ABSTRACT
In this report, a nationally representative sample of kindergarten through 12th grade (K-12) public school principals were asked about their experiences with covering classrooms and hiring staff. In the spring of the 2021-2022 school year, which coincided with the coronavirus disease 2019 (COVID-19) omicron variant surge, most principals struggled to keep classrooms consistently staffed and many reported that hiring had become more challenging since the previous school year. Principals indicated that a lack of substitute teachers -- not an increase in open teaching positions -- was the main reason for classroom coverage shortages. In addition to day-to-day coverage issues, most principals reported that teacher vacancies were on the rise. Most of these principals believed that vacancies had grown more difficult to fill than in the prior school year, largely because of declining applicant counts. Principals' preferences when hiring teachers lend further insight into potential drivers of hiring challenges. A large majority of principals expressed strong preferences for like-minded teachers whose mindsets aligned with the vision and culture of the schools. Few principals prioritized the diversity of the educator workforce at their schools.
ABSTRACT
Wastewater surveillance has been widely used to track the prevalence of SARS-CoV-2 in communities. Although some studies have investigated the decay of SARS-CoV-2 RNA in wastewater, understanding about its fate during wastewater transport in real sewers is still limited. This study aims to assess the impact of sewer biofilms on the dynamics of SARS-CoV-2 RNA concentration in naturally contaminated real wastewater (raw influent wastewater without extra SARS-CoV-2 virus/gene seeding) using a simulated laboratory-scale sewer system. The results indicated that, with the sewer biofilms, a 90% concentration reduction of the SARS-CoV-2 RNA was observed within 2 h both in wastewater of gravity (GS, gravity-driven sewers) and rising main (RM, pressurized sewers) sewer reactors. In contrast, the 90% reduction time was 8–26 h in control reactors without biofilms. The concentration reduction of SARS-CoV-2 RNA in wastewater was significantly more in the presence of sewer biofilms. In addition, an accumulation of c.a. 260 and 110 genome copies/cm2 of the SARS-CoV-2 E gene was observed in the sewer biofilm samples from RM and GS reactors within 12 h, respectively. These results confirmed that the in-sewer concentration reduction of SARS-CoV-2 RNA in wastewater was likely caused by the partition to sewer biofilms. The need to investigate the in-sewer dynamic of SARS-CoV-2 RNA, such as the variation of RNA concentration in influent wastewater caused by biofilm attachment and detachment, was highlighted by the significantly enhanced reduction rate of SARS-CoV-2 RNA in wastewater of sewer biofilm reactors and the accumulation of SARS-CoV-2 RNA in sewer biofilms. Further research should be conducted to investigate the in-sewer transportation of SARS-CoV-2 and their RNA and evaluate the role of sewer biofilms in leading to underestimates of COVID-19 prevalence in communities.
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This research aims to grasp the evolution of consumer demand and improve the resilience of the hotel industry under the public health crisis (COVID-19). Online reviews of 7,679 hotels in 10 cities were collected from Ctrip, China's major online hotel platform. Then, we applied opinion mining and time evolution to mine the change in consumer demand before, during, and after the COVID-19 pandemic. Findings show that some consumer demands (e.g. epidemic safety) will change during the outbreak period. However, during the nonoutbreak period, users were more concerned about their own check-in experience (e.g. hotel facilities, front desk services). This article provides new ideas for identifying the dynamic value of online reviews. We suggest that businesses focus on ensuring hotel safety during the crisis period. The results contribute essential theoretical and practical significance to the hotel industry's crisis management during public health crises.
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Container ports operate in more challenging and volatile environments at present times. Events such as US-China trade tensions and the COVID-19 pandemic severely affect numerous container ports at various levels. Strategies pursued by container ports are key to port development and management amidst these challenges. Drawing on configuration theory, this research employs Fuzzy-set Qualitative Comparative Analysis to investigate the relation between port strategies and container throughput. The research contributes to the literature by proposing an approach to account for complexity of the port sector and offers insights into strategies adopted by major container ports. The research further identifies 10 port strategies and proposed indicators that can represent the essence of these strategies. Being able to represent strategies in a quantitative format is important for strategy analysis and performance evaluation. Results reveal that major container ports employ a combination of strategies which address both the supply and demand-side aspects of the port business. Growing digitalization and digitization coupled with advancements in information capture, diagnostics capabilities and predictive abilities means a greater role for data analytics to influence container port strategy and performance. Implications for port managers, policy makers and researchers from the perspective of port policy and management are proposed.
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In supply chain management (SCM), product classification and demand forecasting are crucial pillars to ensure companies to have production in the right category and quantity for long-term profitability. Due to COVID-19 from 2019, the automobile industry has been seriously negatively affected as the demand dropped dramatically. Therefore, it is necessary to make reasonable product classification and accurate demand forecasting to facilitate automobile companies in SCM to reduce unpopular product manufacture and unnecessary storage costs. In this paper, the Canada automobile market has been chosen with the period from 1946 to 2022. To classify a number of different types of motor vehicles into several categories with general characteristics, K-means Clustering method is applied. With the seasonal patterns and random generated features for auto sales, the time series models ARIMA and SARIMA are adopted for demand forecasting. According to the analysis, the automobiles fitting in the category with high demand and low price are valuable for further production. In addition, SARIMA Model is more accurate and fits better than ARIMA Model for both the training and test datasets for long-term prediction. The classification and forecasting results shed light on guiding manufacturers to adjust production schemes and ensuring auto dealers to predict more accurate sales in order to optimize the strategic planning. © 2023 SPIE.
ABSTRACT
Supply chain managers are forced to develop crisis-induced strategies due to the complexity of crises, as opposed to the more traditional strategies that prioritize competitive priorities. The increasing frequency and severity of recent crises, such as the coronavirus outbreaks, widespread product recalls, and financial crises, highlight the need for introspective and retrospective socio-economic insights on the contexts, priorities, and themes of supply chain management in times of crisis. This article's goal is to review the literature on supply chain management during times of crisis, organizing the relevant body of scholarly work in a systematic way, outlining current research methodologies, capturing strategic priorities and themes of complexity in research studies, and highlighting opportunities for additional research. Four factors for restorative priorities are identified by the review, which is based on a systematic analysis of 250 academic publications from 1996 to 2021 and reflects operations strategy in times of crisis: Critical supplies with important services, prompt action with restoration, safety with security, and traceability with transparency are just a few examples. The analysis also reveals that network configurations and business cycle complexity, optimal choices and provisioning system complexity, complicated learning processes and demand forecast are all sources of operational complexity during crises. The build-to-cycle, organic capabilities, and operational mindfulness framings for supply chain management in emergency situations are suggested with the use of review insights. The article ends with suggestions for future research on supply chain improvements, diagnosis, solidarity, mapping, temporariness, and thresholds, as well as optimal selection issues on connecting crisis network allocations with cross-functionalities and connecting crisis systems investments with liabilities.
ABSTRACT
After the global tourism industry has experienced the impact of the pandemic, it is critical that people gain confidence in traveling and have the impression that staying in hotels is now safe, because only in this way tourism businesses such as hotels can be fully successful in recovering. For this reason, the researchers guided by a descriptive research design and quantitative research approach, aimed to determine what people think about staying in a hotel, particularly in terms of safety and security, price, location, and service quality, in the time of COVID-19 pandemic recovery stage, focused on the local community of Calamba City, Laguna, Philippines, being one of richest cities in the country and the place where the researchers reside. Moreover, a comparative analysis of the perspective of the respondents has been performed in terms of their age, sex, and educational attainment, identifying which age, sex and educational attainment groups have more positive or negative attitude, and a higher or lower level of hotel stay intention compared with other groups. Being the first study that has assessed the tourism market particularly in terms of their perspective on hotel stay as the hospitality industry attempts to recover from the impact of the pandemic, this is expected to provide a clear picture of the need for management of hotels to continuously work on marketing efforts highlighting the information that it is now safe to practice tourism and stay in their establishments, hence, serving as a guide in coming up with promotional strategies and an action plan, as well as a motivation for researchers who wish to determine the same in their locality or country.
ABSTRACT
Broiler chicken eggs are one of the main and strategic foods for the people of Indonesia and contribute to regional and national inflation. Broiler egg production in Indonesia differs between regions. Areas with a surplus of eggs tend to have lower prices than areas with a deficit. This research is to measure the transmission of broiler egg prices between markets in surplus and deficit areas, using weekly price time series data for the period January 2018-December 2021. Areas of surplus broiler eggs, East Java Province (the highest broiler egg production in Indonesia) which become one of the main suppliers to the Province of East Nusa Tenggara as a deficit area. Using the Johannsen cointegration test it is found that there is no cointegration or there is no relationship between the surplus and deficit regions in the long term but not in the short term. Factors of marketing infrastructure, market information systems, and geographical conditions can be obstacles to the absence of cointegration. The VAR (Vector Auto-Regressive) Vector Error Correction model (VECM) test, found that price transmission occurred between surplus and deficit areas, meaning that between the two regions, there was market integration prior to Covid. The transmission has weakened, and due to the Covid situation, there have been restrictions on the movement of people and goods. The government and other market players need to study the response of the broiler egg market, in the short and long term so that market players can make the right policies.
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PurposeThis study evaluates the impact of online menus and perceived convenience of online food ordering on consumer purchase intention and shows how a desire for food creates a relationship between an online menu and a customer's purchase intention. Suggestions for management are proposed to design an effective menu to improve business performance in the competitive market in Vietnam.Design/methodology/approachThe paper follows a quantitative method. Quantitative research aims to analyze and critically evaluate the research question(s) to discover new factors.FindingsFindings indicate a positive relationship between menu visual appeal (MV), menu informativeness (MI), desire for food (DF), the perceived convenience (PC) of ordering food online and intention to purchase (PI). The attractiveness of images and information is a significant factor affecting diners' desire to eat, while the demand for food and the convenience of ordering food online are also factors affecting purchase intention.Practical implicationsThe study confirms the importance of online menus to purchase intention. Economically, when supply and demand are reasonable, the market is stable and technology develops. In terms of social, hygiene, attractiveness and price factors, it is helpful to have an overview. Research is the premise for further studies with factors from menu to customer trust.Originality/valueThe study provides a solid foundation for further studies on restaurant menu elements as well as a new perspective on how restaurants improve their dishes.
ABSTRACT
The use of machine learning and data-driven methods for predictive analysis of power systems offers the potential to accurately predict and manage the behavior of these systems by utilizing large volumes of data generated from various sources. These methods have gained significant attention in recent years due to their ability to handle large amounts of data and to make accurate predictions. The importance of these methods gained particular momentum with the recent transformation that the traditional power system underwent as they are morphing into the smart power grids of the future. The transition towards the smart grids that embed the high-renewables electricity systems is challenging, as the generation of electricity from renewable sources is intermittent and fluctuates with weather conditions. This transition is facilitated by the Internet of Energy (IoE) that refers to the integration of advanced digital technologies such as the Internet of Things (IoT), blockchain, and artificial intelligence (AI) into the electricity systems. It has been further enhanced by the digitalization caused by the COVID-19 pandemic that also affected the energy and power sector. Our review paper explores the prospects and challenges of using machine learning and data-driven methods in power systems and provides an overview of the ways in which the predictive analysis for constructing these systems can be applied in order to make them more efficient. The paper begins with the description of the power system and the role of the predictive analysis in power system operations. Next, the paper discusses the use of machine learning and data-driven methods for predictive analysis in power systems, including their benefits and limitations. In addition, the paper reviews the existing literature on this topic and highlights the various methods that have been used for predictive analysis of power systems. Furthermore, it identifies the challenges and opportunities associated with using these methods in power systems. The challenges of using these methods, such as data quality and availability, are also discussed. Finally, the review concludes with a discussion of recommendations for further research on the application of machine learning and data-driven methods for the predictive analysis in the future smart grid-driven power systems powered by the IoE.
ABSTRACT
The transition to Electric Vehicles (EV) in place of traditional internal combustion engines is increasing societal demand for electricity. The ability to integrate the additional demand from EV charging into forecasting electricity demand is critical for maintaining the reliability of electricity generation and distribution. Load forecasting studies typically exclude households with home EV charging, focusing on offices, schools, and public charging stations. Moreover, they provide point forecasts which do not offer information about prediction uncertainty. Consequently, this paper proposes the Long Short-Term Memory Bayesian Neural Networks (LSTM-BNNs) for household load forecasting in presence of EV charging. The approach takes advantage of the LSTM model to capture the time dependencies and uses the dropout layer with Bayesian inference to generate prediction intervals. Results show that the proposed LSTM-BNNs achieve accuracy similar to point forecasts with the advantage of prediction intervals. Moreover, the impact of lockdowns related to the COVID-19 pandemic on the load forecasting model is examined, and the analysis shows that there is no major change in the model performance as, for the considered households, the randomness of the EV charging outweighs the change due to pandemic.
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Water use was impacted significantly by the COVID-19 pandemic. Although previous studies quantitatively investigated the effects of COVID-19 on water use, the relationship between water-use variation and COVID-19 dynamics (i.e., the spatial-temporal characteristics of COVID-19 cases) has received less attention. This study developed a two-step methodology to unravel the impact of COVID-19 pandemic dynamics on water-use variation. First, using a water-use prediction model, the water-use change percentage (WUCP) indicator, which was calculated as the relative difference between modeled and observed water use, i.e., water-use variation, was used to quantify the COVID-19 effects on water use. Second, two indicators, i.e., the number of existing confirmed cases (NECC) and the spatial risk index (SRI), were applied to characterize pandemic dynamics, and the quantitative relationship between WUCP and pandemic dynamics was examined by means of regression analysis. We collected and analyzed 6-year commercial water-use data from smart meters of Zhongshan District in Dalian City, Northeast China. The results indicate that commercial water use decreased significantly, with an average WUCP of 59.4%, 54.4%, and 45.7%during the three pandemic waves, respectively, in Dalian. Regression analysis showed that there was a positive linear relationship between water-use changes (i.e., WUCP) and pandemic dynamics (i.e., NECC and SRI). Both the number of COVID-19 cases and their spatial distribution impacted commercial water use, and the effects were weakened by restriction strategy improvement, and the accumulation of experience and knowledge about COVID-19. This study provides an in-depth understanding of the impact of COVID-19 dynamics on commercial water use. The results can be used to help predict water demand under during future pandemic periods or other types of natural and human-made disturbance.
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From 2018 to 2022, on average, 70% of the Brazilian effective electric generation was produced by hydropower, 10% by wind power, and 20% by thermal power plants. Over the last five years, Brazil suffered from a series of severe droughts. As a result, hydropower generation was reduced, but demand growth was also declined as results of the COVID-19 pandemic and economic recession. From 2012 to 2022, the Brazilian reservoir system operated with, on average, only 40% of the active storage, but storage recovered to normal levels in the first three months of 2022. Despite large capacity of storage reservoirs, high volatility of the marginal cost of energy was observed in recent years. In this paper, we used two optimization models, NEWAVE and HIDROTERM for our study. These two models were previously developed for mid-range planning of the operation of the Brazilian interconnected power system. We used these two models to optimize the operation and compared the results with observed operational records for the period of 2018-2022. NEWAVE is a stochastic dual dynamic programming model which aggregates the system into four subsystems and 12 equivalent reservoirs. HIDROTERM is a nonlinear programming model that considers each of the 167 individual hydropower plants of the system. The main purposes of the comparison are to assess cooperation opportunities with the use of both models and better understand the impacts of increasing uncertainties, seasonality of inflows and winds, demand forecasts, decisions about storage in reservoirs, and thermal production on energy prices. © World Environmental and Water Resources Congress 2023.All rights reserved
ABSTRACT
This research determines the impacts of COVID-19 US on crawfish production and consumption for 2020 and 2021 using an Equilibrium Displacement Model. In the US, crawfish is one of the seafood commodities where most production is consumed by domestic consumers (7% of domestic consumption is from imports). Crawfish and rice are complementary. Therefore, the impacts of COVID-19 on crawfish consumption simultaneously influence rice production and crawfish producers and consumers. In the first year of COVID-19 (2020), the reduction in crawfish retail demand caused negative effects on final consumers and producers. However, crawfish consumption recovered significantly in the second year (2021), which could compensate for the loss in 2020. Overall, consumer and producer gains ranged from $549 to $626 million if the COVID-19 pandemic only impacted retail consumption. However, in 2021, the increase in production costs due to higher oil/diesel prices and other input prices caused the farm supply to decrease. As a result, total welfare gains ranged from $200 to $228 million. If the demand in 2021 did not increase, but the crawfish farm supply decreased, consumer and producer losses ranged from $929 to $1045 million. Overall, the total effects of COVID-19 on consumers and producers for 2020 and 2021 depend on its effects in 2021. If the demand in 2021 increased following the decrease in farm supply, consumers and producers would benefit from the shocks of COVID-19 due to higher post-COVID-19 demand.
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Irrigation is increasingly being called upon to help stabilize and grow food and water security in the face of multiple crises;these crises include climate change, but also recent global food and energy price crises, including the 2007/08 food and energy price crises, and the more recent crises triggered by the COVID-19 pandemic and the war on Ukraine. While irrigation development used to focus on public, large-scale, surface- and reservoir-fed systems, over the last several decades, private small-scale investments in groundwater irrigation have grown in importance and are expected to see rapid future growth, particularly in connection with solar-powered pumping systems. But is irrigation 'fit-for-purpose' to support population growth, economic development, and multiple food, energy and climate crises? This paper reviews how fit-for-purpose irrigation is with a focus on economies of scale of surface and groundwater systems, and a particular examination of systems in Sub-Saharan Africa where the need for expansion is largest. The review finds challenges for both larger surface and smaller groundwater systems in the face of growing demand for irrigated agriculture and dwindling and less reliable water supplies. To support resilience of the sector, we propose both a holistic design and management improvement agenda for larger surface systems, and a series of suggestions to improve sustainability concerns of groundwater systems.
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This paper begins with a survey of recent commodity price developments that highlights the magnitude of this price surge and identifies the rapid rise in wheat prices as a key element. The analysis in this paper focuses on the extent to which domestic markets are insulated from these changes and on the resulting impacts on world prices. An econometric analysis using Error Correction Models finds stable long-term relationships between world wheat prices and most domestic prices of wheat and wheat products, but with considerable variation across countries in the rate of price transmission. A case study of the price shocks during the Covid pandemic and the Ukraine food price crisis finds that price insulation roughly doubled the overall increase in world wheat prices and raised their volatility both during periods of price increase and price decline.
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Insights on the indirect effects of the COVID-19 pandemic are critical for designing and implementing policies to alleviate the food security burden it may have caused, and for bolstering rural communities against similar macroeconomic shocks in the future. Yet estimating the causal effects of the pandemic is difficult due to its ubiquitous nature and entanglement with other shocks. In this descriptive study, we combine high-resolution satellite imagery to control for plot-level rainfall with household socio-economic panel data from 2014, 2016, 2019 and 2020, to differentiate the effect of the pandemic from climatic shocks on food security in Morogoro, Tanzania. We find evidence of decreased incomes, increased prices of staple foods, and increased food insecurity in 2020 relative to previous years, and link these changes to the pandemic by asking households about their perceptions of COVID-19. Respondents overwhelmingly attribute economic hardships to the pandemic, with perceived impacts differing by asset level.
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The paper presents the reaction of the Romanian cereal market to the disruption of trade flows caused by certain shocks, such as the COVID-19 pandemic, which lead to changes with high impact on the functioning of this market, representing an important test for the resilience of the sector. Due to trade liberalization in global markets, including agri-food markets, the competitiveness of exports has become increasingly important, contributing to the creation of the country's competitive advantage. Any restrictions to trade in agri-food products can distort trade flows, and this disruption will have an impact on supply and prices. Maintaining a balance between imports and exports is essential to ensure domestic market stability. International trade in agri-food products plays an important role in global food security. The results show that Romania mainly exports unprocessed agricultural products, with cereals having the largest share in the export structure, cereal supply is dependent on climate change, yet it is one of the products with the lowest volatility. The cereal market shows a more elastic reaction to price responses, even though demand for staple foods is generally inelastic.
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The COVID-19 pandemic and the anti-pandemic measures taken have significantly affected the activities of the society and the associated need for mobility, as well as the transport behaviour of inhabitants. The goal of this research is to assess the impact of the COVID-19 pandemic on the change in the demand of residents for suburban bus transport (SBT) services in the regions of Slovakia. Due to the impact of the pandemic of COVID-19, there was a decrease in the number of passengers transported by SBT as well as a decrease in the supply of bus services offered in all the regions under study. The decrease in the number of passengers in the pandemic year was caused not only by COVID-19 and the anti-pandemic measures, but also by the reduction in the supply of SBT. The research confirmed the relation between the reduction in the offer of SBT and the decrease in passenger demand. © 2023 Vladimír Konečný et al.
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This paper examines regional differences in the demand for digital skills based on an analysis of 9 million vacancies posted on the Unified Digital Platform ‘Work in Russia' in 2018–2022. We examine approaches used in the literature to classify digital skills and using it develop our own classification. The paper studies the advantages and limitations of various indicators of the demand for digital skills. We suggest that the ratio between the share of vacancies requiring digital skills of a certain group in the region and the labor force population should be used as the most appropriate one. The results of the study show that in Russia there is still a significant regional differentiation in the employer's demand for all selected groups. Differentiation increased with the beginning of the COVID-19 pandemic, and decreased slightly in 2021–2022. We reveal that regions with a higher level of economic development have higher requirements for digital skills. Digital skills are more often required in regions specialized on primary production and less often in agricultural regions. Of the federal districts, a slightly higher level of demand for digital skills is observed in the Ural and Far Eastern federal districts, while a significantly lower level is observed in the North Caucasus federal district. © 2023 Spatial Economics. All rights reserved.