Spatiotemporal evolution of SARS-CoV-2 Alpha and Delta variants during large nationwide outbreak of COVID-19, Vietnam, 2021

We analyzed 1,303 SARS-CoV-2 whole-genome sequences from Vietnam, and found the Alpha and Delta variants were responsible for a large nationwide outbreak of COVID-19 in 2021. The Delta variant was confined to the AY.57 lineage and caused >1.7 million infections and >32,000 deaths. Viral transmission was strongly affected by nonpharmaceutical interventions

Safety and efficacy of fluoxetine on functional outcome after acute stroke (AFFINITY): a randomised, double-blind, placebo-controlled trial

Background Trials of fluoxetine for recovery after stroke report conflicting results. The Assessment oF FluoxetINe In sTroke recoverY (AFFINITY) trial aimed to show if daily oral fluoxetine for 6 months after stroke improves functional outcome in an ethnically diverse population. Methods AFFINITY was a randomised, parallel-group, double-blind, placebo-controlled trial done in 43 hospital stroke units in Australia (n=29), New Zealand (four), and Vietnam (ten). Eligible patients were adults (aged ≥18 years) with a clinical diagnosis of acute stroke in the previous 2–15 days, brain imaging consistent with ischaemic or haemorrhagic stroke, and a persisting neurological deficit that produced a modified Rankin Scale (mRS) score of 1 or more. Patients were randomly assigned 1:1 via a web-based system using a minimisation algorithm to once daily, oral fluoxetine 20 mg capsules or matching placebo for 6 months. Patients, carers, investigators, and outcome assessors were masked to the treatment allocation. The primary outcome was functional status, measured by the mRS, at 6 months. The primary analysis was an ordinal logistic regression of the mRS at 6 months, adjusted for minimisation variables. Primary and safety analyses were done according to the patient's treatment allocation. The trial is registered with the Australian New Zealand Clinical Trials Registry, ACTRN12611000774921. Findings Between Jan 11, 2013, and June 30, 2019, 1280 patients were recruited in Australia (n=532), New Zealand (n=42), and Vietnam (n=706), of whom 642 were randomly assigned to fluoxetine and 638 were randomly assigned to placebo. Mean duration of trial treatment was 167 days (SD 48·1). At 6 months, mRS data were available in 624 (97%) patients in the fluoxetine group and 632 (99%) in the placebo group. The distribution of mRS categories was similar in the fluoxetine and placebo groups (adjusted common odds ratio 0·94, 95% CI 0·76–1·15; p=0·53). Compared with patients in the placebo group, patients in the fluoxetine group had more falls (20 [3%] vs seven [1%]; p=0·018), bone fractures (19 [3%] vs six [1%]; p=0·014), and epileptic seizures (ten [2%] vs two [<1%]; p=0·038) at 6 months. Interpretation Oral fluoxetine 20 mg daily for 6 months after acute stroke did not improve functional outcome and increased the risk of falls, bone fractures, and epileptic seizures. These results do not support the use of fluoxetine to improve functional outcome after stroke

Comparing the Order of Multi-Layered Modifiers in English, Chinese and Vietnamese in Language Teaching

From the cross-linguistic perspective and cognitive linguistic theory, this study has analysed the rules of multi-layered modifiers in English, Chinese, and Vietnamese, pointing out their common points and differences. Although all three languages belong to the SVO (subject-verb-object) type but modifiers in English and Chinese are in front of the core words, which shows that English and Chinese belong to the language in the left branch, but modifiers in Vietnamese, they are behind the core words which shows that Vietnamese belongs to the right branch. All the three languages have one thing in common, whether they are on the left or on the right branch, in which modifiers have the closest relationship with the core words that will stand nearest to them. Other modifiers that have a non-intimate relationship with the core words will stand further away from them. Thus, mastering this feature of the three types of languages will help in language teaching and learning

Computational study for OWA Traveling Salesman Problem

International audienceGiven a set of cities and distances to travel between each pair of them, the Traveling Salesman Problem (TSP) is to find the shortest tour which visits every city exactly once and returns to the starting city. In some practical cases, the balance between edges in the tour is as important as the total distance. It motivates us to study an equitable version of TSP where the optimal tour satisfies: i) Pareto optimality (i.e. not improvable on all distances simultaneously), ii)fairness (or the balance between edges). In this work, we study a variant of TSP where the ordered weighted averaging (OWA) is used to control both Pareto efficiency and fairness, we call this problem OWATSP. OWA imposes implicitly the balance between edges by Pigou-Dalton transfer principle claiming that a transfer from a richer resource to a poorer one results in a fairer distribution.OWATSP belongs to the class of fair combinatorial optimization considered in [2]. The main challenge of this problem class is the non-linearity of OWA objective. Fortunately, it can be cast to Mixed-Integer Programs (MIPs) by several existing linearization methods [3, 1]. However, the time spent to solve exact formulations increases considerably with the size of instances and might reach hours for small-size ones. To tackle the issue, an iterative algorithm based on Lagrangian relaxation [2] is proposed and verified the efficiency of several fair optimization problems related to matching. In this paper, we focus on algorithms to solve efficiently large-size instances of OWATSP - an NP-hard problem

A Branch-and-Cut algorithm for the Balanced Traveling Salesman Problem

The balanced traveling salesman problem (BTSP) is a variant of the traveling salesman problem, in which one seeks a tour that minimizes the difference between the largest and smallest edge costs in the tour. The BTSP, which is obviously NP-hard, was first investigated by Larusic and Punnen in 2011 [9]. They proposed several heuristics based on the double-threshold framework, which converge to good-quality solutions though not always optimal (e.g. 27 provably optimal solutions were found among 65 TSPLIB instances of at most 500 vertices). In this paper, we design a special-purpose branch-and-cut algorithm for solving exactly the BTSP. In contrast with the classical TSP, due to the BTSP's objective function, the efficiency of algorithms for solving the BTSP depends heavily on determining correctly the largest and smallest edge costs in the tour. In the proposed branch-and-cut algorithm, we develop several mechanisms based on local cutting planes, edge elimination, and variable fixing to locate more and more precisely those edge costs. Other important ingredients of our algorithm are heuristics for improving the lower and upper bounds of the branch-and-bound tree. Experiments on the same TSPLIB instances show that our algorithm was able to solve to optimality 63 out of 65 instances

Improving Subtour Elimination Constraint Generation in Branch-and-Cut Algorithms for the TSP with Machine Learning

International audienceBranch-and-Cut is a widely-used method for solving integer programming problems exactly. In recent years, researchers have been exploring ways to use Machine Learning to improve the decision-making process of Branch-and-Cut algorithms. While much of this research focuses on selecting nodes, variables, and cuts [11,9,26], less attention has been paid to designing efficient cut generation strategies in Branch-and-Cut algorithms, despite its large impact on the algorithm performance. In this paper, we focus on improving the generation of subtour elimination constraints, a core and compulsory class of cuts in Branch-and-Cut algorithms devoted to solving the Traveling Salesman Problem, which is one of the most studied combinatorial optimization problems. Our approach takes advantage of Machine Learning to address two questions before launching the separation routine to find cuts at a node of the search tree: 1) Do violated subtour elimination constraints exist? 2) If yes, is it worth generating them? We consider the former as a binary classification problem and adopt a Graph Neural Network as a classifier. By formulating subtour elimination constraint generation as a Markov decision problem, the latter can be handled through an agent trained by reinforcement learning. Our method can leverage the underlying graph structure of fractional solutions in the search tree to enhance its decision-making. Furthermore, once trained, the proposed Machine Learning model can be applied to any graph of any size (in terms of the number of vertices and edges). Numerical results show that our approach can significantly accelerate the performance of subtour elimination constraints in Branchand-Cut algorithms for the Traveling Salesman Problem

MỘT SỐ ĐẶC ĐIỂM HÌNH THÁI VÀ SINH HỌC CỦA BƯỚM PHƯỢNG LỚN PAPILIO MEMNON LINNAEUS, 1758 (PAPILIONIDAE) Ở THÀNH PHỐ HUẾ VÀ VÙNG PHỤ CẬN

Great Mormon (Papilio memnon Linnaeus, 1758) is one of the big-sized beautiful butterflies, and in spite of possessing wide distribution, they are rare in nature. This study was carried out from January 2018 to March 2019 in Hue City and the adjacent areas. The results show that the mature butterflies are relatively large (the forewing mean is 71.65 ± 0.59 mm). Female mature butterflies are in two morphologies: non-mimetic and mimetic female. Female butterflies are less numerous than male butterflies. The newly hatched larvae have a relatively rough morphology. The first instar larvae have an initial body length of 4.05 ± 0.15 mm; later, they become more slippery. The fifth instar larvae have an average body length of 54.90 ± 2.11 mm. The pupae have an average length of 39.27 ± 0.61 mm, pointed vertex, and parallel outer. The narrow top is divided into two horn-like structures by a spear-shaped space. Great Mormon larvae eat six plant species belonging to the Rutaceae family, namely Citrus au-rantifolia, C. grandis, C. sinensis, Clausena excavata, Zanthoxylum nitidum, and Atalantia buxifolia. Under the semi-natural culture conditions with the temperature of 27–40 °C (average 37.32 ± 0.27 °C) and relative humidity 70–98% (average 88.69 ± 0.48%), the caterpillars were fed on fresh pomelo leaves (Citrus grandis), and the mature individuals were cultured with several flowers including Ixora coccinea, Lantanacamara, and 50% diluted honey. The life cycle of the butterflies (from egg to mature butterfly) is 33–56 days (average of 49.8 ± 4.2 days).Bướm phượng lớn Papilio memnon Linnaeus, 1758 là một trong những loài bướm đẹp, kích thước lớn, mặc dù chúng là loài phân bố rộng nhưng chỉ gặp rải rác vài cá thể ngoài tự nhiên. Nghiên cứu về loài bướm này được thực hiện từ tháng 1/2018 đến tháng 3/2019 tại thành phố Huế và các vùng phụ cận. Kết quả cho thấy bướm trưởng thành khá lớn (cánh trước dài trung bình 71,65 ± 0,59 mm), bướm cái trưởng thành có 2 dạng hình thái: dạng không đuôi và dạng có đuôi; bướm cái có số lượng ít hơn bướm đực. Sâu mới nở có hình thái khá xù xì, cuối tuổi 1 đạt 4,05 ± 0,15 mm; ở các tuổi về sau, sâu càng trở nên trơn láng, sâu non cuối tuổi 5 đạt kích thước trung bình 54,90 ± 2,11 mm. Nhộng dài trung bình 39,27 ± 0,61 mm; phần thóp thu hẹp, phía ngoài song song, đỉnh thóp chẻ thành hai sừng bởi khoảng trống hình mũi mác rất đặc trưng. Sâu non sử dụng sáu loài cây thuộc họ Cam chanh (Rutaceae) làm thức ăn, gồm: Citrus aurantifolia, C. grandis, C. sinensis, Clausena excavata, Glycosmis pentaphylla và Zanthoxylum nitidum. Trong điều kiện nuôi bán tự nhiên với nhiệt độ từ 27 đến 40 °C (trung bình (TB) 37,32 ± 0,27 °C) và độ ẩm tương đối 70–98% (TB 88,69 ± 0,48%), sâu non được nuôi bằng lá bưởi tươi (Citrus grandis), trưởng thành thả trong lồng có hoa cây bông trang (Ixora coccinea), hoa cây bông ổi (Lantana camara) và mật ong pha loãng 50%. Vòng đời của chúng khoảng 33–56 ngày (TB 49,8 ± 4,2 ngày)

Generalized Nash Fairness solutions for Bi-Objective Minimization Problems

In this paper, we consider a special case of Bi-Objective Optimization (BOO), called Bi-Objective Minimization (BOM), where two objective functions to be minimized take only positive values. As well as for BOO, most methods proposed in the literature for solving BOM focus on computing the Pareto-optimal solutions that represent different trade offs between two objectives. However, it may be difficult for a central decision-maker to determine the preferred solutions due to a huge number of solutions in the Pareto set. We propose a novel criterion for selecting the preferred Pareto-optimal solutions by introducing the concept of ρ-Nash Fairness (ρ-N F ) solutions inspired from the definitionof proportional fairness. The ρ-N F solutions are the Paretooptimal solutions achieving some proportional Nash equilibrium between the two objectives. The positive parameter ρ is introduced to reflect the relative importance of the first objective to the second one. For this work, we will discuss some existential and algorithmic questions about the ρ-N F solutions by first showing their existence for BOM. Furthermore, the set of ρ-N F solutions can be a strict subset of the Pareto set. As there are possibly many ρ-N F solutions, we focus on extreme ρ-N F solutions achieving the smallest values for one of the objectives. Then, we propose two Newton-based iterative algorithms for finding extremeρ-N F solutions. Finally, we present computational results on some instances of the Bi-Objective Travelling Salesman Problem and the Bi-Objective Shortest Path Problem

Depression and associated factors among infertile women at Tu Du hospital, Vietnam: a cross-sectional study

Background: About 40,000 infertile couples visit Tu Du Hospital, Vietnam for consultation and treatment of infertility per year. Depression in infertile female patients not only influences mental wellbeing, but also affects the effectiveness of infertility treatment. The study aimed to determine the depression prevalence in infertile female patients and associated factors. Methods: A cross-sectional study was conducted during April–July 2016 with 401 infertile women visiting the Department of Infertility at Tu Du Hospital . The PHQ-9 scale was used to measure depressive symptoms. Face-to-face interviewing was conducted using a structured questionaire. Participants were also inquired about demographic characteristics, socio-economic status, infertility related characteristics and family and social relationships. Results: The depression prevalence was 12.2%, with a cut-off score ≥10 on PHQ-9 scale. Depression in infertile female patients was associated with infertility caused by the husband (AOR=3.09, 95% CI=1.44–6.63), infertility caused by both spouses (AOR=3.63, 95% CI=1.26–10.48), alcohol-addicted husband (AOR=4.83, 95% CI=1.32–17.58), and with wife’s previous antidepressant use (AOR=48.1, 95% CI=4.83–47.96) Conclusions: Assessment of depressive symptoms should be assessed at an early stage among infertile female patients for timely mental health support

A comparative study of linearization methods for Ordered Weighted Average

International audienceWe consider a fair version of combinatorial optimization, which aims for both Pareto-efficiency and fairness of a solution. A possible approach to achieve the objectives simultaneously is to use the Ordered Weighted Average (OWA) aggregating function, which can be formulated into mix-integer programming (MIP) formulations. In this paper, we study two MIP formulations proposed in the literature for the OWA in the context of fair combinatorial optimization. On the one hand, we prove that both MIP formulations are equivalent in terms of linear relaxations. On the other hand, we estimate the quality with regard to the OWA value of an optimal solution of original combinatorial optimization. An experimental evaluation of the MIP formulations in tackling OWA Traveling Salesman Problem is also presented