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We explore the effect of lottery presentation formats on elicitation of risk preferences using a popular probability varying task (Holt & Laury. The American Economic Review 92 (5), 1644-1655. 2002) and a payoff-varying task (Drichoutis & Lusk. Journal of Risk and Uncertainty 53 (2), 89-106. 2016). The presentation formats use horizontal bars that vary either the width or height of the bars (or both at the same time) to help subjects in judging how large or small probabilities and monetary amounts are in a given choice set. These graphical formats are compared to a text only format. We complement our choice data with eye tracking data that enriches our structural models with additional information regarding how visual attention varies with the presented information. While we find no statistically significant effects of presentation formats on elicited parameters for risk preferences, we find that eye tracking information not only is associated with preference parameters, but it also changes the inferences with respect to which decision theory better fits the choice data.

We offer two new fraud prediction metrics: the AB-score, which is based on Benford's Law, and the ABF-score, which combines the AB-score with the well-known F-score model from the seminal work by Dechow et al. (2011). Multiple performance evaluation metrics show that the ABF-score provides the highest accuracy, while the AB-score substantially expands the scope over which misreporting can be predicted. Additionally, both models are easier to estimate than other popular models while delivering similar accuracy. Our models perform well in- and out-of-sample and across alternative misstatement proxies. Back-of-the-envelope calculations suggest that our improved precision (over the F-score model) could save stakeholders about $14.34 billion (in 2016 dollars) annually. Finally, in a case study approach using a sample of notorious financial frauds, we show that our models offer sharper identification of fraud with an expanded scope that correctly identifies far more fraudulent firm-years.

This paper analyzes the economic effects of student loans in a segmented educational market. The motivation here draws upon some studies that verify differences in labor income returns and repayment difficulties depending on the characteristics of the institution attended by the student. I put forward a neo-Kaleckian model that considers three types of households: rentiers (RHs), lower-skilled workers (LSWs), and higher-skilled workers (HSWs). Moreover, a cost-minimizing representative firm combines physical capital and labor in effective units in the production process, which also features some labor skill substitution, thus generating a bargaining process between the different worker groups over the wage gap. The main result is that, for a debt-financed human capital investment, the conditions that drive long-term economic growth do not necessarily align with those that reduce the wage gap and household debt. In fact, in some cases, widening the wage gap may be a necessary condition for boosting economic activity and human capital accumulation. However, this investment might not drive down wage inequality and could raise concerns about household debt.

Since the start of the pandemic, routine vaccination has been delayed or suspended in many countries. Over one-third of countries have had continued disruptions to routine immunization programs in 2021. Since 2020, there has been an increase in zero-dose children, leading to a substantial immunity gap in many developing countries. Unvaccinated children have become susceptible to outbreaks due to missed immunization doses. Post-pandemic, measles, polio, and cholera outbreaks have occurred. To address this challenge, UNICEF, the World Health Organization (WHO), and the Global Alliance for Vaccines and Immunization (GAVI) have launched the Big Catch-up campaign to accelerate catch-up efforts, promote recovery, and enhance routine immunization programs in priority countries.This paper outlines the prevalent challenges associated with reaching underserved children over the past four years, with a focus on the implementation of catch-up and recovery initiatives in 10 countries in two regions of Africa (East and Southern Africa (ESAR) and West and Central Africa Region (WCAR).The study findings indicated that it was feasible to identify children who had not received any vaccinations in the country by utilizing various data sources documented in research papers. An initial obstacle encountered was locating these children within the community. Thematic analysis of the plans culminated in categorizing the primary challenge, initially involving vaccinating children over 2 years old per national policy. The subsequent difficulties encompassed identifying these children, generating demand for vaccinations among older children, and reporting on the population of children who had not received any vaccines. The strategies primarily revolved around fortifying the data system to capture these zero-dose children, implementing catch-up campaigns, and the Periodic Intensification of Routine Immunization (PIRI). The successful execution of the proposed strategies may diminish immunity gaps and reinforce routine immunization services, aligning with the IA2030 agenda.

NATIONAL BESTSELLERWas one of the most unheralded captains of the Toronto Maple Leafs also one of the greatest players in the history of hockey's most popular team? In telling his story of turmoil in Toronto's Ballard years (and with Don Cherry's Mississauga Ice Dogs), growing up in an environment filled with alcohol and alcoholism, and his own struggles and battles, Rick Vaive finally sets the record straight.In the storied history of the Toronto Maple Leafs, no player scored fifty goals in a season until Rick Vaive in 1981-82. He did it three years in a row (only two others have scored 50 since) before being unceremoniously stripped of his captaincy and traded out of town, and he did it for a promising team that was nonetheless largely stuck at the bottom of the standings. So why isn't his number 22 hanging from the rafters of the Leafs'rink and his name as revered in Leafs lore as Gilmour, Sundin and Clark? You could blame it on a team that lost far more than it won. You could blame Harold Ballard and his erratic ownership. You could blame the fans, the media...Rick Vaive doesn't blame anybody. Sometimes, life just doesn't go your way. He'd know. Growing up in a household plagued by alcoholism, the gifted young hockey player took shelter in the company of his grandmother and a blind and severely disabled uncle. Rick learned quickly that there are more valuable things in life than hockey. Even after his promising coaching career stopped dead when it ran into Don Cherry in Mississauga--one of the worst seasons in Ontario junior hockey history--he still doesn't point fingers. Life is too sweet for regrets, but learning that lesson can be one hell of a ride.

The aim of this study was to assess the effect of long-term catch crop application on the structural properties of humin, which is considered the most recalcitrant fraction of soil organic matter. Soil samples from a 30-year field experiment on triticale cultivated with and without catch crops were analysed to determine the total organic carbon content and fractional composition of humic substances. Meanwhile, humin isolated from bulk soil was analysed to determine its elemental composition and spectroscopic properties measured with UV-Vis, fluorescence, and 13C-CPMAS-NMR. It was found that catch crop farming enhanced the formation of highly reactive humus substances, like low-molecular-weight fractions and humic acids, while decreasing the humin fraction. The higher H/C and O/C atomic ratios of humin and the UV-Vis, fluorescence, and 13C-CPMAS-NMR results confirmed a higher share of oxygen-containing functional groups in humin isolated from the soil with catch crop rotation, also corroborating its greater aliphatic nature. Under the conditions of our field experiment, the results indicated that organic residues from catch crops quickly undergo the decay process and are transformed mainly into highly reactive humus substances, which can potentially improve soil health, while mineral fertilisation alone without catch crops favours the stabilisation and sequestration of carbon. [ABSTRACT FROM AUTHOR]

Automating contact-rich manipulation of viscoelastic objects with rigid robots faces challenges including dynamic parameter mismatches, unstable contact oscillations, and spatiotemporal force-deformation coupling. In our prior work, a Compliance-Aware Tactile Control and Hybrid Deformation Regulation (CATCH-FORM-3D) strategy fulfills robust and effective manipulations of 3D viscoelastic objects, which combines a contact force-driven admittance outer loop and a PDE-stabilized inner loop, achieving sub-millimeter surface deformation accuracy and ±5% force tracking. However, this strategy requires fine-tuning of object-specific parameters and task-specific calibrations, to bridge this gap, a CATCH-FORM-ACTer is proposed, by enhancing CATCH-FORM-3D with a framework of Action Chunking with Transformer (ACT). An intuitive teleoperation system performs Learning from Demonstration (LfD) to build up a long-horizon sensing, decision-making and execution sequences. Unlike conventional ACT methods focused solely on trajectory planning, our approach dynamically adjusts stiffness, damping, and diffusion parameters in real time during multi-phase manipulations, effectively imitating human-like force-deformation modulation. Experiments on single arm/bimanual robots in three tasks show better force fields patterns and thus $10\%-20\%$ higher success rates versus conventional methods, enabling precise, safe interactions for industrial, medical or household scenarios.

The use of marine protected areas (MPAs) is expanding around the world. MPAs can have a wide variety of objectives (e.g., science, conservation, food security, cultural value), and scientific guidance on how to design MPAs to achieve objectives is often based on simulation modeling. Many different models may all provide an answer to questions such as the predicted change in population biomass and fisheries catches resulting from th implementation of an MPA. When multiple levels of model complexity are all in theory capable of answering the same question, and the models cannot be confronted with data directly, the decision of what level of model complexity to use can be ad hoc. In this, paper I compare the predicted effects of MPAs on catch and biomass produced by a spatially explicit age-structured multi-species and multi-fleet (High-definition) model to the predictions generated by a two-patch surplus production (Low-definition) model, fitted to emulate the High-definition model. I found that in many cases, the predictions made by the two models were markedly different, with the Low-definition model frequently predicting substantially higher biomass benefits from MPAs than the High-definition model, and in some cases incorrectly estimating the direction (positive or negative) of the MPA effects. However, I also found that the Low-definition model has strategic value for broad classification and ranking exercises. My results show that care should be taken in selecting and interpreting the results of MPA simulation models and that research is needed to understand what models are best suited to what policy recommendations when multiple viable options exist.

Spillover is a term commonly applied to the dispersal of fish and/or larvae from inside a closed area to areas open to fishing. The presence of spillover is often quantified by measuring gradients in attributes such as abundance or catch rates near the boundaries of closed areas or by measuring higher abundance inside closed areas compared to outside. It is commonly assumed that such gradients or ratios indicate that the closed area has benefitted the fishery and the total abundance of fish. We explore this assumption using a spatially explicit model of closed areas with different intensities of fishing and fish movement, and we find that such gradients will be expected any time there is higher abundance inside the closed area. However, such gradients do not necessarily indicate a benefit to the fishery either in terms of total catch or catch rate, and unless pre-closure fishing was intense, total abundance is not expected to rise significantly. We examine case studies that argue that spillover exists and leads to fishery benefits. We then evaluate the evidence for net benefits in these case studies and find those with evidence of net benefits all come from places where fishing pressure was intense. While most analysis come from quite small coastal closed areas, two studies of very large open-ocean closed areas are discussed, and we find that both suggest little overall impact on the tuna populations that support the main commercial fisheries affected by the closures in question.

Fish welfare is a crucial issue that needs to be addressed in fisheries. Thus, the scope of the fair-fish database - an online open-access platform - was expanded from aquaculture (farm branch) to fisheries (catch branch). It provides farm and catch welfare profiles (WelfareChecks) of aquatic species based on literature reviews. In the catch branch, each WelfareCheck encompasses a species in relation to a specific fishing method used to catch it, assessing 10 criteria covering welfare hazards throughout the steps of the catching process: prospection, setting, catching, emersion, release from gear, bycatch avoidance, sorting, discarding, storing, and stunning/slaughter. In each criterion, we assess the likelihood and potential of experiencing good welfare under minimal and high-standard fisheries conditions, respectively, besides the certainty level about these. A final WelfareScore is provided for each profile, which serves as a benchmark for assessing and improving fish welfare. Since its publication in 2023, we have published five WelfareChecks. The goal is to increase the number of profiles for several fished species and catching methods over time. In conclusion, the catch branch of the fair-fish database serves as an open-access source providing an overview of the welfare of a fished species given a certain catching method. It is a reliable tool that raises public awareness of fish welfare, provides scientists with insight into knowledge gaps, and offers practitioners with suggestions about how to avoid welfare risks.

Capture in nearshore fisheries is the leading threat to coastal elasmobranchs, of which more than 75 % are threatened with extinction globally. Limited knowledge of these highly dynamic fisheries impedes the design and implementation of stakeholder-inclusive policies for conservation. To address this, we developed an interdisciplinary approach, combining landing data with fishing geo-locations, Very High resolution (VHR) satellite imagery and fisher interviews to model elasmobranch catch dynamics and map areas of high catch potential. We compared how elasmobranch catch rates varied by species ecology, habitat, and fisheries characteristics in Visakhapatnam and Malvan, two regions on the east and west coasts of India, respectively. We sampled 2209 fishing trips across three oceanographic seasons from landing sites at both locations in 2022-23. We recorded 5578 elasmobranchs from >20 species of which at least 13 were categorised as ‘Threatened’. Gillnets, hook and line and trawl nets were the most common gears, but their use and catch rates varied considerably. Elasmobranchs had a higher catch risk on the eastern site (where they may be specifically targeted) and were generally larger. Catch rates were higher in shallow regions on the west coast and in the summer at both sites. Importantly, we demonstrate that drivers of elasmobranch catch were site and fishery specific, underscoring the need for more local-scale research for planning conservation actions. Our framework provides a robust method to study the highly dynamic and diverse nature of nearshore fisheries, which can inform conservation actions and, at the same, time, enable a bottom-up approach to conserving elasmobranchs.

Accurate disease detection in fruit leaves is crucial for preserving crop health and enhancing agricultural productivity. Fruits hold significant economic value and nutritional importance, but their vulnerability to diseases severely impacts both yield and quality. This research proposes a novel Multi-level Attention DenseNet-based Deep Convolutional Neural Network (MADDCNN) model to improve fruit disease detection. The model addresses challenges such as varying lighting conditions, complex backgrounds, and overlapping symptoms, which hinder detection accuracy in existing systems. By incorporating the Catch Fish Optimization (CFO) technique, MADDCNN enhances multi-fruit classification accuracy and optimizes parameter tuning for better performance. To further enhance the detection process, the model employs the Hybrid Runge Kutta DeepLabV3+ (HRKD) method for segmenting disease-affected areas, enabling more precise identification and isolation of infected regions. This segmentation step significantly boosts classification accuracy and reliability in agricultural environments. The novelty of this research lies in the integration of MADDCNN with CFO and HRKD, enabling severity classification, adaptive optimization, and precise segmentation under varying agricultural conditions. The MADDCNN model outperforms other methods in key metrics, achieving 98.34% accuracy, 97.85% precision, 97.91% recall, and 98.12% F1 score. Furthermore, it demonstrates computational efficiency, processing each image in just 1.5 s. Overall, the MADDCNN approach offers a sustainable, efficient solution for detecting fruit diseases, addressing the challenges of varying conditions and complex symptoms, and contributing to enhanced agricultural practices.