Model selection with decision support model for US natural gas consumption forecasting

Reliable prediction of natural gas consumption helps make the right decisions ensuring sustainable economic growth. This problem is addressed here by introducing a hybrid mathematical model defined as the Choquet integral-based model. Model selection is based on decision support model to consider the model performance more comprehensively. Different from the previous literature, we focus on the interaction between models when combine models. This paper adds grey accumulation generating operator to Holt-Winters model to capture more information in time series, and the grey wolf optimizer obtains the associated parameters. The proposed model can deal with seasonal (short-term) variability using season auto-regression moving average computation. Besides, it uses the long short term memory neural network to deal with long-term variability. The effectiveness of the developed model is validated on natural gas consumption due to the COVID-19 pandemic in the USA. For this, the model is customized using the publicly available datasets relevant to the USA energy sector. The model shows better robustness and outperforms other similar models since it consider the interaction between models. This means that it ensures reliable perdition, taking the highly uncertain factor (e.g., the COVID-19) into account. © 2023 Elsevier Ltd

How Message Frames Promote People's Willingness to Get Vaccinated? The Mediation Role of Perceived Net Benefits

Objectives: This study aims to evaluate the association among framed messages (egoism-, altruism-, and loss-framed information), perceived net benefits (PNB), and willingness to receive a COVID-19 vaccine. Methods: A between-subject survey experiment was designed to assess the above association. A total of 1,316 individuals were included in this study. The participants were randomly assigned to one control group (receiving non-framed information) and three experimental groups (receiving egoism-, altruism-, and loss-framed information). The participants then reported their vaccination willingness and perceived effectiveness and side effects of vaccination. PNB was determined by subtracting the perceived side effects from perceived effectiveness. Results: Compared with the control group, participants in the experimental groups exhibited stronger vaccination willingness. Higher PNB levels were associated with enhanced vaccination willingness. However, only loss-framed messages indirectly affected vaccination willingness through PNB. Conclusion: PNB can mediate the impact of message framing on vaccination willingness. However, the mediation effect of PNB was only found in the relationship between loss-framed messages and vaccination willingness. Copyright © 2023 Li, Gong, Tang and Zhou.

The personalized recommendation for OTA flight cancellation and change services during the pandemic

The COVID-19 pandemic has had a dramatic impact on people’s travels. Due to the recurrent pandemic and regionally different policies in China, travelers must pay a lot for flight cancellations and changes. To accommodate this, online travel agencies (OTA) can provide a more flexible ancillary as a supplement to the airline company's services. Here, we introduced the upgraded all-in-one (AIO) service package, which offers compensation for flight delays, changes, or refund. We also designed a dynamic recommendation engine (DRE), which can make real-time personalized recommendations. Backed by AB testing, the machine learning-based DRE not only raises the package attach rate without interrupting the flight ordering process, but also helps the customers cut cost when making flight cancellations or changes.

Study on the construction workforce management based on lean construction in the context of COVID-19

Purpose: The construction industry is facing challenges not only for workers' mobility in the pandemic situation but also for Lean Construction (LC) practise in responding to the high-quality development during the post-pandemic. As such, this paper presents a construction workforce management framework based on LC to manage both the emergency goal in migrant worker management and the long-term goal in labour productivity improvement in China. Design/methodology/approach: The framework is created based on the integrated culture and technology strategies of LC. A combination of qualitative and quantitative methods is taken to explore factors influencing the mobility of construction workers and to measure labour productivity improvement. The case study approach is adopted to demonstrate the framework application. Findings: For method application, a time-and-motion study and Percent Plan Complete indicator are proposed to offer labour productivity measurements of “resources efficiency” and “flow efficiency”. Moreover, the case study provides an industry level solution for construction workforce management and Lean Construction culture shaping, as well as witnesses the LC culture and technology strategies alignment contributing to LC practise innovation. Originality/value: Compared with previous studies which emphasised solely LC techniques rather than socio-technical system thinking, the proposed integration framework as well as implementation of “Worker's Home” and “Lean Work Package” management models in the COVID-19 pandemic contribute to new extensions of both the fundamental of knowledge and practise in LC. © 2022, Emerald Publishing Limited.

Assessment of PM2.5 Chemical Composition and Air Quality Monitoring: Implications of Air Pollutants Emissions from a Night Market in Kaohsiung City, Taiwan

There are around 300 night markets in Taiwan, and they have been drawing an increasing number of tourists in recent years. As a result, public awareness over air quality in the night markets has grown tremendously. In response to this, a specific night market in Kaohsiung City was chosen for this study in order to characterize the existing air quality in and around the night markets. In this present study, we employed an Industrial Source Complex Short-Term (ISCST3) air quality model for the simulation of PM2.5 diffusions. The model as a technique can simulate the pollutants emissions, diffusions, transportation, and pollution sources in specific areas and subsequently evaluate the influence between the source and the receiver. Therefore, we compared pollutants emissions data from several air quality monitoring stations with our sampling data of three different sampling sites in Kaohsiung City. The findings of this study showed that the average concentration of PM2.5 was in the range of 29-61 mu g m(-3) during opening hours of the night market, whereas the average concentration of PM2.5 range was between 22-38 mu g m(-3) before the night market opening hours. The concentration of metallic elements (ME) (Mg, Na, Cr, Mn, Fe, Cu, Al, Ba, Cd, Pb and Ca) was determined with the support of Inductively Coupled Plasma Optical Emission Spectroscopy (ICP-OES). During the night market opening hours, the result disclosed that the ME concentrations in PM2.5 was in an increasing order as follows: Na > Fe > Al > Ca. With respect to the concentration of carbonaceous species, our results showed that the highest total carbon (TC) concentration was found to be 6.52 mu g m(-3) during the downwind sampling interval. The highest elemental carbon (EC) and organic carbon (OC) concentration were found to be 6.53 mu g m(-3) and 2.70 mu g m(-3) of the PM2.5 concentration, respectively. This study's findings have significant consequences for Taiwan policymakers and urban planners, particularly those responsible for coordinating environmental protection and economic development in cities. Therefore, policy actions to abate urban air pollution can be attained on diverse governing echelons, resulting in synergistic effects such as a reduction in climate change impacts.

Analysis and prediction of economic cross-correlation under COVID-19 based on MF-LSTM and WNN

In the era of COVID-19, it is particularly important to analyze the correlation of economic indicators and propose corresponding policies. In this paper, a number of industry indicators that have an important impact on the economy are selected, and normalization, interpolation, and PCA operations are performed on them. Based on the MF-LSTM neural network, this paper analyzes the many-to-one correlation between industry indicators and macroeconomic indicators. Furthermore, based on the WNN neural network, wavelet analysis is used to predict the impact of macroeconomic indicators on people's livelihood indicators under time series. Based on the above model, the coupling relationship between industry indicators and macroeconomic indicators and the development trend of people's livelihood indicators for a period of time in the future have been obtained, and the accuracy of the model has also been verified. © 2021 ACM.

Denali, a phase 3B multicenter, open-label single-arm study of roxadustat: Operational learnings within us dialysis organizations

Background: Roxadustat is an oral hypoxia-inducible factor prolyl hydroxylase (HIF-PH) inhibitor that promotes erythropoiesis and improves iron availability in patients with anemia of chronic kidney disease (CKD). This trial aims to provide practical data on roxadustat use in dialysis patients with anemia via a semi-pragmatic evaluation of introduction into providers' practices (Fresenius Medical Care). Methods: This open-label, single-arm study assesses the efficacy and safety of roxadustat in correcting/maintaining hemoglobin (Hb) in patients with CKD-related anemia receiving in-center/home dialysis at nine US sites (NCT04410198). Initial roxadustat dose is weight-based (erythropoiesis-stimulating agent [ESA]-naïve patients) or guided by an ESA dose-conversion algorithm (ESA patients), in this trial targeting Hb=11±1 g/dL. Roxadustat dose is titrated every 4 weeks based on Hb level or rate of change, with 24-week treatment duration and up to 1-year extension. Efficacy is assessed by change from baseline in Hb and proportion of patients achieving mean Hb ≥10 g/dL averaged over weeks 16-24. Exploratory endpoints include time to first red blood cell transfusion, proportion of patients achieving mean Hb ≥10 g/dL in first 8 weeks, intravenous iron use, and dosing adherence. Safety endpoints include treatment-emergent adverse events (AE), with COVID-19 positivity an AE of special interest. Results: This ongoing trial was successfully initiated and enrolled (n=203) during the COVID-19 pandemic, with modifications for home dialysis. Baseline characteristics appear representative of the US dialysis population (Table). Conclusions: This trial adds to phase 3 studies of roxadustat by evaluating its use in treating anemia of CKD in home/in-center dialysis patients during the COVID-19 pandemic, while providing a view into operationalization and ease of real-world use. Full study results will be presented. (Table Presented) .

Feasibility Study of Detection of Coronavirus Disease 2019 with Microwave Medical Imaging

This paper studies the feasibility of detecting the pneumonia due to the COVID-19 with microwave medical imaging. One challenge while formulating such a problem is to identify the disease in lungs whose dielectric permittivity is dynamically fluctuating with the respiration. In this paper, we utilize this feature by assuming that the permittivity of the disease has minor variation at microwave frequencies during the respiration, and thus the dielectric variance of the pixels at the diseased site over a number of consecutive images significantly differs from those of the other tissues in the thorax. Based on this assumption, we propose two approaches that make use of the a priori information (API) on the position of the heart and the symmetry of the thorax, respectively, to identify a diseased lung. Finally, these two approaches are numerically validated on a thorax phantom, and their performance is compared.

Effect of Perceived Organizational Support on Fatigue of Chinese Nurses during the COVID-19 Pandemic: Resilience as a Mediator

As working for the nurse is believed to be one of the most stressful professions, nurses are particularly at risk of experiencing fatigue. Especially during the COVID-19 pandemic, fatigue among nurses may affect physical and mental health seriously, which is attracting increasing attention of researchers and clinical practitioners to find effective prevention measures to alleviate nurses' fatigue. This study aims to investigate the mediational effect of resilience on the relationship between nurses' perceived organizational support and fatigue. A total of 476 nurses from secondary and above hospitals in Hebei province, China during March and April in 2020, were investigated with Perceived Organizational Support Scale, Resilience Scale and Work Fatigue Inventory. Structural equation modeling was conducted to explore the mechanisms of nurses' perceived organizational support on fatigue. Results revealed that perceived organizational support can decrease the nurses' mental/physical/emotional fatigue through the mediating role of resilience. These findings guide for hospital managers to identify effective prevention strategies to alleviate the fatigue of clinical nurses.

Risk factors for disease severity, unimprovement, and mortality in COVID-19 patients in Wuhan, China.

OBJECTIVE: In December 2019, coronavirus disease (COVID-19) emerged in Wuhan. However, the characteristics and risk factors associated with disease severity, unimprovement and mortality are unclear and our objective is to throw some light on these. METHODS: All consecutive patients diagnosed with COVID-19 admitted to the Renmin Hospital of Wuhan University from January 11 to February 6, 2020, were enrolled in this retrospective cohort study. RESULTS: A total of 663 COVID-19 patients were included in this study. Among these, 247 (37.3%) had at least one kind of chronic disease; 0.5% of the patients (n = 3) were diagnosed with mild COVID-19, while 37.8% (251/663), 47.5% (315/663), and 14.2% (94/663) were in moderate, severe, and critical conditions, respectively. In our hospital, during follow-up 251 of 663 patients (37.9%) improved and 25 patients died, a mortality rate of 3.77%. Older patients (>60 years old) and those with chronic diseases were prone to have a severe to critical COVID-19 condition, to show unimprovement, and to die (p <0.001, <0.001). Multivariate logistic regression analysis identified being male (OR = 0.486, 95%CI 0.311-0.758; p 0.001), having a severe COVID-19 condition (OR = 0.129, 95%CI 0.082-0.201; p <0.001), expectoration (OR = 1.796, 95%CI 1.062-3.036; p 0.029), muscle ache (OR = 0.309, 95%CI 0.153-0.626; p 0.001), and decreased albumin (OR = 1.929, 95%CI 1.199-3.104; p 0.007) as being associated with unimprovement in COVID-19 patients. CONCLUSION: Male sex, a severe COVID-19 condition, expectoration, muscle ache, and decreased albumin were independent risk factors which influence the improvement of COVID-19 patients.