst for BRM. The reduction mode significantly affected the catalytic performance of the C-700-2.0 precursor. The sample reduced at 750 °C for 1.5 h (R-750-1.5) possessed the optimum activity, with conversions of CH4 and CO2 reaching equilibrium (98% and 84%, respectively) and an ideal H2/CO ratio of 2 at the reaction temperature of 700 °C.

In this study, silver nanoparticles (AgNPs) loaded on pre-formed CeO2 nanorods (Ag-Ce) were synthesized using Jasminum subtriplinerve Blume leaf extract as a reducing agent. The mesoporous structure of CeO2 was confirmed by the N2 adsorption/desorption study, indicating type ІV isotherms with an H3-type hysteresis loop. The high specific surface area of the Ag-Ce samples was discovered in the range of 74–80 m2/g with an average pore diameter of 23.2–23.4 Å, and a pore volume of 0.091–0.093 cm3/g (pure CeO2 of 72 m2/g, 23 Å, and 0.098 cm3/g, respectively). Silver nanoparticles with spherical shapes (5–10 nm in diameter) were well dispersed on the surface of CeO2 nanorods (100–200 nm in length and 20–30 nm in diameter), as shown by X-ray diffraction (XRD), Energy-dispersive X-ray (EDX) and high-resolution transmission electron microscopy (HR-TEM). Their catalytic reactivity was investigated in the hydrogenation of p-nitrophenol (p-NP) toward p-aminophenol (p-AP) using NaBH4 at room temperature, exhibiting better reactivity of the Ag-Ce samples than pure CeO2 nanorods. The 0.2Ag-Ce sample (0.2 wt.% Ag) showed the complete conversion of p-NP toward p-AP after 3 min using 0.2 g/L catalysts.

Citrus maxima peel (CMP) was utilized for the green synthesis of multifunctional silver-biochar material (Ag-CMPB). An alternative, cost-effective and eco-friendly technique was performed using the CMP extract as a reducing agent for the biosynthesis of Ag-CMPB material, in which biochar is prepared from CMP residue. Ag-CMPB nanomaterials were characterized by various physico-chemical methods such as XRD, HRTEM, BET, FT-IR, EDX, and PZC. The results revealed the formation of the face-centered cubic structure of Ag with the particle size in a range of 3 − 10 nm and the stacking structure of layers of biochar. Flavonoids and phenolic acids in CMP extract as a reducing agent of Ag-CMPB material. The specific surface area of Ag-CMPB was determined to be 79.2 m2/g with a pore diameter of 22.4 Å. Ag-CMPB showed high efficiency in the short time in removing organic compounds. The adsorption efficiency was recognized in the following sequence for POPs adsorption: Methyl blue > Rhodamine B > Methyl orange, with the adsorption capacities of 95.5, 69.3, and 51.5 mg/g, respectively. The antibacterial activity of Ag-CMPB was evaluated on five bacteria against B. cereus, E. coli, P. aeruginosa, S. aureus, and Salmonella by the zone of inhibition and MIC value.

The spread of the 2019 novel coronavirus disease (COVID-19) has engulfed the world with a rapid, unexpected, and far-reaching global crisis. In the study of COVID-19, Geographic Information Systems (GIS) and Remote Sensing (RS) have played an important role in many aspects, especially in the fight against COVID-19. This review summarises 102 scientific papers on applications of GIS and RS on studies of the COVID-19 pandemic. In this study, two themes of GIS and RS-related applications are grouped into the six categories of studies of the COVID-19 including spatio-temporal changes, WebGISbased mapping, the correlation between the COVID-19 and natural, socio-economic factors, and the environmental impacts. The findings of this study provide insight into how to apply new techniques (GIS and RS) to better understand, better manage the evolution of the COVID-19 pandemic and effectively assess its impacts.

An outbreak of the 2019 Novel Coronavirus Disease (COVID-19) in China caused by the emergence of Severe Acute Respiratory Syndrome CoronaVirus 2 (SARSCoV2) spreads rapidly across the world and has negatively affected almost all countries including such the developing country as Vietnam. This study aimed to analyze the spatial clustering of the COVID-19 pandemic using spatial auto-correlation analysis. The spatial clustering including spatial clusters (high-high and low-low), spatial outliers (low-high and high-low), and hotspots of the COVID-19 pandemic were explored using the local Moran’s I and Getis-Ord’s G* i statistics. The local Moran’s I and Moran scatterplot were first employed to identify spatial clusters and spatial outliers of COVID-19. The Getis-Ord’s G* i statistic was then used to detect hotspots of COVID-19. The method has been illustrated using a dataset of 86,277 locally transmitted cases confirmed in two phases of the fourth COVID-19 wave in Vietnam. It was shown that significant low-high spatial outliers and hotspots of COVID-19 were first detected in the NorthEastern region in the first phase, whereas, high-high clusters and low-high outliers and hotspots were then detected in the Southern region of Vietnam. The present findings confirm the effectiveness of spatial auto-correlation in the fight against the COVID-19 pandemic, especially in the study of spatial clustering of COVID-19. The insights gained from this study may be of assistance to mitigate the health, economic, environmental, and social impacts of the COVID-19 pandemic.

Cellulose was extracted from the banana stem by chemical method and the factors affecting the extraction process such as concentration of NaOH and H2O2, as well as the assisted microwave time were investigated. Design-Expert software with Response Surface Methodology was used in the modeling and optimization of the cellulose extraction process. The results of XRD, FT-IR, SEM were also used to determine the physicochemical properties of cellulose obtained from the banana stem. The results of the modeling and optimization process of cellulose extraction showed the efficiency of the process and the high applicability of cellulose from the banana stem to create valuable industrial products.

Nylon 66, which is an important membrane class used in manufacturing of chitin and chitosan, have a number of features that can be improved by surface functionalizations into a novel composite structure with support of ultrasound and silica gel (SiG) catalyst in a doubled amidation reaction. Firstly, nylon 66/para-phenylenediamine thin film composite (NP-TFC) is prepared from commercial nylon 66 membrane in an ultrasound assisted hydrolysis-amidation reaction. Secondly, carboxylic functionalized multi-walled carbon nanotubes (MWCNT-COOH) are grafted on the NP fiber in an ultrasound assisted/SiG-catalyzed amidation reaction, where para-phenylenediamine (pPD) role is cross-linking. As an excellent result confirmed by either Fourier transform infrared (FTIR), Raman spectrometry or scanning electron microscopic (SEM), bundled MWCNTs bridges are easily built in SiG-catalyzed ethanol media to connect nylon 66 fibers at distances of 0.3–1 μm. The vacuum filtration test confirmed that as-prepared nylon 66/pPD/MWCNTs structure has superior Ca2+ rejection efficiency to that of original nylon 66.

In many cases of sliding control, the exact nonlinear functions of dynamic equations are difficult to be obtained. Because of this reason, the uncertainty of nonlinear functions of system can cause bad response under sliding control. In this paper, we suggest an adaptive fuzzy sliding control method which can balance a SIMO system. This method still guarantees the stability of system by Lyapunov criteria. Otherwise, the problem of uncertainty of nonlinear system is solved. The object chosen in this paper is pendubot - a popular model in control laboratory. The success of this method is proved by simulation results.

The ion adsorption capacity has been added to the nylon 6 microfiltration (MF) membrane by grafting the fibers in its structure with carboxylated multi-walled carbon nanotubes (MWCNT-COOH). Through a direct three-step functionalization reaction with hexamethylenediamine (HMDA) and MWCNT-COOH, the multi-walled carbon nanotubes grafted nylon (MWCNT@nylon) fibers are made up of original nylon 6 and intermediate amino-enriched (nylon-NH _2 ) membranes. Chemical structure analysis shows that HMDA and MWCNTs were grafted to the nylon 6 fiber side wall, without causing damage to either the nylon 6 backbone or the pore size of the original membrane. The SEM images confirm this and further confirm that the plexus of MWCNT-COOH has a pore size of approximately 50 nm, covering the nylon 6 fibers. The sidewall of nylon-NH _2 and MWCNT@nylon fibers contains hydrophilic groups (amino and carboxylic), allowing the as-prepared membranes to reduce the wetting angle from about 70.3° to about 108.1°. This special structure opens new possibilities for polyamide membranes as well as mass production by the proposed simple method.

In this work, high-performance liquid chromatography in combination with inductively coupled plasma dynamic reaction cell quadrupole mass spectrometry was introduced and optimized for speciation analysis of five major arsenic species including arsenobetain (AsB), arsenite (As(III)), monomethylarsonic (MMA), dimethylarsenonic acid (DMA), and arsenate (As(V)) in rice samples. Five arsenic compounds were separated on a Hamilton PRP X100 strong anion-exchange column employed with the mobile phase that is compatible with mass spectrometry, containing ammonium carbonate, methanol, and disodium ethylenediaminetetraacetic acid. Arsenic compounds were detected online by inductively coupled plasma dynamic reaction cell quadrupole mass spectrometry utilizing oxygen as the reaction gas at a flow rate of 0.7 mL·min−1. Five selected arsenic species were baseline separated at the optimum experimental conditions. The excellent LOD and LOQ values of the developed method were achieved in the range of 0.5 to 2.9 μg·kg−1 and 1.7 to 9.6 μg·kg−1 for all species of arsenic, respectively. The ionization effect in plasma during chromatographic gradient elution was systematically investigated by using postcolumn injector. Arsenic compounds in rice samples were extracted by diluted nitric acid at elevated temperature. The extraction efficiency and the interconversion of target compounds during sample preparation were also assessed. The full validation of the developed method was performed by using certified reference material, BRC 211, from European Institute of Reference and Standard for speciation analysis. The recovery of all selected arsenic species was in the range of 70 to 135.5%. The validated method was also applied to analyze rice samples collected from some contaminated rice fields. The results showed that As(III), DMA, and As(V) were found in all rice samples. Average concentration (range) of inorganic arsenic and DMA in all rice samples were 130.3 (65.5–228.1) and 32 (8.2–133.01) μg·kg−1, respectively. However, total concentration of inorganic arsenic in most of investigated rice samples was below the maximum residual level according to US-FDA and European Union standards.

Clean label is an important trend in the food industry. It aims at washing foods of chemicals perceived as unhealthy by consumers. Microorganisms are present in many foods (usually fermented), they exhibit a diversity of metabolism and some can bring probiotic properties. They are usually well considered by consumers and, with progresses in the knowledge of their physiology and behavior, they can become very precise tools to produce or degrade specific compounds. They are thus an interesting means to obtain clean label foods. In this review, we propose to discuss some current research to use microorganisms to produce clean label foods with examples improving sensorial, textural, health and nutritional properties.

Lysin motif (LysM) domains are found in many bacterial peptidoglycan hydrolases. They can bind non-covalently to peptidoglycan and have been employed to display heterologous proteins on the bacterial cell surface. In this study, we aimed to use a single LysM domain derived from a putative extracellular transglycosylase Lp_3014 of Lactobacillus plantarum WCFS1 to display two different lactobacillal β-galactosidases, the heterodimeric LacLM-type from Lactobacillus reuteri and the homodimeric LacZ-type from Lactobacillus delbrueckii subsp. bulgaricus, on the cell surface of different Lactobacillus spp. The β-galactosidases were fused with the LysM domain and the fusion proteins, LysM-LacLMLreu and LysM-LacZLbul, were successfully expressed in Escherichia coli and subsequently displayed on the cell surface of L. plantarum WCFS1. β-Galactosidase activities obtained for L. plantarum displaying cells were 179 and 1153 U per g dry cell weight, or the amounts of active surface-anchored β-galactosidase were 0.99 and 4.61 mg per g dry cell weight for LysM-LacLMLreu and LysM-LacZLbul, respectively. LysM-LacZLbul was also displayed on the cell surface of other Lactobacillus spp. including L. delbrueckii subsp. bulgaricus, L. casei and L. helveticus, however L. plantarum is shown to be the best among Lactobacillus spp. tested for surface display of fusion LysM-LacZLbul, both with respect to the immobilization yield as well as the amount of active surface-anchored enzyme. The immobilized fusion LysM-β-galactosidases are catalytically efficient and can be reused for several repeated rounds of lactose conversion. This approach, with the β-galactosidases being displayed on the cell surface of non-genetically modified food-grade organisms, shows potential for applications of these immobilized enzymes in the synthesis of prebiotic galacto-oligosaccharides.