The building sector is the largest energy consumer accounting for 40% of global energy usage. An energy forecast model supports decision-makers to manage electric utility management. Identifying ...optimal values of hyperparameters of prediction models is challenging. Therefore, this study develops a novel time-series Wolf-Inspired Optimized Support Vector Regression (WIO-SVR) model to predict 48-step-ahead energy consumption in buildings. The proposed model integrates the support vector regression (SVR) and the grey wolf optimizer (GWO) in which the SVR model serves as a prediction engine while the GWO is used to optimize the hyperparameters of the SVR model. The 30-min energy data from various buildings in Vietnam were adopted to validate model performance. Buildings include one commercial building, one hospital building, three authority buildings, three university buildings, and four office buildings. The dataset is divided into the learning data and the test data. The performance of the WIO-SVR was superior to baseline models including the SVR, random forests (RF), M5P, and decision tree learner (REPTree). The WIO-SVR model obtained the highest value of correlation coefficient (R) with 0.90. The average root-mean-square error (RMSE) of the WIO-SVR was 2.02 kWh which was more accurate than those of the SVR model with 10.95 kWh, the RF model with 16.27 kWh, the M5P model with 17.73 kWh, and the REPTree model with 26.44 kWh. The proposed model improved 442.0-1207.9% of the predictive accuracy in RMSE. The reliable WIO-SVR model provides building managers with useful references in efficient energy management.
•Friction efficiency is significantly dependent on layer thickness and surface layer property.•Local deformation is hindered by the interface, resulting in very few plastic defects below the ...interface.•Surface layer property or interface controls the plastic deformation of the layer thickness variation.•Friction coefficient is related to the thermal softening and strain rate hardening of the material for different scratch rates.
The tribological performance of CuTa/CuTa amorphous/amorphous nanomultilayers is investigated through molecular dynamics simulation in the present work. There is a significant effect of layer thickness and surface layer properties on scratching force, which results in reduced friction efficiency with small layer thickness for Cu80Ta20/Cu20Ta80 specimen, while the frictional performance between the two models of Cu80Ta20/Cu20Ta80 and Cu20Ta80/Cu80Ta20 exhibits the opposite at the critical layer thickness of 1.5 nm. It is found that the surface layer property controls plastic deformation for specimens with great layer thicknesses, while the interface plays a significant role in plastic deformation with small layer thickness. For the increase in scratching speed, the friction coefficient tends to gently decrease for a cutting depth of 1.0 nm, and it increases slightly for machining depths of 2.0 nm and 2.5 nm in relation to the thermal softening and strain rate hardening of the material.
Vietnam is a lower middle-income country with no national surveillance system for hospital-acquired infections (HAIs). We assessed the prevalence of hospital-acquired infections and antimicrobial use ...in adult intensive care units (ICUs) across Vietnam.
Monthly repeated point prevalence surveys were systematically conducted to assess HAI prevalence and antimicrobial use in 15 adult ICUs across Vietnam. Adults admitted to participating ICUs before 08:00 a.m. on the survey day were included.
Among 3287 patients enrolled, the HAI prevalence was 29.5% (965/3266 patients, 21 missing). Pneumonia accounted for 79.4% (804/1012) of HAIs Most HAIs (84.5% 855/1012) were acquired in the survey hospital with 42.5% (363/855) acquired prior to ICU admission and 57.5% (492/855) developed during ICU admission. In multivariate analysis, the strongest risk factors for HAI acquired in ICU were: intubation (OR 2.76), urinary catheter (OR 2.12), no involvement of a family member in patient care (OR 1.94), and surgery after admission (OR 1.66). 726 bacterial isolates were cultured from 622/1012 HAIs, most frequently Acinetobacter baumannii (177/726 24.4%), Pseudomonas aeruginosa (100/726 13.8%), and Klebsiella pneumoniae (84/726 11.6%), with carbapenem resistance rates of 89.2%, 55.7%, and 14.9% respectively. Antimicrobials were prescribed for 84.8% (2787/3287) patients, with 73.7% of patients receiving two or more. The most common antimicrobial groups were third generation cephalosporins, fluoroquinolones, and carbapenems (20.1%, 19.4%, and 14.1% of total antimicrobials, respectively).
A high prevalence of HAIs was observed, mainly caused by Gram-negative bacteria with high carbapenem resistance rates. This in combination with a high rate of antimicrobial use illustrates the urgent need to improve rational antimicrobial use and infection control efforts.
Health personnel and community workers are at the front line of the COVID-19 emergency response and need to be equipped with adequate knowledge related to epidemics for an effective response. This ...study aimed to identify the coverage of COVID-19 health information via different sources accessed by health workers and community workers in Vietnam. A cross-sectional study using a web-based survey was carried out from January to February 2020 in Vietnam. Respondent-driven sampling (RDS) was used for recruiting participants. We utilized the exploratory factor analysis (EFA) to examine the construct validity of the questionnaire. A higher percentage of participants knew about "Clinical and pathogen characteristics of COVID-19", compared to "Regulations and policies related to COVID-19". The percentage of participants accessing the information on "Guidelines and policies on prevention and control of COVID-19" was the lowest, especially among medical students. "Mass media and peer-educators" channels had a higher score of accessing COVID-19 information, compared to "Organizations/ agencies/ associations" sources. Participants consumed most of their COVID-19 information via "Internet, online newspapers, social networks". Our findings indicate an urgency to re-design training programs and communication activities for a more effective dissemination of information related to the COVID-19 epidemic or epidemics in general.
The sine cosine algorithm (SCA) is widely recognized for its efficacy in solving optimization problems, although it encounters challenges in striking a balance between exploration and exploitation. ...To improve these limitations, a novel model, termed the novel sine cosine algorithm (nSCA), is introduced. In this advanced model, the roulette wheel selection (RWS) mechanism and opposition-based learning (OBL) techniques are integrated to augment its global optimization capabilities. A meticulous evaluation of nSCA performance has been carried out in comparison with state-of-the-art optimization algorithms, including multi-verse optimizer (MVO), salp swarm algorithm (SSA), moth-flame optimization (MFO), grasshopper optimization algorithm (GOA), and whale optimization algorithm (WOA), in addition to the original SCA. This comparative analysis was conducted across a wide array of 23 classical test functions and 29 CEC2017 benchmark functions, thereby facilitating a comprehensive assessment. Further validation of nSCA utility has been achieved through its deployment in five distinct engineering optimization case studies. Its effectiveness and relevance in addressing real-world optimization issues have thus been emphasized. Across all conducted tests and practical applications, nSCA was found to outperform its competitors consistently, furnishing more effective solutions to both theoretical and applied optimization problems.
Vietnam, classified as a developing nation, encounters numerous challenges within its construction sector, including the scarcity of comprehensive and documented historical data regarding risks and a ...deficiency in embracing contemporary methodologies to mitigate the impact of risk factors on construction project objectives. This paper outlines initial findings from an ongoing research endeavor that centers on implementing Lean Construction (LC) techniques to enhance construction management practices specifically for marble floor finishing work within Vietnam. Therefore, this study aims to apply the construction lean principle combined with discrete-event simulation (DES) by using EZStrobe to simulate the marble floor finishing process in reality, from observing and collecting data of each activity in the actual process on the site. By building, running simulations, and resulting from real-world simulations, we'll understand the sources of waste, and then apply lean construction principles through methods such as just in time, reduce the batch size and resources priorities, and multi-skilled teams for the initial construction process. The study's lean modeling results has led to a 13% reduction in construction cycle time, a 141% improvement in process efficiency, a 268% enhancement in average productivity, and a 96% reduction in labor cost. The result has become the reference document resource for the managers and construction engineers to improve the performance of not only general finishing work but also marble floor finishing work.
Meta-heuristic algorithms distinguish themselves from conventional optimization methods owing to their intrinsic adaptability and straightforward implementation. Among them, the sine cosine algorithm ...(SCA) is lauded for its ability to transition seamlessly between exploration and exploitation phases throughout the optimization process. However, there exists potential for enhancing the balance that SCA maintains between exploration and exploitation. To augment the proficiency in global optimization of SCA, an innovative strategy-nSCA-that integrates the roulette wheel selection (RWS) with opposition-based learning was formulated. The robustness of nSCA was rigorously evaluated against leading-edge methods such as the genetic algorithm (GA), particle swarm optimization, moth-flame optimization, ant lion optimization, and multi-verse optimizer, as well as the foundational SCA. This evaluation included benchmarks set by both CEC 2019 and CEC 2021 test functions. Additionally, the performance of nSCA was confirmed through numerous practical optimization problems, emphasizing its effectiveness in applied settings. In all evaluations, nSCA consistently showcased superior performance compared to its evolutionary algorithm counterparts, delivering top-tier solutions for both benchmark functions and real-world optimization challenges. Given this compelling evidence, one can posit that nSCA serves as a strong candidate for addressing intricate optimization challenges found in real-world contexts, regardless of whether they are of a discrete or continuous nature.
The present study focuses on the problem of vehicle routing with limited capacity, with the objective of minimizing the transportation distance required to serve h clients with predetermined ...locations and needs. The aim is to create k trips that cover the shortest possible distance. To achieve this goal, a hybrid whale optimization algorithm (hGWOA) is proposed, which combines the whale optimization algorithm (WOA) with the grey wolf optimizer (GWO). The proposed hybrid model is comprised of two main steps. First step, the GWO's hunting mechanism is integrated transitioning to the utilization phase of WOA, and a newly devised state is introduced that is linked to GWO. In the second step, a novel technique is incorporated into the exploration mission phase to enhance the resolve after per iteration. The algorithm's performance is assessed and compared with other modern algorithms, including the GWO, WOA, ant lion optimizer (ALO), and dragonfly algorithm (DA) using 23 benchmark test functions and CEC2017 benchmark test function. The results indicate that the hybrid hGWOA method outperforms other algorithms in terms of delivery distance optimization for scenarios involving scale and complexity. These findings are corroborated through case studies related to cement delivery and a real-world scenario in Viet Nam.
The global construction industry plays a pivotal role, yet its unique characteristics pose distinctive challenges. Each construction project, marked by its individuality, substantial value, intricate ...scale, and constrained adaptability, confronts crucial limitations concerning time and cost. Despite contributing significantly to environmental concerns throughout construction activities and infrastructure operations, environmental considerations remain insufficiently addressed by project managers. This research introduces an improved rendition of the muti-objective grasshopper optimization algorithm (MOGOA), termed eMOGOA, as a novel methodology to tackle time, cost, and carbon dioxide emission trade-off problems (TCCP) in construction project management. To gauge its efficacy, a case study involving 29 activities is employed. eMOGOA amalgamates MOGOA, tournament selection (TS), and opposition-based learning (OBL) techniques to enhance the performance of the original MOGOA. The outcomes demonstrate that eMOGOA surpasses other optimization algorithms, such as MODA, MOSMA, MOALO and MOGOA when applied to TCCP. These findings underscore the efficiency and relevance of the eMOGOA algorithm within the realm of construction project management.
Managing a construction project is challenging because of cost, time, safety, and quality considerations. In the most projects, the cost of construction is one of the most critical aspect because ...material cost alone accounts for significant ratio of the total project. Therefore, the cost of construction materials should be controlled. In this study, we proposed the use of material requirements planning (MRP) to control the cost of construction materials. After determining the demand for the materials required for construction, we estimated both the quantity of materials required and time taken to deliver the materials to the construction site. Although economic order quantity models have been applied to analyze construction material costs, they do not accurately reflect concerns related to material cost. Therefore, we used the material supply chain model (construction logistics planning) to analyze material costs. To optimize MRP according to the current progress of a project, a novel approach combining the dragonfly algorithm (DA) and particle swarm optimization algorithm (PSO) was proposed. To verify the advanced searchability of the DA–PSO algorithm, the algorithm was compared with the gray wolf and the genetic algorithms.
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