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Due to extreme weather conditions and anomalous events such as the COVID-19 pandemic, utilities and grid operators worldwide face unprecedented challenges. These unanticipated changes in trends introduce new uncertainties in conventional short-term electricity demand forecasting (EDF) since its result depends on recent usage as an input variable. In order to quantify the uncertainty of EDF effectively, this paper proposes a comprehensive probabilistic EFD method based on Gaussian process regression (GPR) and kernel density estimation (KDE). GPR is a non-parametric method based on Bayesian theory, which can handle the uncertainties in EDF using limited data. Mobility data is incorporated to manage uncertainty and pattern changes and increase forecasting model scalability. This study first performs a correlation study for feature selection that comprises weather, renewable and non-renewable energy, and mobility data. Then, different kernel functions of GPR are compared, and the optimal function is recommended for real applications. Finally, real data are used to validate the effectiveness of the proposed model and are elaborated with three scenarios. Comparison results with other conventional adopted methods show that the proposed method can achieve high forecasting accuracy with a minimum quantity of data while addressing forecasting uncertainty, thus improving decision-making.
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Background: Since the start of the COVID-19 epidemic in late 2019, there have been more than 152 affected regions and countries with over 110,000 confirmed cases outside mainland China. Methods: We analysed COVID-19 cases among travellers from mainland China to different regions and countries, comparing the region- and country-specific rates of detected and confirmed cases per flight volume to estimate the relative sensitivity of surveillance in different regions and countries. Results: Although travel restrictions from Wuhan City and other cities across China may have reduced the absolute number of travellers to and from China, we estimated that more than two thirds (70%, 95% CI: 54% - 80%, compared to Singapore;75%, 95% CI: 66% - 82%, compared to multiple countries) of cases exported from mainland China have remained undetected. Conclusions: These undetected cases potentially resulted in multiple chains of human-to-human transmission outside mainland China.
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PurposeBased on text content analysis using big data, this study aims to explore differences in guest perceptions of peer-to-peer accommodations before and after COVID-19 to provide suggestions for the development of these properties in China postpandemic.Design/methodology/approachA guest perception dictionary was established by collecting Ctrip customer reviews of peer-to-peer accommodations. After data cleaning, thematic word analysis and semantic association network analysis were used to explore perceptions and thematic differences before and after COVID-19.FindingsThis research constructed a multidimensional framework of guest-perceived values for peer-to-peer accommodation in the context of COVID-19. The findings showed that the emphasis on functionality in peer-to-peer accommodation changed;perceived emotional values associated with peer-to-peer stays were more complex;perceived social values decreased, host–guest interactions were reduced and online communication became a stronger trend;tourist preferences for types of experiences changed, and people changed their destination selections;perceived conditional value was reflected in perceived risks, and the perceptions of environmental health, service and physical risks increased.Research limitations/implicationsThis research has constructed a multidimensional framework of tourist perceived value on the basis of peer-to-peer accommodation context and epidemic background and has thus shown the changes in tourist perceived value of peer-to-peer accommodation before and after COVID-19.Originality/valueTo the best of authors' knowledge, this research constitutes the first attempt to explore the perceptual differences for peer-to-peer accommodations before and after COVID-19 based on an extensive data set of online reviews from multiple provinces of China.
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"Digital transformation of education” is a hot topic of present educational reform and the development tendency of future educational innovation. Digital transformation of education, as a process stressing technology-driven educational innovation and reform, boasts the importance of development in its birth and practice. It is driven jointly by social and cultural transitions, the impetus of technical innovation, the guidance of national policies, and the endogenous development of the education system. The logic of practice of the transformation can be regarded as the behavior choice dominated by value and intentions, and the underlying generation principles in practice comprise the "problem-driven + concept-led” principle, the "system evolution + innovative breakthrough” principle, and the "value assessment + iterative optimization” principle. At present, under the background of constructing the new ecology of a smart society and a high-quality education system, and under the pressure of COVID-19 pandemic, the transformation faces golden development opportunities. Nonetheless, the present transformation practice still has some differences from the expected value orientation. Therefore, it is required to research and develop a mature digital education model or framework, carry out a national digital education preparation campaign, work for pilot schemes for the transformation at all levels and of all kinds, attach importance to theoretical research on education relating to the digital transformation of education, and more actively embrace the new wave of educational reform worldwide.
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PurposeThis paper aims to identify and assess global risks in the supply chain performance.Design/methodology/approachFirst, global risks are identified and classified according to three criteria: content, probability and context. A set of supply chain performance indicators are then defined by the theory of resource-based view and balanced scorecard. Structural equation modeling is adopted to access risks in the global supply chain.FindingsThis article contributes to the supply chain risk management literature by providing a detailed operationalization of global supply chain risk constructs, e.g. natural disasters, war and terrorism, fire accidents, economic and political instability, social and cultural grievances, decease. Empirical results reveal that the supply chain is predominantly regarded as being vulnerable as the proposed model of risks can explain up to 12.6% variance of supplier performance, 25.2% innovation and learning, 23% internal business, 40.6% customer service and 32.4% finance.Research limitations/implicationsThese risks are relevant contextual variables in strategic supply chain decisions. Supply chain managers should keep in mind acceptable cost/benefit tradeoffs in their firms' mitigation efforts associated with major contingency risks. This research advocates the allocation of scarce resources to adopt the supply chain strategies of avoidance, speculative and postponement.Originality/valueThe application of the strategic content/process/context to explain global supply chain performance is an interesting approach. Moreover, globalization trends and the COVID-19 perspectives are considered to be the main reasons for increasing such complex factors. Data on validating research models collected during the COVID-19 pandemic reflect the topicality of this study.
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BackgroundSome COVID-19 patients with similar quantitative CT measurements had variable clinical presentation and outcome. The absence of reasonable clinical explanations, such as pre-existing comorbidities or vascular complications, adds to the confusion. The authors believed that neglecting the impact of certain severe morphologic features could be an alternative radiological explanation. This study aims to optimize the initial CT staging of COVID-19 and propose a new combined morphologic/volumetric CT severity index (CTSI) to solve this clinico-radiological mismatch.ResultsThis multi-center study included two major steps. The first step of the study entailed a standardized combined morphologic/volumetric CT severity analyses to propose a new optimized CTSI. This was conducted retrospectively during the period from June till September 2020. It included 379 acutely symptomatic COVID-19 patients. They were clinically classified according to their oxygen saturation and respiratory therapeutic requirements into three groups: group A (mild 298/79%), group B (borderline severity 57/15%), and group C (severe/critical 24/6%). The morphologic and volumetric assessment of their HRCT was analyzed according to severity, by two consultant radiologists in consensus. A new 25 point-CTSI has been created, combining eight morphological CT patterns [M1:M8;8 points] and four grades of volumetric scores [S1:S4;17 points]. The addition of the M5 pattern (air bubble sign), M6 pattern (early fibrosis and architectural distortion), or M7 pattern (crazy-paving) proved to increase the clinical severity. The second step of the study entailed a standardized blinded/independent validation analysis for the proposed CTSI. This was prospectively conducted on other 132 patients during October 2020 and independently performed by other two consultant radiologists. Validation results reached 80.2% sensitivity, 91.8% specificity, AUROC-curve = 0.8356, and 90.9% accuracy.ConclusionA new optimized CTSI with accepted validation is proposed for initial staging of COVID-19 patients, using combined morphologic/volumetric assessment instead of the quantitative assessment alone. It could solve the clinico-radiological mismatch among patients with similar quantitative CT results and variable clinical presentation during the absence of pre-existing comorbidities or vascular complications.
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The prominent growth of social media users, particularly during the COVID-19 pandemic, allows social media to become a potential platform for product promotion. The presence of digital influencers as online opinion leaders has been proven to influence brand value perception, establish brand engagement, and encourage consumer purchase intention. As the country with the fourth-largest population globally, Indonesia is a lucrative market for cosmetic products. In addition to the rising number of the millennial generation who is interested in beauty products, the male segment which is no less concerned about appearance also expands the cosmetics market. A quantitative method was applied using an online questionnaire to 220 samples. The findings reveal that perceived influence has a positive effect on purchase intention with brand engagement in self-concept and brand expected value as the mediating variables. Likewise, brand engagement in self-concept has a positive effect on brand expected value. This study recommends XYZ Cosmetics to continue enrolling Tasya Farasya, considering that, as a mega-influencer with enormous Instagram followers in Indonesia, she can drive the perception and behavior of her followers.
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Portfolio selection is a major topic for investors to allocate their assets and maximize their profit under constrained risk. For uncertain investment behavior in a vagueness environment, some researchers have devoted themselves to this field of fuzzy portfolio models for portfolio selection. Especially, Tsaur, Chiu and Huang in 2021 defined guaranteed return rates to excess investment for securities whose return rates are bigger than the guaranteed return rates in the fuzzy portfolio selection. However, an independent investor has original ideas in investment, and thus we need to consider more types of risk attitudes for an investor's portfolio selection when the guaranteed return rates are used to excess investment. To manage the excess investment by the risk preference, a new concept of s dimensions of excess investment is introduced to perceive the risk attitude of an investor for portfolio selection. Finally, we present a numerical example of a portfolio selection problem to illustrate the proposed model. This example shows that the higher dimensions of excess investment derive lower expected return rates with lower constrained risk than that of dimension s = 1;and we suggest lower risk preference should select a higher dimension of excess investment. Then, the dimension of excess investment s = 2 can be applied for portfolio selection when the risk preference is lower.
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Local government debt is the biggest “gray rhino” of China’s economy and one of the most significant factors affecting the sustainability of economic growth. We use the macroeconomic data of China’s real economy development level and local government debt from 2000 to 2020 to investigate the impact of local government debt on the real economy using the spatial Durbin model, focusing on the impact of the local government debt scale on the development of the real economy in jurisdictions and non-jurisdictions and the intermediation effect of finance under the geospatial correlation characteristics of economic development. The results show that the spatial correlation of the real economy between jurisdictions prevails and the correlation deepens over time. The scale of local government debt in China has exceeded a reasonable threshold, and the crowding-out effect of debt expansion on the real economy is obvious and not limited by jurisdictions, with significant spatial spillover effects. Financial marketization can effectively mitigate the crowding-out effect of local government debt on the real economy. These findings provide useful references for mapping the correlated development characteristics of local government debt and the real economy in China, effectively preventing local government debt risks and high leverage of the real economy and financial systemic risks, and providing effective insights for other countries to resolve government debt problems, prevent crises, and promote local economic development.
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The air quality of the living area influences human health to a certain extent. Therefore, it is particularly important to detect the quality of indoor air. However, traditional detection methods mainly depend on chemical analysis, which has long been criticized for its high time cost. In this research, a rapid air detection method for the indoor environment using laser-induced breakdown spectroscopy (LIBS) and machine learning was proposed. Four common scenes were simulated, including burning carbon, burning incense, spraying perfume and hot shower which often led to indoor air quality changes. Two steps of spectral measurements and algorithm analysis were used in the experiment. Moreover, the proposed method was found to be effective in distinguishing different kinds of aerosols and presenting sensitivity to the air compositions. In this paper, the signal was isolated by the forest, so the singular values were filtered out. Meanwhile, the spectra of different scenarios were analyzed via the principal component analysis (PCA), and the air environment was classified by K-Nearest Neighbor (KNN) algorithm with an accuracy of 99.2%. Moreover, based on the establishment of a high-precision quantitative detection model, a back propagation (BP) neural network was introduced to improve the robustness and accuracy of indoor environment. The results show that by taking this method, the dynamic prediction of elements concentration can be realized, and its recognition accuracy is 96.5%.
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This paper aims to examine mindfulness as a potential mediator of readiness for change to work Performance among health workers such as Nurse, Doctors, and back offices in hospitals in Indonesia. We are using the quantitative method in this research. The partial least square-structural equation modeling (PLS-SEM) approach analyzed the data. The population is 206 health workers in Indonesia as a sample of research. A direct and indirect correlation between Readiness for Change to Work Performance through Mindfulness value is 0. 736 with ß 15.731. Mindfulness is proven to function as a mediator moderate.
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In construction industry, with the management of time and cost of the project, risk management is also especially important. Recently, COVID-19 pandemic has brought a huge crisis on construction sector. During this crisis, risk management becomes even more crucial to avoid further losses in the project. This study aims at identifying the risks involved in construction project during COVID-19 crisis, analyse them and develop a plan to bring the project back on schedule. Possible risks involved in construction sector due to COVID-19 are identified and defined. The risks are classified based on the categories like commercial risk, health and safety risk, completion risk etc. The project was analysed for all categories of risks using Expected Value Method (EVM) for statistical analysis. EVM evaluates the average outcome when the future events may or may not happen. Based on the analysis, Composite Likelihood factor, Composite Impact factor and risk severity has been computed. The EVM results shown that the commercial risk would be at a high level with a risk severity equals to 0.034 and completion risk would be at a low level with a risk severity equals to 0.003. Using this approach, the occurrence of risks at various stages of the project can also be predicted. EVM is found to be a convenient and accurate method to identify risks that might occur and prepare a contingency plan to avoid further losses. © 2023, Canadian Society for Civil Engineering.
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Since the beginning of 2020, China has banned the consumption of wild animals to combat the spread of zoonoses. Most existing studies focus on the intention and behavior of wildlife consumption and their causes;however, few have looked at public willingness to resist wildlife consumption, as well as the cause and effects of such actions. In this study, a framework for an extended theory of planned behavior was constructed. Based on a 7-point Likert scale, a sample of 1194 respondents from eight provinces across China was obtained through an online survey. Structural equation modeling was used to analyze netizen behavioral intention to resist consuming wild animals and their causes to provide a reference for the implementation and optimization of relevant policies. The study model passed the goodness-of-fit test, confirming the robustness of the results. The results showed that Chinese netizens’ intention to resist consuming wild animals was moderate, with 55.19% willing to participate in activities against it, i.e., it is important to resist eating wild animals as a standard. Attitude, subjective norm, perceived behavioral control, and past experience of the Chinese netizen had significant positive effects on resistance intention, i.e., (1) netizens’ current living area with severe outbreaks were more likely to resist wildlife consumption, (2) highly knowledge level netizens were more likely to resist wildlife consumption than less knowledgeable ones, and (3) lower income level had higher behavioral intentions of netizens. The findings suggest that the government must take a lead role in wildlife protection and strengthen its restrictions, laws, and regulations. The media should also be used to promote conservation and popularize a protective message in favor of wild animals. Public quality and assurance of wildlife protection should be culturally reinforced to effectively ban the illegal trade of wild animals and their products.
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Background: The dissemination of recommendations on low-value care alone may not lead to physicians’ behavioral changes. The aim of this study was to evaluate whether a multifaceted behavioral intervention among internal medicine residents could reduce low-value care in hospitalized patients. Methods: A pre–post quality improvement intervention was conducted at the Internal Medicine Division of La Tour hospital (Geneva, Switzerland) from May 2020 to October 2021. The intervention period (3 months) consisted of a multifaceted informational intervention with audits and educative feedback about low-value care. The pre- and post-intervention periods including the same six calendar months were compared in terms of number of blood samples per patient day, prescription rates of benzodiazepines (BZDs) and proton pump inhibitors (PPIs), as well as safety indicators including potentially avoidable readmissions, premature deaths and complications. results: A total of 3400 patients were included in this study;1095 (32.2%) and 1155 (34.0%) were, respectively, hospitalized during the pre- and post-intervention periods. Patient characteristics were comparable between the two periods. Only the number of blood tests per patient day and the BZD prescription rate at discharge were significantly reduced in the post-intervention phase (pre: 0.54 ± 0.43 vs. post: 0.49 ± 0.60, p ≤ 0.001;pre: 4.2% vs. post: 1.7%, p = 0.003, respectively). PPI prescription rates remained comparable. Safety indicators analyses revealed no significant differences between the two periods of interest. Conclusions: Our results demonstrate a modest but statistically significant effect of a multifaceted educative intervention in reducing the number of blood tests and the BZD prescription rate at discharge in hospitalized patients. Limiting low-value services is very challenging and additional long-term interventions are necessary for wider implementation.
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The 5G system requires an optimum coding technique to achieve the high diversity gain, low bit error rate (BER), and low detection complexity. Various coding techniques were developed in recent times for improving the diversity performance of the MIMO systems. Space-time-coding (STC) is used to fulfill the requirement of handling large data flow in the 5G wireless communications. It is highly required to optimize the orthogonal nature of STC. The paper proposed a novel design of the optimum linearly scalable dispersion code (O-LSDC). In this paper, an optimum coefficient-based O-LSDC is designed based on the elementary matrix operations, unitary matrix normalization technique, and coefficient mapping strategy. Mapped coefficients are linearly solved for optimum value estimation. To find the optimum solution of the LSDC codes, five cases of LSDC are defined based on the scaling coefficients and then performance is evaluated against the BER vs. SNR. Evaluating the simulation results in terms of error probabilities for the five different orthonormal LSDC, this work simulates the system for multiple antennas using the Rayleigh fading MIMO system model. Also, evaluating the impact of the proposed LSDC over the BER performance for the varied number of Monte Carlo iterations, then the performance graph is plotted for multiple-antennas system. The proposed O-LSDC under Rayleigh fading channel using the M-PSK modulation enhances the performance of the 5G and beyond communication system in terms of BER and SNR.
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The mixture of generalised linear models (MGLM) requires knowledge about each mixture component’s specific exponential family (EF) distribution. This assumption is relaxed and a mixture of semi-parametric generalised linear models (MSPGLM) approach is proposed, which allows for unknown distributions of the EF for each mixture component while much of the parametric structure of the traditional MGLM is retained. Such an approach inherently allows for both symmetric and non-symmetric component distributions, frequently leading to non-symmetrical response variable distributions. It is assumed that the random component of each mixture component follows an unknown distribution of the EF. The specific member can either be from the standard class of distributions or from the broader set of admissible distributions of the EF which is accessible through the semi-parametric procedure. Since the inverse link functions of the mixture components are unknown, the MSPGLM estimates each mixture component’s inverse link function using a kernel smoother. The MSPGLM algorithm alternates the estimation of the regression parameters with the estimation of the inverse link functions. The properties of the proposed MSPGLM are illustrated through a simulation study on the separable individual components. The MSPGLM procedure is also applied on two data sets.
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The concept of education for sustainable development (ESD) belongs to the latter exogenous type. It was not put forward as an independent concept at first. At present, it is necessary to shift from the approach to an understanding concentrating on sustainable development to an interpretation that focuses on the structure of education. Under the guidance of online technology’s pursuit of efficiency, effect, simplicity and materialization, the process of ESD embedded with online technology has highlighted its value of tools, while lacking rules and guidance of the teleological rationality, hence it cannot achieve the value tendency of humanism which accords with its reason and being good to it. In the course of realistic education, the instrumental value and humanistic value of ESD are always intertwined together, and the value choices of educational entities are often manifested as bounded rationality. Therefore, around the theme of ESD, any two of the players of educational organizations constitute the “evolutionary game with bounded rationality”. Based on the result of an evolutionary game, ESD should pay more attention to the humanization of purpose, the contextualizing of content, the experience of process and the rationalization of technology, to fully realize the return of humanistic value of ESD.
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Objectives. The March 2020 lockdown mandated institution closures and forced educators to provide remote instruction. We intended to gain an in-depth and holistic understanding of dental faculty’s experience and perceptions during this mandatory shift. Insights were gathered in 6 key areas: prior remote instruction experience;magnitude of transition and frequency of instruction methods used;training received;preferred teaching methods based on how they promote student learning, interaction, engagement, and performance;social impact;and future of teaching. Methods. An anonymous QualtricsXM survey was distributed to all dental faculties at University of Detroit Mercy in May 2020. Data was collected over a three-week period and analyzed using Statistical Package for the Social Sciences (SPSS) Statistics 26 (IBM;Armonk, New York, USA). Pearson chi-squared test and Fisher’s exact test were used to evaluate categorical data. Results. Out of the 30 participants who transitioned to online remote instruction, 16 (53.3%) reported an age between 30 and 49 years and 14 (46.7%) reported ≥50 years of age. Our analysis revealed that faculty in 30–49-year age range had no prior online synchronous teaching experience. The transition was described as interesting, useful, time-consuming, and challenging. In-person live teaching received highest number of first place rankings (50%), and online asynchronous method received highest number of last place rankings (42.86%). Faculties in >50 year age range expressed maximum satisfaction with online small-group discussions, and those in 30–49 years age group were more satisfied with one-on-one online discussion. Conclusion. Our study finds that the faculty tends to gravitate to online methods that are similar to or mimic their past in-person teaching environment. Forced remote instruction gave new content delivery experiences to faculties of all ranges of age and experience in teaching. This exposure is likely to result in a more widespread adoption of a variety of instructional methods in future. Our study strongly suggests that traditional in-person instruction is the preferred method for delivery of content and is likely better for student learning. Majority of the faculties believed that the future of dental education would be a mix of both in-class and online instruction. In summer and fall 2021, our institution adopted hybrid, flexible models for didactic instruction to allow students the opportunity to digest content in their preferred method. Careful planning and systematic, continued training will be required to enhance technological skills and to incorporate newer models into didactic and clinical instruction. For the success of blended learning, courses will need redesigning of assessment methods, commitment, and support from administrators.
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An infectious disease can cause a detrimental effect on national security. A group such as the military called a “closed population”, which is a subset of the general population but has many distinct characteristics, must survive even in the event of a pandemic. Hence, it requires its own distinct solution during a pandemic. In this study, we investigate a simulation analysis for implementing an agent-based model that reflects the characteristics of agents and the environment in a closed population and finds effective control measures for making the closed population functional in the course of disease spreading.