Gustavsengamble7374

Women in agriculture are involved in agricultural activities and are solely responsible for household-level unpaid work. They face severe time trade-offs between agricultural and household activities across crop seasons. Recent literature suggests that these time trade-offs may negatively impact their nutrition. However, there is no quantitative evidence exploring this relationship within an agricultural context. This paper addresses this research gap by analyzing the relationship between women's time trade-offs and their nutritional outcomes. Using a unique ten-month primary panel data of 960 women from India, our findings show that women are severely time-constrained, as they contribute significantly to agricultural as well as domestic work. Our results show that during peak seasons relative to lean seasons, women's time trade-offs (rising opportunity cost of time) are negatively associated with the intake of calories, proteins, iron,zinc and Vitamin A. 3Methyladenine We show that this negative relationship is manifested severely among women who are landless and cultivate paddy alone (food crop) or paddy and cotton (mixed crop). This study highlights the gendered role of agricultural activities in rural households and the need to recognize time as a scarce resource when implementing policies and programs involving women in agriculture. We contribute to the literature of agriculture-nutrition linkages by examining the the time use pathway in detail. Besides providing novel metrics, we discuss several policy implications to reduce women's time constraints and enhance their nutrition.Natural disaster and food insecurity are prevalent in Haiti. Natural disasters may cause long-term food insecurity. Microfinance programs may provide resilience against this outcome. The objectives of this study were 1) to assess the association between the impact of Hurricane Matthew and long-term food insecurity and 2) to understand whether this association varies by participants' membership in a microfinance program. In 2017-2018, we interviewed 304 Haitian female microfinance clients. We used log-binomial regression to evaluate the association between hurricane Matthew impact and long-term food insecurity, with evaluation of effect modification by timing of microfinance exposure. We found that one year after the hurricane, participants who were severely impacted by the hurricane were more likely to report poor dietary diversity and moderate to severe household hunger, compared to the less severely impacted participants. Both associations became insignificant among those who received their first microfinance loan before the hurricane. Natural disasters like hurricanes are associated with long-term food insecurity at individual and household levels. Microfinance programs might improve post-hurricane long-term food security.Contract farming has emerged as a popular mechanism to encourage vertical coordination in developing country agriculture. Yet, there is a lack of consensus on its ability to spur structural transformation in rural economies. We present results from a field experiment on contract farming for rice production in Benin. While all contracts have positive effects on welfare and productivity measures, we find that the simplest contract has impacts nearly as large as contracts with additional attributes. This suggests that once price risk is resolved through the offer of a fixed-price contract, farmers are able to address other constraints on their own.This article examines valuation and payment practices of psychoanalysts in Buenos Aires, Argentina. Psychoanalysts do not use explicit sliding scales but rather reach an agreement about fees in conversation with the patient. This negotiation is conducted with some principles of gift-giving, where parties try to give more, rather than through competitive bargaining (an inverted bazaar). Drawing on the sociology of money, morals and markets, and valuation studies literatures, I distinguish four factors to explain this 1) Some formally produced prices as well as market mechanisms shape benchmarks for fees, but the peculiar service psychologists offer (which makes quality judgments hard), the way patients and therapists are matched, and the lack of public information about prices allow for high flexibility in price-setting; these are structural factors that remain unsaid in the conversation on fees. 2) A professional narrative that highlights a responsibility towards patients that should not be contaminated by economic interest. 3) Psychoanalysts' elaborations on the meanings of the payment, which should reflect the uniqueness of each patient and the bond analyst-patient and symbolize the patient's commitment to treatment, involving a cost and a loss beyond the economic. 4) The prevalence of cash, face-to-face payment without intermediaries, which helps desacralize the analyst and disentangle the session from the rest of the economic life of the analyst, but impedes evading moralization of the transaction. Payments in psychoanalysis are delicate arrangements, and analysts often stress about valuation and payments. They have to be careful to ensure this flexibility results in morally acceptable transactions.Small islands have unique environmental characteristics that make them prone or vulnerable to natural and human-induced hazards. The ability of a community to measure and assess its own characteristics (i.e., connectedness, risk and vulnerability, procedures on disaster planning, response and recovery, and available resources) contributes to the improvement of its capacity to better deal with, survive, and recover from disasters. Thus, we undertook this study to measure the resilience of a small island community using a tool developed by the Torrens Resilience Institute. We conducted a survey among 37 local government officials and 192 local community residents in the Island Province of Guimaras from August to December 2018 using a structured questionnaire following a simple random sampling method. Our results show that Guimaras is facing various natural and anthropogenic hazards. However, local officials and community residents agreed that Guimaras is in the "Going Well Zone" (i.e., the island community is likely to be extremely resilient to any disaster) and that there is no significant difference (t-test, α = 0.05) in their ratings on disaster preparedness. As sun, sand, and sea tourism is a growing industry worldwide, the assessment that small island tourist destinations such as Guimaras is a resilient community would have positive impacts on the tourism industry, possibility leading to the sustainable development of coastal communities with tourism as a major source of supplemental or alternative livelihoods while reducing pressure on overexploited fish stocks.Understanding the uptake and clearance kinetics of new drugs and contrast agents is an important aspect of drug development that typically involves a combination of imaging and analysis of harvested organs. Although these techniques are well-established and can be quantitative, they generally do not preserve high resolution biodistribution information. In this context, fluorescence whole-body cryo-imaging is a promising technique for recovering 3D drug/agent biodistributions at a high resolution throughout an entire study animal at specific time points. A common challenge associated with fluorescence imaging in tissue is that agent signal can be confounded by endogenous fluorescence signal which is often observed in the visible window. One method to address this issue is to acquire hyperspectral images and spectrally unmix agent signal from confounding autofluorescence signals using known spectral bases. Herein, we apply hyperspectral whole-body cryo-imaging and spectral unmixing to examine the distribution of multiple fluorescent agents in excretion organ regions.During the epidemic of COVID-19, Computed Tomography (CT) is used to help in the diagnosis of patients. link2 Most current studies on this subject appear to be focused on broad and private annotated data which are impractical to access from an organization, particularly while radiologists are fighting the coronavirus disease. It is challenging to equate these techniques since they were built on separate datasets, educated on various training sets, and tested using different metrics. In this research, a deep learning semantic segmentation architecture for COVID-19 lesions detection in limited chest CT datasets will be presented. The proposed model architecture consists of the encoder and the decoder components. The encoder component contains three layers of convolution and pooling, while the decoder contains three layers of deconvolutional and upsampling. The dataset consists of 20 CT scans of lungs belongs to 20 patients from two sources of data. The total number of images in the dataset is 3520 CT scans with its labelled images. The dataset is split into 70% for the training phase and 30% for the testing phase. Images of the dataset are passed through the pre-processing phase to be resized and normalized. Five experimental trials are conducted through the research with different images selected for the training and the testing phases for every trial. The proposed model achieves 0.993 in the global accuracy, and 0.987, 0.799, 0.874 for weighted IoU, mean IoU and mean BF score accordingly. The performance metrics such as precision, sensitivity, specificity and F1 score strengthens the obtained results. link3 The proposed model outperforms the related works which use the same dataset in terms of performance and IoU metrics.Reverse-Transcription Polymerase Chain Reaction (RT-PCR) method is currently the gold standard method for detection of viral strains in human samples, but this technique is very expensive, take time and often leads to misdiagnosis. The recent outbreak of COVID-19 has led scientists to explore other options such as the use of artificial intelligence driven tools as an alternative or a confirmatory approach for detection of viral pneumonia. In this paper, we utilized a Convolutional Neural Network (CNN) approach to detect viral pneumonia in x-ray images using a pretrained AlexNet model thereby adopting a transfer learning approach. The dataset used for the study was obtained in the form of optical Coherence Tomography and chest X-ray images made available by Kermany et al. (2018, https//doi.org/10.17632/rscbjbr9sj.3) with a total number of 5853 pneumonia (positive) and normal (negative) images. To evaluate the average efficiency of the model, the dataset was split into on 5050, 6040, 7030, 8020 and 9010 for training and testing respectively. To evaluate the performance of the model, 10 K Cross-validation was carried out. The performance of the model using overall dataset was compared with the means of cross-validation and the currents state of arts. The classification model has shown high performance in terms of accuracy, sensitivity and specificity. 7030 split performed better compare to other splits with accuracy of 98.73%, sensitivity of 98.59% and specificity of 99.84%.A smart and scalable system is required to schedule various machine learning applications to control pandemics like COVID-19 using computing infrastructure provided by cloud and fog computing. This paper proposes a framework that considers the use case of smart office surveillance to monitor workplaces for detecting possible violations of COVID effectively. The proposed framework uses deep neural networks, fog computing and cloud computing to develop a scalable and time-sensitive infrastructure that can detect two major violations wearing a mask and maintaining a minimum distance of 6 feet between employees in the office environment. The proposed framework is developed with the vision to integrate multiple machine learning applications and handle the computing infrastructures for pandemic applications. The proposed framework can be used by application developers for the rapid development of new applications based on the requirements and do not worry about scheduling. The proposed framework is tested for two independent applications and performed better than the traditional cloud environment in terms of latency and response time.