How top-down and bottom-up attention modulate risky choice

We examine how bottom-up (or stimulus-driven) and top-down (or goal-driven) processes govern the distribution of attention in risky choice. In three experiments, participants chose between a certain payoff and the chance of receiving a payoff drawn randomly from an array of eight numbers. We tested the hypothesis that initial attention is driven by perceptual properties of the stimulus (e.g., font size of the numbers), but subsequent choice is goal-driven (e.g., win the best outcome). Two experiments in which task framing (goal driven) and font size (stimulus driven) were manipulated demonstrated that payoffs with the highest values and the largest font sizes had the greatest impact on choice. The third experiment added a number in large font to the array, which could not be an outcome of the gamble (i.e., a distractor). Eye movement and choice data indicated that although the distractor attracted attention, it had no influence on option selection. Together with computational modeling analyses, the results suggest that perceptual salience can induce bottom-up effects of overt selection but that the perceived value of information is the crucial arbiter of intentional control over risky choice.

Sustainability Assessment of Reuse and Recycling Management Options for End-of-Life Computers-Korean and Japanese Case Study Analysis

The depletion of natural resources and global warming have increased in severity globally. In the industrial field, assembly products, such as electronic products, should be disassembled for recycling and reuse to deal with these problems. Reuse and recycling can contribute to reducing GreenHouse Gas (GHG) emissions and less depletion of natural resources since GHG emissions for virgin material production can be saved using reused components and recycled materials. However, each component of selling revenue and material-based GHG emissions depends on the country because of the different energy mixes of electrical power. Moreover, each collected component embedded in End-of-Life (EOL) products needs to be selected as a life cycle option based on its remaining life. The purpose of this study is to decide life cycle options such as reuse, recycling, and disposal of each component environmentally-friendly and economically in Korea and Japanese cases for computers. Firstly, selecting the life cycle option for each component was formulated by 0–1 integer programming with ε constraints. Next, GHG emissions, profits, and costs in Korea and Japan were estimated and analyzed for each component. Finally, Korean and Japanese cases were analyzed to obtain an economic value in the same material-based GHG saving rate with each component’s life cycle option selection by comparing each EOL product data. In the experiments, GHG recovery efficiency was higher in Japan 43 [g/Yen] than one in Korea 28 [g/Yen]. Therefore, it was better to retrieve and reutilize the components in Korea. However, if the maximum GHG recovery efficiency is desired, Japan is a better option.

Food waste treatment option selection through spherical fuzzy AHP

In line with the increase in consciousness on sustainability in today’s global world, great emphasis has been attached to food waste management. Food waste is a complex issue to manage due to uncertainties on quality, quantity, location, and time of wastes, and it involves different decisions at many stages from seed to post-consumption. These ambiguities re-quire that some decisions should be handled in a linguistic and ambiguous environment. That forces researchers to benefit from fuzzy sets mostly utilized to deal with subjectivity that causes uncertainty. In this study, as a novel approach, the spherical fuzzy analytic hierarchy process (SFAHP) was used to select the best food treatment option. In the model, four main criteria (infrastructural, governmental, economic, and environmental) and their thirteen sub-criteria are considered. A real case is conducted to show how the proposed model can be used to assess four food waste treatment options (composting, anaerobic digestion, landfilling, and incineration). Also, a sensitivity analysis is generated to check whether the evaluations on the main criteria can change the results or not. The proposed model aims to create a subsidiary tool for decision makers in relevant companies and institutions.

Visceral Artery Aneurysms Embolization and Other Interventional Options: State of the Art and New Perspectives

Visceral artery aneurysms (VAAs) are rare, usually asymptomatic and incidentally discovered during a routine radiological examination. Shared guidelines suggest their treatment in the following conditions: VAAs with diameter larger than 2 cm, or 3 times exceeding the target artery; VAAs with a progressive growth of at least 0.5 cm per year; symptomatic or ruptured VAAs. Endovascular treatment, less burdened by morbidity and mortality than surgery, is generally the preferred option. Selection of the best strategy depends on the visceral artery involved, aneurysm characteristics, the clinical scenario and the operator’s experience. Tortuosity of VAAs almost always makes embolization the only technically feasible option. The present narrative review reports state of the art and new perspectives on the main endovascular and other interventional options in the treatment of VAAs. Embolization techniques and materials, use of covered and flow-diverting stents and percutaneous approaches are accurately analyzed based on the current literature. Visceral artery-related considerations and targeted approaches are also provided and discussed.

Donor Anti-Spike Immunity is Related to Recipient Recovery and Can Predict the Efficacy of Convalescent Plasma Units.

Background: The novel coronavirus, SARS-CoV2 that causes COVID-19 has resulted in the death of more than 2.31 million people within the last year and yet no cure exists. Whereas passive immunization with COVID-19 convalescent plasma (CCP) provides a safe and viable option, selection of optimal units for therapy and lack of clear therapeutic benefit from transfusion remain as barriers to the use of CCP. Study design and methods: To identify plasma that is expected to benefit recipients, we measured anti-SARS-CoV2 antibody levels using clinically available serological assays and correlated with the neutralizing activity of CCP from donors. Neutralizing titer of plasma samples was measured by assaying infectivity of SARS-CoV-2 spike protein pseudotyped retrovirus particles in the presence of dilutions of plasma samples. We also used this assay to identify evidence of passive transfusion of neutralizing activity in CCP recipients. Results: Viral neutralization and anti-spike protein antibodies in 109 samples from 87 plasma donors were highly varied but modestly correlated with each other. Recipients who died of COVID-19 were found to have been transfused with units with lower anti-spike antibody levels and neutralizing activity. Passive transfer of neutralization activity was documented in 62% of antibody naive plasma recipients. Conclusions: Since viral neutralization is the goal of CCP transfusion, our observations not only support the use of anti-spike SARS-CoV2 serology tests to identify beneficial CCP units, but also support the therapeutic value of convalescent plasma with high titers of anti-spike antibodies.

Acute Lysergic Acid Diethylamide Does Not Influence Reward-Driven Decision Making of C57BL/6 Mice in the Iowa Gambling Task

While interest in psychedelic drugs in the fields of psychiatry and neuroscience has re-emerged in recent last decades, the general understanding of the effects of these drugs remains deficient. In particular, there are gaps in knowledge on executive functions and goal-directed behaviors both in humans and in commonly used animal models. The effects of acute doses of psychedelic lysergic acid diethylamide (LSD) on reward-driven decision making were explored using the mouse version of the Iowa Gambling Task. A total of 15 mice were trained to perform in a touch-screen adaptation of the rodent version of the Iowa Gambling Task, after which single acute doses of LSD (0.025, 0.1, 0.2, 0.4 mg/kg), serotonin 2A receptor-selective agonist 25CN-NBOH (1.5 mg/kg), d-amphetamine (2.0 mg/kg), and saline were administered before the trial. 25CN-NBOH and the three lowest doses of LSD showed no statistically significant changes in option selection or in general functioning during the gambling task trials. The highest dose of LSD (0.4 mg/kg) significantly decreased premature responding and increased the omission rate, but had no effect on option selection in comparison with the saline control. Amphetamine significantly decreased the correct responses and premature responding while increasing the omission rate. In conclusion, mice can perform previously learned, reward-driven decision-making tasks while under the acute influence of LSD at a commonly used dose range.

Remanufacturing Option Selection with Disassembly for Recovery Rate and Profit

In order to cope with the issue of depletion of natural resources, expectations for economical designs of the closed-loop supply chains of products that include remanufacturing in their lifecycle have recently significantly grown. However, since disassembly of a product to remanufacture it is costly due to high labor costs, the lifecycle option of remanufacturing an end of life product by disassembly and reassembly needs to be established environmentally as well as economically. In this study, we propose a remanufacturing option selection method that takes recovery rates and profits into account. First, a bill of materials of a product is prepared to create data for remanufacturing. Next, its remanufacturing option selection is formulated by using the 0-1 integer programming. Lastly, the proposed remanufacturing option selection method is verified by analyzing the sensitivities of the recovery rates and selling prices of the remanufactured products using the ϵ constraint method. The proposed method that takes remanufacturing into account has demonstrated a generating larger profits than a conventional method maintaining high recovery rates at the same levels in a case study.

Enabling Reliability-Driven Optimization Selection with Gate Graph Attention Neural Network

Modern compilers provide a huge number of optional compilation optimization options. It is necessary to select the appropriate compilation optimization options for different programs or applications. To mitigate this problem, machine learning is widely used as an efficient technology. How to ensure the integrity and effectiveness of program information is the key to problem mitigation. In addition, when selecting the best compilation optimization option, the optimization goals are often execution speed, code size, and CPU consumption. There is not much research on program reliability. This paper proposes a Gate Graph Attention Neural Network (GGANN)-based compilation optimization option selection model. The data flow and function-call information are integrated into the abstract syntax tree as the program graph-based features. We extend the deep neural network based on GGANN and build a learning model that learns the heuristics method for program reliability. The experiment is performed under the Clang compiler framework. Compared with the traditional machine learning method, our model improves the average accuracy by 5–11% in the optimization option selection for program reliability. At the same time, experiments show that our model has strong scalability.

Review of Learning-Assisted Power System Optimization

Machine learning, with a dramatic breakthrough in recent years, is showing great potential to upgrade the power system optimization toolbox. Understanding the strength and limitation of machine learning approaches is crucial to answer when and how to integrate them in various power system optimization tasks. This paper pays special attention to the coordination between machine learning approaches and optimization models, and carefully evaluates to what extent such data-driven analysis may benefit the rule-based optimization. A series of typical references are selected and categorized into four kinds: the boundary parameter improvement, the optimization option selection, the surrogate model and the hybrid model. This taxonomy provides a novel perspective to understand the latest research progress and achievements. We further discuss several key challenges and provide an in-depth comparison on the features and designs of different categories. Deep integration of machine learning approaches and optimization models is expected to become the most promising technical trend.

Review of Learning-Assisted Power System Optimization

Machine learning, with a dramatic breakthrough in recent years, is showing great potential to upgrade the power system optimization toolbox. Understanding the strength and limitation of machine learning approaches is crucial to answer when and how to integrate them in various power system optimization tasks. This paper pays special attention to the coordination between machine learning approaches and optimization models, and carefully evaluates to what extent such data-driven analysis may benefit the rule-based optimization. A series of typical references are selected and categorized into four kinds: the boundary parameter improvement, the optimization option selection, the surrogate model and the hybrid model. This taxonomy provides a novel perspective to understand the latest research progress and achievements. We further discuss several key challenges and provide an in-depth comparison on the features and designs of different categories. Deep integration of machine learning approaches and optimization models is expected to become the most promising technical trend.