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Abstract Increasing the use of renewable energy in microgrids (MGs) offers environmental and economic benefits. However, the unpredictable and intermittent nature of available resources poses challenges for optimal MG scheduling. Hybrid AC–DC microgrids provide a solution, seamlessly integrating renewables while reducing energy losses and improving power grid reliability. Additionally, incentive‐based demand response programs promote flexible energy consumption, further mitigating the variability of renewable generation and enhancing grid stability. This paper investigates the challenges and potential of high renewable penetration in hybrid AC–DC MGs, analysing the role of demand response programs in system optimization. The microgrid's energy management is modelled using MILP, while a Stackelberg game represents the demand response program. These models are integrated to optimize energy management and demand response jointly. Simulations demonstrate the cost‐saving benefits of this integrated framework, achieved through coordinated flexible resource scheduling and incentive‐based demand response programming.

Abstract The advancement of Photovoltaic technology has undergone rapid acceleration in recent years. Nonetheless, the most significant drawback of Photovoltaic is its intermittence, making it an obvious source of power fluctuation. This study proposes a novel scheme for real‐time or intraday PV power forecasting by adopting two predictive models, namely, White‐box and Combination. The White‐box model is implemented employing mathematical calculations and statistics called Exceedance Probability. Meanwhile, the Combination model is an aggregation of several predictive models' outputs including the White‐box model and benchmark ones by dynamically adjusting the weight coefficient of each model based on their forecasting accuracy. The experimental results, which are verified on two PV systems corresponding to two case studies located at Vietnam and Australia, indicate that the two proposed models outperform other referenced models as nMAPE improves approximately 40% and 38% in terms of the first and second case study, respectively. In particular, the White‐box model shows superiority by updating the forecast every 10 min, which can adapt to the fluctuation of weather conditions whereas the Combination one yields acceptable precision, indicating its flexible application.

Abstract Background Misfolding and aggregation of amyloid β (Aβ), along with neurofibrillary tangles consisting of aggregated Tau species, are pathological hallmarks of Alzheimer’s disease (AD) onset and progression. In this study, we hypothesized the clearance of Aβ aggregates from the brain and body into the gut. Methods To investigate this, we used surface-based fluorescence intensity distribution analysis (sFIDA) to determine the Aβ aggregate concentrations in feces from 26 AD patients and 31 healthy controls (HC). Results Aβ aggregates were detectable in human feces and their concentrations were elevated in AD patients compared to HC (specificity 90.3%, sensitivity 53.8%). Conclusion Thus, fecal Aβ aggregates constitute a non-invasive biomarker candidate for diagnosing AD. Whether digestion-resistant Aβ aggregates in feces are secreted via the liver and bile or directly from the enteric neuronal system remains to be elucidated.

ABSTRACT Aims/Introduction Blood glucose meters are commonly used at the bedside, but most of the meters used in Hung Vuong Hospital (Ho Chi Minh City, Vietnam) are built for self‐monitoring and might not be suitable for determining glucose levels in patients. In this study, we aimed to validate the performance of six frequently used meters in our hospital using the Clinical & Laboratory Standards Institute (CLSI) standard, and investigate the hematocrit impact on the accuracy of these meters. Materials and Methods A total of 135 pregnant women who underwent a 75‐g oral glucose tolerance test consented to participate in the study at Hung Vuong Hospital. Whole blood glucose levels were measured in duplicate using meters, and hematocrit levels were measured using an Alinity h‐series analyzer. Within 5 min, plasma glucose levels were measured twice in a row using the Cobas c502 reference analyzer. For accuracy and precision, the hematocrit effect was assed using CLSI POCT12‐A3. Results Out of six evaluated meters, three meters qualified. For CLSI criterion at glucose concentration of 5.55 mmol/L, Accu‐Chek Inform II, Accu‐Chek Performa and OneTouch VerioVue achieved 97.31%, 98.08% and 99.62%, respectively. For CLSI criterion at 4.17 mmol/L, these three achieved 100%. Accu‐Chek Inform II and Accu‐Chek Performa showed an inverse correlation between glucose level and hematocrit with slopes of −0.500 (95% confidence interval −0.678 to −0.322) and −0.396 (95% confidence interval −0.569 to −0.224), whereas OneTouch VerioVue was not affected by hematocrit, with a slope of 0.207 (95% confidence interval −0.026 to 0.440). Conclusions Blood glucose meters' measurements can be affected by hematocrit, and might provide readings not within an acceptable bias. Medical organizations need to verify or validate before using on patients.

Abstract The gap between electricity supply and demand, which is the electricity imbalance, can have negative economic impacts on retail electricity suppliers. This paper presents a methodology for designing demand response (DR) programs while mitigating the negative impacts. The DR programs require the retailers to set target electricity consumption and prices of economic incentives for customers who cooperate with the DR programs. First, the target electricity consumption is determined by solving a problem of social welfare maximization in which the objective function is the sum of economic surplus of the retailers and their customers and impacts of electricity imbalance. The optimal electricity prices or the optimal rebate levels are then calculated under the principle that rational customers act to maximize their economic surplus. Through numerical simulations, the authors confirmed that the proposed methodology mitigated the impacts of electricity imbalance. Meanwhile, results of the numerical simulations showed that in practical situations, there were cases where the retailers unable to reduce the electricity imbalance profitably. Therefore, the authors additionally discuss ways to assess the electricity imbalance, which motivates the retailers to reduce the imbalance, in such cases.

This study investigates the impact of migration on the well-being of children under 5 in Vietnam. For the purpose of this study migration is measured by the proportion of residents who moved from one province to another five years ago, while children’s well-being is proxied by nutritional status, height and weight for age, education, and childcare. Our estimation results show that migration increases the probability of child access to food, improves their nutritional status, and enhances childcare. Interestingly, the effects of migration on the nutritional status of children under 5 tend to be greater for children who has already had a better nutritional status. We find that income is a channel through which migration affects the well-being of children. Our findings suggest that promoting migration can be an effective tool for improving the well-being of children in Vietnam.

Abstract Islet amyloid polypeptide (IAPP) is co-secreted with insulin from pancreatic ß-cells. Its oligomerisation is regarded as disease driving force in type 2 diabetes (T2D) pathology. Up to now, IAPP oligomers have been detected in affected tissues. IAPP oligomer concentrations in blood have not been analysed so far. Using the IAPP single-oligomer-sensitive and monomer-insensitive surface-based fluorescence intensity distribution analysis (sFIDA) technology, levels of IAPP oligomers in blood plasma from healthy controls and people with T2D in different disease stages where determined. Subsequently, the level of IAPP oligomerisation was introduced as the ratio between the IAPP oligomers determined with sFIDA and the total IAPP concentration determined with ELISA. Highest oligomerisation levels were detected in plasma of people with T2D without late complication and without insulin therapy. Their levels stand out significantly from the control group. Healthy controls presented with the lowest oligomerisation levels in plasma. In people with T2D without complications, IAPP oligomerisation levels correlated with disease duration. The results clearly demonstrate that IAPP oligomerisation in insulin-naïve patients correlates with duration of T2D. Although a correlation per se does not identify, which is cause and what is consequence, this result supports the hypothesis that IAPP aggregation is the driving factor of T2D development and progression. The alternative and conventional hypothesis explains development of T2D with increasing insulin resistance causing exhaustion of pancreatic ß-cells due to over-secretion of insulin, and thus IAPP, too, resulting in subsequent IAPP aggregation and fibril deposition in the pancreas. Further experiments and comparative analyses with primary tissues are warranted.

Abstract Anelloviruses are nonpathogenic viruses that comprise a major portion of the human virome. Despite being ubiquitous in the human population, anelloviruses (ANVs) remain poorly understood. Basic features of the virus, such as the identity of its capsid protein and the structure of the viral particle, have been unclear until now. Here, we use cryogenic electron microscopy to describe the first structure of an ANV-like particle. The particle, formed by 60 jelly roll domain-containing ANV capsid proteins, forms an icosahedral particle core from which spike domains extend to form a salient part of the particle surface. The spike domains come together around the 5-fold symmetry axis to form crown-like features. The base of the spike domain, the P1 subdomain, shares some sequence conservation between ANV strains while a hypervariable region, forming the P2 subdomain, is at the spike domain apex. We propose that this structure renders the particle less susceptible to antibody neutralization by hiding vulnerable conserved domains while exposing highly diverse epitopes as immunological decoys, thereby contributing to the immune evasion properties of anelloviruses. These results shed light on the structure of anelloviruses and provide a framework to understand their interactions with the immune system.

Abstract Background Ensuring ongoing first-aid training for primary healthcare providers (PHPs) is one of the critical strategies for providing quality health services and contributing to achieving universal health coverage. However, PHPs have received insufficient attention in terms of training and capacity building, especially in the remote areas of low-to-middle-income countries. This study evaluated the effectiveness of a first-aid training program for PHPs on a Vietnamese island and explored their perspectives and experiences regarding first-aid implementation. Methods A mixed-methods study was conducted among 39 PHPs working in community healthcare centers. The quantitative method utilized a quasi-experimental design to evaluate participants’ first-aid knowledge at three time points: pre-training, immediately post-training, and three months post-training. Sixteen of the PHPs participated in subsequent semi-structured focus group interviews using the qualitative method. Quantitative data were analyzed using repeated measures analysis of variance (ANOVA), while qualitative data were subjected to thematic analysis. Results The quantitative results showed a significant improvement in both the overall mean first-aid knowledge scores and the subdimensions of the first-aid knowledge scores among healthcare providers post-training. There was a statistically significant difference between the baseline and immediate posttest and follow-up knowledge scores (p 0.05). Three main themes emerged from the focus group discussions: perception of first-aid in remote areas, facilitators and barriers. Participants identified barriers, including infrastructure limitations, shortage of the primary healthcare workforce, inadequate competencies, and insufficient resources. Conversely, receiving considerable support from colleagues and the benefits of communication technologies in implementing first aid were mentioned as facilitators. The training bolstered the participants’ confidence in their first-aid responses, and there was a desire for continued education. Conclusions Implementing periodic first-aid refresher training for PHPs in a nationwide resource-limited setting can contribute significantly to achieving universal health coverage goals. This approach potentially enhances the preparedness of healthcare providers in these areas to deliver timely and effective first aid during emergencies, which may lead to more consistent primary healthcare services despite various challenges.

Abstract In this paper, we would like to examine whether stable de Sitter inflationary solutions appear within power-law extensions of the Starobinsky model. In particular, we will address general constraints for the existence along with the stability of de Sitter inflationary solutions in a general case involving not only the Starobinsky $$R^2$$ R 2 term but also an additional power-law $$R^n$$ R n one. According to the obtained results, we will be able to identify which extension is more suitable for an early inflationary phase rather than a late-time cosmic acceleration phase. To be more specific, we will consider several values of n to see whether the corresponding de Sitter inflationary solutions are stable or not.

Drought ranks among the costliest of all climate-related phenomena and manifests in various forms, posing significant challenges in understanding its influence on agriculture and natural ecosystems. Mainland Southeast Asia (MSEA), a significant region of tropical agriculture and vegetation ecosystems, has become increasingly susceptible to drought hazards. In this study, we characterized and assessed vegetation dynamics and their drought impacts using correlation analysis and explainable machine learning methods under different vegetation types and elevation zones during the dry growing seasons from 2000 to 2022. Specifically, we characterized the vegetation dynamics and their trend in space and time. Next, we assessed vegetation-drought responses in consideration of meteorological, hydrological, and agricultural droughts under different land cover types and elevation characteristics. Lastly, we used an explainable machine learning method to quantify the drivers and impacts of multifaceted droughts on natural and undisturbed vegetation ecosystems. Our findings revealed that nearly 70% of the MSEA region experienced a greening trend despite large areas of vegetative damage during the drought years. Vietnam witnessed increasing vegetation condition in most observed years while the declining trend was mainly found in Cambodia and southern Laos. Vegetation-drought responses showed that tropical vegetation had a high sensitivity to drought conditions, and stronger responses were observed in rainfed crop, mixed forest, and deciduous forest at lower altitude areas. In natural and undisturbed ecosystems, short-term meteorological and agricultural drought disturbances accounted for nearly 93% of variations in tropical vegetation. Among the different examined drought indices, the 3-month Standardized Precipitation Evapotranspiration (SPEI-3) and Temperature Condition Index (TCI) were identified as the factors having the largest influence, together explaining about 55% of the observed natural undisturbed vegetation variations. These findings deepen our understanding of tropical vegetation-drought responses and the underlying drivers of natural and undisturbed ecosystems. Such insights could provide valuable information to assist national and local governments in the MSEA region in developing effective drought management and adaptation programs to safeguard tropical agricultural production and natural ecosystems amidst growing climate challenges.

Background: Receiving hemodialysis treatment makes end-stage renal disease (ESRD) patients highly vulnerable amidst the COVID-19 pandemic. Hence, their kidney disease quality of life (KDQOL) is affected. We aimed to examine the association between fear of COVID-19 (FCoV-19) and KDQOL, and the effect modification of Health literacy (HL) on this association.Material and Methods: A survey was conducted at 8 hospitals from July 2020 to March 2021 on 972 patients. Data collection includes socio-demographic factors, clinical parameters, HL, digital healthy diet literacy (DDL), hemodialysis diet knowledge (HDK), FCoV-19, suspected COVID-19 symptoms (S-COVID-19-S), and KDQOL.Results: Higher HL scores B = 0.13 (95% CI = 0.06–0.21, p = 0.001) and HDK scores B = 0.58 (95% CI = 0.31–0.85, p = 0.001) were associated with higher KDQOL scores. Whereas, S-COVID-19-S B = −6.12 (95% CI = −7.66 to – 4.58, p = 0.001) and FCoV-19 B = −0.91 (95% CI = −1.03 to – 0.80, p = 0.001) were associated with lower KDQOL scores. Notably, higher HL scores significantly attenuate the negative impact of FCoV-19 on overall KDQOL and the kidney disease component summary.Conclusions: In hemodialysis patients, FCoV-19 and S-COVID-19-S were associated with a lower KDQOL. Health literacy significantly mitigates the negative impact of FCoV-19 on KDQOL. Strategic public health interventions to improve HL are suggested to protect patient’s KDQOL during the pandemic.

This paper presents a two-port microstrip patch antenna system with compact size and high isolation for full-duplex and multiple-input-multiple-output (MIMO) applications. The proposed miniaturization approach is to employ compact microstrip patch antenna and decoupling network as well. Here, two quarter-wavelength patch antennas are arranged in the H-plane coupled configuration with element spacing of about 0.008 λ at the center operating frequency. Besides, a defected ground structure is chosen as the decoupling network, which doesn't require any additional space and contributes to significantly reducing the overall antenna dimensions. The final antenna design with compact size of 0.49 λ × 0.37 λ × 0.01 λ has operating band from 2.45 to 2.48 GHz, in which isolation is better than 30 dB and the maximum isolation is 42 dB. Besides, the proposed antenna also achieved comparable gain of 3.8 dBi and good diversity performance. In comparison with other related works, the proposed design has smallest overall size with extremely small element spacing while obtaining comparable isolation enhancement.

Abstract The role of mitochondria peptides in the spreading of glioblastoma remains poorly understood. In this study, we investigated the mechanism underlying intracranial glioblastoma progression. Our findings demonstrate that the mitochondria-derived peptide, humanin, plays a significant role in enhancing glioblastoma progression through the intratumoral activation of the integrin alpha V (ITGAV)–TGF beta (TGFβ) signaling axis. In glioblastoma tissues, humanin showed a significant upregulation in the tumor area compared to the corresponding normal region. Utilizing multiple in vitro pharmacological and genetic approaches, we observed that humanin activates the ITGAV pathway, leading to cellular attachment and filopodia formation. This process aids the subsequent migration and invasion of attached glioblastoma cells through intracellular TGFβR signaling activation. In addition, our in vivo orthotopic glioblastoma model provides further support for the pro-tumoral function of humanin. We observed a correlation between poor survival and aggressive invasiveness in the humanin-treated group, with noticeable tumor protrusions and induced angiogenesis compared to the control. Intriguingly, the in vivo effect of humanin on glioblastoma was significantly reduced by the treatment of TGFBR1 inhibitor. To strengthen these findings, public database analysis revealed a significant association between genes in the ITGAV–TGFβR axis and poor prognosis in glioblastoma patients. These results collectively highlight humanin as a pro-tumoral factor, making it a promising biological target for treating glioblastoma.

Vancomycin is a critical antibiotic frequently utilized in clinical settings, with therapeutic drug monitoring (TDM) strongly advised to optimize treatment efficacy and mitigate the risk of adverse effects. However, current methods for measuring vancomycin levels in human plasma are hindered by long analysis times and complicated sample preparations. Thus, this study developed and validated a novel UPLC-MS/MS method for a rapid (with a running time of 3.5 min) and simple analysis of plasma vancomycin. To quantify vancomycin concentration in human plasma, we have developed and validated the UPLC-MS/MS method with high sensitivity, specificity, and accuracy, meeting the strict criteria according to the Food and Drugs Administration (FDA) guidelines for validation biological analysis methods. Vancomycin and atenolol (internal standard) underwent positive electrospray ionization (ESI+) and detection in multi-reaction monitoring (MRM) mode. The selected MRM transitions were m/z 725.66→144.16 for vancomycin and m/z 267.29→189.96 for atenolol. Plasma samples were precipitated using a simple mixture containing acetonitrile, methanol, and formic acid as a pH adjuster. The separation was performed using the Poroshell 120 Phenyl Hexyl Column (4.6 × 150 mm, 2.7 μm) maintained at 25 °C for 3.5 min. Isocratic elution with a mobile phase (methanol and 0.1% formic acid in a 40:60 v/v ratio) at a flow rate of 0.5 mL/min was employed. The method showed linearity (0.1–75 μg/mL) with a coefficient of determination above 0.9994 and a lower limit of quantification at 0.1 μg/mL. Precision, both intraday and interday, was below 10%, and accuracy ranged from 91.70% to 111.57%. System suitability, selectivity, stability, carryover, dilution, recovery, and matrix effect validation results all met acceptable criteria. The established UPLC-MS/MS method is expected to be a rapid, simple, and reliable tool for drug monitoring and pharmacokinetic studies, enhancing patient care during vancomycin administration.

Abstract Hypervirulent Klebsiella pneumoniae (hvKp) is a significant cause of severe invasive infections in Vietnam, yet data on its epidemiology, population structure and dynamics are scarce. We screened hvKp isolates from patients with bloodstream infections (BSIs) at a tertiary infectious diseases hospital in Vietnam and healthy individuals, followed by whole genome sequencing and plasmid analysis. Among 700 BSI-causing Kp strains, 100 (14.3%) were hvKp. Thirteen hvKp isolates were identified from 350 rectal swabs of healthy adults; none from 500 rectal swabs of healthy children. The hvKp isolates were genetically diverse, encompassing 17 sequence types (STs), predominantly ST23, ST86 and ST65. Among the 113 hvKp isolates, 14 (12.6%) carried at least one antimicrobial resistance (AMR) gene, largely mediated by IncFII, IncR, and IncA/C plasmids. Notably, the acquisition of AMR conjugative plasmids facilitated horizontal transfer of the non-conjugative virulence plasmid between K. pneumoniae strains. Phylogenetic analysis demonstrated hvKp isolates from BSIs and human carriage clustered together, suggesting a significant role of intestinal carriage in hvKp transmission. Enhanced surveillance is crucial to understand the factors driving intestinal carriage and hvKp transmission dynamics for informing preventive measures. Furthermore, we advocate the clinical use of our molecular assay for diagnosing hvKp infections to guide effective management.

Background Digital Health Literacy (DHL) is crucial in navigating digital health environments, yet few studies focus on older adults. Objective Explore the associations of digital health information and resource utilization, IT-related social support, and barriers/enhancers to digital health service usage with DHL among older adults. Methods A cross-sectional study was conducted from January 2022 to April 2023, involving 417 individuals over the age of 60 who were interviewed using an instrument for collecting data on DHL, social support, barriers/enhancers influencing use of digital health resources and personal/demographic data. Multi-regression models were used to examine the associations. Results Higher DHL scores were associated with daily use of digital interaction with healthcare ( B = 0.28; 95% confidence interval [CI] = 0.07, 0.49; p = .01), daily use of other digital health resources ( B = 0.22; 95% CI = 0.05, 0.40; p = .01), ease in finding assistance for online navigation ( B = 0.27; 95% CI = 0.08, 0.45; p = .01), self-perceived digital proficiency (“usually very good at surfing the internet,” B = 0.35; 95% CI = 0.18, 0.52; p = .01), assistance from relatives/others in internet browsing ( B = 0.20; 95% CI = 0.02, 0.37; p = .02), and having access to a computer, tablet, or smartphone ( B = 0.29; 95% CI = 0.11, 0.47, p = .01). Conversely, barriers like “no access to a computer, tablet, or phone at all times” ( B = −0.19; 95% CI = −0.34, −0.04; p = .01), “difficulty understanding online content” ( B = −0.22; 95% CI = −0.36, −0.07; p = .01), and “believing to be too old for online services” ( B = −0.18; 95% CI = −0.32, −0.03; p = .02) were associated with lower DHL scores. Conclusions Engagement with digital health platforms, including making online appointments and accessing personal health records, is associated with higher DHL levels. Support from relatives or others, a modifiable attribute, is also associated with elevated DHL among older adults.

Objective: To identify the efficacy and tolerability of Proteoglycan F in patients with primary knee OA.Design: A 24-week randomized, placebo-controlled, double-blind clinical trial with two arms: (1) Proteoglycan F (received 10 ​mg proteoglycan daily, for 24 weeks) and (2) control group (received placebo). Knee symptoms and joint cartilage status (evaluated by ultrasound and MRI of knee joints), quality of life, serum cytokine levels (IL-1β and TNF-α), and safety evaluation were measured before, during, and after the treatment. Results: After 24-week treatment, pain reduction (in the KOOS pain score) of at least 20% and at least 50% (NRS scale) compared to baseline in the PGF group was significantly higher than those in the control group. The PGF group had greater reductions in the total scores of subchondral bone marrow edema, and bone cocoon under cartilage on knee MRI (classification according to WORMs), which were −2.27 (-4.0; −0.51) and −1.77 (-3.08; −0.46), respectively (p ​

Background and purpose: Minimally invasive surgery for ICH has shown a significantly improved functional outcome for selected ICH patients. This study aimed to evaluate the factors influencing the outcome of supratentorial ICH patients treated with stereotactic computed tomographic (CT) guided recombinant tissue plasminogen activator (rt-PA). Methods: We retrospectively evaluated data from 80 consecutive supratentorial ICH patients between December 2017 and July 2020, analyzing their 180-day outcomes, and identifying clinical, radiological factors for good prognosis within 180 days. Results: The mean age was 55.6 years (SD 11.4). The median preoperative Glasgow Coma Scale (GCS) score was 9 (interquartile range - IQR 6–12). The mean final ICH volume was 26.5 ml (SD 27.5), with ICH volume reduction by an average of 66.7 %. Six months after the procedure, 40 patients (50 %) had favorable outcomes (modified Rankin Scale [mRS]) score 0–3). In multivariate analysis, age (odds ratio [OR] = 0.939, 95 % confidence interval (CI) = 0.894 – 0.986; p = 0.012), the GCS score before operation (OR = 1.525, 95 % CI = 1.008 – 2.309; p = 0.046), the initial midline shift (OR = 0.808, 95 % CI = 0.661 – 0.988; p = 0.038), and the residual volume hematoma (OR = 0.949, 95 % CI = 0.908 – 0.992, p = 0.020) were the significant predictors of a favorable 180-day outcome. Receiver operating characteristic curve analysis confirmed that the best cut-off point for predicting the good functional 180-day outcome was a GCS score of 9 before the procedure [area under the curve: 0.721, 95 % CI = 0.608 – 0.833, p = 0.001]. Conclusion: CT-guided thrombolysis and aspiration can be safe and effective in reducing ICH volume. A preoperation GCS score above or equal to 9 and younger age were associated with improved long-term outcomes in patients with supratentorial ICH.

Breadfruit's substantial carbohydrate content makes it a viable starch source, specifically resistant starch (RS) that helps prevent chronic diseases. This study investigated the effects of enzyme hydrolysis and microwave treatment (MT) on enriching type III RS in breadfruit. It also determined its structural and functional properties, including swelling power, solubility index, water absorption capacity, oil binding capacity, and syneresis. MT at 30 W.g-1 for 3 min resulted in the highest RS content of 74.8%, significantly surpassing pullulanase hydrolysis (1.0 U.g-1 dry basis for 12 h) at 17.3% RS. The breadfruit starch granules exhibited a regular shape, approximately 7.9 µm in length, whereas modified granules were less than 11 µm, along with observable deformation in their structural shape. In conclusion, the study demonstrates the efficacy of MT for enhancing RS content in breadfruit, highlighting its potential as a healthy functional ingredient and starch substitute.

Objectives The prevalence of posttraumatic stress disorder (PTSD) has increased, particularly among individuals who have recovered from coronavirus disease 2019 (COVID-19) infection. Health literacy is considered a “social vaccine” that helps people respond effectively to the pandemic. We aimed to investigate the association between long COVID-19 and PTSD, and to examine the modifying role of health literacy in this association. Methods A cross-sectional study was conducted at 18 hospitals and health centers in Vietnam from December 2021 to October 2022. We recruited 4,463 individuals who had recovered from COVID-19 infection for at least 4 weeks. Participants provided information about their sociodemographics, clinical parameters, health-related behaviors, health literacy (using the 12-item short-form health literacy scale), long COVID-19 symptoms and PTSD (Impact Event Scale-Revised score of 33 or higher). Logistic regression models were used to examine associations and interactions. Results Out of the study sample, 55.9% had long COVID-19 symptoms, and 49.6% had PTSD. Individuals with long COVID-19 symptoms had a higher likelihood of PTSD (odds ratio [OR], 1.86; 95% confidence interval [CI], 1.63–2.12; p

White blood cell (WBC) recognition is still a challenging problem because of the high variability and complexity of blood cell images. Blood cell images can vary in quality, resolution, contrast, illumination, staining, and background. Blood cells can also vary in shape, size, color, texture, and distribution. Moreover, blood cells can overlap, cluster, or deform, making them challenging to segment and identify. This paper proposed an efficient automatic illumination compensation algorithm using singular value decomposition in the cosine domain (CSVDC) to enhance WBC images in the preprocessing step. Firstly, the WBC color image is split into three color channels and then mapped to the frequency domain using the discrete cosine transform (DCT) to get their DCT coefficient matrices. Next, the compensation coefficients are constructed based on the DC terms and DCT coefficient matrices’ most significant singular values. The DCT coefficient matrices are then linearly adjusted by multiplying with the compensation coefficients. Finally, three color channels are reconstructed using the inverted DCT to get the enhanced WBC image. Experimental results for the four most common PBC_dataset_normal_DIB, Raabin-WBC, BCCD, and Munich AML Morphology datasets using state-of-art deep learning models, including VGG16, GoogLeNet, and RestNet, illustrate the effectiveness of the CSVDC algorithm. In particular, on the PBC_dataset_normal_DIB dataset, when using the ResNet, the proposed enhanced WBC images have a higher average recognition rate compared to the original, ASVDF, ASVDW, and AHOSVD images by 3.82%, 2.69%, 11.37%, and 8.62%, respectively. Experimental results show that our method dramatically improves deep learning-based WBC recognition accuracy.

Medical image data often face the problem of data scarcity and costly annotation processes. To overcome this, our study introduces a novel transfer learning method for medical image classification. We present a multimodal learning framework that incorporates the pre-trained PubMedCLIP model and multimodal feature fusion. Prompts of different complexities are combined with images as inputs to the proposed model. Our findings demonstrate that this approach significantly enhances image classification tasks while reducing the burden of annotation costs. Our study underscores the potential of PubMedCLIP in revolutionizing medical image analysis through its prompt-based approach and showcases the value of multi-modality for training robust models in healthcare. Code is available at:https://github.com/HongJapan/MTL_prompt_medical.git.

Abstract Among the most prevalent neurodevelopmental disorders, Autism Spectrum Disorder (ASD) is highly diverse showing a broad phenotypic spectrum. ASD also couples with a broad range of mutations, both de novo and inherited. In this study, we used a proprietary SNP genotyping chip to analyze the genomic DNA of 250 Vietnamese children diagnosed with ASD. Our Single Nucleotide Polymorphism (SNP) genotyping chip directly targets more than 800 thousand SNPs in the genome. Our primary focus was to identify pathogenic/likely pathogenic mutations that are potentially linked to more severe symptoms of autism. We identified and validated 23 pathogenic/likely pathogenic mutations in this initial study. The data shows that these mutations were detected in several cases spanning multiple biological pathways. Among the confirmed SNPs, mutations were identified in genes previously known to be strongly associated with ASD such as SLCO1B1, ACADSB, TCF4, HCP5, MOCOS, SRD5A2, MCCC2, DCC, and PRKN while several other mutations are known to associate with autistic traits or other neurodevelopmental disorders. Some mutations were found in multiple patients and some patients carried multiple pathogenic/likely pathogenic mutations. These findings contribute to the identification of potential targets for therapeutic solutions in what is considered a genetically heterogeneous neurodevelopmental disorder.

Abstract In this paper, we investigate the validity of the so-called cosmic no-hair conjecture in the framework of anisotropic inflation models of non-canonical scalar fields non-minimally coupled to a two-form field. In particular, we focus on two typical k-inflation and Dirac–Born–Infeld inflation models, in which we find a set of exact anisotropic power-law inflationary solutions. Interestingly, these solutions are shown to be stable and attractive during an inflationary phase using the dynamical system analysis. The obtained results indicate that the non-minimal coupling between the scalar and two-form fields acts as a non-trivial source of generating stable spatial anisotropies during the inflationary phase and therefore violates the prediction of the cosmic no-hair conjecture, even when the scalar field is of non-canonical forms. In connection with the Planck 2018 data, tensor-to-scalar ratios of these anisotropic solutions are investigated. As a result, it appears that the tensor-to-scalar ratio of the anisotropic power-law inflationary solution of k-inflation model turns out to be more highly consistent with the Planck 2018 data than that of Dirac-Born-Infeld model.

Soil Erosion Susceptibility Mapping (SESM) is one of the practical approaches for managing and mitigating soil erosion. This study applied four Machine Learning (ML) models, namely the Multilayer Perceptron (MLP) classifier, AdaBoost, Ridge classifier, and Gradient Boosting classifier to perform SESM in a region of Nghe An province, Vietnam. The development of these models incorporated seven factors influencing soil erosion: slope degree, slope aspect, curvature, elevation, Normalized Difference Vegetation Index (NDVI), rainfall, and soil type. These factors were determined based on 685 identified soil erosion locations. According to SHapley Additive exPlanations (SHAP) analysis, soil type emerged as the most significant factor influencing soil erosion. Among all the developed models, the Gradient Boosting classifier demonstrated the highest prediction power, followed by the MLP classifier, Ridge classifier, and AdaBoost, respectively. Therefore, the Gradient Boosting classifier is recommended for accurate SESM in other regions too, taking into account the local geo-environmental factors. HIGHLIGHTS Soil erosion has been modeled and a soil erosion susceptibility map was generated.; Several ML models, including the MLP classifier, Ada Boost, Ridge classifier, and Gradient Boosting classifier were implemented.; Developed models were tuned using the Grid Search CV technique.; The Gradient Boosting classifier performed the best.; About 33% of the study area has a high and very high susceptibility to soil erosion occurrence.;

Construction workplaces often face unforeseen collision hazards due to a decline in auditory situational awareness among on-foot workers, leading to severe injuries and fatalities. Previous studies that used auditory signals to prevent collision hazards focused on employing a classical beamforming approach to determine equipment sounds’ Direction of Arrival (DOA). No existing frameworks implement a neural network-based approach for both equipment sound classification and localization. This paper presents an innovative framework for sound classification and localization using multichannel sound datasets artificially synthesized in a virtual three-dimensional space. The simulation synthesized 10,000 multi-channel datasets using just fourteen single sound source audiotapes. This training includes a two-staged convolutional recurrent neural network (CRNN), where the first stage learns multi-label sound event classes followed by the second stage to estimate their DOA. The proposed framework achieves a low average DOA error of 30 degrees and a high F-score of 0.98, demonstrating accurate localization and classification of equipment near workers’ positions on the site.

Since the advent of gene-targeting technology in embryonic stem cells, mice have become a primary model organism for investigating human gene function due to the striking genomic similarities between the two species. With the introduction of the CRISPR/Cas9 system for genome editing in mice, the pace of loss-of-function analysis has accelerated significantly. This has led to the identification of numerous genes that play crucial roles in male reproductive processes, including meiosis, chromatin condensation, flagellum formation in the testis, sperm maturation in the epididymis, and fertilization in the oviduct. Despite the advancements, the functions of many genes, particularly those enriched in male reproductive tissues, remain largely unknown. In our study, we focused on 15 genes and generated 13 gene-deficient mice [4933411K16Rik, Adam triple (Adam20, Adam25, and Adam39), BC048671, Cfap68, Gm4846, Gm4984, Gm13570, Nt5c1b, Ppp1r42, Saxo4, Sh3d21, Spz1, and Tektl1] to elucidate their roles in male fertility. Surprisingly, all 13 gene-deficient mice exhibited normal fertility in natural breeding experiments, indicating that these genes are not essential for male fertility. These findings have important implications as they may help prevent other research laboratories from duplicating efforts to generate knockout mice for genes that do not demonstrate an apparent phenotype related to male fertility. By shedding light on the dispensability of these genes, our study contributes to a more efficient allocation of research resources in the exploration of male reproductive biology.

Abstract INTRODUCTION Soluble amyloid beta (Aβ) oligomers have been suggested as initiating Aβ related neuropathologic change in Alzheimer's disease (AD) but their quantitative distribution and chronological sequence within the AD continuum remain unclear. METHODS A total of 526 participants in early clinical stages of AD and controls from a longitudinal cohort were neurobiologically classified for amyloid and tau pathology applying the AT(N) system. Aβ and tau oligomers in the quantified cerebrospinal fluid (CSF) were measured using surface‐based fluorescence intensity distribution analysis (sFIDA) technology. RESULTS Across groups, highest Aβ oligomer levels were found in A+ with subjective cognitive decline and mild cognitive impairment. Aβ oligomers were significantly higher in A+T− compared to A−T− and A+T+. APOE ε4 allele carriers showed significantly higher Aβ oligomer levels. No differences in tau oligomers were detected. DISCUSSION The accumulation of Aβ oligomers in the CSF peaks early within the AD continuum, preceding tau pathology. Disease‐modifying treatments targeting Aβ oligomers might have the highest therapeutic effect in these disease stages. Highlights Using surface‐based fluorescence intensity distribution analysis (sFIDA) technology, we quantified Aβ oligomers in cerebrospinal fluid (CSF) samples of the DZNE‐Longitudinal Cognitive Impairment and Dementia (DELCODE) cohort Aβ oligomers were significantly elevated in mild cognitive impairment (MCI) Amyloid‐positive subjects in the subjective cognitive decline (SCD) group increased compared to the amyloid‐negative control group Interestingly, levels of Aβ oligomers decrease at advanced stages of the disease (A+T+), which might be explained by altered clearing mechanisms

This study investigated what features undergraduate EFL learners perceive as affecting the difficulty of model paragraphs. Four hundred and seventy-five Vietnamese undergraduates participated in a partial least squares structural equation model design. They ranked five paragraphs from easiest to most difficult and responded to a 10-point Likert questionnaire regarding 11 features (titles, paragraph length, vocabulary, vocabulary in context, rhetorical organization, paragraph structure, sentence length, punctuation, signal words, interest, background knowledge). The results showed that eight variables (titles, vocabulary, vocabulary in context, sentence length, rhetorical organization, paragraph structure, interest, background knowledge) had a significant direct effect and four variables (vocabulary, sentence length, rhetorical organization, background knowledge) had mediating effects. The model accounted for 0.508 R 2 of students’ perceptions, with a moderate to high predictive relevance ( Q 2 = 0.35). The paper also discusses the results’ implications for those in writing studies and the publishing industry. Suggestions for future study are also presented.

Introduction: 3D pharmacophore models describe the ligand’s chemical interactions in their bioactive conformation. They offer a simple but sophisticated approach to decipher the chemically encoded ligand information, making them a valuable tool in drug design.Methods: Our research summarized the key studies for applying 3D pharmacophore models in virtual screening for 6,944 compounds of APJ receptor agonists. Recent advances in clustering algorithms and ensemble methods have enabled classical pharmacophore modeling to evolve into more flexible and knowledge-driven techniques. Butina clustering categorizes molecules based on their structural similarity (indicated by the Tanimoto coefficient) to create a structurally diverse training dataset. The learning method combines various individual pharmacophore models into a set of pharmacophore models for pharmacophore space optimization in virtual screening.Results: This approach was evaluated on Apelin datasets and afforded good screening performance, as proven by Receiver Operating Characteristic (AUC score of 0.994 ± 0.007), enrichment factor of (EF1% of 50.07 ± 0.211), Güner-Henry score of 0.956 ± 0.015, and F-measure of 0.911 ± 0.031.Discussion: Although one of the high-scoring models achieved statistically superior results in each dataset (AUC of 0.82; an EF1% of 19.466; GH of 0.131 and F1-score of 0.071), the ensemble learning method including voting and stacking method balanced the shortcomings of each model and passed with close performance measures.

BackgroundThe COVID-19 pandemic has underscored the significance of adopting healthy lifestyles to mitigate the risk of severe outcomes and long-term consequences. ObjectiveThis study focuses on assessing the prevalence and clustering of 5 unhealthy lifestyle behaviors among Vietnamese adults after recovering from COVID-19, with a specific emphasis on sex differences. MethodsThe cross-sectional data of 5890 survivors of COVID-19 in Vietnam were analyzed from December 2021 to October 2022. To examine the sex differences in 5 unhealthy lifestyle behaviors (smoking, drinking, unhealthy diet, physical inactivity, and sedentary behavior), the percentages were plotted along with their corresponding 95% CI for each behavior. Latent class analysis was used to identify 2 distinct classes of individuals based on the clustering of these behaviors: the “less unhealthy” group and the “more unhealthy” group. We examined the sociodemographic characteristics associated with each identified class and used logistic regression to investigate the factors related to the “more unhealthy” group. ResultsThe majority of individuals (male participants: 2432/2447, 99.4% and female participants: 3411/3443, 99.1%) exhibited at least 1 unhealthy behavior, with male participants being more susceptible to multiple unhealthy behaviors. The male-to-female ratio for having a single behavior was 1.003, but it escalated to 25 for individuals displaying all 5 behaviors. Male participants demonstrated a higher prevalence of combining alcohol intake with sedentary behavior (949/2447, 38.8%) or an unhealthy diet (861/2447, 35.2%), whereas female participants tended to exhibit physical inactivity combined with sedentary behavior (1305/3443, 37.9%) or an unhealthy diet (1260/3443, 36.6%). Married male participants had increased odds of falling into the “more unhealthy” group compared to their single counterparts (odds ratio [OR] 1.45, 95% CI 1.14-1.85), while female participants exhibited lower odds (OR 0.65, 95% CI 0.51-0.83). Female participants who are underweight showed a higher likelihood of belonging to the “more unhealthy” group (OR 1.11, 95% CI 0.89-1.39), but this was not observed among male participants (OR 0.6, 95% CI 0.41-0.89). In both sexes, older age, dependent employment, high education, and obesity were associated with higher odds of being in the “more unhealthy” group. ConclusionsThe study identified notable sex differences in unhealthy lifestyle behaviors among survivors of COVID-19. Male survivors are more likely to engage in unhealthy behaviors compared to female survivors. These findings emphasize the importance of tailored public health interventions targeting sex-specific unhealthy behaviors. Specifically, addressing unhealthy habits is crucial for promoting post–COVID-19 health and well-being.

Drought has become one of the most devastating natural risks of agricultural production and the environment in almost all climate regions. Thus, understanding the spatiotemporal characteristics of drought and its associated impacts is crucial in drought early warning management and adaptation efforts. In this study, we used the 3-month Standardized Precipitation Index (SPI-3) obtained from Climate Hazards Group InfraRed Precipitation with Station data (CHIRPS) to investigate the space–time characteristics of drought conditions. Also, this study examined the impact of the SPI-based drought on vegetation health conditions using the Advanced Very High Resolution Radiometer (AVHRR) Normalized Difference Vegetation Index (NDVI) time-series data from 1981 to 2020. The results revealed that the region experiences drought primarily between July and September, with the most prolonged drought events lasting up to five months. Morocco suffered from more frequent droughts than other countries in the region. The Mann-Kendall test showed that the trend of drought became drier over the last decade, whereas the period from 1981 to 2010 witnessed either wetting or no trends. This study also found that the response of crops and grasslands showed higher correlation with the SPI-3 and that the response of vegetation to droughts was higher during the dry season. The findings of this study provide useful information to support local and regional drought planning and adaptation programs and enhance the understanding of drought development in the region.

Abstract Cardiovascular disease (CVD) and its risk factors seem to be linked with deteriorated graft function and persists as the major cause of mortality in kidney transplant recipients (KTRs). Diet quality is associated with CVD prevention in the healthy population, however, less study focuses on KTRs. The study aimed to determine the association between diet quality indices and lipid profile abnormalities as risk factors for CVD in KTRs. This prospective study enrolled 106 KTRs who had functioning allografts from September 2016. Lipid profiles included low-density lipoprotein cholesterol (LDL-C), high-density lipoprotein cholesterol (HDL-C), total cholesterol (TC), and triglyceride (TG) and were based on the National Cholesterol Education Program Adult Treatment Panel III recommendations. Three-day dietary data were collected by a well-trained registered dietitian. The Alternative Healthy Eating Index-Taiwan (AHEI-Taiwan), Alternative Healthy Eating Index-2010 (AHEI-2010), and Healthy Eating Index-2015 (HEI-2015) scores were calculated and divided into quartiles and compared accordingly. KTRs’ mean LDL-C, HDL-C, TC, and TG levels were 119.8 ± 36.6 mg/dL, 52.0 ± 17.9 mg/dL, 205.8 ± 43.9 mg/dL, and 160.2 ± 121.6 mg/dL, respectively. Compared with the lowest quartile, only the highest quartile of AHEI-Taiwan had lower TC and LDL-C levels. After adjustment for age, gender, energy, Charlson comorbidity index, transplant duration, and dialysis duration, logistic regression analysis revealed that the highest quartile of AHEI-Taiwan had 82% (odds ratio [OR], 0.18; 95% confidence interval [CI] 0.04–0.72, p

Abstract Epacadostat (EPA), the most advanced IDO1 inhibitor, in combination with PD-1 checkpoint inhibitor, has failed in a recent Phase III clinical trial for treating metastatic melanoma. Here we report an EPA nanovesicle therapeutic platform (Epacasome) based on chemically attaching EPA to sphingomyelin via an oxime-ester bond highly responsive to hydrolase cleavage. Via clathrin-mediated endocytosis, Epacasome displays higher cellular uptake and enhances IDO1 inhibition and T cell proliferation compared to free EPA. Epacasome shows improved pharmacokinetics and tumour accumulation with efficient intratumoural drug release and deep tumour penetration. Additionally, it outperforms free EPA for anticancer efficacy, potentiating PD-1 blockade with boosted cytotoxic T lymphocytes (CTLs) and reduced regulatory T cells and myeloid-derived suppressor cells responses in a B16-F10 melanoma model in female mice. By co-encapsulating immunogenic dacarbazine, Epacasome further enhances anti-tumor effects and immune responses through the upregulation of NKG2D-mediated CTLs and natural killer cells responses particularly when combined with the PD-1 inhibitor in the late-stage metastatic B16-F10-Luc2 model in female mice. Furthermore, this combination prevents tumour recurrence and prolongs mouse survival in a clinically relevant, post-surgical melanoma model in female mice. Epacasome demonstrates potential to synergize with PD-1 blockade for improved response to melanoma immunotherapy.

Camellia quephongensis Hakoda et Ninh is a yellow-flowered camellia that inhabits the Que Phong District, Nghe An Province, North-Central Vietnam, and its taxon includes approximately 50 species from South China and Vietnam. Researchers have primarily focused on the taxonomy and biochemistry of medicinal substances found in camellia flowers and on horticultural studies of their propagation. Consequently, habitat characteristics and adaptation mechanisms still need to be better understood. Thus, this study investigated the habitats of C. quephongensis in terms of landscape and stand composition, analyzed the morphological variabilities and age distribution patterns among different habitat types, and explored its adaptation mechanisms. The results revealed that this species preferentially inhabited forests on vulnerable slopes and stream banks, which were frequently disturbed by slope failure or flooding, while its multi-stemming traits were key factors in recovering from damage and persisting in such habitats. Relatively stable sites with fewer disturbances can be a source habitat, whereas frequently disturbed sites can be a sink habitat for the populations. Regular thinning of the shrub and herb layers in the plantation maintained a stand composition similar to that of natural stands; thus, succession control is recommended to conserve the naturally growing site of this species. The findings of this study will aid in the future conservation and restoration of growing areas.

Abstract Salmonella enterica serotype 1,4,[5],12:i:- (Typhimurium monophasic variant) of sequence type (ST) 34 has emerged as the predominant pandemic genotype in recent decades. Despite increasing reports of resistance to antimicrobials in Southeast Asia, Salmonella ST34 population structure and evolution remained understudied in the region. Here we performed detailed genomic investigations on 454 ST34 genomes collected from Vietnam and diverse geographical sources to elucidate the pathogen’s epidemiology, evolution and antimicrobial resistance. We showed that ST34 has been introduced into Vietnam in at least nine occasions since 2000, forming five co-circulating major clones responsible for paediatric diarrhoea and bloodstream infection. Most expansion events were associated with acquisitions of large multidrug resistance plasmids of IncHI2 or IncA/C2. Particularly, the self-conjugative IncA/C2 pST34VN2 (co-transferring bla CTX-M-55, mcr-3.1, and qnrS1) underlies local expansion and intercontinental spread in two separate ST34 clones. At the global scale, Southeast Asia was identified as a potential hub for the emergence and dissemination of multidrug resistant Salmonella ST34, and mutation analysis suggests of selection in antimicrobial responses and key virulence factors.

In recent decades, the dramatic transformation from conventional energy to renewables, such as photovoltaic (PV), has been extensively occurred to address the increasing electricity demand and environmental issues. Nevertheless, the operation of PV systems is adversely affected by the intermittence of meteorological factors, such as solar irradiance. Therefore, accurate PV power generation forecast has played a vital role to stimulate the integration of renewable energy sources into the power grid. This study proposes a novel scheme for short-term PV power forecast based on Transformer Neural Network (TransNN) integrated with Convolutional Neural Network (CNN). In addition, Variational Mode Decomposition (VMD) is adopted in the data pre-processing stage to enhance the forecast accuracy. The performance of the proposed model is validated utilizing two real-world datasets, and five benchmark models are dedicated to forecast outcome comparison. The predicted results indicated the superiority of the proposed model compared to the referenced models by achieving the Mean Absolute Error (MAE) values under 1 kW in all cases.

Low-voltage ride-through (LVRT) capability is crucial for wind power plants that are grid-connected. A grid code requires wind farms to remain on-grid and inject a specific reactive current when a grid fault occurs. The requirements are satisfied by a cooperative control scheme of a battery energy storage system (BESS) and grid-side converter (GSC) for improving the LVRT capacity of wind turbine generators using permanent magnet synchronous generators. The proposed approach uses the BESS to absorb a power difference that appears in the back-to-back converters during the LVRT operation. Hence, the DC-link voltage is regulated by the BESS. In addition, the GSC control allows reactive current to be injected into the grid according to the grid code requirements. Finally, the wind turbine generators, which use the proposed approach, are built in MATLAB/Simulink. The theoretical analysis of the proposed method is validated by simulation.

Microgrids (MGs) with a high penetration of renewable energy are becoming increasingly popular, mainly due to the need for a sustainable and environmentally friendly power system. However, the stochastic characteristic of renewable energy sources makes it a considerable challenge when designing a microgrid. Appropriate installation of energy storage systems (ESSs) such as battery and hydrogen storage systems are needed to counter the intermittent nature of energy sources. This study presents a comparison and evaluation of eight different metaheuristic approaches for optimizing the size of a hydrogen storage-based microgrid, with the aims of minimizing the microgrid’s cost and ensuring the ability to regulate the energy flow within the system. In addition, the optimization algorithm considers the power of the photovoltaic (PV) system, electrolyzer, fuel cell, and the capacity of the battery and hydrogen tank as decision variables. Results of numerical simulations proved that, under the above problem framework, the particle swarm optimization algorithm outperforms the rest. The algorithm is able to produce an optimized microgrid with a 25.3% lower annual system cost compared to the worst-performing algorithm. Its ability to escape the local optimum solution is also showcased.

This study investigates the impact of substituting lactose with maltodextrin in milk–tea formulations to enhance their physicochemical and structural properties. Various lactose-to-maltodextrin ratios (100:0, 90:10, 85:15, 80:20, 75:25) were evaluated in both post-pasteurized and concentrated skim milk–tea (SM-T) and whole milk–tea (WM-T) formulations. Concentration significantly improved the zeta potential, pH, and browning index in both SM-T and WM-T compared to pasteurization. L:M ratios of 90:10 and 75:25 in WM-T and 90:10 and 80:20 in SM-T showed higher phenolic preservation after concentration due to structural changes resulting from the addition of maltodextrin and water removal during prolonged heating. The preservation effect of phenolic components in both WM-T and SM-T is governed by many mechanisms including pH stabilization, zeta potential modulation, protein interactions, complex formation, and encapsulation effects. Therefore, optimizing milk–tea stability and phenolic preservation through L:M ratio adjustments provides a promising approach for enhancing milk–tea properties.

The rapid increase in the production of powdered milk–tea blends is driven by a growing awareness of the presence of highly nutritious bioactive compounds and consumer demand for convenient beverages. However, the lack of literature on the impact of heat-induced component interactions during processing hinders the production of high-quality milk–tea powders. The production process of milk–tea powder blends includes the key steps of pasteurization, evaporation, and spray drying. Controlling heat-induced interactions, such as protein–protein, protein–carbohydrate, protein–polyphenol, carbohydrate–polyphenol, and carbohydrate–polyphenol, during pasteurization, concentration, and evaporation is essential for producing a high-quality milk–tea powder with favorable physical, structural, rheological, sensory, and nutritional qualities. Adjusting production parameters, such as the type and the composition of ingredients, processing methods, and processing conditions, is a great way to modify these interactions between components in the formulation, and thereby, provide improved properties and storage stability for the final product. Therefore, this review comprehensively discusses how molecular-level interactions among proteins, carbohydrates, and polyphenols are affected by various unit operations during the production of milk–tea powders.

This paper introduces the design of a circularly polarized metasurface-based antenna array for C-band satellite applications that owns broadband operation and high gain characteristics. The single radiating element comprises a Y-shape patch and an above-placed 2 × 2 unit-cell metasurface. Further improvement in operating bandwidth and broadside gain is achieved by arranging four single elements in a 2 × 2 configuration and a sequential-phase feed network. A prototype has been fabricated and measured to validate the feasibility of the proposed antenna array. The measured operating bandwidth is 20% (4.50–5.50 GHz), which is an overlap between a −10 dB impedance bandwidth of 29.8% (4.50–5.99 GHz) and a 3 dB axial ratio bandwidth of 20% (4.50–5.50 GHz). Across this operating band, the peak broadside gain is 10.5 dBi. Compared with the recently published studies, the proposed array is a prominent design for producing a wide operating bandwidth and relatively high gains while maintaining the overall compact dimensions.

BackgroundDeclining cognitive function (CF) and physical function (PF) relate to poorer health-related quality of life (HRQoL) in older adults. As health literacy (HL) facilitates health information utilization, it may mediate links between functionality and HRQoL appraisals. This study examined HL as an intermediary between joint CF and PF contributions and HRQoL in Hong Kong older adults.Methods490 older adults aged 50–80 years completed assessments from March to July 2021. Health Literacy Survey Questionnaire Short Form 12 questions (HLS-SF12), Montreal Cognitive Assessment (MoCA), Senior Fitness Test (SFT) and 12-Item Short-Form Health Survey version 2 (SF-12v2) were used to assess HL, CF, PF and HRQoL, respectively. Path analysis tested a model with HL mediating CF/PF predictors and HRQoL outcome.ResultsResults for direct effects indicated that CF significantly associated with PF (β = 0.115, SE = 0.012, p