Final Choices Research Articles

The ATLAS ITk strip detector system for the Phase-II LHC upgrade

ATLAS is currently preparing for the HL-LHC upgrade, with an all-silicon Inner Tracker (ITk) that will replace the current Inner Detector. The ITk will feature a pixel detector surrounded by a strip detector, with the strip system consisting of four Barrel layers and six endcap disks. After completion of final design reviews in key areas, such as Sensors, Modules, Front-End electronics and ASICs, a large scale prototyping program has been completed successfully. We present an overview of the Strip System, and highlight the final design choices of sensors, modules and ASICs. We will summarise results achieved during prototyping and the current status of production and pre-production on various detector components, with an emphasis on QA and QC procedures.

Coordination of Renewable Energy Integration and Peak Shaving through Evolutionary Game Theory

This paper presents a novel approach to optimizing the coordination between renewable energy generation enterprises and power grid companies using evolutionary game theory. The research focuses on resolving conflicts and distributing benefits between these key stakeholders in the context of large-scale renewable energy integration. A theoretical model based on replicator dynamics is developed to simulate and analyze the evolutionary stable strategies of power generation enterprises and grid companies with particular emphasis on peak shaving services and electricity bidding. These simulations are based on theoretical models and do not incorporate real-world data directly, but they aim to replicate scenarios that reflect realistic behaviors within the electricity market. The model is validated through dynamic simulation under various scenarios, demonstrating that the final strategic choices of both thermal power and renewable energy enterprises tend to evolve towards either high-price or low-price bidding strategies, significantly influenced by initial system parameters. Additionally, this study explores how the introduction of peak shaving compensation affects the coordination process and stability of renewable energy integration, providing insights into improving grid efficiency and enhancing renewable energy adoption. Although the results are simulation-based, they are designed to offer practical recommendations for grid management and policy development, particularly for the integration of renewable energies such as wind power in competitive electricity markets. The findings suggest that effective government regulation, alongside well-designed compensation mechanisms, can help establish a balanced interest distribution between stakeholders. By offering a clear framework for analyzing the dynamics of renewable energy integration, this work provides valuable policy recommendations to promote cooperation and stability in electricity markets. This study contributes to the understanding of the complex interactions in the electricity market and offers practical solutions for enhancing the integration of renewable energy into the grid.

Decision-Making with Predictions of Others' Likely and Unlikely Choices in the Human Brain.

For better decisions in social interactions, humans often must understand the thinking of others and predict their actions. Since such predictions are uncertain, multiple predictions may be necessary for better decision-making. However, the neural processes and computations underlying such social decision-making remain unclear. We investigated this issue by developing a behavioral paradigm and performing functional magnetic resonance imaging and computational modeling. In our task, female and male participants were required to predict others' choices in order to make their own value-based decisions, as the outcome depended on others' choices. Results showed, to make choices, the participants mostly relied on a value difference (primary) generated from the case where others would make a likely choice, but sometimes they additionally used another value difference (secondary) from the opposite case where others make an unlikely choice. We found that the activations in the posterior cingulate cortex (PCC) correlated with the primary difference while the activations in the right dorsolateral prefrontal cortex (rdlPFC) correlated with the secondary difference. Analysis of neural coupling and temporal dynamics suggested a three-step processing network, beginning with the left amygdala signals for predictions of others' choices. Modulated by these signals, the PCC and rdlPFC reflect the respective value differences for self-decisions. Finally, the medial prefrontal cortex integrated these decision signals for a final decision. Our findings elucidate the neural process of constructing value-based decisions by predicting others and illuminate their key variables with social modulations, providing insight into the differential functional roles of these brain regions in this process.

Hidden flaws behind expert-level accuracy of multimodal GPT-4 vision in medicine

Recent studies indicate that Generative Pre-trained Transformer 4 with Vision (GPT-4V) outperforms human physicians in medical challenge tasks. However, these evaluations primarily focused on the accuracy of multi-choice questions alone. Our study extends the current scope by conducting a comprehensive analysis of GPT-4V’s rationales of image comprehension, recall of medical knowledge, and step-by-step multimodal reasoning when solving New England Journal of Medicine (NEJM) Image Challenges—an imaging quiz designed to test the knowledge and diagnostic capabilities of medical professionals. Evaluation results confirmed that GPT-4V performs comparatively to human physicians regarding multi-choice accuracy (81.6% vs. 77.8%). GPT-4V also performs well in cases where physicians incorrectly answer, with over 78% accuracy. However, we discovered that GPT-4V frequently presents flawed rationales in cases where it makes the correct final choices (35.5%), most prominent in image comprehension (27.2%). Regardless of GPT-4V’s high accuracy in multi-choice questions, our findings emphasize the necessity for further in-depth evaluations of its rationales before integrating such multimodal AI models into clinical workflows.

Towards Controllable Generative Design: A Conceptual Design Generation Approach Leveraging the FBS Ontology and Large Language Models

Abstract Recent research in the field of design engineering is primarily focusing on using AI technologies such as Large Language Models (LLMs) to assist early-stage design. The engineer or designer can use LLMs to explore, validate and compare thousands of generated conceptual stimuli and make final choices. This was seen as a significant stride in advancing the status of the generative approach in computer-aided design. However, it is often difficult to instruct LLMs to obtain novel conceptual solutions and requirement-compliant in real design tasks, due to the lack of transparency and insufficient controllability of LLMs. This study presents an approach to leverage LLMs to infer Function-Behavior-Structure (FBS) ontology for high-quality design concepts. Prompting design based on the FBS model decomposes the design task into three sub-tasks including functional, behavioral, and structural reasoning. In each sub-task, prompting templates and specification signifiers are specified to guide the LLMs to generate concepts. User can determine the selected concepts by judging and evaluating the generated function-structure pairs. A comparative experiment has been conducted to evaluate the concept generation approach. According to the concept evaluation results, our approach achieves the highest scores in concept evaluation, and the generated concepts are more novel, useful, functional, and low-cost compared to the baseline.

Navigating Educational and Urban Landscapes: Middle-Class School Choice Strategies in Local Education Markets

ABSTRACT The middle-classes use school choice as a strategy of class reproduction and comparative advantage. In this article, we show how middle-class parental school choice strategies are spatially dependent and how schooling preferences and final choices are bounded by the social and educational characteristics of the local education market of their neighborhood of residence in the city of Barcelona. Our findings reveal interesting differences relating to the search process and final decisions, which are dependent on parental preferences and risk perception in the different local education markets. Reflections on some policy implications of the analysis are considered in the conclusions.

Exploring the Embodiment of Feminist Growth in Movies: Taking Thelma and Louise as an Example

The film, as a form of cultural expression, usually also assumes the responsibility of reflecting and exploring social issues and presenting them to the masses in a more popular way, and these social issues also include gender issues class issues, and so on. Feminism has been a hot topic in recent years, and it has led to many different issues such as women's independence and the dichotomy between men and women, but the public's understanding of feminism is too shallow and one-sided. Taking the film Thelma and Louise as an example, this study aims to explore the theme of women breaking the boundaries of gender and class to present feminist growth in this film, by analyzing the characters' personalities, their ways of thinking about things, and their final choices. This study likewise presents how the two female protagonists transform from victims to strong and independent women. According to the final results of the study Thelma and Louise successfully present a feminist coming-of-age story that is significant to society as well as the awakening of feminism.

ATLAS ITk strip detector for the Phase-II Upgrade

The inner detector of the present ATLAS experiment has been designed and developed to function in the environment of the present Large Hadron Collider (LHC). At the ATLAS Phase-II Upgrade, the particle densities and radiation levels will exceed current levels by a factor of ten. The instantaneous luminosity is expected to reach unprecedented values, resulting in up to 200 proton-proton interactions in a typical bunch crossing. The new detectors must be faster and they need to be more highly segmented. The sensors used also need to be far more resistant to radiation, and they require much greater power delivery to the front-end systems. At the same time, they cannot introduce excess material which could undermine tracking performance. For those reasons, the inner tracker of the ATLAS detector was redesigned and will be rebuilt completely. The ATLAS Upgrade Inner Tracker (ITk) consists of several layers of silicon particle detectors. The innermost layers will be composed of silicon pixel sensors, and the outer layers will consist of silicon microstrip sensors. This contribution focuses on the strip region of the ITk. The central part of the strip tracker (barrel) will be composed of rectangular short (∼2.5 cm) and long (∼5 cm) strip sensors. The forward regions of the strip tracker (end-caps) consist of six disks per side, with trapezoidal shaped sensors of various lengths and strip pitches. After the completion of final design reviews in key areas, such as Sensors, Modules, Front-End electronics, and ASICs, a large scale prototyping program has been completed in all areas successfully. We present an overview of the Strip System and highlight the final design choices of sensors, module designs and ASICs. We will summarise results achieved during prototyping and the current status of pre-production and production on various detector components, with an emphasis on QA and QC procedures.

The Barriers to Shared Decision-making in Exercise Prescription for Children With Asthma: A Qualitative Study From Parents’ Perspective

The knowledge of barriers from the parental perspective is essential for facilitating shared decision-making in the field of pediatric asthma. Participants who were parents of children with a diagnosis of asthma were recruited, and in-depth, semistructured interviews were conducted. The interview transcripts were analyzed thematically using framework methods. Seventeen participants undertook interviews. Three themes and nine subthemes emerged: (1) decision-making need level-limited understanding of decision-making knowledge, ambiguity regarding self-empowerment roles, and lack of family member support; (2) decision-making support level-insufficient ability to evaluate information, inefficient communication with health care professionals, and excessive use of professional terminology; and (3) decision-making outcome level-doubts about the final decision-making choices, time constraints on decision-making, and absence of mechanisms to track decisions made. The findings would serve as crucial foundations for the development of decision-aid programs within the context of pediatric asthma.

Demand Steering in a Last-Mile Delivery Problem with Home and Pickup Point Delivery Options

To increase the efficiency of last-mile delivery, online retailers can adopt pickup points in their operations. The retailer may then incentivize customers to steer them from home to pickup point delivery to reduce costs. However, it is usually uncertain whether the customer accepts this incentive to switch to pickup delivery. This setup gives rise to a new last-mile delivery problem with integrated incentive and routing decisions under uncertainty. We model this problem as a two-stage stochastic program with decision-dependent uncertainty. In the first stage, a retailer decides which customers to incentivize. However, customers’ reaction to the incentive is stochastic: they may accept the offer and switch to pickup point delivery, or they may decline the offer and stick with home delivery. In the second stage, after customers’ final delivery choices are revealed, a vehicle route is planned to serve customers via the delivery option of their choice. We develop an exact branch-and-bound algorithm and propose several heuristics to improve the algorithm’s scalability. Our algorithm solves instances with up to 50 customers, realizing on average 4%–8% lower last-mile delivery costs compared with the commonly applied approaches in the industry that do not use incentives or offer incentives to all customers. We also develop a benchmark policy that gives very fast solutions with a 2% average optimality gap for small instances and up to 2% average cost increase compared with the heuristic solutions. Funding: This project received funding from the European Union’s Horizon 2020 research and innovation program under the Marie Skłodowska-Curie grant agreement [Grant 765395]. Supplemental Material: The online appendix is available at https://doi.org/10.1287/trsc.2023.0287 .

Exploring parents’ child care search and decision-making processes

Searching for child care is an important stageof the parental child care decision-making process. Although previous studies have identified a series of factors related to parents’ child care decision-making, little is known about parents’ child care search processes. Using the 2012 National Survey of Early Careand Education (NSECE) dataset, this study aims to identify family and community characteristics related to parents’ child care search behaviors and their final care choices. The study examined the search strategies of parents of preschoolers (36–60 months), including the type of programs that parents consider, the number of options that parents consider, and the sources of information they consult in the process of making child care decisions. Results showed that 43 % of parents only considered one option during their child care search. The type of child care considered, and the source of information parents used during their child care search were significantly related to their final choices. As many states are currently developing the consumer education program to help parents select child care arrangement, the findings from this study have direct implications for policy and practices.

Overview of material choices for HL-LHC collimators

In view of the High-Luminosity upgrade of the Large Hadron Collider (HL-LHC) at CERN, different materials were investigated for the upgrade of the LHC collimation system. A key objective was to determine how the jaws of the new collimators could be manufactured to meet the demanding requirements of HL-LHC, such as thermo-mechanical robustness and stability, beam coupling impedance, Ultra-High Vacuum (UHV), etc. During the Long-Shutdown 2 (LS2), five primary and ten secondary low-impedance collimators were already produced using novel materials. For LS3, in addition to more secondary collimators, the production and installation of other types of devices, including tertiaries and physics-debris collimators, is planned. This paper details the final mate-rial choices and rationale for each collimator family.

Latin particles in De Tristitia Christi: the fine-tuning of word choice

This paper aims to identify and analyze some of the substitutions of the particle sed (> ceterum/verum/at), visible in the manuscript of More’s De Tristitia. In this study, the theoretical framework of Functional Discourse Grammar is applied for the first time, and it helps to explain some of the reasons underlying More’s final choices. One of the clearest conclusions is that More tries to show that the particle chosen does not function as a conjunction at the sentence level but as an adversative connector at the presentational level; this level has to do with the presentation and organization of the information in the text, and can serve to offer an explanation, justification, clarification, context, etc. between the various textual units. The examination of the passages in context also shows how More was careful to avoid repetitions which could diminish the elegance of the chosen linguistic expression.

Depression Detection using AI, ML and NLP

Suicide is one of the most serious social health issues that exist in today's culture. We can refer suicidal thoughts to act of killing yourself intentionally. It can be used as a suicide risk measure. India is among the top countries among in the world to have annual suicide rate. Social networks have been developed as a first-rate factor for its users to communicate with their interested friends and proportion their captions, photos, and videos reflecting their moods, emotions, and sentiments. To increase and put in force a version which takes a facial expression image as an enter and symptoms. Based on that, it predicts if the patient is suffering from anxiety, depression or not. We can train version using photographs of various emotions in it & will use it for prediction. Image captioning can be accomplished after prediction for higher visualization of report. We will also use text mining (NLP) technique on various inputs taken in the form of audio and video to convert it into text. At final, we can make final choices based on above two techniques. And with this, we will be able to generate a detailed dashboard of user disease status and his history. With the help of all this data we can design webapp for above system. We will use CNN algorithm for speed up detection of depressed character instances and approach to become aware of high-quality answers of mental health troubles. We suggest system learning method as an efficient and scalable technique. We document an implementation of the proposed method. We have evaluated the efficiency of our proposed technique the usage of a set of various psycholinguistic features. We show that our proposed method can extensively improve the accuracy and category blunders price.

Selection of solar panel using entropy TOPSIS technique

Energy surplus has always been problematic in isolated and countryside areas of the globe, both monetarily and technically. Prioritizing energy sources to electrify a rural hamlet is the most challenging stage. Because no one option is perfect, pooling resources is always a good idea. The combination of monetary, technological, and eco-friendly factors in planned decision-making complicates building an independent off-grid source of electricity. Due to their cheap cost and low noise, standalone solar systems have been seen as viable and rapidly developing green energy sources, particularly for rural electrification. However, these systems' lowest energy conversion and highest capital expenses are their principal drawbacks. As a result, numerous factors should be considered before installing the system, including PV panel types and configurations, PV module mathematical models, storage batteries, environmental requirements, sizing strategy based on techno-economic objectives, and final design choices. This research aims to present a method for choosing the optimum solar panel for rural electrification based on multi-criteria decision-making (MCDM) strategy. The SPs are associated with various characteristics, like Cost Per Watt, INR (SPC-1), and Nameplate Max. Power rating; Watt (SPC-2), Vmax; Volts (SPC-3), Imax; Amperes (SPC-4), Voc; Volts (SPC-5), Isc; Amperes (SPC-6), Panel; efficiency(%) (SPC-7), Number of cells: quantity (SPC-8) and Weight;kg (SPC-9). The SPC-1 and SPC-9 are nonbeneficial criteria, and the SPC-2 to SPC-8 are beneficial criteria. As a result, the TOPSIS procedure is applied to choose the optimum option out of the 16 options. For equal weight, the MCS value is 0.626, whereas the entropy weight approach has an MCS value of 0.817. Following that, using the TOPSIS technique, SP-12 ranks second in alternatives. If the first option isn't available on the market, the second can be chosen.

Opportunities or Threats? The Role of Entrepreneurial Risk Perception in Shaping the Entrepreneurial Motivation

Entrepreneurial risk is an important factor that individuals must consider when starting their own business. The COVID-19 continues to rage, bringing great challenges to China’s economy and entrepreneurial activities. In this study, college students encounter greater entrepreneurial risks. There are two opposing views on the role of entrepreneurial risk in shaping individual entrepreneurial motivation: one view that risk is a threat, and the other view that entrepreneurial risk contains opportunity. Existing studies have discussed the issues from individual factors and environment factors, respectively, ignoring the combined effects of individual and environment factors. Person–situation transactions theory points out that individuals usually make their final behavior choices based on their comprehensive evaluation of environmental factors and themselves. Therefore, individual and environment factors should be integrated to investigate the effect of entrepreneurial risk on entrepreneurial motivation. Based on the person–situation transactions theory, this study establishes a theoretical model that entrepreneurial risk perception influences necessity and opportunity entrepreneurial motivation through entrepreneurial self-efficacy and discusses the moderating effect of entrepreneurship policy. A questionnaire survey was conducted on 595 fresh graduates from eight universities in China to obtain relevant data and the Structural Equation modelling was established to test the hypothesis. The results confirm that without the influence of external factors, college students regard entrepreneurial risk as a great threat, which not only weakens their entrepreneurial self-efficacy, but also reduces their necessity and opportunity entrepreneurial motivation. However, with the support of good entrepreneurial policies, entrepreneurial risk can be transformed into an opportunity to improve the entrepreneurs motivation driven by necessity of college students, but the impact on the opportunity motivation is not significant. This study deeply analyzes the dual characteristics of entrepreneurial risk perception in the process of shaping individual entrepreneurial motivation, expands the related research on entrepreneurial risk perception and entrepreneurial motivation, and has important implications for the government and universities to formulate entrepreneurial policies for college students.

Agenda Democracy

The study of agenda setting has become curiously disconnected from democratic theory and democratization. Following Schattschneider, Dahl, and recent developments in political theory, I call for its reintegration in theoretical and empirical realms. The concept of agenda democracy allows for better understanding of contests over institutions, significant historical-institutional transformations, the study of inequality and its mechanisms of generation and maintenance, and the building and undermining of democracy. Agenda democracy requires a broad understanding of agendas (beyond a mere menu of final policy choices), recognizes that many democratic regimes have institutions that systematically render agendas nondemocratic, and compels us to look at the interstices of institutions and society (party transformation, petition and grievance mechanisms, advocacy campaigns, initiatives to expand what I call the shortlist of the possible) for moments of significant change. Agenda democracy compels the examination of democratizing agenda restrictions, the study of conservative organizations in politics, and the consideration of decomposing the term “movement.”

Climate change summit: testing the impact of role playing games on crossing the knowledge to action gap

Climate change decision-making happens at the intersection of knowledge, values, and experience, usually requiring action with incomplete information. Yet, climate change education prioritizes improving knowledge gaps, not communication and contextual understanding. Role-playing games highlight the interaction of evidence and values in decision-making; they have also been shown to help participants cope with uncertainty. Here, we used a role-playing activity in 8 undergraduate science class sections to explore how role-playing affected climate change decision-making. We found that students selected significantly different climate proposals based on differing affinities with their characters. Students’ articulation of ‘uncertainty’ and ‘concreteness’ in justifying their final choices were significantly related to their chosen proposals. Students’ understanding of circumstance and perspective in driving different priorities/decisions increased significantly. This activity is scalable and broadly applicable; by empowering participants to discuss data and values and make decisions with incomplete information, we might encourage confident participation in the global climate conversation.

Analyzing entry strategies for co-opetitive supply chains with the learning effect

PurposeThis study aims to investigate whether the contract manufacturer (CM) should take the first-mover advantage in the end-product without supplying core components to the original equipment manufacturer (OEM) immediately, or should fully squeeze the benefit of the learning effect through an amplified production quantity by letting the OEM enter the end-product market early.Design/methodology/approachThe authors propose a two-period model for a supply chain consisting of a CM and an OEM where the CM has four alternative entry strategies concerning it competition to the OEM in the end-product market. For each strategy, the authors derive the equilibrium solutions of the two firms using a backward approach. Comparison leads to the CM’s final choices among the four strategies.FindingsFor both CM and OEM, the monopoly and the first-entry strategies will be dominated by either the post-entry or the simultaneous-entry strategy, and thus, their preferred strategy is chosen from the latter two. Regarding the two firms choices between the post- and simultaneous-entry strategy, the CM prefers the post-entry strategy when the OEMs brand premium is at a moderate level, whereas the OEM prefers the post-entry strategy when its brand premium is low, and the learning effect can amplify the interval for the CMs adopting the post-entry strategy as well as changes the interval for the OEMs preference related to the two strategies.Originality/valueThis paper is the first one to explore the optimal strategy for a CM to maximize its profit in a co-opetitive supply chain situation with a CM and an OEM. The authors believe that our paper contributes to both literature and the market.

Cosmo-Paleontology: Statistics of Fossil Groups in a Gravity-Only Simulation

We present a detailed study of fossil group candidates identified in "Last Journey", a gravity-only cosmological simulation covering a $(3.4\, h^{-1}\mathrm{Gpc})^3$ volume with a particle mass resolution of $m_p \approx 2.7 \times 10^9\, h^{-1}\mathrm{M}_\odot$. The simulation allows us to simultaneously capture a large number of group-scale halos and to resolve their internal structure. Historically, fossil groups have been characterized by high X-ray brightness and a large luminosity gap between the brightest and second brightest galaxy in the group. In order to identify candidate halos that host fossil groups, we use halo merger tree information to introduce two parameters: a luminous merger mass threshold ($M_\mathrm{LM}$) and a last luminous merger redshift cut-off ($z_\mathrm{LLM}$). The final parameter choices are informed by observational data and allow us to identify a plausible fossil group sample from the simulation. The candidate halos are characterized by reduced substructure and are therefore less likely to host bright galaxies beyond the brightest central galaxy. We carry out detailed studies of this sample, including analysis of halo properties and clustering. We find that our simple assumptions lead to fossil group candidates that form early, have higher concentrations, and are more relaxed compared to other halos in the same mass range.