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Foreign exchange risk management is well researched in the context of multinational enterprises, but how small and medium sized exporting firms manage their forex risk is still largely unexplored, especially through an international business lens. This study investigates how New Zealand and Australian exporting small and medium-sized enterprises (SMEs) manage foreign exchange (forex) risk, and the impact of increasing internationalisation on this. The study draws on two theoretical perspectives to assist the investigation: the resource-based view and internationalisation theory. The surveys were distributed to New Zealand and Australian exporting firms across a full range of industry sectors represented in the business database provided by Kompass. Statistical analyses, including exploratory factor analysis, and confirmatory factor analysis were conducted to test the measurement model, structural model and research hypotheses. The study identifies four determinants of forex risk strategy of exporting SMEs: degree of internationalisation, forex exposure, perceived forex risk and resources. Organisational and human resources are shown to have a key mediation role in the model. The study provides insights into key determinants of forex risk management and their interrelationships in the little examined context of exporting SMEs.

This paper studies the relationship between accounting information reflected in financial statements and stock return in Vietnam Stock Market. The authors propose a research model to define the relationship. Besides, machine learning algorithms are used in researching and forecasting, with data obtained from observed firms during the period from 2009 to 2020. Research results show that gradient boosting algorithm has the best self-reporting performance and finan-cial ratios also have great impact on stock returns, including operating income growth, stock earnings volatility, dividend yield, earnings before tax-to-equity ratio, cash holding ratio, and accrual quality. Based on the research results, the authors make some recommendations for investors, firms, and policy makers.

Objective: The article covers current issues related to proving the facts of causing moral injury resulting from a crime under the legislation of the Russian Federation. Authors review the reason to amend the legislation of criminal procedure of the Russian Federation in order to develop a mandatory norm prescribing the mandatory appointment of a forensic examination when establishing the fact that a crime has caused moral injury expressed in additional costs incurred by damage to health.Methods: The analysis of the provisions of the current criminal procedure law of Russia, as well as foreign countries (Great Britain), allows the authors to analyze the existing problems and propose rational ways to solve them.Results: It has been proved that in the activities to ensure compensation for moral injury resulting from a crime, there is a justified need to use an integrated approach to the use of special knowledge to determine the nature and severity of harm caused to health.Conclusion: The authors substantiate the conclusion that it is necessary to build a unified legal method for assessing the moral injury caused by a crime, which starts from the initiation of a criminal case, and to create guarantees of its full compensation within the framework of a single process without the court transferring decisions on the satisfaction of claims to the civil law plane.

Vibration-based structural damage detection is one of the most promising venues for building smart and automated structural health monitoring applications; however, its applicability is impeded by a large amount of collected vibration data, and the performance could be undermined by degraded data. Therefore, this study develops a robust framework, dubbed AutoBoost-SDD, that can effectively handle contaminated vibration data and provide reliable monitoring results within reasonable computational resources. The proposed method consists of three key components. Firstly, multi-domain feature extraction techniques are utilized to convert high-dimensional raw data into informative feature vectors. Secondly, the auto-encoder deep learning architecture is leveraged to refine feature vectors of contaminated data. Finally, a tree-based boosting machine learning algorithm, namely LightGBM, is employed to assess the structures’ operational states using learned output from the second step. The viability and performance of the proposed framework are illustrated via three case studies involving numerical data of a 5-degree of freedom system, a 2D frame structure, and experimental data of a large-scale 18-story frame structure from the literature. The results show that the AutoBoost-SDD framework is able to provide reasonable detection results despite the presence of various contaminations, including noisy, missing, and anomalous data.

The reliability of building structures subjected to ground motion is a time-consuming, complex, and iterative computation problem involving multidiscipline theories such as probability theory, reliability theory, and dynamic structural analysis. In order to address this challenge, this study proposes a highly efficient approach based on the subset simulation and a deep learning-based surrogate model. The subset simulation is an efficient sampling strategy that significantly diminishes the number of samples to compute when determining the failure probability, especially for very small ones. On the other hand, the surrogate model based on a deep learning algorithm can deliver equivalently accurate structures’ responses compared to the well-known finite element method with markedly smaller time complexity, given appropriate available training data. The efficiency and effectiveness of the proposed approach are demonstrated in detail through three case studies with increasing complexity: a 1-DoF problem, a 2D frame, and a 3D structure based on experimental data showing a reduction up to two orders of magnitude in time complexity compared to the Monte Carlo simulation using finite element method.

The study is conducted to investigate the impact of corporate governance and ownership on business performance in listed firms on the Vietnamese Stock Exchange. The study employed the general regression method (GLS) with a sample of 506 listed firms in the period 2008–2020. The study demonstrated the impacts of corporate governance and ownership on firm performance. First, the size of the Board of Directors (BOD), state ownership, and foreign ownership have a positive impact on firm performance. On the contrary, the ratio of independent members in the BOD and the percentage of members of the BOD who are major shareholders have a negative impact on firm performance. An interesting finding is that the BOD with female members, the duality of director and chairman, and the ratio of independent members have a negative impact on the Board of Management. In contrast, firms with no female members in the BOD have a positive relationship with firm performance. The empirical results and recommendations in this study might be good instructions for firms to improve their firm performance.

Burkitt lymphoma (BL) is an aggressive lymphoma type that is currently treated by intensive chemoimmunotherapy. Despite the favorable clinical outcome for most patients with BL, chemotherapy-related toxicity and disease relapse remain major clinical challenges, emphasizing the need for innovative therapies. Using genome-scale CRISPR-Cas9 screens, we identified B-cell receptor (BCR) signaling, specific transcriptional regulators, and one-carbon metabolism as vulnerabilities in BL. We focused on serine hydroxymethyltransferase 2 (SHMT2), a key enzyme in one-carbon metabolism. Inhibition of SHMT2 by either knockdown or pharmacological compounds induced anti-BL effects in vitro and in vivo. Mechanistically, SHMT2 inhibition led to a significant reduction of intracellular glycine and formate levels, which inhibited the mTOR pathway and thereby triggered autophagic degradation of the oncogenic transcription factor TCF3. Consequently, this led to a collapse of tonic BCR signaling, which is controlled by TCF3 and is essential for BL cell survival. In terms of clinical translation, we also identified drugs such as methotrexate that synergized with SHMT inhibitors. Overall, our study has uncovered the dependency landscape in BL, identified and validated SHMT2 as a drug target, and revealed a mechanistic link between SHMT2 and the transcriptional master regulator TCF3, opening up new perspectives for innovative therapies.

Output-only structural health monitoring is a highly active research direction because it is a promising methodology for building digital twin applications providing near-real-time monitoring results of the structure. However, one of the technical bottlenecks is how to work effectively with multiple high-dimensional vibration signals. To address this question, this study develops a two-stage data-driven framework based on various advanced techniques, such as time-series feature extractions, self-learning, graph neural network, and machine learning algorithms. At first, multiple features in statistical, time, and spectral domains, are extracted from raw vibration data; then, they subsequently enter a graph convolution network to account for the spatial correlation of sensor locations. After that, the high-performance adaptive boosting machine learning algorithm is leveraged to assess structures' health states. This method allows for learning a lower-dimensional yet informative representation of vibration data; thus, the subsequent monitoring tasks could be performed with reduced time complexity and economical computational resources. The performance of the proposed method is qualitatively and quantitatively demonstrated through two examples involving both numerical and experimental structural data. Furthermore, comparison and robustness studies are carried out, showing that the proposed approach outperforms various machine learning/deep learning-based methods in terms of accuracy and noise/missing-robustness.

This article introduces experimental and analytical studies on the resistance under eccentric loads of reinforced fly ash concrete (RFAC) columns, in which fly ash (FA) is used to partially replace ordinary Portland cement (OPC) with a by-mass ratio of 20%. Based on experimental results of concrete specimens with mean 28-day cylinder strength of 30 MPa, modifications on simplified bi-linear and tri-linear models of stress-strain relationships of OPC concrete specified in the Russian and Vietnamese design standards are proposed. These nonlinear deformation models are incorporated into an analytical approach to establish the resistance of RFAC columns in the form of interaction surface, associated with an assessment method for safety factor based on the principle of inverse distance weighted average (IDWA). Parameters of the proposed analytical approach are determined by test results obtained from eight RFAC column specimens having 150 × 200 (mm) rectangular cross-section, 1600 mm-height, and 4Φ14 longitudinal rebars with yield strength of 362.6 MPa. In the tests, the specimens were loaded with uniaxial eccentricities ranging from 0 to 80 mm until failed. It is shown that with ε’b1 = 0.0022 and kE = 0.91, the corresponding safety factors of bi-linear and tri-linear models validated for the tested specimens are conservative and nearest to unity, proving that the proposed analytical approach is capable of closely predicting the RFAC columns’ resistance.

Studies suggest that quality managemen tprofessionals need a range of skills to simultaneously exploit current operational models and explore digital trans- formation. However, there is limited research on the impact of digitalisation on improvement work, associated practices and the skills and competencies of quality management professionals. To contribute to this gap, this study draws on a framework based on a conceptual combination of the principles of quality management and the field of occupational competence. The study aims to understand how digitalisation influences the role of quality management professionals, by assessing its influence on the professionals’ improvement practices. The study employs a multiple cross-case research design with data from interviews with nine interviewees, and two focus groups. The results show a so far nascent and limited influence of digitalisation on improvement practices, a need for explorative and team-based practices. Moreover, nine needed skills areas to enhance the professionals’ potential to benefit from digi- talisation in improvement work are suggested. These are Integrator, Pragmatic approach based on a good understanding of possibilities, Change management, Process management, General project management, Improvement analysis, Predictive and proactive approach in QM, General IT and Big data proficiency. Funding: Area of Advance Production at Chalmers University of Technology; Area of Advance Production at Chalmers University of Technology; Area of Advance Production at Chalmers University of Technology

Recently, the combination of cognitive radio networks with the nonorthogonal multiple access (NOMA) approach has emerged as a viable option for not only improving spectrum usage but also supporting large numbers of wireless communication connections. However, cognitive NOMA networks are unstable and vulnerable because multiple devices operate on the same frequency band. To overcome this drawback, many techniques have been proposed, such as optimal power allocation and interference cancellation. In this paper, we consider an approach by which the secondary transmitter (STx) is able to find the best licensed channel to send its confidential message to the secondary receivers (SRxs) by using the NOMA technique. To combat eavesdroppers and achieve reasonable performance, a power allocation policy that satisfies both the outage probability (OP) constraint of primary users and the security constraint of secondary users is optimized. The closed-form formulas for the OP at the primary base station and the leakage probability for the eavesdropper are obtained with imperfect channel state information. Furthermore, the throughput of the secondary network is analyzed to evaluate the system performance. Based on that, two algorithms (i.e., the continuous genetic algorithm (CGA) for CR NOMA (CGA-CRN) and particle swarm optimization (PSO) for CR NOMA (PSO-CRN)), are applied to optimize the throughput of the secondary network. These optimization algorithms guarantee not only the performance of the primary users but also the security constraints of the secondary users. Finally, simulations are presented to validate our research results and provide insights into how various factors affect system performance.

Yok Don National Park is located in the tropical rainforest zone on the Central Highlands of Vietnam. The obtained results from the study undertaken on the composition of plant species and forest vegetation in National Park indicated a record of 856 species, 473 genera and 129 families that belongs to the four divisions of vascular plants. These includes: Lycopodiophyta, Polypodiophyta, Pinophyta and Magnoliophyta. Useful plants of 856 taxonomy species listed consists of 498 species of medicinal plants, 157 species of timber plants, 144 species of edible plants, 60 species of ornamental plants, 19 species of industrial plants, 10 species of fiber plants and 38 species of unknown use plants, respectively. During the duration of investigation, Peliosanthes teta Andrews was newly recorded in the forest vegetation of National Park. A variety of forest vegetations in the area under study is described. In this study, four major vegetation types of forest were identified in Yok Don National Park., Мета. Вивчення лісового рослинного покриву Національного парку Йок-Дон, розташованого у зоні тропічних лісів на Центральному високогір’ї В’єтнаму. Матеріали і методи. Лісову рослинність націо- нального парку Йок-Дон досліджували загальновживаними методами. Результати та обговорення. Дослі- дження видового складу рослинності та лісових рослин у Національному парку дали змогу ідентифікувати 856 видів з 473 родів та 129 родин, які належать до чотирьох відділів судинних рослин. До них відносять: Lycopodiophyta, Polypodiophyta, Pinophyta та Magnoliophyta. За ознаками корисності список 856 видів рослин включає: 498 видів лікарських, 157 деревних, 144 їстівних, 60 декоративних, 19 технічних, 10 видів волокнистих рослин та 38 видів невизначеного використання. У процесі обстеження лісової рослинності парку знову було зареєстровано Peliosanthes teta Andrews. Описується різноманіття виявленої на досліджуваній території лісової рослинності. Висновки. Унаслідок виконаних досліджень у Національному парку Йок-Дон було виявлено чотири основні типи лісової рослинності: тропічний широколистяний диптерокарповий, тропічний вічнозелений широколистяний, тропічний напів-вічнозелений широколистяний та змішаний ліс. Отримана інформація сприятиме збереженню та сталому розвитку Національного парку. Окрім корисних рослин у парку було зафіксовано чотири ендемічні види та 16 видів, які підлягають збереженню на глобальному та національному рівнях.

A reconfigurable wearable textile antenna with wide frequency tuning range is demonstrated in this work. The frequency agility is realized by adding a coplanar reconfiguration module to a planar inverted-F antenna (PIFA) constructed in textile technology. The reconfiguration module consists of a small flexible printed circuit board (PCB) which integrates a tuning circuitry with commercial snap-on buttons as electronic-to-textile connectors. Using this module equipped with a varactor diode, the frequency reconfigurability of the PIFA is investigated and optimized parametrically for maximum tuning range, exploiting a smooth transition from a quarter-wave to a half-wave patch resonant mode. A fabricated prototype demonstrates a very wide tuning range of approximately 70% (or one octave) with stable directive radiation characteristics, as predicted by simulations. The robust performance and mechanical stability of the reconfigurable antenna in various on-body and bending configurations validates the viability of the antenna tuning concept and the practicality of the proposed coplanar reconfiguration module.

The large sea urchins (Diadematidae) are widely distributed worldwide, from temperate to tropical waters. Diadematidae is an important herbivore in the coral reef ecosystem through herbivore-algal-coral interaction, which maintains the reef’s health. Many studies of Diadematidae have been performed around world, but in Vietnam, there is little known about the ecology of this group. Here, we first investigated the assemblage structure of Diadematidae in Northern Vietnam. We observed 43 sites of representative coral reefs in Ba Mun - Co To, Cat Ba - Ha Long, and Hon Me. Based on the surveys, we found four species of considerable size sea urchins, of which the most common species is Diadema setosum (79.8% of the total), Enchinothrix calamaris (10.1%), E. diadema (8.3%), and D. savignyi (1.8%). Among three study regions, Hon Me is the most diverse. In addition, Hon Me also has the highest density of sea urchins. However, this value is generally low compared to that in Southern Vietnam and other neighboring countries. Further study is needed to investigate the ecology and reproductive biology of Diadematidae to provide fundamental information for sustainable exploitation and conservation.

When communication infrastructure is destroyed by natural disasters, reconstructing a flexible network for search and rescue or restoring transmission channels is a necessary mission. Therefore, in this paper, we investigate the system performance for the Internet of Things (IoT) using multiple unmanned aerial vehicles (UAVs) in the disaster area. The communication protocol is separated into two phases: In the first phase, due to the resource limitation, UAV relays (URs) harvest energy from a ground power beacon (GPB) to support the communication process. In the second phase, the ground base station (GBS) sends the signal to the first UR by using non-orthogonal multiple access (NOMA) technique. It is noted that the first UR uses its energy harvested from the first phase to forward the signal to the second UR by applying the decode-and-forward (DF) approach. Similarly, the second UR uses DF to transfer the signal to two IoT sensor node (IoTS) clusters, i.e., near cluster and far cluster. Accordingly, we derive the closed-form expression of the throughput for the IoTSs under imperfect channel state information (CSI) with Nakagami-m fading and formulate a new problem of throughput optimization for multi-UAV system. For optimization problem, solutions are found using covariance matrix adaptive evolutionary strategy (CMA-ES) algorithm. The numerical results show that system performance in terms of throughput can be improved by tuning the UAVs’ altitudes and distances from the GBS to suitable values.

This paper investigates the throughput of a non-orthogonal multiple access (NOMA)-based cognitive radio (CR) system with multiple unmanned aerial vehicle (UAV)-assisted relays under system performance and security constraints. We propose a communication protocol that includes an energy harvesting (EH) phase and multiple communication phases. In the EH phase, the multiple UAV relays (URs) harvest energy from a power beacon. In the first communication phase, a secondary transmitter (ST) uses the collected energy to send confidential signals to the first UR using NOMA. Simultaneously, a ground base station communicates with a primary receiver (PR) under interference from the ST. In the subsequent communication phases, the next URs apply the decode-and-forward technique to transmit the signals. In the last communication phase, the Internet of Things destinations (IDs) receive their signals in the presence of an eavesdropper (EAV). Accordingly, the outage probability of the primary network, the throughput of the secondary network, and the leakage probability at the EAV are analyzed. On this basis, we propose a hybrid search method combining particle swarm optimization (PSO) and continuous genetic algorithm (CGA) to optimize the UR configurations and the NOMA power allocation to maximize the throughput of the secondary network under performance and security constraints.

Laparoscopic cholecystectomy becomes standard technique for management symptomatic cholelithiasis, polype. The risk of bile duct injury following laparoscopic cholecystectomy is higher than open cholecystectomy. Purpose: To diagnosis the type of injury bile duct and management bile duct injuries sustained by laparoscopic cholecystectomy. Patients and Methods: Retrospective analyses of the patients undergoing laparoscopic cholecystectomy from 01/1999 to 06/2020. We evaluated the data according to outcome measures, characteristics and treatment results of major bile duct injuries after laparoscopic cholecystectomy. Results: We found 21 patients with major bile duct injuries sustained by laparoscopic cholecystectomy. Only 23.8% of injuries are recognized during operation. Type of injuries include: Lateral injury of common bilde duct were 4 cases, transection of bile duct were 4 cases. Management injuries include: bile duct suture were 4 cases (19.1%), hepaticojejunostomy were 10 cases (47.6%), choledochojejunostomy were 6 cases (28.5%). Conclusion: Common bile duct injury is a rare complication of laparoscopic cholecystectomy (0.35%). Immediate recognition and repair are associated with improved outcome. The optimal management depends on the timing of recognition of injury, the type, extent of bile duct injury, the patient’s conditions and the availability of experienced hepatobiliary surgeons.

The effect of fly ash (FA) dosage on concrete’s compressive strength and stress-strain relationship is investigated in two steps in this article. First, an experimental program was conducted on concrete mixtures designed with 0% (control batch of 30 MPa mean cylinder compressive strength), 10, 20, 30, and 40% of ordinary Portland cement (OPC) mass replaced by FA, which is taken from a new source in an Asia country. The test results showed that compared to other investigated dosages, concrete using 20%FA/OPC mass-replacement gained the most improvement in the 28-day compressive strength and tensile split strength, as well as the compressive strength development. Second, a probabilistic investigation was conducted using Dropout Neural Network, Bayesian Neural Network, and Gaussian Process models. These artificial intelligence-based models were compared to other models reviewed from the literature, showing relatively good results in terms of the statistical metric R2, which are 0.92, 0.9, and 0.88, respectively. The three models were tested and validated with a dataset of 1032 experimental results on FAC collected from the literature. When testing with the experimental results obtained in the first step, a good correlation between the predicted values and the experimental results was observed within the confidence interval of (5%, 95%), showing the reliability of the proposed models. Thus, the stress-strain relationship of fly ash concrete can also be investigated in a probabilistic manner. It is proved in this study that among the proposed models, Dropout Neural Network has the best balance between performance and time complexity.

This chapter describes the current status and challenges regarding the governance of wastewater surveillance systems against COVID-19. Monitoring SARS-CoV-2 in wastewater has been proposed to be a potential tool to understand the actual prevalence of COVID-19 in the community, and it could be an effective tool during the pandemic to monitor the trend as well as beyond the pandemic as an early warning system to prevent future outbreaks. However, challenges to institutionalize wastewater surveillance systems are still abundant and unfolding at a rapid rate given that the international understanding regarding the scientific knowledge and socio-political impacts of COVID-19 are in the developing stages. To better understand the existing challenges and bottlenecks, a comparative study between eight countries across the Asia-Pacific was carried out. Through gaining a better understanding of common issues as well as issues specific to each country, we hope to contribute to building a robust multistakeholder system to monitor SARS-CoV-2 as well as future pathogens in wastewater as an effective disease surveillance system for COVID-19 and unknown epidemics (disease X) in the community level.

In this paper, we propose and investigate the stability of a novel 3-compartment ordinary differential equation (ODE) model of HIV infection of CD4+ T-cells with a mass action term. Similar to various endemic models, the dynamics within the model is fully determined by the basic reproduction term R0. If R0 < 1, the disease-free (zero) equilibrium will be asymptotically stable. On the other hand, if R0 > 1, there exists a positive equilibrium that is globally/orbitally asymptotically stable under certain conditions within the interior of a predefined region. Finally, numerical simulations are conducted to illustrate and verify the results.

Vấn đề phát triển chương trình đào tạo giáo viên mầm non, tiểu học và trung học cơ sở không phải là mới ở các cơ sở đào tạo giáo viên. Song, đào tạo giáo viên vẫn tồn tại rất nhiều vấn đề nan giải, đặc biệt là năng lực nghề nghiệp sau đào tạo không rõ ràng. Bài viết này góp phần làm sáng tỏ nội dung năng lực nghề dạy học, định hướng tổ chức chương trình và hoạt động đào tạo tập trung vào năng lực.

Background: Achalasia is a primary motor disorder of the esophagus characterized by insufficient lower esophageal sphincter relaxation and loss of esophageal peristalsis. Diagnosis is confirmed by clinical symptoms, endoscopic, radiographic and manometric. Although pneumatic dilation has a role in the treatment of achalasia, laparoscopic Heller myotomy is considered by many experts as the best treatment modality for most patients with newly diagnosed achalasia. Objective: To evaluate the efficacy and safety of laparoscopic Heller myotomy with Dor antireflux procedure. Patients and Methods: Prospective analyses of the patients of achalasia undergoing laparoscopic Heller myotomy with Dor antireflux procedure from 01/2012 to 06/2020. We evaluated the data according to outcome measures, characteristics and treatment results of achalasia after laparoscopic Heller myotomy with Dor antireflux procedure. Results: We found 11 patients with achalasia were diagnosed and treated by laparoscopic. 100% of the patients have dysphagia, weight loss; 81.8% have chest pain and regurgitation. Mean operative duration was 148 ± 21 minutes. There was no complications had found in our patients. Follow-up after 3 - 6 months by Eckardt score show that 90.9% had a good result, 01 patient with persistent symtomps (Eckardt score > 3) had to undergo a pneumatic dilation. Conclusion: The results of the laparoscopic Heller myotomy with Dor antireflux procedure were safe and effective. Keywwords: Achalasia, laparoscopic Heller myotomy, Dor antireflux procedure

An integration and modularization solution for a passive computational radio frequency identification (RFID) module is proposed for wireless patient monitoring. In the proposed integrated configuration, all the electronic components are encapsulated within the cavity of a textile antenna. This forms a compact protected embodiment suitable for concealment in a hospital garment, with the aim of being unobtrusive to older patients. In addition, modularization of the sensor and textile antenna is introduced through a convenient snap-on attachment method for the computational RFID module. This is in harmony with simple assembly techniques and facilitates maintenance. Importantly, the resulting customizable configurations, for example, in terms of antenna form factor and materials, can be tailored to suit application requirements, such as performance and disposability. The experimental results demonstrate that the proposed wireless wearable sensor can operate with an antenna broadside gain of 2.74 dBi within a wide bandwidth extending over 893–964 MHz to support seamless worldwide operation across the ultrahigh-frequency (UHF) industrial, scientific, and medical (ISM) bands. The wearable sensor embodiment characterized in the context of a monitoring system attains excellent performance in terms of received signal strength, data read rate, and batteryless operational range.

The free vibration of rotating functionally graded porous (FGP) circular cylindrical shell with different boundary conditions is presented analytically in this study. The porous material properties are assumed to be graded in the thickness direction of the cylindrical shell according to three types of porosity distributions. By using Love’s shell theory and Hamilton’s principle, the governing equations of the rotating FGP cylindrical shell are derived, in which the effects of the centrifugal and Coriolis forces due to rotation are also taken into account. The natural frequencies of the rotating FGP cylindrical shell structure subjected to different boundary conditions are determined by applying Galerkin’s method together with beam functions of longitudinal mode functions. To validate the present results, comparisons between the results of the present method and previous studies are performed; a very good agreement is achieved. Besides, some influences of porous material properties, boundary conditions, circumferential wave number, geometric parameters, Coriolis acceleration, rotating speed on natural frequency as well as critical speed of the rotating FGP cylindrical shell are given.

Tram Chim National Park in Southern Vietnam is a wetland area included in the system of specially protected natural areas (SPNA). For the purposes of land monitoring, we studied Landsat-5 and Sentinel-2B images obtained in 1991, 2006 and 2019. The methods of normalized difference vegetation index (NDVI) and water objects – normalized difference water index (NDWI) were used to estimate the vegetation in National Park. The allocated land is classifi ed by the maximum likelihood method in ENVI 5.3 into categories. For each image, a statistical analysis of the land after classifi cation was performed. Between 1991 and 2019, land changes occurred in about 57 % of the Tram Chim National Park total area. As a result, the wetland area has signifi cantly reduced there due to climate change. However, the area of Melaleuca forests in Tram Chim National Park has increased due to the effi ciency of reforestation in protected areas. Melaleuca forests are also being restored.

Meta-learning has recently received much attention in a wide variety of deep reinforcement learning (DRL). In non-meta-learning, we have to train a deep neural network as a controller to learn a specific control task from scratch using a large amount of data. This way of training has shown many limitations in handling different related tasks. Therefore, meta-learning on control domains becomes a powerful tool for transfer learning on related tasks. However, it is widely known that meta-learning requires massive computation and training time. This paper will propose a novel DRL framework, which is called HCGF-R2-DDPG (Hybrid CPU/GPU Framework for Reptile+ and Recurrent Deep Deterministic Policy Gradient). HCGF-R2-DDPG will integrate meta-learning into a general asynchronous training architecture. The proposed framework will allow utilising both CPU and GPU to boost the training speed for the meta network initialisation. We will evaluate HCGF-R2-DDPG on various Partially Observable Markov Decision Process (POMDP) domains.

In this paper, the dynamics of multi-span thin plates loaded by masses moving with arbitrary trajectories have been numerically studied using modal decomposition. The partial differential equation of motion of the plate is derived from the classical plate theory; then, it is transformed into a number of ordinary differential equations coupled by interaction forces between the moving masses and plate surface. This procedure was successfully validated by comparison with the results of the literature. Moreover, the method applies to various boundary conditions and is not limited by the plate shape in principle. In addition, this method considerably reduces the computation time by a truncation on the number of vibration modes. In the numerical examples, the influence of the moving speed, number of masses, damping ratio, number of spans are examined.

Cash management plays an important role in the business operations. However, holding too much cash results in unnecessary expenses such as opportunity costs, management costs, and representation costs due to negative cash holdings. This study examines the sensitivity of cash flows to cash holdings. The paper uses regression methods for table data, including FEM, REM, GLS, and GMM regression, with a research dataset including non-financial companies listed on Vietnam’s stock market in the period 2008–2018. Empirical results show that cash flows are positively associated with cash holdings levels. At the same time, research has shown an asymmetry in cash flows sensitivity to cash holdings. The study also classified the companies with limited and no financial restrictions. In the Vietnamese context, compared to unrestricted companies, financially restricted companies have a lower cash flows sensitivity. The research results are the basis for enterprises to manage cash better and increase business efficiency in the future.