Ho Chi Minh City University of Science

While ZnO nanoparticles have been regarded as promising photocatalysts for addressing organic pollutants in water, they still pose several challenges, such as low activity under UV–visible illumination and difficulties in the recovery of catalytic powder. To overcome these limitations, in this work, thin films composed of surface-modified ZnO nanoparticles for the first time with labyrinth-like architecture were successfully immobilized on a unique zinc-electroplated copper substrate by using a simple sol–gel spin coating technique followed by our thermal-shock-fluorination method (at 400, 500 or 600 °C). The degradation of several organic dyes, including cationic and anionic dyes, under UVA light and visible light irradiation was used to assess their photocatalytic activity. The results demonstrate that the synergistic effect of fluorination and thermal shock greatly enhanced the photocatalytic performance of our samples. Specifically, this treatment significantly altered the surface texture of ZnO thin films by creating labyrinth-like architecture with surface chain walls and oxygen vacancies, resulting in enhanced activity. Furthermore, by electro-coating Cu plates with oxidized zinc layers, we successfully obtained a thermally and mechanically robust substrate that effectively promotes the attachment of ZnO nanoparticles, improving the applicability of our photocatalysts under harsh conditions as well as their recovery and reuse.

The goal of this study is to provide a benchmark for the use of Monte Carlo simulation when applied to coincidence summing corrections. The examples are based on simple geometries: two types of germanium detectors and four kinds of sources, to mimic eight typical measurement conditions. The coincidence corrective factors are computed for four radionuclides. The exercise input files and calculation results with practical recommendations are made available for new users on a dedicated webpage.

Breast cancer is currently one of the leading causes of death in many countries worldwide. Detecting breast masses early can provide higher chances of survival for patients. However, determining and segmenting benign or malignant breast masses is becoming a challenging issue. Currently, there are a wide range of Convolutional Neural Networks used to address breast mass segmentation and breast cancer classification issues, such as U-Net, SegNet, Mask R-CNN, for segmentation, and Convnet, CNN, ResNet, for classification. However, these solutions are still not effective enough. Therefore, we have solved this problem by applying modern model called Segment Anything Model to predict breast tumor segmentation masks to help doctors identify and evaluate breast tumors and two models EfficientNet B0 combined with Focal Loss and Vision Transformer base to classify breast images as benign or malignant. The experimental results show those modern models achieved high performance with an Intersection over Union score of 96.59% on the CIBS-DDSM dataset. Additionally, the classification model achieved an accuracy of 100% and F1-scores of 100% on the DDSM dataset, outperforming other models. Our technique helps support doctors in identifying breast masses in images and provides reliable predictions for diagnostic purposes, thus improving the effectiveness of breast cancer detection.

We investigate possible effects of unparticles at the MUonE experiment by considering a general model for unparticle with broken scale invariance, characterized by the scaling dimension d and the energy scale $$\mu $$ μ at which the scale invariance is broken. Taking into account available relevant constraints on the couplings of the unparticles with the Standard Model (SM) leptons, we found that the MUonE experiment at the level of 10 ppm systematic accuracy is sensitive to such effects if $$1<d\lesssim 1.4$$ 1 < d ≲ 1.4 and $$1\le \mu \lesssim 12$$ 1 ≤ μ ≲ 12 GeV for vector unparticles. The effects of scalar unparticles are too feeble to be detected. The vector unparticles can induce a significant shift on the best-fit value of $$a_\mu ^\text {had}$$ a μ had at the MUonE, thereby providing an opportunity to detect unparticles or to obtain a new bound on the unparticle-SM couplings in the case of no anomaly.

In this study, the gamma ray-induced Maillard reaction method was carried out for chitosan (CTS) and glucosamine (GA) to improve the water solubility and antibacterial activity. The mixture solution of CTS and GA was exposed to gamma rays at a dose of 25 kGy and freeze-dried to obtain a Maillard reaction product (MRP) powder. The physicochemical and biological properties of the CTS-GA MRP powder were investigated. The CTS-GA MRP powder expressed good solubility at a concentration of 0.05 g/mL. In addition, the result of the antibacterial activity test against Escherichia coli revealed that the CTS-GA MRP powder exhibited highly antibacterial activity at pH 7; in particular, bacterial density was reduced by over 4 logs. Furthermore, the cytotoxicity test of the CTS-GA MRP powder on mouse fibroblast cells (L929) showed non-cytotoxicity with high cell viability (>90%) at concentrations of 0.1–1 mg/mL. Owing to the high antibacterial activity and low cytotoxicity, the water-soluble CTS-GA MRP powder can be used as a favorable natural preservative for food and cosmetics.

Cationic liposomes, specifically 1,2-dioleoyl-3-trimethylammonium-propane (DOTAP) liposomes, serve as successful carriers for guanine-quadruplex (G4) structure-based cytosine-guanine oligodeoxynucleotides (CpG ODNs). The combined benefits of CpG ODNs forming a G4 structure and a non-viral vector carrier endow the ensuing complex with promising adjuvant properties. Although G4-CpG ODN-DOTAP complexes show a higher immunostimulatory effect than naked G4-CpG ODNs, the effects of the complex composition, especially charge ratios, on the production of the pro-inflammatory cytokines interleukin (IL)-6 and interferon (IFN)-α remain unclear. Here, we examined whether charge ratios drive the bifurcation of cytokine inductions in human peripheral blood mononuclear cells. Linear CpG ODN-DOTAP liposome complexes formed micrometer-sized positively charged agglomerates; G4-CpG ODN-DOTAP liposome complexes with low charge ratios (0.5 and 1.5) formed ~250 nm-sized negatively charged complexes. Notably, low-charge-ratio (0.5 and 1.5) complexes induced significantly higher IL-6 and IFN-α levels simultaneously than high-charge-ratio (2 and 2.5) complexes. Moreover, confocal microscopy indicated a positive correlation between the cellular uptake of the complex and amount of cytokine induced. The observed effects of charge ratios on complex size, surface charge, and affinity for factors that modify cellular-uptake, intracellular-activity, and cytokine-production efficiency highlight the importance of a rational complex design for delivering and controlling G4-CpG ODN activity.

Understanding the relationships between social stress and the gastrointestinal microbiota, and how they influence host health and performance is expected to have many scientific and commercial implementations in different species, including identification and improvement of challenges to animal welfare and health. In particular, the study of the stress impact on the gastrointestinal microbiota of pigs may be of interest as a model for human health. A porcine stress model based on repeated regrouping and reduced space allowance during the last 4 weeks of the finishing period was developed to identify stress-induced changes in the gut microbiome composition. The application of the porcine stress model resulted in a significant increase in salivary cortisol concentration over the course of the trial and decreased growth performance and appetite. The applied social stress resulted in 32 bacteria being either enriched (13) or depleted (19) in the intestine and feces. Fecal samples showed a greater number of microbial genera influenced by stress than caecum or colon samples. Our trial revealed that the opportunistic pathogens Treponema and Clostridium were enriched in colonic and fecal samples from stressed pigs. Additionally, genera such as Streptococcus , Parabacteroides , Desulfovibrio , Terrisporobacter , Marvinbryantia , and Romboutsia were found to be enriched in response to social stress. In contrast, the genera Prevotella , Faecalibacterium , Butyricicoccus , Dialister , Alloprevotella , Megasphaera , and Mitsuokella were depleted. These depleted bacteria are of great interest because they synthesize metabolites [e.g., short-chain fatty acids (SCFA), in particular, butyrate] showing beneficial health benefits due to inhibitory effects on pathogenic bacteria in different animal species. Of particular interest are Dialister and Faecalibacterium , as their depletion was identified in a human study to be associated with inferior quality of life and depression. We also revealed that some pigs were more susceptible to pathogens as indicated by large enrichments of opportunistic pathogens of Clostridium, Treponema, Streptococcus and Campylobacter . Generally, our results provide further evidence for the microbiota-gut-brain axis as indicated by an increase in cortisol concentration due to social stress regulated by the hypothalamic–pituitary–adrenal axis, and a change in microbiota composition, particularly of bacteria known to be associated with pathogenicity and mental health diseases.

Hemorrhagic shock is the leading cause of preventable deaths in traumatic accidents, emphasizing the significance of hemostasis-promoting materials and their incorporation into a pre-hospital medical response system. Bacterial cellulose has drawn much attention in tissue engineering due to its versatility and biocompatibility. In this study, a hemostatic dressing was composed of bacterial cellulose (BC) and chitosan oligosaccharide (COS), which is a well-known biological agent. The influence of chitosan oligosaccharide on the morphology, chemical, and physical properties of the fabricated cellulose membranes was evaluated. Additionally, the hemostatic performance of BC-COS membranes was investigated through in vitro and in vivo experiments. The results show that the addition of COS led to a lower mechanical strength while increasing the hemostatic properties of the BC membrane. The BC membrane modified with 2 w/v% COS solution (BC-COS2) demonstrated excellent hemostasis promotion through whole blood assays and mice hemorrhage model. The blood clotting index and blood clotting time of the BC-COS2 sample reached 86.25% and 190 s, respectively. Furthermore, in the in vivo experiments, the group treated using BC-COS2 also presented much lower bleeding stop time and blood loss compared to the untreated group. The results reveal the viability of the cellulosic material as a potential hemostatic dressing.

In this letter, we achieve covert communication for downlink non-orthogonal multiple access systems by reradiating on the public user’s antenna. Specifically, we formulate the problem of maximizing the effective covert rate for the covert user under the constraints of covertness for the warden and quality of service for the public user. For the optimization problem, the power allocation factor at the transmitter and the power fraction of the reradiating processing at the public user, which have a dual effect on the system’s performance, are jointly optimized. Numerical results indicate that: 1) the maximum effective covert rate tends to build up to a specific constant value as the transmit power increases; and 2) there exists an optimal value of the predetermined rate for the covert user to maximize the effective covert rate.

BACKGROUND Following the trend of green and sustainable development, multi‐application materials utilizing biomass waste have been developed, leading that trend is carbon aerogel (CA). In this study, zinc oxide‐doped CA was synthesized from bagasse cellulose (Zn‐BCA) through simple processes: Cross‐linking to form a sol–gel system, freeze‐drying, and pyrolysis. To synthesize suitable materials, precursor and crosslinking ratios, the content of Zn ²⁺ , and pyrolysis temperature in N 2 were investigated. The characteristics of ZnO were investigated through modern methods: Scanning electron microscopy, Raman spectroscopy, X‐ray diffraction, Fourier‐transform infrared spectroscopy, and energy‐dispersive X‐ray spectroscopy. The dye adsorption capacity of Zn‐BCA was evaluated through the UV–Vis method. Furthermore, the electrochemical properties are shown through cyclic voltammetry, galvanostatic charge‐discharge, electrochemical impedance spectroscopy results, and glucose sensing results were obtained through linear sweep voltammetry. RESULTS Zn‐BCA shows potential for multiple applications with crystal violet pigment adsorption capacity up to 39 mg/g, specific capacitance reaching 164 F/g, and glucose sensing capabilities. CONCLUSION Thus, Zn‐BCA shows wide applicability in adsorbents and electrode materials, in addition to using bagasse biomass as green precursor to synthesize materials. © 2023 Society of Chemical Industry (SCI).

Traditional methods for Nom transliteration have been relying on statistical machine translation. While there have been initial attempts to apply neural machine translation (NMT) to Nom transliteration, these approaches have not effectively utilized the shared characteristics of multilingualism. To overcome this, we propose a new method that employs a highly shared parameter multilingual NMT model, using a unified parameter set the encoder and decoder. This approach utilizes a single encoder-decoder pair for all translation directions. Experimental evaluation of our proposed method demonstrates a significant improvement of +6.3 BLEU score compared to the NMT baseline. We also conduct preliminary experiments on incorporating BERT-NMT into multilingual machine translation to enhance the performance of low-resource translation tasks in general.

Graphene has garnered increasing attention for solar energy harvesting owing to its unique features. However, limitations hinder its widespread adoption in solar energy harvesting, comprising the band gapless in the molecular orbital of graphene lattice, its vulnerability to oxidation in oxidative environments, and specific toxic properties that require careful consideration during development. Beyond current challenges, researchers have explored doping graphene with ionic liquids to raise the lifespan of solar cells (SCs). Additionally, they have paid attention to optimizing graphene/Si Schottky junction or Schottky barrier SCs by enhancing the conductivity and work function of graphene, improving silicon's reflectivity, and addressing passivation issues at the surface/interface of graphene/Si, resulting in significant advancements in their power conversion efficiency. Increasing the functional area of graphene-based SCs and designing efficient grid electrodes are also crucial for enhancing carrier collection efficiency. Flaws and contaminants present at the interface between graphene and silicon pose significant challenges. Despite the progress of graphene/Si-based photovoltaic cells still needs to catch up to the efficiency achieved by commercially available Si p-n junction SCs. The low Schottky barrier height, design-related challenges associated with transfer techniques, and high lateral resistivity of graphene contribute to this performance gap. To maximize the effectiveness and robustness of graphene/Si-based photovoltaic cells, appropriate interlayers have been utilized to tune the interface and modulate graphene's functionality. This mini-review will address ongoing research and development endeavors using van der Waals graphene heterojunctions, aiming to overcome the existing limitations and unlock graphene's full potential in solar energy harvesting and smart storage systems.

The utilization of Aga1P anchor protein in the display system for expressing heterologous proteins on the surface of Saccharomyces cerevisiae has been shown to be an ideal approach. This system has the ability to improve the expression of target proteins beyond the cell surface, resulting in increased activity and stability of the expression system. Recent studies have demonstrated that a new L-type lectin from Litopenaeus vannamei (LvLTLC1) has been found to possess the capability of agglutinating Vibrio parahaemolyticus, a pathogen responsible for causing acute hepatopancreatic necrosis disease (AHPND) in shrimp. In this study, LvLTLC1 protein was designed to be expressed on the surface of S. cerevisiae via Aga1P anchor. The expression of LvLTLC1 protein on the surface of S. cerevisiae::pYIP-LvLTLC1-Aga1P was confirmed through the use of analytical techniques including SDS-PAGE, dot blot, and fluorescent immunoassay with LvLTC1-specific antibody. Subsequently, the newly generated yeast strain was evaluated for its ability to agglutinate V. parahaemolyticus and A. hydrophila. The obtained results indicated that S. cerevisiae expressing LvLTLC1 protein on its surface had the ability to agglutinate both AHPND-causing V. parahaemolyticus and A. hydrophila. This newly generated yeast strain could be served as a feed supplement for controlling bacteria in general and AHPND in particular. Graphical abstract

Background Owing to the growing global demand for organ replacement and tissue regeneration, three-dimensional (3D) printing is widely recognized as an essential technology in tissue engineering. Biomaterials become a potential source of raw materials for printing ink by containing factors that promote tissue regeneration. Platelet concentrates are autologous biological products that are capable of doing that. Objectives This study was carried out to create bioinks capable of providing biological signals by combining gelatin–alginate with platelet concentrates. Methods This study combined platelet concentrates, including platelet-rich plasma (PRP) and platelet-rich fibrin (PRF), with gelatin and alginate to create bioinks. Bioink properties, including gelatinization and pH, were assessed before printing. After that, the scaffolds were done, and the growth factor (GF) release and cytotoxicity from these scaffolds were performed. Results Results showed that all the three bioinks, including alginate–gelatin (AG), alginate–gelatin-PRP (AGP), and alginate–gelatin-PRF (AGF) were gelatinized right at the end of bioink fabrication and had a pH around 7. The scaffolds from bioinks supplemented with platelet concentrates secreted GFs that remained for 12 d, and the extracts from them were not cytotoxic for the L929 cell line. Conclusion In summary, bioinks were made by combining AG with platelet concentrates and had properties suitable for creating scaffolds with cell-oriented grafts in the development of artificial tissues and organs.

The discovery of frequent generators of high utility itemsets (FGHUIs) holds great importance as they provide concise representations of frequent high utility itemsets (FHUIs). FGHUIs are crucial for generating nonredundant high utility association rules, which are highly valuable for decision-makers. However, mining FGHUIs poses challenges in terms of scalability, memory usage, and runtime, especially when dealing with dense and large datasets. To overcome these challenges, this paper proposes an efficient approach for mining FGHUIs using a novel lower bound called \(lbu\) on the utility. The approach includes effective pruning strategies that eliminate non-generator high utility branches early in the prefix search tree based on \(lbu\), resulting in faster execution and reduced memory usage. Furthermore, the paper introduces a novel algorithm, MFG-HUI, which efficiently discovers FGHUIs. Experimental results demonstrate that the proposed algorithm outperforms state-of-the-art approaches in terms of efficiency and effectiveness.

LIME [9] is an Explainable AI (XAI) method that can offer local explanation for any Machine Learning model prediction. However, the design of LIME often leads to controversial problems in the explanation, which are mostly due to the randomness of LIME’s neighborhood generating process. In this paper, we contribute a method that can help LIME deliver comprehensible explanation by optimizing feature attribution and kernel width of the generating process. Our method ensures high level of features attribution while keeping kernel width lower than the default setting to remain high locality in the explanation. The study will focus mainly on LIME for tabular data.

In recent times, numerous studies on static knowledge graphs have achieved significant advancements. However, when extending knowledge graphs with temporal information, it poses a complex problem with larger data size, increased complexity in interactions between objects, and a potential for information overlap across time intervals. In this research, we introduce a novel model called TouriER, based on the MetaFormer architecture, to learn temporal features. We also apply a data preprocessing method to integrate temporal information in a reasonable manner. Additionally, the utilization of Fourier Transforms has proven effective in feature extraction. Through experiments on benchmark datasets, the TouriER model has demonstrated better performance compared to well-known models based on standard metrics.