A purely analytical wake model for wind turbines is derived, anchored exclusively in physical interactions between atmospheric turbulence and turbine dynamics, and thus inherently accounting for atmospheric stratification. Unlike empirical models relying on assumed wake deficit shapes or tunable coefficients, this model predicts the wake deficit solely from measurable properties of the inflow, namely, turbulence intensity and the turbulence integral time scale. Systematic validation against Large Eddy Simulations (LES) for both IEA 15MW and NREL 5MW turbines, simulated in Meso-NH under stable, neutral, and unstable conditions, demonstrates excellent agreement across atmospheric regimes. Importantly, the model requires these specific turbulence statistics as input but shows only weak sensitivity to the integral time scale, ensuring robustness even with moderate uncertainties in inflow characterisation. Comparative analysis with the state-of-the-art Super-Gaussian analytical model highlights superior performance of the present approach, particularly for unstable and neutral stratification. These results show that the predictive accuracy gained by incorporating richer inflow physics justifies the need for more comprehensive atmospheric inputs, providing a clear pathway for physically grounded, calibration-free wake modeling in operational wind energy contexts.
Based on the psychological traits of executives, this study investigates the impact and mechanism of Chief Executive Officer (CEO) narcissism on corporate Environmental, Social and Governance (ESG) performance. Based on the upper echelons theory, we measure the degree of CEO narcissism by the size of their handwritten signatures and empirically examine whether and how CEO narcissism affects corporate ESG performance. Using the data of Chinese A-share listed companies during 2009–2022 as the research sample, this paper finds that CEO narcissism is significantly negatively correlated with corporate ESG performance. Further, the mechanism test indicates that CEO narcissism reduces firms' ESG performance mainly through three paths: lowering internal control quality, lowering information transparency and exacerbating financing constraints. The heterogeneity test finds that the negative effect of CEO narcissism on firms' ESG performance is more significant in firms with shorter CEO tenure, lower institutional ownership, firms in non-heavy pollution industries and lower media attention. In addition, the economic consequence test finds that the negative effect of CEO narcissism on ESG performance ultimately leads to a decrease in corporate performance. We adopt a CEO narcissism perspective to innovatively deconstruct the micro-level driving mechanisms behind corporate ESG performance in the Chinese context. It provides a theoretical basis for firms to improve their governance structures to constrain the adverse behaviors of narcissistic CEOs, while empowering regulatory authorities to identify the underlying psychological causes behind differences in corporate ESG performance. This has strategic value for promoting substantive ESG investments by firms.
PurposeThis study aims to analyse the dual dynamics of ownership in Indian family-owned businesses and their impact on real earnings management (REM). Utilizing the Family Ownership to External Ownership Ratio (FEOR), this study explores how family and external shareholders interact and influence REM, addressing a significant gap in the literature on corporate governance in Indian family firms.Design/methodology/approachThis methodology involves a comprehensive analysis of 2,188 Indian family firms (representing 12,290 firm-year data) from 2010 to 2021. The FEOR metric is a proxy for ownership structure and REM is measured using abnormal production costs, discretionary spending and operating cash flows. The study also considers control variables, such as leverage, market-to-book value and audit committee characteristics, to assess the impact on REM.FindingsThis study reveals a significant negative relationship between FEOR and REM, suggesting that increased family ownership is associated with reduced earnings management. However, substantial external blockholder investments tend to pressurize inflated earnings. These results align with international studies, but offer unique insights into the Indian corporate context.Research limitations/implicationsThis study contributes to the understanding of governance dynamics in family-owned firms, particularly in India. It extends the current knowledge by illustrating the complex interplay between family ownership, external blockholder and REM.Practical implicationsThis study underscores the importance of governance structures tailored to the unique challenges of family ownership for family businesses and regulators. These findings suggest that increased family ownership can enhance financial reporting quality and offer guidance for governance improvements.Social implicationsThis study highlights the role of family-owned businesses in maintaining high standards of financial integrity, which is crucial for investor trust and market stability. This underscores the need for governance policies that consider the socioeconomic context of family ownership in emerging economies such as India.Originality/valueThis study introduces the FEOR variable to analyse the impact of ownership structure on REM in family-owned firms. It provides a unique perspective on governance challenges in Indian family businesses, contributing original insights into the intersection of family ownership, external stakeholders and financial reporting practices.
Organizations positively need great benefit quality to draw in financial backers and partners. Quality benefits can give quality data and decrease insight unsettling influences, so they can give a precise evaluation of organization execution. A few variables can impact profit quality, including bookkeeping traditionalism, capital construction, and speculation opportunity sets. Subsequently, this exploration means to audit whether these variables impact income quality. This exploration technique utilizes a quantitative methodology including 4 insurance agency, 15 non-bank monetary organizations, and 35 financial organizations. Of the 35 financial organizations, 25 of them recorded positive benefits for four back to back years. 25 businesses were the total number of samples observed over the course of a year. In this way, the all out perception information gathered more than four years is 100. The information got was broke down with a few tests utilizing SPSS. The exploration results show that bookkeeping traditionalism variables can reinforce profit quality. Capital construction factors play a part in adjusting hazard and return. The quality of earnings data can be improved by using the investment opportunity set factor. In light of the outcomes, it is presumed that bookkeeping traditionalism, capital design and the assortment of venture open doors affect profit quality. Keywords: Earnings quality, Accounting conservatism, Capital structure, Investment opportunity set
Benjamin A. T. Grahama, Lauren Brown, Georgios Chochlakis
et al.
Face-to-face interactions between police officers and the public affect both individual well-being and democratic legitimacy. Many government-public interactions are captured on video, including interactions between police officers and drivers captured on bodyworn cameras (BWCs). New advances in AI technology enable these interactions to be analyzed at scale, opening promising avenues for improving government transparency and accountability. However, for AI to serve democratic governance effectively, models must be designed to include the preferences and perspectives of the governed. This article proposes a community-informed, approach to developing multi-perspective AI tools for government accountability. We illustrate our approach by describing the research project through which the approach was inductively developed: an effort to build AI tools to analyze BWC footage of traffic stops conducted by the Los Angeles Police Department. We focus on the role of social scientists as members of multidisciplinary teams responsible for integrating the perspectives of diverse stakeholders into the development of AI tools in the domain of police -- and government -- accountability.
A numerical framework for simulating progressive failure under high-cycle fatigue loading is validated against experiments of composite quasi-isotropic open-hole laminates. Transverse matrix cracking and delamination are modeled with a mixed-mode fatigue cohesive zone model, covering crack initiation and propagation. Furthermore, XFEM is used for simulating transverse matrix cracks and splits at arbitrary locations. An adaptive cycle jump approach is employed for efficiently simulating high-cycle fatigue while accounting for local stress ratio variations in the presence of thermal residual stresses. The cycle jump scheme is integrated in the XFEM framework, where the local stress ratio is used to determine the insertion of cracks and to propagate fatigue damage. The fatigue cohesive zone model is based on S-N curves and requires static material properties and only a few fatigue parameters, calibrated on simple fracture testing specimens. The simulations demonstrate a good correspondence with experiments in terms of fatigue life and damage evolution.
As AI systems become increasingly prevalent and impactful, the need for effective AI governance and accountability measures is paramount. This paper examines the AI governance landscape, focusing on Anthropic's Claude, a foundational AI model. We analyze Claude through the lens of the NIST AI Risk Management Framework and the EU AI Act, identifying potential threats and proposing mitigation strategies. The paper highlights the importance of transparency, rigorous benchmarking, and comprehensive data handling processes in ensuring the responsible development and deployment of AI systems. We conclude by discussing the social impact of AI governance and the ethical considerations surrounding AI accountability.
José Luis Ros Medina, Daniel Barragán, Edgar A. Ruvalcaba-Gómez
La transparencia es un concepto ampliamente estudiado en las Ciencias Sociales, aunque su rigor conceptual no siempre ha sido suficiente. Uno de los aspectos en los que quizás ha habido más deslices teóricos en relación con la transparencia es precisamente su evaluación. A nivel metodológico, este trabajo incluye un repaso teórico y experiencial de la evaluación de la transparencia con el objetivo de proponer un Índice de Transparencia de las Universidades Latinoamericanas. A pesar de que ha habido experiencias de evaluación concretas en este ámbito, hasta ahora ninguna de ellas ha tenido un enfoque regional ni ha sido sostenible en el tiempo. En particular, el índice propuesto evalúa la publicación de información en seis áreas vitales para transparentar: información institucional y de gobierno, planificación y rendición de cuentas, personal, estudiantes, información económica y financiera y resultados científicos y académicos. La propuesta se basa en la necesidad de contar con una herramienta que permita evaluar de manera rigurosa y sistemática la transparencia en las universidades de la región, que pueda ser aplicada posteriormente.
Political institutions and public administration (General), Accounting. Bookkeeping
Vanessa Ramos da Silva, Edvalda Araújo Leal, Franciele Beck
A pesquisa objetiva compreender e discutir a constituição da folga orçamentária no processo de planejamento e controle de duas empresas familiares com estruturas de gestão distintas, sob a ótica da Teoria da Estruturação. Mediante abordagem qualitativa foram realizados dois estudos de casos em duas empresas familiares. As categorias analisadas foram: perfil do respondente, envolvimento da família na gestão e constituição da folga orçamentária. Foi possível compreender que as empresas compartilham a visão da relevância do processo orçamentário, porém com particularidades quanto à condução deste processo em função do perfil de envolvimento da família na gestão do negócio. Dentre as contribuições da pesquisa tem-se o desenvolvimento da pesquisa na área gerencial com apoio da Teoria da Estruturação, os resultados trazem uma análise aprofundada em duas empresas com composições heterogêneas de gestão, identificou-se o quanto os gestores que utilizam o orçamento conheciam o conceito de folga orçamentária e se a estrutura do Sistema de Controle Gerencial impacta na constituição da folga orçamentária.
While HDMaps are a crucial component of autonomous driving, they are expensive to acquire and maintain. Estimating these maps from sensors therefore promises to significantly lighten costs. These estimations however overlook existing HDMaps, with current methods at most geolocalizing low quality maps or considering a general database of known maps. In this paper, we propose to account for existing maps of the precise situation studied when estimating HDMaps. We identify 3 reasonable types of useful existing maps (minimalist, noisy, and outdated). We also introduce MapEX, a novel online HDMap estimation framework that accounts for existing maps. MapEX achieves this by encoding map elements into query tokens and by refining the matching algorithm used to train classic query based map estimation models. We demonstrate that MapEX brings significant improvements on the nuScenes dataset. For instance, MapEX - given noisy maps - improves by 38% over the MapTRv2 detector it is based on and by 8% over the current SOTA.
Madelyne Xiao, Mona Wang, Anunay Kulshrestha
et al.
We investigate how users perceive social media account verification, how those perceptions compare to platform practices, and what happens when a gap emerges. We use recent changes in Twitter's verification process as a natural experiment, where the meaning and types of verification indicators rapidly and significantly shift. The project consists of two components: a user survey and a measurement of verified Twitter accounts. In the survey study, we ask a demographically representative sample of U.S. respondents (n = 299) about social media account verification requirements both in general and for particular platforms. We also ask about experiences with online information sources and digital literacy. More than half of respondents misunderstand Twitter's criteria for blue check account verification, and over 80% of respondents misunderstand Twitter's new gold and gray check verification indicators. Our analysis of survey responses suggests that people who are older or have lower digital literacy may be modestly more likely to misunderstand Twitter verification. In the measurement study, we randomly sample 15 million English language tweets from October 2022. We obtain account verification status for the associated accounts in November 2022, just before Twitter's verification changes, and we collect verification status again in January 2022. The resulting longitudinal dataset of 2.85 million accounts enables us to characterize the accounts that gained and lost verification following Twitter's changes. We find that accounts posting conservative political content, exhibiting positive views about Elon Musk, and promoting cryptocurrencies disproportionately obtain blue check verification after Twitter's changes. We close by offering recommendations for improving account verification indicators and processes.
Engine failure is a recurring emergency in General Aviation and fixed-wing UAVs, often requiring the pilot or remote operator to carry out carefully planned glides to safely reach a candidate landing strip. We tackle the problem of minimizing the altitude loss of a thrustless aircraft flying towards a designated target position. Extending previous work on optimal glides without obstacles, we consider here trajectory planning of optimal gliding in the the presence of ground obstacles, while accounting for wind effects. Under simplifying model assumptions, in particular neglecting the effect of turns, we characterize the optimal solution as comprising straight glide segments between iteratively-determined extreme points on the obstacles. Consequently, the optimal trajectory is included in an iteratively-defined reduced visibility graph, and can be obtained by a standard graph search algorithm, such as A$^*$. We further quantify the effect of turns to verify a safe near-optimal glide trajectory. We apply our algorithm on a Cessna 172 model, in realistic scenarios, demonstrating both the altitude-loss optimal trajectory calculation, and determination of airstrip reachability.
There is widespread agreement among philosophers about the Mens Rea Asymmetry (MRA), according to which praise requires intent, whereas blame does not. However, there is evidence showing that MRA is descriptively inadequate. We hypothesize that the violations of MRA found in the experimental literature are due to what we call “moral compositionality,” by which we mean that people evaluate the component parts of a moral problem separately and then reach an overall verdict by aggregating the verdicts on the component parts. We have subjected this hypothesis to the test and here report the results of our experiment. We explore several explanations of the experimental findings and conclude that they present a puzzle to moral theory.
Neuroimaging-based prediction methods for intelligence and cognitive abilities have seen a rapid development in literature. Among different neuroimaging modalities, prediction based on functional connectivity (FC) has shown great promise. Most literature has focused on prediction using static FC, but there are limited investigations on the merits of such analysis compared to prediction based on dynamic FC or region level functional magnetic resonance imaging (fMRI) times series that encode temporal variability. To account for the temporal dynamics in fMRI data, we propose a deep neural network involving bi-directional long short-term memory (bi-LSTM) approach that also incorporates feature selection mechanism. The proposed pipeline is implemented via an efficient GPU computation framework and applied to predict intelligence scores based on region level fMRI time series as well as dynamic FC. We compare the prediction performance for different intelligence measures based on static FC, dynamic FC, and region level time series acquired from the Adolescent Brain Cognitive Development (ABCD) study involving close to 7000 individuals. Our detailed analysis illustrates that static FC consistently has inferior prediction performance compared to region level time series or dynamic FC for unimodal rest and task fMRI experiments, and in almost all cases using a combination of task and rest features. In addition, the proposed bi-LSTM pipeline based on region level time series identifies several shared and differential important brain regions across task and rest fMRI experiments that drive intelligence prediction. A test-retest analysis of the selected features shows strong reliability across cross-validation folds. Given the large sample size from ABCD study, our results provide strong evidence that superior prediction of intelligence can be achieved by accounting for temporal variations in fMRI.
A. Feder Cooper, Emanuel Moss, Benjamin Laufer
et al.
In 1996, Accountability in a Computerized Society [95] issued a clarion call concerning the erosion of accountability in society due to the ubiquitous delegation of consequential functions to computerized systems. Nissenbaum [95] described four barriers to accountability that computerization presented, which we revisit in relation to the ascendance of data-driven algorithmic systems--i.e., machine learning or artificial intelligence--to uncover new challenges for accountability that these systems present. Nissenbaum's original paper grounded discussion of the barriers in moral philosophy; we bring this analysis together with recent scholarship on relational accountability frameworks and discuss how the barriers present difficulties for instantiating a unified moral, relational framework in practice for data-driven algorithmic systems. We conclude by discussing ways of weakening the barriers in order to do so.
This article continues a series of publications on the issues of accounting, analysis and assessment of the corporate social responsibility in a rapidly changing environment when society and business face new challenges. The authors provide methodological solutions to the problem of studying the social responsibility of Russian public companies based on the analysis of data disclosed in their financial and non-financial statements. The research uses abduction methods; logical analysis; logical content analysis of financial and non-financial reporting standards in terms of requirements for disclosure of social aspects; expert analysis of social reporting of Russian companies; linguistic analysis in order to identify the practice of using certain terms and concepts in the financial and non-financial reporting. The analysis of corporate reporting standards made it possible to propose a new model for analyzing disclosures in the field of corporate social responsibility. The authors reveal the most important issues of the corporate social responsibility by assessing the trends in the social sphere, identifying opportunities and threats for its further development connected with the evolution of society, economy and ecology, ongoing digitalization processes. Procedures of researching business social responsibility are developed and fully described. The documentary base of the research includes International Financial Reporting Standards, Sustainable Development Reporting Standards, Basic Performance Indicators of the Russian Union of Industrialists and Entrepreneurs (RSPP) as well as financial and non-financial reports of Russian companies operating in production industries. The results of the research are the proposals to apply the multi-stage approach, which makes it possible to assess the social responsibility of certain economic entities through the prism of global tasks the country and the world community face with. The analysis of the complex problems of business and civilization allows us to identify five top problems in the light of which the social responsibility of large companies should be assessed, as well as to build a logical connection between these top problems and the key topics of disclosures in corporate reports.
Nahid Abedi, Ali Asghar Taherabadi, Farshid Kheirollahi
et al.
Objective: The main purpose of this study is to investigate the effect of learning methods including STAD, JIGSAW and virtual education on achieving students' cognitive goals of financial statements based on Bloom's classification.
Methods: In this study, the extent of learning is divided according to Bloom's classification into six levels of knowledge, understanding, application, analysis, composition and evaluation. Pre-testing and post-testing techniques were used to collect data and analysis of covariance and Scheffe post-hoc test was utilized to examine the hypothesis. The statistical population of the study includes all students of accounting at Islamic Azad University of Jiroft, taking intermediate accounting 1 course, during the first and second semesters of the academic year 2019-2020. The study sample includes four intermediate accounting 1 course groups, namely 105 people (80 in three experimental groups and 25 in control groups), all four groups are taught by one of the researchers and the final grades of the students were considered as their learning index.
Results: The results of analysis of covariance showed that pre-testing scores did not have a significant effect on post-testing scores. On the other hand, the analysis of covariance significance level for knowledge, understanding, application and analysis in groups was zero; Therefore, it is proved that there is a significant difference between the post-testing scores (learning scores) of the groups in these four levels. The results of Scheffe test revealed that the learning methods of STAD and Jigsaw have a significant effect on students' learning levels; but there is no significant difference between virtual education and lecture classes.
Conclusion: These results generally indicate that current E-Learning programs are not of the required quality; but using the capacity of STAD and JIGSAW methods can provide a favorable context for learning and strengthening the dynamism and promoting the culture of teamwork among students.
Blockchain rewriting with fine-grained access control allows a user to create a transaction associated with a set of attributes, while another user (or modifier) who possesses enough rewriting privileges from a trusted authority satisfying the attribute set can rewrite the transaction. However, it lacks accountability and is not designed for open blockchains that require no trust assumptions. In this work, we introduce accountable fine-grained blockchain rewriting in a permissionless setting. The property of accountability allows the modifier's identity and her rewriting privileges to be held accountable for the modified transactions in case of malicious rewriting (e.g., modify the registered content from good to bad). We first present a generic framework to secure blockchain rewriting in the permissionless setting. Second, we present an instantiation of our approach and show its practicality through evaluation analysis. Last, we demonstrate that our proof-of-concept implementation can be effectively integrated into open blockchains.
A case-control study is designed to help determine if an exposure is associated with an outcome. However, since case-control studies are retrospective, they are often subject to recall bias. Recall bias can occur when study subjects do not remember previous events accurately. In this paper, we first define the estimand of interest: the causal odds ratio (COR) for a case-control study. Second, we develop estimation approaches for the COR and present estimates as a function of recall bias. Third, we define a new quantity called the \textit{R-factor}, which denotes the minimal amount of recall bias that leads to altering the initial conclusion. We show that a failure to account for recall bias can significantly bias estimation of the COR. Finally, we apply the proposed framework to a case-control study of the causal effect of childhood physical abuse on adulthood mental health.