Indian agriculture and rural economy in terms of the COVID-19 pandemic

The aim of the paper is the analysis of the changes in Indian agriculture during the pandemic. The following research questions were put forward: What is the diversification of the Indian agriculture and rural economy in terms of COVID-19?;What is the percentage change in overall agricultural commodities production, in farm-gate prices for commodities in agriculture and the allied sector and in the availability of agri-inputs? The theoretical part of the paper depicted the following issues: India's role in global farming;rural economy issues, economic recovery, and the agricultural sector in terms of the COVID-19 pandemic. The studies that were conducted include: documentation, statistical, comparative, and dynamics analysis. The results showed that the Indian agriculture and rural economy in terms of COVID-19 had different tendencies. With businesses across all industries forced to shut down, the economy ground to a halt. Even while farming operations were spared during the lockdown's early stages, the agricultural value chain nonetheless experienced widespread disruptions. This was devastating to the rural Indian economy.

Measuring carbon emissions performance of Japan's metal industry: Energy inputs, agglomeration, and the potential for green recovery reduction

This study measures the total factor carbnon dioxide (CO2) emissions performance of the metal industry, iron and steel, nonferrous metal, and metal processing industries in 39 Japanese prefectures from 2008 to 2019. The true fixed-effects panel stochastic frontier model identifies regional carbon efficiency as well as the inefficiency determinants. The main results are as follows. First, a decrease in the coal ratio and an increase in the electricity ratio in total energy consumption improves efficiency. This result suggests that electrification in the metal industry, especially conversion from blast furnaces to electric furnaces in the iron and steel industry, contributes to reducing carbon emissions. Second, industrial agglomeration improves carbon emissions performance in the metal industry. This implies that agglomeration and decarbonization policies focusing on there are more effective, rather than a uniform national policy. Third, compared to the cumulative CO2 emissions over the sample period, 49,017 × 103 tons, the cumulative CO2 mitigation potential is 29,703 × 103 tons, indicating that CO2 emissions can be reduced by 60.6% without affecting the output. Forth, to examine the green economic recovery with efficiency in Japan's metal industry after COVID-19, we present a simple scenario analysis where a k% replacement coal ratio with an electricity ratio in total energy consumption, assuming that each prefecture will achieve the maximum CO2 emission amount during the sample period. By replacing 10% of the coal ratio with the electricity ratio, CO2 emissions can be reduced by 23.0%. In the case of a 20% replacement, CO2 emissions can be reduced by 33.0%. Our results show that Japan's targets in the post-COVID-19 green recovery process should be a decrease in coal consumption, an increase in electricity, and industrial agglomeration. © 2023 Elsevier Ltd

Emerging data inputs for infectious diseases surveillance and decision making.

Infectious diseases create a significant health and social burden globally and can lead to outbreaks and epidemics. Timely surveillance for infectious diseases is required to inform both short and long term public responses and health policies. Novel data inputs for infectious disease surveillance and public health decision making are emerging, accelerated by the COVID-19 pandemic. These include the use of technology-enabled physiological measurements, crowd sourcing, field experiments, and artificial intelligence (AI). These technologies may provide benefits in relation to improved timeliness and reduced resource requirements in comparison to traditional methods. In this review paper, we describe current and emerging data inputs being used for infectious disease surveillance and summarize key benefits and limitations.

Stratification in parents' selection of developmentally appropriate books for children: register-based evidence from Danish public libraries

This paper studies socioeconomic gradients in parents' selection of developmentally appropriate children's books from public libraries. I draw on developmental gradients research to hypothesize that families with high socioeconomic status (SES) select a higher share of books that match children's developmental stage in order to best improve children's learning environments. Based on Danish registry data on all books borrowed from public libraries in 2020, I find that highly educated families are more likely to use libraries and borrow more books, but they do not select a larger share of developmentally appropriate books;in fact, they select a slightly lower share. In contrast, high-income families borrow both a little more books and a little higher share of developmentally appropriate books, than low-income families. The supplementary analyses show that results are robust across families with children of different ages and to account for nonrandom selection into the sample of library users, socioeconomic differences in children's reading skills, and the impact of library lockdowns due to Covid-19. I conclude that stratification in library book selection is more prominent concerning the voraciousness with which highly educated parents provide reading inputs (more books) than how discriminating they are in terms of selecting developmentally appropriate books.

PVT-COV19D: COVID-19 Detection Through Medical Image Classification Based on Pyramid Vision Transformer

With the outbreak of COVID-19, a large number of relevant studies have emerged in recent years. We propose an automatic COVID-19 diagnosis model based on PVTv2 and the multiple voting mechanism. To accommodate the different dimensions of the image input, we classified the images using the Transformer model, sampled the images in the dataset according to the normal distribution, and fed the sampling results into the PVTv2 model for training. A large number of experiments on the COV19-CT-DB dataset demonstrate the effectiveness of the proposed method. Our method won the sixth place in the (2nd) COVID19 Detection Challenge of ECCV 2022 Workshop: AI-enabled Medical Image Analysis - Digital Pathology & Radiology/COVID19. Our code is publicly available at https://github.com/MenSan233/Team-Dslab-Solution. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

COVID-19, living income differential and decline in income of cocoa producers in C..te d'Ivoire. (Agriculture et syst..mes alimentaires face .. la Covid-19.) [French]

Cocoa farmers in C..te d'Ivoire are mostly below the poverty line. In September 2019, the Ivorian and Ghanaian governments imposed the Living Income Differential (LID) on private companies, an additional $ 400 per ton compared to the international market price, passed on to the producer price (farm gate price). At the beginning of 2020, the Covid-19 arose. In this dual context, how did prices change? Has the hope of increased income been achieved? Three approaches are used: (a) monitoring of the selling price of cocoa beans and monitoring of the price of purchased cocoa farming inputs and basic necessities for households;(b) monthly monitoring of farm gate cocoa price in 2020-2021;(c) an analysis of national production data from C..te d'Ivoire and Ghana, the world price, variations in the demand for beans by the grinding industry, and the price paid to producers, over 20 years. The first result is a very temporary and limited rise in the farm gate price of cocoa at the end of 2020, then its fall in 2021 as the price of inputs and basic necessities soar. The 2021-2022 campaign is even more harmful with a tightening of the price scissor. It is therefore the failure of the LID, but the role of Covid-19 in this failure is very nuanced with regard to the declarations of the State and the multinationals. The drop in prices and the loss of income for cocoa farmers in 2020-2022 rather fits into the economic theory of games. Without control of their supply, an agreement between two companies or countries cannot work. The failure is part of a largely endogenous structural change: demographic growth, policies to encourage migration and deforestation, opacity of the sector and finally continued growth of the supply of cocoa from C..te d'Ivoire on the international market.

How to improve the future efficiency of Covid-19 treatment centers? A hybrid framework combining artificial neural network and congestion approach of data envelopment analysis.

Covid-19 virus (severe acute respiratory syndrome coronavirus 2, SARS-CoV-2) threatens the health of human beings worldwide, imposing a concern for the world and prompting governments to control the contagion. Although vaccination is a proper tool to control the transmission, the efficient allocation of limited health-care resources to massive patients can improve the effectiveness of medical services. Relying on the Artificial Neural Network (ANN), the aim of this research is to enhance the future efficiency of Covid-19 treatment centers by forecasting their efficiency and providing benchmarks. To do this, we use the congestion approach of data envelopment analysis (DEA) based on the theory of economies of scale principles. In the traditional input-oriented DEA, inefficient decision-making units (DMUs) can become efficient merely by reducing the inputs. However, this may not always be true in real-world applications such as improving the efficiency of COVID-19 treatment centers (DMUs). Meaning that the treatment centers with less congested inputs (e.g., ventilators, test equipment, pulmonologists, and nurses, etc.) normally have higher mortality rates. For this reason, in this study, we take the congested inputs approach into account to provide proper benchmarks for the inefficient treatment centers. According to the congestion approach of DEA, an optimum increase in congested inputs can lead to a greater than a proportional increment in outputs. In other words, if more respiratory equipment, pulmonologists, patient rooms, nurses and beds, etc. are allocated to Covid-19 treatment centers, not only the number of deaths (undesirable outputs) are decreased, but also the number of recoveries (desirable outputs) are increased. Such an optimal rise in the congested inputs is determined in pairwise comparisons derived from the model. Accordingly, in this study, first, considering the congestion approach of DEA and historical data of five periods, we identify the initial efficiency of Iranian Covid-19 treatment centers. Then, by running ANN, we forecast the future inputs and outputs, the overall efficiency, and rank of the treatment centers. By doing this, the prospective efficient and inefficient DMUs are identified, and appropriate benchmarks are determined.

Impact of COVID-19 pandemic on agro-inputs distribution in Ogun State, Nigeria

AJOL : This study investigated the impact of the COVID-19 pandemic on agro-inputs distribution and sales along the agricultural supply chain (ASC) in Ogun State, Nigeria. A simple random sampling technique was used to select 96 agro-dealers from the Abeokuta and Ilaro ADP zones of Ogun State. Data were collected and analysed using a computer assisted personalized interviewing (CAPI) system. Findings showed that 89% of the respondents sold agro-inputs in the shops, 26% in the rural villages, and 19.8% at market stands. Due to the lockdown, 68.8% closed down shops for 35 - 39 days, 92.7% found it difficult to move agro-inputs from the stores to the villages, and 85.4% incurred high costs in transporting agro-inputs from urban to the rural areas. Likewise, 61.5% reported low patronage of fewer than 10 customers per week while the average weekly sales dropped from ?60,000 to 15,600 for maize seeds, ?78,000 to 27,000 for herbicides, and ?336,000 to 120,000 for fertilizers. The lockdown was significantly affected patronage (β = 0.64) and sales (β = 0.72). The COVID-19 pandemic affected patronage and sales of agro-inputs in Ogun State

Multiple-Inputs Convolutional Neural Network for COVID-19 Classification and Critical Region Screening From Chest X-ray Radiographs: Model Development and Performance Evaluation.

Background: The COVID-19 pandemic is becoming one of the largest, unprecedented health crises, and chest X-ray radiography (CXR) plays a vital role in diagnosing COVID-19. However, extracting and finding useful image features from CXRs demand a heavy workload for radiologists. Objective: The aim of this study was to design a novel multiple-inputs (MI) convolutional neural network (CNN) for the classification of COVID-19 and extraction of critical regions from CXRs. We also investigated the effect of the number of inputs on the performance of our new MI-CNN model. Methods: A total of 6205 CXR images (including 3021 COVID-19 CXRs and 3184 normal CXRs) were used to test our MI-CNN models. CXRs could be evenly segmented into different numbers (2, 4, and 16) of individual regions. Each region could individually serve as one of the MI-CNN inputs. The CNN features of these MI-CNN inputs would then be fused for COVID-19 classification. More importantly, the contributions of each CXR region could be evaluated through assessing the number of images that were accurately classified by their corresponding regions in the testing data sets. Results: In both the whole-image and left- and right-lung region of interest (LR-ROI) data sets, MI-CNNs demonstrated good efficiency for COVID-19 classification. In particular, MI-CNNs with more inputs (2-, 4-, and 16-input MI-CNNs) had better efficiency in recognizing COVID-19 CXRs than the 1-input CNN. Compared to the whole-image data sets, the efficiency of LR-ROI data sets showed approximately 4% lower accuracy, sensitivity, specificity, and precision (over 91%). In considering the contributions of each region, one of the possible reasons for this reduced performance was that nonlung regions (eg, region 16) provided false-positive contributions to COVID-19 classification. The MI-CNN with the LR-ROI data set could provide a more accurate evaluation of the contribution of each region and COVID-19 classification. Additionally, the right-lung regions had higher contributions to the classification of COVID-19 CXRs, whereas the left-lung regions had higher contributions to identifying normal CXRs. Conclusions: Overall, MI-CNNs could achieve higher accuracy with an increasing number of inputs (eg, 16-input MI-CNN). This approach could assist radiologists in identifying COVID-19 CXRs and in screening the critical regions related to COVID-19 classifications.

Model Predictive Control Tailored to Epidemic Models

We propose a model predictive control (MPC) approach for minimising the social distancing and quarantine measures during a pandemic while maintaining a hard infection cap. To this end, we study the admissible and the maximal robust positively invariant set (MRPI) of the standard SEIR compartmental model with control inputs. Exploiting the fact that in the MRPI all restrictions can be lifted without violating the infection cap, we choose a suitable subset of the MRPI to define terminal constraints in our MPC routine and show that the number of infected people decays exponentially within this set. Furthermore, under mild assumptions we prove existence of a uniform bound on the time required to reach this terminal region (without violating the infection cap) starting in the admissible set. The findings are substantiated based on a numerical case study. © 2022 EUCA.

Where did the input crisis originate and what to do to mitigate its impact? [Spanish]

This article examines the increase in prices of farm inputs (herbicides, insecticides, fungicides and fertilizers) in 2021 and the reasons for the increase, including the rise in energy costs, the disruption caused by outbreaks of new variants of COVID-19, and the increase in international freight prices. It is concluded that there are definitely external factors, beyond local control, that are drastically affecting the prices of farm inputs, a situation that directly harms the Colombian agri-food market by significantly increasing production costs due to the high dependence on imported products and raw materials necessary in the national agrochemical and fertilizer industry. There is a need to implement technologies and cultivation practices that lead to the reduction and/or rationalization in the use of inputs, seeking to reduce production costs and increase yields.

Impact of the COVID-19 pandemic on agriculture in India: cross-sectional results from a nationally representative survey

The COVID-19 pandemic has disrupted agriculture in India in many ways, yet no nationally representative survey has been conducted to quantify these impacts. The three objectives of this study were to evaluate how the pandemic has influenced: (1) cropping patterns and input use, (2) farmers' willingness to adopt sustainable agricultural practices, and (3) farmers' COVID-19 symptoms. Phone surveys were conducted between December 2020 and January 2021 with farmers who had previously participated in a nationally representative survey. Values are reported as weighted percent (95% confidence interval). A total of 3,637 farmers completed the survey;59% (56-61%) were small/marginal farmers;72% (69-74%) were male;and 52% (49-55%) had a below poverty line ration card. A majority of farmers (84% [82-86%]) reported cultivating the same crops in 2019 and 2020. Farmers who reported a change in their cropping patterns were more likely to be cultivating vegetables (p = 0.001) and soybean (p<0.001) and less likely to be cultivating rice (p<0.001). Concerning inputs, 66% (63-68%) of farmers reported no change in fertilizers;66% (64-69%) reported no change in pesticides;and 59% (56-62%) reported no change in labor. More than half of farmers (62% [59-65%]) were interested in trying sustainable farming, primarily because of government schemes or because their peers were practicing it. About one-fifth (18% [15-21%]) of farmers reported COVID-19 symptoms in the past month (cough, fever, or shortness of breath) and among those with symptoms, 37% (28-47%) reported it affected their ability to work. In conclusion, COVID-19 infections had started to impact farmers' productivity even during the first wave in India. Most farmers continued to grow the same crops with no change in input use. However, many expressed an interest in learning more about practicing sustainable farming. Findings will inform future directions for resilient agri-food systems.

Method for the Analysis of Health Personnel Availability in a Pandemic Crisis Scenario through Monte Carlo Simulation

During pandemic times, difficulties and problems related to the health sector are evident as the number of patients coming to health centers is higher compared to normal situations. This increase in the number of patients is typical of the pandemic, due to the high level of contagion in the population. Health personnel have a higher risk of infection, due to their sharing the work of caring for positive patients, so the infection rate is much higher. Hence, it remains necessary to understand the behavior of infection of health personnel, in order to be prepared to deal with the care of patients. Accordingly, in this research, we present a method to estimate different scenarios of infection and assess the probability of occurrence, so we can estimate the infection rate of health personnel. We present a simulation of 21 possible scenarios with 100 workers and a minimum of 80% needed to guarantee patient care. The results show that it is more likely that a 50% contagion scenario will occur, with an acceptable probability of 20%.

Skewed Auto-Regressive Process with Exogenous Input Variables: An Application in the Administered Vaccine Doses on Covid-19 Spread

This study focuses on the prevalence of COVID-19 disease along with vaccination in the United States. We have considered the daily total infected cases of COVID-19 with total vaccinated cases as exogenous input and modeled them using light/heavy tailed auto-regressive with exogenous input model based on the innovations that belong to the flexible class of the two-piece scale mixtures of normal (TP-SMN) family. We have shown that the prediction of COVID-19 spread is affected by the rate of vaccine injection. In fact, the presence of exogenous input variables in time series models not only increases the accuracy of modeling, but also causes better and closer approximations in some issues including predictions. An Expectation-Maximization (EM) type algorithm has been considered for finding the maximum likelihood (ML) estimations of the model parameters, and modeling as well as predicting the infected numbers of COVID-19 in the presence of the vaccinated cases in the US.

Impact assessment of COVID 19 on cotton economy of India

The impact of COVID 19 on the economy in general is no doubt ravaging and its impact on agriculture is complex and varied across diverse segments that form the agricultural value chain. Cotton has a complex supply chain that stretch from input suppliers, farmers, traders, ginning factories, spinning mills, textile companies and oil processors. The study was designed to capture the panoramic view of world and national cotton economy during the pandemic period and its impact on cotton fanning in India. Cotton prices declined in the initial months for January to April, 2020 and later recouped once the lock down restrictions were phased out. As such from the study during the year 2020-2021, it was noticed in general, as per CAB estimates, cotton fanning in India was not Effected in its area and production excepting in north zone which was not due to lock down but for the pest attack and lack of irrigation facilities. Districtwise analysis confirmed that labour availability for loading and unloading and its transport was the major impediment especially in the southern zone while it was market uncertainty in the other zones. During the COVID 19 pandemic year, the cotton value chain, like others, had faced unprecedented disruptions. Cotton farmers and supply chain actors should work together to make sure that the farmers have secured acquaintance to sell their cotton. Farmers' protection should be considered a priority in getting the minimal requirements regarding the input supply, logistics and remuneration for their produce.

PSO-Optimized CoVID-19 MLP-NARX Mortality Prediction Model

Mortality prediction models localized for Malaysia is limited, warranting a research gap to study further. A predictive model for CoVID-19 mortality prediction is presented in this paper. The model utilized the MLP-NARX structure. Parameters for the model were optimized using PSO. Prediction results yielded average MSE value of 8.1141x10x(-7) with acceptable validation results.

A value-oriented psychological contract: Generational differences amidst a global pandemic.

The COVID-19 pandemic has changed the landscape of working conditions world-wide, fast tracking the reality of the digital-driven workplace. Concepts such as remote working, working-from-home and hybrid working models are now considered as the "new normal." Employes are expected to advance, flourish and survive in this digitally connected landscape. Different age and generational groups may experience this new organizational landscape differently and may expect different organizational outcomes in exchange for their inputs. Accordingly, the study investigated differences regarding the value-oriented psychological contract expectations of employes from different generational groups. An ANOVA test for significant mean differences and a post hoc test for multiple comparisons were conducted on a sample of (N = 293) employes in the services industry in Southern Africa (85%) and other European countries (15%). The observed generational cohort differences regarding value-orientated psychological contract expectations for job characteristics and work-life balance could be utilized to develop interventions and strategies to promote retention of employes in the post-pandemic digital-orientated workplace.

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.

COVID-19 Detection from Chest Radiograph Images of Patients using Neural Networks

The fast escalation of COVID-19 since its outbreak has affected the health care system globally. To pare the dissemination of the disease it is important to detect COVID-19 positive cases at an early stage and isolate the patients as early as possible. So, an effective and timely method for detecting the disease is required. Despite the fact that RT-PCR is the benchmark for COVID-19 detection and is very accurate, it is time-consuming. Applying deep learning along with radiography may help in diagnosing patients with COVID-19 much fast. In the paper, we propose a deep learning-based CNN architecture inspired by AlexNet which takes a three-channel image input. © 2022 IEEE.

Contract Incompleteness and the Boundaries of the Firm in Times of COVID-19

In this paper, we study the firm's boundaries in times of COVID-19. Ownership and location decisions govern sourcing and shape firms' boundaries. Adopting incomplete contracts theory/international economics perspective, we investigate the determinants of ownership and location decisions and explore COVID-19-induced changes in firms' boundaries. Drawing on survey data from a sample of Italian firms, our estimates suggest that input specificity drives the ownership decision, whereas the location decision depends on productivity. Few firms re-consider sourcing because of the pandemic, suggesting a great deal of inertia in firm's boundaries.