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Establishing and maintaining a highly-trained pharmaceutical workforce is crucial for serving the healthcare needs of the population. Globally, various factors have been associated with the decision of students leaving high school to pursue an undergraduate degree in pharmacy, however, this topic has not yet been explored in Vietnam. To explore the motivations of first-year undergraduate pharmacy students to study pharmacy at university. A cross-sectional study was conducted on 428 first-year pharmacy students enrolled in undergraduate pharmacy programs from across all universities in Can Tho, Vietnam, including one public university (193 students) and two private universities (235 students), from November 2022 to February 2023. Exploratory factor analysis identified five underlying factors associated with the choice of students to study pharmacy, with internal consistency assessed using Cronbach's Alpha ([alpha]): personal factors ([alpha] = 0.86), career ([alpha] = 0.88), advice ([alpha] = 0.74), reputation ([alpha] = 0.84), and promotion ([alpha] = 0.76). Of these factors, personal factors had the greatest impact (mean = 4.13), while the least influential factor was promotion (mean = 2.92). Compared to public university students, private university students were more influenced by career (p = 0.019), advice (p = 0.002), and promotion (p < 0.001) when making the decision to study pharmacy. This study identifies the factors associated with the decision to pursue pharmacy education. These findings may be of interest to educational institutions addressing how they attract students and meet the workforce needs of the pharmaceutical industry and healthcare system.

Peer-to-peer recommender systems play a critical role in online shopping in Vietnam. This paper aims to identify the relationship between Recommendation Quality and Purchase Intention and the moderating effects of Attitude and Trust on this relationship. Partial Least Squares Structural Equation Modeling was used as a research method. The sample consisted of 365 respondents who frequently use recommender system when shopping online. Data were collected using non-probability sampling method. The questionnaire is delivered to online customers who frequently rely on peer-to-peer recommender systems to make a purchase decision. The results show that Recommendation Transparency, Recommendation Accuracy, Recommendation Novelty, and Recommendation Diversity are positively related to Recommendation Quality. Consequently, Recommendation Quality has a positive impact on Attitude, Trust, and Purchase Intention. Besides, Attitude has a positive impact on online Purchase Intention. Trust also has a positive impact on online Purchase Intention. Practical implications are proposed to improve the impacts of peer-to-peer recommender systems on online shopping.

The article deals with the problem of a dynamic model of the truck crane considering the elasticity of the rope and the ground. The dynamic model of a truck crane in the vertical plane has been established. When considering the oscillation of a truck crane, it is a dynamic system with a large number of degrees of freedom. It is crucial to develop a dynamic model for the truck crane. This model will allow us to evaluate the dynamic response of the machine, analyze the vibration of the heavy object being lifted and lowered, and subsequently study stability and control problems. In this paper, the dynamic models of the cranes in the working plane have been established, built by Lagrange's multiplier equations, which are systems of differential-algebraic equations with the generalized coordinates of the machine's motions and the elastic coordinates. The Lagrange equation provides a simple method for solving dynamic problems. The advantage of this equation is that the form and number of equations do not depend on the number of objects in the investigated system, nor do they depend on the way the objects move. The number of Lagrange equations depends only on the number of degrees of freedom of the system.

The present study simultaneously optimizes the three objective functions of pre-tensioned concrete (PT) beams, such as construction and material cost, beam weights, and beam depth, where multiple objectives optimizations (MOO) conflicting with each other are performed. MOO is performed based on 21 input parameters including tendon parameters and the 21 output parameters. Preassigned input parameters are defined by 16 equalities and design requirements are also implemented during the optimization through 19 code-based inequalities. Finally, the five-step ANN-based algorithms are presented to find optimized design parameters subject to external loads within ranges prescribed by inequalities. A Pareto frontier is presented based on the combinations of weight fractions representing contributions made by the three objective functions. ANN-based optimizations are capable of quantifying tendon ratios and rebar areas when concrete sections crack under service loads while optimizing multi-design targets and objective functions at the same time. The ANN-based optimized designs are verified with a structural mechanics-based software, AutoPT. Reductions of 9.1%, 30.1%, and 10.4% in beam depths, costs, and weights, respectively, obtained based on a Pareto frontier which simultaneously optimizes multi-objective functions are compared with probable designs by human engineers, demonstrating a design efficiency for a pre-tensioned concrete (PT) beam.

Summary: Objective: The aim of this study was to determine factors including biochemical markers, dietary habits, and social-anthropometric associated with uncontrolled hypertension among elderly hypertension in rural regions of Vietnam. Methods: This study included 272 hypertension patients aged over 60 years. Demographic data, anthropometric measurements, and risk factors associated with hypertension were from the cross-sectional study of 272 customers at the Medical Examination Department, Samson General Hospital, Thanh Hoa Province, Vietnam. Biochemical markers including total cholesterol, triglycerides, and fasting blood glucose were measured. Dietary habits were by using a frequency questionnaire. Uncontrolled hypertension was defined as systolic blood pressure of ≥140 mmHg and/or diastolic blood pressure of ≥90 mmHg in a patient taking anti-hypertensive medication.Generalized multiple linear regression was used to assess the impact of related factors on blood pressure, which were used as the dependent variables. Logistic regression analysis was used to calculate the odds ratios (ORs) and 95% confidence intervals (95% CIs) of related factors for hypertension status. Results: The study found that men had more uncontrolled hypertension than women among the elderly population. The prevalences of hyperglycemia, hypertriglyceridemia, and hypercholesterolemia were high in both controlled and uncontrolled groups. There was an inverse association between fruits (p=0.021) and vegetable consumption (p=0.038) and a positive correlation between salty habits (p=0.040) and processed food (p

Background Subtraction (BgS) is a widely researched technique to develop online Change Detection algorithms for static video cameras. Many BgS methods have employed the unsupervised, adaptive approach of Gaussian Mixture Model (GMM) to produce decent backgrounds, but they lack proper consideration of scene semantics to produce better foregrounds. On the other hand, with considerable computational expenses, BgS with Deep Neural Networks (DNN) is able to produce accurate background and foreground segments. In our research, we blend both approaches for the best. First, we formulated a network called Convolutional Density Approximation (CDA) for direct density estimation of background models. Then, we propose a self-supervised training strategy for CDA to adaptively capture high-frequency color distributions for the corresponding backgrounds. Finally, we show that background models can indeed assist foreground extraction by an efficient Neural Motion Subtraction (NeMos) network. Our experiments verify competitive results in the balance between effectiveness and efficiency.

Association rule mining (ARM) is a technique for discovering meaningful associations within databases, typically handling discrete and categorical data. Recent advancements in ARM have concentrated on refining calculations to reveal connections among various databases. The integration of shuffled frog leaping optimization (SFLO) processes has played a crucial role in this pursuit. This paper introduces an innovative SFLO-based method for performance analysis. To generate association rules, we utilize the apriori algorithm and incorporate frog encoding within the SFLO method. A key advantage of this approach is its one-time database filtering, significantly boosting efficiency in terms of CPU time and memory usage. Furthermore, we enhance the optimization process's efficacy and precision by employing multiple measures with the modified SFLO techniques for mining such information.The proposed approach, implemented using MongoDB, underscores that our performance analysis yields notably superior outcomes compared to alternative methods. This research holds implications for fruit shape database mining, providing robust support for fruit class classification.

An analytical method for analyzing the thermal vibration of multi-directional functionally graded porous rectangular plates in fluid media with novel porosity patterns is developed in this study. Mechanical properties of MFG porous plates change according to the length, width, and thickness directions for various materials and the porosity distribution which can be widely applied in many fields of engineering and defence technology. Especially, new porous rules that depend on spatial coordinates and grading indexes are proposed in the present work. Applying Hamilton’s principle and the refined higher-order shear deformation plate theory, the governing equation of motion of an MFG porous rectangular plate in a fluid medium (the fluid-plate system) is obtained. The fluid velocity potential is derived from the boundary conditions of the fluid-plate system and is used to compute the extra mass. The Galerkin-Vlasov solution is used to solve and give natural frequencies of MFG porous plates with various boundary conditions in a fluid medium. The validity and reliability of the suggested method are confirmed by comparing numerical results of the present work with those from available works in the literature. The effects of different parameters on the thermal vibration response of MFG porous rectangular plates are studied in detail. These findings demonstrate that the behavior of the structure within a liquid medium differs significantly from that within a vacuum medium. Thereby, they offer appropriate operational approaches for the structure when employed in various mediums.

Recent experimental findings have shown that tantalum single crystals display strong anisotropy during Taylor impact testing in stark contrast to isotropic deformation in polycrystalline counterparts. In this study, a coupled dislocation dynamics and finite element model was developed to simulate the complex stress field under dynamic loading of a Taylor impact test and track the intricate evolution of the dislocation microstructure. Our model allowed us to investigate detailed motion of dislocations and their mutual interactions and the effect of varying simulation parameters, such as sample size, initial dislocation density, crystallographic orientation, and temperature. Simulation results show good agreement with experimental observations and shed light on the mechanical response at small-scale under extreme loading conditions. In addition, resolved shear stress analysis incorporating the effect of shear stress from impact was performed to quantitatively support and provide a means to understand the model predictions of the impact foot shape.

Abstract The paper presents a strength-failure mechanism for colloidal detachment by breakage and permeability decline in reservoir rocks. The current theory for permeability decline due to colloidal detachment, including microscale mobilisation mechanisms, mathematical and laboratory modelling, and upscaling to natural reservoirs, is developed only for detrital particles with detachment that occurs against electrostatic attraction. We establish a theory for detachment of widely spread authigenic particles due to breakage of the particle-rock bonds, by integrating beam theory of particle deformation, failure criteria, and creeping flow. Explicit expressions for stress maxima in the beam yield a graphical technique to determine the failure regime. The core-scale model for fines detachment by breakage has a form of maximum retention concentration of the fines, expressing rock capacity to produce breakable fines. This closes the governing system for authigenic fines transport in rocks. Matching of the lab coreflood data by the analytical model for 1D flow exhibits two-population particle behaviour, attributed to simultaneous detachment and migration of authigenic and detrital fines. High agreement between the laboratory and modelling data for 16 corefloods validates the theory. The work is concluded by geo-energy applications to (i) clay breakage in geological faults, (ii) typical reservoir conditions for kaolinite breakage, (iii) well productivity damage due to authigenic fines migration, and (iv) feasibility of fines breakage in various geo-energy extraction technologies.

Context: Adverse drug interactions increase the risk of hospitalization, treatment costs, unfavorable treatment outcomes, and prolonged hospital stays. However, there are a limited number of studies conducted in city-affiliated healthcare settings in Vietnam. Aims: To identify drug interactions in outpatient treatment prescriptions at a city-based healthcare center in the southern region of Vietnam. Methods: A cross-sectional descriptive study was conducted on outpatient treatment prescriptions stored in the outpatient department of a city-affiliated healthcare center from January to June, 2023. The study used two databases for drug interaction checking: the Drug Interactions Checker and Medscape Interactions Checker. Univariate and multivariate regression analyses were used to identify predictors of clinically significant drug interactions. Results: Among 1203 surveyed prescriptions, 36.7% contained at least one clinically-significant drug interaction, with 3% of prescriptions having severe interactions. The highest frequency of drug interactions involved amlodipine and metformin (9.9%). Patients with five or more prescribed medications (OR = 3.305; 95% CI: 2.320-4.708), hypertension (OR = 7.142; 95% CI: 4.658-10.950), dyslipidemia (OR = 2.242; 95% CI: 1.665-3.020), and non-insulin-dependent diabetes (OR = 2.447; 95% CI: 1.817-3.295) had a higher likelihood of experiencing clinically significant drug interactions. Conclusions: This study revealed that over one-third of outpatient prescriptions were associated with clinically-significant drug interactions. This study highlights the importance of clinical pharmacists screening prescriptions to detect and reduce interactions, as well as the application of computerized alert systems with intelligent screening software to minimize drug interactions.

Design optimizations of round reinforced concrete columns based on artificial neural networks (ANNs) have been investigated in previous studies with only one pair of axial load (${P_u}$) and bending moment (${M_u}$). In this study, ANNs are generalized to be applicable to multiple load pairs by reshaping weight matrices of ANNs to prevent retraining of ANNs on the large datasets. Generalized ANN-based Lagrange optimizations are proposed for structural designs of round reinforced concrete columns with multiple load combinations. The present study modularizes the weight matrix of ANNs which considers one load pair to completely capture multiple factored loads. An optimal design by ANNs based on the modularized weight matrix and Lagrange optimization techniques using the Karush–Kuhn–Tucker (KKT) conditions was performed and validated with large datasets. Design examples performed by an ANN-based method and structural mechanics demonstrate accuracies of safety factors (SF) as small as 1% − 2%, which confirms the applicability of the proposed ANNs. Based on the present study, ANNs with modularized weight matrices aid engineers in optimizing round reinforced concrete columns subject to multiple loads.

This study investigates the shrinkage characteristics of various materials in metal 3D printing using Material Extrusion (ME) technology. The materials examined include 17-4PH Stainless Steel V1 and V2, Inconel 625, H13 Tool Steel V1, and A2 Tool Steel. Experiments reveal that shrinkage rates vary significantly among these materials, with 17-4PH Stainless Steel V1 exhibiting the lowest average shrinkage rate of 16.2%, while Inconel 625 shows the highest average shrinkage rate of 24.5%. These findings are critical for improving dimensional accuracy in metal 3D printing. Additionally, results demonstrate that print orientation affects shrinkage. The analysis of product accuracy reveals inconsistencies between printed dimensions and design specifications, likely influenced by printing parameters. The conclusion underscores the importance of selecting appropriate printing materials and optimizing parameters to ensure dimensional accuracy in 3D printed products.

A dynamic system is considered where the regulating impact is the product of the control signal on the output variable of a linear dynamic system driven by the same applied control. The essence of the proposed method consists in the dynamic linearization of a nonlinear control operator, which makes it possible to guarantee a desired regulating impact. In a particular case, this approach corresponds to vector (field-oriented) control. It is shown that dynamic linearization based on the internal model method makes it possible to decompose a nonlinear system into a cascade of two subsystems. The proposed regulator consists of two blocks connected in series where the first block solves the problem of regulation with the Luenberger observer, and the second block compensates for a nonlinear dynamic operator. To demonstrate the effectiveness of the proposed approach, an example of numerical modeling of a neutrally stable plant with an output adaptive control is given. In practice, this method may be in demand in the tasks of controlling induction and synchronous motors and multi-link robotic manipulators.

In this study, the evolution of the frame in a structural behavior’s perspective is focused, identifying how cracks influence the degradations of the frame from the camber cracks to plastic hinges. An extensive experimental investigation for the H-shaped post-tensioned precast frame module with monolithic beam-column connections using bolted plate connections is performed to identify the ductile behavior of the H-shaped post-tensioned precast frame module. The inelastic stresses are observed to be redistributed until the failure mechanism with the three plastic hinges are formed in the prestressed beam, which dissipates an inelastic energy in a ductile fashion. No noticeable deformation in plates and bolts for both the first and second floor of the precast frame is found at the final stroke when the experiment was terminated, elucidating that bolted plate connections provide the frame with connections similar to the concrete monolithic joints. Structural behaviors are also investigated using nonlinear finite element analyses based on a damaged concrete plasticity model, unraveling the principles governing crack formations within tested frames. This investigatory approach yields valuable insights for designing post-tensioned precast frames with mechanical column joints. The proposed frame is verified as an option for modular off-site construction to replace cast-in-concrete monolithic frames.

Background: Among pharmacy workers, low workplace wellbeing can lead to reduced effectiveness. However, to date, studies on this issue are limited within the community pharmacy setting in Vietnam. Objectives: This study was conducted to identify the component aspects of workplace wellbeing and their associations with demographic characteristics. Methods: The cross-sectional descriptive study was conducted in Can Tho, Vietnam. Self-administered questionnaires were hand-delivered to all pharmacy workers working at selected community pharmacies. The workplace wellbeing scale comprised 18 items. Results: In total, 382 pharmacy workers participated in this study. Factor analysis revealed three fundamental aspects to workplace wellbeing: Factor 1 – perceived self-worth and job satisfaction, Factor 2 – positive emotions with work, and Factor 3 – negative emotions with work. Factor 1 showed a positive correlation with Factor 2, with a correlation coefficient (ρ) of 0.509, while both Factor 1 (ρ = −0.399) and Factor 2 (ρ = −0.416) demonstrated negative correlations with Factor 3. Higher income was associated with higher positive emotions with work (P = 0.008), higher perceived self-worth and job satisfaction (P = 0.013), and lower negative emotions with work (P < 0.001). Conclusion: Workplace wellbeing of pharmacy workers in their professional environments was associated with financial aspects. These findings suggest that policies aimed at improving income for pharmacy workers could bring benefits to enhancing job satisfaction and workplace wellbeing.

Typhonium obtusum is described as a new species endemic to Vietnam. It is unique in the genus in having an oblong-elliptic spathe limb with an obtuse apex and yellowish-greenish filiform staminodes with a down-curved acumen. The ecology, distribution and assessment of the conservation status of the new taxon, as well as a key to all known Typhonium species in Vietnam, are provided.

TikTok is increasingly influential in promoting brand awareness and boosting purchase intention. From a social media marketing perspective, brand awareness significantly influences consumers’ purchasing decisions. The primary objective of this study is to investigate and measure the factors influencing the brand awareness of TikTok users through social media marketing communications. The paper employs exploratory factor analysis, Cronbach’s Alpha test, and regression analysis to test the hypotheses. 24 observed variables were divided into six groups. Each variable was assessed using a 5-point Likert scale. The sample size includes 240 valid responses from TikTok users collected via convenience sampling. The questionnaire was distributed to TikTok users from Vietnam via the link on Google Forms. The collected data were processed by SPSS 20 software. The results suggest that six independent variables positively affect brand recognition via social media marketing on TikTok, with a 95% confidence interval at Sig. = 0.000 (0.05). Each of the six variables positively impacts initial expectations and is statistically significant at 1%. The results show that trends (Beta = 0.299) are the most decisive factors impacting brand awareness of TikTok users. Besides, electronic word of mouth (Beta = 0.242), influencer (Beta = 0.220), entertainment (Beta = 0.206), interaction (Beta = 0.200), and storytelling (Beta = 0.179) also positively affect the brand awareness of TikTok users. Limitations and further research suggest that marketers should investigate the role of artificial intelligence (AI) in the consumer’s brand awareness-constructing process.

This study investigates the influence of friction on the buckling behavior of thin, elastic, spherical shells under uniform external pressure. The study spans a range of geometric parameters, from shallow shells to hemispheres. Three different end-edge boundary conditions—clamped, hinged, and frictional ends—are considered across a wide range of friction coefficients using an axisymmetric model and nonlinear buckling analysis. The spherical shell becomes increasingly susceptible to buckling when the friction coefficient falls below the converged friction coefficient. A formula is developed to estimate this converged friction coefficient for each geometric parameter. Furthermore, a boundary separating the effects of friction on critical pressure into distinct regions is established, and equations predicting critical pressure within each region are provided. The study also finds that friction influences the buckling mode transition in the shells. Due to significant changes in the theta angle of the no-bending point with increasing geometric parameter and friction coefficient, buckling mode transitions occur at lower friction coefficients in wider spherical shells. These findings provide valuable insights into the intricate interplay between geometric parameter, friction, and buckling behavior in shells. In practical applications, this study can be used to assess and enhance the safety and reliability of spherical shells.