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This study investigates the entropy generation and heat transfer of water density inversion region in a square cavity with the different relative positions of hot and cold walls by numerical simulation. Mathematical models are solved using the EES (Engineering Equation Solver) program. A physical model is a 38 mm square cavity filled with water, with cold and hot wall temperatures maintained at 0oC and 10oC. Results indicated that the Nusselt number of cases 1 (cold and hot walls on both sides) is higher than case 2 (hot wall above, cold wall below) and case 3 (cold wall above, hot wall below) by 1.582 and 1.059 times, respectively. The average total generation per unit volume in case 1 increases by 1.577 times and 1.059 times, respectively, compared to cases 2 and 3, which almost correspondingly increase with heat transfer capacity. Entropy generation by fluid friction is negligible

There has been an outstanding improvement in injection technology in the spark ignition (SI) engines’ fuel supply systems, from traditional carburettors with throttle body injection (TBI), manifold port or multi-point injection (MPI) to direct injection (DI). This paper has developed an MPI intake manifold model and investigated fuel injector angles using a multiphase CFD package provided by FloEFD software. A wide range of injector angles from 22 to 30 degrees has been investigated in order to evaluate the influence of the injector angle on the wall-wetting issue, a critical problem of manifold injection systems in SI engines. The intake air pressure differential in the manifold was also evaluated. The results show that the fuel injector angle affects the multiphase flow in the MPI system. The manifold diameter meets the design specifications, and the pressure differential in the manifold is quite small. With an injector angle of 25 degrees, the wall-wetting issue is minimal. This model could be used for further studies on engine performance and emission formation.

Most heavy precipitation events and extreme flooding over the U.S. Pacific coast can be linked to prevalent atmospheric river (AR) conditions. Thus, reliable quantitative precipitation estimation with a rich spatiotemporal resolution is vital for water management and early warning systems of flooding and landslides over these regions. At the same time, high-quality near-real-time measurements of AR precipitation remain challenging due to the complex topographic features of land surface and meteorological conditions of the region: specifically, orographic features occlude radar measurements while infrared-based algorithms face challenges, differentiating between both cold brightband (BB) precipitation and the warmer nonbrightband (NBB) precipitation. It should be noted that the latter precipitation is characterized by greater orographic enhancement. In this study, we evaluate the performance of a recently developed near-real-time satellite precipitation algorithm: Precipitation Estimation from Remotely Sensed Information Using Artificial Neural Networks (PERSIANN) Dynamic Infrared–Rain Rate-Now (PDIR-Now). This model is primarily dependent on infrared information from geostationary satellites as input; consequently, PDIR-Now has the advantage of short data latency, 15–60-min delay between observation to precipitation product delivery. The performance of PDIR-Now is analyzed with a focus on AR-related events for cases dominated by NBB and BB precipitation over the Russian River basin. In our investigations, we utilize S-band (3-GHz) precipitation profilers with Joss/Parsivel disdrometer measurements at the Middletown and Santa Rosa stations to classify BB and NBB precipitation events. In general, our analysis shows that PDIR-Now is more skillful in retrieving precipitation rates over both BB and NBB events across the topologically complex study area as compared to PERSIANN-Cloud Classification System (CCS). Also, we discuss the performance of well-known operational near-real-time precipitation products from 2017 to 2019. Conventional categorical and volumetric categorical indices, as well as continuous statistical metrics, are used to show the differences between various high-resolution precipitation products such as Multi-Radar Multi-Sensor (MRMS).

In this research, the effect of the colling channel design on the warpage of a thin-wall injection molding plastic product has been numerically simulated. Three cooling channel with different designs are considered. The results indicate that the total displacement of warpage on product significantly depends on cooling channel layout. Using a cooling channel with conformal form, the heat accumulation inside the box is released. Therefore, the maximum total displacement dramatically reduces from 1.05 mm in the original design to 0.19 mm. Though increasing conformal cooling channel length will even decrease the maximum displacement to 0.09 mm, the jump of coolant pressure to 86.7 MPa is a big drawback. Therefore, the final suitable design for this system is the short conformal cooling channel.

This paper analyzes a model of aid allocation equalizingthe opportunity between recipient countries to reach acommon poverty reduction goal. We propose a fair andefficient aid allocation based on a multicriteria principle.The model considers structural handicaps in recipientcountries in terms of lack of human capital and economicvulnerability, their initial poverty, and the naturalgap between the growth rate required to reach a developmentgoal and the observed one. We show that ourproposed aid allocation favors poor and vulnerablecountries with our multicriteria principle. It substantiallydiffers from the observed allocation. Analyses alsoshed light on the impact of the donors' aversion to thelow natural growth gap in recipient countries on theoptimal aid allocation and the marginal efficiency of aid.

There is a current wave of a new generation of digital solutions based on intelligent systems, hybrid digital twins and AI-driven optimization tools to assure quality in smart factories. Such digital solutions heavily depend on quality-related information within the supply chain business ecosystem to drive zero-waste value chains. To empower zero-waste value chain strategies with meaningful, reliable, and trustful data, there must be a solution for end-to-end industrial data traceability, trust, and security across multiple process chains or even inter-organizational supply chains. In this paper, we first present Product, Process, and Data quality services to drive zero-waste value chain strategies. Following this, we present the Trusted Framework (TF), which is a key enabler for the secure and effective sharing of quality-related information within the supply chain business ecosystem, and thus for quality optimization actions towards zero-defect manufacturing. The TF specification includes the data model and format of the Process/Product/Data (PPD) Quality Hallmark, the OpenAPI exposed to factory system and a comprehensive Identity Management layer, for secure horizontal- and vertical quality data integration. The PPD hallmark and the TF already address some of the industrial needs to have a trusted approach to share quality data between the different stakeholders of the production chain to empower zero-waste value chain strategies.

IntroductionInfection with Plasmodium vivax is a recognized cause of severe malaria including deaths. The exact burden and patterns of severe P. vivax monoinfections is however still not well quantified, especially in P. vivax endemic regions. We examined the magnitude and patterns of severe malaria caused by monoinfections of P. vivax and associated predictors among patients admitted to a tertiary care center for malaria in Vietnam.MethodsA retrospective cohort study was conducted based on the patients’ medical records at the Hospital for Tropical Diseases from January 2015 to December 2018. Extracted information included demographic, epidemiologic, clinical, laboratory and treatment characteristics.ResultsMonoinfections with P. vivax were found in 153 (34.5, 95% CI 30.3–39.1%) patients of whom, uncomplicated and severe malaria were documented in 89.5% (137/153, 95% CI 83.7–93.5%) and 10.5% (16/153, 95% CI 6.5–16.3%), respectively. Patterns of severe malaria included jaundice (8 cases), hypoglycemia (3 cases), shock (2 cases), anemia (2 cases), and cerebral malaria (1 case). Among 153 patients, 73 (47.7%) had classic malaria paroxysm, 57 (37.3%) had >7 days of illness at the time of admission, and 40 (26.1%) were referred from other hospitals. A misdiagnosis as having other diseases from malaria cases coming from other hospitals was up to 32.5% (13/40). Being admitted to hospital after day 7th of illness (AOR = 6.33, 95% CI 1.14–35.30, p = 0.035) was a predictor of severe malaria. Severe malaria was statistically associated with longer hospital length of stay (p = 0.035). Early and late treatment failures and recrudescence were not recorded. All patients recovered completely.DiscussionThis study confirms the emergence of severe vivax malaria in Vietnam which is associated with delayed hospital admission and increased hospital length of stay. Clinical manifestations of P. vivax infection can be misdiagnosed which results in delayed treatment. To meet the goal of malaria elimination by 2030, it is crucial that the non-tertiary hospitals have the capacity to quickly and correctly diagnose malaria and then provide treatment for malaria including P. vivax infections. More robust studies need to be conducted to fully elucidate the magnitude of severe P. vivax in Vietnam.

Gas temperature is an essential parameter in estimating production rate and pressure model inside the production tubing. Three heat transfer mechanisms named as conduction, convection and radiation have been applied to identify the gas temperature declination. Gas wells with bottom hole temperature greater than 160oC and gas rates reaching 55 million standard ft3 per day (MMscf/d) indicate a higher heat loss due to convection than the other two mechanisms. Conduction is the main factor in explaining heat diffusion to the surrounding at the top of the well. The study presents a strong similarity in value compared to the field data by combining Gray correlation and heat transfer model to predict the bottom hole pressure with an error of approximately 3%. Additionally, the gas temperature affects gas rate prediction through gas viscosity and Z factor. With the gas composition mostly containing C1 (70.5%), gas viscosity and Z coefficient at the wellhead are not as high as 0.017 cp and 0.92 respectively. It is possible to have a two-phase flow, then a temperature model along the production tubing is necessary to ensure the gas production rate.

Sustainable tourism has become a global concern in recent years. An approach is proposed to evaluate the sustainability of hotels and to compare their real actions and promotions. Following a study of relevant literature, a set of criteria for so-called green hotels was proposed. Multi-criteria decision making methods were then used, including the best–worst method to calculate the weights of all criteria, and the fuzzy technique-for-order-of-preference-by-similarity-to-ideal-solution method was then implemented to rank sustainability performance. The proposed model was applied to evaluate five hotels in Vietnam. The results showed that the criterion named ‘green policy and regulation’ played the most significant role in the sustainability practice of hotels. Sensitivity analysis was also applied to validate the research framework and eliminate the effects of bias. The proposed model is recommended for use in various other fields.

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.

Endolichenic fungi are considered a promising source of new materials. For further evaluation of some biological activities of the Trichoderma strains isolated from lichens Dirinaria spp. and Cryptothecia spp., their antifungal and antibacterial activities were screened by the methods of dual culture and environmental toxicity. Substrate degradation was evaluated using the qualitative enzyme assays. Fourteen strains of Trichoderma spp. were isolated from 60 lichen samples. All the isolates were able to inhibit the radial growth of tested fungal strains (Bipolaris spp., Colletotrichum spp., Corynespora cassiicola, and Fusarium spp.). Otherwise, only 12/14 isolates were found capable of competing for substrates with Ralstonia solanacearum. The cell-free supernatant obtained from the cultures possessed both antifungal and antibacterial activities. The antagonistic activity of the isolates was selective. Most of the isolates were able to degrade at least one of the investigated substrates, namely cellulose, pectin, and starch. All strains could produce peroxidase; none of the isolates possessed laccase and tyrosinase. A potential antagonistic fungal strain VDT6 has been identified as Trichoderma harzianum. The assessment results indicated that the Trichoderma isolates could be used in agriculture as biological control agents.

Manufacturing converts raw materials into finished products using machine tools for controlled material removal or deposition. It can be observed using sensors installed within and around machine tools. These sensors measure quantities, such as vibrations, cutting forces, temperature, currents, power consumption, and acoustic emission, to diagnose defects and enable zero-defect manufacturing as part of the Industry 4.0 vision. The continuity of high-quality sensor data streams is fundamental to predicting phenomena, such as geometric deformations, surface roughness, excessive coolant use, and imminent tool wear with adequate accuracy and appropriate timing. However, in practice, data acquired by some sensors can be of poor quality and unsuitable for prediction due to sensor faults stemming from environmental factors. In this paper, we answer if we can repair erroneous data in a faulty sensor based on data simultaneously available in redundant sensors that observe the same process. We present a machine learning pipeline to synthesize virtual sensors that can step in for faulty sensors to maintain reasonable quality and continuity in sensor data streams. We have validated the synthesized virtual sensors in four industrial case studies.

The Internet of Things (IoT) and Cyber-Physical Systems (CPS) are the backbones of Industry 4.0, where data quality is crucial for decision support. Data quality in these systems can deteriorate due to sensor failures or uncertain operating environments. Our objective is to summarize and assess the research efforts that address data quality in data-centric CPS/IoT industrial applications. We systematically review the state-of-the-art data quality techniques for CPS and IoT in Industry 4.0 through a systematic literature review (SLR) study. We pose three research questions, define selection and exclusion criteria for primary studies, and extract and synthesize data from these studies to answer our research questions. Our most significant results are (i) the list of data quality issues, their sources, and application domains, (ii) the best practices and metrics for managing data quality, (iii) the software engineering solutions employed to manage data quality, and (iv) the state of the data quality techniques (data repair, cleaning, and monitoring) in the application domains. The results of our SLR can help researchers obtain an overview of existing data quality issues, techniques, metrics, and best practices. We suggest research directions that require attention from the research community for follow-up work.

Recent developments in “headline-making” deep neural networks (DNNs), specifically convolutional neural networks (CNNs), along with advancements in computational power, open great opportunities to integrate massive amounts of real-time observations to characterize spatiotemporal structures of surface precipitation. This study aims to develop a CNN algorithm, named Deep Neural Network High Spatiotemporal Resolution Precipitation Estimation (Deep-STEP), that ingests direct satellite passive microwave (PMW) brightness temperatures (Tbs) at emission and scattering frequencies combined with infrared (IR) Tbs from geostationary satellites and surface information to automatically extract geospatial features related to the precipitable clouds. These features allow the end-to-end Deep-STEP algorithm to instantaneously map surface precipitation intensities with a spatial resolution of 4 km. The main advantages of Deep-STEP, as compared to current state-of-the-art techniques, are 1) it learns and estimates complex precipitation systems directly from raw measurements in near–real time, 2) it uses the automatic spatial neighborhood feature extraction approach, and 3) it fuses coarse-resolution PMW footprints with IR images to reliably retrieve surface precipitation at a high spatial resolution. We anticipate our proposed DNN algorithm to be a starting point for more sophisticated and efficient precipitation retrieval systems in terms of accuracy, fine spatial pattern detection skills, and computational costs.

In this study, glucomannan-poly(acrylic acid) hydrogels were synthesized under different conditions. The hydrogels consist of glucomannan and acrylic acid and are crosslinked by N,N-methylene-bis-(acrylamide). The structure and morphology of the hydrogels were investigated by using Fourier-transform infrared spectroscopy (FT-IR) and scanning electron microscope (SEM). The swelling ratio, biodegradation and pH-sensitive properties, relationship between hydrogel swelling rate and 5-ASA adsorption-desorption capacity, influence of medium pH on 5-ASA desorption, and 5-ASA adsorption kinetics were studied. The swelling ratio of the synthesized hydrogel samples is 16.70–56.21 times. This ratio is low in the pH 1.0 media and increases significantly in the pH 7,4 media. The hydrogels are biodegradable in the presence of cellulase (400 U·mg–1) in a pH 7.4 phosphate buffer at 37 °C (69.8% after ten days; the cellulase concentration 1.5 g·L­–1). The hydrogels exhibit high adsorption and desorption capacities for 5-ASA. The adsorption kinetics follows the pseudo-first-order model. These hydrogels can be applied to smart drug delivery systems.

Pervious concrete pavement has been used widely as an effective practice for water management in low-impact development techniques. The hydrologic performance of pervious concrete pavement depends significantlyon the rainfall intensity and the designed slope. This study assessed the hydrologic performance of pervious concrete pavement by evaluating the time for surface ponding via finite element analysis. A series of simulations were carried out to explore the relationship between hydrologic performance and pervious concrete pavement by the Hydrus 2D program. The research’s results showed that as the slope increased, the time of surface ponding also increased. The data indicated that the slope variable had a low impact on the water level in pervious concrete pavement under a constant rainfall intensity. Observation of the effect of rainfall intensity showed that when the rainfall intensity increased twofold, the time for surface ponding dropped about two times. Furthermore, when surface ponding appeared, pervious concrete pavement at higher rainfall intensity had lower water content. The rainfall intensity also significantly affects the hydrologicperformance of the pervious concrete pavement. This study only assessed the hydrologic performance by using the time for surface ponding via finite element analysis. Further experimental studies should be conducted to examine the relationship of other factors to the hydrologic performance of pervious concrete pavement.

Purpose The purpose of this paper is to investigate how emotional experience factors affect the obligation-related commitment. The emotional experience factors consist of positive factor, job satisfaction and negative factor, and emotional exhaustion from the study of Wong and Tay (2010); both facets have been studied with affective commitment but yet lack of study about normative commitment. Affective and normative commitments are both components of organizational commitment; while affective commitment refers to the intention of keeping the position because of the desire but not by any other attachment, whereas normative commitment is defined as staying for the reasons related to obligation. Also, by applying withdrawal construct of validation of job-hopping behavior, a connection was constructed between normative commitment and the two separated motives of job-hopping behavior, advance motive and escape motive (Lake et al., 2017). Design/methodology/approach To achieve such objectives, quantitative research is implicated using the primary data collected from online and offline surveys from employees who are currently working in many information technology (IT)-related companies in Ho Chi Minh City. As for analyzing tactic and method, SmartPLS software is applied to ensure the reliability and validity of the factors as well as confirming the significant and developing the hypotheses from the proposed construct. Findings The findings reveal the significant affect from workplace escape motive that leads to the behavior of job hop, but not from the career enhancement. The result also revealed the effect of indebted obligation when most of the employees maintain the low level of normative commitment and the proper increase in emotional exhaustion and job satisfaction can help to improve the normative commitment of such employees. Originality/value This study makes its contribution and recommendations to human resource management in IT industry.

Sustainable development has become a global tendency in the growth orientation of governments. Renewable energies such as wind and solar have attracted many concerns in reducing the level of fossil fuels dependence. One of the most significant stages in such a transmission process is determining the most suitable location. This article aims to propose a novelty approach combining two multiple criteria decision making models, namely data envelopment analysis (DEA) and analysis hierarchy process (AHP) to recommend the most optimal site for wind power plants. Vietnam has been one of the most successful ASEAN nations in attracting renewable energy investments; hence, the article considers it as a case study. In the first stage, there are five inputs and outputs conducted by DEA models to choose the five most potential alternatives among 12 locations for the next stage. In the second stage, the AHP model is implemented on five main criteria and 20 subcriteria that are referred to the options of experienced experts. The findings show that Ninh Thuan province, which is obtained with the highest final score (0.374), is the most suitable location for wind power plants construction. The article has contributed to renewable literature in terms of methodologies as well as to practice for both public and private stakeholders.

This paper uses Molecular Dynamics (MD) method to study the influence of high temperature (T) and high pressure (P) on the structure and phase transition of Fe2O3 materials. The results show that, when increasing the temperature from T = 300 K to T = 7000 K, P = 0.0 GPa, the size (1) of the Fe2O3 materials increases, the energy (E) increases, the length link (r) decreased, the number of structural units FeO4, FeO5 increased, and FeO6 decreased. Similarly, as the pressure (P) is increased, from P = 0 GPa to P = 360 GPa at temperatures T, l decreases, E increases, r decreases, FeO4 decreases and disappears, FeO5 decreases, and FeO6 increases at high P with P ≥ 150 GPa, FeO5 disappeared at P ≥ 250 GPa and only FeO6 appeared at T = 2300, 7000 K. In addition, when increasing T, P, the bond angle of Fe–O–Fe, O–Fe–O decreases, E increases, r decreases, l increases when T increases and l decreases when P increases, leading to the number of structural units FeO4, FeO5 increasing and FeO6 decreasing when T increases and vice versa when P increases. In addition, the phase transition temperature (Tm), Tm = 2300 K was determined. All the obtained results will be the basis for future experimental studies of amorphous Fe2O3 materials.

Atmospheric mercury (Hg) cycling is sensitive to climate-driven changes, but links with various teleconnections remain unestablished. Here, we revealed the El Niño-Southern Oscillation (ENSO) influence on gaseous elemental mercury (GEM) concentrations recorded at a background station in East Asia using the Hilbert-Huang transform (HHT). The timing and magnitude of GEM intrinsic variations were clearly distinguished by ensemble empirical mode decomposition (EEMD), revealing the amplitude of the GEM concentration interannual variability (IAV) is greater than that for diurnal and seasonal variability. We show that changes in the annual cycle of GEM were modulated by significant IAVs at time scales of 2-7 years, highlighted by a robust GEM IAV-ENSO relationship of the associated intrinsic mode functions. With confirmation that ENSO modulates the GEM annual cycle, we then found that weaker GEM annual cycles may have resulted from El Niño-accelerated Hg evasion from the ocean. Furthermore, the relationship between ENSO and GEM is sensitive to extreme events (i.e., 2015-2016 El Niño), resulting in perturbation of the long-term trend and atmospheric Hg cycling. Future climate change will likely increase the number of extreme El Niño events and, hence, could alter atmospheric Hg cycling and influence the effectiveness evaluation of the Minamata Convention on Mercury.

Glucomannan/graphene oxide (GM/GO) hydrogel was synthesized by using calcium hydroxide as the crosslinker. The synthesized material was characterized by using IR, XRD, SEM, EDX and RAMAN technology. The composite hydrogel was used for removal of organic dyes from aqueous solution. The results showed that the GM/GO hydrogel had a porous structure and a high adsorption capacity toward methylene blue (MB). The pseudo-second-order kinetic model could fit the rate equation of MB adsorption onto the GM/GO hydrogel. The adsorption of MB onto GM/GO hydrogel was a spontaneous process. In addition, the equilibrium adsorption isotherm data indicated that equilibrium data were fitted to the Langmuir isotherm and the maximum dye adsorption capacity was 198,69 mg.g-1. Moreover, the hydrogel was stable and easily recovered and adsorption capacity was around 97% of the initial saturation adsorption capacity after being used five times.