Ambiguity tolerance and confusion avoidance in the intent to purchase farmed fish

Aquaculture products are presently becoming a crucial part of consumers’ diets. However, asymmetric information regarding farmed fish exposes consumers to ambiguity and often makes them confused. Therefore, this study aims to ascertain the levels of ambiguity tolerance (AT) and confusion avoidance (CA) related to farmed fish and to test if consumers’ AT and CA influence their purchase intent (PI) of such fish. The impact of demographics on consumers’ CA and AT is also explored. The data were obtained through a survey conducted on a randomly selected sample of 1053 households who either purchase and have purchased fish. The collected data were analyzed employing explorative factor analysis, structural equation modeling, and a simple regression model. The study demonstrates that AT had a positive and significant impact on the PI of farmed fish, and also reduced CA. However, the current high level of CA does not influence the PI of farmed fish

Consumers’ Preferences for the Traceability Information of Seafood Safety

Consumers’ Preferences for the Traceability Information of Seafood SafetyDue to importing food and the perpetual changes from conventional wet markets to supermarkets in emerging markets, consumers have the opportunity to base their buying decisions on traceability systems. Seafood traceability systems involve information on production mode, inspection notes, sustainable sources, and sources of origin to provide consumer protection and help ensure that all seafood is safe to consume. This study aims to explore seafood markets by assessing the demand for traceability information attributes by utilising data from an experimental survey in an emerging market such as Bangladesh. The data were analysed using descriptive statistics, exploratory factor analysis, and a conditional logit model. The results demonstrate that consumers are concerned regarding vitamins, cholesterol, and preservatives, while they are little concerned about microbiological contamination, pesticide residues, genetic modification, and additives or artificial colours. The difference between the mean willingness to pay (WTP) for traditional and sustainable farmed fish is higher than that between the mean WTP for conventional and sustainable wild fish. In a ranked-choice voting system, the ‘production mode’ and ‘claim of safety control (e.g., being formalin-free)’ were the first and second most influential attributes in fish choices. The outcomes of the econometric model revealed that consumers are more likely to prefer traceability information about fish control (e.g., formalin-free), and they want to pay a price premium for this information. Alternatively, consumers are less likely to prefer farmed and imported fish, and their WTP for these fishes are highly inflated. This finding may be because consumers use wild and local origin as a cue for food safety or quality. This study hopes that the effects of such traceability information will optimise the production process and supply chain and help make seafood recall management more effective

Deep Ensemble Learning with Frame Skipping for Face Anti-Spoofing

Face presentation attacks, also known as spoofing attacks, pose a significant threat to biometric systems that rely on facial recognition systems, such as access control systems, mobile payments, and identity verification systems. To prevent spoofing, several video-based methods have been presented in the literature that analyze facial motion in successive video frames. However, estimating the motion between adjacent frames is a challenging task and requires high computational cost. In this paper, we reformulate the face anti-spoofing task as a motion prediction problem and introduce a deep ensemble learning model with a frame skipping mechanism. The proposed frame skipping is based on a uniform sampling approach where the original video is divided into fixed size video clips. In this way, every nth frame of the clip is selected to ensure that the temporal patterns can easily be perceived during the training of three different recurrent neural networks (RNNs). Motivated by the performance of each RNNs, a meta-model is developed to improve the overall recognition performance by combining the predictions of the individual RNNs. Extensive experiments were conducted on four datasets, and state-of-the-art performance is reported for MSU-MFSD (3.12\%), Replay-Attack (11.19\%), and OULU-NPU (12.23\%) using half total error rate (HTER) in the most challenging cross-dataset test scenario

Saliency-based Video Summarization for Face Anti-spoofing

Due to the growing availability of face anti-spoofing databases, researchers are increasingly focusing on video-based methods that use hundreds to thousands of images to assess their impact on performance. However, there is no clear consensus on the exact number of frames in a video required to improve the performance of face anti-spoofing tasks. Inspired by the visual saliency theory, we present a video summarization method for face anti-spoofing tasks that aims to enhance the performance and efficiency of deep learning models by leveraging visual saliency. In particular, saliency information is extracted from the differences between the Laplacian and Wiener filter outputs of the source images, enabling identification of the most visually salient regions within each frame. Subsequently, the source images are decomposed into base and detail layers, enhancing representation of important information. The weighting maps are then computed based on the saliency information, indicating the importance of each pixel in the image. By linearly combining the base and detail layers using the weighting maps, the method fuses the source images to create a single representative image that summarizes the entire video. The key contribution of our proposed method lies in demonstrating how visual saliency can be used as a data-centric approach to improve the performance and efficiency of face presentation attack detection models. By focusing on the most salient images or regions within the images, a more representative and diverse training set can be created, potentially leading to more effective models. To validate the method's effectiveness, a simple deep learning architecture (CNN-RNN) was used, and the experimental results showcased state-of-the-art performance on five challenging face anti-spoofing datasets

The economic cost of weeds in dryland cotton production systems of Australia

Economic losses and costs associated with weeds in dryland cotton production are important, both for growers and for industry bodies when making decisions about research priorities and research and development funding. A survey was conducted to provide information on weed types, control strategies and estimated costs to growers. We used information from the survey to estimate conventional financial losses due to weeds, and as a basis for evaluating aggregate economic (society) impacts. An economic surplus model was used to estimate the aggregate societal impact of weeds for three production regions in north-eastern Australia. The annual economic costs associated with weeds were estimated to be $41 million, and the on-farm financial costs were$25 million. While these are past (sunk) costs, and based on a total removal of weeds, the approach outlined here can be used to begin evaluating likely future returns from technologies or management improvements for different agricultural problems.Weeds, Dryland Cotton, and Economics, Crop Production/Industries, Environmental Economics and Policy,

Effect of Different Concentrations of Plant Growth Hormones for in Vitro Regeneration of Rice Varieties BRRI Dhan 28 and BRRI Dhan 29

A method for in-vitro propagation of BRRI dhan 28 and BRRI dhan 29 was developed by using seed embryos as explants on MS media and half strength MS media containing different concentrations of plant growth regulators and hormones. In case of BRRI dhan 28, approximately 10

Development of an Efficient in Vitro Regeneration System for Endangered Wild Orange Citrus Chrysocarpa L.

A method for in-vitro propagation of wild type Indian orange (Citrus chrysocarpa L.) was developed by shoot organogenesis from seed. Mature seed embryos were used as explants and treated with different hormones and plant growth regulators on MS medium for callus, shoots and roots induction. For callus inductio

Out-of-pocket expenditure for seeking health care for sick children younger than 5 years of age in Bangladesh: findings from cross-sectional surveys, 2009 and 2012

Background: Bangladesh has committed to universal health coverage, and options to decrease household out-of-pocket expenditure (OPE) are being explored. Understanding the determinants of OPE is an essential step. This study aimed to estimate and identify determinants of OPE in seeking health care for sick under-five children. Methods: Cross-sectional data was collected by structured questionnaire in 2009 (n = 7362) and 2012 (n = 6896) from mothers of the under-five children. OPE included consultation fees and costs of medicine, diagnostic tests, hospital admission, transport, accommodation, and food. Expenditure is expressed in US dollars and adjusted for inflation. Linear regression was used for ascertaining the determinants of OPE. Results: Between 2009 and 2012, the median OPE for seeking care for a sick under-five child increased by ~ 50%, from USD 0.82 (interquartile range 0.39\u20131.49) to USD 1.22 (0.63\u20132.36) per child/visit. Increases were observed in every component OPE measured, except for consultation fees which decreased by 12%. Medicine contributed the major portion of overall OPE. Higher overall OPE for care seeking was associated with a priority illness (20% increase), care from trained providers (90% public/~ 2-fold private), residing in hilly/wet lands areas (20%) , and for mothers with a secondary education (19%). Conclusion: OPE is a major barrier to quality health care services and access to appropriate medicine is increasing in rural Bangladesh. To support the goal of universal health care coverage, geographic imbalances as well as expanded health financing options need to be explored

Consumers’ Willingness to Pay (WTP) for Organically Farmed Fish in Bangladesh

This study aims to assess the market potential for organically farmed shrimp. The rank-ordered logit model was employed to investigate consumer perceptions; the findings reveal that consumers prefer organic shrimp from mariculture, and inland-farmed shrimp to the coastal version. The willingness to pay (WTP) for conventional shrimp amongst consumers with low knowledge is less than that for organic shrimp amongst highly knowledgeable ones. In addition, the lower WTP for organic shrimp compared with safe shrimp amongst those with a medium knowledge level shows that the organically farmed shrimp market is lagging behind due to limited knowledge and confusion