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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.

Coal fires are a persistent threat to major coal-producing countries in the world. Thus, it is very important to delineate the potential risks of coal fires. This study presents a method for the delineation of coal fire risk areas from Landsat-8 TIRS data. Land surface temperatures (LSTs) were first retrieved from the Landsat-8 TIRS images. The degree of spatial autocorrelation among these LSTs was then identified using local Moran's I statistic. Thermal-related anomalies for the delineation of coal fire risk areas were identified by setting the MEAN+2*SDEV (SDEV is the standard deviation), MEAN+3*SDEV, and MEAN+4*SDEV formulas as thresholds on the local Moran's I statistic. These coal fire risk areas were finally validated using known coal fire sites and cross-validated by comparing them with those obtained from hot spot analysis. A case study of the Na Duong coal field (northern Vietnam) has shown that coal fire risks at moderate and high levels were mainly detected in the center of the coal field. The higher values of local Moran's I statistic, the higher levels of coal fire risks. These coal fire risks were mainly concentrated around known coal fire sites. These results reveal that Landsat-8 TIRS data can effectively delineate coal fire risk areas. [ABSTRACT FROM AUTHOR]

Metal oxides, in many cases, exhibit n-type semiconductors due to the existence of oxygen vacancies in the lattice. Therefore, the interactions of oxygen being in medium with oxygen vacancies during the annealing process can change concentrations of defects that will cause variations in optical and electric properties of such materials. However, research on such interactions for commercial FTO, ITO, and TiO2 products has been limited. This paper summarizes the results of some experiments conducted to determine the influence of thermal annealing media on the optical, electrical properties of thin films of these products. The thermal media considered are air medium, 101 torr low vacuum condition, and Argon gas environments, and the annealing condition is set at 450°C for 20 minutes. It is found that while FTO films change for the better after annealing, ITO films trend towards worse, and TiO2 films have the most photoconversion efficiency (Isc = 0.27 mA, η = 0.37%) under the moderate oxygen concentration environment. [ABSTRACT FROM AUTHOR]

In this paper, we propose a combining classifier method based on the Bayesian inference framework. Specifically, the outputs of base classifiers (called Level1 data or meta-data) are utilized in a combiner to produce the final classification. In our ensemble system, each class in the training set induces a distribution on the Level1 data, which is modeled by a multivariate Gaussian distribution. Traditionally, the parameters of the Gaussian are estimated using a maximum likelihood approach. However, maximum likelihood estimation cannot be applied since the covariance matrix of Level1 data of each class is not full rank. Instead, we propose to estimate the multivariate Gaussian distribution of Level1 data of each class by using the Variational Inference method. Experiments conducted on eighteen UCI Machine Learning Repository datasets and a selected 10-class CLEF2009 medical imaging database demonstrated the advantage of our method compared with several well-known ensemble methods. [ABSTRACT FROM AUTHOR]

The escalating challenge of antibiotic resistance has driven the innovation of new antibacterial and antifouling materials. Recent developments focus on nature-inspired topographical engineering and nanostructured surfaces to combat resistant bacteria. This review discusses these advances, emphasizing the potential of nanoantibiotics and biopolymers. Nanoantibiotics revitalize drug effectiveness by encapsulating them in nanoparticles, presenting a new strategy to fight pathogens. Biopolymers, eco-friendly and biodegradable, emerge as a sustainable alternative, with applications in food safety and beyond. The exploration of these materials signifies a leap in design, fabrication, and the possibility of cost-effective, large-scale production, highlighting a promising avenue for commercial applications to tackle antibiotic resistance and biofouling effectively.

Background and Objectives: A comprehensive nutritional management is necessary for favourable outcomes in patients with chronic kidney disease (CKD). We aimed to assess the changes in nutritional status and disease progression with nutritional management where renal replacement therapy (RRT) was not in place. Methods and Study Design: A quasi-experiment intervention was conducted on 70 CKD patients at stages 3-5 from July to December 2022. Participants were excluded if they underwent RRT, including dialysis (hemodialysis or peritoneal dialysis), or kidney transplantation. The nutritional regimen covered nutritional counseling, samples of the dietary menu, and supplement products. We evaluated nutritional status using Subjective Global Assessment (SGA) scale and sub-clinical blood test at T0 (hospital admission) and T1 (two weeks after the admission or 24 hours before the discharge). Results: After the intervention, the number of patients classified as malnutrition or at risk of malnourished reduced significantly (65.7% to 54.3% and 25.7% and 5.7%, respectively). The serum concentration of urea, creatinine and parathyroid hormone decreased remarkably, especially in patients receiving nutritional management. In the intervention group, the dietary pattern provided increased intakes of calcium and iron at T1, while phosphorus, sodium and potassium decreased after follow-up. Nausea/vomiting, loss of appetite, tiredness and sleep disorders were improved in the intervention compared to the control group. Conclusions: Nutritional therapy enhanced the nutritional status, and quality of dietary and renal function in CKD patients without RRT. Applying nutrition education and treatment at an early stage can slow CKD progression, which should be applicable elsewhere in Vietnam. [ABSTRACT FROM AUTHOR]

This study presents a method to investigate meteorological drought characteristics using multiple climate models for multiple timescales under two representative concentration pathway (RCP) scenarios, RCP4.5 and RCP8.5, during 2021–2050. The methods of delta change factor, unequal weights, standardized precipitation index, Mann–Kendall and Sen's slope are proposed and applied with the main purpose of reducing uncertainty in climate projections and detection of the projection trends in meteorological drought. Climate simulations of three regional climate models driven by four global climate models are used to estimate weights for each run on the basic of rank sum. The reliability is then assessed by comparing a weighted ensemble climate output with observations during 1989–2008. Timescales of 1, 3, 6, 9, 12, and 24 months are considered to calculate the standardized precipitation index, taking the Vu Gia-Thu Bon (VG-TB) as a pilot basin. The results show efficient precipitation simulations using unequal weights. In the same timescales, the occurrence of moderately wet events is smaller than that of moderately dry events under the RCP4.5 scenario during 2021–2050. Events classified as "extremely wet", "extremely dry", "very wet" and "severely dry" are expected to rarely occur under the RCP8.5 scenario. [ABSTRACT FROM AUTHOR]

Chronic wounds often result in multiple infections with various kinds of bacteria and uncontrolled wound exudate, resulting in several healthcare issues. Advanced medicated nanofibres prepared by electrospinning have gained much attention for their topical application on infected chronic wounds. The objective of this work is to enhance the critical variables of ciprofloxacin-loaded polycaprolactone-silk sericin (PCL/SS-PVA-CIP) nanofibre production via the process of electrospinning. To examine the antibacterial effectiveness of PCL/SS-PVA-CIP nanocomposites, the material was tested against P. aeruginosa and S. aureus. The combination of PCL/SS-PVA-CIP exhibited potent inhibitory properties, with the most effective concentrations of ciprofloxacin (CIP) being 3 μg/g and 7.0 μg/g for each bacterium, respectively. The biocompatibility was evaluated by conducting cell reduction and proliferation studies using the human epidermal keratinocyte (HaCaT) cells and human gingival fibroblasts (HGFs) in vitro cell lines. The PCL/SS-PVA-CIP showed good cell compatibility with HaCaT and HGF cells, with effective proliferation even at antibiotic doses of up to 7.0 μg/g. The drug release effectiveness of the nanocomposites was assessed at various concentrations of CIP, resulting in a maximum cumulative release of 76.5% and 74.4% after 72 h for CIP concentrations of 3 μg/g and 7 μg/g, respectively. In summary, our study emphasizes the possibility of combining silk sericin (SS) and polycaprolactone (PCL) loading with CIP nanocomposite for wound management.

IntroductionAs sufficient nutrition helps alleviate catabolic stress and modulate the systemic inflammatory response of the body, it plays an indispensable role in the good prognosis of critically ill patients. Thus, this study aimed to investigate the malnutrition of patients with severe COVID-19 and its association with adverse treatment outcomes.MethodsWe conducted a retrospective cross-sectional study in two provincial hospitals in Hanoi from February to April 2022. Participants were patients with severe COVID-19 admitted to the Intensive Care Unit (ICU). Malnutrition risk were evaluated by Nutritional Risk Screening-2002 (NRS), Global Leadership Initiative on Malnutrition (GLIM), Prognostic Nutritional Index (PNI), and the adverse prognosis was assessed by Acute Physiology and Chronic Health Evaluation II (APACHE II). The multivariate receiver-operating characteristic (ROC) curve was applied to estimate the predictive ability of those criteria regarding worse treatment results.ResultsThe percentages of malnutrition measured by NRS, GLIM, PNI, and BMI were 62.6, 51.5, 42.9, and 16.6%, respectively. Patients with more severe malnutrition assessed by GLIM, PNI, and having above target fasting blood glucose (FBG) (≥10.0 mmol/L) were more likely to have higher APACHE scores. PNI had a better diagnostic performance than NRS and BMI (AUC = 0.84, 0.81, and 0.82, respectively). In addition, FBG revealed a good prognostic implication (AUC = 0.84).ConclusionA relatively high percentage of patients experienced moderate and severe malnutrition regardless of screening tools. Individuals at higher risk of malnutrition and high FBG were predicted to have more adverse treatment outcomes. It is recommended that nutritional screening should be conducted regularly, and personalizing nutritional care strategies is necessary to meet patients’ nutrient demands and prevent other nutrition-related complications.

In the flame retardancy of the biopolymer matrix and natural fibre reinforcement containing green composites, researchers face multiple challenges, such as low thermal stability, the candlewick effect of fibres and compatibility issues. Cellulosic fibres have been shown to have char-promoting properties and to advantageously interact with intumescent systems. In this work, melamine-polyphosphate was combined with neat or flame-retardant-treated cellulosic fibres differing in fibre length to obtain intumescent flame retarded poly(lactic acid) composites. The effect of the cellulose fibre length was evaluated in both forms. The structure-property relationships were evaluated by thermal and flammability test methods. It was found that the formation and the structure of the fire-protecting char are influenced by the length of the cellulose fibres, and thus it noticeably affects the effectiveness of the flame-retardant system. Cellulose fibres with an average length of 30–60 µm were found to contribute the best to the formation of an integrated fibrous-intumescent char structure with enhanced barrier characteristics.

Nanosized multi-drug delivery systems provide synergistic effects between drugs and bioactive compounds, resulting in increased overall efficiency and restricted side effects compared to conventional single-drug chemotherapy. In this study, we develop an amphiphilic heparin-poloxamer P403 (HP403) nanogel that could effectively co-load curcuminoid (Cur) and cisplatin hydrate (CisOH) (HP403@CisOH@Cur) via two loading mechanisms. The HP403 nanogels and HP403@CisOH@Cur nanogels were closely analyzed with 1H-NMR spectroscopy, FT-IR spectroscopy, TEM, and DLS, exhibiting high stability in spherical forms. In drug release profiles, accelerated behavior of Cur and CisOH at pH 5.5 compared with neutral pH was observed, suggesting effective delivery of the compounds in tumor sites. In vitro studies showed high antitumor activity of HP403@CisOH@Cur nanogels, while in vivo assays showed that the dual-drug platform prolonged the survival time of mice and prevented tail necrosis. In summary, HP403@CisOH@Cur offers an intriguing strategy to achieve the cisplatin and curcumin synergistic effect in a well-designed delivery platform that increases antitumor effectiveness and overcomes undesired consequences caused by cisplatin in breast cancer treatment.

Jeokimyasal verilerin saha dağılım desenlerinin belirlenmesi çalışmalarında uzamsal küme ve aykırı değerler büyük önem taşır. Bunlar, sadece cevherleşmenin temel özelliklerini değil, aynı zamanda maden yatağının ve cevher mineralinin alansal dağılımını da karakterize ederler. Bu çalışmada, çok değişkenli verinin alansal dağılım deseni konusunda yeni bir yöntem tanıtılacak olup bu yöntemde stabil Mahalanobis uzaklığı ile lokal Moran’s I1 değeri baz alınmıştır. Uzamsal matriksi oluştururken, aralığı belirlemek üzere öncelikle Moran’s I uzamsal korelogramı kullanılmıştır. Daha sonra, belirli bir elementler topluluğu için stabil Mehalanobis aralığı hesaplanmıştır. Son olarak, hem uzamsal birlik derecesini ölçmek hem de, uzamsal küme ve aykırı değerler dahil olmak üzere Cu, Au, Mo, Ag, Pb, Zn, As ve Sb birliklerinin uzamsal dağılım desenlerini belirlemek için yersel Moran’s I1 istatistiği uygulanmıştır. Hem ham veri hem de Box-Cox dönüşüm verisi üzerinde altı değişik ölçekte (2km, 4km, 6km, 8km, 10km ve 12km) uzamsal desen analizi yapılmıştır. Elde edilen sonuçlar, Moran’s I1 ve stabil Mahalanobis ile tanımlanan uzamsal küme ve uzamsal aykırı değer alanlarının nesnel gerçeklik ile uyum sağladıklarını, ayrıca çalışma alanında bilinen maden yatakları ile de uyumlu bir ilişki gösterdiklerini ortaya koymuştur.

In this work, a simultaneous liquefaction, saccharification, and fermentation (SLSF) process at very high gravity (VHG) of broken rice for potable ethanol production was developed at pilot scale. The SLSF-VHG process was performed in a unique fermenter, at 30 °C. Rice flour (RF) was dissolved in tap water to reach 311.5 g/1 dry matter and then the mixture was simul-taneousely liquefied, saccharified, and fermented. Thanks to the addition of a raw starch hydrolyzing enzyme containing a mixture of alpha-amylase and gluco-amylase (Stargen 002 at 991.8 GAU/kg RF), gluco-amylase (Amigase Mega L at 0.035% w/w), protease (Fermgen at 600 SAPU/kg RF), active dry yeast Saccharomyces cereuisiae (Red Ethanol at 3.5 x 107 cells/ml), KH2PO4 (4.8mM), and urea (16.0 mM). Under these conditions, the SLSF-VHG process finished after 120 h and achieved an ethanol content of 17.6% v/v corresponding to 86.3% of the theoretical ethanol yield. We scaled up this SLSF process at very high gravity at pilot scale (251) and achieved an ethanol content of 17.0% v/v corresponding to a yield of 83.2% of the theoretical ethanol yield. Rice-based distillers dried grains (DDG) was produced from the whole stillage of SLSF process at very high gravity by being plate-filtered and dried. The obtained DDG had high contents of crude protein (47.5%) and fibers (15.8%). Our results suggest, the SLSF under VHG condition of broken rice as well as the recovery of protein-rich DDG could have a great potential for the ethanol and animal feeding industry in Vietnam. [ABSTRACT FROM AUTHOR]