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Aims: To investigate the efficacy and safety of Cerebrolysin and Cerebrolysin plus nootropics in the routine treatment of patients with acute ischemic stroke (AIS). Background: Acute ischemic stroke (AIS) is a leading cause of disability with unmet treatment needs lacking effective drug therapy. Multimodal drugs modulating stroke pathophysiology as Cerebrolysin constitute a good therapeutic option. Objective: In this study, we assessed the effects of Cerebrolysin and Cerebrolysin plus nootropics, in comparison with other nootropic drugs alone, on functional, neurological and cognitive recovery of patients with AIS in Vietnam. Methods.: This non-interventional, controlled, open-label, prospective and multicenter study included 398 AIS patients (234 males) treated with Cerebrolysin (n=190; 20 i.v. infusions of 10 ml), other nootropics (comparator group; n=86), or a combination of both (n=122). The study primary endpoint was the modified Ranking Scale (mRS) score on day 90. Secondary endpoints included study-period change in NIHSS score; percentage of well-recovered (mRS 0-2) patients, the proportion of good NIHSS response (≥6 points) cases, and MoCA scores at day 90; and safety indicators. Results: Compared with other nootropics, both Cerebrolysin and combined therapy induced significant improvements (p11) cases only. No drug-related adverse events were reported. Conclusion: Cerebrolysin alone or combined with other nootropics was effective and safe in routine AIS treatment, during both acute and recovery phases, which supports its use in daily clinical practice. Other: According to the results of this multicenter study, the importance of reducing differences in the treatment regimens of AIS in Vietnam should be further emphasized.

This paper presents a novel DSO support tool with visualisation capability for forecasting network congestion in distribution systems with a high level of renewables. To incorporate the uncertainties in the distribution systems, the probabilistic power flow framework has been utilised. An advanced photovoltaic production forecast based on sky images and a load forecast using an artificial neural network is used as the input to the tool. In addition, advanced load models and operating modes of photovoltaic inverters have been incorporated into the tool. The tool has been applied in case studies to perform congestion forecasts for two real distribution systems to validate its usability and scalability. The results from case studies demonstrated that the tool performs satisfactorily for both small and large networks and is able to visualise the cumulative probabilities of nodes voltage deviation and network components (branches and transformers) congestion for a variety of forecast horizons as desired by the DSO. The results have also shown that explicit inclusion of load‐voltage dependency models would improve the accuracy of the congestion forecast. For demonstrating the applicability of the tool, it has been integrated into an existing distribution management system via the IoT platform of a DMS vendor, Atos Worldgrid.

Gait analysis has been studied for a long time and applied to fields such as security, sport, and medicine. In particular, clinical gait analysis has played a significant role in improving the quality of healthcare. With the growth of machine learning technology in recent years, deep learning-based approaches to gait analysis have become popular. However, a large number of samples are required for training models when using deep learning, where the amount of available gait-related data may be limited for several reasons. This paper discusses certain techniques that can be applied to enable the use of deep learning for gait analysis in case of limited availability of data. Recent studies on the clinical applications of deep learning for gait analysis are also reviewed, and the compatibility between these applications and sensing modalities is determined. This article also provides a broad overview of publicly available gait databases for different sensing modalities.

Cone-beam computed tomography (CBCT), a modern technique with many applications, is becoming increasingly important and essential in many areas of economics and life. CBCT equipment has been commercialized in both hardware and software. However, having a device system suitable for a specific group of objects will be the best. This work used Monte Carlo simulation to optimize the design of a Cone-beam Computed Tomography (CBCT) for lightweight and small-size casts. Factors affecting image quality such as radiation-source, flat-panel detector, and geometrical distance were investigated. Simulation results indicate that an X-ray generator with a focal spot size of 4 × 4 $${\upmu \mathrm{m}}^{2}$$ , high voltage of 240 kV, and a radiation intensity of $${10}^{13}$$ photon/s with a flat panel CsI (Tl) detector of 0.3 mm thickness and a pixel size of 0.1–0.2 mm, is suitable for a CBCT to inspect small objects in industry. The Monte Carlo model was validated against experiments and to evaluate some characteristics of the existing system.

A patient initially diagnosed as having central serous chorioretinopathy (CSC) presented to a clinic with recurrence of pancreatic cancer manifesting as choroidal metastasis. He was initially diagnosed with CSC by a local ophthalmologist 8 weeks earlier and subsequently presented to our clinic for second opinion after further loss of vision. His medical history was significant for locally advanced pancreatic cancer that was resected by pancreaticoduodenectomy and was treated with adjuvant Folfirinox chemotherapy that was completed 12 months earlier. On examination, there was a large serous retinal detachment overlying a large pale ill-defined elevated choroidal lesion. A diagnosis of choroidal metastasis from recurrence of his pancreatic cancer was made. The diagnosis of choroidal metastasis of his pancreatic cancer represented recurrence of his pancreatic cancer that is associated with high mortality. Early recognition by clinical assessment may allow timely management with chemotherapy and radiation, and potentially prolong survival.

Numerical modeling for the safety-related equipment used in a nuclear power plant (i.e., cabinet facilities) plays an essential role in seismic risk assessment. A full finite element model is often time-consuming for nonlinear time history analysis due to its computational modeling complexity. Thus, this study aims to generate a simplified model that can capture the nonlinear behavior of the electrical cabinet. Accordingly, the distributed plasticity approach was utilized to examine the stiffness-degradation effect caused by the local buckling of the structure. The inherent dynamic characteristics of the numerical model were validated against the experimental test. The outcomes indicate that the proposed model can adequately represent the significant behavior of the structure, and it is preferred in practice to perform the nonlinear analysis of the cabinet. Further investigations were carried out to evaluate the seismic behavior of the cabinet under the influence of the constitutive law of material models. Three available models in OpenSees (i.e., linear, bilinear, and Giuffre-Menegotto-Pinto (GMP) model) were considered to provide an enhanced understating of the seismic responses of the cabinet. It was found that the material nonlinearity, which is the function of its smoothness, is the most effective parameter for the structural analysis of the cabinet. Also, it showed that implementing nonlinear models reduces the seismic response of the cabinet considerably in comparison with the linear model.

Servelle-Martorell syndrome, also known as angio-osteohypotrophic syndrome, is a congenital venous malformation that rarely involves the arterial system. There are many different, mainly conservative, treatment methods. We present the case of a 28-year-old woman with a history of left arm and back lesions, which appeared at birth and had enlarged over time. She had been diagnosed with a vascular malformation but had not yet received treatment. She presented to our hospital with numbness in her left arm and was diagnosed with Servelle-Martorell syndrome based on computed tomography (CT) angiography. This case emphasizes that CT angiography is a useful modality for evaluating the extent of the venous malformations in patients with Servelle-Martorell syndrome.

Predicting the behavior of concrete in a Concrete-Filled Steel Tubular (CFST) column is challenging due to the sensitivity to input parameters such as the size of the cross-section, the material modeling, and the boundary conditions. The present paper proposes a new modified finite element model to predict the behavior and strength of a CFST subjected to axial compression. The development is based on the concrete damaged plasticity model, with its stress-strain relationship revised from the available model. The predicted accuracy of the modified model is verified via a wide range of experimental tests. The proposed model has more accuracy than the available models in predicting the ultimate compression strength. The results show good agreement with the test data, allowing its use in modeling CFST columns.

The aim of this study was to evaluate the effects of cancer treatment with chemotherapy for the trace elements in nails of patients with colorectal cancer. The contents of trace elements in the nails of 102 patients with colorectal cancer (including treated and untreated) and 60 healthy subjects were analyzed using k0-standardization method of neutron activation analysis. All participants aged between 32 and 76. Result of this study showed that the contents of nine trace elements (As, Br, Co, Cr, Hg, Fe, Sc, Se, and Zn) in the nails were measured. Furthermore, this study showed that there was significant difference in the contents of the elements Fe, Se, and Zn in both colon and rectal cancer patient groups between untreated and treated subjects with chemotherapy. We conclude that the contents of Fe, Se, and Zn in the nail samples may be used to evaluate of the colorectal cancer risk, and they may be affected by chemotherapy.

This study sought to assess the cost-effectiveness of population-based tobacco control interventions, which included health promotion and education, smoke-free models, cessation programs, warning on package, marketing bans, and raising tax. Standardized activity-based costing ingredient approach was applied with the provider perspective to calculate interventions cost from 2013 to 2017. The potential health impacts of the aforementioned interventions were calculated through a Microsoft Excel-based modeling adapted from Higashi et al and Ngalesoni et al. All six population-based tobacco control interventions were highly cost-effective with ranges from 1405 VND (Vietnamese Dong) to 135 560 VND per DALY (disability-adjusted life year) averted. It was identified that raising cigarette taxes and applying health warnings on tobacco packages are the most favorable, cost-effective interventions. The results from this study provide a robust message that calls for increased attention and efforts in developing an appropriate policy agenda, which jointly integrates both political and community-based interventions, to maximize intervention impact on tobacco use.

The prevalence of epiretinal membrane (ERM) is 7-11.8%, with increasing age being the most important risk factor. Although most ERM is idiopathic, common secondary causes include cataract surgery, retinal vascular disease, uveitis and retinal tears. The myofibroblastic pre-retinal cells are thought to transdifferentiate from glial and retinal pigment epithelial cells that reach the retinal surface via defects in the internal limiting membrane (ILM) or from the vitreous cavity. Grading schemes have evolved from clinical signs to optical coherence tomography (OCT) based classification with associated features such as the cotton ball sign. Features predictive of better prognosis include absence of ectopic inner foveal layers, cystoid macular oedema, acquired vitelliform lesions and ellipsoid and cone outer segment termination defects. OCT-Angiography shows reduced size of the foveal avascular zone. Vitrectomy with membrane peeling remains the mainstay of treatment for symptomatic ERMs. Additional ILM peeling reduces recurrence but is associated with anatomical changes including inner retinal dimpling. This article is protected by copyright. All rights reserved.

3D Filtered Back Projection (FBP) is a three-dimensional reconstruction algorithm usually used in Cone Beam Computed Tomography (CBCT) system. FBP is one of the most popular algorithms due to its reconstruction is fast while quality of the result is acceptable. It can also handle a more considerable amount of data with same computer performance with other algorithms. However, the quality of a reconstructed image by the FBP algorithm strongly depends on spatial filters and denoising filters applied to projections. In this paper an evaluation of the reconstructed image quality of the CBCT system by using different denoising filters and spatial filters to find out the best filters for the CBCT system is performed. The result shows that, there is a significantly decrease of the noise of projection with the combination of Median and Gaussian filters. The reconstructed image has high resolution with Cosine filter and becomes more sharpen with Hanning filter.

In this paper, the method of digital twin integrated power-hardware-in-the-loop for the assessment of distributed renewable energy resources is presented. The digital twin of the electrical grid is created via synchronization between the real-time simulator and the supervision, control and data acquisition system. The digital twin can be integrated to larger system with the addition of simulated devices, with or without reconfiguration of topology. The setup is then completed with power-hardware-in-the-loop interface where the real distributed energy resource can be physically integrated. The impact can then be analysed locally and globally as it propagates through the desired deployment environment.

We study the problem of virtual machine (VM) placement and migration in a data center. In the current approaches, VMs are assigned to physical servers using on-demand provisioning. Such an approach is simple but it often results in a poor performance due to resource fragmentation. Additionally, sub-optimal VM placement usually generates unneeded VM migration and unnecessary cross network traffic. The efficiency of a datacenter therefore significantly depends on how VMs are provisioned and where they are placed. A good placement scheme will not only improve the quality of service but also reduce the operation cost of the data center. In this paper, we study the problem of optimal VM placement and migration to minimize resource usage and power consumption in a data center. We formulate the optimization problem as a joint multiple objective function and solve it by leveraging the framework of convex optimization. Due to the intractable nature of the combinatorial optimization, we then propose Multi-level Join VM Placement and Migration (MJPM) algorithms based on the relaxed convex optimization framework to approximate the optimal solution. The theoretical analysis demonstrates the effectiveness of our proposed algorithms that substantially increases data center efficiency. In addition, our extensive simulation results on different practical topologies show significant performance improvement over the existing approaches.

Objectives This study aims to analyze lesions on computed tomography (CT) images and digital subtraction angiography (DSA) of ruptured cerebral arteriovenous malformation (AVM). Methods Cross-sectional description of 82 patients' cerebral bleeding due to rupture of AVM, determined by multislice computed tomography or cerebral DSA at Bach Mai Hospital. Results Patients with ruptured AVM are commonly under 40 years old (62.2%), average age: 35.1 ± 11.2. On CT or DSA images, it is common to see AVM rupture causing cerebral bleeding in the supratentorial region (91.5%), with 83% cerebral lobe bleeding, mainly small and moderate hematoma volume ( p p Conclusions On MSCT and DSA images, ruptured AVMs often cause lobar hemorrhage in young people. AVMs are usually small to moderate in size. The feeding arteries are mainly derived from middle and posterior cerebral arteries, and drained mainly by superficial veins. The Spetzler–Martin classification and the supplementary grading scales are used for ranking the severity of the lesion, as well as for choosing AVM treatment options and assessing the prognosis.

This paper provides the practical application of a linear shrinkage framework on Vietnam stock market. The cumulative data points observed in this analysis are 468 weeks from January 2011 to December 2019. All the companies listed on Ho Chi Minh City Stock Exchange (HOSE), except the companies under two years period from Initial Public Offering (IPO), are considered. The cumulative number of stocks picked is therefore 350 companies. The VNINDEX, which is the Vietnam Stock Index, is used as a reference index for shrinking to a single-index model. The empirical results show that the shrinkage of covariance matrix for portfolio optimization gives the promising results for the investors on Vietnam stock market. The shrinkage method helps the investors to produce the optimal portfolio in the sense of having higher profit with lower levels of risk compared to the portfolio of the traditional SCM method. Moreover, the portfolio turnover of shrinkage method is always kept at low magnitudes, and this makes the shrinkage portfolios save much transaction costs and reduce the liquidity risks in the trading process. In addition, the ability of shrinkage method in making profit is once again confirmed by the Alpha coefficient that achieves a high positive value.

Background “Heat-not-burn” tobacco products are designed to heat processed tobacco instead of combusting it, thus significantly reducing the formation of harmful and potentially harmful constituents (HPHCs) found in cigarette smoke, and ultimately reducing the risk of smoking-related diseases. The Carbon-Heated Tobacco Product (CHTP), a heat-not-burn tobacco product similar in appearance and use ritual to cigarettes, has been developed for smokers who would otherwise continue smoking as an alternative to cigarettes. To evaluate reduced risk of harm potential of CHTP, it is critical to quantify exposure to HPHCs and consequent biological pathway disturbances involved in disease onset in smokers who switch to CHTP. Methods In this 2-arm, parallel-group study, adult healthy smokers, not willing to quit, were randomized to switch to CHTP 1.2 (n = 80) or to continue using cigarettes (n = 40) for 5 days in confinement followed by 85 days in an ambulatory setting. Endpoints included biomarkers of exposure (BoExp) to HPHCs, and to nicotine, urinary excretion of mutagenic constituents (Ames assay), CYP1A2 activity, biomarkers of effect, and safety. Results In switchers to CHTP, BoExp were 40%-95% lower compared to smokers after 5 days of product use, with sustained reductions (36%-93%) observed on Day 90. Urine mutagenicity and CYP1A2 activity were also lower in the CHTP group. Exposure to nicotine was higher in the CHTP group at Day 5, but was similar between the two groups at Day 90. Favorable changes in some biomarkers of effect were observed in the CHTP group showing reductions in white blood cell count, soluble intracellular adhesion molecule-1, and 11-dehydro-thromboxane B2, respectively, indicative of reduced inflammation, endothelial dysfunction, and platelet activation. Conclusions Switching from cigarettes to CHTP resulted in significantly reduced exposure to HPHCs and was associated with observed improvements in some biomarkers of effect representative of pathomechanistic pathways underlying the development of smoking-related diseases.

The “Globalization 4.0” has challenged Vietnamese Higher education to meet the new requirements of generating qualified labor force: self-direct their learning and make their own decision, to adjust and develop their perspectives and their actions consequently. The current teaching and learning approaches mostly based on behaviorism are not enough to allow Higher education to achieve the new objective. Meanwhile, though constructivist learning has been implemented for years, its use was limited to activate student learning and equipped them with professional skills and competencies instead of focusing on developing their perspectives and worldview. To enhance the success of higher education in terms of achieving new educational objective, this paper will introduce transformative learning as a perspective that would be contribute to radical and comprehensive change in this educational level.

The fifth generation of cellular technologies, 5G, promises to deliver a wide variety of services. However, the first release of 5G (3GPP Rel-15) did not consider multicast capabilities in the 5G system including both access and core networks. This article fills the gap by describing solutions for 5G mobile core network architecture incorporating multicast and broadcast capabilities. Two innovative solutions to provision multicast and broadcast, based on 3GPP Rel-15 arechitecture, have been proposed and analyzed. One is 5G architecture friendly with low imprint over Rel-15 while the other is based on the evolution of LTE Broadcast architecture. The architectural solutions feature two new main transmission modes: transparent multicast transport and point-to-multipoint services. The former enables the 5G network as a network pipe supporting multicast traffic providing a cost-effective delivery mode for multimedia while the latter enables multicast and broadcast as a service. The paper also presents a convergent fixed wireline and mobile network architecture based on the proposed mobile network architecture altenatives. The proposed architecture alternatives are suitable for different verticals and applications such as Digital Terrestrial Television, Public Warning, Internet of Things, V2X (vehicle to everything) and mission critical communications (MCC).

Structural modification in the electrical cabinet is investigated by a proposed procedure that comprises of an experimental, analytical and numerical solution. This research emphasizes the linear dynamic analysis of the cabinet that is studied under the seismic excitation to demonstrate the real behavior of the cabinets in NPP. To this end, an actual electric cabinet is experimentally tested using an impact hammer test which reveals the fundamental parameters of the cabinet. The Frequency-domain decomposition (FDD) method is used to extract the dynamic properties of the cabinet from the experiment which is then used for numerical modeling. To validate the dynamic properties of the cabinet an analytical solution is suggested. The calibrated model is analyzed under the floor response obtained from the Connecticut nuclear power plant structure excited by Tabas 1978 (Mw 7.4) earthquake. Eventually, the grouping effect of the cabinets is proposed which represents the influence on the dynamic modification. This grouping of the cabinets is described more sophisticatedly by the theoretical understating, which results in a significant change in the seismic response. Considering the grouping effects will be helpful in the assessment of the real seismic behavior, design, and performance of cabinets.

In this paper an action recognition method that can adaptively handle the problems of variations in camera viewpoint is introduced. Our contribution is three-fold. First, a space-sampling algorithm based on affine transform in multiple scales is proposed to yield a series of different viewpoints from a single one. A histogram of dense optical flow is then extracted over each fixed-size patch for a given generated viewpoint as a local feature descriptor. Second, a dimension selection procedure is also proposed to retain only the dimensions that have distinctive information and discard the unnecessary ones in the feature vector space. Third, to adapt to a situation in which video data in multiple viewpoints are used for training; an extended method with a voting algorithm is also introduced to increase the recognition accuracy. By conducting experiments using both simulated and realistic datasets (http://www.aislab.org/index.php/en/mvar-datasets), the proposed method is validated. The method is found to be accurate and capable of maintaining its accuracy under a wide range of viewpoint changes. In addition, the method is less sensitive to variations in subject scale, subject position, action speed, partial occlusion, and background. The method is also validated by comparing with state-of-the-art view-invariant action recognition methods using well-known i3DPost and MuHAVi public datasets.

Locally administered steroids have a long history in ophthalmology for the treatment of inflammatory conditions. Anterior segment conditions tend to be treated with topical steroids whilst posterior segment conditions generally require periocular, intravitreal or systemic administration for penetration. Over recent decades, the clinical applications of periocular steroid delivery have expanded to a wide range of conditions including macular oedema from retino‐vascular conditions. Formulations have been developed with the aim to provide practical, targeted, longer‐term and more efficacious therapy whilst minimizing side effects. Herein, we provide a comprehensive overview of the types of periocular steroid delivery, their clinical applications in ophthalmology and their side effects.

This study provides an insight of the nonlinear behavior of the Offshore Wind Turbine (OWT) structure using the distributed plasticity approach. The fiber section beam-column element is applied to construct the finite element model. The accuracy of the proposed model is verified using linear analysis via the comparison of the dynamic characteristics. For collapse risk assessment of OWT, the nonlinear effects considering the earthquake Incident Angle (IA) have been evaluated first. Then, the Incremental Dynamic Analysis (IDA) has been executed using a set of 20 near-fault records. Lastly, fragility curves are developed to evaluate the vulnerability of structures for different limit states. Attained results justify the accuracy of the proposed approach for the structural response against the ground motions and other environmental loads. It indicates that effects of static wind and wave loads along with the earthquake loads should be considered during the risk assessment of the OWT structure.

Background and Purpose: Tandem occlusive lesion, a major challenge for thrombectomy in acute anterior circulation strokes, is poorly represented in randomized trials. This study demonstrates the findings of thrombectomy in tandem occlusion and comparative analysis of two treatment groups (extracranial versus intracranial first subgroup). Patients and Methods: We enrolled and divided 17 patients with acute tandem ischemic stroke who received endovascular treatment into two groups. Group 1 with completed (100%) internal carotid artery (ICA) occlusion was treated by an extracranial stent, whereas Group 2 with severe (70%–99%) ICA occlusion was prioritized with intracranial thrombectomy. Data of clinical parameters, imaging and angiographic results, periprocedural complications, and results after 3 months were collected and analyzed. Results: The mean age of patients was 70.2 ± 8.8 years, and males accounted for 94.1%. The National Institutes of Health Stroke Scale and Alberta Stroke Program Early CT Score (ASPECTS) baseline were 16.6 ± 4.2 and 7.6 ± 1.1, respectively, with occlusive side was 52.9% on the right. Group 1 including ten cases (58.8%) was treated extracranial lesion with carotid stent before intracranial thrombectomy, and Group 2 with seven cases (41.2%) was prioritized intracranial thrombectomy. In total 17 procedures, there were ten stent retrievers (58.8%), four aspirations (23.5%), and three Solumbra (17.7%). No re-occlusion of carotid stent postoperation was recorded. The good revascularization (thrombolysis in cerebral infarction 2b-3) was archived in 82.4% of patients, while symptomatic hemorrhage was seen in 2 cases (11.8%). Three months after treatment, patients with favorable clinical outcome (Modified Rankin Scale ≤2) accounted for 47.1%. Conclusion: Our study determined a promising outcome with reasonable good recanalization and clinical recovery for endovascular intervention in tandem ischemic. In the subgroup of treatment, “extracranial stent first” had more complex disease with completed ICA occlusion which required longer procedure time may lead to worse outcome.

This paper proposes a simplified approach for assessing and predicting the seismic risks for electrical cabinets in nuclear power plants (NPPs). The method is a combination of fragility analysis an...

This paper presents a hybrid technique of combining the BERT embedding method and the graph convolutional neural network. This combination is then employed to solve the text classification problem. Initially, we apply the BERT embedding method to the whole corpus in order to transform all the texts into numerical vectors. Then, the graph convolutional neural network will be applied to these numerical vectors to classify these texts into their appropriate classes. Especially, in our approach, we need only a few labeled texts for the model training. For the illustration, in this paper, we use the BBC news and the IMDB movie reviews datasets to perform our experiments, showing that the performance of the graph convolutional neural network model is better than the performances of the combination of the BERT embedding method with other classical machine learning models.

Face recognition is a very significant branch of application in the pattern recognition area. It has multiple applications in the military and finance, to name a few. In reality, sometimes we have to deal with sparse representation of the facial data. In this paper, to deal with sparsity, two advanced versions of Sparse PCA are developed, including Proximal Gradient Sparse PCA (PG Sparse PCA) and Fast Iterative Shrinkage-Thresholding Algorithm Sparse PCA (FISTA Sparse PCA). Then they will be respectively applied to solve the face recognition problem by considering their combination with nearest-neighbor method and with the kernel ridge regression method. Experimental results illustrate that the accuracies of PG Sparse PCA and FISTA Sparse PCA are equivalent in the combination with nearest-neighbor, while PG Sparse PCA performs better than FISTA Sparse PCA in the case of using kernel ridge regression. However, we recognize that the computing process of FISTA Sparse PCA, on average, is always faster than the PG sparse PCA version due to the use of a fast proximal gradient version.

Background The World Health Organization recommends breastfeeding for at least two years (24 months or more) after birth. In Vietnam, 22% of women continue breastfeeding for at least two years. The aim of this study was to determine the sociodemographic and psychosocial characteristics of mother-baby dyads associated with breastfeeding for 24 months or more in a rural setting in Vietnam. Methods A secondary analysis was conducted on existing data obtained from a prospective study in Ha Nam, Vietnam. Women were recruited when they were pregnant and were followed up until 36 months after giving birth. The data were collected between 2009 and 2011. The associations between sociodemographic and psychosocial characteristics and continued breastfeeding for 24 months or more were examined using a multivariable logistic regression model. Results Overall, 363 women provided complete data which were included in the analyses. Among those, 20.9% breastfed for 24 months or more. Women who were 31 years old or older were more likely to breastfeed for 24 months or more than women who were 20 years old or younger (adjusted odds ratio, AOR, 9.54 [95% CI 2.25, 40.47]). Women who gave birth to girls were less likely to breastfeed for 24 or more months than women who had boys (AOR 0.44; 95% CI 0.25, 0.80). Conclusions This study provides evidence that may be useful for policy-makers to help improve breastfeeding practices for all children in Vietnam by targeting policy towards younger women and women with girls to promote continued breastfeeding for at least 24 months.

Crab species are economically important crustaceans with high demand in markets. Development of the culture system resulted in an increase in disease outbreaks. Disquisition and application of methods that can prevent and control diseases during the crab culture period are crucially essential. The application of prebiotics and probiotics to replace the overuse of antibiotics has practical significance in sustainability of the crab farming. Previously, both prebiotics and probiotics have shown to confer benefits in growth promotion, immunostimulation, and disease control in crab culture. These components can directly stimulate humoral immunity by increasing activities of lysozyme, phagocytosis, respiratory burst, and phenoloxidase. They also activate the cell-mediated immunity by binding to pattern recognition receptors expressed on immune cells and modulating cell signal transduction pathways. Furthermore, the mechanism of action and effectiveness in use of prebiotics and probiotics are summarized in this study, indicating the potential application of prebiotics and probiotics as feed additives in crab farming.

More and more software projects are being consolidated into ecosystems to increase their discovery, usability, and usefulness. Some of the most popular ecosystems exist in npmjs, Python Package Indexing, and Apache Maven Repository. It is difficult for developers to relate these projects and use them to their full potential because of their number, the spread and depth of their features, and their intrinsic and accidental complexities. We present a technique—SECO Storms Maker—to capture and present the essential information from projects in an ecosystem to help developers join, use, and contribute to the ecosystem. We generate word-clouds based on the projects’ documentation via tokenization and distribution frequency. We identify relations among projects using grammar patterns scanning after part-of-speech tagging. We put these word-clouds into a graph to ease navigation and exploration. We evaluate our technique by manually building a ground truth and comparing a randomly-selected project with SECO to show its benefits.

International audience; As more distributed renewable energy resources are connected and peer-to-peer energy trading frameworks are forming, the traditional business model of the distribution system operator and energy retailers faces emerging load and price risks. In this paper, two sustainable and complementary business models for the DSO the new energy ecosystem are proposed: DSO as the platform operator of the P2P energy exchange framework (including flexibility market and electric vehicle market) and DSO as the service supplier. The two complementary business models exploit the competitive advantages of the DSO (infrastructure, expertise) and provide the DSO with sustainable income stream to complement the revenue reduction under intermittent scenario of demand and DRES production.

International audience; Vast integration of new technologies to enable smart control of the power grid requires a reliable, efficient and resilient communication infrastructure. Today, many communication protocols (e.g., IEC 61850, OPC UA, Modbus, Internet, WiMAX, 4G, Wi-Fi, etc.) and technologies (e.g., PLC, GSM, Optic fiber, RF radio mesh, Cellular, etc.) are established for the smart grid applications. In case of the stability guarantee of smart grid, the Quality of Service (QoS) is a challenge to be considered. One of the major concerns in data delivery over the network is a low latency message transmission to ensure the time critical tasks, e.g., control and protection tasks. In this context, the main contribution of this paper is to propose a model methodology for the communication network delay in smart grid applications. To be compatible with the further goal of delay predictive compensation method, the present paper proposes a message transmission delay estimation method using Long Short-Term Memory (LSTM) neural network. To this end, Python is the chosen programming language, including its required libraries for the considered application. Delay values measured on a real-time HV/MV substation application are used as input data for validation purpose.

In light of the rapid development of distributed energy resources (DERs), especially photovoltaic (PV) systems, a new kind of grid has been formulated called MGs. These grids can be operated in both grid-connected and islanded modes. Their faults currents can be bi-directional and vary in a wide range between two operation modes. Moreover, for achieving a successful islanding of MGs, the fault ride through (FRT) and dynamic voltage support requirements of DERs and PVs should be fulfilled. Therefore, the protection concept for such new grids is totally different from the traditional distribution networks. The protection scheme of MGs should not only protect them against all kind of faults but also be coordinated with the interface protection of the connected DERs. Although a number of studies have undertaken the MG protection into consideration, there is still room for further development. In this paper, the authors were determined to propose a new protection scheme that can protect MGs against all faults and ensure their smooth transition between two operation modes. The proposed protection scheme was validated on a MG simulated in the software environment DIgSILENT|PowerFactory with real data provided by a local utility company.

STATCOM is a FACTS device installed in power systems for damping stability and increasing power transmission capacity. The algorithms used to control the STATCOM often use a PID controller. However, the PID controller is not robust for its stability control in highly nonlinear and complex systems. This paper presents the application firefly algorithm for improving the STATCOM controller to enhance the stability in a network connected to a wind energy system. The wind energy system is represented as a Doubly Fed Induction Generator (DFIG) driven by a Wind Turbine (WT). The power grid is simulated by an IEEE standard 14-bus system encompassing 14 nodes, 5 sources, and 11 loads. The studied wind power system is one of 5 sources that supply power to the network. Simulation results are performed with different disturbance conditions for testing the stability of the studied system. It can be concluded from simulation results that the proposed STATCOM controller helps improve the studied system's stability.

This study presents a simple, efficient, accurate method for nonlinear time-history earthquake analysis of spatial steel frames. The proposed new fiber plastic hinge method simulating only one element for each member captures the time-history dynamic behavior of steel frames as accurately as sophisticated plastic zone methods. Stability functions and the geometric stiffness matrix are employed for predicting the second-order effects that aim to minimize computational time and computer resources. Residual stresses are considered through two fiber plastic hinges by assigning initial stress values. A numerical integration procedure using Newmark integration combined with the Newton-Raphson balanced iteration algorithm is developed to find a solution to nonlinear dynamic equilibrium equations of the structural system. Shear deformation effects are also considered in the dynamic analysis. The proposed software, named Direct Advanced Analysis and Design (DAAD), is proved to be accurate and reliable as compared with the analysis results generated by expensive commercial Finite Element Analysis (FEA) software packages. The proposed DAAD program can be improved for performance-based direct design and analysis., Earthquakes; Fiber plastic hinges; Geometric nonlinearity; Inelasticity; Stability functions; Steel frames.

This paper introduces a method to encode the blur operators of an arbitrary dataset of sharp-blur image pairs into a blur kernel space. Assuming the encoded kernel space is close enough to in-the-wild blur operators, we propose an alternating optimization algorithm for blind image deblurring. It approximates an unseen blur operator by a kernel in the encoded space and searches for the corresponding sharp image. Unlike recent deep-learning-based methods, our system can handle unseen blur kernel, while avoiding using complicated handcrafted priors on the blur operator often found in classical methods. Due to the method’s design, the encoded kernel space is fully differentiable, thus can be easily adopted in deep neural network models. Moreover, our method can be used for blur synthesis by transferring existing blur operators from a given dataset into a new domain. Finally, we provide experimental results to confirm the effectiveness of the proposed method. The code is available at https://github.com/VinAIResearch/blur-kernelspace-exploring.

The objective of this work is to segment high-resolution images without overloading GPU memory usage or losing the fine details in the output segmentation map. The memory constraint means that we must either downsample the big image or divide the image into local patches for separate processing. However, the former approach would lose the fine details, while the latter can be ambiguous due to the lack of a global picture. In this work, we present MagNet, a multi-scale framework that resolves local ambiguity by looking at the image at multiple magnification levels. MagNet has multiple processing stages, where each stage corresponds to a magnification level, and the output of one stage is fed into the next stage for coarse-to-fine information propagation. Each stage analyzes the image at a higher resolution than the previous stage, recovering the previously lost details due to the lossy downsampling step, and the segmentation output is progressively refined through the processing stages. Experiments on three high-resolution datasets of urban views, aerial scenes, and medical images show that MagNet consistently outperforms the state-of-the-art methods by a significant margin., Accepted to CVPR'21

Makeup transfer is the task of applying on a source face the makeup style from a reference image. Real-life makeups are diverse and wild, which cover not only color-changing but also patterns, such as stickers, blushes, and jewelries. However, existing works overlooked the latter components and confined makeup transfer to color manipulation, focusing only on light makeup styles. In this work, we propose a holistic makeup transfer framework that can handle all the mentioned makeup components. It consists of an improved color transfer branch and a novel pattern transfer branch to learn all makeup properties, including color, shape, texture, and location. To train and evaluate such a system, we also introduce new makeup datasets for real and synthetic extreme makeup. Experimental results show that our framework achieves the state of the art performance on both light and extreme makeup styles. Code is available at https://github.com/VinAIResearch/CPM.

Patient experience is being widely considered in evaluating the quality of health care services. This is a cross-sectional study with 860 inpatients hospitalized in 4 clinical departments (General Internal Medicine; Cardiology; General Surgery; Surgery, and Orthopedic Trauma) of the Kien Giang General Hospital, from April to June 2020. Data was collected through a two-part questionnaire used to evaluate the inpatient experience during hospital treatment, using the Likert scale 5 points. The patient’s experience is classified into 3 aspects (environment—facilities, healthcare staff's care, treatment information). In total, 815 participants responded to the interview (94.8%). The rates of patients having a positive experience on the environment and facilities, the care of health workers, and treatment information are 31.7%, 85.9%, and 74.2%, respectively. The patient’s positive overall experience rate is 65.5%. Factors related to the patient's overall experience are the department of treatment, residential area, age, and employment status ( P

Open and reproducible research practices increase the reusability and impact of scientific research. The reproducibility of research results is influenced by many factors, most of which can be addressed by improved education and training. Here we describe how workshops developed by the Reproducibility for Everyone (R4E) initiative can be customized to provide researchers at all career stages and across most disciplines with education and training in reproducible research practices. The R4E initiative, which is led by volunteers, has reached more than 3000 researchers worldwide to date, and all workshop materials, including accompanying resources, are available under a CC-BY 4.0 license at https://www.repro4everyone.org/.

As a prerequisite for probiotics to establish and settle in the host intestine, adhesion is an essential criterion for probiotic screening. However, adhesion of probiotics in the intestines of one of the most important cultured fish species globally, the grass carp Ctenopharyngodon idellus, remains understudied. Here, we assessed the adhesion properties of a novel potentially probiotic bacterial strain isolated from the grass carp intestine: Bacillus paralicheniformis FA6 (henceforth FA6). To indirectly determine the adhesion, we compared the hydrophobicity of FA6 with eight other bacterial strains, and found that FA6 had comparatively high hydrophobicity. To assess adhesion directly, we supplemented the feed with FA6 for 2 weeks, and then used immunofluorescence to study the dynamics of FA6 for 220 days after discontinuing the supplementation. The FA6 fluorescence signal plateaued between days 40 and 60, and remained visible in the grass carp guts until day 80. In addition, FA6 was re-isolated on day 220 from intestinal contents. Our results suggest that FA6 has high adhesion ability, and can colonize the grass carp intestine for long periods of time. This research provides a promising foundation for the application of FA6 in aquaculture.

Precise diagnosis of the severity of dengue fever (DF) and its timely treatment reduce its mortality, but the sensitivity of clinical presentations is low for the classification of severity. Research on biomarkers is ongoing, and some studies in adults have shown the utility of C reactive protein (CRP) in the early diagnosis of dengue severity. However, the role of CRP in children with DF is still unknown. The aim of this study was to determine the value of CRP in distinguishing between DF and severe DF. A cross-sectional study was conducted from May 2016 to April 2017 at Children's Hospital 1 in Ho Chi Minh City, Vietnam. Serum CRP was tested on days 4 and 5 of the disease. Laboratory tests for DF were based on either a positivity for non-structured glycoprotein-1 antigen (NS1Ag) or enzyme-linked immunosorbent assay (ELISA) for immunoglobulin (Ig) M in acute phase serum. Among 270 patients, 29.6% had DF, 26.7% had DF with warning signs, and 43.7% had severe DF. The CRP level was measured in 123 patients on day 4 and 147 patients on day 5 of the illness. The median CRP level for DF was 2.4 mg/L, for DF with warning signs was 6.7 mg/L, and for severe DF was 7.3 mg/L. The CRP level was higher on day 4 than on day 5. The CRP level showed a statistically significant difference between the group with circulatory dysfunctions (p=0.02) or liver dysfunction (p= 0.04) and the other patients. The CRP cut-off point on day 4 that distinguished DF and severe DF was 5.8 mg/L and the area under the receiver operating characteristic curve (AUC) was 0.89, with a sensitivity of 82.9% and specificity of 80%. The CRP concentration in the early stage of illness may, therefore, help to distinguish between DF and severe DF.

In-situ monitoring systems for additive manufacturing processes drive the possibility for identification of defects during printing, rendering both time and cost savings as the process can be stopped if a failure is detected. However, the relationship between defects identified during printing to the actual mechanical performance of the printed part has yet to be established. In this paper, we report the design and development of an optical based image processing in-situ monitoring system implemented on the selective laser melting process. The system consisted of an optical camera, a mirror and a set of light emitting diode lights. 316 L stainless steel specimens were fabricated with varying energy densities. Features captured in the optical images during printing were identified and quantified by image processing techniques. Results from density tests based on Archimedes’ principle and mechanical properties from tensile tests have demonstrated a correlation with the processed optical images of the specimens, validating the potential of inferring mechanical performance of printed parts based on features identified through the optical based image processing in-situ monitoring system. Accepted version

Background Vietnam has limited evidence about the burden of common and severe mental disorders among adults to inform policy. The aim of this paper was to estimate the prevalence of common and severe mental disorders among adults and factors associated with them in Vietnam. Methods We conducted a cross-sectional household survey among people aged at least 16 years in Thanh Hoa and Ben Tre provinces which are nationally representative of the North and the South of Vietnam. The World Health Organization Self-Reporting Questionnaire 24 was used to screen for clinically significant symptoms of common and severe mental disorders at the individual level. Household characteristics were obtained in face-to-face interviews with the household heads. A multilevel mixed-effects logistic regression model was used to identify associated factors of the common and severe mental disorders. Results Among 611 households which included 1528 adults, the point prevalence of clinically significant symptoms was 14.4% for common mental disorders and 8.2% for severe mental disorders after weighting by age groups. Common mental disorders were associated with social factors including lived in a Northern rather than a Southern province, disadvantaged household economic status, in which a family member(s) misused alcohol, the family lacking links to social organisations able to provide instrumental support, and the individual not having completed primary school. Severe mental disorders had fewer associations with social factors compared to common mental disorders, but were associated with living in the Northern province, disadvantaged household economic status, family violence and being older than 50 years. Conclusions The prevalence of clinically significant symptoms of common and severe mental disorders among adults in Vietnam was higher than in high income countries and had a strong association with household characteristics. The result indicates that a community-based approach to reduce household risk factors and to provide instrumental support might be an effective strategy to alleviate the burden of mental health problems in Vietnam.

Pacific white shrimp is the major farmed shrimp species in the world. It is known to be very sensitive to the environmental and management changes such as intensification, which is one of the primary necessities to increase shrimp production, but represents a stressful condition, and needs to be managed properly to diminish its negative effects in aquaculture. In this study, juvenile Pacific white shrimp were fed diets supplementing with equal quantities of Astragalus membranaceus, Codonopsis pilosula and Glycyrrhiza uralensis extracts in different concentrations (0, 1.0, 2.0 and 3.0 g/kg) for 8 weeks. We subsequently estimated the effects of supplemented diets on growth performance, haematological parameters, and histological changes of hepatopancreas. Results revealed that the supplemented mixture had no benefits on growth and survival rate, but had favourable effects on haemocyte count, number of granulocytes, and hemocyanin concentration in the haemolymph (p > 0.05). We observed that R cells and B cells were increased in hepatopancreatic tubules of shrimps fed on supplemented diets. Besides, a diet containing 2.0 g/kg of extract mixture maintained the decreased levels of alanine aminotransferase (p 0.05). Among the used concentrations, 2.0 g/kg seemed to be the most suitable concentration.

An approach for collapse risk assessment is proposed to evaluate the vulnerability of electric cabinet in nuclear power plants. The lognormal approaches, namely maximum likelihood estimation and linear regression, are introduced to establish the fragility curves. These two fragility analyses are applied for the numerical models of cabinets considering various boundary conditions, which are expressed by representing restrained and anchored models at the base. The models have been built and verified using the system identification (SI) technique. The fundamental frequency of the electric cabinet is sensitive because of many attached devices. To bypass this complex problem, the average spectral acceleration (Sa¯) in the range of period that cover the first mode period is chosen as an intensity measure on the fragility function. The nonlinear time history analyses for cabinet are conducted using a suite of 40 ground motions. The obtained curves with different approaches are compared, and the variability of risk assessment is evaluated for restrained and anchored models. The fragility curves obtained for anchored model are found to be closer each other, compared to the fragility curves for restrained model. It is also found that the support boundary conditions played a significant role in acceleration response of cabinet. Keywords: Fragility curve, Seismic intensity measure, System identification, Lognormal assumption, Average spectral acceleration, Linear regression