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Abstract Introduction Most hospitalized patients required invasive or non-invasive ventilation and High Flow Nasal Cannula (HFNC). Therefore, this study was conducted to describe the characteristics of patients with severe Coronavirus Disease—2019 (COVID-19) treated by HFNC and its effectiveness for reducing the rate of intubated—mechanical ventilation in the Intensive Care Unit (ICU) of Phu Chanh COVID-19 Department—Binh Duong General Hospital. Methods It was a cross-sectional and descriptive study. All severe patients with COVID-19 with acute respiratory failure eligible for the study were included. Patient characteristics, clinical symptoms, laboratory results, and treatment methods were collected for analysis; parameters and data related to HFNC treatment and follow-up were analysed. Results 80 patients, aged of 49.7 ± 16.6 years, were treated with HFNC at admission in ICU. 14 patients had type 2 diabetes (17.5%), 3 patients had chronic respiratory disease (3.8%), 19 patients had high blood pressure (23.8%), and 5 patients with other comorbidities (7.4%). The majority of patients with severe COVID-19 had typical symptoms of COVID-19 such as shortness of breath (97.5%), intensive tired (81.3%), cough (73.7%), anosmia (48.3%), ageusia (41.3%), and fever (26.3%). The results of arterial blood gases demonstrated severe hypoxia under optimal conventional oxygen therapy (PaO2 = 52.5 ± 17.4 mmHg). Respiratory rate, SpO2, PaO2 were significantly improved after using HFNC at 1st day, 3rd day and 7th day (P

Abstract Recent advances in AI and intelligent vehicle technology hold the promise of revolutionizing mobility and transportation through advanced driver assistance systems (ADAS). Certain cognitive factors, such as impulsivity and inhibitory control have been shown to relate to risky driving behavior and on-road risk-taking. However, existing systems fail to leverage such factors in assistive driving technologies adequately. Varying the levels of these cognitive factors could influence the effectiveness and acceptance of ADAS interfaces. We demonstrate an approach for personalizing driver interaction via driver safety interfaces that are are triggered based on the inference of the driver’s latent cognitive states from their driving behavior. To accomplish this, we adopt a data-driven approach and train a recurrent neural network to infer impulsivity and inhibitory control from recent driving behavior. The network is trained on a population of human drivers to infer impulsivity and inhibitory control from recent driving behavior. Using data collected from a high-fidelity vehicle motion simulator experiment, we demonstrate the ability to deduce these factors from driver behavior. We then use these inferred factors to determine instantly whether or not to engage a driver safety interface. This approach was evaluated using leave-one-out cross validation using actual human data. Our evaluations reveal that our personalized driver safety interface that captures the cognitive profile of the driver is more effective in influencing driver behavior in yellow light zones by reducing their inclination to run through them.

Abstract Background Participatory approaches have become a widely applied research approach. Despite their popularity, there are many challenges associated with the evaluation of participatory projects. Here we describe an evaluation of a community-based participatory research study of underserved communities in Ho Chi Minh City (HCMC), Vietnam at risk for hepatitis C virus. The goals of our evaluation were to explore the main benefits and challenges of implementing and participating in a participatory study and to describe study impacts. Methods We conducted two meetings with leaders and members of the participating groups followed by in-depth interviews with 10 participants. We then held a dissemination meeting with over 70 participants, including the representatives of each group, researchers from non-governmental organizations (community-based, national and international), and govenrment officials from the Vietnam Ministry of Health and the Department of Health of HCMC. Results Results include four categories where we describe first the participatory impacts, followed by the collaborative impacts. Then we describe the benefits and challenges of creating and belonging to one of the groups, from members’ and leaders’ points of view. Finally, we describe the key suggestions that participants provided for future research. Conclusion In conclusion, the evaluation approach led to both a research reflection on the ‘success’ of the project and enabled participants themselves to reflect on the outcomes and benefits of the study from their point of view.

The main content of the article refers to the consideration of factors and their influence on the implementation of occupational safety and hygiene (OSH) risk assessment at enterprises operating in fields with high risks of occupational safety and hygiene in Vietnam. Although the Law on Occupational Safety and Hygiene in Vietnam has been in effect for 5 years, both employees and employers have not yet fully and effectively applied contents related to workplace safety and hygiene. In addition, some guiding contents of this work gradually reveal certain limitations. Based on the guidance on OSH risk assessment currently being applied in Vietnam, combined with standards related to corporate social responsibility (CSR) according to ISO 26000, ISO 45001. The author has developed a questionnaire to survey employers at 230 enterprises operating in industries and occupations with high risks of labor accidents and occupational diseases according to regulations. The results show that there are four factors that affect the implementation of OSH risk assessment in the following order: (1) the ability of business managers, (2) the sense of responsibility of employees and their representatives, (3) labor safety training and (4) legal regulations and safety instructions. In addition, there are many opinions on further improving regulations and OSH risk assessment processes. Based on the results of this research, we would like to make some recommendations to improve the legal system related to the OSH risk assessment process and CSR, and help relevant parties better understand the benefits and raise their awareness of the issue.

This manuscript examines the chassis design of compact electric vehicles fueled by fuel cells, focusing on ensuring structural strength, optimizing weight, and integrating fuel cell systems. Consequently, a thorough design framework is created, which includes the selection of materials, study of the structure, and safety concerns. We employ advanced computational techniques and finite element analysis to create models and simulate numerous design scenarios, guaranteeing optimal performance under diverse operating circumstances. The chassis is designed to meet the precise needs of fuel cell systems, such as hydrogen storage and safety measures. The findings indicate that a meticulously engineered chassis can greatly improve the effectiveness and security of compact electric vehicles propelled by fuel cells. The findings contribute to the wider field of sustainable vehicle design and offer practical insights for future research and development in electric vehicle (EV) technology. Furthermore, this thesis asserts that incorporating fuel cell technology into the chassis design of compact electric vehicles offers a feasible route to achieve more sustainable and efficient urban transportation options.

Trypoxylon Latreille, 1796 (Hymenoptera, Crabronidae) consists of 633 known species worldwide and the genus is divided into numerous species groups. In Vietnam, 19 species of Trypoxylon have been recorded to date. In this study, a new species, Trypoxylon sicklum Pham and Antropov sp. nov. is described and illustrated from Vinh Phuc Province, Vietnam. The new species is the second member of the Trypoxylon fulvocollare species group. A key to species in the Trypoxylon fulvocollaris group is presented.Trypoxylon sicklum Pham and Antropov sp. nov., is described as a new species and is the second member of the Trypoxylon fulvocollare species group. A key to species in the Trypoxylon fulvocollaris group is presented.

Today, with the Industrial Revolution 4.0, digital transformation is taking place strongly in many different fields. Digital transformation can bring a competitive advantage to enterprises. However, enterprises also spend a lot of money to implement digital transformation. Therefore, whether or not digital transformation increases company performance is always of concern to company leaders. This paper aims to analyse the impact of Digital Transformation on Financial Performance, including Revenue, Profit and ROA in Vietnam. Digital Transformation is represented by the variables Cloud Computing, Big Data Analytics, Artificial Intelligence, and the Internet of Things. We used Google Forms to survey 314 leaders of enterprises in Vietnam. Then, we used descriptive statistics, correlation and regression analysis to analyse the data. Research results show that Cloud Computing, Big Data Analytics, and Artificial Intelligence positively impact financial performance. Enterprises that apply digital transformation have better financial performance than other enterprises. In contrast, there is no connection between the Internet of Things and financial performance. In addition, Firm type and Firm size also affect financial performance. FDI enterprises have higher performance than others, and more giant-sized enterprises have higher performance than others.

This presentation explored how the University of Birmingham’s Academic Skills Centre (ASC) is responding to urgent needs to equip taught students with the skills and knowledge for effective use of generative AI (GAI) tools for study. As Martin Compton (2023) noted, there is an ‘ongoing drive to centre information literacy and critical-thinking skills in the development of our students’. The introduction of tools like ChatGPT and Copilot reshapes how knowledge is acquired and applied, underscoring this imperative. With expertise in embedding information literacy and critical thinking across a range of disciplines, learning developers are uniquely equipped to adapt these skills to the challenges posed by GAI. Through this adaptation, students will be better positioned to navigate the complexities of studying and learning in environments influenced by GAI. Learning developers can take the lead in shaping the ethical frameworks and guidance for using GAI tools. Our ASC produced the university’s student-facing guidance on using GAI tools and is currently working with postgraduate students — ‘GAI Ambassadors’ — to co-create an interactive and accessible Canvas resource. A co-creation approach has allowed these students to be active participants in their own learning (Katz, 2021) whilst ensuring they bring their unique perspective to using these tools. Ultimately, this presentation had a dual purpose: to allow us and our students to showcase what we have produced and to share best practice; secondly, advocating for learning developers, alongside their students, to be at the forefront of leading university policy on how and when GAI tools should be used.

Abstract Objectives According to the WHO, more than 50 million people have epilepsy. Among them, nearly 80% of patients with epilepsy live in developing countries and 75% of them do not have access to treatment. The ketogenic diet (KD) has been shown as an effective alternative for patients with drug‐resistant epilepsy. Although it has been studied in Asia, no such studies have been conducted in Vietnam. The purpose of this study was to verify the feasibility and tolerability of KD in children with refractory epilepsies in Vietnam. Methods Children with drug‐resistant epilepsy followed at Children's Hospital, Vietnam treated by KD were included in a prospective study from June 2019 to October 2021. Side‐effects, retention rate, number, and duration of seizures were recorded after 1, 3, 6, 9 and 12 months of KD. Patients were considered as respondents when a 50% seizure frequency was reached. Tolerance and acceptability of the KD were closely monitored. Results Forty‐six children were included but KD was contraindicated for one patient. Due to the COVID pandemic, we had to rely on internet exchanges to stay in touch with families. Meals had to be adapted to Vietnamese culinary habits. The retention rate decreased from 82.2% at 1 month to 40% at 12 months of follow‐up. The incidence of side effects was 44.4% and occurred mainly during the first month. Fifteen patients out of 45 were considered as responders after 12 months. Significance Our study was the first attempt to introduce KD in Vietnam. It demonstrated that this diet was feasible and well tolerated. The KD diet resulted in significant improvement for 30% of our patients with drug‐resistant epilepsy. This percentage is lower than in some studies but warrants the use of KD as a valuable alternative in a country where many patients lack access to recent treatments.

Camellia specimens were collected from Mt. Ba Vi, Ba Vi National Park, during field surveys in 2022 (flowers) and 2023 (fruit). On the basis of morphological analyses, we determined that the morphology of these specimens matched that of Camellia tonkinensis. The characteristics of the specimens included papery-textured, elliptic or broad elliptic leaves with acuminate to narrowly acuminate apices, obtuse to rounded bases, petioles with channeled adaxial surfaces, tomentose gynoecia, 3(–4)-coccate capsules, and 1(–2) seeds per locule. A comparison of the morphological characteristics of the 2022 and 2023 collections (DL 220901, Dl 230201) was made with the type specimen (B. Balansa 3860, 1887) and the 2014 collections from Ngoc Son-Ngo Luong Nature Reserve (N. Q. Hieu et al. CPC616, CPC673, CPC712, CPC776). The characteristics and photographs of the rediscovered species from Mt. Ba Vi are presented herein. From the latest field surveys, the IUCN conservation status of Camellia tonkinensis was assessed as Critically Endangered (CR).

BackgroundMedical examinations contain repeatedly measured data from multiple visits, including imaging variables collected from different modalities. However, the utility of such data for the prediction of time-to-event is unknown, and only a fraction of the data is typically used for risk prediction. We hypothesized that multimodal longitudinal imaging data could improve dynamic disease prognosis of cardiovascular and renal disease (CVRD).MethodsIn a multi-centered cohort of 5,114 CARDIA participants, we included 166 longitudinal imaging variables from five imaging modalities: Echocardiography (Echo), Cardiac and Abdominal Computed Tomography (CT), Dual-Energy x-ray Absorptiometry (DEXA), Brain Magnetic Resonance Imaging (MRI) collected from young adulthood to mid-life over 30 years (1985–2016) to perform dynamic survival analysis of CVRD events using machine learning dynamic survival analysis (Dynamic-DeepHit, LTRCforest, and Extended Cox for Time-varying Covariates). Risk probabilities were continuously updated as new data were collected. Model performance was assessed using integrated AUC and C-index and compared to traditional risk factors.ResultsLongitudinal imaging data, even when being irregularly collected with high missing rates, improved CVRD dynamic prediction (0.03 in integrated AUC, up to 0.05 in C-index compared to traditional risk factors; best model's C-index = 0.80–0.83 up to 20 years from baseline) from young adulthood followed up to midlife. Among imaging variables, Echo and CT variables contributed significantly to improved risk estimation. Echo measured in early adulthood predicted midlife CVRD risks almost as well as Echo measured 10–15 years later (0.01 C-index difference). The most recent CT exam provided the most accurate prediction for short-term risk estimation. Brain MRI markers provided additional information from cardiac Echo and CT variables that led to a slightly improved prediction.ConclusionsLongitudinal multimodal imaging data readily collected from follow-up exams can improve CVRD dynamic prediction. Echocardiography measured early can provide a good long-term risk estimation, while CT/calcium scoring variables carry atherosclerotic signatures that benefit more immediate risk assessment starting in middle-age.

Mangroves play an important role in coastal estuarine areas with different ecological functions, such as reducing the impact of waves and currents, accumulating biomass and sequestering carbon. However, estimation of terrestrial biomass in mangrove areas, especially in Vietnam, has not been fully studied. The application of unmanned aerial vehicles (UAV), mounted with multispectral cameras combined with field verification is an effective method for estimating terrestrial biomass for mangroves, as it reduces field survey time and allows for greater spatial range research. In this study, ground biomass was estimated for the mangrove area in the Dong Rui commune, based on multispectral image data obtained from UAV and survey results in 16 standard cells measuring actual biomass according to four regression models: Log-Log, Log-Lin, Lin-Log and Lin-Lin. The results of comparing the data from these four models show that the log-log model has the highest accuracy with a high correlation coefficient (R2 = 0.831). Based on the results of the analysis and selection of ground-based biomass estimation models, a biomass map was established for the UAV flying area in the Dong Rui mangrove forest with biomass values ranging from 20 Mg/ha to 150 Mg/ha. In summary, we present a biomass estimation method through four basic linear regression models for mangrove areas, based on multispectral image data obtained from ultrahigh-resolution UAV. The resulting research results can serve as a basis for managers to calculate and synchronise the payment of carbon services, thus contributing to effectively promoting the livelihoods of local people.

Abstract Introduction The fourth outbreak of COVID-19 with the delta variant in Vietnam was very fierce due to the limited availability of vaccines and the lack of healthcare resources. During that period, the high mortality of patients with severe and critical COVID-19 caused many concerns for the health system, especially the intensive care units. This study aimed to analyze the predictive factors of death and survival in patients with severe and critical COVID-19. Methods We conducted a cross-sectional and descriptive study on 151 patients with severe and critical COVID-19 hospitalized in the Intensive Care Unit of Binh Duong General Hospital. Results Common clinical symptoms of severe and critical COVID-19 included shortness of breath (97.4%), fatigue (89.4%), cough (76.8%), chest pain (47.7%), loss of smell (48.3%), loss of taste (39.1%), and headache (21.2%). The abnormal biochemical features were leukopenia (2.1%), anemia, thrombocytopenia (18%), hypoxia with low PaO2 (34.6%), hypocapnia with reduced PaCO2 (29.6%), and blood acidosis (18.4%). Common complications during hospitalization were septic shock (15.2%), cardiogenic shock (5.3%), and embolism (2.6%). The predictive factors of death were being female, age > 65 years, cardiovascular comorbidity, thrombocytopenia (

Conamomum vietnamense, a new species of Zingiberaceae family, has been discovered and described from Tay Nguyen (Central Highlands, Vietnam) in 2022. The present study described the preparation of essential oil from leaves and rhizomes of the plant by hydrodistillation process. Then, the chemical composition of these essential oils was analyzed by Gas Chromatography-Mass Spectrometry (GC-MS), which indicated that limonene (18.74 and 26.20%) and eucalyptol (40.47 and 49.49%) were the main components, respectively. The essential oils also showed moderate antimicrobial activities against Gram-positive bacterial strains (Enterococcus faecalis ATCC 299212, Staphylococcus aureus ATCC 25923, Bacillus cereus ATCC 14579), Gram-negative bacterial strains (Escherichia coli ATCC 25922, Pseudomonas aeruginosa ATCC 27853, Salmonella enterica ATCC 13076), and a pathogenic yeast (Candida albicans ATCC 10231) in the MIC range of 32–256 μg/mL, which was comparable to those of positive controls, streptomycin and cycloheximide. For the first time, the chemical composition and antimicrobial activity of the essential oil of C. vietnamense were studied.

Traffic routing is an essential service in most intelligent transportation systems (ITS), especially in urban traffic networks of large cities. However, this is a challenge issue as these traffic networks are complicated and traffic conditions change dynamically. Existing routing solutions from technology giants such as Google Maps, Waze are still insufficient in terms of accuracy (effectiveness) and efficiency (performance), especially applying in developing countries where traffic infrastructures have not yet matured enough, leading to degradation of transportation performance, safety while increasing traffic jam, economic loss, air and noise pollution. This work proposes an effective routing approach in large cities to avoid traffic congestion, enhancing transportation capacity and safety. Concretely, this work designed an appropriate urban traffic data organization scheme and devised a novel approach to searching solutions in an optimal routing algorithm, thus improve the routing performance significantly. Results from real-field experiments confirm the effectiveness and the efficiency of the proposed approaches which are prominent compared to the state-of-the-art solutions. The proposed approaches have been applied in a real urban traffic network of Ho Chi Minh City, the biggest city in Vietnam whose traffic is complicated. The experimental results and the practical deployed system reveal that the proposed mechanisms are ready to be applied in real-world applications.

Locked thumb metacarpophalangeal (MCP) joint has been known as a relatively rare condition. We present a case of a 25-year-old male presented with a traumatic locked thumb MCP joint of the right hand. Clinical presentation showed the joint was in a hyperextention position and resistant to active flexion and extension. The radial sesamoid was distally displaced on X-ray film and entrapped into the joint on CT scan. The mechanism of the locked MCP joint was thought as a “pull-hook lock” between the pointed proximal edge of the sesamoid and the deformed volar groove on the cartilaginous surface of the metacarpal head. Surgical treatment included partially releasing the insertion of the abductor pollicis brevis (APB) and flexor pollicis brevis (FPB) muscles to move the entrapped radial sesamoid, and resurfacing the deformed metacarpal head.

Abstract Background MiRNAs are small non-coding RNAs that post-transcriptionally regulate gene expression in organisms ranging from viruses to mammals. There is great relevance in understanding how miRNAs regulate genes involved in the growth, development, and maturation of the many parasitic worms (helminths) that together afflict more than 2 billion people. Results Here, we describe the miRNAs expressed by each of the predominant intra-mammalian development stages of Fasciola hepatica, a foodborne flatworm that infects a wide range of mammals worldwide, most importantly humans and their livestock. A total of 124 miRNAs were profiled, 72 of which had been previously reported and three of which were conserved miRNA sequences described here for the first time. The remaining 49 miRNAs were novel sequences of which, 31 were conserved with F. gigantica and the remaining 18 were specific to F. hepatica. The newly excysted juveniles express 22 unique miRNAs while the immature liver and mature bile duct stages each express 16 unique miRNAs. We discovered several sequence variant miRNAs (IsomiRs) as well as miRNA clusters that exhibit strict temporal expression paralleling parasite development. Target analysis revealed the close association between miRNA expression and stage-specific changes in the transcriptome; for example, we identified specific miRNAs that target parasite proteases known to be essential for intestinal wall penetration (cathepsin L3). Moreover, we demonstrate that miRNAs fine-tune the expression of genes involved in the metabolic pathways that allow the parasites to move from an aerobic external environment to the anerobic environment of the host. Conclusions These results provide novel insight into the regulation of helminth parasite development and identifies new genes and miRNAs for therapeutic development to limit the virulence and pathogenesis caused by F. hepatica.

Background: Theta burst stimulation (TBS) is an efficient noninvasive neuromodulation paradigm that has been widely adopted, clinically. However, the efficacy of TBS treatment remains similarly modest as conventional 10 Hz repetitive transcranial magnetic stimulation (rTMS). Objective/hypothesis: To develop a new TBS paradigm that enhances the effects of TMS administration while maintaining high time-efficiency. Methods: We describe here a new TMS paradigm, named High-Density Theta Burst Stimulation (hdTBS). This paradigm delivers up to 6 pulses per burst, as opposed to only 3 in conventional TBS, while maintaining the inter-burst interval of 200 ms (or 5 Hz) – a critical parameter in inducing long-term potentiation. This paradigm was implemented on a TMS stimulator developed in-house; its physiological effects were assessed in the motor cortex of awake rats using a rodent specific focal TMS coil. Microwire electrodes were implanted into each rat's limb muscles to longitudinally record motor-evoked potential (MEP). Four different TBS paradigms (3, 4, 5 or 6 pulses per burst, 200 s per session) were tested; MEP signals were recorded immediately before (baseline) and up to 35 min post each TBS session. Results: We developed a stimulator based on a printed-circuit board strategy. The stimulator was able to deliver stable outputs of up to 6 pulses per burst. Animal experiments (n = 15) revealed significantly different aftereffects induced by the four TBS paradigms (Friedman test, p = 0.018). Post hoc analysis further revealed that, in comparison to conventional 3-pulse TBS, 5- and 6-pulse TBS enhanced the aftereffects of MEP signals by 56% and 92%, respectively, while maintaining identical time efficiency. Conclusion(s): A new stimulation paradigm is proposed, implemented and tested in the motor cortex of awake rats using a focal TMS coil developed in the lab. We observed enhanced aftereffects as assessed by MEP, with no obvious adverse effects, suggesting the translational potentials of this paradigm.

The issue of how to economically harvest and recover algal biomass has always been of concern to green wastewater treatment systems utilizing microalgal technology. This study explores the efficiency of biomass recovery from Chlorella Vulgaris using an electrocoagulation-flocculation method, aiming to optimize the environmentally friendly harvesting of algal biomass. Utilizing a response surface methodology (RSM) with a central composite design (CCD), the research investigates the impact of pH, current intensity, reaction time, and salinity on recovery efficiency. Optimal conditions were identified as a pH of 6.5, current intensity of 1.5A, reaction time of 20 minutes, and a salinity of 15ppt, achieving an optimal recovery efficiency of 84.36%. The findings underscore the significant influence of pH, where deviations from the optimum value substantially reduced efficiency. This study confirms that electrocoagulation is a viable and effective method for enhancing Chlorella Vulgaris biomass recovery, offering a sustainable alternative to traditional chemical flocculation methods by reducing dependency on chemical coagulants and minimizing production costs. The results provide a solid scientific basis for future enhancements in the sustainable industrial application of algae, promoting environmental protection and economic viability.

Background and Aims: To describe the clinical characteristics and evaluate the results of office hysteroscopy in patients with recurrent implantation failure at Tam Anh General Hospital. Methods: A descriptive study on 151 patients at Tam Anh General Hospital who had failed embryo transfer twice or more consecutively was carried out from January 2021 to March 2023. Results: 151 patients were included in the study, 41,1% of which had unexplained infertility. The mean age and duration of infertility were 34,5 ± 5,2 years and 5,7 ± 3,4 years, respectively. 55,0% of patients exhibited uterine cavity abnormalities during office hysteroscopy, with the most common is endometritis accounting for 29.8%. The median time to perform the procedure was 3 (2) minutes, the mean VAS pain score was 3,2 ± 1,1 points. No complications related to office hysteroscopy were noted. Conclusions: A office hysteroscopy was able to detect intrauterine pathologies which were missed by other investigative modalities. In individuals who have experienced repeated implantation failure, office hysteroscopy is a safe and practical approach that should be recognized as a crucial tool for intrauterine evaluation.

Background: Papillary thyroid carcinoma (PTC) is the most commonly diagnosed differentiated thyroid carcinoma. There is controversy about performing upfront lobectomy vs thyroidectomy for smaller well differentiated thyroid carcinoma. Methods: A retrospective study from 2015 to 2020 was conducted consisting of consecutive patients with a preoperative malignant (Bethesda VI) cytology on fine needle aspirate (FNA) consistent with PTC. Specific ultrasonographic features such as taller than wide, hypoechogenicity, irregular margins, internal vascularity and microcalcifications were recorded. Criteria for exclusion was the presence of positive lymph nodes, extrathyroidal extension, familial thyroid carcinoma and bilateral disease detected preoperatively. Outcome was defined as a lobectomy being adequate treatment or a completion thyroidectomy recommended based on current 2015 ATA guidelines. Results: Preoperative malignant cytological nodules (Bethesda VI) with irregular margins on sonography were significantly (p = 0.025) at increased risk (OR = 2.48) of requiring a completion thyroidectomy. There was also no statistically significant difference between groups when stratified by size with 50% of tumours between 1 and 2 cm requiring a completion thyroidectomy. Conclusions: The presence of irregular margins on ultrasound predicts an increased risk of requiring a completion thyroidectomy. Specific consideration of this sonographic finding should be made when counselling patients who have cytologically confirmed papillary thyroid carcinoma regarding the best choice of thyroid operation.

The process of damage identification in Structural Health Monitoring (SHM) gives us a lot of practical information about the current status of the inspected structure. The target of the process is to detect damage status by processing data collected from sensors, followed by identifying the difference between the damaged and the undamaged states. Different machine learning techniques have been applied to attempt to extract features or knowledge from vibration data, however, they need to learn prior knowledge about the factors affecting the structure. In this paper, a novel method of structural damage detection is proposed using a hybrid convolution neural network and recurrent neural network. A convolution neural network is used to extract deep features while recurrent neural network is trained to learn the long-term historical dependency in time series data. This proposed method which combines two types of features -spatial and temporal- helps to increase discrimination ability when being compared with the one that contains deep features only. Finally, the neural network is applied to categorize the time series into two states - undamaged and damaged. The accuracy of the proposed method was tested on a benchmark dataset of Z24-bridge (Switzerland). The result shows that the hybrid method provides a high level of accuracy in damage identification of the tested structure

Trust in the government is an important dimension of happiness according to the World Happiness Report (Skelton, 2022). Recently, social media platforms have been exploited to erode this trust by spreading hate-filled, violent, anti-government sentiment. This trend was amplified during the COVID-19 pandemic to protest the government-imposed, unpopular public health and safety measures to curb the spread of the coronavirus. Detection and demotion of anti-government rhetoric, especially during turbulent times such as the COVID-19 pandemic, can prevent the escalation of such sentiment into social unrest, physical violence, and turmoil. This article presents a classification framework to identify anti-government sentiment on Twitter during politically motivated, anti-lockdown protests that occurred in the capital of Michigan. From the tweets collected and labeled during the pair of protests, a rich set of features was computed from both structured and unstructured data. Employing feature engineering grounded in statistical, importance, and principal components analysis, subsets of these features are selected to train popular machine learning classifiers. The classifiers can efficiently detect tweets that promote an anti-government view with around 85% accuracy. With an F1-score of 0.82, the classifiers balance precision against recall, optimizing between false positives and false negatives. The classifiers thus demonstrate the feasibility of separating anti-government content from social media dialogue in a chaotic, emotionally charged real-life situation, and open opportunities for future research.

This study presents a phytochemical survey of two common intertidal red algal species, Bostrychia scorpioides and Catenella caespitosa, regarding their MAA (mycosporine-like amino acid) composition, which are known as biogenic sunscreen compounds. Six novel MAAs from Bostrychia scorpioides named bostrychines and two novel MAAs from Catenella caespitosa named catenellines were isolated using a protocol which included silica gel column chromatography, flash chromatography on reversed phase material and semipreparative HPLC (High-Performance Liquid Chromatography). The structure of the novel MAAs was elucidated using NMR (Nuclear Magnetic Resonance) and HR-MS (High-Resolution Mass Spectrometry), and their absolute configuration was confirmed by ECD (Electronic Circular Dichroism). All isolated MAAs possess a cyclohexenimine scaffold, and the metabolites from B. scorpioides are related to the known MAAs bostrychines A-F, which contain glutamine, glutamic acid and/or threonine in their side chains. The new MAAs from C. caespitosa contain taurine, an amino sulfonic acid that is also present in another MAA isolated from this species, namely, catenelline. Previous and new data confirm that intertidal red algae are chemically rich in MAAs, which explains their high tolerance against biologically harmful ultraviolet radiation.

This work examines the use of accelerometers to identify vibrational patterns that can effectively predict the state of a 3D printer, which could be useful for predictive maintenance. Prototypes using both a simple rectangular shape and a more complex Octopus shape were fabricated and evaluated. Fast Fourier Transform, Spectrogram, and machine learning models, such as Principal Component Analysis and Support Vector Machine, were employed for data analysis. The results indicate that vibrational signals can be used to predict the state of a 3D printer. However, the position of the accelerometers is crucial for vibration-based fault detection. Specifically, the sensor closest to the nozzle could predict the state of the 3D printer faster at a 71% greater sensitivity compared to sensors mounted on the frame and print bed. Therefore, the model presented in this study is appropriate for vibrational fault detection in 3D printers.

Abstract Traumatic brain injury (TBI) is a leading neurological cause of death and disability across the world. Early characterization of TBI severity could provide a window for therapeutic intervention and contribute to improved outcome. We hypothesized that granular electronic health record data available in the first 24 h following admission to the intensive care unit (ICU) can be used to differentiate outcomes at discharge. Working from two ICU datasets we focused on patients with a primary admission diagnosis of TBI whose length of stay in ICU was ≥ 24 h (N = 1689 and 127). Features derived from clinical, laboratory, medication, and physiological time series data in the first 24 h after ICU admission were used to train elastic-net regularized Generalized Linear Models for the prediction of mortality and neurological function at ICU discharge. Model discrimination, determined by area under the receiver operating characteristic curve (AUC) analysis, was 0.903 and 0.874 for mortality and neurological function, respectively. Model performance was successfully validated in an external dataset (AUC 0.958 and 0.878 for mortality and neurological function, respectively). These results demonstrate that computational analysis of data routinely collected in the first 24 h after admission accurately and reliably predict discharge outcomes in ICU stratum TBI patients.

ABSTRACT During bacterial endospore formation, the developing spore is internalized into the mother cell through a phagocytic-like process called engulfment, which involves synthesis and hydrolysis of peptidoglycan. Engulfment peptidoglycan hydrolysis requires the widely conserved and well-characterized DMP complex, composed of SpoIID, SpoIIM, and SpoIIP. In contrast, although peptidoglycan synthesis has been implicated in engulfment, the protein players involved are less well defined. The widely conserved SpoIIIAH-SpoIIQ interaction is also required for engulfment efficiency, functioning like a ratchet to promote membrane migration around the forespore. Here, we screened for additional factors required for engulfment using transposon sequencing in Bacillus subtilis mutants with mild engulfment defects. We discovered that YrvJ, a peptidoglycan hydrolase, and the MurA paralog MurAB, involved in peptidoglycan precursor synthesis, are required for efficient engulfment. Cytological analyses suggest that both factors are important for engulfment when the DMP complex is compromised and that MurAB is additionally required when the SpoIIIAH-SpoIIQ ratchet is abolished. Interestingly, despite the importance of MurAB for sporulation in B. subtilis, phylogenetic analyses of MurA paralogs indicate that there is no correlation between sporulation and the number of MurA paralogs and further reveal the existence of a third MurA paralog, MurAC, within the Firmicutes. Collectively, our studies identify two new factors that are required for efficient envelop remodeling during sporulation and highlight the importance of peptidoglycan precursor synthesis for efficient engulfment in B. subtilis and likely other endospore-forming bacteria. IMPORTANCE In bacteria, cell envelope remodeling is critical for cell growth and division. This is also the case during the development of bacteria into highly resistant endospores (spores), known as sporulation. During sporulation, the developing spore becomes internalized inside the mother cell through a phagocytic-like process called engulfment, which is essential to form the cell envelope of the spore. Engulfment involves both the synthesis and hydrolysis of peptidoglycan and the stabilization of migrating membranes around the developing spore. Importantly, although peptidoglycan synthesis has been implicated during engulfment, the specific genes that contribute to this molecular element of engulfment have remained unclear. Our study identifies two new factors that are required for efficient envelope remodeling during engulfment and emphasizes the importance of peptidoglycan precursor synthesis for efficient engulfment in the model organism Bacillus subtilis and likely other endospore-forming bacteria. Finally, our work highlights the power of synthetic screens to reveal additional genes that contribute to essential processes during sporulation.

Abstract Background After a decade of silence, an outbreak of the contagious and Asian endemic disease, goat pox re-emerged in North Vietnam affecting more than 1800 heads with a mortality rate of 6.5%. The inevitable impact of goat pox on hide quality, breeding, chevon and milk production has resulted in a significant economic losses to the developing goat industry of Vietnam. In the act of establishing an effective control of this devastating disease, tracing the source of re-emergence via a phylogenetic study was carried out to reveal their genetic relatedness. Either skin scab or papule from the six affected provinces were collected, cultured into Vero cells followed by restricted enzyme digestion of targeted P32 gene DNA encoding. The P32 gene was then cloned and transformed into E.coli competent cells for further sequencing. Results The isolated sequence is deposited into GenBank under Accession No. MN317561/VNUAGTP1. The phylogenetic tree revealed high similarity of nucleotide and amino acid sequences to references goat pox strains accounting for 99.6 and 99.3, respectively. The Vietnamese strain is clustered together with currently circulating goat pox virus in China, India and Pakistan which suggested the origin of South China. Conclusions This Vietnam isolate is clustered together with other Asian goat pox strains indicating the dissemination of a common goat pox virus within this continent.

This paper designs a 1 × 2 switchable dual-mode optical 90° hybrid device on the silicon-on-insulator (SOI) platform for the mode division multiplexing hybridized coherent transmission systems in optical communication. Our proposed hybrid uses various 2 × 2 multimode interference (MMI) couplers, symmetric Y-junctions, and thermo-optic phase shifters. The suggested hybrid device working principle is proved on theoretical analysis about the multimode interference and the transfer matrix relation. The optimization of geometrical parameters and metallic micro-heaters is executed through the numerical simulation method. The proposed hybrid coupler expresses lots of optical performance advantages, with an insertion loss lower than 2.5 dB, a common-mode rejection ratio (CMRR) better than -24 dB, and phase error smaller than 4° in the 50-nm wavelength bandwidth of the third telecom window for both two TE modes. Besides, the device is proven large geometrical tolerances in terms of width and height tolerances larger than ±50 nm and ±6 nm in the 1.5-dB variation limit of transmission, respectively. In addition, the proposed device attains relatively low power consumption under 75 mW and an ultrafast switching time below 10 μs. Such advantages of good performances make the device a promising potential for broadband and intra-chip MDM-hybridized coherent optical communication systems and photonic integrated circuits.

In the last decade, the synthesis of reversible logic circuits has become a trending topic because of its future necessity and importance. Many methods have been studied and proposed, for instance, transformation-based, search-based, cycle-based, ESOP-based and BDD-based methods. Each of them has its limitation related to time processing, ancilla and garbage line, quantum cost. This study develops an algorithm that could synthesize quantum circuits based on mixed-polarity Toffoli gate and a variant of binary decision diagram (BDD) called reduced-ordered-complemented edge-bdd (ROCBDD). It is an optimized method of BDD to reduce nodes in the representation of Boolean functions, which leads to adding more lines and quantum gates in the reversible circuit. First, the differences between synthesis using ROCBDD, the traditional BDD and others methods are introduced. Then, we define a new structure consisting of mixed-polarity Toffoli gates relied on nodes of ROCBDD. An efficient algorithm to match BDD representation to the reversible logic circuit is also mentioned. Finally, the experimental results show that our algorithm has better synthesizing costs than previous BDD-based methods.

Summary: Background: Primary hyperparathyroidism is a prevalent disease with proven benefits for appropriately selected patients who undergo parathyroidectomy. The ability to accurately predict expected single adenoma gland weight as the cause based on preoperative biochemical tests could improve cure rates in a minimally invasive approach. Objective: To assess the correlation between parathyroid weight and preoperative parathyroid hormone and calcium levels in patients with primary hyperparathyroidism with a solitary adenoma and determine if these could be used to predict expected parathyroid weight. Methods: Patients with primary hyperparathyroidism who underwent curative parathyroidectomy from 2013 to 2018 was retrospectively analysed. Results: There is a strong positive correlation r = 0.602 between preoperative PTH levels with respect to parathyroid weight (p

BackgroundApproximately 1. 07 million people in Vietnam are infected with hepatitis C virus (HCV). To address this epidemic, the South East Asian Research Collaborative in Hepatitis (SEARCH) launched a 600-patient cohort study and two clinical trials, both investigating shortened treatment strategies for chronic HCV infection with direct-acting antiviral drugs. We conducted ethnographic research with a subset of trial participants and found that the majority were aware of HCV infection and its implications and were motivated to seek treatment. However, people who inject drugs (PWID), and other groups at risk for HCV were under-represented, although injecting drug use is associated with high rates of HCV.Material and MethodsWe designed a community-based participatory research (CBPR) study to engage in dialogues surrounding HCV and other community-prioritized health issues with underserved groups at risk for HCV in Ho Chi Minh City. The project consists of three phases: situation analysis, CBPR implementation, and dissemination. In this paper, we describe the results of the first phase (i.e., the situation analysis) in which we conducted desk research and organized stakeholder mapping meetings with representatives from local non-government and community-based organizations where we used participatory research methods to identify and analyze key stakeholders working with underserved populations.ResultsTwenty six institutions or groups working with the key underserved populations were identified. Insights about the challenges and dynamics of underserved communities were also gathered. Two working groups made up of representatives from the NGO and CBO level were formed.DiscussionUsing the information provided by local key stakeholders to shape the project has helped us to build solid relationships, give the groups a sense of ownership from the early stages, and made the project more context specific. These steps are not only important preliminary steps for participatory studies but also for other research that takes place within the communities.

Purpose: The research aims to investigate the individual quality management (QM) factors impact on business performance in manufacturing firms. We then assess their influence and look at how industry 4.0 affects business performance directly. Furthermore, the study will demonstrate the moderate effect of industry 4.0 on the interaction between QM and business performance. Methodology/Approach: The authors used two methodologies. The first is qualitative methods, by interviewing experts to develop a realistically appropriate model. The second is quantitative methods, by carrying out the survey and getting 84 observations. This research used the multivariate data analysis technique PLS-SEM to evaluate three measurement models: formative measurement model, reflective measurement model, and structural model. Findings: The QM was operationalised as a multi–dimensional construct. According to the findings, these individual QM factors significantly affect the organisation’s performance, including customer satisfaction, employee satisfaction and quality performance. Besides, QM and Industry 4.0 have directly impact on performance. The study also shows that industry 4.0 has a positive moderating role in the relationship between QM and business performance. Research Limitation/Implication: The survey was only done in emerging countries and the sample size is limited. There are other QM and business performance measurement parameters that have not yet to be discussed. Originality/Value of paper: The study contributes to the QM literature by building a business quality model, in which the role of Industry 4.0 was explored.

The process of damage identification in Structural Health Monitoring (SHM) gives us a lot of practical information about the current status of the inspected structure. The target of the process is to detect damage status by processing data collected from sensors, followed by identifying the difference between the damaged and the undamaged states. Different machine learning techniques have been applied to attempt to extract features or knowledge from vibration data, however, they need to learn prior knowledge about the factors affecting the structure. In this paper, a novel method of structural damage detection is proposed using convolution neural network and recurrent neural network. A convolution neural network is used to extract deep features while recurrent neural network is trained to learn the long-term historical dependency in time series data. This method with combining two types of features increases discrimination ability when compares with it to deep features only. Finally, the neural network is applied to categorize the time series into two states - undamaged and damaged. The accuracy of the proposed method was tested on a benchmark dataset of Z24-bridge (Switzerland). The result shows that the hybrid method provides a high level of accuracy in damage identification of the tested structure.

Layered metamaterial beam structures are gaining attention in a variety of fields including vibration attenuation and energy harvesting. Exhaustive research on single-beam metamaterial vibration attenuation structures using local resonators exists in literature. Moreover, there are recent attempts at modelling double-layered beams with different kinds of constraints. The double-layered beam models in literature are limited to simple beams and not extended to metamaterials with local resonators. This article is focused on developing a design criterion and a modelling platform for layered metamaterial structures with multiple beams and local resonators for vibration isolation. The model is developed using Euler-Bernoulli beam equations, superposition of mode shapes and Galerkin methods. A prototype layered metamaterial structure is fabricated and characterized experimentally. The prototype consists of horizontal beams, local resonators forming unit cells, and vertical beams linkages. Each local resonator consists of cantilevers with tip masses. Results show good agreement between model and experiment. Two major bandgaps are observed at 190–410 Hz and 550–710 Hz. Results reveal that the low frequency bandgap can be further reduced through the design of the local resonators. Results also show that alternating the length of the local resonators causes a shift in the first frequency bandgap. An increase in the number of local resonators opens up extra frequency bandgaps at lower frequencies with the drawback of reducing the depth in vibration transmissibility. Moreover, the higher frequency bandgaps are mostly affected by the horizontal beams. An increase in the length of the horizontal beams, while the number and design of the local resonators are fixed, broadens the second frequency bandgap and shifts it to lower values.

Retrieving three-dimensional (3D) shape information from a single two-dimensional (2D) image has recently gained enormous attention in a variety of fields. In spite of recent advancements in algorithms and hardware developments, the easy-to-use characteristics and the accuracy of the 3D shape reconstruction are always of great interest. This paper presents a robust 3D shape reconstruction technique that integrates structured-light 3D imaging scheme with deep convolutional neural network (CNN) learning. The structured-light patterns facilitate the featuring process while the CNN modeling surpasses the complexity of the traditional 3D shape reconstructions. In the supervised learning pipeline, the input is either a single fringe-pattern or a single speckle-pattern image, and the output is its corresponding high-accuracy 3D shape label. Unlike the well-received autoencoder-based CNN model, a global guidance network path with multi-scale feature fusion is introduced into the CNN model to improve the accuracy of the 3D shape reconstruction. Experimental evaluations have been conducted to demonstrate the validity and robustness of the proposed technique, which provides a promising tool for ever-increasing scientific research and engineering applications.

The katanin family of microtubule-severing enzymes is critical for cytoskeletal rearrangements that affect key cellular processes like division, migration, signaling, and homeostasis. In humans, aberrant expression, or dysfunction of the katanins, is linked to developmental, proliferative, and neurodegenerative disorders. Here, we review current knowledge on the mammalian family of katanins, including an overview of evolutionary conservation, functional domain organization, and the mechanisms that regulate katanin activity. We assess the function of katanins in dividing and non-dividing cells and how their dysregulation promotes impaired ciliary signaling and defects in developmental programs (corticogenesis, gametogenesis, and neurodevelopment) and contributes to neurodegeneration and cancer. We conclude with perspectives on future katanin research that will advance our understanding of this exciting and dynamic class of disease-associated enzymes.

This paper develops a computational model for nonlinear bending analysis of functionally graded (FG) plates using a four-node quadrilateral element SQ4T within the context of the first order shear deformation theory (FSDT). In particular, the construction of the nonlinear geometric equations are based on Total Lagrangian approach in which the motion at the present state compared with the initial state is considered to be large. Small strain-large displacement theory of von Kármán is used in nonlinear formulations of the quadrilateral element SQ4T with twice interpolation strategy (TIS). The solution of the nonlinear equilibrium equations is obtained by the iterative method of Newton-Raphson with the appropriate convergence criteria. The present numerical results are compared with the other numerical results available in the literature in order to demonstrate the effectiveness of the developed element. These results also contribute a better knowledge and understanding of nonlinear bending behaviors of these structures.

Cenozoic basalt regions contain various natural resources that can be used for socio-economic development. Different quantitative and qualitative methods have been applied to understand the geological and geomorphological characteristics of basalt formations. Nowadays the integration of remote sensing and geographic information systems (GIS) has become a powerful method to distinguish geological formations. In this paper, authors combined satellite and fieldwork data to analyze the structure and morphology of highland geological formations in order to distinguish two main volcanic eruption episodes. Based on remote sensing analysis in this study, different spectral band ratios were generated to select the best one for basalt classification. Lastly, two spectral combinations (including band ratios 4/3, 6/2, 7/4 in Landsat 8 and 3/2, 5/1, 7/3 in Landsat 7) were chosen for the Maximum Likelihood classification. The final geological map based on the integration of Landsat 7 and 8 outcomes shows precisely the boundary of the basalt formations with the accuracy up to 93.7%. This outcome contributed significantly to the correction of geological maps. In further studies, authors suggest the integration of Landsat 7 and 8 data in geological studies and natural resource and environmental management at both local and regional scales.