Evolution of oilseed sector in Romania - an analysis from the perspective of effects produced by the COVID-19 pandemic

Romania ranks first in the European Union for the production of sunflower seeds, third for the production of soybeans and seventh for the production of rapeseed. The paper aims to analyse the effects produced by the COVID-19 pandemic on the evolution of the oilseed sector in Romania. Thus, the following indicators were analysed: evolution of areas under oilseeds, total oilseed production and average yields, as well as the volatility of selling prices for oilseeds. The results of the study reveal that Romania has been the largest producer of sunflower seeds in the European Union. The average yields in sunflower, soybeans and rapeseed have shown great variations in the analysed period. According to Eurostat data, it can be noticed that although Romania is the third large producer of oilseeds in the EU, the average yields continue to be low compared to those from other large EU producers. Yields are also among the most volatile in the EU. The selling prices for soybeans showed a higher increase in the year 2020 than in 2019 in Romania, compared to the increase in the average selling prices of the EU-27 (+9.89%). The selling prices for rapeseed also had a higher increase in 2020 than in 2019 in Romania, compared to the increase in the average selling prices of the EU-27 (+2.31%).

An Assessment of Unconventional Monetary Policy During COVID-19 Pandemic in India

We employ event study methodology to analyze the impact of unprecedented unconventional monetary policy (UMP) measures employed by the Reserve Bank of India to fortify monetary transmission mechanism and to restore financial stability. We find that the UMP announcements result in a decline in bond yields and yield spread as well as increase in market capitalization and sectoral portfolio of stock returns. Evaluating the relative efficacy of UMP measures, we find that targeted long-term repo operation announcements are more effective in easing bond yields than mere long-term repo operations. Our findings provide beneficial inference for day-traders and investors as asset prices increase significantly and durable goods producing stock returns found to be higher than those of non-durable goods. The lessons that can be drawn for the emerging market economy central banks, who do not have enough space to conduct conventional monetary policy and even when they do not face zero lower bound interest rate, they still can employ UMP tools to directly influence banks cost of funds, and long-term bond yields and interest rates, and in turn, portfolio of stock returns and investments to stimulate aggregate demand. JEL Classification: C13, C54, E52, E65

Customized Luminescent Multiplexed Quick-Response Codes as Reliable Temperature Mobile Optical Sensors for eHealth and Internet of Things

The need to sense and track in real time through sustainable and multifunctional labels is exacerbated by the COVID-19 pandemic, where the simultaneous measurement of body temperature and the fast tracking of people is required. One of the big challenges is to develop effective low-cost systems that can promote healthcare provision everywhere and for that, smarter and personalized Internet of things (IoT) devices are a pathway in large exploration, toward cost reduction and sustainability. Using the concept of color-multiplexed quick response (QR) codes, customized smart labels formed by two independent layers and smart location patterns provide simultaneous tracking and multiple synchronous temperature reading with maximum sensitivity values of 8.5% K-1 in the physiological temperature range, overwhelming the state-of-the-art optical sensor for healthcare services provided electronically via the internet (eHealth) and mobile sensors (mHealth).

NLP-based feature extraction for the detection of COVID-19 misinformation videos on YouTube

We present a simple NLP methodology for detecting COVID-19 misinformation videos on YouTube by leveraging user comments. We use transfer learning pre-trained models to generate a multi-label classifier that can categorize conspiratorial content. We use the percentage of misinformation comments on each video as a new feature for video classification. We show that the inclusion of this feature in simple models yields an accuracy of up to 82.2%. Furthermore, we verify the significance of the feature by performing a Bayesian analysis. Finally, we show that adding the first hundred comments as tf-idf features increases the video classifier accuracy by up to 89.4%. © ACL 2020.All right reserved.

Carbon nanotube substrates enhance SARS-CoV-2 spike protein ion yields in matrix-assisted laser desorption-ionization mass spectrometry

Nanostructured surfaces enhance ion yields in matrix-assisted laser desorption-ionization mass spectrometry (MALDI-MS). The spike protein complex, S1, is one fingerprint signature of Sars-CoV-2 with a mass of 75 kDa. Here, we show that MALDI-MS yields of Sars-CoV-2 spike protein ions in the 100 kDa range are enhanced 50-fold when the matrix-analyte solution is placed on substrates that are coated with a dense forest of multi-walled carbon nanotubes, compared to yields from uncoated substrates. Nanostructured substrates can support the development of mass spectrometry techniques for sensitive pathogen detection and environmental monitoring. © 2023 Author(s).

Optimization of gasification process parameters for COVID-19 medical masks using response surface methodology

Due to the COVID-19 pandemic, large amounts of medical wastes have been produced and their disposal has resulted in environmental and human health problems. This medical waste may include face masks, gloves, face shields, goggles, coverall suits, and other related wastes, such as hand sanitizer and disinfectant containers. To address this issue, the effect was investigated of gasification process parameters (type of COVID-19 medical mask based on the polypropylene ratio, pressure, steam ratio, and temperature) on hydrogen syngas and cold gas efficiency. The gasification model was developed using process modeling based on the Aspen Plus software. Response surface methodology with a 3k statistical factorial design was used to optimize the process aiming for the highest hydrogen yield and cold gas efficiency. Analysis of variance showed that both the steam ratio and temperature were significant parameters regarding the hydrogen yield and cold gas efficiency. Proposed models were constructed with very high accuracy based on their coefficient of determination (R2) values being greater than 0.97. The optimum conditions were: 65 % polypropylene in the mixture, a pressure of 1 bar, a steam ratio of 0.38, and a temperature of 900 °C, producing a maximum hydrogen yield of 40.61 % and cold gas efficiency of 81.43 %. These results supported the efficacy of the primary design for steam gasification using a mixture of plastic wastes as feedstock. The hydrogen could be utilized in chemical applications, whereas the efficiency could be used as a basis for further development of the process. © 2022 THE AUTHORS

Euro area sovereign bond risk premia before and during the Covid-19 pandemic.

We provide a novel modeling framework to decompose euro area sovereign bond yields into five distinct components: ( i ) expected future short-term risk-free rates and a term premium, ( i i ) a default risk premium, ( i i i ) redenomination risk premium, ( i v ) liquidity risk premium, and ( v ) segmentation (convenience) premium. Identification is achieved by considering sovereign yields jointly with other rates, including sovereign credit default swap spreads with and without redenomination as a credit event trigger. We illustrate our model by studying yield components embedded in German, French, Italian, and Spanish sovereign bonds, before and after the onset of the Covid-19 pandemic in 2020, and by examining the impact of European Central Bank (ECB) monetary policy and European Union (EU) fiscal policy announcements in response to the pandemic. We find that all five risk premia became sizable following the onset of the pandemic, and that both monetary and fiscal policy announcements had a pronounced effect on yields, mostly through default, redenomination, and segmentation (convenience) premia.

Model Evaluation of Secondary Chemistry due to Disinfection of Indoor Air with Germicidal Ultraviolet Lamps

Air disinfection using germicidal ultraviolet light (GUV) has received increasing attention during the COVID-19 pandemic. GUV uses UVC lamps to inactivate microorganisms, but it also initiates photochemistry in air. However, GUV's indoor-air-quality impact has not been investigated in detail. Here, we model the chemistry initiated by GUV at 254 ("GUV254") or 222 nm ("GUV222") in a typical indoor setting for different ventilation levels. Our analysis shows that GUV254, usually installed in the upper room, can significantly photolyze O3, generating OH radicals that oxidize indoor volatile organic compounds (VOCs) into more oxidized VOCs. Secondary organic aerosol (SOA) is also formed as a VOC-oxidation product. GUV254-induced SOA formation is of the order of 0.1-1 mu g/m3 for the cases studied here. GUV222 (described by some as harmless to humans and thus applicable for the whole room) with the same effective virus-removal rate makes a smaller indoor-air-quality impact at mid-to-high ventilation rates. This is mainly because of the lower UV irradiance needed and also less efficient OH-generating O3 photolysis than GUV254. GUV222 has a higher impact than GUV254 under poor ventilation due to a small but significant photochemical production of O3 at 222 nm, which does not occur with GUV254.

The effect of Eurosystem asset purchase programmes on euro area sovereign bond yields during the COVID-19 pandemic.

We investigate the effect of Eurosystem asset purchase programmes (APP) on the monthly yields of 10-year sovereign bonds for 11 euro area sovereigns during January-December 2020. The analysis is based on time-varying coefficient methods applied to monthly panel data covering the period 2004m09-2020m12. During 2020, APP contributed to an average decline in yields estimated in the range of 58-76 bps. In December 2020, the effect per EUR trillion ranged between 34 bps in Germany and 159 bps in Greece. Stronger effects generally display diminishing returns. Our findings suggest that a sharp decline in the size of the APP in the aftermath of the COVID-19 crisis could lead to very sharp increases in bond yields, particularly in peripheral countries. The analysis additionally reveals a differential response to global risks between core and peripheral countries, with the former enjoying safe-haven benefits. Markets' perceptions of risk are found to be significantly affected by credit ratings, which is in line with recent evidence based on constant parameter methods.

Remote Sensing-Based Evaluation of Heat Stress Damage on Paddy Rice Using NDVI and PRI Measured at Leaf and Canopy Scales

Extremely high air temperature at the heading stage of paddy rice causes a yield reduction due to the increasing spikelet sterility. Quantifying the damage to crops caused by high temperatures can lead to more accurate estimates of crop yields. The remote sensing technique evaluates crop conditions indirectly but provides information related to crop physiology, growth, and yield. In this study, we aim to assess the crop damage caused by heat stress in paddy rice examined under elevated air temperatures in a temperature gradient field chamber from 2016 to 2019, using remote-sensed vegetation indices. A leaf-spectrometer, field-spectrometers, and a multi-spectral camera were used to monitor the conditions of paddy rice. Although, in the leaf- and canopy-scales, the values of normalized difference vegetation index (NDVI) and photochemical reflectance index (PRI) decreased after the heading of rice under normal conditions, the decreasing sensitivity of NDVI and PRI was different depending on the degree of physiological heat stress by high temperature conditions. The NDVI after the heading under extremely high air temperature was not dropped and remained the value before heading. The PRI decreased at all air temperature conditions after the heading;the PRI of the plot exposed to the elevated air temperature was higher than that under ambient air temperature. Further, the relative change in NDVI and PRI after the heading exhibited a strong relationship with the ripening ratio of paddy rice, which is the variable related to crop yield. These remote-sensing results aid in evaluating the crop damage caused by heat stress using vegetation indices. [ FROM AUTHOR] Copyright of Agronomy is the property of MDPI and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full . (Copyright applies to all s.)

Covid-19, sovereign risk and monetary policy: Evidence from the European Monetary Union

This paper investigates the impact of Covid-19 pandemic and monetary policy measures adopted by the European Central Bank (ECB) on the sovereign risk for the European Monetary Union (EMU) countries for the period between March-2020 and November-2020 using daily data. The impact of Covid-19 and monetary policy shocks on the credit default swap rates and bond yields are investigated relying on a fixed effects panel regression model for five core (Germany, France, Austria, Netherlands, Belgium) and three periphery (Italy, Portugal and Spain) countries. To investigate the cross-country differences in the responses, the interactions of the independent variables with periphery dummy and other country-specific variables are included in the regressions. The results of the empirical analysis suggest that Covid-19 shock increased the sovereign risk in the periphery EMU countries significantly and monetary policy measures have been effective in easing financial conditions in these countries. The results are insignificant for the core countries. The results also show that financial stability alleviates the negative impact of Covid-19 on the sovereign risk.

Impact of Coronavirus Oil Price shock on Bond Yields

This research paper looks into how the crude oil prices impact the bond yields issued by the central government of the top ten oil-importing nations before and after the outbreak of Coronavirus and analyze and compare the correlation the bond yields have with crude oil. All the top ten importers are classified into clusters based on their products to identify similarities between them and understand the correlation with oil prices before and after the coronavirus pandemic. The paper has looked into the Central bank's interest rates of these nations to explain the difference in properties that the clusters exhibited during the two periods (I.e., before and after the Coronavirus outbreak of Coronavirus).The control of these policy interest rates plays a vital role in shielding the bond yields from the shocks of oil prices. Nations that we're able to revitalize confidence in investors through their actions by adjusting their interest rates were having a better position than nations that could not change their interest rates.

STUDY ON THE NEW MANAGEMENT STRATEGY FOR AGRICULTURAL FARMS IN ROMANIA, AS A RESULT OF THE ENERGY CRISIS FROM 2021-2022

In Romania, in 2020-2022, a series of events took place which led to the need to rethink and adapt the management of agricultural farms to the new conditions. It is about the health crisis caused by the Covid-19 pandemic, which overlapped with one of the driest agricultural years (2019-2020), but also about the energy crisis, that for agriculture meant the unprecedented price increase for inputs and technologies. Under these conditions, the cost of food has increased from the very first stage of the production process, namely obtaining the raw material. Analyzing the data provided by the Romanian Commodities Exchange, the upward trend in prices starting with January 2020 and lasted until February 2022 can be observed. For the main crops in Romania, namely wheat, corn and sunflower, trading prices increased with 26-73%. At the same time, there are significant increases in inputs, which in turn are caused by higher prices in the energy sector - 443% for natural gas and 194% for electricity. Petroleum products were no exception, with a doubling of the price between the beginning of 2020 and February 2022. All these have led farmers to look for new solutions in order to ensure, at least partially, the necessary fertilizers from natural sources, through crops that are able to bring nitrogen into the soil. Reducing costs per hectare by introducing legumes (especially soy) into crop rotation and merging technological works are only the first proposed solutions, and in the coming years more innovations will be implemented in agricultural management.

The Impact of COVID-19 Pandemic on Government Bond Yields

The COVID-19 pandemic is a real shock to society and business and financial markets. The government bond market is an essential part of financial markets, especially in difficult times, because it is a source of government funding. The majority of existing ESG studies report positive impacts on corporate financial performance regarding environmental, social, and governance. Thus, understanding governments’ financial practices and their relevant ESG implications is insufficient. This research aims to value the impact of the COVID-19 pandemic on different government bond curve sectors. We try to identify the reactions to the COVID-19 pandemic in the government bond market and analyze separate tenors of government bond yields in different regions. We have chosen Germany and the United States government bond yields of 10, 5, and 3 years tenor for the analysis. As independent variables, we have chosen daily cases of COVID-19 and daily deaths from COVID-19 at the country and global levels. We used daily data from 02 January 2020–19 March 2021, and divided this period into three stages depending on the COVID-19 pandemic data. We employed the methods of correlation-regression analysis (ordinary least squares and least squares with breakpoints) and VAR-based impulse response functions to evaluate the effect of the COVID-19 pandemic on government bond yields both in the long and short run. Our analysis revealed the impact of the spread of the COVID-19 pandemic on government bond yields differs depending on the country and the assessment period. The short-term responses vary in direction, strength, and duration;the long-term response of Germany’s yields appeared to be more negative (indicating the decrease of the yields), while the response of the United States yields appeared to be more positive (i.e., increase of yields). Copyright © 2022 Zhou, Teresienė, Keliuotytė-Staniulėnienė, Kanapickiene, Dong and Kaab Omeir.

Feeding the Nation-Dynamic Customer Contacting for E-Fulfillment in Times of Crisis

The outbreak of the COVID-19 pandemic led demand for online grocery orders for both click & collect (C&C) and attended home delivery (AHD) to outstrip delivery capacity by a wide margin. In the United Kingdom, the booking systems of some e-retailers could not handle the flood of incoming requests, forcing the retailers to proactively reach out to certain priority customer segments with the aim of serving as many high-priority customers as possible. To determine when to contact each customer segment in this extraordinary demand environment, we investigate the new demand management concept of proactively contacting customers. We first develop a decision policy for the C&C fulfillment method to address the problem of when to contact customers. We then extend this approach to the AHD setting. To cope with increased problem complexity, we propose a three-step procedure to solve this problem. First, we subdivide the delivery area into smaller subareas;second, we select the most promising subareas;and third, we determine which customers to contact within the chosen subareas. To gain managerial insights and to show the practical benefits of our approaches, we apply both approaches to realistic data from the London area. Our results show that proactive customer contacting allows for a tailored allocation of scarce capacity in e-groceries. We conclude with a discussion on how these contacting techniques can be valuable in postcrisis times.

2020 Annual report of the Southwest Minnesota Farm Business Management Association

The average net farm income for the 108 farms included in the 2020 annual report of the Southwest Minnesota Farm Business Management Association was $322,402, up more than 100% from the preceding year. Much of this increased profitability resulted from improved crop prices in the third and fourth quarters of 2020 as well as improved profitability for livestock producers. Government payments related to the impacts of the COVID pandemic were also a big factor. Profits for association members were at their highest levels since 2012. Crop producers saw higher earnings based on above average yields, higher harvest season prices, and increased government payments. Livestock markets were severely impacted by the pandemic especially in the second quarter of the year. Earnings for all types of livestock operations were up primarily because of COVID related government payments. Without those payments, many livestock producers would have suffered severe losses.

Evaluation of crude slate and processing of recycle effects on H-Oil performance

The LUKOIL Neftohim Burgas (LNB) H-Oil hydrocracker performance at different operating conditions, and different crude oils, and processing of recycle of partially blended fuel oil (PBFO) was assessed. The lower throughput, a result from the Covid 19 pandemic, allowed the LNB H-Oil hydrocracker to operate at a capacity lower than 50% of the design and to process recycle of PBFO achieving a vacuum residue conversion of 93%. The impact of crude oil slate, and processing of recycle of PBFO on conversion and on the unconverted hydrocracked vacuum residue quality was discussed. Intercriteria analysis was employed to evaluate the statistically meaningful relations of the operating conditions to the H-Oil conversion and yields. Regression equations were developed and discussed. [Received: April 16, 2021;Accepted: 28, 2021]

Efficiency Analysis of the Crop Production in China in 2019 and 2020: Role of Uncertainty Perceptions in COVID-19

This paper employs data envelopment analysis (DEA) to determine crop production efficiency in 15 major provinces of China during 2019-2020. The total power of agricultural machinery, the application amount of chemical fertilizer, the irrigation area of cultivated land, the area of grain sowing, and the total capacity of reservoirs in each province are defined as the input items. The production of food, production of oil plants, and production of fruits are considered output items. According to the findings from the DEA, the most efficient crop production is observed in Shandong and Xinjiang provinces. We also discuss the role of farmers' uncertainty perceptions in COVID-19. By cluster analysis, the provinces with large grain sown area and high grain yield are Henan and Heilongjiang, the provinces with moderate grain production in the grain sown area are Hunan, Hubei, Jiangxi, Guizhou, and Yunnan, and Xinjiang, Shandong, Hebei, Anhui, Sichuan, Jiangsu, Inner Mongolia, and Jilin are the provinces with low grain production. [ FROM AUTHOR] Copyright of Discrete Dynamics in Nature & Society is the property of Hindawi Limited and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full . (Copyright applies to all s.)

Impacts of Extreme Climate Events on Future Rice Yields in Global Major Rice-Producing Regions.

Under the dual impacts of climate change and COVID-19, there are great risks to the world's food security. Rice is one of the three major food crops of the world. Assessing the impact of climate change on future rice production is very important for ensuring global food security. This article divides the world's main rice-producing regions into four regions and uses a multivariate nonlinear model based on historical economic and climatic data to explore the impacts of historical extreme climatic events and economic factors on rice yield. Based on these historical models, future climatic data, and economic data under different shared socioeconomic pathways (SSPs), the yields of four major rice-producing regions of the world under different climate change scenarios (SSP126, SSP245, and SSP585) are predicted. The research results reveal that under different climate change scenarios, extreme high-temperature events (Tx90p) and extreme precipitation events (Rx5day, R99pTOT) in the four major rice-producing regions have an upward trend in the future. Extreme low-temperature events (Tn10p) have a downward trend. In the rice-producing regions of Southeast Asia and South America, extreme precipitation events will increase significantly in the future. The prediction results of this model indicate that the rice output of these four major rice-producing regions will show an upward trend in the future. Although extreme precipitation events will have a negative impact on rice production, future increases in rice planting areas, economic development, and population growth will all contribute to an increase in rice production. The increase in food demand caused by population growth also brings uncertainty to global food security. This research is helpful for further understanding climate change trends and risks to global rice-production areas in the future and provides an important reference for global rice-production planning and risk management.

A Bidirectional LSTM Model for Classifying Chatbot Messages

Online channels, e.g., Facebook Messenger and Line, are widely used especially in COVID-19 pandemic. To quickly respond to their customer, chatbot system are implemented in many companies or organizations, connected to those channels. The Office of Registrar, Thammasat University also implements a chatbot to answer questions from students. An important step in the chatbot system is to know an intention of a question message. A bidirectional LSTM model is employed to classify a question message from the chatbot system into five intention classes. The experimental results shows that the obtained model yields an accuracy of 0.80 on our validation dataset. © 2021 IEEE.